Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

PubMed Central

Doubková, Marcela; Van Dijk, Albert I.J.M.; Sabel, Daniel; Wagner, Wolfgang; Blöschl, Günter

2012-01-01

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling. PMID:23483015

[Determination of soil exchangeable base cations by using atomic absorption spectrophotometer and extraction with ammonium acetate].

PubMed

Zhang, Yu-ge; Xiao, Min; Dong, Yi-hua; Jiang, Yong

2012-08-01

A method to determine soil exchangeable calcium (Ca), magnesium (Mg), potassium (K), and sodium (Na) by using atomic absorption spectrophotometer (AAS) and extraction with ammonium acetate was developed. Results showed that the accuracy of exchangeable base cation data with AAS method fits well with the national standard referential soil data. The relative errors for parallel samples of exchangeable Ca and Mg with 66 pair samples ranged from 0.02%-3.14% and 0.06%-4.06%, and averaged to be 1.22% and 1.25%, respectively. The relative errors for exchangeable K and Na with AAS and flame photometer (FP) ranged from 0.06%-8.39% and 0.06-1.54, and averaged to be 3.72% and 0.56%, respectively. A case study showed that the determination method for exchangeable base cations by using AAS was proven to be reliable and trustable, which could reflect the real situation of soil cation exchange properties in farmlands.

[Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging].

PubMed

Yang, Shun-hua; Zhang, Hai-tao; Guo, Long; Ren, Yan

2015-06-01

Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control. The results indicated that soil or- ganic matter was significantly positively correlated with relative elevation whilst it had a significantly negative correlation with stream power index. Semivariance analysis showed that both soil organic matter content and its residuals (including ordinary least square regression residual and GWR resi- dual) had strong spatial autocorrelation. Interpolation accuracies by different methods were esti- mated based on a data set of 98 validation samples. Results showed that the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of RK were respectively 39.2%, 17.7% and 20.6% lower than the corresponding values of OK, with a relative-improvement (RI) of 20.63. GWRK showed a similar tendency, having its ME, MAE and RMSE to be respectively 60.6%, 23.7% and 27.6% lower than those of OK, with a RI of 59.79. Therefore, both RK and GWRK significantly improved the accuracy of OK interpolation of soil organic matter due to their in- corporation of auxiliary variables. In addition, GWRK performed obviously better than RK did in this study, and its improved performance should be attributed to the consideration of sample spatial locations.

Four reference soil and rock samples for measuring element availability in the Western Energy Regions

USGS Publications Warehouse

Crock, J.G.; Severson, R.C.

1980-01-01

Attaining acceptable precision in extractable element determinations is more difficult than in total element determinations. In total element determinations, dissolution of the sample is qualitatively checked by the clarity of the solution and the absence of residues. These criteria cannot be used for extracts. Possibilities for error are introduced in virtually every step in soil extractions. Therefore, the use of reference materials whose homogeneity and element content are reasonably well known is essential for determination of extractable elements. In this report, estimates of homogeneity and element content are presented for four reference samples. Bulk samples of about 100 kilograms of each sample were ground to pass an 80-mesh sieve. The samples were homogenized and split using a Jones-type splitter. Fourteen splits of each reference sample were analyzed for total content of Ca, Co, Cu, Fe, K, Mg, Mn, Na, and Zn; DTPA-extractable Cd, Co, Cu, Fe, Mn, Ni, Pb, and Zn; exchangeable Ca, Mg, K, and Na; cation exchange capacity water-saturation-extractable Ca, Mg, K, Na, C1, and SO4; soil pH; and hot-water-extractable boron. Error measured between splits was small, indicating that the samples were homogenized adequately and that the laboratory procedure provided reproducible results.

Transfer of Cadmium from Soil to Vegetable in the Pearl River Delta area, South China

PubMed Central

Zhang, Huihua; Chen, Junjian; Zhu, Li; Yang, Guoyi; Li, Dingqiang

2014-01-01

The purpose of this study was to investigate the regional Cadmium (Cd) concentration levels in soils and in leaf vegetables across the Pearl River Delta (PRD) area; and reveal the transfer characteristics of Cadmium (Cd) from soils to leaf vegetable species on a regional scale. 170 paired vegetables and corresponding surface soil samples in the study area were collected for calculating the transfer factors of Cadmium (Cd) from soils to vegetables. This investigation revealed that in the study area Cd concentration in soils was lower (mean value 0.158 mg kg−1) compared with other countries or regions. The Cd-contaminated areas are mainly located in west areas of the Pearl River Delta. Cd concentrations in all vegetables were lower than the national standard of Safe vegetables (0.2 mg kg−1). 88% of vegetable samples met the standard of No-Polluted vegetables (0.05 mg kg−1). The Cd concentration in vegetables was mainly influenced by the interactions of total Cd concentration in soils, soil pH and vegetable species. The fit lines of soil-to-plant transfer factors and total Cd concentration in soils for various vegetable species were best described by the exponential equation (), and these fit lines can be divided into two parts, including the sharply decrease part with a large error range, and the slowly decrease part with a low error range, according to the gradual increasing of total Cd concentrations in soils. PMID:25247431

Transfer of cadmium from soil to vegetable in the Pearl River Delta area, South China.

PubMed

Zhang, Huihua; Chen, Junjian; Zhu, Li; Yang, Guoyi; Li, Dingqiang

2014-01-01

The purpose of this study was to investigate the regional Cadmium (Cd) concentration levels in soils and in leaf vegetables across the Pearl River Delta (PRD) area; and reveal the transfer characteristics of Cadmium (Cd) from soils to leaf vegetable species on a regional scale. 170 paired vegetables and corresponding surface soil samples in the study area were collected for calculating the transfer factors of Cadmium (Cd) from soils to vegetables. This investigation revealed that in the study area Cd concentration in soils was lower (mean value 0.158 mg kg(-1)) compared with other countries or regions. The Cd-contaminated areas are mainly located in west areas of the Pearl River Delta. Cd concentrations in all vegetables were lower than the national standard of Safe vegetables (0.2 mg kg(-1)). 88% of vegetable samples met the standard of No-Polluted vegetables (0.05 mg kg(-1)). The Cd concentration in vegetables was mainly influenced by the interactions of total Cd concentration in soils, soil pH and vegetable species. The fit lines of soil-to-plant transfer factors and total Cd concentration in soils for various vegetable species were best described by the exponential equation (y = ax(b)), and these fit lines can be divided into two parts, including the sharply decrease part with a large error range, and the slowly decrease part with a low error range, according to the gradual increasing of total Cd concentrations in soils.

Selection of Optimal Auxiliary Soil Nutrient Variables for Cokriging Interpolation

PubMed Central

Song, Genxin; Zhang, Jing; Wang, Ke

2014-01-01

In order to explore the selection of the best auxiliary variables (BAVs) when using the Cokriging method for soil attribute interpolation, this paper investigated the selection of BAVs from terrain parameters, soil trace elements, and soil nutrient attributes when applying Cokriging interpolation to soil nutrients (organic matter, total N, available P, and available K). In total, 670 soil samples were collected in Fuyang, and the nutrient and trace element attributes of the soil samples were determined. Based on the spatial autocorrelation of soil attributes, the Digital Elevation Model (DEM) data for Fuyang was combined to explore the coordinate relationship among terrain parameters, trace elements, and soil nutrient attributes. Variables with a high correlation to soil nutrient attributes were selected as BAVs for Cokriging interpolation of soil nutrients, and variables with poor correlation were selected as poor auxiliary variables (PAVs). The results of Cokriging interpolations using BAVs and PAVs were then compared. The results indicated that Cokriging interpolation with BAVs yielded more accurate results than Cokriging interpolation with PAVs (the mean absolute error of BAV interpolation results for organic matter, total N, available P, and available K were 0.020, 0.002, 7.616, and 12.4702, respectively, and the mean absolute error of PAV interpolation results were 0.052, 0.037, 15.619, and 0.037, respectively). The results indicated that Cokriging interpolation with BAVs can significantly improve the accuracy of Cokriging interpolation for soil nutrient attributes. This study provides meaningful guidance and reference for the selection of auxiliary parameters for the application of Cokriging interpolation to soil nutrient attributes. PMID:24927129

Minimizing Artifacts and Biases in Chamber-Based Measurements of Soil Respiration

NASA Astrophysics Data System (ADS)

Davidson, E. A.; Savage, K.

2001-05-01

Soil respiration is one of the largest and most important fluxes of carbon in terrestrial ecosystems. The objectives of this paper are to review concerns about uncertainties of chamber-based measurements of CO2 emissions from soils, to evaluate the direction and magnitude of these potential errors, and to explain procedures that minimize these errors and biases. Disturbance of diffusion gradients cause underestimate of fluxes by less than 15% in most cases, and can be partially corrected for with curve fitting and/or can be minimized by using brief measurement periods. Under-pressurization or over-pressurization of the chamber caused by flow restrictions in air circulating designs can cause large errors, but can also be avoided with properly sized chamber vents and unrestricted flows. Somewhat larger pressure differentials are observed under windy conditions, and the accuracy of measurements made under such conditions needs more research. Spatial and temporal heterogeneity can be addressed with appropriate chamber sizes and numbers and frequency of sampling. For example, means of 8 randomly chosen flux measurements from a population of 36 measurements made with 300 cm2 chambers in tropical forests and pastures were within 25% of the full population mean 98% of the time and were within 10% of the full population mean 70% of the time. Comparisons of chamber-based measurements with tower-based measurements of total ecosystem respiration require analysis of the scale of variation within the purported tower footprint. In a forest at Howland, Maine, the differences in soil respiration rates among very poorly drained and well drained soils were large, but they mostly were fortuitously cancelled when evaluated for purported tower footprints of 600-2100 m length. While all of these potential sources of measurement error and sampling biases must be carefully considered, properly designed and deployed chambers provide a reliable means of accurately measuring soil respiration in terrestrial ecosystems.

Comparison of different interpolation methods for spatial distribution of soil organic carbon and some soil properties in the Black Sea backward region of Turkey

NASA Astrophysics Data System (ADS)

Göl, Ceyhun; Bulut, Sinan; Bolat, Ferhat

2017-10-01

The purpose of this research is to compare the spatial variability of soil organic carbon (SOC) in four adjacent land uses including the cultivated area, the grassland area, the plantation area and the natural forest area in the semi - arid region of Black Sea backward region of Turkey. Some of the soil properties, including total nitrogen, SOC, soil organic matter, and bulk density were measured on a grid with a 50 m sampling distance on the top soil (0-15 cm depth). Accordingly, a total of 120 samples were taken from the four adjacent land uses. Data was analyzed using geostatistical methods. The methods used were: Block kriging (BK), co - kriging (CK) with organic matter, total nitrogen and bulk density as auxiliary variables and inverse distance weighting (IDW) methods with the power of 1, 2 and 4. The methods were compared using a performance criteria that included root mean square error (RMSE), mean absolute error (MAE) and the coefficient of correlation (r). The one - way ANOVA test showed that differences between the natural (0.6653 ± 0.2901) - plantation forest (0.7109 ± 0.2729) areas and the grassland (1.3964 ± 0.6828) - cultivated areas (1.5851 ± 0.5541) were statistically significant at 0.05 level (F = 28.462). The best model for describing spatially variation of SOC was CK with the lowest error criteria (RMSE = 0.3342, MAE = 0.2292) and the highest coefficient of correlation (r = 0.84). The spatial structure of SOC could be well described by the spherical model. The nugget effect indicated that SOC was moderately dependent on the study area. The error distributions of the model showed that the improved model was unbiased in predicting the spatial distribution of SOC. This study's results revealed that an explanatory variable linked SOC increased success of spatial interpolation methods. In subsequent studies, this case should be taken into account for reaching more accurate outputs.

Soil sail content estimation in the yellow river delta with satellite hyperspectral data

USGS Publications Warehouse

Weng, Yongling; Gong, Peng; Zhu, Zhi-Liang

2008-01-01

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487-1527, 1971-1991, 2032-2092, and 2163-2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area. ?? 2008 CASI.

Ensemble learning for spatial interpolation of soil potassium content based on environmental information.

PubMed

Liu, Wei; Du, Peijun; Wang, Dongchen

2015-01-01

One important method to obtain the continuous surfaces of soil properties from point samples is spatial interpolation. In this paper, we propose a method that combines ensemble learning with ancillary environmental information for improved interpolation of soil properties (hereafter, EL-SP). First, we calculated the trend value for soil potassium contents at the Qinghai Lake region in China based on measured values. Then, based on soil types, geology types, land use types, and slope data, the remaining residual was simulated with the ensemble learning model. Next, the EL-SP method was applied to interpolate soil potassium contents at the study site. To evaluate the utility of the EL-SP method, we compared its performance with other interpolation methods including universal kriging, inverse distance weighting, ordinary kriging, and ordinary kriging combined geographic information. Results show that EL-SP had a lower mean absolute error and root mean square error than the data produced by the other models tested in this paper. Notably, the EL-SP maps can describe more locally detailed information and more accurate spatial patterns for soil potassium content than the other methods because of the combined use of different types of environmental information; these maps are capable of showing abrupt boundary information for soil potassium content. Furthermore, the EL-SP method not only reduces prediction errors, but it also compliments other environmental information, which makes the spatial interpolation of soil potassium content more reasonable and useful.

A Comparative Assessment of the Influences of Human Impacts on Soil Cd Concentrations Based on Stepwise Linear Regression, Classification and Regression Tree, and Random Forest Models

PubMed Central

Qiu, Lefeng; Wang, Kai; Long, Wenli; Wang, Ke; Hu, Wei; Amable, Gabriel S.

2016-01-01

Soil cadmium (Cd) contamination has attracted a great deal of attention because of its detrimental effects on animals and humans. This study aimed to develop and compare the performances of stepwise linear regression (SLR), classification and regression tree (CART) and random forest (RF) models in the prediction and mapping of the spatial distribution of soil Cd and to identify likely sources of Cd accumulation in Fuyang County, eastern China. Soil Cd data from 276 topsoil (0–20 cm) samples were collected and randomly divided into calibration (222 samples) and validation datasets (54 samples). Auxiliary data, including detailed land use information, soil organic matter, soil pH, and topographic data, were incorporated into the models to simulate the soil Cd concentrations and further identify the main factors influencing soil Cd variation. The predictive models for soil Cd concentration exhibited acceptable overall accuracies (72.22% for SLR, 70.37% for CART, and 75.93% for RF). The SLR model exhibited the largest predicted deviation, with a mean error (ME) of 0.074 mg/kg, a mean absolute error (MAE) of 0.160 mg/kg, and a root mean squared error (RMSE) of 0.274 mg/kg, and the RF model produced the results closest to the observed values, with an ME of 0.002 mg/kg, an MAE of 0.132 mg/kg, and an RMSE of 0.198 mg/kg. The RF model also exhibited the greatest R2 value (0.772). The CART model predictions closely followed, with ME, MAE, RMSE, and R2 values of 0.013 mg/kg, 0.154 mg/kg, 0.230 mg/kg and 0.644, respectively. The three prediction maps generally exhibited similar and realistic spatial patterns of soil Cd contamination. The heavily Cd-affected areas were primarily located in the alluvial valley plain of the Fuchun River and its tributaries because of the dramatic industrialization and urbanization processes that have occurred there. The most important variable for explaining high levels of soil Cd accumulation was the presence of metal smelting industries. The good performance of the RF model was attributable to its ability to handle the non-linear and hierarchical relationships between soil Cd and environmental variables. These results confirm that the RF approach is promising for the prediction and spatial distribution mapping of soil Cd at the regional scale. PMID:26964095

A Comparative Assessment of the Influences of Human Impacts on Soil Cd Concentrations Based on Stepwise Linear Regression, Classification and Regression Tree, and Random Forest Models.

PubMed

Qiu, Lefeng; Wang, Kai; Long, Wenli; Wang, Ke; Hu, Wei; Amable, Gabriel S

2016-01-01

Soil cadmium (Cd) contamination has attracted a great deal of attention because of its detrimental effects on animals and humans. This study aimed to develop and compare the performances of stepwise linear regression (SLR), classification and regression tree (CART) and random forest (RF) models in the prediction and mapping of the spatial distribution of soil Cd and to identify likely sources of Cd accumulation in Fuyang County, eastern China. Soil Cd data from 276 topsoil (0-20 cm) samples were collected and randomly divided into calibration (222 samples) and validation datasets (54 samples). Auxiliary data, including detailed land use information, soil organic matter, soil pH, and topographic data, were incorporated into the models to simulate the soil Cd concentrations and further identify the main factors influencing soil Cd variation. The predictive models for soil Cd concentration exhibited acceptable overall accuracies (72.22% for SLR, 70.37% for CART, and 75.93% for RF). The SLR model exhibited the largest predicted deviation, with a mean error (ME) of 0.074 mg/kg, a mean absolute error (MAE) of 0.160 mg/kg, and a root mean squared error (RMSE) of 0.274 mg/kg, and the RF model produced the results closest to the observed values, with an ME of 0.002 mg/kg, an MAE of 0.132 mg/kg, and an RMSE of 0.198 mg/kg. The RF model also exhibited the greatest R2 value (0.772). The CART model predictions closely followed, with ME, MAE, RMSE, and R2 values of 0.013 mg/kg, 0.154 mg/kg, 0.230 mg/kg and 0.644, respectively. The three prediction maps generally exhibited similar and realistic spatial patterns of soil Cd contamination. The heavily Cd-affected areas were primarily located in the alluvial valley plain of the Fuchun River and its tributaries because of the dramatic industrialization and urbanization processes that have occurred there. The most important variable for explaining high levels of soil Cd accumulation was the presence of metal smelting industries. The good performance of the RF model was attributable to its ability to handle the non-linear and hierarchical relationships between soil Cd and environmental variables. These results confirm that the RF approach is promising for the prediction and spatial distribution mapping of soil Cd at the regional scale.

Analytical method for nitroaromatic explosives in radiologically contaminated soil for ISO/IEC 17025 accreditation

DOE PAGES

Boggess, Andrew; Crump, Stephen; Gregory, Clint; ...

2017-12-06

Here, unique hazards are presented in the analysis of radiologically contaminated samples. Strenuous safety and security precautions must be in place to protect the analyst, laboratory, and instrumentation used to perform analyses. A validated method has been optimized for the analysis of select nitroaromatic explosives and degradative products using gas chromatography/mass spectrometry via sonication extraction of radiologically contaminated soils, for samples requiring ISO/IEC 17025 laboratory conformance. Target analytes included 2-nitrotoluene, 4-nitrotoluene, 2,6-dinitrotoluene, and 2,4,6-trinitrotoluene, as well as the degradative product 4-amino-2,6-dinitrotoluene. Analytes were extracted from soil in methylene chloride by sonication. Administrative and engineering controls, as well as instrument automationmore » and quality control measures, were utilized to minimize potential human exposure to radiation at all times and at all stages of analysis, from receiving through disposition. Though thermal instability increased uncertainties of these selected compounds, a mean lower quantitative limit of 2.37 µg/mL and mean accuracy of 2.3% relative error and 3.1% relative standard deviation were achieved. Quadratic regression was found to be optimal for calibration of all analytes, with compounds of lower hydrophobicity displaying greater parabolic curve. Blind proficiency testing (PT) of spiked soil samples demonstrated a mean relative error of 9.8%. Matrix spiked analyses of PT samples demonstrated that 99% recovery of target analytes was achieved. To the knowledge of the authors, this represents the first safe, accurate, and reproducible quantitative method for nitroaromatic explosives in soil for specific use on radiologically contaminated samples within the constraints of a nuclear analytical lab.« less

Analytical method for nitroaromatic explosives in radiologically contaminated soil for ISO/IEC 17025 accreditation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boggess, Andrew; Crump, Stephen; Gregory, Clint

Here, unique hazards are presented in the analysis of radiologically contaminated samples. Strenuous safety and security precautions must be in place to protect the analyst, laboratory, and instrumentation used to perform analyses. A validated method has been optimized for the analysis of select nitroaromatic explosives and degradative products using gas chromatography/mass spectrometry via sonication extraction of radiologically contaminated soils, for samples requiring ISO/IEC 17025 laboratory conformance. Target analytes included 2-nitrotoluene, 4-nitrotoluene, 2,6-dinitrotoluene, and 2,4,6-trinitrotoluene, as well as the degradative product 4-amino-2,6-dinitrotoluene. Analytes were extracted from soil in methylene chloride by sonication. Administrative and engineering controls, as well as instrument automationmore » and quality control measures, were utilized to minimize potential human exposure to radiation at all times and at all stages of analysis, from receiving through disposition. Though thermal instability increased uncertainties of these selected compounds, a mean lower quantitative limit of 2.37 µg/mL and mean accuracy of 2.3% relative error and 3.1% relative standard deviation were achieved. Quadratic regression was found to be optimal for calibration of all analytes, with compounds of lower hydrophobicity displaying greater parabolic curve. Blind proficiency testing (PT) of spiked soil samples demonstrated a mean relative error of 9.8%. Matrix spiked analyses of PT samples demonstrated that 99% recovery of target analytes was achieved. To the knowledge of the authors, this represents the first safe, accurate, and reproducible quantitative method for nitroaromatic explosives in soil for specific use on radiologically contaminated samples within the constraints of a nuclear analytical lab.« less

A soil map of a large watershed in China: applying digital soil mapping in a data sparse region

NASA Astrophysics Data System (ADS)

Barthold, F.; Blank, B.; Wiesmeier, M.; Breuer, L.; Frede, H.-G.

2009-04-01

Prediction of soil classes in data sparse regions is a major research challenge. With the advent of machine learning the possibilities to spatially predict soil classes have increased tremendously and given birth to new possibilities in soil mapping. Digital soil mapping is a research field that has been established during the last decades and has been accepted widely. We now need to develop tools to reduce the uncertainty in soil predictions. This is especially challenging in data sparse regions. One approach to do this is to implement soil taxonomic distance as a classification error criterion in classification and regression trees (CART) as suggested by Minasny et al. (Geoderma 142 (2007) 285-293). This approach assumes that the classification error should be larger between soils that are more dissimilar, i.e. differ in a larger number of soil properties, and smaller between more similar soils. Our study area is the Xilin River Basin, which is located in central Inner Mongolia in China. It is characterized by semi arid climate conditions and is representative for the natural occurring steppe ecosystem. The study area comprises 3600 km2. We applied a random, stratified sampling design after McKenzie and Ryan (Geoderma 89 (1999) 67-94) with landuse and topography as stratifying variables. We defined 10 sampling classes, from each class 14 replicates were randomly drawn and sampled. The dataset was split into 100 soil profiles for training and 40 soil profiles for validation. We then applied classification and regression trees (CART) to quantify the relationships between soil classes and environmental covariates. The classification tree explained 75.5% of the variance with land use and geology as most important predictor variables. Among the 8 soil classes that we predicted, the Kastanozems cover most of the area. They are predominantly found in steppe areas. However, even some of the soils at sand dune sites, which were thought to show only little soil formation, can be classified as Kastanozems. Besides the Kastanozems, Regosols are most common at the sand dune sites as well as at sites that are defined as bare soil which are characterized by little or no vegetation. Gleysols are mostly found at sites in the vicinity of the Xilin river, which are connected to the groundwater. They can also be found in small valleys or depressions where sub-surface waters from neighboring areas collect. The richest soils are found in mountain meadow areas. Pedogenetic conditions here are most favorable and lead to the formation of Chernozems with deep humic Ah horizons. Other soil types that occur in the study area are Arenosols, Calcisols, Cambisol and Phaeozems. In addition, soil taxonomic distance is implemented into the decision tree procedure as a measure of classification error. The results of incorporating taxonomic distance as a loss function in the decision tree will be compared with the standard application of the decision tree.

Evaluation of WES One-Dimensional Dynamic Soil Testing Procedures.

DTIC Science & Technology

1983-06-01

relations: 18 AC - (2K +4G (6)a so I so r (6) Dividing the stress reduction Aa by a from equation (5), we obtain ana r estimate of the fractional error in...walls, the radial expansion of the soil caused by the expansion of the steel side walls, and the nonuniform stress and strain states in the sample...is applied rapidly, the stress state in the soil at the steel base may be very different from that at the top surface of the soil. With such

Approach of regionalisation c-stocks in forest soils on a national level

NASA Astrophysics Data System (ADS)

Wellbrock, Nicole; Höhle, Juliane; Dühnelt, Petra; Holzhausen, Marieanna

2010-05-01

Introduction In December 2006, the German government decided to manage forests as carbon sinks to reduce greenhouse gas emissions in accordance with Article 3.4 of the Kyoto Protocol. The National Forest Monitoring data contribute to the fulfilment of these reporting commitments. In Germany, National Forest Monitoring includes the systematical extensive National Soil Condition Survey (BZE) and the detailed case studies (Level-II) which determine the processes within forests. This complex monitoring system is appropriate to Germany's greenhouse gas reporting (THG 2008 to 2012). The representative BZE plots can be used to obtain regional data for the National Carbon Stock Inventory. Here, an approach adopting a combination of geostatistics and regression analysis is preferred. The difficulty of showing the statistical significance of expected small changes while carbon stocks are generally high is one of the major challenges in carbon stock monitoring. However, through intensive preparation and cooperation with the forestry authorities of each federal state, the errors uncured in determining changes in carbon stocks in forest soils, which must be stipulated in greenhouse gas monitoring, could be minimised. In contrast to the detailed soil case studies, in which essentially the sources of error occur repeatedly in carbon stock change calculations, the BZE data can be stratified to form plots with homogenous properties, thereby reducing the standard error of estimate. Subsequently, the results of the stratification are projected across Germany, the reporting unit for greenhouse gas monitoring. National Forest Monitoring The BZE represents a national, systematic sampling inventory of the condition of forest soils. The first BZE inventory (BZE I: 1987 to 1993) was carried out on a systematic 8 x 8 km grid on the same sampling plots adopted in the Forest Condition Survey (WZE). In some areas the network of sampling plots involves 1900 grid points. The first BZE I survey was repeated after 15 years, between 2006 and 2008, by the national and the state authorities in cooperation. Afterwards, extensive laboratory and statistical analyses were conducted. Necessary parameters are listed in table 1. Upscaling approach There are different approaches for presenting extensive carbon stock data (Baritz et al., 2006). The availability of georeference plots means one can merge the point data with map data. In Germany, an approach was tested that used homogenous soil areas und plot-information from the national soil inventory. For every soil area c-stocks were regionalised. Only information form BZE-plots were involved which were characteristic for the soil area. The indicators were soil type and substrate class. For every soil area the forest areas were taken in account to calculate c-stock per forest area. The sum of every c-stock per soil area is the c-stock in forest soils of Germany. Tab.1: List of parameters for the carbon inventory (BZE II) Components Parameters Point level Field sampling Width of depth classes, Fine roots, humus (< 2 cm), dry bulk density, stone content, area of humus layer sampled, height a.s.l., litterfall, deadwood (from 10 cm) Analysis C content, fine soil fraction, weight of humus layer, Carbon stock calculations Carbon stock Regional Level Plot Soil type, parent material, vegetation type or forest Regionalisation Soil and land use maps, statistical models, ecological regions, digital elevation models, climate regions

A statistical method for estimating rates of soil development and ages of geologic deposits: A design for soil-chronosequence studies

USGS Publications Warehouse

Switzer, P.; Harden, J.W.; Mark, R.K.

1988-01-01

A statistical method for estimating rates of soil development in a given region based on calibration from a series of dated soils is used to estimate ages of soils in the same region that are not dated directly. The method is designed specifically to account for sampling procedures and uncertainties that are inherent in soil studies. Soil variation and measurement error, uncertainties in calibration dates and their relation to the age of the soil, and the limited number of dated soils are all considered. Maximum likelihood (ML) is employed to estimate a parametric linear calibration curve, relating soil development to time or age on suitably transformed scales. Soil variation on a geomorphic surface of a certain age is characterized by replicate sampling of soils on each surface; such variation is assumed to have a Gaussian distribution. The age of a geomorphic surface is described by older and younger bounds. This technique allows age uncertainty to be characterized by either a Gaussian distribution or by a triangular distribution using minimum, best-estimate, and maximum ages. The calibration curve is taken to be linear after suitable (in certain cases logarithmic) transformations, if required, of the soil parameter and age variables. Soil variability, measurement error, and departures from linearity are described in a combined fashion using Gaussian distributions with variances particular to each sampled geomorphic surface and the number of sample replicates. Uncertainty in age of a geomorphic surface used for calibration is described using three parameters by one of two methods. In the first method, upper and lower ages are specified together with a coverage probability; this specification is converted to a Gaussian distribution with the appropriate mean and variance. In the second method, "absolute" older and younger ages are specified together with a most probable age; this specification is converted to an asymmetric triangular distribution with mode at the most probable age. The statistical variability of the ML-estimated calibration curve is assessed by a Monte Carlo method in which simulated data sets repeatedly are drawn from the distributional specification; calibration parameters are reestimated for each such simulation in order to assess their statistical variability. Several examples are used for illustration. The age of undated soils in a related setting may be estimated from the soil data using the fitted calibration curve. A second simulation to assess age estimate variability is described and applied to the examples. ?? 1988 International Association for Mathematical Geology.

Sources of errors and uncertainties in the assessment of forest soil carbon stocks at different scales-review and recommendations.

PubMed

Vanguelova, E I; Bonifacio, E; De Vos, B; Hoosbeek, M R; Berger, T W; Vesterdal, L; Armolaitis, K; Celi, L; Dinca, L; Kjønaas, O J; Pavlenda, P; Pumpanen, J; Püttsepp, Ü; Reidy, B; Simončič, P; Tobin, B; Zhiyanski, M

2016-11-01

Spatially explicit knowledge of recent and past soil organic carbon (SOC) stocks in forests will improve our understanding of the effect of human- and non-human-induced changes on forest C fluxes. For SOC accounting, a minimum detectable difference must be defined in order to adequately determine temporal changes and spatial differences in SOC. This requires sufficiently detailed data to predict SOC stocks at appropriate scales within the required accuracy so that only significant changes are accounted for. When designing sampling campaigns, taking into account factors influencing SOC spatial and temporal distribution (such as soil type, topography, climate and vegetation) are needed to optimise sampling depths and numbers of samples, thereby ensuring that samples accurately reflect the distribution of SOC at a site. Furthermore, the appropriate scales related to the research question need to be defined: profile, plot, forests, catchment, national or wider. Scaling up SOC stocks from point sample to landscape unit is challenging, and thus requires reliable baseline data. Knowledge of the associated uncertainties related to SOC measures at each particular scale and how to reduce them is crucial for assessing SOC stocks with the highest possible accuracy at each scale. This review identifies where potential sources of errors and uncertainties related to forest SOC stock estimation occur at five different scales-sample, profile, plot, landscape/regional and European. Recommendations are also provided on how to reduce forest SOC uncertainties and increase efficiency of SOC assessment at each scale.

Statistical design and analysis of environmental studies for plutonium and other transuranics at NAEG ''safety-shot'' sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilbert, R.O.; Eberhardt, L.L.; Fowler, E.B.

This paper is centered around the use of stratified random sampling for estimating the total amount (inventory) of $sup 239-240$Pu and uranium in surface soil at ten ''safety-shot'' sites on the Nevada Test Site (NTS) and Tonopah Test Range (TTR) that are currently being studied by the Nevada Applied Ecology Group (NAEG). The use of stratified random sampling has resulted in estimates of inventory at these desert study sites that have smaller standard errors than would have been the case had simple random sampling (no stratification) been used. Estimates of inventory are given for $sup 235$U, $sup 238$U, and $supmore » 239-240$Pu in soil at A Site of Area 11 on the NTS. Other results presented include average concentrations of one or more of these isotopes in soil and vegetation and in soil profile samples at depths to 25 cm. The regression relationship between soil and vegetation concentrations of $sup 235$U and $sup 238$U at adjacent sampling locations is also examined using three different models. The applicability of stratified random sampling to the estimation of concentration contours of $sup 239-240$Pu in surface soil using computer algorithms is also investigated. Estimates of such contours are obtained using several different methods. The planning of field sampling plans for estimating inventory and distribution is discussed. (auth)« less

Impact of Data Assimilation on Cost-Accuracy Tradeoff in Multi-Fidelity Models at the Example of an Infiltration Problem

NASA Astrophysics Data System (ADS)

Sinsbeck, Michael; Tartakovsky, Daniel

2015-04-01

Infiltration into top soil can be described by alternative models with different degrees of fidelity: Richards equation and the Green-Ampt model. These models typically contain uncertain parameters and forcings, rendering predictions of the state variables uncertain as well. Within the probabilistic framework, solutions of these models are given in terms of their probability density functions (PDFs) that, in the presence of data, can be treated as prior distributions. The assimilation of soil moisture data into model predictions, e.g., via a Bayesian updating of solution PDFs, poses a question of model selection: Given a significant difference in computational cost, is a lower-fidelity model preferable to its higher-fidelity counter-part? We investigate this question in the context of heterogeneous porous media, whose hydraulic properties are uncertain. While low-fidelity (reduced-complexity) models introduce a model error, their moderate computational cost makes it possible to generate more realizations, which reduces the (e.g., Monte Carlo) sampling or stochastic error. The ratio between these two errors determines the model with the smallest total error. We found assimilation of measurements of a quantity of interest (the soil moisture content, in our example) to decrease the model error, increasing the probability that the predictive accuracy of a reduced-complexity model does not fall below that of its higher-fidelity counterpart.

SOIL AND SEDIMENT SAMPLING METHODS | Science ...

EPA Pesticide Factsheets

The EPA Office of Solid Waste and Emergency Response's (OSWER) Office of Superfund Remediation and Technology Innovation (OSRTI) needs innovative methods and techniques to solve new and difficult sampling and analytical problems found at the numerous Superfund sites throughout the United States. Inadequate site characterization and a lack of knowledge of surface and subsurface contaminant distributions hinders EPA's ability to make the best decisions on remediation options and to conduct the most effective cleanup efforts. To assist OSWER, NERL conducts research to improve their capability to more accurately, precisely, and efficiently characterize Superfund, RCRA, LUST, oil spills, and brownfield sites and to improve their risk-based decision making capabilities, research is being conducted on improving soil and sediment sampling techniques and improving the sampling and handling of volatile organic compound (VOC) contaminated soils, among the many research programs and tasks being performed at ESD-LV.Under this task, improved sampling approaches and devices will be developed for characterizing the concentration of VOCs in soils. Current approaches and devices used today can lose up to 99% of the VOCs present in the sample due inherent weaknesses in the device and improper/inadequate collection techniques. This error generally causes decision makers to markedly underestimate the soil VOC concentrations and, therefore, to greatly underestimate the ecological

Characterizing Satellite Rainfall Errors based on Land Use and Land Cover and Tracing Error Source in Hydrologic Model Simulation

NASA Astrophysics Data System (ADS)

Gebregiorgis, A. S.; Peters-Lidard, C. D.; Tian, Y.; Hossain, F.

2011-12-01

Hydrologic modeling has benefited from operational production of high resolution satellite rainfall products. The global coverage, near-real time availability, spatial and temporal sampling resolutions have advanced the application of physically based semi-distributed and distributed hydrologic models for wide range of environmental decision making processes. Despite these successes, the existence of uncertainties due to indirect way of satellite rainfall estimates and hydrologic models themselves remain a challenge in making meaningful and more evocative predictions. This study comprises breaking down of total satellite rainfall error into three independent components (hit bias, missed precipitation and false alarm), characterizing them as function of land use and land cover (LULC), and tracing back the source of simulated soil moisture and runoff error in physically based distributed hydrologic model. Here, we asked "on what way the three independent total bias components, hit bias, missed, and false precipitation, affect the estimation of soil moisture and runoff in physically based hydrologic models?" To understand the clear picture of the outlined question above, we implemented a systematic approach by characterizing and decomposing the total satellite rainfall error as a function of land use and land cover in Mississippi basin. This will help us to understand the major source of soil moisture and runoff errors in hydrologic model simulation and trace back the information to algorithm development and sensor type which ultimately helps to improve algorithms better and will improve application and data assimilation in future for GPM. For forest and woodland and human land use system, the soil moisture was mainly dictated by the total bias for 3B42-RT, CMORPH, and PERSIANN products. On the other side, runoff error was largely dominated by hit bias than the total bias. This difference occurred due to the presence of missed precipitation which is a major contributor to the total bias both during the summer and winter seasons. Missed precipitation, most likely light rain and rain over snow cover, has significant effect on soil moisture and are less capable of producing runoff that results runoff dependency on the hit bias only.

Enhancement of MS2D Bartington point measurement of soil magnetic susceptibility

NASA Astrophysics Data System (ADS)

Fabijańczyk, Piotr; Zawadzki, Jarosław

2015-04-01

Field magnetometry is fast method used to assess the potential soil pollution. The most popular device used to measure the soil magnetic susceptibility on the soil surface is a MS2D Bartington. Single reading using MS2D device of soil magnetic susceptibility is low time-consuming but often characterized by considerable errors related to the instrument or environmental and lithogenic factors. Typically, in order to calculate the reliable average value of soil magnetic susceptibility, a series of MS2D readings is performed in the sample point. As it was analyzed previously, such methodology makes it possible to significantly reduce the nugget effect of the variograms of soil magnetic susceptibility that is related to the micro-scale variance and measurement errors. The goal of this study was to optimize the process of taking a series of MS2D readings, whose average value constitutes a single measurement, in order to take into account micro-scale variations of soil magnetic susceptibility in proper determination of this parameter. This was done using statistical and geostatistical analyses. The analyses were performed using field MS2D measurements that were carried out in the study area located in the direct vicinity of the Katowice agglomeration. At 150 sample points 10 MS2D readings of soil magnetic susceptibility were taken. Using this data set, series of experimental variograms were calculated and modeled. Firstly, using single random MS2D reading for each sample point, and next using the data set increased by adding one more MS2D reading, until their number reached 10. The parameters of variogram: nugget effect, sill and range of correlation were used to determine the most suitable number of MS2D readings at sample point. The distributions of soil magnetic susceptibility at sample point were also analyzed in order to determine adequate number of readings enabling to calculate reliable average soil magnetic susceptibility. The research leading to these results has received funding from the Polish-Norwegian Research Programme operated by the National Centre for Research and Development under the Norwegian Financial Mechanism 2009-2014 in the frame of Project IMPACT - Contract No Pol-Nor/199338/45/2013. References: Zawadzki J., Magiera T., Fabijańczyk P., 2007. The influence of forest stand and organic horizon development on soil surface measurement of magnetic susceptibility. Polish Journal of Soil Science, XL(2), 113-124 Zawadzki J., Fabijańczyk P., Magiera T., Strzyszcz Z., 2010. Study of litter influence on magnetic susceptibility measurements of urban forest topsoils using the MS2D sensor. Environmental Earth Sciences, 61(2), 223-230.

High-resolution moisture profiles from full-waveform probabilistic inversion of TDR signals

NASA Astrophysics Data System (ADS)

Laloy, Eric; Huisman, Johan Alexander; Jacques, Diederik

2014-11-01

This study presents an novel Bayesian inversion scheme for high-dimensional undetermined TDR waveform inversion. The methodology quantifies uncertainty in the moisture content distribution, using a Gaussian Markov random field (GMRF) prior as regularization operator. A spatial resolution of 1 cm along a 70-cm long TDR probe is considered for the inferred moisture content. Numerical testing shows that the proposed inversion approach works very well in case of a perfect model and Gaussian measurement errors. Real-world application results are generally satisfying. For a series of TDR measurements made during imbibition and evaporation from a laboratory soil column, the average root-mean-square error (RMSE) between maximum a posteriori (MAP) moisture distribution and reference TDR measurements is 0.04 cm3 cm-3. This RMSE value reduces to less than 0.02 cm3 cm-3 for a field application in a podzol soil. The observed model-data discrepancies are primarily due to model inadequacy, such as our simplified modeling of the bulk soil electrical conductivity profile. Among the important issues that should be addressed in future work are the explicit inference of the soil electrical conductivity profile along with the other sampled variables, the modeling of the temperature-dependence of the coaxial cable properties and the definition of an appropriate statistical model of the residual errors.

Hyperspectral analysis of soil organic matter in coal mining regions using wavelets, correlations, and partial least squares regression.

PubMed

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Wang, Xuchen

2016-02-01

Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation--partial least squares regression (PLSR) method effectively solves the information loss problem of correlation--multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400-1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R(2) = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions.

Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method.

PubMed

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Xi, Xiuxiu

2015-07-23

The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance (measured by an ASD FieldSpec 3 spectroradiometer) and TN based on spectral reflectance curves of soil samples collected from subsided land which is determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]'), (correlation coefficients, p < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided lands caused by the extraction of natural resources including groundwater, oil and coal.

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data.

PubMed

Hu, Junguo; Zhou, Jian; Zhou, Guomo; Luo, Yiqi; Xu, Xiaojun; Li, Pingheng; Liang, Junyi

2016-01-01

Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data

PubMed Central

Hu, Junguo; Zhou, Jian; Zhou, Guomo; Luo, Yiqi; Xu, Xiaojun; Li, Pingheng; Liang, Junyi

2016-01-01

Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points. PMID:26807579

Design and Test of a Soil Profile Moisture Sensor Based on Sensitive Soil Layers

PubMed Central

Liu, Cheng; Qian, Hongzhou; Cao, Weixing; Ni, Jun

2018-01-01

To meet the demand of intelligent irrigation for accurate moisture sensing in the soil vertical profile, a soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of five groups of sensing probes, a data processor, and some accessory components. Low-resistivity copper rings were used as components of the sensing probes. Composable simulation of the sensor’s sensing probes was carried out using a high-frequency structure simulator. According to the effective radiation range of electric field intensity, width and spacing of copper ring were set to 30 mm and 40 mm, respectively. A parallel resonance circuit of voltage-controlled oscillator and high-frequency inductance-capacitance (LC) was designed for signal frequency division and conditioning. A data processor was used to process moisture-related frequency signals for soil profile moisture sensing. The sensor was able to detect real-time soil moisture at the depths of 20, 30, and 50 cm and conduct online inversion of moisture in the soil layer between 0–100 cm. According to the calibration results, the degree of fitting (R2) between the sensor’s measuring frequency and the volumetric moisture content of soil sample was 0.99 and the relative error of the sensor consistency test was 0–1.17%. Field tests in different loam soils showed that measured soil moisture from our sensor reproduced the observed soil moisture dynamic well, with an R2 of 0.96 and a root mean square error of 0.04. In a sensor accuracy test, the R2 between the measured value of the proposed sensor and that of the Diviner2000 portable soil moisture monitoring system was higher than 0.85, with a relative error smaller than 5%. The R2 between measured values and inversed soil moisture values for other soil layers were consistently higher than 0.8. According to calibration test and field test, this sensor, which features low cost, good operability, and high integration, is qualified for precise agricultural irrigation with stable performance and high accuracy. PMID:29883420

Design and Test of a Soil Profile Moisture Sensor Based on Sensitive Soil Layers.

PubMed

Gao, Zhenran; Zhu, Yan; Liu, Cheng; Qian, Hongzhou; Cao, Weixing; Ni, Jun

2018-05-21

To meet the demand of intelligent irrigation for accurate moisture sensing in the soil vertical profile, a soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of five groups of sensing probes, a data processor, and some accessory components. Low-resistivity copper rings were used as components of the sensing probes. Composable simulation of the sensor’s sensing probes was carried out using a high-frequency structure simulator. According to the effective radiation range of electric field intensity, width and spacing of copper ring were set to 30 mm and 40 mm, respectively. A parallel resonance circuit of voltage-controlled oscillator and high-frequency inductance-capacitance (LC) was designed for signal frequency division and conditioning. A data processor was used to process moisture-related frequency signals for soil profile moisture sensing. The sensor was able to detect real-time soil moisture at the depths of 20, 30, and 50 cm and conduct online inversion of moisture in the soil layer between 0⁻100 cm. According to the calibration results, the degree of fitting ( R ²) between the sensor’s measuring frequency and the volumetric moisture content of soil sample was 0.99 and the relative error of the sensor consistency test was 0⁻1.17%. Field tests in different loam soils showed that measured soil moisture from our sensor reproduced the observed soil moisture dynamic well, with an R ² of 0.96 and a root mean square error of 0.04. In a sensor accuracy test, the R ² between the measured value of the proposed sensor and that of the Diviner2000 portable soil moisture monitoring system was higher than 0.85, with a relative error smaller than 5%. The R ² between measured values and inversed soil moisture values for other soil layers were consistently higher than 0.8. According to calibration test and field test, this sensor, which features low cost, good operability, and high integration, is qualified for precise agricultural irrigation with stable performance and high accuracy.

Representativeness of laboratory sampling procedures for the analysis of trace metals in soil.

PubMed

Dubé, Jean-Sébastien; Boudreault, Jean-Philippe; Bost, Régis; Sona, Mirela; Duhaime, François; Éthier, Yannic

2015-08-01

This study was conducted to assess the representativeness of laboratory sampling protocols for purposes of trace metal analysis in soil. Five laboratory protocols were compared, including conventional grab sampling, to assess the influence of sectorial splitting, sieving, and grinding on measured trace metal concentrations and their variability. It was concluded that grinding was the most important factor in controlling the variability of trace metal concentrations. Grinding increased the reproducibility of sample mass reduction by rotary sectorial splitting by up to two orders of magnitude. Combined with rotary sectorial splitting, grinding increased the reproducibility of trace metal concentrations by almost three orders of magnitude compared to grab sampling. Moreover, results showed that if grinding is used as part of a mass reduction protocol by sectorial splitting, the effect of sieving on reproducibility became insignificant. Gy's sampling theory and practice was also used to analyze the aforementioned sampling protocols. While the theoretical relative variances calculated for each sampling protocol qualitatively agreed with the experimental variances, their quantitative agreement was very poor. It was assumed that the parameters used in the calculation of theoretical sampling variances may not correctly estimate the constitutional heterogeneity of soils or soil-like materials. Finally, the results have highlighted the pitfalls of grab sampling, namely, the fact that it does not exert control over incorrect sampling errors and that it is strongly affected by distribution heterogeneity.

Designing a national soil erosion monitoring network for England and Wales

NASA Astrophysics Data System (ADS)

Lark, Murray; Rawlins, Barry; Anderson, Karen; Evans, Martin; Farrow, Luke; Glendell, Miriam; James, Mike; Rickson, Jane; Quine, Timothy; Quinton, John; Brazier, Richard

2014-05-01

Although soil erosion is recognised as a significant threat to sustainable land use and may be a priority for action in any forthcoming EU Soil Framework Directive, those responsible for setting national policy with respect to erosion are constrained by a lack of robust, representative, data at large spatial scales. This reflects the process-orientated nature of much soil erosion research. Recognising this limitation, The UK Department for Environment, Food and Rural Affairs (Defra) established a project to pilot a cost-effective framework for monitoring of soil erosion in England and Wales (E&W). The pilot will compare different soil erosion monitoring methods at a site scale and provide statistical information for the final design of the full national monitoring network that will: provide unbiased estimates of the spatial mean of soil erosion rate across E&W (tonnes ha-1 yr-1) for each of three land-use classes - arable and horticultural grassland upland and semi-natural habitats quantify the uncertainty of these estimates with confidence intervals. Probability (design-based) sampling provides most efficient unbiased estimates of spatial means. In this study, a 16 hectare area (a square of 400 x 400 m) positioned at the centre of a 1-km grid cell, selected at random from mapped land use across E&W, provided the sampling support for measurement of erosion rates, with at least 94% of the support area corresponding to the target land use classes. Very small or zero erosion rates likely to be encountered at many sites reduce the sampling efficiency and make it difficult to compare different methods of soil erosion monitoring. Therefore, to increase the proportion of samples with larger erosion rates without biasing our estimates, we increased the inclusion probability density in areas where the erosion rate is likely to be large by using stratified random sampling. First, each sampling domain (land use class in E&W) was divided into strata; e.g. two sub-domains within which, respectively, small or no erosion rates, and moderate or larger erosion rates are expected. Each stratum was then sampled independently and at random. The sample density need not be equal in the two strata, but is known and is accounted for in the estimation of the mean and its standard error. To divide the domains into strata we used information on slope angle, previous interpretation of erosion susceptibility of the soil associations that correspond to the soil map of E&W at 1:250 000 (Soil Survey of England and Wales, 1983), and visual interpretation of evidence of erosion from aerial photography. While each domain could be stratified on the basis of the first two criteria, air photo interpretation across the whole country was not feasible. For this reason we used a two-phase random sampling for stratification (TPRS) design (de Gruijter et al., 2006). First, we formed an initial random sample of 1-km grid cells from the target domain. Second, each cell was then allocated to a stratum on the basis of the three criteria. A subset of the selected cells from each stratum were then selected for field survey at random, with a specified sampling density for each stratum so as to increase the proportion of cells where moderate or larger erosion rates were expected. Once measurements of erosion have been made, an estimate of the spatial mean of the erosion rate over the target domain, its standard error and associated uncertainty can be calculated by an expression which accounts for the estimated proportions of the two strata within the initial random sample. de Gruijter, J.J., Brus, D.J., Biekens, M.F.P. & Knotters, M. 2006. Sampling for Natural Resource Monitoring. Springer, Berlin. Soil Survey of England and Wales. 1983 National Soil Map NATMAP Vector 1:250,000. National Soil Research Institute, Cranfield University.

Aspects of spatial and temporal aggregation in estimating regional carbon dioxide fluxes from temperate forest soils

NASA Technical Reports Server (NTRS)

Kicklighter, David W.; Melillo, Jerry M.; Peterjohn, William T.; Rastetter, Edward B.; Mcguire, A. David; Steudler, Paul A.; Aber, John D.

1994-01-01

We examine the influence of aggregation errors on developing estimates of regional soil-CO2 flux from temperate forests. We find daily soil-CO2 fluxes to be more sensitive to changes in soil temperatures (Q(sub 10) = 3.08) than air temperatures (Q(sub 10) = 1.99). The direct use of mean monthly air temperatures with a daily flux model underestimates regional fluxes by approximately 4%. Temporal aggregation error varies with spatial resolution. Overall, our calibrated modeling approach reduces spatial aggregation error by 9.3% and temporal aggregation error by 15.5%. After minimizing spatial and temporal aggregation errors, mature temperate forest soils are estimated to contribute 12.9 Pg C/yr to the atmosphere as carbon dioxide. Georeferenced model estimates agree well with annual soil-CO2 fluxes measured during chamber studies in mature temperate forest stands around the globe.

Soil Bulk Density by Soil Type, Land Use and Data Source: Putting the Error in SOC Estimates

NASA Astrophysics Data System (ADS)

Wills, S. A.; Rossi, A.; Loecke, T.; Ramcharan, A. M.; Roecker, S.; Mishra, U.; Waltman, S.; Nave, L. E.; Williams, C. O.; Beaudette, D.; Libohova, Z.; Vasilas, L.

2017-12-01

An important part of SOC stock and pool assessment is the assessment, estimation, and application of bulk density estimates. The concept of bulk density is relatively simple (the mass of soil in a given volume), the specifics Bulk density can be difficult to measure in soils due to logistical and methodological constraints. While many estimates of SOC pools use legacy data in their estimates, few concerted efforts have been made to assess the process used to convert laboratory carbon concentration measurements and bulk density collection into volumetrically based SOC estimates. The methodologies used are particularly sensitive in wetlands and organic soils with high amounts of carbon and very low bulk densities. We will present an analysis across four database measurements: NCSS - the National Cooperative Soil Survey Characterization dataset, RaCA - the Rapid Carbon Assessment sample dataset, NWCA - the National Wetland Condition Assessment, and ISCN - the International soil Carbon Network. The relationship between bulk density and soil organic carbon will be evaluated by dataset and land use/land cover information. Prediction methods (both regression and machine learning) will be compared and contrasted across datasets and available input information. The assessment and application of bulk density, including modeling, aggregation and error propagation will be evaluated. Finally, recommendations will be made about both the use of new data in soil survey products (such as SSURGO) and the use of that information as legacy data in SOC pool estimates.

Error and Uncertainty Analysis for Ecological Modeling and Simulation

DTIC Science & Technology

2001-12-01

management (LRAM) accounting for environmental, training, and economic factors. In the ELVS methodology, soil erosion status is used as a quantitative...Monte-Carlo approach. The optimization is realized through economic functions or on decision constraints, such as, unit sample cost, number of samples... nitrate flux to the Gulf of Mexico. Nature (Brief Communication) 414: 166-167. (Uncertainty analysis done with SERDP software) Gertner, G., G

Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Maximization-Complementary Superiority (LCMCS) Method

PubMed Central

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Xi, Xiuxiu

2015-01-01

The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance (measured by an ASD FieldSpec 3 spectroradiometer) and TN based on spectral reflectance curves of soil samples collected from subsided land which is determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]′), (correlation coefficients, p < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided lands caused by the extraction of natural resources including groundwater, oil and coal. PMID:26213935

Spatial prediction of near surface soil water retention functions using hydrogeophysics and empirical orthogonal functions

NASA Astrophysics Data System (ADS)

Gibson, Justin; Franz, Trenton E.

2018-06-01

The hydrological community often turns to widely available spatial datasets such as the NRCS Soil Survey Geographic database (SSURGO) to characterize the spatial variability of soil properties. When used to spatially characterize and parameterize watershed models, this has served as a reasonable first approximation when lacking localized or incomplete soil data. Within agriculture, soil data has been left relatively coarse when compared to numerous other data sources measured. This is because localized soil sampling is both expensive and time intense, thus a need exists in better connecting spatial datasets with ground observations. Given that hydrogeophysics is data-dense, rapid, non-invasive, and relatively easy to adopt, it is a promising technique to help dovetail localized soil sampling with spatially exhaustive datasets. In this work, we utilize two common near surface geophysical methods, cosmic-ray neutron probe and electromagnetic induction, to identify temporally stable spatial patterns of measured geophysical properties in three 65 ha agricultural fields in western Nebraska. This is achieved by repeat geophysical observations of the same study area across a range of wet to dry field conditions in order to evaluate with an empirical orthogonal function. Shallow cores were then extracted within each identified zone and water retention functions were generated in the laboratory. Using EOF patterns as a covariate, we quantify the predictive skill of estimating soil hydraulic properties in areas without measurement using a bootstrap validation analysis. Results indicate that sampling locations informed via repeat hydrogeophysical surveys, required only five cores to reduce the cross-validation root mean squared error by an average of 64% as compared to soil parameters predicted by a commonly used benchmark, SSURGO and ROSETTA. The reduction to five strategically located samples within the 65 ha fields reduces sampling efforts by up to ∼90% as compared to the common practice of soil grid sampling every 1 ha.

Quantitative evaluation of the CEEM soil sampling intercomparison.

PubMed

Wagner, G; Lischer, P; Theocharopoulos, S; Muntau, H; Desaules, A; Quevauviller, P

2001-01-08

The aim of the CEEM soil project was to compare and to test the soil sampling and sample preparation guidelines used in the member states of the European Union and Switzerland for investigations of background and large-scale contamination of soils, soil monitoring and environmental risk assessments. The results of the comparative evaluation of the sampling guidelines demonstrated that, in soil contamination studies carried out with different sampling strategies and methods, comparable results can hardly be expected. Therefore, a reference database (RDB) was established by the organisers, which acted as a basis for the quantitative comparison of the participants' results. The detected deviations were related to the methodological details of the individual strategies. The comparative evaluation concept consisted of three steps: The first step was a comparison of the participants' samples (which were both centrally and individually analysed) between each other, as well as with the reference data base (RDB) and some given soil quality standards on the level of concentrations present. The comparison was made using the example of the metals cadmium, copper, lead and zinc. As a second step, the absolute and relative deviations between the reference database and the participants' results (both centrally analysed under repeatability conditions) were calculated. The comparability of the samples with the RDB was categorised on four levels. Methods of exploratory statistical analysis were applied to estimate the differential method bias among the participants. The levels of error caused by sampling and sample preparation were compared with those caused by the analytical procedures. As a third step, the methodological profiles of the participants were compiled to concisely describe the different procedures used. They were related to the results to find out the main factors leading to their incomparability. The outcome of this evaluation process was a list of strategies and methods, which are problematic with respect to comparability, and should be standardised and/or specified in order to arrive at representative and comparable results in soil contamination studies throughout Europe. Pre-normative recommendations for harmonising European soil sampling guidelines and standard operating procedures have been outlined in Wagner G, Desules A, Muntau H, Theocharopoulos S. Comparative Evaluation of European Methods for Sampling and Sample Preparation of Soils for Inorganic Analysis (CEEM Soil). Final Report of the Contract SMT4-CT96-2085, Sci Total Environ 2001;264:181-186. Wagner G, Desaules A, Munatu H. Theocharopolous S, Quevauvaller Ph. Suggestions for harmonising sampling and sample pre-treatment procedures and improving quality assurance in pre-analytical steps of soil contamination studies. Paper 1.7 Sci Total Environ 2001b;264:103-118.

Factors affecting cadmium absorbed by pistachio kernel in calcareous soils, southeast of Iran.

PubMed

Shirani, H; Hosseinifard, S J; Hashemipour, H

2018-03-01

Cadmium (Cd) which does not have a biological role is one of the most toxic heavy metals for organisms. This metal enters environment through industrial processes and fertilizers. The main objective of this study was to determine the relationships between absorbed Cd by pistachio kernel and some of soil physical and chemical characteristics using modeling by stepwise regression and Artificial Neural Network (ANN), in calcareous soils in Rafsanjan region, southeast of Iran. For these purposes, 220 pistachio orchards were selected, and soil samples were taken from two depths of 0-40 and 40-80cm. Besides, fruit and leaf samples from branches with and without fruit were taken in each sampling point. The results showed that affecting factors on absorbed Cd by pistachio kernel which were obtained by regression method (pH and clay percent) were not interpretable, and considering unsuitable vales of determinant coefficient (R 2 ) and Root Mean Squares Error (RMSE), the model did not have sufficient validity. However, ANN modeling was highly accurate and reliable. Based on its results, soil available P and Zn and soil salinity were the most important factors affecting the concentration of Cd in pistachio kernel in pistachio growing areas of Rafsanjan. Copyright © 2017 Elsevier B.V. All rights reserved.

Data assimilation with soil water content sensors and pedotransfer functions in soil water flow modeling

USDA-ARS?s Scientific Manuscript database

Soil water flow models are based on a set of simplified assumptions about the mechanisms, processes, and parameters of water retention and flow. That causes errors in soil water flow model predictions. Soil water content monitoring data can be used to reduce the errors in models. Data assimilation (...

Electrical and magnetic properties of rock and soil

USGS Publications Warehouse

Scott, J.H.

1983-01-01

Field and laboratory measurements have been made to determine the electrical conductivity, dielectric constant, and magnetic permeability of rock and soil in areas of interest in studies of electromagnetic pulse propagation. Conductivity is determined by making field measurements of apparent resisitivity at very low frequencies (0-20 cps), and interpreting the true resistivity of layers at various depths by curve-matching methods. Interpreted resistivity values are converted to corresponding conductivity values which are assumed to be applicable at 10^2 cps, an assumption which is considered valid because the conductivity of rock and soil is nearly constant at frequencies below 10^2 cps. Conductivity is estimated at higher frequencies (up to 10^6 cps) by using statistical correlations of three parameters obtained from laboratory measurements of rock and soil samples: conductivity at 10^2 cps, frequency and conductivity measured over the range 10^2 to 10^6 cps. Conductivity may also be estimated in this frequency range by using field measurements of water content and correlations of laboratory sample measurements of the three parameters: water content, frequency, and conductivity measured over the range 10^2 to 10^6 cps. This method is less accurate because nonrandom variation of ion concentration in natural pore water introduces error. Dielectric constant is estimated in a similar manner from field-derived conductivity values applicable at 10^2 cps and statistical correlations of three parameters obtained from laboratory measurements of samples: conductivity measured at 10^2 cps, frequency, and dielectric constant measured over the frequency range 10^2 to 10^6 cps. Dielectric constant may also be estimated from field measurements of water content and correlations of laboratory sample measurements of the three parameters: water content, frequency, and dielectric constant measured from 10^2 to 10^6 cps, but again, this method is less accurate because of variation of ion concentration of pore water. Special laboratory procedures are used to measure conductivity and dielectric constant of rock and soil samples. Electrode polarization errors are minimized by using an electrode system that is electrochemically reversible-with ions in pore water.

Positive matrix factorization as source apportionment of soil lead and cadmium around a battery plant (Changxing County, China).

PubMed

Xue, Jian-long; Zhi, Yu-you; Yang, Li-ping; Shi, Jia-chun; Zeng, Ling-zao; Wu, Lao-sheng

2014-06-01

Chemical compositions of soil samples are multivariate in nature and provide datasets suitable for the application of multivariate factor analytical techniques. One of the analytical techniques, the positive matrix factorization (PMF), uses a weighted least square by fitting the data matrix to determine the weights of the sources based on the error estimates of each data point. In this research, PMF was employed to apportion the sources of heavy metals in 104 soil samples taken within a 1-km radius of a lead battery plant contaminated site in Changxing County, Zhejiang Province, China. The site is heavily contaminated with high concentrations of lead (Pb) and cadmium (Cd). PMF successfully partitioned the variances into sources related to soil background, agronomic practices, and the lead battery plants combined with a geostatistical approach. It was estimated that the lead battery plants and the agronomic practices contributed 55.37 and 29.28%, respectively, for soil Pb of the total source. Soil Cd mainly came from the lead battery plants (65.92%), followed by the agronomic practices (21.65%), and soil parent materials (12.43%). This research indicates that PMF combined with geostatistics is a useful tool for source identification and apportionment.

Using 50 years of soil radiocarbon data to identify optimal approaches for estimating soil carbon residence times

NASA Astrophysics Data System (ADS)

Baisden, W. T.; Canessa, S.

2013-01-01

In 1959, Athol Rafter began a substantial programme of systematically monitoring the flow of 14C produced by atmospheric thermonuclear tests through organic matter in New Zealand soils under stable land use. A database of ∼500 soil radiocarbon measurements spanning 50 years has now been compiled, and is used here to identify optimal approaches for soil C-cycle studies. Our results confirm the potential of 14C to determine residence times, by estimating the amount of ‘bomb 14C’ incorporated. High-resolution time series confirm this approach is appropriate, and emphasise that residence times can be calculated routinely with two or more time points as little as 10 years apart. This approach is generally robust to the key assumptions that can create large errors when single time-point 14C measurements are modelled. The three most critical assumptions relate to: (1) the distribution of turnover times, and particularly the proportion of old C (‘passive fraction’), (2) the lag time between photosynthesis and C entering the modelled pool, (3) changes in the rates of C input. When carrying out approaches using robust assumptions on time-series samples, multiple soil layers can be aggregated using a mixing equation. Where good archived samples are available, AMS measurements can develop useful understanding for calibrating models of the soil C cycle at regional to continental scales with sample numbers on the order of hundreds rather than thousands. Sample preparation laboratories and AMS facilities can play an important role in coordinating the efficient delivery of robust calculated residence times for soil carbon.

A quantitative comparison of soil moisture inversion algorithms

NASA Technical Reports Server (NTRS)

Zyl, J. J. van; Kim, Y.

2001-01-01

This paper compares the performance of four bare surface radar soil moisture inversion algorithms in the presence of measurement errors. The particular errors considered include calibration errors, system thermal noise, local topography and vegetation cover.

Three-Dimensional Mapping of Soil Organic Carbon by Combining Kriging Method with Profile Depth Function.

PubMed

Chen, Chong; Hu, Kelin; Li, Hong; Yun, Anping; Li, Baoguo

2015-01-01

Understanding spatial variation of soil organic carbon (SOC) in three-dimensional direction is helpful for land use management. Due to the effect of profile depths and soil texture on vertical distribution of SOC, the stationary assumption for SOC cannot be met in the vertical direction. Therefore the three-dimensional (3D) ordinary kriging technique cannot be directly used to map the distribution of SOC at a regional scale. The objectives of this study were to map the 3D distribution of SOC at a regional scale by combining kriging method with the profile depth function of SOC (KPDF), and to explore the effects of soil texture and land use type on vertical distribution of SOC in a fluvial plain. A total of 605 samples were collected from 121 soil profiles (0.0 to 1.0 m, 0.20 m increment) in Quzhou County, China and SOC contents were determined for each soil sample. The KPDF method was used to obtain the 3D map of SOC at the county scale. The results showed that the exponential equation well described the vertical distribution of mean values of the SOC contents. The coefficients of determination, root mean squared error and mean prediction error between the measured and the predicted SOC contents were 0.52, 1.82 and -0.24 g kg(-1) respectively, suggesting that the KPDF method could be used to produce a 3D map of SOC content. The surface SOC contents were high in the mid-west and south regions, and low values lay in the southeast corner. The SOC contents showed significant positive correlations between the five different depths and the correlations of SOC contents were larger in adjacent layers than in non-adjacent layers. Soil texture and land use type had significant effects on the spatial distribution of SOC. The influence of land use type was more important than that of soil texture in the surface soil, and soil texture played a more important role in influencing the SOC levels for 0.2-0.4 m layer.

Three-Dimensional Mapping of Soil Organic Carbon by Combining Kriging Method with Profile Depth Function

PubMed Central

Chen, Chong; Hu, Kelin; Li, Hong; Yun, Anping; Li, Baoguo

2015-01-01

Understanding spatial variation of soil organic carbon (SOC) in three-dimensional direction is helpful for land use management. Due to the effect of profile depths and soil texture on vertical distribution of SOC, the stationary assumption for SOC cannot be met in the vertical direction. Therefore the three-dimensional (3D) ordinary kriging technique cannot be directly used to map the distribution of SOC at a regional scale. The objectives of this study were to map the 3D distribution of SOC at a regional scale by combining kriging method with the profile depth function of SOC (KPDF), and to explore the effects of soil texture and land use type on vertical distribution of SOC in a fluvial plain. A total of 605 samples were collected from 121 soil profiles (0.0 to 1.0 m, 0.20 m increment) in Quzhou County, China and SOC contents were determined for each soil sample. The KPDF method was used to obtain the 3D map of SOC at the county scale. The results showed that the exponential equation well described the vertical distribution of mean values of the SOC contents. The coefficients of determination, root mean squared error and mean prediction error between the measured and the predicted SOC contents were 0.52, 1.82 and -0.24 g kg-1 respectively, suggesting that the KPDF method could be used to produce a 3D map of SOC content. The surface SOC contents were high in the mid-west and south regions, and low values lay in the southeast corner. The SOC contents showed significant positive correlations between the five different depths and the correlations of SOC contents were larger in adjacent layers than in non-adjacent layers. Soil texture and land use type had significant effects on the spatial distribution of SOC. The influence of land use type was more important than that of soil texture in the surface soil, and soil texture played a more important role in influencing the SOC levels for 0.2-0.4 m layer. PMID:26047012

Application of spatial methods to identify areas with lime requirement in eastern Croatia

NASA Astrophysics Data System (ADS)

Bogunović, Igor; Kisic, Ivica; Mesic, Milan; Zgorelec, Zeljka; Percin, Aleksandra; Pereira, Paulo

2016-04-01

With more than 50% of acid soils in all agricultural land in Croatia, soil acidity is recognized as a big problem. Low soil pH leads to a series of negative phenomena in plant production and therefore as a compulsory measure for reclamation of acid soils is liming, recommended on the base of soil analysis. The need for liming is often erroneously determined only on the basis of the soil pH, because the determination of cation exchange capacity, the hydrolytic acidity and base saturation is a major cost to producers. Therefore, in Croatia, as well as some other countries, the amount of liming material needed to ameliorate acid soils is calculated by considering their hydrolytic acidity. For this research, several interpolation methods were tested to identify the best spatial predictor of hidrolitic acidity. The purpose of this study was to: test several interpolation methods to identify the best spatial predictor of hidrolitic acidity; and to determine the possibility of using multivariate geostatistics in order to reduce the number of needed samples for determination the hydrolytic acidity, all with an aim that the accuracy of the spatial distribution of liming requirement is not significantly reduced. Soil pH (in KCl) and hydrolytic acidity (Y1) is determined in the 1004 samples (from 0-30 cm) randomized collected in agricultural fields near Orahovica in eastern Croatia. This study tested 14 univariate interpolation models (part of ArcGIS software package) in order to provide most accurate spatial map of hydrolytic acidity on a base of: all samples (Y1 100%), and the datasets with 15% (Y1 85%), 30% (Y1 70%) and 50% fewer samples (Y1 50%). Parallel to univariate interpolation methods, the precision of the spatial distribution of the Y1 was tested by the co-kriging method with exchangeable acidity (pH in KCl) as a covariate. The soils at studied area had an average pH (KCl) 4,81, while the average Y1 10,52 cmol+ kg-1. These data suggest that liming is necessary agrotechnical measure for soil conditioning. The results show that ordinary kriging was most accurate univariate interpolation method with smallest error (RMSE) in all four data sets, while the least precise showed Radial Basis Functions (Thin Plate Spline and Inverse Multiquadratic). Furthermore, it is noticeable a trend of increasing errors (RMSE) with a reduced number of samples tested on the most accurate univariate interpolation model: 3,096 (Y1 100%), 3,258 (Y1 85%), 3,317 (Y1 70%), 3,546 (Y1 50%). The best-fit semivariograms show a strong spatial dependence in Y1 100% (Nugget/Sill 20.19) and Y1 85% (Nugget/Sill 23.83), while a further reduction of the number of samples resulted with moderate spatial dependence (Y1 70% -35,85% and Y1 50% - 32,01). Co-kriging method resulted in a reduction in RMSE compared with univariate interpolation methods for each data set with: 2,054, 1,731 and 1,734 for Y1 85%, Y1 70%, Y1 50%, respectively. The results show the possibility for reducing sampling costs by using co-kriging method which is useful from the practical viewpoint. Reduced number of samples by half for determination of hydrolytic acidity in the interaction with the soil pH provides a higher precision for variable liming compared to the univariate interpolation methods of the entire set of data. These data provide new opportunities to reduce costs in the practical plant production in Croatia.

Overestimation of Crop Root Biomass in Field Experiments Due to Extraneous Organic Matter.

PubMed

Hirte, Juliane; Leifeld, Jens; Abiven, Samuel; Oberholzer, Hans-Rudolf; Hammelehle, Andreas; Mayer, Jochen

2017-01-01

Root biomass is one of the most relevant root parameters for studies of plant response to environmental change, soil carbon modeling or estimations of soil carbon sequestration. A major source of error in root biomass quantification of agricultural crops in the field is the presence of extraneous organic matter in soil: dead roots from previous crops, weed roots, incorporated above ground plant residues and organic soil amendments, or remnants of soil fauna. Using the isotopic difference between recent maize root biomass and predominantly C3-derived extraneous organic matter, we determined the proportions of maize root biomass carbon of total carbon in root samples from the Swiss long-term field trial "DOK." We additionally evaluated the effects of agricultural management (bio-organic and conventional), sampling depth (0-0.25, 0.25-0.5, 0.5-0.75 m) and position (within and between maize rows), and root size class (coarse and fine roots) as defined by sieve mesh size (2 and 0.5 mm) on those proportions, and quantified the success rate of manual exclusion of extraneous organic matter from root samples. Only 60% of the root mass that we retrieved from field soil cores was actual maize root biomass from the current season. While the proportions of maize root biomass carbon were not affected by agricultural management, they increased consistently with soil depth, were higher within than between maize rows, and were higher in coarse (>2 mm) than in fine (≤2 and >0.5) root samples. The success rate of manual exclusion of extraneous organic matter from root samples was related to agricultural management and, at best, about 60%. We assume that the composition of extraneous organic matter is strongly influenced by agricultural management and soil depth and governs the effect size of the investigated factors. Extraneous organic matter may result in severe overestimation of recovered root biomass and has, therefore, large implications for soil carbon modeling and estimations of the climate change mitigation potential of soils.

Average variograms to guide soil sampling

NASA Astrophysics Data System (ADS)

Kerry, R.; Oliver, M. A.

2004-10-01

To manage land in a site-specific way for agriculture requires detailed maps of the variation in the soil properties of interest. To predict accurately for mapping, the interval at which the soil is sampled should relate to the scale of spatial variation. A variogram can be used to guide sampling in two ways. A sampling interval of less than half the range of spatial dependence can be used, or the variogram can be used with the kriging equations to determine an optimal sampling interval to achieve a given tolerable error. A variogram might not be available for the site, but if the variograms of several soil properties were available on a similar parent material and or particular topographic positions an average variogram could be calculated from these. Averages of the variogram ranges and standardized average variograms from four different parent materials in southern England were used to suggest suitable sampling intervals for future surveys in similar pedological settings based on half the variogram range. The standardized average variograms were also used to determine optimal sampling intervals using the kriging equations. Similar sampling intervals were suggested by each method and the maps of predictions based on data at different grid spacings were evaluated for the different parent materials. Variograms of loss on ignition (LOI) taken from the literature for other sites in southern England with similar parent materials had ranges close to the average for a given parent material showing the possible wider application of such averages to guide sampling.

Improved intact soil-core carbon determination applying regression shrinkage and variable selection techniques to complete spectrum laser-induced breakdown spectroscopy (LIBS).

PubMed

Bricklemyer, Ross S; Brown, David J; Turk, Philip J; Clegg, Sam M

2013-10-01

Laser-induced breakdown spectroscopy (LIBS) provides a potential method for rapid, in situ soil C measurement. In previous research on the application of LIBS to intact soil cores, we hypothesized that ultraviolet (UV) spectrum LIBS (200-300 nm) might not provide sufficient elemental information to reliably discriminate between soil organic C (SOC) and inorganic C (IC). In this study, using a custom complete spectrum (245-925 nm) core-scanning LIBS instrument, we analyzed 60 intact soil cores from six wheat fields. Predictive multi-response partial least squares (PLS2) models using full and reduced spectrum LIBS were compared for directly determining soil total C (TC), IC, and SOC. Two regression shrinkage and variable selection approaches, the least absolute shrinkage and selection operator (LASSO) and sparse multivariate regression with covariance estimation (MRCE), were tested for soil C predictions and the identification of wavelengths important for soil C prediction. Using complete spectrum LIBS for PLS2 modeling reduced the calibration standard error of prediction (SEP) 15 and 19% for TC and IC, respectively, compared to UV spectrum LIBS. The LASSO and MRCE approaches provided significantly improved calibration accuracy and reduced SEP 32-55% over UV spectrum PLS2 models. We conclude that (1) complete spectrum LIBS is superior to UV spectrum LIBS for predicting soil C for intact soil cores without pretreatment; (2) LASSO and MRCE approaches provide improved calibration prediction accuracy over PLS2 but require additional testing with increased soil and target analyte diversity; and (3) measurement errors associated with analyzing intact cores (e.g., sample density and surface roughness) require further study and quantification.

Particle-size distribution models for the conversion of Chinese data to FAO/USDA system.

PubMed

Shangguan, Wei; Dai, YongJiu; García-Gutiérrez, Carlos; Yuan, Hua

2014-01-01

We investigated eleven particle-size distribution (PSD) models to determine the appropriate models for describing the PSDs of 16349 Chinese soil samples. These data are based on three soil texture classification schemes, including one ISSS (International Society of Soil Science) scheme with four data points and two Katschinski's schemes with five and six data points, respectively. The adjusted coefficient of determination r (2), Akaike's information criterion (AIC), and geometric mean error ratio (GMER) were used to evaluate the model performance. The soil data were converted to the USDA (United States Department of Agriculture) standard using PSD models and the fractal concept. The performance of PSD models was affected by soil texture and classification of fraction schemes. The performance of PSD models also varied with clay content of soils. The Anderson, Fredlund, modified logistic growth, Skaggs, and Weilbull models were the best.

Accounting for the measurement error of spectroscopically inferred soil carbon data for improved precision of spatial predictions.

PubMed

Somarathna, P D S N; Minasny, Budiman; Malone, Brendan P; Stockmann, Uta; McBratney, Alex B

2018-08-01

Spatial modelling of environmental data commonly only considers spatial variability as the single source of uncertainty. In reality however, the measurement errors should also be accounted for. In recent years, infrared spectroscopy has been shown to offer low cost, yet invaluable information needed for digital soil mapping at meaningful spatial scales for land management. However, spectrally inferred soil carbon data are known to be less accurate compared to laboratory analysed measurements. This study establishes a methodology to filter out the measurement error variability by incorporating the measurement error variance in the spatial covariance structure of the model. The study was carried out in the Lower Hunter Valley, New South Wales, Australia where a combination of laboratory measured, and vis-NIR and MIR inferred topsoil and subsoil soil carbon data are available. We investigated the applicability of residual maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC) simulation methods to generate parameters of the Matérn covariance function directly from the data in the presence of measurement error. The results revealed that the measurement error can be effectively filtered-out through the proposed technique. When the measurement error was filtered from the data, the prediction variance almost halved, which ultimately yielded a greater certainty in spatial predictions of soil carbon. Further, the MCMC technique was successfully used to define the posterior distribution of measurement error. This is an important outcome, as the MCMC technique can be used to estimate the measurement error if it is not explicitly quantified. Although this study dealt with soil carbon data, this method is amenable for filtering the measurement error of any kind of continuous spatial environmental data. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of soil solution sampling techniques to assess metal fluxes from contaminated soil to groundwater.

PubMed

Coutelot, F; Sappin-Didier, V; Keller, C; Atteia, O

2014-12-01

The unsaturated zone plays a major role in elemental fluxes in terrestrial ecosystems. A representative chemical analysis of soil pore water is required for the interpretation of soil chemical phenomena and particularly to assess Trace Elements (TEs) mobility. This requires an optimal sampling system to avoid modification of the extracted soil water chemistry and allow for an accurate estimation of solute fluxes. In this paper, the chemical composition of soil solutions sampled by Rhizon® samplers connected to a standard syringe was compared to two other types of suction probes (Rhizon® + vacuum tube and Rhizon® + diverted flow system). We investigated the effects of different vacuum application procedures on concentrations of spiked elements (Cr, As, Zn) mixed as powder into the first 20 cm of 100-cm columns and non-spiked elements (Ca, Na, Mg) concentrations in two types of columns (SiO2 sand and a mixture of kaolinite + SiO2 sand substrates). Rhizon® was installed at different depths. The metals concentrations showed that (i) in sand, peak concentrations cannot be correctly sampled, thus the flux cannot be estimated, and the errors can easily reach a factor 2; (ii) in sand + clay columns, peak concentrations were larger, indicating that they could be sampled but, due to sorption on clay, it was not possible to compare fluxes at different depths. The different samplers tested were not able to reflect the elemental flux to groundwater and, although the Rhizon® + syringe device was more accurate, the best solution remains to be the use of a lysimeter, whose bottom is kept continuously at a suction close to the one existing in the soil.

Genesis and properties of wetland soils by VIS-NIR-SWIR as a technique for environmental monitoring.

PubMed

Demattê, José Alexandre Melo; Horák-Terra, Ingrid; Beirigo, Raphael Moreira; Terra, Fabrício da Silva; Marques, Karina Patrícia Prazeres; Fongaro, Caio Troula; Silva, Alexandre Christófaro; Vidal-Torrado, Pablo

2017-07-15

Wetlands are important ecosystems characterized by redoximorphic environments producing typical soil forming processes and organic carbon accumulation. Assessments and management of these areas are dependent on knowledge about soil characteristics and variability. By reflectance spectroscopy, information about soils can be obtained since their spectral behaviors are directly related to their chemical, physical, and mineralogical properties reflecting the pedogenetic processes and environment conditions. Our aims were: (a) to characterize the main soil classes of wetlands regarding their spectral behaviors in VIS-NIR-SWIR (350-2500 nm) and relate them to pedogenesis and environmental conditions, (b) to determine spectral ranges (bands) with greater expression of the main soil properties, (c) to identify spectral variations and similarities between hydromorphic soils from wetlands and other soils under different moisture conditions, and (d) to propose spectral models to quantify some chemical and physical soil properties used as environmental quality indicators. Nine soil profiles from the Pantanal region (Mato Grosso State, Brazil) and one from the Serra do Espinhaço Meridional (Minas Gerais State, Brazil) were investigated. Spectral morphology interpretation allowed identifying horizon differences regarding shape, absorption features and reflectance intensity. Some pedogenetic processes of wetland soils related to organic carbon accumulation and oxide iron variation were identified by spectra. Principal Component Analysis allowed discriminating soils from wetland and outside this area (oxidic environment). Quantification of organic carbon was possible with R 2 of 0.90 and low error. Quantification of clay content was masked by soils with organic carbon content over 2% where it was not possible to quantify with high R 2 and low error both properties when dataset has soil samples with high organic carbon content. By reflectance spectroscopy, important characteristics of wetland soils can be identified and used to distinguish from soils of different environments at low costs, reduced time, and with environmental quality. Copyright © 2017 Elsevier Ltd. All rights reserved.

[A site index model for Larix principis-rupprechtii plantation in Saihanba, north China].

PubMed

Wang, Dong-zhi; Zhang, Dong-yan; Jiang, Feng-ling; Bai, Ye; Zhang, Zhi-dong; Huang, Xuan-rui

2015-11-01

It is often difficult to estimate site indices for different types of plantation by using an ordinary site index model. The objective of this paper was to establish a site index model for plantations in varied site conditions, and assess the site qualities. In this study, a nonlinear mixed site index model was constructed based on data from the second class forest resources inventory and 173 temporary sample plots. The results showed that the main limiting factors for height growth of Larix principis-rupprechtii were elevation, slope, soil thickness and soil type. A linear regression model was constructed for the main constraining site factors and dominant tree height, with the coefficient of determination being 0.912, and the baseline age of Larix principis-rupprechtii determined as 20 years. The nonlinear mixed site index model parameters for the main site types were estimated (R2 > 0.85, the error between the predicted value and the actual value was in the range of -0.43 to 0.45, with an average root mean squared error (RMSE) in the range of 0.907 to 1.148). The estimation error between the predicted value and the actual value of dominant tree height for the main site types was in the confidence interval of [-0.95, 0.95]. The site quality of the high altitude-shady-sandy loam-medium soil layer was the highest and that of low altitude-sunny-sandy loam-medium soil layer was the lowest, while the other two sites were moderate.

Impacts of sampling design and estimation methods on nutrient leaching of intensively monitored forest plots in the Netherlands.

PubMed

de Vries, W; Wieggers, H J J; Brus, D J

2010-08-05

Element fluxes through forest ecosystems are generally based on measurements of concentrations in soil solution at regular time intervals at plot locations sampled in a regular grid. Here we present spatially averaged annual element leaching fluxes in three Dutch forest monitoring plots using a new sampling strategy in which both sampling locations and sampling times are selected by probability sampling. Locations were selected by stratified random sampling with compact geographical blocks of equal surface area as strata. In each sampling round, six composite soil solution samples were collected, consisting of five aliquots, one per stratum. The plot-mean concentration was estimated by linear regression, so that the bias due to one or more strata being not represented in the composite samples is eliminated. The sampling times were selected in such a way that the cumulative precipitation surplus of the time interval between two consecutive sampling times was constant, using an estimated precipitation surplus averaged over the past 30 years. The spatially averaged annual leaching flux was estimated by using the modeled daily water flux as an ancillary variable. An important advantage of the new method is that the uncertainty in the estimated annual leaching fluxes due to spatial and temporal variation and resulting sampling errors can be quantified. Results of this new method were compared with the reference approach in which daily leaching fluxes were calculated by multiplying daily interpolated element concentrations with daily water fluxes and then aggregated to a year. Results show that the annual fluxes calculated with the reference method for the period 2003-2005, including all plots, elements and depths, lies only in 53% of the cases within the range of the average +/-2 times the standard error of the new method. Despite the differences in results, both methods indicate comparable N retention and strong Al mobilization in all plots, with Al leaching being nearly equal to the leaching of SO(4) and NO(3) with fluxes expressed in mol(c) ha(-1) yr(-1). This illustrates that Al release, which is the clearest signal of soil acidification, is mainly due to the external input of SO(4) and NO(3).

Combining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlands.

PubMed

Egberth, Mikael; Nyberg, Gert; Næsset, Erik; Gobakken, Terje; Mauya, Ernest; Malimbwi, Rogers; Katani, Josiah; Chamuya, Nurudin; Bulenga, George; Olsson, Håkan

2017-12-01

Soil carbon and biomass depletion can be used to identify and quantify degraded soils, and by using remote sensing, there is potential to map soil conditions over large areas. Landsat 8 Operational Land Imager satellite data and airborne laser scanning data were evaluated separately and in combination for modeling soil organic carbon, above ground tree biomass and below ground tree biomass. The test site is situated in the Liwale district in southeastern Tanzania and is dominated by Miombo woodlands. Tree data from 15 m radius field-surveyed plots and samples of soil carbon down to a depth of 30 cm were used as reference data for tree biomass and soil carbon estimations. Cross-validated plot level error (RMSE) for predicting soil organic carbon was 28% using only Landsat 8, 26% using laser only, and 23% for the combination of the two. The plot level error for above ground tree biomass was 66% when using only Landsat 8, 50% for laser and 49% for the combination of Landsat 8 and laser data. Results for below ground tree biomass were similar to above ground biomass. Additionally it was found that an early dry season satellite image was preferable for modelling biomass while images from later in the dry season were better for modelling soil carbon. The results show that laser data is superior to Landsat 8 when predicting both soil carbon and biomass above and below ground in landscapes dominated by Miombo woodlands. Furthermore, the combination of laser data and Landsat data were marginally better than using laser data only.

Soil pH Errors Propagation from Measurements to Spatial Predictions - Cost Benefit Analysis and Risk Assessment Implications for Practitioners and Modelers

NASA Astrophysics Data System (ADS)

Owens, P. R.; Libohova, Z.; Seybold, C. A.; Wills, S. A.; Peaslee, S.; Beaudette, D.; Lindbo, D. L.

2017-12-01

The measurement errors and spatial prediction uncertainties of soil properties in the modeling community are usually assessed against measured values when available. However, of equal importance is the assessment of errors and uncertainty impacts on cost benefit analysis and risk assessments. Soil pH was selected as one of the most commonly measured soil properties used for liming recommendations. The objective of this study was to assess the error size from different sources and their implications with respect to management decisions. Error sources include measurement methods, laboratory sources, pedotransfer functions, database transections, spatial aggregations, etc. Several databases of measured and predicted soil pH were used for this study including the United States National Cooperative Soil Survey Characterization Database (NCSS-SCDB), the US Soil Survey Geographic (SSURGO) Database. The distribution of errors among different sources from measurement methods to spatial aggregation showed a wide range of values. The greatest RMSE of 0.79 pH units was from spatial aggregation (SSURGO vs Kriging), while the measurement methods had the lowest RMSE of 0.06 pH units. Assuming the order of data acquisition based on the transaction distance i.e. from measurement method to spatial aggregation the RMSE increased from 0.06 to 0.8 pH units suggesting an "error propagation". This has major implications for practitioners and modeling community. Most soil liming rate recommendations are based on 0.1 pH unit increments, while the desired soil pH level increments are based on 0.4 to 0.5 pH units. Thus, even when the measured and desired target soil pH are the same most guidelines recommend 1 ton ha-1 lime, which translates in 111 ha-1 that the farmer has to factor in the cost-benefit analysis. However, this analysis need to be based on uncertainty predictions (0.5-1.0 pH units) rather than measurement errors (0.1 pH units) which would translate in 555-1,111 investment that need to be assessed against the risk. The modeling community can benefit from such analysis, however, error size and spatial distribution for global and regional predictions need to be assessed against the variability of other drivers and impact on management decisions.

Stochastic error model corrections to improve the performance of bottom-up precipitation products for hydrologic applications

NASA Astrophysics Data System (ADS)

Maggioni, V.; Massari, C.; Ciabatta, L.; Brocca, L.

2016-12-01

Accurate quantitative precipitation estimation is of great importance for water resources management, agricultural planning, and forecasting and monitoring of natural hazards such as flash floods and landslides. In situ observations are limited around the Earth, especially in remote areas (e.g., complex terrain, dense vegetation), but currently available satellite precipitation products are able to provide global precipitation estimates with an accuracy that depends upon many factors (e.g., type of storms, temporal sampling, season, etc.). The recent SM2RAIN approach proposes to estimate rainfall by using satellite soil moisture observations. As opposed to traditional satellite precipitation methods, which sense cloud properties to retrieve instantaneous estimates, this new bottom-up approach makes use of two consecutive soil moisture measurements for obtaining an estimate of the fallen precipitation within the interval between two satellite overpasses. As a result, the nature of the measurement is different and complementary to the one of classical precipitation products and could provide a different valid perspective to substitute or improve current rainfall estimates. However, uncertainties in the SM2RAIN product are still not well known and could represent a limitation in utilizing this dataset for hydrological applications. Therefore, quantifying the uncertainty associated with SM2RAIN is necessary for enabling its use. The study is conducted over the Italian territory for a 5-yr period (2010-2014). A number of satellite precipitation error properties, typically used in error modeling, are investigated and include probability of detection, false alarm rates, missed events, spatial correlation of the error, and hit biases. After this preliminary uncertainty analysis, the potential of applying the stochastic rainfall error model SREM2D to correct SM2RAIN and to improve its performance in hydrologic applications is investigated. The use of SREM2D for characterizing the error in precipitation by SM2RAIN would be highly useful for the merging and the integration steps in its algorithm, i.e., the merging of multiple soil moisture derived products (e.g., SMAP, SMOS, ASCAT) and the integration of soil moisture derived and state of the art satellite precipitation products (e.g., GPM IMERG).

Characterization and forensic analysis of soil samples using laser-induced breakdown spectroscopy (LIBS).

PubMed

Jantzi, Sarah C; Almirall, José R

2011-07-01

A method for the quantitative elemental analysis of surface soil samples using laser-induced breakdown spectroscopy (LIBS) was developed and applied to the analysis of bulk soil samples for discrimination between specimens. The use of a 266 nm laser for LIBS analysis is reported for the first time in forensic soil analysis. Optimization of the LIBS method is discussed, and the results compared favorably to a laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) method previously developed. Precision for both methods was <10% for most elements. LIBS limits of detection were <33 ppm and bias <40% for most elements. In a proof of principle study, the LIBS method successfully discriminated samples from two different sites in Dade County, FL. Analysis of variance, Tukey's post hoc test and Student's t test resulted in 100% discrimination with no type I or type II errors. Principal components analysis (PCA) resulted in clear groupings of the two sites. A correct classification rate of 99.4% was obtained with linear discriminant analysis using leave-one-out validation. Similar results were obtained when the same samples were analyzed by LA-ICP-MS, showing that LIBS can provide similar information to LA-ICP-MS. In a forensic sampling/spatial heterogeneity study, the variation between sites, between sub-plots, between samples and within samples was examined on three similar Dade sites. The closer the sampling locations, the closer the grouping on a PCA plot and the higher the misclassification rate. These results underscore the importance of careful sampling for geographic site characterization.

Accounting for uncertainty in pedotransfer functions in vulnerability assessments of pesticide leaching to groundwater.

PubMed

Stenemo, Fredrik; Jarvis, Nicholas

2007-09-01

A simulation tool for site-specific vulnerability assessments of pesticide leaching to groundwater was developed, based on the pesticide fate and transport model MACRO, parameterized using pedotransfer functions and reasonable worst-case parameter values. The effects of uncertainty in the pedotransfer functions on simulation results were examined for 48 combinations of soils, pesticides and application timings, by sampling pedotransfer function regression errors and propagating them through the simulation model in a Monte Carlo analysis. An uncertainty factor, f(u), was derived, defined as the ratio between the concentration simulated with no errors, c(sim), and the 80th percentile concentration for the scenario. The pedotransfer function errors caused a large variation in simulation results, with f(u) ranging from 1.14 to 1440, with a median of 2.8. A non-linear relationship was found between f(u) and c(sim), which can be used to account for parameter uncertainty by correcting the simulated concentration, c(sim), to an estimated 80th percentile value. For fine-textured soils, the predictions were most sensitive to errors in the pedotransfer functions for two parameters regulating macropore flow (the saturated matrix hydraulic conductivity, K(b), and the effective diffusion pathlength, d) and two water retention function parameters (van Genuchten's N and alpha parameters). For coarse-textured soils, the model was also sensitive to errors in the exponent in the degradation water response function and the dispersivity, in addition to K(b), but showed little sensitivity to d. To reduce uncertainty in model predictions, improved pedotransfer functions for K(b), d, N and alpha would therefore be most useful. 2007 Society of Chemical Industry

Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.

The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals

NASA Astrophysics Data System (ADS)

Dong, Jianzhi; Crow, Wade T.; Bindlish, Rajat

2018-01-01

Knowledge of the temporal error structure for remotely sensed surface soil moisture retrievals can improve our ability to exploit them for hydrologic and climate studies. This study employs a triple collocation analysis to investigate both the total variance and temporal autocorrelation of errors in Soil Moisture Active and Passive (SMAP) products generated from two separate soil moisture retrieval algorithms, the vertically polarized brightness temperature-based single-channel algorithm (SCA-V, the current baseline SMAP algorithm) and the dual-channel algorithm (DCA). A key assumption made in SCA-V is that real-time vegetation opacity can be accurately captured using only a climatology for vegetation opacity. Results demonstrate that while SCA-V generally outperforms DCA, SCA-V can produce larger total errors when this assumption is significantly violated by interannual variability in vegetation health and biomass. Furthermore, larger autocorrelated errors in SCA-V retrievals are found in areas with relatively large vegetation opacity deviations from climatological expectations. This implies that a significant portion of the autocorrelated error in SCA-V is attributable to the violation of its vegetation opacity climatology assumption and suggests that utilizing a real (as opposed to climatological) vegetation opacity time series in the SCA-V algorithm would reduce the magnitude of autocorrelated soil moisture retrieval errors.

Stand-alone error characterisation of microwave satellite soil moisture using a Fourier method

USDA-ARS?s Scientific Manuscript database

Error characterisation of satellite-retrieved soil moisture (SM) is crucial for maximizing their utility in research and applications in hydro-meteorology and climatology. Error characteristics can provide insights for retrieval development and validation, and inform suitable strategies for data fus...

Error in Radar-Derived Soil Moisture due to Roughness Parameterization: An Analysis Based on Synthetical Surface Profiles

PubMed Central

Lievens, Hans; Vernieuwe, Hilde; Álvarez-Mozos, Jesús; De Baets, Bernard; Verhoest, Niko E.C.

2009-01-01

In the past decades, many studies on soil moisture retrieval from SAR demonstrated a poor correlation between the top layer soil moisture content and observed backscatter coefficients, which mainly has been attributed to difficulties involved in the parameterization of surface roughness. The present paper describes a theoretical study, performed on synthetical surface profiles, which investigates how errors on roughness parameters are introduced by standard measurement techniques, and how they will propagate through the commonly used Integral Equation Model (IEM) into a corresponding soil moisture retrieval error for some of the currently most used SAR configurations. Key aspects influencing the error on the roughness parameterization and consequently on soil moisture retrieval are: the length of the surface profile, the number of profile measurements, the horizontal and vertical accuracy of profile measurements and the removal of trends along profiles. Moreover, it is found that soil moisture retrieval with C-band configuration generally is less sensitive to inaccuracies in roughness parameterization than retrieval with L-band configuration. PMID:22399956

The applicability of the Lamendin method to skeletal remains buried for a 16-year period: a cautionary note.

PubMed

De Angelis, Danilo; Mele, Elia; Gibelli, Daniele; Merelli, Vera; Spagnoli, Laura; Cattaneo, Cristina

2015-01-01

The Lamendin method is widely reported as one of the most reliable means of age estimation of skeletal remains, but very little is known concerning the influence of burial in soil. This study aimed at verifying the reliability of the Lamendin method on corpses buried for 16 years in a cemetery. The Lamendin and the Prince and Ubelaker methods were applied. In all age groups except the 40- to 49-year-olds, the error was higher in the buried sample. The age-at-death error ranged between 10.7 and 36.8 years for the Lamendin method (vs. the reported 7.3-18.9 years) and 9.5 and 35.7 for the Prince and Ubelaker one (vs. the original 5.2-32.6 years); in all age groups, the error is closer to that found on archeological populations. These results suggest caution in applying the Lamendin method to forensic cases of human remains buried even for a brief period under soil. © 2014 American Academy of Forensic Sciences.

Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions

NASA Technical Reports Server (NTRS)

Nearing, Grey S.; Mocko, David M.; Peters-Lidard, Christa D.; Kumar, Sujay V.; Xia, Youlong

2016-01-01

Model benchmarking allows us to separate uncertainty in model predictions caused 1 by model inputs from uncertainty due to model structural error. We extend this method with a large-sample approach (using data from multiple field sites) to measure prediction uncertainty caused by errors in (i) forcing data, (ii) model parameters, and (iii) model structure, and use it to compare the efficiency of soil moisture state and evapotranspiration flux predictions made by the four land surface models in the North American Land Data Assimilation System Phase 2 (NLDAS-2). Parameters dominated uncertainty in soil moisture estimates and forcing data dominated uncertainty in evapotranspiration estimates; however, the models themselves used only a fraction of the information available to them. This means that there is significant potential to improve all three components of the NLDAS-2 system. In particular, continued work toward refining the parameter maps and look-up tables, the forcing data measurement and processing, and also the land surface models themselves, has potential to result in improved estimates of surface mass and energy balances.

Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions.

PubMed

Nearing, Grey S; Mocko, David M; Peters-Lidard, Christa D; Kumar, Sujay V; Xia, Youlong

2016-03-01

Model benchmarking allows us to separate uncertainty in model predictions caused by model inputs from uncertainty due to model structural error. We extend this method with a "large-sample" approach (using data from multiple field sites) to measure prediction uncertainty caused by errors in (i) forcing data, (ii) model parameters, and (iii) model structure, and use it to compare the efficiency of soil moisture state and evapotranspiration flux predictions made by the four land surface models in the North American Land Data Assimilation System Phase 2 (NLDAS-2). Parameters dominated uncertainty in soil moisture estimates and forcing data dominated uncertainty in evapotranspiration estimates; however, the models themselves used only a fraction of the information available to them. This means that there is significant potential to improve all three components of the NLDAS-2 system. In particular, continued work toward refining the parameter maps and look-up tables, the forcing data measurement and processing, and also the land surface models themselves, has potential to result in improved estimates of surface mass and energy balances.

Spatial variation in the degradation rate of the pesticides isoproturon, azoxystrobin and diflufenican in soil and its relationship with chemical and microbial properties.

PubMed

Bending, Gary D; Lincoln, Suzanne D; Edmondson, Rodney N

2006-01-01

The extent of within field variability in the degradation rate of the pesticides isoproturon, azoxystrobin and diflufenican, and the role of intrinsic soil factors and technical errors in contributing to the variability, was investigated in sites on sandy-loam and clay-loam. At each site, 40 topsoil samples were taken from a 160 x 60 m area, and pesticides applied in the laboratory. Time to 25% dissipation (DT25) ranged between 13 and 61 weeks for diflufenican, 5.6 and 17.2 weeks for azoxystrobin, and 0.3 and 12.5 weeks for isoproturon. Variability in DT25 was higher in the sandy-loam in which there was also greatest variability in soil chemical and microbial properties. Technical error associated with pesticide extraction, analysis and lack of model fit during derivation of DT25 accounted for between 5.3 and 25.8% of the variability for isoproturon and azoxystrobin, but could account for almost all the variability for diflufenican. Azoxystrobin DT25, sorption and pH were significantly correlated.

Progress, Potential and Pitfalls in the Use of Bomb 14C to Constrain Soil Carbon Dynamics

NASA Astrophysics Data System (ADS)

Baisden, W. T.

2007-12-01

Forty four years have passed since atmospheric testing of thermonuclear weapons injected a major 14C spike into the atmosphere-biosphere-hydrosphere system. The use of bomb 14C, in combination with millennial decay of 14C, remains the most effective empirical tool for constraining rates of carbon (C) cycling in soils at timescales beyond experimental manipulations (>5 years). In the last 20 years, accelerator mass spectrometry has greatly increased the potential and throughput of soil 14C studies. At present, atmospheric Δ14C appears to be stabilizing at more constant values as a result of reinjection of bomb 14C from decadal storage in forests and soils. This means that current and future studies using bomb 14C have different sensitivities and uncertainties compared to those carried out during periods of rapid Δ14C decline such as the 1970s, 80s and 90s. Bomb 14C proves most effective when archived soil samples are available: simply using bulk Δ14C from samples collected at two or more times can surpass single time point Δ14C from soil fractions in providing robust C cycling rates. Of course, measurement of Δ14C in soil fractions from time series samples can significantly improve estimates of C cycling parameters. Samples collected between ca. 1965 and 1995 have now greatly surpassed pre-bomb samples in utility, although pre-bomb samples retain considerable usefulness for estimating the size of inert (millennial) C pools. Major pitfalls in the use of bomb 14C, particularly for single time point samples and fractions, are mainly associated with model assumptions. For example, calculated residence times can be highly sensitive to a minor component of old C (<10% of total C). Similarly, calculated residence times are also highly dependent upon rates of soil C accumulation or loss. A final key source of error is lag times between C fixation from atmospheric CO2 and incorporation in the measured soil C pool, either due to long-lived plant tissue, or residence times in other soil pools/horizons. All work using Δ14C should consider sensitivity and uncertainty related to these issues. Major potential exists in the use of Δ14C to constrain soil C dynamics as a function of soil depth, in relation to major unexplained losses of soil C, and to probe the mechanisms and rates of soil organic matter stabilization. These areas of major potential all lay outside conventional use of Δ14C to calculate simple residence times.

Natural sampling strategy

NASA Technical Reports Server (NTRS)

Hallum, C. R.; Basu, J. P. (Principal Investigator)

1979-01-01

A natural stratum-based sampling scheme and the aggregation procedures for estimating wheat area, yield, and production and their associated prediction error estimates are described. The methodology utilizes LANDSAT imagery and agrophysical data to permit an improved stratification in foreign areas by ignoring political boundaries and restratifying along boundaries that are more homogeneous with respect to the distribution of agricultural density, soil characteristics, and average climatic conditions. A summary of test results is given including a discussion of the various problems encountered.

Stochastic estimation of plant-available soil water under fluctuating water table depths

NASA Astrophysics Data System (ADS)

Or, Dani; Groeneveld, David P.

1994-12-01

Preservation of native valley-floor phreatophytes while pumping groundwater for export from Owens Valley, California, requires reliable predictions of plant water use. These predictions are compared with stored soil water within well field regions and serve as a basis for managing groundwater resources. Soil water measurement errors, variable recharge, unpredictable climatic conditions affecting plant water use, and modeling errors make soil water predictions uncertain and error-prone. We developed and tested a scheme based on soil water balance coupled with implementation of Kalman filtering (KF) for (1) providing physically based soil water storage predictions with prediction errors projected from the statistics of the various inputs, and (2) reducing the overall uncertainty in both estimates and predictions. The proposed KF-based scheme was tested using experimental data collected at a location on the Owens Valley floor where the water table was artificially lowered by groundwater pumping and later allowed to recover. Vegetation composition and per cent cover, climatic data, and soil water information were collected and used for developing a soil water balance. Predictions and updates of soil water storage under different types of vegetation were obtained for a period of 5 years. The main results show that: (1) the proposed predictive model provides reliable and resilient soil water estimates under a wide range of external conditions; (2) the predicted soil water storage and the error bounds provided by the model offer a realistic and rational basis for decisions such as when to curtail well field operation to ensure plant survival. The predictive model offers a practical means for accommodating simple aspects of spatial variability by considering the additional source of uncertainty as part of modeling or measurement uncertainty.

Psychrometric measurement of soil water potential: Stability of calibration and test of pressure-plate samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, T.L.; Gee, G.W.; Heller, P.R.

1990-08-01

A commercially available thermocouple psychrometer sample changer (Decagon SC-10A) was used to measure the water potential of field soils ranging in texture from sand to silty clay loam over a range of {minus}0.5 to {minus}20.0 MPa. The standard error of prediction based on regression statistics was generally between 0.04 and 0.14 MPa at {minus}5 MPa. Replacing the measuring junction of the unit changed the calibration slightly; however, it did not significantly alter measurement accuracy. Calibration curves measured throughout a year of testing are consistent and indicate no systematic drift in calibration. Most measurement uncertainty is produced by shifts in themore » intercept in the calibration equation rather than the slope. Both the variability in intercept and the regression error seem to be random. Measurements taken with the SC-10A show that water potential in both sand and silt loam samples removed from 1.5-MPa pressure plates was often 0.5 to 1.0 MPa greater than the 1.5-MPa applied pressure. Limited data from 0.5-MPa pressure plates show close agreement between SC-10A measurements and pressure applied to these more permeable plates.« less

Use of Electronic Hand-held Devices for Collection of Savannah River Site Environmental Data - 13329

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marberry, Hugh; Moore, Winston

2013-07-01

Savannah River Nuclear Solutions has begun using Xplore Tablet PC's to collect data in the field for soil samples, groundwater samples, air samples and round sheets at the Savannah River Site (SRS). EPA guidelines for groundwater sampling are incorporated into the application to ensure the sample technician follows the proper protocol. The sample technician is guided through the process for sampling and round sheet data collection by a series of menus and input boxes. Field measurements and well stabilization information are entered into the tablet for uploading into Environmental Restoration Data Management System (ERDMS). The process helps to eliminate inputmore » errors and provides data integrity. A soil sample technician has the ability to collect information about location of sample, field parameter, describe the soil sample, print bottle labels, and print chain of custody for the sample that they have collected. An air sample technician has the ability to provide flow, pressure, hours of operation, print bottle labels and chain of custody for samples they collect. Round sheets are collected using the information provided in the various procedures. The data are collected and uploaded into ERDMS. The equipment used is weather proof and hardened for the field use. Global Positioning System (GPS) capabilities are integrated into the applications to provide the location where samples were collected and to help sample technicians locate wells that are not visited often. (authors)« less

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network.

PubMed

Jia, Zhenyi; Zhou, Shenglu; Su, Quanlong; Yi, Haomin; Wang, Junxiao

2017-12-26

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.

A Comparison of Soil Moisture Retrieval Models Using SIR-C Measurements over the Little Washita River Watershed

NASA Technical Reports Server (NTRS)

Wang, J. R.; Hsu, A.; Shi, J. C.; ONeill, P. E.; Engman, E. T.

1997-01-01

Six SIR-C L-band measurements over the Little Washita River watershed in Chickasha, Oklahoma during 11-17 April 1994 have been analyzed for studying the change of soil moisture in the region. Two algorithms developed recently for estimation of moisture content in bare soil were applied to these measurements and the results were compared with those sampled on the ground. There is a good agreement between the values of soil moisture estimated by either one of the algorithms and those measured from ground sampling for bare or sparsely vegetated fields. The standard error from this comparison is on the order of 0.05-0.06 cu cm/cu cm, which is comparable to that expected from a regression between backscattering coefficients and measured soil moisture. Both algorithms provide a poor estimation of soil moisture or fail to give solutions to areas covered with moderate or dense vegetation. Even for bare soils the number of pixels that bear no numerical solution from the application of either one of the two algorithms to the data is not negligible. Results from using one of these algorithms indicate that the fraction of these pixels becomes larger as the bare soils become drier. The other algorithm generally gives a larger fraction of these pixels when the fields are vegetation-covered. The implication and impact of these features are discussed in this article.

Using digital soil maps to infer edaphic affinities of plant species in Amazonia: Problems and prospects.

PubMed

Moulatlet, Gabriel Massaine; Zuquim, Gabriela; Figueiredo, Fernando Oliveira Gouvêa; Lehtonen, Samuli; Emilio, Thaise; Ruokolainen, Kalle; Tuomisto, Hanna

2017-10-01

Amazonia combines semi-continental size with difficult access, so both current ranges of species and their ability to cope with environmental change have to be inferred from sparse field data. Although efficient techniques for modeling species distributions on the basis of a small number of species occurrences exist, their success depends on the availability of relevant environmental data layers. Soil data are important in this context, because soil properties have been found to determine plant occurrence patterns in Amazonian lowlands at all spatial scales. Here we evaluate the potential for this purpose of three digital soil maps that are freely available online: SOTERLAC, HWSD, and SoilGrids. We first tested how well they reflect local soil cation concentration as documented with 1,500 widely distributed soil samples. We found that measured soil cation concentration differed by up to two orders of magnitude between sites mapped into the same soil class. The best map-based predictor of local soil cation concentration was obtained with a regression model combining soil classes from HWSD with cation exchange capacity (CEC) from SoilGrids. Next, we evaluated to what degree the known edaphic affinities of thirteen plant species (as documented with field data from 1,200 of the soil sample sites) can be inferred from the soil maps. The species segregated clearly along the soil cation concentration gradient in the field, but only partially along the model-estimated cation concentration gradient, and hardly at all along the mapped CEC gradient. The main problems reducing the predictive ability of the soil maps were insufficient spatial resolution and/or georeferencing errors combined with thematic inaccuracy and absence of the most relevant edaphic variables. Addressing these problems would provide better models of the edaphic environment for ecological studies in Amazonia.

[Simulation of cropland soil moisture based on an ensemble Kalman filter].

PubMed

Liu, Zhao; Zhou, Yan-Lian; Ju, Wei-Min; Gao, Ping

2011-11-01

By using an ensemble Kalman filter (EnKF) to assimilate the observed soil moisture data, the modified boreal ecosystem productivity simulator (BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station, Jiangsu Province of China during the growth seasons in 2000-2004. After the assimilation of observed data, the determination coefficient, root mean square error, and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943, 0.018-0.042, and 0.021-0.041, respectively, with the simulation precision improved significantly, as compared with that before assimilation, indicating the applicability of data assimilation in improving the simulation of soil moisture. The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture.

A new statistic to express the uncertainty of kriging predictions for purposes of survey planning.

NASA Astrophysics Data System (ADS)

Lark, R. M.; Lapworth, D. J.

2014-05-01

It is well-known that one advantage of kriging for spatial prediction is that, given the random effects model, the prediction error variance can be computed a priori for alternative sampling designs. This allows one to compare sampling schemes, in particular sampling at different densities, and so to decide on one which meets requirements in terms of the uncertainty of the resulting predictions. However, the planning of sampling schemes must account not only for statistical considerations, but also logistics and cost. This requires effective communication between statisticians, soil scientists and data users/sponsors such as managers, regulators or civil servants. In our experience the latter parties are not necessarily able to interpret the prediction error variance as a measure of uncertainty for decision making. In some contexts (particularly the solution of very specific problems at large cartographic scales, e.g. site remediation and precision farming) it is possible to translate uncertainty of predictions into a loss function directly comparable with the cost incurred in increasing precision. Often, however, sampling must be planned for more generic purposes (e.g. baseline or exploratory geochemical surveys). In this latter context the prediction error variance may be of limited value to a non-statistician who has to make a decision on sample intensity and associated cost. We propose an alternative criterion for these circumstances to aid communication between statisticians and data users about the uncertainty of geostatistical surveys based on different sampling intensities. The criterion is the consistency of estimates made from two non-coincident instantiations of a proposed sample design. We consider square sample grids, one instantiation is offset from the second by half the grid spacing along the rows and along the columns. If a sample grid is coarse relative to the important scales of variation in the target property then the consistency of predictions from two instantiations is expected to be small, and can be increased by reducing the grid spacing. The measure of consistency is the correlation between estimates from the two instantiations of the sample grid, averaged over a grid cell. We call this the offset correlation, it can be calculated from the variogram. We propose that this measure is easier to grasp intuitively than the prediction error variance, and has the advantage of having an upper bound (1.0) which will aid its interpretation. This quality measure is illustrated for some hypothetical examples, considering both ordinary kriging and factorial kriging of the variable of interest. It is also illustrated using data on metal concentrations in the soil of north-east England.

Evaluating the utility of mid-infrared spectral subspaces for predicting soil properties.

PubMed

Sila, Andrew M; Shepherd, Keith D; Pokhariyal, Ganesh P

2016-04-15

We propose four methods for finding local subspaces in large spectral libraries. The proposed four methods include (a) cosine angle spectral matching; (b) hit quality index spectral matching; (c) self-organizing maps and (d) archetypal analysis methods. Then evaluate prediction accuracies for global and subspaces calibration models. These methods were tested on a mid-infrared spectral library containing 1907 soil samples collected from 19 different countries under the Africa Soil Information Service project. Calibration models for pH, Mehlich-3 Ca, Mehlich-3 Al, total carbon and clay soil properties were developed for the whole library and for the subspace. Root mean square error of prediction was used to evaluate predictive performance of subspace and global models. The root mean square error of prediction was computed using a one-third-holdout validation set. Effect of pretreating spectra with different methods was tested for 1st and 2nd derivative Savitzky-Golay algorithm, multiplicative scatter correction, standard normal variate and standard normal variate followed by detrending methods. In summary, the results show that global models outperformed the subspace models. We, therefore, conclude that global models are more accurate than the local models except in few cases. For instance, sand and clay root mean square error values from local models from archetypal analysis method were 50% poorer than the global models except for subspace models obtained using multiplicative scatter corrected spectra with which were 12% better. However, the subspace approach provides novel methods for discovering data pattern that may exist in large spectral libraries.

Overestimation of Crop Root Biomass in Field Experiments Due to Extraneous Organic Matter

PubMed Central

Hirte, Juliane; Leifeld, Jens; Abiven, Samuel; Oberholzer, Hans-Rudolf; Hammelehle, Andreas; Mayer, Jochen

2017-01-01

Root biomass is one of the most relevant root parameters for studies of plant response to environmental change, soil carbon modeling or estimations of soil carbon sequestration. A major source of error in root biomass quantification of agricultural crops in the field is the presence of extraneous organic matter in soil: dead roots from previous crops, weed roots, incorporated above ground plant residues and organic soil amendments, or remnants of soil fauna. Using the isotopic difference between recent maize root biomass and predominantly C3-derived extraneous organic matter, we determined the proportions of maize root biomass carbon of total carbon in root samples from the Swiss long-term field trial “DOK.” We additionally evaluated the effects of agricultural management (bio-organic and conventional), sampling depth (0–0.25, 0.25–0.5, 0.5–0.75 m) and position (within and between maize rows), and root size class (coarse and fine roots) as defined by sieve mesh size (2 and 0.5 mm) on those proportions, and quantified the success rate of manual exclusion of extraneous organic matter from root samples. Only 60% of the root mass that we retrieved from field soil cores was actual maize root biomass from the current season. While the proportions of maize root biomass carbon were not affected by agricultural management, they increased consistently with soil depth, were higher within than between maize rows, and were higher in coarse (>2 mm) than in fine (≤2 and >0.5) root samples. The success rate of manual exclusion of extraneous organic matter from root samples was related to agricultural management and, at best, about 60%. We assume that the composition of extraneous organic matter is strongly influenced by agricultural management and soil depth and governs the effect size of the investigated factors. Extraneous organic matter may result in severe overestimation of recovered root biomass and has, therefore, large implications for soil carbon modeling and estimations of the climate change mitigation potential of soils. PMID:28298919

Estimating spatially distributed soil texture using time series of thermal remote sensing - a case study in central Europe

NASA Astrophysics Data System (ADS)

Müller, Benjamin; Bernhardt, Matthias; Jackisch, Conrad; Schulz, Karsten

2016-09-01

For understanding water and solute transport processes, knowledge about the respective hydraulic properties is necessary. Commonly, hydraulic parameters are estimated via pedo-transfer functions using soil texture data to avoid cost-intensive measurements of hydraulic parameters in the laboratory. Therefore, current soil texture information is only available at a coarse spatial resolution of 250 to 1000 m. Here, a method is presented to derive high-resolution (15 m) spatial topsoil texture patterns for the meso-scale Attert catchment (Luxembourg, 288 km2) from 28 images of ASTER (advanced spaceborne thermal emission and reflection radiometer) thermal remote sensing. A principle component analysis of the images reveals the most dominant thermal patterns (principle components, PCs) that are related to 212 fractional soil texture samples. Within a multiple linear regression framework, distributed soil texture information is estimated and related uncertainties are assessed. An overall root mean squared error (RMSE) of 12.7 percentage points (pp) lies well within and even below the range of recent studies on soil texture estimation, while requiring sparser sample setups and a less diverse set of basic spatial input. This approach will improve the generation of spatially distributed topsoil maps, particularly for hydrologic modeling purposes, and will expand the usage of thermal remote sensing products.

Geostatistical approach for assessing soil volumes requiring remediation: validation using lead-polluted soils underlying a former smelting works.

PubMed

Demougeot-Renard, Helene; De Fouquet, Chantal

2004-10-01

Assessing the volume of soil requiring remediation and the accuracy of this assessment constitutes an essential step in polluted site management. If this remediation volume is not properly assessed, misclassification may lead both to environmental risks (polluted soils may not be remediated) and financial risks (unexpected discovery of polluted soils may generate additional remediation costs). To minimize such risks, this paper proposes a geostatistical methodology based on stochastic simulations that allows the remediation volume and the uncertainty to be assessed using investigation data. The methodology thoroughly reproduces the conditions in which the soils are classified and extracted at the remediation stage. The validity of the approach is tested by applying it on the data collected during the investigation phase of a former lead smelting works and by comparing the results with the volume that has actually been remediated. This real remediated volume was composed of all the remediation units that were classified as polluted after systematic sampling and analysis during clean-up stage. The volume estimated from the 75 samples collected during site investigation slightly overestimates (5.3% relative error) the remediated volume deduced from 212 remediation units. Furthermore, the real volume falls within the range of uncertainty predicted using the proposed methodology.

An improved triple collocation algorithm for decomposing autocorrelated and white soil moisture retrieval errors

USDA-ARS?s Scientific Manuscript database

If not properly account for, auto-correlated errors in observations can lead to inaccurate results in soil moisture data analysis and reanalysis. Here, we propose a more generalized form of the triple collocation algorithm (GTC) capable of decomposing the total error variance of remotely-sensed surf...

Visible and Near-Infrared Spectroscopy Analysis of a Polycyclic Aromatic Hydrocarbon in Soils

PubMed Central

Okparanma, Reuben N.; Mouazen, Abdul M.

2013-01-01

Visible and near-infrared (VisNIR) spectroscopy is becoming recognised by soil scientists as a rapid and cost-effective measurement method for hydrocarbons in petroleum-contaminated soils. This study investigated the potential application of VisNIR spectroscopy (350–2500 nm) for the prediction of phenanthrene, a polycyclic aromatic hydrocarbon (PAH), in soils. A total of 150 diesel-contaminated soil samples were used in the investigation. Partial least-squares (PLS) regression analysis with full cross-validation was used to develop models to predict the PAH compound. Results showed that the PAH compound was predicted well with residual prediction deviation of 2.0–2.32, root-mean-square error of prediction of 0.21–0.25 mg kg−1, and coefficient of determination (r 2) of 0.75–0.83. The mechanism of prediction was attributed to covariation of the PAH with clay and soil organic carbon. Overall, the results demonstrated that the methodology may be used for predicting phenanthrene in soils utilizing the interrelationship between clay and soil organic carbon. PMID:24453798

Learning class descriptions from a data base of spectral reflectance of soil samples

NASA Technical Reports Server (NTRS)

Kimes, D. S.; Irons, J. R.; Levine, E. R.; Horning, N. A.

1993-01-01

Consideration is given to a program developed to learn class descriptions from positive and negative training examples of spectral reflectance data of bare soils. It is a combination of 'learning by example' and the generate-and-test paradigm and is designed to provide a robust learning environment that can handle error-prone data. The program was tested by having it learn class descriptions of various categories of organic carbon content, iron oxide content, and particle size distribution in soils. These class descriptions were then used to classify an array of targets. The program found the sequence of relationships between bands that contained the most important information to distinguish the classes. Physical explanations for the class descriptions obtained are presented.

Comment on 'Shang S. 2012. Calculating actual crop evapotranspiration under soil water stress conditions with appropriate numerical methods and time step. Hydrological Processes 26: 3338-3343. DOI: 10.1002/hyp.8405'

NASA Technical Reports Server (NTRS)

Yatheendradas, Soni; Narapusetty, Balachandrudu; Peters-Lidard, Christa; Funk, Christopher; Verdin, James

2014-01-01

A previous study analyzed errors in the numerical calculation of actual crop evapotranspiration (ET(sub a)) under soil water stress. Assuming no irrigation or precipitation, it constructed equations for ET(sub a) over limited soil-water ranges in a root zone drying out due to evapotranspiration. It then used a single crop-soil composite to provide recommendations about the appropriate usage of numerical methods under different values of the time step and the maximum crop evapotranspiration (ET(sub c)). This comment reformulates those ET(sub a) equations for applicability over the full range of soil water values, revealing a dependence of the relative error in numerical ET(sub a) on the initial soil water that was not seen in the previous study. It is shown that the recommendations based on a single crop-soil composite can be invalid for other crop-soil composites. Finally, a consideration of the numerical error in the time-cumulative value of ET(sub a) is discussed besides the existing consideration of that error over individual time steps as done in the previous study. This cumulative ET(sub a) is more relevant to the final crop yield.

[Prediction of soil nutrients spatial distribution based on neural network model combined with goestatistics].

PubMed

Li, Qi-Quan; Wang, Chang-Quan; Zhang, Wen-Jiang; Yu, Yong; Li, Bing; Yang, Juan; Bai, Gen-Chuan; Cai, Yan

2013-02-01

In this study, a radial basis function neural network model combined with ordinary kriging (RBFNN_OK) was adopted to predict the spatial distribution of soil nutrients (organic matter and total N) in a typical hilly region of Sichuan Basin, Southwest China, and the performance of this method was compared with that of ordinary kriging (OK) and regression kriging (RK). All the three methods produced the similar soil nutrient maps. However, as compared with those obtained by multiple linear regression model, the correlation coefficients between the measured values and the predicted values of soil organic matter and total N obtained by neural network model increased by 12. 3% and 16. 5% , respectively, suggesting that neural network model could more accurately capture the complicated relationships between soil nutrients and quantitative environmental factors. The error analyses of the prediction values of 469 validation points indicated that the mean absolute error (MAE) , mean relative error (MRE), and root mean squared error (RMSE) of RBFNN_OK were 6.9%, 7.4%, and 5. 1% (for soil organic matter), and 4.9%, 6.1% , and 4.6% (for soil total N) smaller than those of OK (P<0.01), and 2.4%, 2.6% , and 1.8% (for soil organic matter), and 2.1%, 2.8%, and 2.2% (for soil total N) smaller than those of RK, respectively (P<0.05).

Influences of sampling size and pattern on the uncertainty of correlation estimation between soil water content and its influencing factors

NASA Astrophysics Data System (ADS)

Lai, Xiaoming; Zhu, Qing; Zhou, Zhiwen; Liao, Kaihua

2017-12-01

In this study, seven random combination sampling strategies were applied to investigate the uncertainties in estimating the hillslope mean soil water content (SWC) and correlation coefficients between the SWC and soil/terrain properties on a tea + bamboo hillslope. One of the sampling strategies is the global random sampling and the other six are the stratified random sampling on the top, middle, toe, top + mid, top + toe and mid + toe slope positions. When each sampling strategy was applied, sample sizes were gradually reduced and each sampling size contained 3000 replicates. Under each sampling size of each sampling strategy, the relative errors (REs) and coefficients of variation (CVs) of the estimated hillslope mean SWC and correlation coefficients between the SWC and soil/terrain properties were calculated to quantify the accuracy and uncertainty. The results showed that the uncertainty of the estimations decreased as the sampling size increasing. However, larger sample sizes were required to reduce the uncertainty in correlation coefficient estimation than in hillslope mean SWC estimation. Under global random sampling, 12 randomly sampled sites on this hillslope were adequate to estimate the hillslope mean SWC with RE and CV ≤10%. However, at least 72 randomly sampled sites were needed to ensure the estimated correlation coefficients with REs and CVs ≤10%. Comparing with all sampling strategies, reducing sampling sites on the middle slope had the least influence on the estimation of hillslope mean SWC and correlation coefficients. Under this strategy, 60 sites (10 on the middle slope and 50 on the top and toe slopes) were enough to ensure the estimated correlation coefficients with REs and CVs ≤10%. This suggested that when designing the SWC sampling, the proportion of sites on the middle slope can be reduced to 16.7% of the total number of sites. Findings of this study will be useful for the optimal SWC sampling design.

Surface modeling of soil antibiotics.

PubMed

Shi, Wen-jiao; Yue, Tian-xiang; Du, Zheng-ping; Wang, Zong; Li, Xue-wen

2016-02-01

Large numbers of livestock and poultry feces are continuously applied into soils in intensive vegetable cultivation areas, and then some veterinary antibiotics are persistent existed in soils and cause health risk. For the spatial heterogeneity of antibiotic residues, developing a suitable technique to interpolate soil antibiotic residues is still a challenge. In this study, we developed an effective interpolator, high accuracy surface modeling (HASM) combined vegetable types, to predict the spatial patterns of soil antibiotics, using 100 surface soil samples collected from an intensive vegetable cultivation area located in east of China, and the fluoroquinolones (FQs), including ciprofloxacin (CFX), enrofloxacin (EFX) and norfloxacin (NFX), were analyzed as the target antibiotics. The results show that vegetable type is an effective factor to be combined to improve the interpolator performance. HASM achieves less mean absolute errors (MAEs) and root mean square errors (RMSEs) for total FQs (NFX+CFX+EFX), NFX, CFX and EFX than kriging with external drift (KED), stratified kriging (StK), ordinary kriging (OK) and inverse distance weighting (IDW). The MAE of HASM for FQs is 55.1 μg/kg, and the MAEs of KED, StK, OK and IDW are 99.0 μg/kg, 102.8 μg/kg, 106.3 μg/kg and 108.7 μg/kg, respectively. Further, RMSE simulated by HASM for FQs (CFX, EFX and NFX) are 106.2 μg/kg (88.6 μg/kg, 20.4 μg/kg and 39.2 μg/kg), and less 30% (27%, 22% and 36%), 33% (27%, 27% and 43%), 38% (34%, 23% and 41%) and 42% (32%, 35% and 51%) than the ones by KED, StK, OK and IDW, respectively. HASM also provides better maps with more details and more consistent maximum and minimum values of soil antibiotics compared with the measured data. The better performance can be concluded that HASM takes the vegetable type information as global approximate information, and takes local sampling data as its optimum control constraints. Copyright © 2015 Elsevier B.V. All rights reserved.

Soil emissivity and reflectance spectra measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sobrino, Jose A.; Mattar, Cristian; Pardo, Pablo

We present an analysis of the laboratory reflectance and emissivity spectra of 11 soil samples collected on different field campaigns carried out over a diverse suite of test sites in Europe, North Africa, and South America from 2002 to 2008. Hemispherical reflectance spectra were measured from 2.0 to 14 {mu}m with a Fourier transform infrared spectrometer, and x-ray diffraction analysis (XRD) was used to determine the mineralogical phases of the soil samples. Emissivity spectra were obtained from the hemispherical reflectance measurements using Kirchhoff's law and compared with in situ radiance measurements obtained with a CIMEL Electronique CE312-2 thermal radiometer andmore » converted to emissivity using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) temperature and emissivity separation algorithm. The CIMEL has five narrow bands at approximately the same positions as the ASTER. Results show a root mean square error typically below 0.015 between laboratory emissivity measurements and emissivity measurements derived from the field radiometer.« less

Static sampling of dynamic processes - a paradox?

NASA Astrophysics Data System (ADS)

Mälicke, Mirko; Neuper, Malte; Jackisch, Conrad; Hassler, Sibylle; Zehe, Erwin

2017-04-01

Environmental systems monitoring aims at its core at the detection of spatio-temporal patterns of processes and system states, which is a pre-requisite for understanding and explaining their baffling heterogeneity. Most observation networks rely on distributed point sampling of states and fluxes of interest, which is combined with proxy-variables from either remote sensing or near surface geophysics. The cardinal question on the appropriate experimental design of such a monitoring network has up to now been answered in many different ways. Suggested approaches range from sampling in a dense regular grid using for the so-called green machine, transects along typical catenas, clustering of several observations sensors in presumed functional units or HRUs, arrangements of those cluster along presumed lateral flow paths to last not least a nested, randomized stratified arrangement of sensors or samples. Common to all these approaches is that they provide a rather static spatial sampling, while state variables and their spatial covariance structure dynamically change in time. It is hence of key interest how much of our still incomplete understanding stems from inappropriate sampling and how much needs to be attributed to an inappropriate analysis of spatial data sets. We suggest that it is much more promising to analyze the spatial variability of processes, for instance changes in soil moisture values, than to investigate the spatial variability of soil moisture states themselves. This is because wetting of the soil, reflected in a soil moisture increase, is causes by a totally different meteorological driver - rainfall - than drying of the soil. We hence propose that the rising and the falling limbs of soil moisture time series belong essentially to different ensembles, as they are influenced by different drivers. Positive and negative temporal changes in soil moisture need, hence, to be analyzed separately. We test this idea using the CAOS data set as a benchmark. Specifically, we expect the covariance structure of the positive temporal changes of soil moisture to be dominated by the spatial structure of rain- and through-fall and saturated hydraulic conductivity. The covariance in temporarily decreasing soil moisture during radiation driven conditions is expect to be dominated by the spatial structure of retention properties and plant transpiration. An analysis of soil moisture changes has furthermore the advantage that those are free from systematic measurement errors.

Short term spatio-temporal variability of soil water-extractable calcium and magnesium after a low severity grassland fire in Lithuania.

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Martin, David

2014-05-01

Fire has important impacts on soil nutrient spatio-temporal distribution (Outeiro et al., 2008). This impact depends on fire severity, topography of the burned area, type of soil and vegetation affected, and the meteorological conditions post-fire. Fire produces a complex mosaic of impacts in soil that can be extremely variable at small plot scale in the space and time. In order to assess and map such a heterogeneous distribution, the test of interpolation methods is fundamental to identify the best estimator and to have a better understanding of soil nutrients spatial distribution. The objective of this work is to identify the short-term spatial variability of water-extractable calcium and magnesium after a low severity grassland fire. The studied area is located near Vilnius (Lithuania) at 54° 42' N, 25° 08 E, 158 masl. Four days after the fire, it was designed in a burned area a plot with 400 m2 (20 x 20 m with 5 m space between sampling points). Twenty five samples from top soil (0-5 cm) were collected immediately after the fire (IAF), 2, 5, 7 and 9 months after the fire (a total of 125 in all sampling dates). The original data of water-extractable calcium and magnesium did not respected the Gaussian distribution, thus a neperian logarithm (ln) was applied in order to normalize data. Significant differences of water-extractable calcium and magnesium among sampling dates were carried out with the Anova One-way test using the ln data. In order to assess the spatial variability of water-extractable calcium and magnesium, we tested several interpolation methods as Ordinary Kriging (OK), Inverse Distance to a Weight (IDW) with the power of 1, 2, 3 and 4, Radial Basis Functions (RBF) - Inverse Multiquadratic (IMT), Multilog (MTG), Multiquadratic (MTQ) Natural Cubic Spline (NCS) and Thin Plate Spline (TPS) - and Local Polynomial (LP) with the power of 1 and 2. Interpolation tests were carried out with Ln data. The best interpolation method was assessed using the cross validation method. Cross-validation was obtained by taking each observation in turn out of the sample pool and estimating from the remaining ones. The errors produced (observed-predicted) are used to evaluate the performance of each method. With these data, the mean error (ME) and root mean square error (RMSE) were calculated. The best method was the one which had the lower RMSE (Pereira et al. in press). The results shown significant differences among sampling dates in the water-extractable calcium (F= 138.78, p< 0.001) and extractable magnesium (F= 160.66; p< 0.001). Water-extractable calcium and magnesium was high IAF decreasing until 7 months after the fire, rising in the last sampling date. Among the tested methods, the most accurate to interpolate the water-extractable calcium were: IAF-IDW1; 2 Months-IDW1; 5 months-OK; 7 Months-IDW4 and 9 Months-IDW3. In relation to water-extractable magnesium the best interpolation techniques were: IAF-IDW2; 2 Months-IDW1; 5 months- IDW3; 7 Months-TPS and 9 Months-IDW1. These results suggested that the spatial variability of these water-extractable is variable with the time. The causes of this variability will be discussed during the presentation. References Outeiro, L., Aspero, F., Ubeda, X. (2008) Geostatistical methods to study spatial variability of soil cation after a prescribed fire and rainfall. Catena, 74: 310-320. Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. Modelling the impacts of wildfire on ash thickness in a short-term period, Land Degradation and Development, (In Press), DOI: 10.1002/ldr.2195

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

PubMed Central

Zhou, Shenglu; Su, Quanlong; Yi, Haomin

2017-01-01

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution. PMID:29278363

Spatial-temporal variability of soil moisture and its estimation across scales

NASA Astrophysics Data System (ADS)

Brocca, L.; Melone, F.; Moramarco, T.; Morbidelli, R.

2010-02-01

The soil moisture is a quantity of paramount importance in the study of hydrologic phenomena and soil-atmosphere interaction. Because of its high spatial and temporal variability, the soil moisture monitoring scheme was investigated here both for soil moisture retrieval by remote sensing and in view of the use of soil moisture data in rainfall-runoff modeling. To this end, by using a portable Time Domain Reflectometer, a sequence of 35 measurement days were carried out within a single year in seven fields located inside the Vallaccia catchment, central Italy, with area of 60 km2. Every sampling day, soil moisture measurements were collected at each field over a regular grid with an extension of 2000 m2. The optimization of the monitoring scheme, with the aim of an accurate mean soil moisture estimation at the field and catchment scale, was addressed by the statistical and the temporal stability. At the field scale, the number of required samples (NRS) to estimate the field-mean soil moisture within an accuracy of 2%, necessary for the validation of remotely sensed soil moisture, ranged between 4 and 15 for almost dry conditions (the worst case); at the catchment scale, this number increased to nearly 40 and it refers to almost wet conditions. On the other hand, to estimate the mean soil moisture temporal pattern, useful for rainfall-runoff modeling, the NRS was found to be lower. In fact, at the catchment scale only 10 measurements collected in the most "representative" field, previously determined through the temporal stability analysis, can reproduce the catchment-mean soil moisture with a determination coefficient, R2, higher than 0.96 and a root-mean-square error, RMSE, equal to 2.38%. For the "nonrepresentative" fields the accuracy in terms of RMSE decreased, but similar R2 coefficients were found. This insight can be exploited for the sampling in a generic field when it is sufficient to know an index of soil moisture temporal pattern to be incorporated in conceptual rainfall-runoff models. The obtained results can address the soil moisture monitoring network design from which a reliable soil moisture temporal pattern at the catchment scale can be derived.

The natural abundance of 13C with different agricultural management by NIRS with fibre optic probe technology.

PubMed

Fuentes, Mariela; González-Martín, Inmaculada; Hernández-Hierro, Jose Miguel; Hidalgo, Claudia; Govaerts, Bram; Etchevers, Jorge; Sayre, Ken D; Dendooven, Luc

2009-06-30

In the present study the natural abundance of (13)C is quantified in agricultural soils in Mexico which have been submitted to different agronomic practices, zero and conventional tillage, retention of crop residues (with and without) and rotation of crops (wheat and maize) for 17 years, which have influenced the physical, chemical and biological characteristics of the soil. The natural abundance of C13 is quantified by near infrared spectra (NIRS) with a remote reflectance fibre optic probe, applying the probe directly to the soil samples. Discriminate partial least squares analysis of the near infrared spectra allowed to classify soils with and without residues, regardless of the type of tillage or rotation systems used with a prediction rate of 90% in the internal validation and 94% in the external validation. The NIRS calibration model using a modified partial least squares regression allowed to determine the delta(13)C in soils with or without residues, with multiple correlation coefficients 0.81 and standard error prediction 0.5 per thousand in soils with residues and 0.92 and 0.2 per thousand in soils without residues. The ratio performance deviation for the quantification of delta(13)C in soil was 2.5 in soil with residues and 3.8 without residues. This indicated that the model was adequate to determine the delta(13)C of unknown soils in the -16.2 per thousand to -20.4 per thousand range. The development of the NIR calibration permits analytic determinations of the values of delta(13)C in unknown agricultural soils in less time, employing a non-destructive method, by the application of the fibre optic probe of remote reflectance to the soil sample.

Benefit of Modeling the Observation Error in a Data Assimilation Framework Using Vegetation Information Obtained From Passive Based Microwave Data

NASA Technical Reports Server (NTRS)

Bolten, John D.; Mladenova, Iliana E.; Crow, Wade; De Jeu, Richard

2016-01-01

A primary operational goal of the United States Department of Agriculture (USDA) is to improve foreign market access for U.S. agricultural products. A large fraction of this crop condition assessment is based on satellite imagery and ground data analysis. The baseline soil moisture estimates that are currently used for this analysis are based on output from the modified Palmer two-layer soil moisture model, updated to assimilate near-real time observations derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The current data assimilation system is based on a 1-D Ensemble Kalman Filter approach, where the observation error is modeled as a function of vegetation density. This allows for offsetting errors in the soil moisture retrievals. The observation error is currently adjusted using Normalized Difference Vegetation Index (NDVI) climatology. In this paper we explore the possibility of utilizing microwave-based vegetation optical depth instead.

Overestimation of organic phosphorus in wetland soils by alkaline extraction and molybdate colorimetry.

PubMed

Turner, Benjamin L; Newman, Susan; Reddy, K Ramesh

2006-05-15

Accurate information on the chemical nature of soil phosphorus is essential for understanding its bioavailability and fate in wetland ecosystems. Solution phosphorus-31 nuclear magnetic resonance (31P NMR) spectroscopy was used to assess the conventional colorimetric procedure for phosphorus speciation in alkaline extracts of organic soils from the Florida Everglades. Molybdate colorimetry markedly overestimated organic phosphorus by between 30 and 54% compared to NMR spectroscopy. This was due in large part to the association of inorganic phosphate with organic matter, although the error was exacerbated in some samples by the presence of pyrophosphate, an inorganic polyphosphate that is not detected by colorimetry. The results have important implications for our understanding of phosphorus biogeochemistry in wetlands and suggest that alkaline extraction and solution 31p NMR spectroscopy is the only accurate method for quantifying organic phosphorus in wetland soils.

Can weighing lysimeter ET represent surrounding field ET well enough to test flux station measurements of daily and sub-daily ET?

USDA-ARS?s Scientific Manuscript database

Weighing lysimeters and neutron probes are two tools used to determine the change in soil water storage that is needed to solve for evapotranspiration (ET) using the soil water balance equation. Errors in the soil water balance due to errors in determination of precipitation and irrigation are commo...

Estimating soil matric potential in Owens Valley, California

USGS Publications Warehouse

Sorenson, Stephen K.; Miller, R.F.; Welch, M.R.; Groeneveld, D.P.; Branson, F.A.

1988-01-01

Much of the floor of the Owens Valley, California, is covered with alkaline scrub and alkaline meadow plant communities, whose existence is dependent partly on precipitation and partly on water infiltrated into the rooting zone from the shallow water table. The extent to which these plant communities are capable of adapting to and surviving fluctuations in the water table depends on physiological adaptations of the plants and on the water content, matric potential characteristics of the soils. Two methods were used to estimate soil matric potential in test sites in Owens Valley. The first was the filter-paper method, which uses water content of filter papers equilibrated to water content of soil samples taken with a hand auger. The other method of estimating soil matric potential was a modeling approach based on data from this and previous investigations. These data indicate that the base 10 logarithm of soil matric potential is a linear function of gravimetric soil water content for a particular soil. Estimates of soil water characteristic curves were made at two sites by averaging the gravimetric soil water content and soil matric potential values from multiple samples at 0.1 m depths derived by using the hand auger and filter paper method and entering these values in the soil water model. The characteristic curves then were used to estimate soil matric potential from estimates of volumetric soil water content derived from neutron-probe readings. Evaluation of the modeling technique at two study sites indicated that estimates of soil matric potential within 0.5 pF units of the soil matric potential value derived by using the filter paper method could be obtained 90 to 95% of the time in soils where water content was less than field capacity. The greatest errors occurred at depths where there was a distinct transition between soils of different textures. (Lantz-PTT)

Near infrared spectroscopy for determination of various physical, chemical and biochemical properties in Mediterranean soils.

PubMed

Zornoza, R; Guerrero, C; Mataix-Solera, J; Scow, K M; Arcenegui, V; Mataix-Beneyto, J

2008-07-01

The potential of near infrared (NIR) reflectance spectroscopy to predict various physical, chemical and biochemical properties in Mediterranean soils from SE Spain was evaluated. Soil samples (n=393) were obtained by sampling thirteen locations during three years (2003-2005 period). These samples had a wide range of soil characteristics due to variations in land use, vegetation cover and specific climatic conditions. Biochemical properties also included microbial biomarkers based on phospholipid fatty acids (PLFA). Partial least squares (PLS) regression with cross validation was used to establish relationships between the NIR spectra and the reference data from physical, chemical and biochemical analyses. Based on the values of coefficient of determination (r(2)) and the ratio of standard deviation of validation set to root mean square error of cross validation (RPD), predicted results were evaluated as excellent (r(2)>0.90 and RPD>3) for soil organic carbon, Kjeldahl nitrogen, soil moisture, cation exchange capacity, microbial biomass carbon, basal soil respiration, acid phosphatase activity, β-glucosidase activity and PLFA biomarkers for total bacteria, Gram positive bacteria, actinomycetes, vesicular-arbuscular mycorrhizal fungi and total PLFA biomass. Good predictions (0.81

A Comparison of Selected Statistical Techniques to Model Soil Cation Exchange Capacity

NASA Astrophysics Data System (ADS)

Khaledian, Yones; Brevik, Eric C.; Pereira, Paulo; Cerdà, Artemi; Fattah, Mohammed A.; Tazikeh, Hossein

2017-04-01

Cation exchange capacity (CEC) measures the soil's ability to hold positively charged ions and is an important indicator of soil quality (Khaledian et al., 2016). However, other soil properties are more commonly determined and reported, such as texture, pH, organic matter and biology. We attempted to predict CEC using different advanced statistical methods including monotone analysis of variance (MONANOVA), artificial neural networks (ANNs), principal components regressions (PCR), and particle swarm optimization (PSO) in order to compare the utility of these approaches and identify the best predictor. We analyzed 170 soil samples from four different nations (USA, Spain, Iran and Iraq) under three land uses (agriculture, pasture, and forest). Seventy percent of the samples (120 samples) were selected as the calibration set and the remaining 50 samples (30%) were used as the prediction set. The results indicated that the MONANOVA (R2= 0.82 and Root Mean Squared Error (RMSE) =6.32) and ANNs (R2= 0.82 and RMSE=5.53) were the best models to estimate CEC, PSO (R2= 0.80 and RMSE=5.54) and PCR (R2= 0.70 and RMSE=6.48) also worked well and the overall results were very similar to each other. Clay (positively correlated) and sand (negatively correlated) were the most influential variables for predicting CEC for the entire data set, while the most influential variables for the various countries and land uses were different and CEC was affected by different variables in different situations. Although the MANOVA and ANNs provided good predictions of the entire dataset, PSO gives a formula to estimate soil CEC using commonly tested soil properties. Therefore, PSO shows promise as a technique to estimate soil CEC. Establishing effective pedotransfer functions to predict CEC would be productive where there are limitations of time and money, and other commonly analyzed soil properties are available. References Khaledian, Y., Kiani, F., Ebrahimi, S., Brevik, E.C., Aitkenhead-Peterson, J. 2016. Assessment and monitoring of soil degradation during land use change using multivariate analysis. Land Degradation and Development. doi: 10.1002/ldr.2541.

K/Ar dating of lunar soils. II

NASA Technical Reports Server (NTRS)

Alexander, E. C., Jr.; Bates, A.; Coscio, M. R., Jr.; Dragon, J. C.; Murthy, V. R.; Pepin, R. O.; Venkatesan, T. R.

1976-01-01

An attempt is made to identify those K/Ar techniques which extract the most reliable chronological information from lunar soils and to define the situations in which the best data are obtainable. Results are presented for determinations of the exposure and K/Ar ages of five lunar soil samples, which were performed by applying correlation techniques for a two-component argon structure to stepwise-heated and neutron-irradiated aliquots of grain-sized separates. It is found that ages deduced from Ar-40/surface-correlated Ar-36 vs K-40/surface-correlated Ar-36 and analogous plots of data from grain-sized separates appear to be the best available K/Ar ages of submature to mature lunar soils, that ages deduced from Ar-40 vs Ar-36 and analogous plots which assume a uniform K content can be significantly in error, and that stepwise-heating (Ar-40)-(Ar-39) experiments yield useful information only for simple immature soils where the K-Ar systematics are dominated by a single component.

An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method

NASA Astrophysics Data System (ADS)

Lamorski, Krzysztof; Šimūnek, Jiří; Sławiński, Cezary; Lamorska, Joanna

2017-02-01

In this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models' development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models' estimations. Developed models are validated and compared with well-known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE = 0.018 m3/m3) that are significantly lower than those for the M77 (RMSE = 0.025 m3/m3) or KP87 (RMSE = 0. 047 m3/m3) models.

Using SMAP to identify structural errors in hydrologic models

NASA Astrophysics Data System (ADS)

Crow, W. T.; Reichle, R. H.; Chen, F.; Xia, Y.; Liu, Q.

2017-12-01

Despite decades of effort, and the development of progressively more complex models, there continues to be underlying uncertainty regarding the representation of basic water and energy balance processes in land surface models. Soil moisture occupies a central conceptual position between atmosphere forcing of the land surface and resulting surface water fluxes. As such, direct observations of soil moisture are potentially of great value for identifying and correcting fundamental structural problems affecting these models. However, to date, this potential has not yet been realized using satellite-based retrieval products. Using soil moisture data sets produced by the NASA Soil Moisture Active/Passive mission, this presentation will explore the use of the remotely-sensed soil moisture data products as a constraint to reject certain types of surface runoff parameterizations within a land surface model. Results will demonstrate that the precision of the SMAP Level 4 Surface and Root-Zone soil moisture product allows for the robust sampling of correlation statistics describing the true strength of the relationship between pre-storm soil moisture and subsequent storm-scale runoff efficiency (i.e., total storm flow divided by total rainfall both in units of depth). For a set of 16 basins located in the South-Central United States, we will use these sampled correlations to demonstrate that so-called "infiltration-excess" runoff parameterizations under predict the importance of pre-storm soil moisture for determining storm-scale runoff efficiency. To conclude, we will discuss prospects for leveraging this insight to improve short-term hydrologic forecasting and additional avenues for SMAP soil moisture products to provide process-level insight for hydrologic modelers.

Metrological assessment of the methods for measuring the contents of acids and ion metals responsible for the exchangeable acidity of soils

NASA Astrophysics Data System (ADS)

Vanchikova, E. V.; Shamrikova, E. V.; Bespyatykh, N. V.; Kyz"yurova, E. V.; Kondratenok, B. M.

2015-02-01

Metrological characteristics—precision, trueness, and accuracy—of the results of measurements of the exchangeable acidity and its components by the potentiometric titration method were studied on the basis of multiple analyses of the soil samples with the examination of statistical data for the outliers and their correspondence to the normal distribution. Measurement errors were estimated. The applied method was certified by the Metrological Center of the Uralian Branch of the Russian Academy of Sciences (certificate no. 88-17641-094-2013) and included in the Federal Information Fund on Assurance of Measurements (FR 1.31.2013.16382).

Determination of Aroclor 1260 in soil samples by gas chromatography with mass spectrometry and solid-phase microextraction.

PubMed

Zhang, Mengliang; Jackson, Glen P; Kruse, Natalie A; Bowman, Jennifer R; Harrington, Peter de B

2014-10-01

A novel fast screening method was developed for the determination of polychlorinated biphenyls that are constituents of the commercial mixture, Aroclor 1260, in soil matrices by gas chromatography with mass spectrometry combined with solid-phase microextraction. Nonequilibrium headspace solid-phase microextraction with a 100 μm polydimethylsiloxane fiber was used to extract polychlorinated biphenyls from 0.5 g of soil matrix. The use of 2 mL of saturated potassium dichromate in 6 M sulfuric acid solution improved the reproducibility of the extractions and the mass transfer of the polychlorinated biphenyls from the soil matrix to the microextraction fiber via the headspace. The extraction time was 30 min at 100°C. The percent recoveries, which were evaluated using an Aroclor 1260 standard and liquid injection, were within the range of 54.9-65.7%. Two-way extracted ion chromatogram data were used to construct calibration curves. The relative error was <±15% and the relative standard deviation was <15%, which are respective measures of the accuracy and precision. The method was validated with certified soil samples and the predicted concentrations for Aroclor 1260 agreed with the certified values. The method was demonstrated to be linear from 10 to 1000 ng/g for Aroclor 1260 in dry soil. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Ecological risk assessment on heavy metals in soils: Use of soil diffuse reflectance mid-infrared Fourier-transform spectroscopy

PubMed Central

Wang, Cheng; Li, Wei; Guo, Mingxing; Ji, Junfeng

2017-01-01

The bioavailability of heavy metals in soil is controlled by their concentrations and soil properties. Diffuse reflectance mid-infrared Fourier-transform spectroscopy (DRIFTS) is capable of detecting specific organic and inorganic bonds in metal complexes and minerals and therefore, has been employed to predict soil composition and heavy metal contents. The present study explored the potential of DRIFTS for estimating soil heavy metal bioavailability. Soil and corresponding wheat grain samples from the Yangtze River Delta region were analyzed by DRIFTS and chemical methods. Statistical regression analyses were conducted to correlate the soil spectral information to the concentrations of Cd, Cr, Cu, Zn, Pb, Ni, Hg and Fe in wheat grains. The principal components in the spectra influencing soil heavy metal bioavailability were identified and used in prediction model construction. The established soil DRIFTS-based prediction models were applied to estimate the heavy metal concentrations in wheat grains in the mid-Yangtze River Delta area. The predicted heavy metal concentrations of wheat grain were highly consistent with the measured levels by chemical analysis, showing a significant correlation (r2 > 0.72) with acceptable root mean square error RMSE. In conclusion, DRIFTS is a promising technique for assessing the bioavailability of soil heavy metals and related ecological risk. PMID:28198802

Radon-222 concentrations in ground water and soil gas on Indian reservations in Wisconsin

USGS Publications Warehouse

DeWild, John F.; Krohelski, James T.

1995-01-01

For sites with wells finished in the sand and gravel aquifer, the coefficient of determination (R2) of the regression of concentration of radon-222 in ground water as a function of well depth is 0.003 and the significance level is 0.32, which indicates that there is not a statistically significant relation between radon-222 concentrations in ground water and well depth. The coefficient of determination of the regression of radon-222 in ground water and soil gas is 0.19 and the root mean square error of the regression line is 271 picocuries per liter. Even though the significance level (0.036) indicates a statistical relation, the root mean square error of the regression is so large that the regression equation would not give reliable predictions. Because of an inadequate number of samples, similar statistical analyses could not be performed for sites with wells finished in the crystalline and sedimentary bedrock aquifers.

Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions

PubMed Central

Hengl, Tomislav; Heuvelink, Gerard B. M.; Kempen, Bas; Leenaars, Johan G. B.; Walsh, Markus G.; Shepherd, Keith D.; Sila, Andrew; MacMillan, Robert A.; Mendes de Jesus, Jorge; Tamene, Lulseged; Tondoh, Jérôme E.

2015-01-01

80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008–2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management—organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15–75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data. PMID:26110833

Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions.

PubMed

Hengl, Tomislav; Heuvelink, Gerard B M; Kempen, Bas; Leenaars, Johan G B; Walsh, Markus G; Shepherd, Keith D; Sila, Andrew; MacMillan, Robert A; Mendes de Jesus, Jorge; Tamene, Lulseged; Tondoh, Jérôme E

2015-01-01

80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008-2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management--organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15-75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data.

Hydraulic conductivity of a sandy soil at low water content after compaction by various methods

USGS Publications Warehouse

Nimmo, John R.; Akstin, Katherine C.

1988-01-01

To investigate the degree to which compaction of a sandy soil influences its unsaturated hydraulic conductivity K, samples of Oakley sand (now in the Delhi series; mixed, thermic, Typic Xeropsamments) were packed to various densities and K was measured by the steady-state centrifuge method. The air-dry, machine packing was followed by centrifugal compression with the soil wet to about one-third saturation. Variations in (i) the impact frequency and (ii) the impact force during packing, and (iii) the amount of centrifugal force applied after packing, produced a range of porosity from 0.333 to 0.380. With volumetric water content θ between 0.06 and 0.12, K values were between 7 × 10−11 and 2 × 10−8 m/s. Comparisons of K at a single θ value for samples differing in porosity by about 3% showed as much as fivefold variation for samples prepared by different packing procedures, while there generally was negligible variation (within experimental error of 8%) where the porosity difference resulted from a difference in centrifugal force. Analysis involving capillary-theory models suggests that the differences in K can be related to differences in pore-space geometry inferred from water retention curves measured for the various samples.

Regional prediction of soil organic carbon content over croplands using airborne hyperspectral data

NASA Astrophysics Data System (ADS)

Vaudour, Emmanuelle; Gilliot, Jean-Marc; Bel, Liliane; Lefebvre, Josias; Chehdi, Kacem

2015-04-01

This study was carried out in the framework of the Prostock-Gessol3 and the BASC-SOCSENSIT projects, dedicated to the spatial monitoring of the effects of exogenous organic matter land application on soil organic carbon storage. It aims at identifying the potential of airborne hyperspectral AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with both contrasted soils and SOC contents, located in the western region of Paris, France. Soils comprise hortic or glossic luvisols, calcaric, rendzic cambisols and colluvic cambisols. Airborne AISA-Eagle data (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks which were georeferenced. Tracks were atmospherically corrected using a set of 22 synchronous field spectra of both bare soils, black and white targets and impervious surfaces. Atmospherically corrected track tiles were mosaicked at a 2 m-resolution resulting in a 66 Gb image. A SPOT4 satellite image was acquired the same day in the framework of the SPOT4-Take Five program of the French Space Agency (CNES) which provided it with atmospheric correction. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then NDVI calculation and thresholding enabled to map agricultural fields with bare soil. All 18 sampled sites known to be bare at this very date were correctly included in this map. A total of 85 sites sampled in 2013 or in the 3 previous years were identified as bare by means of this map. Predictions were made from the mosaic spectra which were related to topsoil SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples. The use of the total sample including 27 sites under cloud shadows led to non-significant results. Considering 43 sites outside cloud shadows only, median validation root-mean-square errors (RMSE) were ~4-4.5 g. kg-1. An additional set of 15 samples with bare soils led to similar RMSE values. Such results are only slightly better than those resulting from an earlier study with multispectral satellite images (Vaudour et al., 2013). The influence of soil surface condition and particularly soil roughness is discussed.

Spatial Variability of the Topsoil Organic Carbon in the Moso Bamboo Forests of Southern China in Association with Soil Properties

PubMed Central

Zhang, Houxi; Zhuang, Shunyao; Qian, Haiyan; Wang, Feng; Ji, Haibao

2015-01-01

Understanding the spatial variability of soil organic carbon (SOC) must be enhanced to improve sampling design and to develop soil management strategies in terrestrial ecosystems. Moso bamboo (Phyllostachys pubescens Mazel ex Houz.) forests have a high SOC storage potential; however, they also vary significantly spatially. This study investigated the spatial variability of SOC (0-20 cm) in association with other soil properties and with spatial variables in the Moso bamboo forests of Jian’ou City, which is a typical bamboo hometown in China. 209 soil samples were collected from Moso bamboo stands and then analyzed for SOC, bulk density (BD), pH, cation exchange capacity (CEC), and gravel content (GC) based on spatial distribution. The spatial variability of SOC was then examined using geostatistics. A Kriging map was produced through ordinary interpolation and required sample numbers were calculated by classical and Kriging methods. An aggregated boosted tree (ABT) analysis was also conducted. A semivariogram analysis indicated that ln(SOC) was best fitted with an exponential model and that it exhibited moderate spatial dependence, with a nugget/sill ratio of 0.462. SOC was significantly and linearly correlated with BD (r = −0.373**), pH (r = −0.429**), GC (r = −0.163*), CEC (r = 0.263**), and elevation (r = 0.192**). Moreover, the Kriging method requires fewer samples than the classical method given an expected standard error level as per a variance analysis. ABT analysis indicated that the physicochemical variables of soil affected SOC variation more significantly than spatial variables did, thus suggesting that the SOC in Moso bamboo forests can be strongly influenced by management practices. Thus, this study provides valuable information in relation to sampling strategy and insight into the potential of adjustments in agronomic measure, such as in fertilization for Moso bamboo production. PMID:25789615

Reflectance spectroscopy for the assessment of soil salt content in soils of the yellow river delta of China

USGS Publications Warehouse

Weng, Yongling; Gong, P.; Zhu, Z.

2008-01-01

There has been growing interest in the use of reflectance spectroscopy as a rapid and inexpensive tool for soil characterization. In this study, we collected 95 soil samples from the Yellow River Delta of China to investigate the level of soil salinity in relation to soil spectra. Sample plots were selected based on a field investigation and the corresponding soil salinity classification map to maximize variations of saline characteristics in the soil. Spectral reflectances of air-dried soil samples were measured using an Analytical Spectral Device (ASD) spectrometer (350-2500 nm) with an artificial light source. In the Yellow River Delta, the dominant chemical in the saline soil was NaCl and MgCl2. Soil spectra were analysed using two-thirds of the available samples, with the remaining one-third withheld for validation purposes. The analysis indicated that with some preprocessing, the reflectance at 1931-2123 nm and 2153-2254 nm was highly correlated with soil salt content (SSC). In the spectral region of 1931-2123 nm, the correlation R ranged from -0.80 to -0.87. In the region of 2153-2254 nm, the SSC was positively correlated with preprocessed reflectance (0.79-0.88). The preprocessing was done by fitting a convex hull to the reflectance curve and dividing the spectral reflectance by the value of the corresponding convex hull band by band. This process is called continuum removal, and the resulting ratio is called continuum removed reflectance (CR reflectance). However, the SSC did not have a high correlation with the unprocessed reflectance, and the correlation was always negative in the entire spectrum (350-2500 nm) with the strongest negative correlation at 1981 nm (R = -0.63). Moreover, we found a strong correlation (R=0.91) between a soil salinity index (SSI: Constructed using CR reflectance at 2052 nm and 2203 nm) and SSC. We estimated SSC as a function of SSI and SSI' (SSI': Constructed using unprocessed reflectance at 2052 nm and 2203 nm) using univariate regression. Validation of the estimation of SSC was conducted by comparing the estimated SSC with the holdout sample points. The comparison produced an estimated root mean squared error (RMSE) of 0.986 (SSC ranging from 0.06 to 12.30 g kg-1) and R2 of 0.873 for SSC with SSI as independent variable and RMSE of 1.248 and R2 of 0.8 for SSC with SSI' as independent variable. This study showed that a soil salinity index developed for CR reflectance at 2052 nm and 2203 nm on the basis of spectral absorption features of saline soil can be used as a quick and inexpensive method for soil salt-content estimation.

Minimising methodological biases to improve the accuracy of partitioning soil respiration using natural abundance 13C.

PubMed

Snell, Helen S K; Robinson, David; Midwood, Andrew J

2014-11-15

Microbial degradation of soil organic matter (heterotrophic respiration) is a key determinant of net ecosystem exchange of carbon, but it is difficult to measure because the CO2 efflux from the soil surface is derived not only from heterotrophic respiration, but also from plant root and rhizosphere respiration (autotrophic). Partitioning total CO2 efflux can be achieved using the different natural abundance stable isotope ratios (δ(13)C) of root and soil CO2. Successful partitioning requires very accurate measurements of total soil efflux δ(13)CO2 and the δ(13)CO2 of the autotrophic and heterotrophic sources, which typically differ by just 2-8‰. In Scottish moorland and grass mesocosm studies we systematically tested some of the most commonly used techniques in order to identify and minimise methodological errors. Typical partitioning methods are to sample the total soil-surface CO2 efflux using a chamber, then to sample CO2 from incubated soil-free roots and root-free soil. We investigated the effect of collar depth on chamber measurements of surface efflux δ(13)CO2 and the effect of incubation time on estimates of end-member δ(13)CO2. (1) a 5 cm increase in collar depth affects the measurement of surface efflux δ(13)CO2 by -1.5‰ and there are fundamental inconsistencies between modelled and measured biases; (2) the heterotrophic δ(13)CO2 changes by up to -4‰ within minutes of sampling; we recommend using regression to estimate the in situ δ(13)CO2 values; (3) autotrophic δ(13)CO2 measurements are reliable if root CO2 is sampled within an hour of excavation; (4) correction factors should be used to account for instrument drift of up to 3‰ and concentration-dependent non-linearity of CRDS (cavity ringdown spectroscopy) analysis. Methodological biases can lead to large inaccuracies in partitioning estimates. The utility of stable isotope partitioning of soil CO2 efflux will be enhanced by consensus on the optimum measurement protocols and by minimising disturbance, particularly during chamber measurements. Copyright © 2014 John Wiley & Sons, Ltd.

Triple collocation based merging of satellite soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

We propose a method for merging soil moisture retrievals from space borne active and passive microwave instruments based on weighted averaging taking into account the error characteristics of the individual data sets. The merging scheme is parameterized using error variance estimates obtained from u...

Organic carbon stocks and sequestration rates of forest soils in Germany.

PubMed

Grüneberg, Erik; Ziche, Daniel; Wellbrock, Nicole

2014-08-01

The National Forest Soil Inventory (NFSI) provides the Greenhouse Gas Reporting in Germany with a quantitative assessment of organic carbon (C) stocks and changes in forest soils. Carbon stocks of the organic layer and the mineral topsoil (30 cm) were estimated on the basis of ca. 1.800 plots sampled from 1987 to 1992 and resampled from 2006 to 2008 on a nationwide grid of 8 × 8 km. Organic layer C stock estimates were attributed to surveyed forest stands and CORINE land cover data. Mineral soil C stock estimates were linked with the distribution of dominant soil types according to the Soil Map of Germany (1 : 1 000 000) and subsequently related to the forest area. It appears that the C pool of the organic layer was largely depending on tree species and parent material, whereas the C pool of the mineral soil varied among soil groups. We identified the organic layer C pool as stable although C was significantly sequestered under coniferous forest at lowland sites. The mineral soils, however, sequestered 0.41 Mg C ha(-1) yr(-1) . Carbon pool changes were supposed to depend on stand age and forest transformation as well as an enhanced biomass input. Carbon stock changes were clearly attributed to parent material and soil groups as sandy soils sequestered higher amounts of C, whereas clayey and calcareous soils showed small gains and in some cases even losses of soil C. We further showed that the largest part of the overall sample variance was not explained by fine-earth stock variances, rather by the C concentrations variance. The applied uncertainty analyses in this study link the variability of strata with measurement errors. In accordance to other studies for Central Europe, the results showed that the applied method enabled a reliable nationwide quantification of the soil C pool development for a certain period. © 2014 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Organic carbon stocks and sequestration rates of forest soils in Germany

PubMed Central

Grüneberg, Erik; Ziche, Daniel; Wellbrock, Nicole

2014-01-01

The National Forest Soil Inventory (NFSI) provides the Greenhouse Gas Reporting in Germany with a quantitative assessment of organic carbon (C) stocks and changes in forest soils. Carbon stocks of the organic layer and the mineral topsoil (30 cm) were estimated on the basis of ca. 1.800 plots sampled from 1987 to 1992 and resampled from 2006 to 2008 on a nationwide grid of 8 × 8 km. Organic layer C stock estimates were attributed to surveyed forest stands and CORINE land cover data. Mineral soil C stock estimates were linked with the distribution of dominant soil types according to the Soil Map of Germany (1 : 1 000 000) and subsequently related to the forest area. It appears that the C pool of the organic layer was largely depending on tree species and parent material, whereas the C pool of the mineral soil varied among soil groups. We identified the organic layer C pool as stable although C was significantly sequestered under coniferous forest at lowland sites. The mineral soils, however, sequestered 0.41 Mg C ha−1 yr−1. Carbon pool changes were supposed to depend on stand age and forest transformation as well as an enhanced biomass input. Carbon stock changes were clearly attributed to parent material and soil groups as sandy soils sequestered higher amounts of C, whereas clayey and calcareous soils showed small gains and in some cases even losses of soil C. We further showed that the largest part of the overall sample variance was not explained by fine-earth stock variances, rather by the C concentrations variance. The applied uncertainty analyses in this study link the variability of strata with measurement errors. In accordance to other studies for Central Europe, the results showed that the applied method enabled a reliable nationwide quantification of the soil C pool development for a certain period. PMID:24616061

Predicting the Mineral Composition of Dust Aerosols. Part 2; Model Evaluation and Identification of Key Processes with Observations

NASA Technical Reports Server (NTRS)

Perlwitz, J. P.; Garcia-Pando, C. Perez; Miller, R. L.

2015-01-01

A global compilation of nearly sixty measurement studies is used to evaluate two methods of simulating the mineral composition of dust aerosols in an Earth system model. Both methods are based upon a Mean Mineralogical Table (MMT) that relates the soil mineral fractions to a global atlas of arid soil type. The Soil Mineral Fraction (SMF) method assumes that the aerosol mineral fractions match the fractions of the soil. The MMT is based upon soil measurements after wet sieving, a process that destroys aggregates of soil particles that would have been emitted from the original, undisturbed soil. The second method approximately reconstructs the emitted aggregates. This model is referred to as the Aerosol Mineral Fraction (AMF) method because the mineral fractions of the aerosols differ from those of the wet-sieved parent soil, partly due to reaggregation. The AMF method remedies some of the deficiencies of the SMF method in comparison to observations. Only the AMF method exhibits phyllosilicate mass at silt sizes, where they are abundant according to observations. In addition, the AMF quartz fraction of silt particles is in better agreement with measured values, in contrast to the overestimated SMF fraction. Measurements at distinct clay and silt particle sizes are shown to be more useful for evaluation of the models, in contrast to the sum over all particles sizes that is susceptible to compensating errors, as illustrated by the SMF experiment. Model errors suggest that allocation of the emitted silt fraction of each mineral into the corresponding transported size categories is an important remaining source of uncertainty. Evaluation of both models and the MMT is hindered by the limited number of size-resolved measurements of mineral content that sparsely sample aerosols from the major dust sources. The importance of climate processes dependent upon aerosol mineral composition shows the need for global and routine mineral measurements.

Predicting the mineral composition of dust aerosols – Part 2: Model evaluation and identification of key processes with observations

DOE PAGES

Perlwitz, J. P.; Perez Garcia-Pando, C.; Miller, R. L.

2015-10-21

A global compilation of nearly sixty measurement studies is used to evaluate two methods of simulating the mineral composition of dust aerosols in an Earth system model. Both methods are based upon a Mean Mineralogical Table (MMT) that relates the soil mineral fractions to a global atlas of arid soil type. The Soil Mineral Fraction (SMF) method assumes that the aerosol mineral fractions match the fractions of the soil. The MMT is based upon soil measurements after wet sieving, a process that destroys aggregates of soil particles that would have been emitted from the original, undisturbed soil. The second methodmore » approximately reconstructs the emitted aggregates. This model is referred to as the Aerosol Mineral Fraction (AMF) method because the mineral fractions of the aerosols differ from those of the wet-sieved parent soil, partly due to reaggregation. The AMF method remedies some of the deficiencies of the SMF method in comparison to observations. Only the AMF method exhibits phyllosilicate mass at silt sizes, where they are abundant according to observations. In addition, the AMF quartz fraction of silt particles is in better agreement with measured values, in contrast to the overestimated SMF fraction. Measurements at distinct clay and silt particle sizes are shown to be more useful for evaluation of the models, in contrast to the sum over all particles sizes that is susceptible to compensating errors, as illustrated by the SMF experiment. Model errors suggest that allocation of the emitted silt fraction of each mineral into the corresponding transported size categories is an important remaining source of uncertainty. Evaluation of both models and the MMT is hindered by the limited number of size-resolved measurements of mineral content that sparsely sample aerosols from the major dust sources. In conclusion, the importance of climate processes dependent upon aerosol mineral composition shows the need for global and routine mineral measurements.« less

ALGORITHM TO REDUCE APPROXIMATION ERROR FROM THE COMPLEX-VARIABLE BOUNDARY-ELEMENT METHOD APPLIED TO SOIL FREEZING.

USGS Publications Warehouse

Hromadka, T.V.; Guymon, G.L.

1985-01-01

An algorithm is presented for the numerical solution of the Laplace equation boundary-value problem, which is assumed to apply to soil freezing or thawing. The Laplace equation is numerically approximated by the complex-variable boundary-element method. The algorithm aids in reducing integrated relative error by providing a true measure of modeling error along the solution domain boundary. This measure of error can be used to select locations for adding, removing, or relocating nodal points on the boundary or to provide bounds for the integrated relative error of unknown nodal variable values along the boundary.

Assessing Statistically Significant Heavy-Metal Concentrations in Abandoned Mine Areas via Hot Spot Analysis of Portable XRF Data

PubMed Central

Kim, Sung-Min; Choi, Yosoon

2017-01-01

To develop appropriate measures to prevent soil contamination in abandoned mining areas, an understanding of the spatial variation of the potentially toxic trace elements (PTEs) in the soil is necessary. For the purpose of effective soil sampling, this study uses hot spot analysis, which calculates a z-score based on the Getis-Ord Gi* statistic to identify a statistically significant hot spot sample. To constitute a statistically significant hot spot, a feature with a high value should also be surrounded by other features with high values. Using relatively cost- and time-effective portable X-ray fluorescence (PXRF) analysis, sufficient input data are acquired from the Busan abandoned mine and used for hot spot analysis. To calibrate the PXRF data, which have a relatively low accuracy, the PXRF analysis data are transformed using the inductively coupled plasma atomic emission spectrometry (ICP-AES) data. The transformed PXRF data of the Busan abandoned mine are classified into four groups according to their normalized content and z-scores: high content with a high z-score (HH), high content with a low z-score (HL), low content with a high z-score (LH), and low content with a low z-score (LL). The HL and LH cases may be due to measurement errors. Additional or complementary surveys are required for the areas surrounding these suspect samples or for significant hot spot areas. The soil sampling is conducted according to a four-phase procedure in which the hot spot analysis and proposed group classification method are employed to support the development of a sampling plan for the following phase. Overall, 30, 50, 80, and 100 samples are investigated and analyzed in phases 1–4, respectively. The method implemented in this case study may be utilized in the field for the assessment of statistically significant soil contamination and the identification of areas for which an additional survey is required. PMID:28629168

Assessing Statistically Significant Heavy-Metal Concentrations in Abandoned Mine Areas via Hot Spot Analysis of Portable XRF Data.

PubMed

Kim, Sung-Min; Choi, Yosoon

2017-06-18

To develop appropriate measures to prevent soil contamination in abandoned mining areas, an understanding of the spatial variation of the potentially toxic trace elements (PTEs) in the soil is necessary. For the purpose of effective soil sampling, this study uses hot spot analysis, which calculates a z -score based on the Getis-Ord Gi* statistic to identify a statistically significant hot spot sample. To constitute a statistically significant hot spot, a feature with a high value should also be surrounded by other features with high values. Using relatively cost- and time-effective portable X-ray fluorescence (PXRF) analysis, sufficient input data are acquired from the Busan abandoned mine and used for hot spot analysis. To calibrate the PXRF data, which have a relatively low accuracy, the PXRF analysis data are transformed using the inductively coupled plasma atomic emission spectrometry (ICP-AES) data. The transformed PXRF data of the Busan abandoned mine are classified into four groups according to their normalized content and z -scores: high content with a high z -score (HH), high content with a low z -score (HL), low content with a high z -score (LH), and low content with a low z -score (LL). The HL and LH cases may be due to measurement errors. Additional or complementary surveys are required for the areas surrounding these suspect samples or for significant hot spot areas. The soil sampling is conducted according to a four-phase procedure in which the hot spot analysis and proposed group classification method are employed to support the development of a sampling plan for the following phase. Overall, 30, 50, 80, and 100 samples are investigated and analyzed in phases 1-4, respectively. The method implemented in this case study may be utilized in the field for the assessment of statistically significant soil contamination and the identification of areas for which an additional survey is required.

Mobility and bio-availability of heavy metals in anthropogenically contaminated alluvial (deluvial) meadow soils (EUTRIC FLUVISOLS)

NASA Astrophysics Data System (ADS)

Dinev, Nikolai; Hristova, Mariana; Tzolova, Venera

2015-04-01

The total content of heavy metals is not sufficient to assess the pollution and the risk for environment as it does not provide information for the type and solubility of heavy metals' compounds in soils. The purpose was to study and determine the mobility of heavy metals in anthropogenically contaminated alluvial (delluvial) meadow soils spread around the non-ferrous plant near the town of Asenovgrad in view of risk assessment for environment pollution. Soil samples from monitoring network (1x1 km) was used. The sequential extraction procedure described by Zein and Brummer (1989) was applied. Results showed that the easily mobilizable cadmium compounds predominate in both contaminated and not contaminated soils. The stable form of copper (associated with silicate minerals, carbonates or amorphous and crystalline oxide compounds) predominates only in non polluted soils and reviles the risk of the environment contamination. Lead spreads and accumulates as highly soluble (mobile) compounds and between 72.3 and 99.6 percent of the total lead is bioavailable in soils. The procedure is very suitable for studying the mobility of technogenic lead and copper in alluvial soils with neutral medium reaction and in particular at the high levels of cadmium contamination. In soils with alkaline reaction - polluted and unpolluted the error of analysis increases for all studied elements.

Estimating soil matric potential in Owens Valley, California

USGS Publications Warehouse

Sorenson, Stephen K.; Miller, Reuben F.; Welch, Michael R.; Groeneveld, David P.; Branson, Farrel A.

1989-01-01

Much of the floor of Owens Valley, California, is covered with alkaline scrub and alkaline meadow plant communities, whose existence is dependent partly on precipitation and partly on water infiltrated into the rooting zone from the shallow water table. The extent to which these plant communities are capable of adapting to and surviving fluctuations in the water table depends on physiological adaptations of the plants and on the water content, matric potential characteristics of the soils. Two methods were used to estimate soil matric potential in test sites in Owens Valley. The first, the filter-paper method, uses water content of filter papers equilibrated to water content of soil samples taken with a hand auger. The previously published calibration relations used to estimate soil matric potential from the water content of the filter papers were modified on the basis of current laboratory data. The other method of estimating soil matric potential was a modeling approach based on data from this and previous investigations. These data indicate that the base-10 logarithm of soil matric potential is a linear function of gravimetric soil water content for a particular soil. The slope and intercepts of this function vary with the texture and saturation capacity of the soil. Estimates of soil water characteristic curves were made at two sites by averaging the gravimetric soil water content and soil matric potential values from multiple samples at 0.1-m depth intervals derived by using the hand auger and filter-paper method and entering these values in the soil water model. The characteristic curves then were used to estimate soil matric potential from estimates of volumetric soil water content derived from neutron-probe readings. Evaluation of the modeling technique at two study sites indicated that estimates of soil matric potential within 0.5 pF units of the soil matric potential value derived by using the filter-paper method could be obtained 90 to 95 percent of the time in soils where water content was less than field capacity. The greatest errors occurred at depths where there was a distinct transition between soils of different textures.

Error sources in passive and active microwave satellite soil moisture over Australia

USDA-ARS?s Scientific Manuscript database

Development of a long-term climate record of soil moisture (SM) involves combining historic and present satellite-retrieved SM data sets. This in turn requires a consistent characterization and deep understanding of the systematic differences and errors in the individual data sets, which vary due to...

The error structure of the SMAP single and dual channel soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

Knowledge of the temporal error structure for remotely-sensed surface soil moisture retrievals can improve our ability to exploit them for hydrology and climate studies. This study employs a triple collocation type analysis to investigate both the total variance and temporal auto-correlation of erro...

Spatial variability of soil carbon, pH, available phosphorous and potassium in organic farm located in Mediterranean Croatia

NASA Astrophysics Data System (ADS)

Bogunović, Igor; Pereira, Paulo; Šeput, Miranda

2016-04-01

Soil organic carbon (SOC), pH, available phosphorus (P), and potassium (K) are some of the most important factors to soil fertility. These soil parameters are highly variable in space and time, with implications to crop production. The aim of this work is study the spatial variability of SOC, pH, P and K in an organic farm located in river Rasa valley (Croatia). A regular grid (100 x 100 m) was designed and 182 samples were collected on Silty Clay Loam soil. P, K and SOC showed moderate heterogeneity with coefficient of variation (CV) of 21.6%, 32.8% and 51.9%, respectively. Soil pH record low spatial variability with CV of 1.5%. Soil pH, P and SOC did not follow normal distribution. Only after a Box-Cox transformation, data respected the normality requirements. Directional exponential models were the best fitted and used to describe spatial autocorrelation. Soil pH, P and SOC showed strong spatial dependence with nugget to sill ratio with 13.78%, 0.00% and 20.29%, respectively. Only K recorded moderate spatial dependence. Semivariogram ranges indicate that future sampling interval could be 150 - 200 m in order to reduce sampling costs. Fourteen different interpolation models for mapping soil properties were tested. The method with lowest Root Mean Square Error was the most appropriated to map the variable. The results showed that radial basis function models (Spline with Tension and Completely Regularized Spline) for P and K were the best predictors, while Thin Plate Spline and inverse distance weighting models were the least accurate. The best interpolator for pH and SOC was the local polynomial with the power of 1, while the least accurate were Thin Plate Spline. According to soil nutrient maps investigated area record very rich supply with K while P supply was insufficient on largest part of area. Soil pH maps showed mostly neutral reaction while individual parts of alkaline soil indicate the possibility of penetration of seawater and salt accumulation in the soil profile. Future research should focus on spatial patterns on soil pH, electrical conductivity and sodium adsorption ratio. Keywords: geostatistics, semivariogram, interpolation models, soil chemical properties

Estimating Root Mean Square Errors in Remotely Sensed Soil Moisture over Continental Scale Domains

NASA Technical Reports Server (NTRS)

Draper, Clara S.; Reichle, Rolf; de Jeu, Richard; Naeimi, Vahid; Parinussa, Robert; Wagner, Wolfgang

2013-01-01

Root Mean Square Errors (RMSE) in the soil moisture anomaly time series obtained from the Advanced Scatterometer (ASCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E; using the Land Parameter Retrieval Model) are estimated over a continental scale domain centered on North America, using two methods: triple colocation (RMSETC ) and error propagation through the soil moisture retrieval models (RMSEEP ). In the absence of an established consensus for the climatology of soil moisture over large domains, presenting a RMSE in soil moisture units requires that it be specified relative to a selected reference data set. To avoid the complications that arise from the use of a reference, the RMSE is presented as a fraction of the time series standard deviation (fRMSE). For both sensors, the fRMSETC and fRMSEEP show similar spatial patterns of relatively highlow errors, and the mean fRMSE for each land cover class is consistent with expectations. Triple colocation is also shown to be surprisingly robust to representativity differences between the soil moisture data sets used, and it is believed to accurately estimate the fRMSE in the remotely sensed soil moisture anomaly time series. Comparing the ASCAT and AMSR-E fRMSETC shows that both data sets have very similar accuracy across a range of land cover classes, although the AMSR-E accuracy is more directly related to vegetation cover. In general, both data sets have good skill up to moderate vegetation conditions.

On-site semi-quantitative analysis for ammonium nitrate detection using digital image colourimetry.

PubMed

Choodum, Aree; Boonsamran, Pichapat; NicDaeid, Niamh; Wongniramaikul, Worawit

2015-12-01

Digital image colourimetry was successfully applied in the semi-quantitative analysis of ammonium nitrate using Griess's test with zinc reduction. A custom-built detection box was developed to enable reproducible lighting of samples, and was used with the built-in webcams of a netbook and an ultrabook for on-site detection. The webcams were used for colour imaging of chemical reaction products in the samples, while the netbook was used for on-site colour analysis. The analytical performance was compared to a commercial external webcam and a digital single-lens reflex (DSLR) camera. The relationship between Red-Green-Blue intensities and ammonium nitrate concentration was investigated. The green channel intensity (IG) was the most sensitive for the pink-violet products from ammonium nitrate that revealed a spectrometric absorption peak at 546 nm. A wide linear range (5 to 250 mgL⁻¹) with a high sensitivity was obtained with the built-in webcam of the ultrabook. A considerably lower detection limit (1.34 ± 0.05mgL⁻¹) was also obtained using the ultrabook, in comparison with the netbook (2.6 ± 0.2 mgL⁻¹), the external web cam (3.4 ± 0.1 mgL⁻¹) and the DSLR (8.0 ± 0.5 mgL⁻¹). The best inter-day precision (over 3 days) was obtained with the external webcam (0.40 to 1.34%RSD), while the netbook and the ultrabook had 0.52 to 3.62% and 1.25 to 4.99% RSDs, respectively. The relative errors were +3.6, +5.6 and -7.1%, on analysing standard ammonium nitrate solutions of known concentration using IG, for the ultrabook, the external webcam, and the netbook, respectively, while the DSLR gave -4.4% relative error. However, the IG of the pink-violet reaction product suffers from interference by soil, so that blank subtraction (|IG-IGblank| or |AG-AGblank|) is recommended for soil sample analysis. This method also gave very good accuracies of -0.11 to -5.61% for spiked soil samples and the results presented for five seized samples showed good correlations between the various imaging devices and spectrophotometer used to determine ammonium nitrate concentrations. Five post-blast soil samples were also analysed and pink-violet product were observed using Griess's test without zinc reduction indicating the absence of ammonium nitrate. This demonstrates significant potential for practical and accurate on-site semi-quantitative determinations of ammonium nitrate concentration. Copyright © 2015 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

Regional prediction of soil organic carbon content over temperate croplands using visible near-infrared airborne hyperspectral imagery and synchronous field spectra

NASA Astrophysics Data System (ADS)

Vaudour, E.; Gilliot, J. M.; Bel, L.; Lefevre, J.; Chehdi, K.

2016-07-01

This study aimed at identifying the potential of Vis-NIR airborne hyperspectral AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with both contrasted soils and SOC contents, located in the western region of Paris, France. Soil types comprised haplic luvisols, calcaric cambisols and colluvic cambisols. Airborne AISA-Eagle data (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks. Tracks were atmospherically corrected then mosaicked at a 2 m-resolution using a set of 24 synchronous field spectra of bare soils, black and white targets and impervious surfaces. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then calculation and thresholding of NDVI from an atmospherically corrected SPOT image acquired the same day enabled to map agricultural fields with bare soil. A total of 101 sites sampled either in 2013 or in the 3 previous years and in 2015 were identified as bare by means of this map. Predictions were made from the mosaic AISA spectra which were related to topsoil SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples, considering 74 sites outside cloud shadows only, and different sampling strategies for selecting calibration samples. Validation root-mean-square errors (RMSE) were comprised between 3.73 and 4.49 g Kg-1 and were ∼4 g Kg-1 in median. The most performing models in terms of coefficient of determination (R2) and Residual Prediction Deviation (RPD) values were the calibration models derived either from Kennard-Stone or conditioned Latin Hypercube sampling on smoothed spectra. The most generalizable model leading to lowest RMSE value of 3.73 g Kg-1 at the regional scale and 1.44 g Kg-1 at the within-field scale and low bias was the cross-validated leave-one-out PLSR model constructed with the 28 near-synchronous samples and raw spectra.

Quantifying Contaminant Mass for the Feasibility Study of the DuPont Chambers Works FUSRAP Site - 13510

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, Carl; Rahman, Mahmudur; Johnson, Ann

2013-07-01

The U.S. Army Corps of Engineers (USACE) - Philadelphia District is conducting an environmental restoration at the DuPont Chambers Works in Deepwater, New Jersey under the Formerly Utilized Sites Remedial Action Program (FUSRAP). Discrete locations are contaminated with natural uranium, thorium-230 and radium-226. The USACE is proposing a preferred remedial alternative consisting of excavation and offsite disposal to address soil contamination followed by monitored natural attenuation to address residual groundwater contamination. Methods were developed to quantify the error associated with contaminant volume estimates and use mass balance calculations of the uranium plume to estimate the removal efficiency of the proposedmore » alternative. During the remedial investigation, the USACE collected approximately 500 soil samples at various depths. As the first step of contaminant mass estimation, soil analytical data was segmented into several depth intervals. Second, using contouring software, analytical data for each depth interval was contoured to determine lateral extent of contamination. Six different contouring algorithms were used to generate alternative interpretations of the lateral extent of the soil contamination. Finally, geographical information system software was used to produce a three dimensional model in order to present both lateral and vertical extent of the soil contamination and to estimate the volume of impacted soil for each depth interval. The average soil volume from all six contouring methods was used to determine the estimated volume of impacted soil. This method also allowed an estimate of a standard deviation of the waste volume estimate. It was determined that the margin of error for the method was plus or minus 17% of the waste volume, which is within the acceptable construction contingency for cost estimation. USACE collected approximately 190 groundwater samples from 40 monitor wells. It is expected that excavation and disposal of contaminated soil will remove the contaminant source zone and significantly reduce contaminant concentrations in groundwater. To test this assumption, a mass balance evaluation was performed to estimate the amount of dissolved uranium that would remain in the groundwater after completion of soil excavation. As part of this evaluation, average groundwater concentrations for the pre-excavation and post-excavation aquifer plume area were calculated to determine the percentage of plume removed during excavation activities. In addition, the volume of the plume removed during excavation dewatering was estimated. The results of the evaluation show that approximately 98% of the aqueous uranium would be removed during the excavation phase. The USACE expects that residual levels of contamination will remain in groundwater after excavation of soil but at levels well suited for the selection of excavation combined with monitored natural attenuation as a preferred alternative. (authors)« less

Application of portable X-ray fluorescence spectrometry in environmental investigation of heavy metal-contaminated sites and comparison with laboratory analysis

NASA Astrophysics Data System (ADS)

Ding, Liang; Wang, Shui; Cai, Bingjie; Zhang, Mancheng; Qu, Changsheng

2018-02-01

In this study, portable X-ray fluorescence spectrometry (pXRF) was used to measure the heavy metal contents of As, Cu, Cr, Ni, Pb and Zn in the soils of heavy metal-contaminated sites. The precision, accuracy and system errors of pXRF were evaluated and compared with traditional laboratory methods to examine the suitability of in situ pXRF. The results show that the pXRF analysis achieved satisfactory accuracy and precision in measuring As, Cr, Cu, Ni, Pb, and Zn in soils, and meets the requirements of the relevant detection technology specifications. For the certified reference soil samples, the pXRF results of As, Cr, Cu, Ni, Pb, and Zn show good linear relationships and coefficients of determination with the values measured using the reference analysis methods; with the exception of Ni, all the measured values were within the 95% confidence level. In the soil samples, the coefficients of determination between Cu, Zn, Pb, and Ni concentrations measured laboratory pXRF and the values measured with laboratory analysis all reach 0.9, showing a good linear relationship; however, there were large deviations between methods for Cr and As. This study provides reference data and scientific support for rapid detection of heavy metals in soils using pXRF in site investigation, which can better guide the practical application of pXRF.

Research on the infiltration processes of lawn soils of the Babao River in the Qilian Mountain.

PubMed

Li, GuangWen; Feng, Qi; Zhang, FuPing; Cheng, AiFang

2014-01-01

Using a Guelph Permeameter, the soil water infiltration processes were analyzed in the Babao River of the Qilian Mountain in China. The results showed that the average soil initial infiltration and the steady infiltration rates in the upstream reaches of the Babao River are 1.93 and 0.99 cm/min, whereas those of the middle area are 0.48 cm/min and 0.21 cm/min, respectively. The infiltration processes can be divided into three stages: the rapidly changing stage (0-10 min), the slowly changing stage (10-30 min) and the stabilization stage (after 30 min). We used field data collected from lawn soils and evaluated the performances of the infiltration models of Philip, Kostiakov and Horton with the sum of squared error, the root mean square error, the coefficient of determination, the mean error, the model efficiency and Willmott's index of agreement. The results indicated that the Kostiakov model was most suitable for studying the infiltration process in the alpine lawn soils.

Prediction of SOC content by Vis-NIR spectroscopy at European scale using a modified local PLS algorithm

NASA Astrophysics Data System (ADS)

Nocita, M.; Stevens, A.; Toth, G.; van Wesemael, B.; Montanarella, L.

2012-12-01

In the context of global environmental change, the estimation of carbon fluxes between soils and the atmosphere has been the object of a growing number of studies. This has been motivated notably by the possibility to sequester CO2 into soils by increasing the soil organic carbon (SOC) stocks and by the role of SOC in maintaining soil quality. Spatial variability of SOC masks its slow accumulation or depletion, and the sampling density required to detect a change in SOC content is often very high and thus very expensive and labour intensive. Visible near infrared diffuse reflectance spectroscopy (Vis-NIR DRS) has been shown to be a fast, cheap and efficient tool for the prediction of SOC at fine scales. However, when applied to regional or country scales, Vis-NIR DRS did not provide sufficient accuracy as an alternative to standard laboratory soil analysis for SOC monitoring. Under the framework of Land Use/Cover Area Frame Statistical Survey (LUCAS) project of the European Commission's Joint Research Centre (JRC), about 20,000 samples were collected all over European Union. Soil samples were analyzed for several physical and chemical parameters, and scanned with a Vis-NIR spectrometer in the same laboratory. The scope of our research was to predict SOC content at European scale using LUCAS spectral library. We implemented a modified local partial least square regression (l-PLS) including, in addition to spectral distance, other potentially useful covariates (geography, texture, etc.) to select for each unknown sample a group of predicting neighbours. The dataset was split in mineral soils under cropland, mineral soils under grassland, mineral soils under woodland, and organic soils due to the extremely diverse spectral response of the four classes. Four every class training (70%) and test (30%) sets were created to calibrate and validate the SOC prediction models. The results showed very good prediction ability for mineral soils under cropland and mineral soils under grassland, with a root mean square error (RMSE) of 3.6 and 7.2 g C kg-1 respectively, while mineral soils under woodland and organic soils predictions were less accurate (RMSE of 11.9 and 51.1 g C kg-1). The RMSE was lower (except for organic soils) when sand content was used as covariate in the selection of the l-PLS predicting neighbours. The obtained results proved that: (i) Although the enormous spatial variability of European soils, the developed modified l-PLS algorithm was able to produce stable calibrations and accurate predictions. (ii) It is essential to invest in spectral libraries built according to sampling strategies, based on soil types, and a standardized laboratory protocol. (iii) Vis-NIR DRS spectroscopy is a powerful and cost effective tool to predict SOC content at regional/continental scales, and should be converted from a pure research discipline into a reference operational method decreasing the uncertainties of SOC monitoring and terrestrial ecosystems carbon fluxes at all scales.

A comparison of interpolation methods for predicting spatial variability of soil organic matter content in Eastern Croatia

NASA Astrophysics Data System (ADS)

Đurđević, Boris; Jug, Irena; Jug, Danijel; Vukadinović, Vesna; Bogunović, Igor; Brozović, Bojana; Stipešević, Bojan

2017-04-01

Soil organic matter (SOM) plays crucial role in soil health and productivity and represents one of the key functions for determining soil degradation and soil suitability for crop production. Nowadays, continuing decline of organic matter in soils in agroecosystems, due to inappropriate agricultural practice (burning and removal of crop residue, overgrazing, inappropriate tillage, etc.) and environmental conditions (climate change, extreme weather conditions, erosion) leads to devastating soil degradation processes and decreases soil productivity. The main objectives of this research is to compare three different interpolation methods (Inverse Distance Weighting IDW, Ordinary kriging OK and Empirical Bayesian Kriging EBK) and provide best spatial predictor in order to ensure detailed analysis of the agricultural land in Osijek-Baranja County, Croatia. A number of 9,099 soil samples have been compiled from layer 0-30 cm and analyzed in laboratory. The average value of SOM in the study area was 2.66%, while 70.7 % of samples had SOM value below 3% in Osijek-Baranja County. Among the applied methods, the lowest root mean square error was recorded under Empirical Bayesian Kriging method which had most accurately assessed soil organic matter. The main advantage of EBK is that the process of creating a valid kriging model is automated so the manual parameter adjusting is eliminated, and this resulted with reduced uncertainty of EBK model. Conducted interpolation and visualization of data showed that 85.7% of agricultural land in Osijek-Baranja County has SOM content lower than 3%, which may indicate some sort of soil degradation process. By using interpolation methods combined with visualization of data, we can detect problematic areas much easier and with additional analysis, suggest measures to repair degraded soils. This kind of approach to problem solving in agriculture can be applied on various agroecological conditions and can significantly facilitate and accelerate the decision-making process, and thus directly affect the profitability and sustainability of agricultural production.

National-Scale Changes in Soil Profile C and N in New Zealand Pastures are Determined by Land Use

NASA Astrophysics Data System (ADS)

Schipper, L. A.; Parfitt, R.; Ross, C.; Baisden, W. T.; Claydon, J.; Fraser, S.

2010-12-01

Grazed pasture is New Zealand’s predominant agricultural land-use and has been relatively recently developed from forest and native grasslands/shrub communities. From the 1850s onwards, land was cleared and exotic pastures established. Phosphorus fertilizer was increasingly used after 1950 which accelerated N fixation by clover. In the last two decades N fertilizers have been used, and grazing intensity has increased, thus affecting soil C and N. Re-sampling of 31 New Zealand soil profiles under grazed pasture measured surprisingly large losses of C and N over the last 2-3 decades (Schipper et al., 2007 Global Change Biology 13:1138-1144). These profiles were predominantly on the most intensively grazed flat land. We extended this re-sampling to 83 profiles (to 90 cm depth), to investigate whether changes in soil C and N stocks also occurred in less intensively managed pasture. Archived soils samples were analysed for total soil C and N alongside the newly collected samples. Intact cores were collected to determine bulk density through the profile. Over an average of 27 years, soils (0-30 cm) in flat dairy pastures significantly lost 0.73±0.16 Mg C ha-1y-1 and 57±16 kg N ha-1y-1 while we observed no change in soil C or N in flat pasture grazed by “dry stock” (e.g., sheep, beef), or in grazed tussock grasslands. Grazed hill country soils (0-30 cm) gained 0.52±0.18 Mg C ha-1y-1 and 66±18 kg N ha-1y-1. The losses of C and N were strongly correlated and C:N ratio has generally declined suggesting soils are becoming N saturated. Losses and gains also occurred in soil layers below 30 cm demonstrating that organic matter throughout the profile was responding to land use. The losses under dairying may be due to greater grazing pressure, fertilizer inputs and exports of C and N. There is evidence that grazing pressure reduces inputs of C below ground, reduces soil microbial C, and that dairy cow urine can mobilise C and N. Gains in hill country pastures may be due to long-term recovery from erosion and disturbance following land clearance. When changes were extrapolated across New Zealand taking into account the different areas of pastures, gains and losses cancelled one another (Table 1) but none-the-less demonstrate considerable alteration of basic soil properties at national scales, and show the usefulness of resampling sites providing that older samples have been archived.Table 1. Change in total C and total N of grazed land for top 30 cm extrapolated across New Zealand. SEM - standard error of the mean

Soil Intake Rates Based on Arsenic in Urine Data | Science ...

EPA Pesticide Factsheets

The ingestion of soil is a potential source of human exposure to environmental contaminants. Several studies have been conducted to estimate the amount of soil ingested by children. The methodology used in these studies has consisted of a mass balance using measurements of certain tracers elements in the feces and urine. There are many uncertainties associated with this approach. The present study uses an innovative approach for deriving soil intake rates. The study uses data collected for children living near a copper smelter in Washington State in the town of Ruston, and from nearby Vashon and Maury Islands. The age of the children included in the study ranged from 2 to 13 years old. Distribution of soil and dust ingestion by children will be estimated based on arsenic concentrations found in urine, soil and air samples collected during three-day visits to each household in four quarters. External peer review comments did not support the use of data to predict soil intake rates due to data variability and measurement issues. The effort as originally proposed has been terminated but collected data and analysis will be used in a new project to evaluate methodologic issues associated with measurement error and variance. The purpose of this task is to conduct an analysis of soil intake rates using environmental and biological measurements of arsenic.

Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non-targeted analysis of comparative samples.

PubMed

Titaley, Ivan A; Ogba, O Maduka; Chibwe, Leah; Hoh, Eunha; Cheong, Paul H-Y; Simonich, Staci L Massey

2018-03-16

Non-targeted analysis of environmental samples, using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC/ToF-MS), poses significant data analysis challenges due to the large number of possible analytes. Non-targeted data analysis of complex mixtures is prone to human bias and is laborious, particularly for comparative environmental samples such as contaminated soil pre- and post-bioremediation. To address this research bottleneck, we developed OCTpy, a Python™ script that acts as a data reduction filter to automate GC × GC/ToF-MS data analysis from LECO ® ChromaTOF ® software and facilitates selection of analytes of interest based on peak area comparison between comparative samples. We used data from polycyclic aromatic hydrocarbon (PAH) contaminated soil, pre- and post-bioremediation, to assess the effectiveness of OCTpy in facilitating the selection of analytes that have formed or degraded following treatment. Using datasets from the soil extracts pre- and post-bioremediation, OCTpy selected, on average, 18% of the initial suggested analytes generated by the LECO ® ChromaTOF ® software Statistical Compare feature. Based on this list, 63-100% of the candidate analytes identified by a highly trained individual were also selected by OCTpy. This process was accomplished in several minutes per sample, whereas manual data analysis took several hours per sample. OCTpy automates the analysis of complex mixtures of comparative samples, reduces the potential for human error during heavy data handling and decreases data analysis time by at least tenfold. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

NASA Astrophysics Data System (ADS)

Citakoglu, Hatice

2017-10-01

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient ( R 2 )) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R 2 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R 2 0.93, 0.91) in estimating soil temperature in Turkey.

Remote Sensing of Soil Moisture: A Comparison of Optical and Thermal Methods

NASA Astrophysics Data System (ADS)

Foroughi, H.; Naseri, A. A.; Boroomandnasab, S.; Sadeghi, M.; Jones, S. B.; Tuller, M.; Babaeian, E.

2017-12-01

Recent technological advances in satellite and airborne remote sensing have provided new means for large-scale soil moisture monitoring. Traditional methods for soil moisture retrieval require thermal and optical RS observations. In this study we compared the traditional trapezoid model parameterized based on the land surface temperature - normalized difference vegetation index (LST-NDVI) space with the recently developed optical trapezoid model OPTRAM parameterized based on the shortwave infrared transformed reflectance (STR)-NDVI space for an extensive sugarcane field located in Southwestern Iran. Twelve Landsat-8 satellite images were acquired during the sugarcane growth season (April to October 2016). Reference in situ soil moisture data were obtained at 22 locations at different depths via core sampling and oven-drying. The obtained results indicate that the thermal/optical and optical prediction methods are comparable, both with volumetric moisture content estimation errors of about 0.04 cm3 cm-3. However, the OPTRAM model is more efficient because it does not require thermal data and can be universally parameterized for a specific location, because unlike the LST-soil moisture relationship, the reflectance-soil moisture relationship does not significantly vary with environmental variables (e.g., air temperature, wind speed, etc.).

Contaminants in landfill soils - Reliability of prefeasibility studies.

PubMed

Hölzle, Ingo

2017-05-01

Recent landfill mining studies have researched the potential for resource recovery using samples from core drilling or grab cranes. However, most studies used small sample numbers, which may not represent the heterogeneous landfill composition. As a consequence, there exists a high risk of an incorrect economic and/or ecological evaluation. The main objective of this work is to investigate the possibilities and limitations of preliminary investigations concerning the crucial soil composition. The preliminary samples of landfill investigations were compared to the excavation samples from three completely excavated landfills in Germany. In addition, the research compared the reliability of prediction of the two investigation methods, core drilling and grab crane. Sampling using a grab crane led to better results, even for smaller investigations of 10 samples. Analyses of both methods showed sufficiently accurate results to make predictions (standard error 5%, level of confidence 95%) for most heavy metals, cyanide and PAH in the dry substance and for sulphate, barium, Benzo[a]pyrene, pH and the electrical conductivity in leachate analyses of soil type waste. While chrome and nickel showed less accurate results, the concentrations of hydrocarbons, TOC, DOC, PCB and fluorine (leachate) were not predictable even for sample numbers of up to 59. Overestimations of pollutant concentrations were more frequently apparent in drilling, and underestimations when using a grab crane. The dispersion of the element and elemental composition had no direct impact on the reliability of prediction. Thus, an individual consideration of the particular element or elemental composition for dry substance and leachate analyses is recommended to adapt the sample strategy and calculate an optimum sample number. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Impact of Model and Rainfall Forcing Errors on Characterizing Soil Moisture Uncertainty in Land Surface Modeling

NASA Technical Reports Server (NTRS)

Maggioni, V.; Anagnostou, E. N.; Reichle, R. H.

2013-01-01

The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

Spatial distribution of particulate organic matter pools, quantified and characterized by mid-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Bornemann, L.; Welp, G.; Amelung, W.

2009-04-01

Comprising more than 60 % of the terrestrial carbon pool, soil organic carbon (SOC) is one of the principal factors regulating the global C-cycle. Against the background of worldwide increasing CO2 emissions, much effort has been put to the modelling of soil-C turnover in order to evaluate its potential for mitigation of climate change. Soil organic matter is an ever changing assemblage of various organic components that interact with the mineral matrix and in dependence of its ecological environment. Carbon storage is thereby assumed to propagate by hierarchical saturation of different carbon pools. A homogeneous distribution of the respective pools within natural environments is unlikely as the controlling soil parameters are subject to spatial and temporal heterogeneity. Several attempts to operationalize this complex soil compartment have been proposed, most of them resting upon a concept of pools with different stability and varying turnover times. Among these pools, particulate organic matter (POM) is considered to be most sensitive to environmental changes and has been shown to explain major parts of the SOC variations. Until today, rather laborious physical and physico-chemical fractionation procedures are most commonly applied for the initialization and validation of POM in C-turnover models. Mid-infrared spectroscopy (MIRS) in combination with partial least squares regression (PLSR) could overcome this problem. The technique is fast, cheap, and requires little sample preparation. All the same, it is an appropriate technique not only for the determination of gross parameters like total soil organic carbon contents, but also for the determination and characterization of minor constituents like black carbon in soils. Basically, the infrared radiation is absorbed by molecules that express a dipole-moment during vibration. As virtually all constituents of soil organic matter and also a multitude of inorganic soil constituents express such a dipole-moment, plentiful chemical information can be extracted from absorption spectra of soil samples. In this work we present the development of calibration models for POM quantification via MIRS-PLSR, and the compilation of a raster data set including SOC and POM of three size classes for the testsite of the SFB-TR32 at Selhausen near Jülich (Germany). The studied test site is an orthic luvisol which has been sampled in a ten times ten meter raster from 0-30 cm depth (n=131). For POM fractionation samples were gently sonicated and material from 2000-250 µm was gained by wet sieving. After a second, more intense sonication, intermediate (250-53 µm) and fine (53-20 µm) material was also gained by wet sieving. All fractions were dried at 40 °C, carbon contents were determined by elemental analysis. For calibration of MIRS-PLSR, SOC contents of 87 bulk soil samples were determined by elemental analysis. Contributions of the different POM fractions to bulk SOC as well as the SOC contents within the particular POM fraction were determined for 36 soil samples by physical particle size fractionation as described above. MIRS-PLSR based predictions for the contribution of POM fractions to bulk soil proved to be satisfactory (R² >0.77) and improved with decreasing particle size. For the predictions of SOC contents in bulk soil and the different POM fractions R² even reached values ≥0.97. Root mean squared errors of the cross validations were in the range of standard deviations of the lab analysis or smaller. As physical fractionation methods are intrinsically susceptible to measurement errors, determination of POM fractions by MIRS analysis may even improve data sets for modelling. Apart from the generally convincing statistical parameters, further evidence for reliable predictions of the contributions of the different POM fractions to bulk SOC could be drawn from the spectral information itself. The spectral features utilized for the determination of the contribution of the different POM fractions to bulk SOC were matching the features for the prediction of the absolute SOC concentrations within the particular fractions. As these predictions were conducted with independent sample sets (bulk soil for the POM contribution and soil fractions for the SOC content within the fraction) the matching structural information for both features of the individual POM fraction indirectly validates the prediction for the POM pools. The latter is especially true as the observed features coincide with the actual knowledge on chemistry and stabilization of POM in soils. For the compilation of a complete raster data-set, the developed calibrations were applied to all of the 131 topsoil samples taken at the SFB-TR32 testsite. Correlation analysis indicated that the coarse and the intermediate POM fractions are related to each other, to bulk SOC content and textural parameters respectively, while the fine POM fraction seems to be independent from these factors. The observed coherences and the applicability of a C-saturation concept will be discussed by visual map-comparison and geostatistical analysis of the determined parameters.

An empirical method to estimate shear wave velocity of soils in the New Madrid seismic zone

USGS Publications Warehouse

Wei, B.-Z.; Pezeshk, S.; Chang, T.-S.; Hall, K.H.; Liu, Huaibao P.

1996-01-01

In this study, a set of charts are developed to estimate shear wave velocity of soils in the New Madrid seismic zone (NMSZ), using the standard penetration test (SPT) N values and soil depths. Laboratory dynamic test results of soil samples collected from the NMSZ showed that the shear wave velocity of soils is related to the void ratio and the effective confining pressure applied to the soils. The void ratio of soils can be estimated from the SPT N values and the effective confining pressure depends on the depth of soils. Therefore, the shear wave velocity of soils can be estimated from the SPT N value and the soil depth. To make the methodology practical, two corrections should be made. One is that field SPT N values of soils must be adjusted to an unified SPT N??? value to account the effects of overburden pressure and equipment. The second is that the effect of water table to effective overburden pressure of soils must be considered. To verify the methodology, shear wave velocities of five sites in the NMSZ are estimated and compared with those obtained from field measurements. The comparison shows that our approach and the field tests are consistent with an error of less than of 15%. Thus, the method developed in this study is useful for dynamic study and practical designs in the NMSZ region. Copyright ?? 1996 Elsevier Science Limited.

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

PubMed Central

Tiyip, Tashpolat; Ding, Jianli; Zhang, Dong; Liu, Wei; Wang, Fei; Tashpolat, Nigara

2017-01-01

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (Rc2), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (Rp2), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance (Rc2 = 0.907, RMSEC = 0.425%, Rp2 = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance (Rc2 = 0.888, RMSEC = 0.446%, Rp2 = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area. PMID:28934274

Soil moisture assimilation using a modified ensemble transform Kalman filter with water balance constraint

NASA Astrophysics Data System (ADS)

Wu, Guocan; Zheng, Xiaogu; Dan, Bo

2016-04-01

The shallow soil moisture observations are assimilated into Common Land Model (CoLM) to estimate the soil moisture in different layers. The forecast error is inflated to improve the analysis state accuracy and the water balance constraint is adopted to reduce the water budget residual in the assimilation procedure. The experiment results illustrate that the adaptive forecast error inflation can reduce the analysis error, while the proper inflation layer can be selected based on the -2log-likelihood function of the innovation statistic. The water balance constraint can result in reducing water budget residual substantially, at a low cost of assimilation accuracy loss. The assimilation scheme can be potentially applied to assimilate the remote sensing data.

Development of a predictive model for lead, cadmium and fluorine soil-water partition coefficients using sparse multiple linear regression analysis.

PubMed

Nakamura, Kengo; Yasutaka, Tetsuo; Kuwatani, Tatsu; Komai, Takeshi

2017-11-01

In this study, we applied sparse multiple linear regression (SMLR) analysis to clarify the relationships between soil properties and adsorption characteristics for a range of soils across Japan and identify easily-obtained physical and chemical soil properties that could be used to predict K and n values of cadmium, lead and fluorine. A model was first constructed that can easily predict the K and n values from nine soil parameters (pH, cation exchange capacity, specific surface area, total carbon, soil organic matter from loss on ignition and water holding capacity, the ratio of sand, silt and clay). The K and n values of cadmium, lead and fluorine of 17 soil samples were used to verify the SMLR models by the root mean square error values obtained from 512 combinations of soil parameters. The SMLR analysis indicated that fluorine adsorption to soil may be associated with organic matter, whereas cadmium or lead adsorption to soil is more likely to be influenced by soil pH, IL. We found that an accurate K value can be predicted from more than three soil parameters for most soils. Approximately 65% of the predicted values were between 33 and 300% of their measured values for the K value; 76% of the predicted values were within ±30% of their measured values for the n value. Our findings suggest that adsorption properties of lead, cadmium and fluorine to soil can be predicted from the soil physical and chemical properties using the presented models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques.

PubMed

Douglas, R K; Nawar, S; Alamar, M C; Mouazen, A M; Coulon, F

2018-03-01

Visible and near infrared spectrometry (vis-NIRS) coupled with data mining techniques can offer fast and cost-effective quantitative measurement of total petroleum hydrocarbons (TPH) in contaminated soils. Literature showed however significant differences in the performance on the vis-NIRS between linear and non-linear calibration methods. This study compared the performance of linear partial least squares regression (PLSR) with a nonlinear random forest (RF) regression for the calibration of vis-NIRS when analysing TPH in soils. 88 soil samples (3 uncontaminated and 85 contaminated) collected from three sites located in the Niger Delta were scanned using an analytical spectral device (ASD) spectrophotometer (350-2500nm) in diffuse reflectance mode. Sequential ultrasonic solvent extraction-gas chromatography (SUSE-GC) was used as reference quantification method for TPH which equal to the sum of aliphatic and aromatic fractions ranging between C 10 and C 35 . Prior to model development, spectra were subjected to pre-processing including noise cut, maximum normalization, first derivative and smoothing. Then 65 samples were selected as calibration set and the remaining 20 samples as validation set. Both vis-NIR spectrometry and gas chromatography profiles of the 85 soil samples were subjected to RF and PLSR with leave-one-out cross-validation (LOOCV) for the calibration models. Results showed that RF calibration model with a coefficient of determination (R 2 ) of 0.85, a root means square error of prediction (RMSEP) 68.43mgkg -1 , and a residual prediction deviation (RPD) of 2.61 outperformed PLSR (R 2 =0.63, RMSEP=107.54mgkg -1 and RDP=2.55) in cross-validation. These results indicate that RF modelling approach is accounting for the nonlinearity of the soil spectral responses hence, providing significantly higher prediction accuracy compared to the linear PLSR. It is recommended to adopt the vis-NIRS coupled with RF modelling approach as a portable and cost effective method for the rapid quantification of TPH in soils. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessing the chronology of bedrock landslides in the Oregon Coastal Range using visible near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Mathabane, N.; Cerovski-Darriau, C.; Sweeney, K. E.; Roering, J. J.

2013-12-01

Obtaining accurate chronological data for landslides is critical to understanding their causes as well as their dynamics. The ability to easily and inexpensively date various parts of a landslide can provide insight not only into the 'When' of landslides but also on the 'How' and 'Why' as well. In this study, we apply visible near-infrared (VisNIR) spectroscopy as a means to date landslide soils in a setting with uniform climate and bedrock lithology. In our Oregon Coast Range site, as sandstone-derived soils weather over time, pedogenic hematite accumulates and alters the color of the soil at a quantifiable and discernable rate. This rate having already been established through a soil chronosequence study, we can use the redness of a soil as a proxy for soil age. This is a potentially economical dating method as it does not rely on expensive radioisotopes and requires only a small amount of sample to process. We collected 39 B-horizon soil samples from 7 different slides and used VisNIR spectroscopy to identify the soil residence time of the landslides. The majority of the samples possessed ages between 75,000 and 150,000 years of age, though several slides registered ages over 200,000 years. The average percent error associated with the landslide ages was ~30-35%, although this value was lower for younger slides (<100,000 years) and greater for slides (>200,000 years). Younger slides were more homogenous in age while older slides exhibited more variability. Additionally, there was lower variability in auger-collected samples when compared to samples collected from road-cuts. Our results suggest that VisNIR spectroscopy may prove a more useful dating method on younger, less disturbed landslides but fail to truly capture the age of older, more complicated slides due to its reliance on a specific pedogentic model for hematite weathering as well as the increased risk for complex slide history. This method could be useful in a regional characterization of landslide chronology for similar biomes and provide scientists with a currently lacking catalogue of ancient landslide ages. Such a catalogue could provide significant insight into the mechanisms and potential triggers of bedrock landslides. Landslide ages and geospatial distribution of the four largest slides sampled in the course of this study. There was significant local variability in slide ages, especially in older, redder slides like Vaughn and Wolf Creek.

A simulation study of the effects of land cover and crop type on sensing soil moisture with an orbital C-band radar

NASA Technical Reports Server (NTRS)

Dobson, M. C.; Ulaby, F. T.; Moezzi, S.; Roth, E.

1983-01-01

Simulated C-band radar imagery for a 124-km by 108-km test site in eastern Kansas is used to classify soil moisture. Simulated radar resolutions are 100 m by 100 m, 1 km by 1 km, and 3 km by 3 km, and each is processed using more than 23 independent samples. Moisture classification errors are examined as a function of land-cover distribution, field-size distribution, and local topographic relief for the full test site and also for subregions of cropland, urban areas, woodland, and pasture/rangeland. Results show that a radar resolution of 100 m by 100 m yields the most robust classification accuracies.

Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Walker, Gregory K.; Mahanama, Sarith P.; Reichle, Rolf H.

2013-01-01

Offline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, e.g., through satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale streamflow forecasts only when evaporation variance is significant relative to the precipitation variance. This condition is met only in the western half of the CONUS domain. Taken together, the two studies demonstrate the utility of a continental-scale land surface modeling system as a tool for addressing the science of hydrological prediction.

Soil moisture from ground-based networks and the North American Land Data Assimilation System Phase 2 Model: Are the right values somewhere in between?

NASA Astrophysics Data System (ADS)

Caldwell, T. G.; Scanlon, B. R.; Long, D.; Young, M.

2013-12-01

Soil moisture is the most enigmatic component of the water balance; nonetheless, it is inherently tied to every component of the hydrologic cycle, affecting the partitioning of both water and energy at the land surface. However, our ability to assess soil water storage capacity and status through measurement or modeling is challenged by error and scale. Soil moisture is as difficult to measure as it is to model, yet land surface models and remote sensing products require some means of validation. Here we compare the three major soil moisture monitoring networks across the US, including the USDA Soil Climate Assessment Network (SCAN), NOAA Climate Reference Network (USCRN), and Cosmic Ray Soil Moisture Observing System (COSMOS) to the soil moisture simulated using the North American Land Data Assimilation System (NLDAS) Phase 2. NLDAS runs in near real-time on a 0.125° (12 km) grid over the US, producing ensemble model outputs of surface fluxes and storage. We focus primarily on soil water storage (SWS) in the upper 0-0.1 m zone from the Noah Land Surface Model and secondarily on the effects of error propagation from atmospheric forcing and soil parameterization. No scaling of the observational data was attempted. We simply compared the extracted time series at the nearest grid center from NLDAS and assessed the results by standard model statistics including root mean square error (RMSE) and mean bias estimate (MBE) of the collocated ground station. Observed and modeled data were compared at both hourly and daily mean coordinated universal time steps. In all, ~300 stations were used for 2012. SCAN sites were found to be particularly troublesome at 5- and 10-cm depths. SWS at 163 SCAN sites departed significantly from Noah with a mean R2 of 0.38 × 0.0.23, a mean RMSE of 14.9 mm with a MBE of -13.5 mm. SWS at 111 USCRN sites has a mean R2 of 0.53 × 0.20, a mean RMSE of 8.2 mm with a MBE of -3.7 mm relative to Noah. Finally, 62 COSMOS sites, the instrument with the largest measurement footprint (0.03 km2), we calculated a mean R2 of 0.53 × 0.21, a mean RMSE of 9.7 mm with a MBE of -0.3 mm. Forcing errors and textural misclassifications correlate well with model biases, indicating that scale and structural errors are equally present in NLDAS. Scaling issues aside, these confounding errors make cal/val missions, such as NASA's upcoming Soil Moisture Active Passive (SMAP) mission, problematic without significant quality control and maintenance of for our monitoring networks. Land surface models, such as NLDAS-2, may provide valuable insight into our soil moisture data and somewhere in between the real values likely exist.

Determining the Diversity and Species Abundance Patterns in Arctic Soils using Rational Methods for Exploring Microbial Diversity

NASA Astrophysics Data System (ADS)

Ovreas, L.; Quince, C.; Sloan, W.; Lanzen, A.; Davenport, R.; Green, J.; Coulson, S.; Curtis, T.

2012-12-01

Arctic microbial soil communities are intrinsically interesting and poorly characterised. We have inferred the diversity and species abundance distribution of 6 Arctic soils: new and mature soil at the foot of a receding glacier, Arctic Semi Desert, the foot of bird cliffs and soil underlying Arctic Tundra Heath: all near Ny-Ålesund, Spitsbergen. Diversity, distribution and sample sizes were estimated using the rational method of Quince et al., (Isme Journal 2 2008:997-1006) to determine the most plausible underlying species abundance distribution. A log-normal species abundance curve was found to give a slightly better fit than an inverse Gaussian curve if, and only if, sequencing error was removed. The median estimates of diversity of operational taxonomic units (at the 3% level) were 3600-5600 (lognormal assumed) and 2825-4100 (inverse Gaussian assumed). The nature and origins of species abundance distributions are poorly understood but may yet be grasped by observing and analysing such distributions in the microbial world. The sample size required to observe the distribution (by sequencing 90% of the taxa) varied between ~ 106 and ~105 for the lognormal and inverse Gaussian respectively. We infer that between 5 and 50 GB of sequencing would be required to capture 90% or the metagenome. Though a principle components analysis clearly divided the sites into three groups there was a high (20-45%) degree of overlap in between locations irrespective of geographical proximity. Interestingly, the nearest relatives of the most abundant taxa at a number of most sites were of alpine or polar origin. Samples plotted on first two principal components together with arbitrary discriminatory OTUs

The isolation of the temperature effect on branched GDGT distribution in an elevation transect of the Eastern Cordillera, Colombia

NASA Astrophysics Data System (ADS)

Anderson, V. J.; Shanahan, T. M.; Saylor, J.; Horton, B. K.

2012-12-01

Recently, the distribution of branched GDGT's (glycerol dialkyl glycerol tetraethers) has been proposed as a proxy for temperature and pH in soils via the MBT/CBT index, and has been used to reconstruct past temperature variations in a number of settings ranging from marine sediments to loess deposits and paleosols. However, empirical calibrations of the MBT/CBT index against temperature show significant scatter, leading to uncertainties as large as ±2 degrees C . In this study we seek to add to and improve upon the existing soil calibration using a new set of samples spanning a large elevation (and temperature) gradient in the Eastern Cordillera of Colombia. At each site we buried temperature loggers to constrain the diurnal and seasonal temperature experienced by each soil sample. Located only 5 degrees north of the equator, our sites experience a very small seasonal temperature variation - most sites display an annual range of less than 4 degrees C. In addition, the pH of all of the soils is almost invariant across the transect, with the vast majority of samples having pH's between 4 and 5. This dataset represents a "best-case" scenario - small variations in seasonal temperature, pH, and well-constrained instrumental data - which allow us to examine the brGDGT-temperature relationship in the absence of major confounding factors such as seasonality and soil chemistry. Interestingly, the relationship between temperature and the MBT/CBT index is not improved using this dataset, suggesting that these factors are not the cause of the anomalous scatter in the calibration dataset. However, we find that using other parameterizations for the regression equation instead of the MBT and CBT indices, the errors in our temperature estimates are significantly reduced.

Combining an experimental study and ANFIS modeling to predict landfill leachate transport in underlying soil-a case study in north of Iran.

PubMed

Yousefi Kebria, D; Ghavami, M; Javadi, S; Goharimanesh, M

2017-12-16

In the contemporary world, urbanization and progressive industrial activities increase the rate of waste material generated in many developed countries. Municipal solid waste landfills (MSWs) are designed to dispose the waste from urban areas. However, discharged landfill leachate, the soluble water mixture that filters through solid waste landfills, can potentially migrate into the soil and affect living organisms by making harmful biological changes in the ecosystem. Due to well-documented landfill problems involving contamination, it is necessary to investigate the long-term influence of discharged leachate on the consistency of the soil beds beneath MSW landfills. To do so, the current study collected vertical deep core samples from different locations in the same unlined landfill. The impacts of effluent leachate on physical and chemical properties of the soil and its propagation depth were studied, and the leachate-transport pattern between successive boreholes was predicted by a developed mathematical model using an adaptive neuro-fuzzy inference system (ANFIS). The decomposition of organic leachate admixtures in the landfill yield is to produce organic acids as well as carbon dioxide, which diminishes the pH level of the landfill soil. The chemical analysis of discharged leachate in the soil samples showed that the concentrations of heavy metals are much lower than those of chloride, COD, BOD 5 , and bicarbonate. Using linear regression and mean square errors between the measured and predicted data, the accuracy of the proposed ANFIS model has been validated. Results show a high correlation between observed and predicated data.

Spectral analysis software improves confidence in plant and soil water stable isotope analyses performed by isotope ratio infrared spectroscopy (IRIS).

PubMed

West, A G; Goldsmith, G R; Matimati, I; Dawson, T E

2011-08-30

Previous studies have demonstrated the potential for large errors to occur when analyzing waters containing organic contaminants using isotope ratio infrared spectroscopy (IRIS). In an attempt to address this problem, IRIS manufacturers now provide post-processing spectral analysis software capable of identifying samples with the types of spectral interference that compromises their stable isotope analysis. Here we report two independent tests of this post-processing spectral analysis software on two IRIS systems, OA-ICOS (Los Gatos Research Inc.) and WS-CRDS (Picarro Inc.). Following a similar methodology to a previous study, we cryogenically extracted plant leaf water and soil water and measured the δ(2)H and δ(18)O values of identical samples by isotope ratio mass spectrometry (IRMS) and IRIS. As an additional test, we analyzed plant stem waters and tap waters by IRMS and IRIS in an independent laboratory. For all tests we assumed that the IRMS value represented the "true" value against which we could compare the stable isotope results from the IRIS methods. Samples showing significant deviations from the IRMS value (>2σ) were considered to be contaminated and representative of spectral interference in the IRIS measurement. Over the two studies, 83% of plant species were considered contaminated on OA-ICOS and 58% on WS-CRDS. Post-analysis, spectra were analyzed using the manufacturer's spectral analysis software, in order to see if the software correctly identified contaminated samples. In our tests the software performed well, identifying all the samples with major errors. However, some false negatives indicate that user evaluation and testing of the software are necessary. Repeat sampling of plants showed considerable variation in the discrepancies between IRIS and IRMS. As such, we recommend that spectral analysis of IRIS data must be incorporated into standard post-processing routines. Furthermore, we suggest that the results from spectral analysis be included when reporting stable isotope data from IRIS. Copyright © 2011 John Wiley & Sons, Ltd.

Critical evaluation of 13C natural abundance techniques to partition soil-surface CO2 efflux

NASA Astrophysics Data System (ADS)

Snell, H.; Midwood, A. J.; Robinson, D.

2013-12-01

Soil is the largest terrestrial store of carbon and the flux of CO2 from soils to the atmosphere is estimated at around 98 Pg (98 billion tonnes) of carbon per year. The CO2 efflux from the soil surface is derived from plant root and rhizosphere respiration (autotrophically fuelled) and microbial degradation of soil organic matter (heterotrophic respiration). Heterotrophic respiration is a key determinant of an ecosystem's long-term C balance, but one that is difficult to measure in the field. One approach involves partitioning the total soil-surface CO2 efflux between heterotrophic and autotrophic components; this can be done using differences in the natural abundance stable isotope ratios (δ13C) of autotrophic and heterotrophic CO2 as the end-members of a simple mixing model. In most natural, temperate ecosystems, current and historical vegetation cover (and therefore also plant-derived soil organic matter) is produced from C3 photosynthesis so the difference in δ13C between the autotrophic and heterotrophic CO2 sources is small. Successful partitioning therefore requires accurate and precise measurements of the δ13CO2 of the autotrophic and heterotrophic end-members (obtained by measuring the δ13CO2 of soil-free roots and root-free soil) and of total soil CO2 efflux. There is currently little consensus on the optimum measurement protocols. Here we systematically tested some of the most commonly used techniques to identify and minimise methodological errors. Using soil-surface chambers to sample total CO2 efflux and a cavity ring-down spectrometer to measure δ13CO2 in a partitioning study on a Scottish moorland, we found that: using soil-penetrating collars leads to a more depleted chamber measurement of total soil δ13CO2 as a result of severing roots and fungal hyphae or equilibrating with δ13CO2 at depth or both; root incubations provide an accurate estimate of in-situ root respired δ13CO2 provided they are sampled within one hour; the δ13CO2 from root-free soil changes rapidly during incubation and even CO2 sampled very soon after excavation is unlikely to give an accurate estimate of the heterotrophic isotope end-member, to solve this we applied non-linear regressions to the change in δ13CO2 with time to derive the heterotrophic end-member in undisturbed soil.

Quantifying soil carbon loss and uncertainty from a peatland wildfire using multi-temporal LiDAR

USGS Publications Warehouse

Reddy, Ashwan D.; Hawbaker, Todd J.; Wurster, F.; Zhu, Zhiliang; Ward, S.; Newcomb, Doug; Murray, R.

2015-01-01

Peatlands are a major reservoir of global soil carbon, yet account for just 3% of global land cover. Human impacts like draining can hinder the ability of peatlands to sequester carbon and expose their soils to fire under dry conditions. Estimating soil carbon loss from peat fires can be challenging due to uncertainty about pre-fire surface elevations. This study uses multi-temporal LiDAR to obtain pre- and post-fire elevations and estimate soil carbon loss caused by the 2011 Lateral West fire in the Great Dismal Swamp National Wildlife Refuge, VA, USA. We also determine how LiDAR elevation error affects uncertainty in our carbon loss estimate by randomly perturbing the LiDAR point elevations and recalculating elevation change and carbon loss, iterating this process 1000 times. We calculated a total loss using LiDAR of 1.10 Tg C across the 25 km2 burned area. The fire burned an average of 47 cm deep, equivalent to 44 kg C/m2, a value larger than the 1997 Indonesian peat fires (29 kg C/m2). Carbon loss via the First-Order Fire Effects Model (FOFEM) was estimated to be 0.06 Tg C. Propagating the LiDAR elevation error to the carbon loss estimates, we calculated a standard deviation of 0.00009 Tg C, equivalent to 0.008% of total carbon loss. We conclude that LiDAR elevation error is not a significant contributor to uncertainty in soil carbon loss under severe fire conditions with substantial peat consumption. However, uncertainties may be more substantial when soil elevation loss is of a similar or smaller magnitude than the reported LiDAR error.

Social-Ecological Patterns of Soil Heavy Metals Based on a Self-Organizing Map (SOM): A Case Study in Beijing, China

PubMed Central

Wang, Binwu; Li, Hong; Sun, Danfeng

2014-01-01

The regional management of trace elements in soils requires understanding the interaction between the natural system and human socio-economic activities. In this study, a social-ecological patterns of heavy metals (SEPHM) approach was proposed to identify the heavy metal concentration patterns and processes in different ecoregions of Beijing (China) based on a self-organizing map (SOM). Potential ecological risk index (RI) values of Cr, Ni, Zn, Hg, Cu, As, Cd and Pb were calculated for 1,018 surface soil samples. These data were averaged in accordance with 253 communities and/or towns, and compared with demographic, agriculture structure, geomorphology, climate, land use/cover, and soil-forming parent material to discover the SEPHM. Multivariate statistical techniques were further applied to interpret the control factors of each SEPHM. SOM application clustered the 253 towns into nine groups on the map size of 12 × 7 plane (quantization error 1.809; topographic error, 0.0079). The distribution characteristics and Spearman rank correlation coefficients of RIs were strongly associated with the population density, vegetation index, industrial and mining land percent and road density. The RIs were relatively high in which towns in a highly urbanized area with large human population density exist, while low RIs occurred in mountainous and high vegetation cover areas. The resulting dataset identifies the SEPHM of Beijing and links the apparent results of RIs to driving factors, thus serving as an excellent data source to inform policy makers for legislative and land management actions. PMID:24690947

Trace level and highly selective determination of urea in various real samples based upon voltammetric analysis of diacetylmonoxime-urea reaction product on the carbon nanotube/carbon paste electrode.

PubMed

Alizadeh, Taher; Ganjali, Mohammad Reza; Rafiei, Faride

2017-06-29

In this study an innovative method was introduced for selective and precise determination of urea in various real samples including urine, blood serum, soil and water. The method was based on the square wave voltammetry determination of an electroactive product, generated during diacetylmonoxime reaction with urea. A carbon paste electrode, modified with multi-walled carbon nanotubes (MWCNTs) was found to be an appropriate electrochemical transducer for recording of the electrochemical signal. It was found that the chemical reaction conditions influenced the analytical signal directly. The calibration graph of the method was linear in the range of 1 × 10 -7 - 1 × 10 -2  mol L -1 . The detection limit was calculated to be 52 nmol L -1 . Relative standard error of the method was also calculated to be 3.9% (n = 3). The developed determination procedure was applied for urea determination in various real samples including soil, urine, plasma and water samples. Copyright © 2017 Elsevier B.V. All rights reserved.

Predicting risk of trace element pollution from municipal roads using site-specific soil samples and remotely sensed data.

PubMed

Reeves, Mari Kathryn; Perdue, Margaret; Munk, Lee Ann; Hagedorn, Birgit

2018-07-15

Studies of environmental processes exhibit spatial variation within data sets. The ability to derive predictions of risk from field data is a critical path forward in understanding the data and applying the information to land and resource management. Thanks to recent advances in predictive modeling, open source software, and computing, the power to do this is within grasp. This article provides an example of how we predicted relative trace element pollution risk from roads across a region by combining site specific trace element data in soils with regional land cover and planning information in a predictive model framework. In the Kenai Peninsula of Alaska, we sampled 36 sites (191 soil samples) adjacent to roads for trace elements. We then combined this site specific data with freely-available land cover and urban planning data to derive a predictive model of landscape scale environmental risk. We used six different model algorithms to analyze the dataset, comparing these in terms of their predictive abilities and the variables identified as important. Based on comparable predictive abilities (mean R 2 from 30 to 35% and mean root mean square error from 65 to 68%), we averaged all six model outputs to predict relative levels of trace element deposition in soils-given the road surface, traffic volume, sample distance from the road, land cover category, and impervious surface percentage. Mapped predictions of environmental risk from toxic trace element pollution can show land managers and transportation planners where to prioritize road renewal or maintenance by each road segment's relative environmental and human health risk. Published by Elsevier B.V.

SMAP Level 4 Surface and Root Zone Soil Moisture

NASA Technical Reports Server (NTRS)

Reichle, R.; De Lannoy, G.; Liu, Q.; Ardizzone, J.; Kimball, J.; Koster, R.

2017-01-01

The SMAP Level 4 soil moisture (L4_SM) product provides global estimates of surface and root zone soil moisture, along with other land surface variables and their error estimates. These estimates are obtained through assimilation of SMAP brightness temperature observations into the Goddard Earth Observing System (GEOS-5) land surface model. The L4_SM product is provided at 9 km spatial and 3-hourly temporal resolution and with about 2.5 day latency. The soil moisture and temperature estimates in the L4_SM product are validated against in situ observations. The L4_SM product meets the required target uncertainty of 0.04 m(exp. 3)m(exp. -3), measured in terms of unbiased root-mean-square-error, for both surface and root zone soil moisture.

Soil permittivity response to bulk electrical conductivity for selected soil water sensors

USDA-ARS?s Scientific Manuscript database

Bulk electrical conductivity can dominate the low frequency dielectric loss spectrum in soils, masking changes in the real permittivity and causing errors in estimated water content. We examined the dependence of measured apparent permittivity (Ka) on bulk electrical conductivity in contrasting soil...

The 'Soil Cover App' - a new tool for fast determination of dead and living biomass on soil

NASA Astrophysics Data System (ADS)

Bauer, Thomas; Strauss, Peter; Riegler-Nurscher, Peter; Prankl, Johann; Prankl, Heinrich

2017-04-01

Worldwide many agricultural practices aim on soil protection strategies using living or dead biomass as soil cover. Especially for the case when management practices are focusing on soil erosion mitigation the effectiveness of these practices is directly driven by the amount of soil coverleft on the soil surface. Hence there is a need for quick and reliable methods of soil cover estimation not only for living biomass but particularly for dead biomass (mulch). Available methods for the soil cover measurement are either subjective, depending on an educated guess or time consuming, e.g., if the image is analysed manually at grid points. We therefore developed a mobile application using an algorithm based on entangled forest classification. The final output of the algorithm gives classified labels for each pixel of the input image as well as the percentage of each class which are living biomass, dead biomass, stones and soil. Our training dataset consisted of more than 250 different images and their annotated class information. Images have been taken in a set of different environmental conditions such as light, soil coverages from between 0% to 100%, different materials such as living plants, residues, straw material and stones. We compared the results provided by our mobile application with a data set of 180 images that had been manually annotated A comparison between both methods revealed a regression slope of 0.964 with a coefficient of determination R2 = 0.92, corresponding to an average error of about 4%. While average error of living plant classification was about 3%, dead residue classification resulted in an 8% error. Thus the new mobile application tool offers a fast and easy way to obtain information on the protective potential of a particular agricultural management site.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robison, W L; Hamilton, T F; Bogen, K

Inter-plant concentration ratios (IPCR), [Bq g{sup -1} {sup 137}Cs in coral atoll tree food-crops/Bq g{sup -1} {sup 137}Cs in leaves of native plant species whose roots share a common soil volume], can replace transfer factors (TF) to predict {sup 137}Cs concentration in tree food-crops in a contaminated area with an aged source term. The IPCR strategy has significant benefits relative to TF strategy for such purposes in the atoll ecosystem. IPCR strategy applied to specific assessments takes advantage of the fact tree roots naturally integrate 137Cs over large volumes of soil. Root absorption of {sup 137}Cs replaces large-scale, expensive soilmore » sampling schemes to reduce variability in {sup 137}Cs concentration due to inhomogeneous radionuclide distribution. IPCR [drinking-coconut meat (DCM)/Scaevola (SCA) and Tournefortia (TOU) leaves (native trees growing on all atoll islands)] are log normally distributed (LND) with geometric standard deviation (GSD) = 1.85. TF for DCM from Enewetak, Eneu, Rongelap and Bikini Atolls are LND with GSD's of 3.5, 3.0, 2.7, and 2.1, respectively. TF GSD for Rongelap copra coconut meat is 2.5. IPCR of Pandanus fruit to SCA and TOU leaves are LND with GSD = 1.7 while TF GSD is 2.1. Because IPCR variability is much lower than TF variability, relative sampling error of an IPCR field sample mean is up 6- to 10-fold lower than that of a TF sample mean if sample sizes are small (10 to 20). Other IPCR advantages are that plant leaf samples are collected and processed in far less time with much less effort and cost than soil samples.« less

Merging bottom-up and top-down precipitation products using a stochastic error model

NASA Astrophysics Data System (ADS)

Maggioni, Viviana; Massari, Christian; Brocca, Luca; Ciabatta, Luca

2017-04-01

Accurate quantitative precipitation estimation is of great importance for water resources management, agricultural planning, and forecasting and monitoring of natural hazards such as flash floods and landslides. In situ observations are limited around the Earth, especially in remote areas (e.g., complex terrain, dense vegetation), but currently available satellite precipitation products are able to provide global precipitation estimates with an accuracy that depends upon many factors (e.g., type of storms, temporal sampling, season etc…). Recently, Brocca et al. (2014) have proposed an alternative approach (i.e., SM2RAIN) that allows to estimate rainfall from space by using satellite soil moisture observations. In contrast with classical satellite precipitation products which sense the cloud properties to retrieve the instantaneous precipitation, this new bottom-up approach makes use of two consecutive soil moisture measurements for obtaining an estimate of the fallen precipitation within the interval between two satellite passes. As a result, the nature of the measurement is different and complementary to the one of classical precipitation products and could provide a different valid perspective to improve current satellite rainfall estimates via appropriate integration between the products (i.e., SM2RAIN plus a classical satellite rainfall product). However, whether SM2RAIN is able or not to improve the performance of any state-of-the-art satellite rainfall product is much dependent upon an adequate quantification and characterization of the relative errors of the products. In this study, the stochastic rainfall error model SREM2D (Hossain et al. 2006) is used for characterizing the retrieval error of both SM2RAIN and a state-of-the-art satellite precipitation product (i.e., 3B42RT). The error characterization serves for an optimal integration between SM2RAIN and 3B42RT for enhancing the capability of the resulting integrated product (i.e. SM2RAIN+3B42RT) in operational hydrology. The study, conducted in Italy for a 5-yr period (2010-2014) using a dense network of raingauges (about 3000) as a benchmark, demonstrates that the integration is able to enhance the correlation and the root mean squared error of SM2RAIN+3B42RT with respect to the parent products. This suggests a potential benefit of merging SM2RAIN derived rainfall with state-of-the-art satellite precipitation estimates for creating a product characterized by higher accuracy and better performance when used in the contest of operational hydrology. REFERENCES 1. Brocca, L.; Ciabatta, L.; Massari, C.; Moramarco, T.; Hahn, S.; Hasenauer, S.; Kidd, R.; Dorigo, W.; Wagner, W.; Levizzani, V. Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. J. Geophys. Res. Atmos. 2014, 119, 5128-5141. 2. Hossain, F.; Anagnostou, E. N. A two-dimensional satellite rainfall error model. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1511-1522.

Evaluating Snowmelt Runoff Processes Using Stable Isotopes in a Permafrost Hillslope

NASA Astrophysics Data System (ADS)

Carey, S. K.

2004-05-01

Conceptual understanding of runoff generation in permafrost regions have been derived primarily from hydrometric information, with isotope and hydrochemical data having only limited application in delineating sources and pathways of water. Furthermore, when stable isotope data are used to infer runoff processes, it often provides conflicting results from hydrometric measurements. In a small subarctic alpine catchment within the Wolf Creek Research Basin, Yukon, Canada, experiments were conducted during the melt period of 2002 and 2003 to trace the stable isotopic signature (d18O) of meltwater from a melting snowpack into permafrost soils and laterally to the stream to identify runoff processes and evaluate sources of error for traditional hydrograph separation studies in snowmelt-dominated permafrost basins. Isotopic variability in the snowpack was recorded at 0.1 m depth intervals during the melt period and compared with the meltwater isotopic signature at the snowpack base collected in lysimeters. Throughout the melt period in both years, there was an isotopic enrichment of meltwater as the season progressed. A downslope transect of wells and piezometers were used to evaluate the influence of infiltrating meltwater and thawing ground on the subsurface d18O signature. As melt began, meltwater infiltrated the frozen porous organic layer, leading to liquid water saturation in the unsaturated pore spaces. Water sampled during this initial melt stage show soil water d18O mirroring that of the meltwater signal. As the melt season progressed, frozen soil began to melt, mixing enriched pre-melt soil water with meltwater. This mixing increased the overall value of d18O obtained from the soil, which gradually increased as thaw progressed. At the end of snowmelt, soil water had a d18O value similar to values from the previous fall, suggesting that much of the initial snowmelt water had been flushed from the hillslope. Results from the hillslope scale are compared with two-component hydrograph separations and sources of error are discussed.

Soil pH Mapping with an On-The-Go Sensor

PubMed Central

Schirrmann, Michael; Gebbers, Robin; Kramer, Eckart; Seidel, Jan

2011-01-01

Soil pH is a key parameter for crop productivity, therefore, its spatial variation should be adequately addressed to improve precision management decisions. Recently, the Veris pH Manager™, a sensor for high-resolution mapping of soil pH at the field scale, has been made commercially available in the US. While driving over the field, soil pH is measured on-the-go directly within the soil by ion selective antimony electrodes. The aim of this study was to evaluate the Veris pH Manager™ under farming conditions in Germany. Sensor readings were compared with data obtained by standard protocols of soil pH assessment. Experiments took place under different scenarios: (a) controlled tests in the lab, (b) semicontrolled test on transects in a stop-and-go mode, and (c) tests under practical conditions in the field with the sensor working in its typical on-the-go mode. Accuracy issues, problems, options, and potential benefits of the Veris pH Manager™ were addressed. The tests demonstrated a high degree of linearity between standard laboratory values and sensor readings. Under practical conditions in the field (scenario c), the measure of fit (r2) for the regression between the on-the-go measurements and the reference data was 0.71, 0.63, and 0.84, respectively. Field-specific calibration was necessary to reduce systematic errors. Accuracy of the on-the-go maps was considerably higher compared with the pH maps obtained by following the standard protocols, and the error in calculating lime requirements was reduced by about one half. However, the system showed some weaknesses due to blockage by residual straw and weed roots. If these problems were solved, the on-the-go sensor investigated here could be an efficient alternative to standard sampling protocols as a basis for liming in Germany. PMID:22346591

Soil pH mapping with an on-the-go sensor.

PubMed

Schirrmann, Michael; Gebbers, Robin; Kramer, Eckart; Seidel, Jan

2011-01-01

Soil pH is a key parameter for crop productivity, therefore, its spatial variation should be adequately addressed to improve precision management decisions. Recently, the Veris pH Manager™, a sensor for high-resolution mapping of soil pH at the field scale, has been made commercially available in the US. While driving over the field, soil pH is measured on-the-go directly within the soil by ion selective antimony electrodes. The aim of this study was to evaluate the Veris pH Manager™ under farming conditions in Germany. Sensor readings were compared with data obtained by standard protocols of soil pH assessment. Experiments took place under different scenarios: (a) controlled tests in the lab, (b) semicontrolled test on transects in a stop-and-go mode, and (c) tests under practical conditions in the field with the sensor working in its typical on-the-go mode. Accuracy issues, problems, options, and potential benefits of the Veris pH Manager™ were addressed. The tests demonstrated a high degree of linearity between standard laboratory values and sensor readings. Under practical conditions in the field (scenario c), the measure of fit (r(2)) for the regression between the on-the-go measurements and the reference data was 0.71, 0.63, and 0.84, respectively. Field-specific calibration was necessary to reduce systematic errors. Accuracy of the on-the-go maps was considerably higher compared with the pH maps obtained by following the standard protocols, and the error in calculating lime requirements was reduced by about one half. However, the system showed some weaknesses due to blockage by residual straw and weed roots. If these problems were solved, the on-the-go sensor investigated here could be an efficient alternative to standard sampling protocols as a basis for liming in Germany.

Uncertainty in Ecohydrological Modeling in an Arid Region Determined with Bayesian Methods

PubMed Central

Yang, Junjun; He, Zhibin; Du, Jun; Chen, Longfei; Zhu, Xi

2016-01-01

In arid regions, water resources are a key forcing factor in ecosystem circulation, and soil moisture is the critical link that constrains plant and animal life on the soil surface and underground. Simulation of soil moisture in arid ecosystems is inherently difficult due to high variability. We assessed the applicability of the process-oriented CoupModel for forecasting of soil water relations in arid regions. We used vertical soil moisture profiling for model calibration. We determined that model-structural uncertainty constituted the largest error; the model did not capture the extremes of low soil moisture in the desert-oasis ecotone (DOE), particularly below 40 cm soil depth. Our results showed that total uncertainty in soil moisture prediction was improved when input and output data, parameter value array, and structure errors were characterized explicitly. Bayesian analysis was applied with prior information to reduce uncertainty. The need to provide independent descriptions of uncertainty analysis (UA) in the input and output data was demonstrated. Application of soil moisture simulation in arid regions will be useful for dune-stabilization and revegetation efforts in the DOE. PMID:26963523

An improved model for soil surface temperature from air temperature in permafrost regions of Qinghai-Xizang (Tibet) Plateau of China

NASA Astrophysics Data System (ADS)

Hu, Guojie; Wu, Xiaodong; Zhao, Lin; Li, Ren; Wu, Tonghua; Xie, Changwei; Pang, Qiangqiang; Cheng, Guodong

2017-08-01

Soil temperature plays a key role in hydro-thermal processes in environments and is a critical variable linking surface structure to soil processes. There is a need for more accurate temperature simulation models, particularly in Qinghai-Xizang (Tibet) Plateau (QXP). In this study, a model was developed for the simulation of hourly soil surface temperatures with air temperatures. The model incorporated the thermal properties of the soil, vegetation cover, solar radiation, and water flux density and utilized field data collected from Qinghai-Xizang (Tibet) Plateau (QXP). The model was used to simulate the thermal regime at soil depths of 5 cm, 10 cm and 20 cm and results were compared with those from previous models and with experimental measurements of ground temperature at two different locations. The analysis showed that the newly developed model provided better estimates of observed field temperatures, with an average mean absolute error (MAE), root mean square error (RMSE), and the normalized standard error (NSEE) of 1.17 °C, 1.30 °C and 13.84 %, 0.41 °C, 0.49 °C and 5.45 %, 0.13 °C, 0.18 °C and 2.23 % at 5 cm, 10 cm and 20 cm depths, respectively. These findings provide a useful reference for simulating soil temperature and may be incorporated into other ecosystem models requiring soil temperature as an input variable for modeling permafrost changes under global warming.

Improvements in the analytical methodology for the residue determination of the herbicide glyphosate in soils by liquid chromatography coupled to mass spectrometry.

PubMed

Botero-Coy, A M; Ibáñez, M; Sancho, J V; Hernández, F

2013-05-31

The determination of glyphosate (GLY) in soils is of great interest due to the widespread use of this herbicide and the need of assessing its impact on the soil/water environment. However, its residue determination is very problematic especially in soils with high organic matter content, where strong interferences are normally observed, and because of the particular physico-chemical characteristics of this polar/ionic herbicide. In the present work, we have improved previous LC-MS/MS analytical methodology reported for GLY and its main metabolite AMPA in order to be applied to "difficult" soils, like those commonly found in South-America, where this herbicide is extensively used in large areas devoted to soya or maize, among other crops. The method is based on derivatization with FMOC followed by LC-MS/MS analysis, using triple quadrupole. After extraction with potassium hydroxide, a combination of extract dilution, adjustment to appropriate pH, and solid phase extraction (SPE) clean-up was applied to minimize the strong interferences observed. Despite the clean-up performed, the use of isotope labelled glyphosate as internal standard (ILIS) was necessary for the correction of matrix effects and to compensate for any error occurring during sample processing. The analytical methodology was satisfactorily validated in four soils from Colombia and Argentina fortified at 0.5 and 5mg/kg. In contrast to most LC-MS/MS methods, where the acquisition of two transitions is recommended, monitoring all available transitions was required for confirmation of positive samples, as some of them were interfered by unknown soil components. This was observed not only for GLY and AMPA but also for the ILIS. Analysis by QTOF MS was useful to confirm the presence of interferent compounds that shared the same nominal mass of analytes as well as some of their main product ions. Therefore, the selection of specific transitions was crucial to avoid interferences. The methodology developed was applied to the analysis of 26 soils from different areas of Colombia and Argentina, and the method robustness was demonstrated by analysis of quality control samples along 4 months. Copyright © 2012 Elsevier B.V. All rights reserved.

Mapping within-field variations of soil organic carbon content using UAV multispectral visible near-infrared images

NASA Astrophysics Data System (ADS)

Gilliot, Jean-Marc; Vaudour, Emmanuelle; Michelin, Joël

2016-04-01

This study was carried out in the framework of the PROSTOCK-Gessol3 project supported by the French Environment and Energy Management Agency (ADEME), the TOSCA-PLEIADES-CO project of the French Space Agency (CNES) and the SOERE PRO network working on environmental impacts of Organic Waste Products recycling on field crops at long time scale. The organic matter is an important soil fertility parameter and previous studies have shown the potential of spectral information measured in the laboratory or directly in the field using field spectro-radiometer or satellite imagery to predict the soil organic carbon (SOC) content. This work proposes a method for a spatial prediction of bare cultivated topsoil SOC content, from Unmanned Aerial Vehicle (UAV) multispectral imagery. An agricultural plot of 13 ha, located in the western region of Paris France, was analysed in April 2013, shortly before sowing while it was still bare soil. Soils comprised haplic luvisols, rendzic cambisols and calcaric or colluvic cambisols. The UAV platform used was a fixed wing provided by Airinov® flying at an altitude of 150m and was equipped with a four channels multispectral visible near-infrared camera MultiSPEC 4C® (550nm, 660nm, 735 nm and 790 nm). Twenty three ground control points (GCP) were sampled within the plot according to soils descriptions. GCP positions were determined with a centimetric DGPS. Different observations and measurements were made synchronously with the drone flight: soil surface description, spectral measurements (with ASD FieldSpec 3® spectroradiometer), roughness measurements by a photogrammetric method. Each of these locations was sampled for both soil standard physico-chemical analysis and soil water content. A Structure From Motion (SFM) processing was done from the UAV imagery to produce a 15 cm resolution multispectral mosaic using the Agisoft Photoscan® software. The SOC content was modelled by partial least squares regression (PLSR) between the laboratory analyses and the multispectral information for the 23 plots. The mean squared error of cross validation (RMSECV) by LOO (Leave One Out) method was 1.97 g of OC per kg of soil. A second correction of the model incorporating the effects of moisture and roughness on reflectance, has improved the quality of the prediction by 18% and a RMSECV of 1.61 g / kg. The model was finally spatialized on the whole plot using ArcGIS® by applying the regression formula on all mosaic pixels. Results are discussed in the light of an additional sampling campaign carried out in October 2015, providing 34 independent samples.

Estimating error cross-correlations in soil moisture data sets using extended collocation analysis

USDA-ARS?s Scientific Manuscript database

Consistent global soil moisture records are essential for studying the role of hydrologic processes within the larger earth system. Various studies have shown the benefit of assimilating satellite-based soil moisture data into water balance models or merging multi-source soil moisture retrievals int...

Chamber measurement of surface-atmosphere trace gas exchange: Numerical evaluation of dependence on soil, interfacial layer, and source/sink properties

NASA Astrophysics Data System (ADS)

Hutchinson, G. L.; Livingston, G. P.; Healy, R. W.; Striegl, R. G.

2000-04-01

We employed a three-dimensional finite difference gas diffusion model to simulate the performance of chambers used to measure surface-atmosphere trace gas exchange. We found that systematic errors often result from conventional chamber design and deployment protocols, as well as key assumptions behind the estimation of trace gas exchange rates from observed concentration data. Specifically, our simulations showed that (1) when a chamber significantly alters atmospheric mixing processes operating near the soil surface, it also nearly instantaneously enhances or suppresses the postdeployment gas exchange rate, (2) any change resulting in greater soil gas diffusivity, or greater partitioning of the diffusing gas to solid or liquid soil fractions, increases the potential for chamber-induced measurement error, and (3) all such errors are independent of the magnitude, kinetics, and/or distribution of trace gas sources, but greater for trace gas sinks with the same initial absolute flux. Finally, and most importantly, we found that our results apply to steady state as well as non-steady-state chambers, because the slow rate of gas diffusion in soil inhibits recovery of the former from their initial non-steady-state condition. Over a range of representative conditions, the error in steady state chamber estimates of the trace gas flux varied from -30 to +32%, while estimates computed by linear regression from non-steady-state chamber concentrations were 2 to 31% too small. Although such errors are relatively small in comparison to the temporal and spatial variability characteristic of trace gas exchange, they bias the summary statistics for each experiment as well as larger scale trace gas flux estimates based on them.

Chamber measurement of surface-atmosphere trace gas exchange--Numerical evaluation of dependence on soil interfacial layer, and source/sink products

USGS Publications Warehouse

Hutchinson, G.L.; Livingston, G.P.; Healy, R.W.; Striegl, Robert G.

2000-01-01

We employed a three-dimensional finite difference gas diffusion model to simulate the performance of chambers used to measure surface-atmosphere tace gas exchange. We found that systematic errors often result from conventional chamber design and deployment protocols, as well as key assumptions behind the estimation of trace gas exchange rates from observed concentration data. Specifically, our simulationshowed that (1) when a chamber significantly alters atmospheric mixing processes operating near the soil surface, it also nearly instantaneously enhances or suppresses the postdeployment gas exchange rate, (2) any change resulting in greater soil gas diffusivity, or greater partitioning of the diffusing gas to solid or liquid soil fractions, increases the potential for chamber-induced measurement error, and (3) all such errors are independent of the magnitude, kinetics, and/or distribution of trace gas sources, but greater for trace gas sinks with the same initial absolute flux. Finally, and most importantly, we found that our results apply to steady state as well as non-steady-state chambers, because the slow rate of gas diffusion in soil inhibits recovery of the former from their initial non-steady-state condition. Over a range of representative conditions, the error in steady state chamber estimates of the trace gas flux varied from -30 to +32%, while estimates computed by linear regression from non-steadystate chamber concentrations were 2 to 31% too small. Although such errors are relatively small in comparison to the temporal and spatial variability characteristic of trace gas exchange, they bias the summary statistics for each experiment as well as larger scale trace gas flux estimates based on them.

Sediment composition for the assessment of water erosion and nonpoint source pollution in natural and fire-affected landscapes.

PubMed

Carkovic, Athena B; Pastén, Pablo A; Bonilla, Carlos A

2015-04-15

Water erosion is a leading cause of soil degradation and a major nonpoint source pollution problem. Many efforts have been undertaken to estimate the amount and size distribution of the sediment leaving the field. Multi-size class water erosion models subdivide eroded soil into different sizes and estimate the aggregate's composition based on empirical equations derived from agricultural soils. The objective of this study was to evaluate these equations on soil samples collected from natural landscapes (uncultivated) and fire-affected soils. Chemical, physical, and soil fractions and aggregate composition analyses were performed on samples collected in the Chilean Patagonia and later compared with the equations' estimates. The results showed that the empirical equations were not suitable for predicting the sediment fractions. Fine particles, including primary clay, primary silt, and small aggregates (<53 μm) were over-estimated, and large aggregates (>53 μm) and primary sand were under-estimated. The uncultivated and fire-affected soils showed a reduced fraction of fine particles in the sediment, as clay and silt were mostly in the form of large aggregates. Thus, a new set of equations was developed for these soils, where small aggregates were defined as particles with sizes between 53 μm and 250 μm and large aggregates as particles>250 μm. With r(2) values between 0.47 and 0.98, the new equations provided better estimates for primary sand and large aggregates. The aggregate's composition was also well predicted, especially the silt and clay fractions in the large aggregates from uncultivated soils (r(2)=0.63 and 0.83, respectively) and the fractions of silt in the small aggregates (r(2)=0.84) and clay in the large aggregates (r(2)=0.78) from fire-affected soils. Overall, these new equations proved to be better predictors for the sediment and aggregate's composition in uncultivated and fire-affected soils, and they reduce the error when estimating soil loss in natural landscapes. Copyright © 2015 Elsevier B.V. All rights reserved.

Evaluation of automated global mapping of Reference Soil Groups of WRB2015

NASA Astrophysics Data System (ADS)

Mantel, Stephan; Caspari, Thomas; Kempen, Bas; Schad, Peter; Eberhardt, Einar; Ruiperez Gonzalez, Maria

2017-04-01

SoilGrids is an automated system that provides global predictions for standard numeric soil properties at seven standard depths down to 200 cm, currently at spatial resolutions of 1km and 250m. In addition, the system provides predictions of depth to bedrock and distribution of soil classes based on WRB and USDA Soil Taxonomy (ST). In SoilGrids250m(1), soil classes (WRB, version 2006) consist of the RSG and the first prefix qualifier, whereas in SoilGrids1km(2), the soil class was assessed at RSG level. Automated mapping of World Reference Base (WRB) Reference Soil Groups (RSGs) at a global level has great advantages. Maps can be updated in a short time span with relatively little effort when new data become available. To translate soil names of older versions of FAO/WRB and national classification systems of the source data into names according to WRB 2006, correlation tables are used in SoilGrids. Soil properties and classes are predicted independently from each other. This means that the combinations of soil properties for the same cells or soil property-soil class combinations do not necessarily yield logical combinations when the map layers are studied jointly. The model prediction procedure is robust and probably has a low source of error in the prediction of RSGs. It seems that the quality of the original soil classification in the data and the use of correlation tables are the largest sources of error in mapping the RSG distribution patterns. Predicted patterns of dominant RSGs were evaluated in selected areas and sources of error were identified. Suggestions are made for improvement of WRB2015 RSG distribution predictions in SoilGrids. Keywords: Automated global mapping; World Reference Base for Soil Resources; Data evaluation; Data quality assurance References 1 Hengl T, de Jesus JM, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, et al. (2016) SoilGrids250m: global gridded soil information based on Machine Learning. Earth System Science Data (ESSD), in review. 2 Hengl T, de Jesus JM, MacMillan RA, Batjes NH, Heuvelink GBM, et al. (2014) SoilGrids1km — Global Soil Information Based on Automated Mapping. PLoS ONE 9(8): e105992. doi:10.1371/journal.pone.0105992

Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements

NASA Technical Reports Server (NTRS)

Carlson, T. N.

1986-01-01

A review is presented of numerical models which were developed to interpret thermal IR data and to identify the governing parameters and surface energy fluxes recorded in the images. Analytic, predictive, diagnostic and empirical models are described. The limitations of each type of modeling approach are explored in terms of the error sources and inherent constraints due to theoretical or measurement limitations. Sample results of regional-scale soil moisture or evaporation patterns derived from the Heat Capacity Mapping Mission and GOES satellite data through application of the predictive model devised by Carlson (1981) are discussed. The analysis indicates that pattern recognition will probably be highest when data are collected over flat, arid, sparsely vegetated terrain. The soil moisture data then obtained may be accurate to within 10-20 percent.

Detection of terrain indices related to soil salinity and mapping salt-affected soils using remote sensing and geostatistical techniques.

PubMed

Triki Fourati, Hela; Bouaziz, Moncef; Benzina, Mourad; Bouaziz, Samir

2017-04-01

Traditional surveying methods of soil properties over landscapes are dramatically cost and time-consuming. Thus, remote sensing is a proper choice for monitoring environmental problem. This research aims to study the effect of environmental factors on soil salinity and to map the spatial distribution of this salinity over the southern east part of Tunisia by means of remote sensing and geostatistical techniques. For this purpose, we used Advanced Spaceborne Thermal Emission and Reflection Radiometer data to depict geomorphological parameters: elevation, slope, plan curvature (PLC), profile curvature (PRC), and aspect. Pearson correlation between these parameters and soil electrical conductivity (EC soil ) showed that mainly slope and elevation affect the concentration of salt in soil. Moreover, spectral analysis illustrated the high potential of short-wave infrared (SWIR) bands to identify saline soils. To map soil salinity in southern Tunisia, ordinary kriging (OK), minimum distance (MD) classification, and simple regression (SR) were used. The findings showed that ordinary kriging technique provides the most reliable performances to identify and classify saline soils over the study area with a root mean square error of 1.83 and mean error of 0.018.

Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation

NASA Astrophysics Data System (ADS)

Wu, Kai; Shu, Hong; Nie, Lei; Jiao, Zhenhang

2018-01-01

Spatially correlated errors are typically ignored in data assimilation, thus degenerating the observation error covariance R to a diagonal matrix. We argue that a nondiagonal R carries more observation information making assimilation results more accurate. A method, denoted TC_Cov, was proposed for soil moisture data assimilation to estimate spatially correlated observation error covariance based on triple collocation (TC). Assimilation experiments were carried out to test the performance of TC_Cov. AMSR-E soil moisture was assimilated with a diagonal R matrix computed using the TC and assimilated using a nondiagonal R matrix, as estimated by proposed TC_Cov. The ensemble Kalman filter was considered as the assimilation method. Our assimilation results were validated against climate change initiative data and ground-based soil moisture measurements using the Pearson correlation coefficient and unbiased root mean square difference metrics. These experiments confirmed that deterioration of diagonal R assimilation results occurred when model simulation is more accurate than observation data. Furthermore, nondiagonal R achieved higher correlation coefficient and lower ubRMSD values over diagonal R in experiments and demonstrated the effectiveness of TC_Cov to estimate richly structuralized R in data assimilation. In sum, compared with diagonal R, nondiagonal R may relieve the detrimental effects of assimilation when simulated model results outperform observation data.

Uncertainty in Pedotransfer Functions from Soil Survey Data

NASA Astrophysics Data System (ADS)

Pachepsky, Y. A.; Rawls, W. J.

2002-05-01

Pedotransfer functions (PTFs) are empirical relationships between hard-to-get soil parameters, i.e. hydraulic properties, and more easily obtainable basic soil properties, such as texture. Use of PTFs in large-scale projects and pilot studies relies on data of soil survey that provides soil basic data as a categorical information. Unlike numerical variables, categorical data cannot be directly used in statistical regressions or neural networks to develop PTFs. Objectives of this work were (a) to find and test techniques to develop PTFs for soil water retention and saturated hydraulic conductivity with soil categorical data as inputs, (b) to evaluate sources of uncertainty in results of such PTFs and to research opportunities of mitigating the uncertainty. We used a subset of about 12,000 samples from the US National Soil characterization database to estimate water retention, and the data set for circa 1000 hydraulic conductivity measurements done in the US. Regression trees and polynomial neural networks based on dummy coding were the techniques tried for the PTF development. The jackknife validation was used to prevent the over-parameterization. Both techniques were equally efficient in developing PTFs, but regression trees gave much more transparent results. Textural class was the leading predictor with RMSE values of about 6.5 and 4.1 vol.% for water retention at -33 and -1500 kPa, respectively. The RMSE values decreased 10% when the laboratory textural analysis was used to establish the textural class. Textural class in the field was determined correctly only in 41% of all cases. To mitigate this source of error, we added slopes, position on the slope classes, and land surface shape classes to the list of PTF inputs. Regression trees generated topotextural groups that encompassed several textural classes. Using topographic variables and soil horizon appeared to be the way to make up for errors made in field determination of texture. Adding field descriptors of soil structure to the field-determined textural class gave similar results. No large improvement was achieved probably because textural class, topographic descriptors and structure descriptors were correlated predictors in many cases. Both median values and uncertainty of the saturated hydraulic conductivity had a power-law decrease as clay content increased. Defining two classes of bulk density helped to estimate hydraulic conductivity within textural classes. We conclude that categorical field soil survey data can be used in PTF-based estimating soil water retention and saturated hydraulic conductivity with quantified uncertainty

From soilscapes to landscapes: A landscape-oriented approach to simulate soil organic carbon dynamics in intensively managed landscapes

USDA-ARS?s Scientific Manuscript database

Most available biogeochemical models focus within a soil profile and cannot adequately resolve contributions of the lighter size fractions of organic rich soils for Enrichment Ratio (ER) estimates, thereby causing unintended errors in Soil Organic Carbon (SOC) storage predictions. These models set E...

Estimation of Soil Moisture Profile using a Simple Hydrology Model and Passive Microwave Remote Sensing

NASA Technical Reports Server (NTRS)

Soman, Vishwas V.; Crosson, William L.; Laymon, Charles; Tsegaye, Teferi

1998-01-01

Soil moisture is an important component of analysis in many Earth science disciplines. Soil moisture information can be obtained either by using microwave remote sensing or by using a hydrologic model. In this study, we combined these two approaches to increase the accuracy of profile soil moisture estimation. A hydrologic model was used to analyze the errors in the estimation of soil moisture using the data collected during Huntsville '96 microwave remote sensing experiment in Huntsville, Alabama. Root mean square errors (RMSE) in soil moisture estimation increase by 22% with increase in the model input interval from 6 hr to 12 hr for the grass-covered plot. RMSEs were reduced for given model time step by 20-50% when model soil moisture estimates were updated using remotely-sensed data. This methodology has a potential to be employed in soil moisture estimation using rainfall data collected by a space-borne sensor, such as the Tropical Rainfall Measuring Mission (TRMM) satellite, if remotely-sensed data are available to update the model estimates.

Spatial Prediction of Soil Classes by Using Soil Weathering Parameters Derived from vis-NIR Spectroscopy

NASA Astrophysics Data System (ADS)

Ramirez-Lopez, Leonardo; Alexandre Dematte, Jose

2010-05-01

There is consensus in the scientific community about the great need of spatial soil information. Conventional mapping methods are time consuming and involve high costs. Digital soil mapping has emerged as an area in which the soil mapping is optimized by the application of mathematical and statistical approaches, as well as the application of expert knowledge in pedology. In this sense, the objective of the study was to develop a methodology for the spatial prediction of soil classes by using soil spectroscopy methodologies related with fieldwork, spectral data from satellite image and terrain attributes in simultaneous. The studied area is located in São Paulo State, and comprised an area of 473 ha, which was covered by a regular grid (100 x 100 m). In each grid node was collected soil samples at two depths (layers A and B). There were extracted 206 samples from transect sections and submitted to soil analysis (clay, Al2O3, Fe2O3, SiO2 TiO2, and weathering index). The first analog soil class map (ASC-N) contains only soil information regarding from orders to subgroups of the USDA Soil Taxonomy System. The second (ASC-H) map contains some additional information related to some soil attributes like color, ferric levels and base sum. For the elaboration of the digital soil maps the data was divided into three groups: i) Predicted soil attributes of the layer B (related to the soil weathering) which were obtained by using a local soil spectral library; ii) Spectral bands data extracted from a Landsat image; and iii) Terrain parameters. This information was summarized by a principal component analysis (PCA) in each group. Digital soil maps were generated by supervised classification using a maximum likelihood method. The trainee information for this classification was extracted from five toposequences based on the analog soil class maps. The spectral models of weathering soil attributes shown a high predictive performance with low error (R2 0.71 to 0.90). The spatial prediction of these attributes also showed a high performance (validations with R2> 0.78). These models allowed to increase spatial resolution of soil weathering information. On the other hand, the comparison between the analog and digital soil maps showed a global accuracy of 69% for the ASC-N map and 62% in the ASC-H map, with kappa indices of 0.52 and 0.45 respectively.

[Using sequential indicator simulation method to define risk areas of soil heavy metals in farmland.

PubMed

Yang, Hao; Song, Ying Qiang; Hu, Yue Ming; Chen, Fei Xiang; Zhang, Rui

2018-05-01

The heavy metals in soil have serious impacts on safety, ecological environment and human health due to their toxicity and accumulation. It is necessary to efficiently identify the risk area of heavy metals in farmland soil, which is of important significance for environment protection, pollution warning and farmland risk control. We collected 204 samples and analyzed the contents of seven kinds of heavy metals (Cu, Zn, Pb, Cd, Cr, As, Hg) in Zengcheng District of Guangzhou, China. In order to overcame the problems of the data, including the limitation of abnormal values and skewness distribution and the smooth effect with the traditional kriging methods, we used sequential indicator simulation method (SISIM) to define the spatial distribution of heavy metals, and combined Hakanson index method to identify potential ecological risk area of heavy metals in farmland. The results showed that: (1) Based on the similar accuracy of spatial prediction of soil heavy metals, the SISIM had a better expression of detail rebuild than ordinary kriging in small scale area. Compared to indicator kriging, the SISIM had less error rate (4.9%-17.1%) in uncertainty evaluation of heavy-metal risk identification. The SISIM had less smooth effect and was more applicable to simulate the spatial uncertainty assessment of soil heavy metals and risk identification. (2) There was no pollution in Zengcheng's farmland. Moderate potential ecological risk was found in the southern part of study area due to enterprise production, human activities, and river sediments. This study combined the sequential indicator simulation with Hakanson risk index method, and effectively overcame the outlier information loss and smooth effect of traditional kriging method. It provided a new way to identify the soil heavy metal risk area of farmland in uneven sampling.

Spatial patterns of (137)Cs inventories and soil erosion from earth-banked terraces in the Yimeng Mountains, China.

PubMed

Zhang, Yunqi; Long, Yi; An, Juan; Yu, Xingxiu; Wang, Xiaoli

2014-10-01

The Yimeng Mountains is one of China's most susceptible regions to soil erosion. In this region, slopes are composed of granite- or gneiss-derived soils that are commonly cultivated using earth-banked terraces. Based on the (137)Cs measurement for nine reference cores, the present study analysed the spatial patterns of (137)Cs inventory and soil erosion using 105 sampling points in a seven-level earth-banked terrace system. The mean (137)Cs inventory, standard deviation, coefficient of variation, and allowable error for the nine reference cores were 987 Bq m(-2), 71 Bq m(-2), 7%, and 6%, respectively, values that may reflect the heterogeneity of the initial (137)Cs fallout deposit. Within each terrace, the (137)Cs inventory generally increases from the rear edge to the front edge, accompanied by a decrease in the erosion rate. This results from planation by tillage and rainfall runoff during the development of the earth-banked terraces. Across the entire seven-level terrace system, (137)Cs inventories decrease from the highest terrace downwards, but increase in the lower terraces, whereas erosion rate displays the opposite trend. These trends are the result of the combined effects of the earth-bank segmented hillslope, the limited protection of the earth banks, and rainfall runoff in combination with tillage. The high coefficients of variation of (137)Cs inventories for the 21 sampling rows, with a mean value of 44%, demonstrate the combined effects of variations in original microtopography, anthropogenic disturbance, the incohesive soils weathered from underlying granite, and the warm climate. Although earth-banked terraces can reduce soil erosion to some extent, the estimated erosion rates for the study area are still very high. Copyright © 2014 Elsevier Ltd. All rights reserved.

An improved thermo-time domain reflectometry method for determination of ice contents in partially frozen soils

NASA Astrophysics Data System (ADS)

Tian, Zhengchao; Ren, Tusheng; Kojima, Yuki; Lu, Yili; Horton, Robert; Heitman, Joshua L.

2017-12-01

Measuring ice contents (θi) in partially frozen soils is important for both engineering and environmental applications. Thermo-time domain reflectometry (thermo-TDR) probes can be used to determine θi based on the relationship between θi and soil heat capacity (C). This approach, however, is accurate in partially frozen soils only at temperatures below -5 °C, and it performs poorly on clayey soils. In this study, we present and evaluate a soil thermal conductivity (λ)-based approach to determine θi with thermo-TDR probes. Bulk soil λ is described with a simplified de Vries model that relates λ to θi. From this model, θi is estimated using inverse modeling of thermo-TDR measured λ. Soil bulk density (ρb) and thermo-TDR measured liquid water content (θl) are also needed for both C-based and λ-based approaches. A theoretical analysis is performed to quantify the sensitivity of C-based and λ-based θi estimates to errors in these input parameters. The analysis indicates that the λ-based approach is less sensitive to errors in the inputs (C, λ, θl, and ρb) than is the C-based approach when the same or the same percentage errors occur. Further evaluations of the C-based and λ-based approaches are made using experimentally determined θi at different temperatures on eight soils with various textures, total water contents, and ρb. The results show that the λ-based thermo-TDR approach significantly improves the accuracy of θi measurements at temperatures ≤-5 °C. The root mean square errors of λ-based θi estimates are only half those of C-based θi. At temperatures of -1 and -2 °C, the λ-based thermo-TDR approach also provides reasonable θi, while the C-based approach fails. We conclude that the λ-based thermo-TDR method can reliably determine θi even at temperatures near the freezing point of water (0 °C).

Differences in Steady-State Net Ammonium and Nitrate Influx by Cold- and Warm-Adapted Barley Varieties 1

PubMed Central

Bloom, Arnold J.; Chapin, F. Stuart

1981-01-01

A flowing nutrient culture system permitted relatively rapid determination of the steady-state net nitrogen influx by an intact barley (Hardeum vulgare L. cv Kombar and Olli) plant. Ion-selective electrodes monitored the depletion of ammonium and nitrate from a nutrient solution after a single pass through a root cuvette. Influx at concentrations as low as 4 micromolar was measured. Standard errors for a sample size of three plants were typically less than 10% of the mean. When grown under identical conditions, a variety of barley bred for cold soils had higher nitrogen influx rates at low concentrations and low temperatures than one bred for warm soils, whereas the one bred for warm soils had higher influx rates at high concentrations and high temperatures. Ammonium was more readily absorbed than nitrate by both varieties at all concentrations and temperatures tested. Ammonium and nitrate influx in both varieties were equally inhibited by low temperatures. PMID:16662052

Error Characterisation and Merging of Active and Passive Microwave Soil Moisture Data Sets

NASA Astrophysics Data System (ADS)

Wagner, Wolfgang; Gruber, Alexander; de Jeu, Richard; Parinussa, Robert; Chung, Daniel; Dorigo, Wouter; Reimer, Christoph; Kidd, Richard

2015-04-01

As part of the Climate Change Initiative (CCI) programme of the European Space Agency (ESA) a data fusion system has been developed which is capable of ingesting surface soil moisture data derived from active and passive microwave sensors (ASCAT, AMSR-E, etc.) flown on different satellite platforms and merging them to create long and consistent time series of soil moisture suitable for use in climate change studies. The so-created soil moisture data records (latest version: ESA CCI SM v02.1 released on 5/12/2014) are freely available and can be obtained from http://www.esa-soilmoisture-cci.org/. As described by Wagner et al. (2012) the principle steps of the data fusion process are: 1) error characterisation, 2) matching to account for data set specific biases, and 3) merging. In this presentation we present the current data fusion process and discuss how new error characterisation methods, such as the increasingly popular triple collocation method as discussed for example by Zwieback et al. (2012) may be used to improve it. The main benefit of an improved error characterisation would be a more reliable identification of the best performing microwave soil moisture retrieval(s) for each grid point and each point in time. In case that two or more satellite data sets provides useful information, the estimated errors can be used to define the weights with which each satellite data set are merged, i.e. the lower its error the higher its weight. This is expected to bring a significant improvement over the current data fusion scheme which is not yet based on quantitative estimates of the retrieval errors but on a proxy measure, namely the vegetation optical depth (Dorigo et al., 2015): over areas with low vegetation passive soil moisture retrievals are used, while over areas with moderate vegetation density active retrievals are used. In transition areas, where both products correlate well, both products are being used in a synergistic way: on time steps where only one of the products is available, the estimate of the respective product is used, while on days where both active and passive sensors provide an estimate, their observations are averaged. REFERENCES Dorigo, W.A., A. Gruber, R. de Jeu, W. Wagner, T. Stacke, A. Löw, C. Albergel, L. Brocca, D. Chung, R. Parinussa, R. Kidd (2015) Evaluation of the ESA CCI soil moisture product using ground-based observations, Remote Sensing of Environment, in press. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012) Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321. Zwieback, S., K. Scipal, W. Dorigo, W. Wagner (2012) Structural and statistical properties of the collocation technique for error characterization, Nonlinear Processes in Geophysics, 19, 69-80.

Determination of irradiated reactor uranium in soil samples in Belarus using 236U as irradiated uranium tracer.

PubMed

Mironov, Vladislav P; Matusevich, Janna L; Kudrjashov, Vladimir P; Boulyga, Sergei F; Becker, J Sabine

2002-12-01

This work presents experimental results on the distribution of irradiated reactor uranium from fallout after the accident at Chernobyl Nuclear Power Plant (NPP) in comparison to natural uranium distribution in different soil types. Oxidation processes and vertical migration of irradiated uranium in soils typical of the 30 km relocation area around Chernobyl NPP were studied using 236U as the tracer for irradiated reactor uranium and inductively coupled plasma mass spectrometry as the analytical method for uranium isotope ratio measurements. Measurements of natural uranium yielded significant variations of its concentration in upper soil layers from 2 x 10(-7) g g(-1) to 3.4 x 10(-6) g g(-1). Concentrations of irradiated uranium in the upper 0-10 cm soil layers at the investigated sampling sites varied from 5 x 10(-12) g g(-1) to 2 x 10(-6) g g(-1) depending on the distance from Chernobyl NPP. In the majority of investigated soil profiles 78% to 97% of irradiated "Chernobyl" uranium is still contained in the upper 0-10 cm soil layers. The physical and chemical characteristics of the soil do not have any significant influence on processes of fuel particle destruction. Results obtained using carbonate leaching of 236U confirmed that more than 60% of irradiated "Chernobyl" uranium is still in a tetravalent form, ie. it is included in the fuel matrix (non-oxidized fuel UO2). The average value of the destruction rate of fuel particles determined for the Western radioactive trace (k = 0.030 +/- 0.005 yr(-1)) and for the Northern radioactive trace (k = 0.035 + 0.009 yr(-1)) coincide within experimental errors. Use of leaching of fission products in comparison to leaching of uranium for study of the destruction rate of fuel particles yielded poor coincidence due to the fact that use of fission products does not take into account differences in the chemical properties of fission products and fuel matrix (uranium).

Uncertainty assessment method for the Cs-137 fallout inventory and penetration depth.

PubMed

Papadakos, G N; Karangelos, D J; Petropoulos, N P; Anagnostakis, M J; Hinis, E P; Simopoulos, S E

2017-05-01

Within the presented study, soil samples were collected in year 2007 at 20 different locations of the Greek terrain, both from the surface and also from depths down to 26 cm. Sampling locations were selected primarily from areas where high levels of 137 Cs deposition after the Chernobyl accident had already been identified by the Nuclear Engineering Laboratory of the National Technical University of Athens during and after the year of 1986. At one location of relatively higher deposition, soil core samples were collected following a 60 m by 60 m Cartesian grid with a 20 m node-to-node distance. Single or pair core samples were also collected from the remaining 19 locations. Sample measurements and analysis were used to estimate 137 Cs inventory and the corresponding depth migration, twenty years after the deposition on Greek terrain. Based on these data, the uncertainty components of the whole sampling-to-results procedure were investigated. A cause-and-effect assessment process was used to apply the law of error propagation and demonstrate that the dominating significant component of the combined uncertainty is that due to the spatial variability of the contemporary (2007) 137 Cs inventory. A secondary, yet also significant component was identified to be the activity measurement process itself. Other less-significant uncertainty parameters were sampling methods, the variation in the soil field density with depth and the preparation of samples for measurement. The sampling grid experiment allowed for the quantitative evaluation of the uncertainty due to spatial variability, also by the assistance of the semivariance analysis. Denser, optimized grid could return more accurate values for this component but with a significantly elevated laboratory cost, in terms of both, human and material resources. Using the hereby collected data and for the case of a single core soil sampling using a well-defined sampling methodology quality assurance, the uncertainty component due to spatial variability was evaluated to about 19% for the 137 Cs inventory and up to 34% for the 137 Cs penetration depth. Based on the presented results and also on related literature, it is argued that such high uncertainties should be anticipated for single core samplings conducted using similar methodology and employed as 137 Cs inventory and penetration depth estimators. Copyright © 2017 Elsevier Ltd. All rights reserved.

A new water retention and hydraulic conductivity model accounting for contact angle

NASA Astrophysics Data System (ADS)

Diamantopoulos, Efstathios; Durner, Wolfgang

2013-04-01

The description of soil water transport in the unsaturated zone requires the knowledge of the soil hydraulic properties, i.e. the water retention and the hydraulic conductivity function. A great amount of parameterizations for this can be found in the literature, the majority of which represent the complex pore space of soils as a bundle of cylindrical capillary tubes of various sizes. The assumption of zero contact angles between water and surface of the grains is also made. However, these assumptions limit the predictive capabilities of these models, leading often to enormous errors in the prediction of water dynamics in soils. We present a pore scale analysis for equilibrium liquid configurations (retention) in angular pores taking the effect of contact angle into account. Furthermore, we propose an alternative derivation of the hydraulic conductivity function, again as a function of the contact angle, assuming flow perpendicular to pore cross sections. Finally, we upscale our model from the pore to the sample scale by assuming a gamma statistical distribution of the pore sizes. Closed form expressions are derived for both sample water retention and conductivity functions. The new model was tested against experimental data from multistep inflow/outflow (MSI/MSO) experiments for a sandy material. They were conducted using ethanol and water as the wetting liquid. Ethanol was assumed to form a zero contact angle with the soil grains. The proposed model described both imbibition and drainage of water and ethanol very well. Lastly, the consideration of the contact angle allowed the description of the observed hysteresis.

Simulating maize yield and bomass with spatial variability of soil field capacity

USGS Publications Warehouse

Ma, Liwang; Ahuja, Lajpat; Trout, Thomas; Nolan, Bernard T.; Malone, Robert W.

2015-01-01

Spatial variability in field soil properties is a challenge for system modelers who use single representative values, such as means, for model inputs, rather than their distributions. In this study, the root zone water quality model (RZWQM2) was first calibrated for 4 yr of maize (Zea mays L.) data at six irrigation levels in northern Colorado and then used to study spatial variability of soil field capacity (FC) estimated in 96 plots on maize yield and biomass. The best results were obtained when the crop parameters were fitted along with FCs, with a root mean squared error (RMSE) of 354 kg ha–1 for yield and 1202 kg ha–1 for biomass. When running the model using each of the 96 sets of field-estimated FC values, instead of calibrating FCs, the average simulated yield and biomass from the 96 runs were close to measured values with a RMSE of 376 kg ha–1 for yield and 1504 kg ha–1 for biomass. When an average of the 96 FC values for each soil layer was used, simulated yield and biomass were also acceptable with a RMSE of 438 kg ha–1 for yield and 1627 kg ha–1 for biomass. Therefore, when there are large numbers of FC measurements, an average value might be sufficient for model inputs. However, when the ranges of FC measurements were known for each soil layer, a sampled distribution of FCs using the Latin hypercube sampling (LHS) might be used for model inputs.

Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States

NASA Astrophysics Data System (ADS)

Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E. A. H.

2017-03-01

Satellite-based near-surface (0-2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [cm3 cm-3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [cm3 cm-3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = -0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone soil moisture over broad extents and has applications in drought predictions and other operational hydrologic modeling purposes.

Application of Terrestrial Microwave Remote Sensing to Agricultural Drought Monitoring

NASA Astrophysics Data System (ADS)

Crow, W. T.; Bolten, J. D.

2014-12-01

Root-zone soil moisture information is a valuable diagnostic for detecting the onset and severity of agricultural drought. Current attempts to globally monitor root-zone soil moisture are generally based on the application of soil water balance models driven by observed meteorological variables. Such systems, however, are prone to random error associated with: incorrect process model physics, poor parameter choices and noisy meteorological inputs. The presentation will describe attempts to remediate these sources of error via the assimilation of remotely-sensed surface soil moisture retrievals from satellite-based passive microwave sensors into a global soil water balance model. Results demonstrate the ability of satellite-based soil moisture retrieval products to significantly improve the global characterization of root-zone soil moisture - particularly in data-poor regions lacking adequate ground-based rain gage instrumentation. This success has lead to an on-going effort to implement an operational land data assimilation system at the United States Department of Agriculture's Foreign Agricultural Service (USDA FAS) to globally monitor variations in root-zone soil moisture availability via the integration of satellite-based precipitation and soil moisture information. Prospects for improving the performance of the USDA FAS system via the simultaneous assimilation of both passive and active-based soil moisture retrievals derived from the upcoming NASA Soil Moisture Active/Passive mission will also be discussed.

Soil moisture optimal sampling strategy for Sentinel 1 validation super-sites in Poland

NASA Astrophysics Data System (ADS)

Usowicz, Boguslaw; Lukowski, Mateusz; Marczewski, Wojciech; Lipiec, Jerzy; Usowicz, Jerzy; Rojek, Edyta; Slominska, Ewa; Slominski, Jan

2014-05-01

Soil moisture (SM) exhibits a high temporal and spatial variability that is dependent not only on the rainfall distribution, but also on the topography of the area, physical properties of soil and vegetation characteristics. Large variability does not allow on certain estimation of SM in the surface layer based on ground point measurements, especially in large spatial scales. Remote sensing measurements allow estimating the spatial distribution of SM in the surface layer on the Earth, better than point measurements, however they require validation. This study attempts to characterize the SM distribution by determining its spatial variability in relation to the number and location of ground point measurements. The strategy takes into account the gravimetric and TDR measurements with different sampling steps, abundance and distribution of measuring points on scales of arable field, wetland and commune (areas: 0.01, 1 and 140 km2 respectively), taking into account the different status of SM. Mean values of SM were lowly sensitive on changes in the number and arrangement of sampling, however parameters describing the dispersion responded in a more significant manner. Spatial analysis showed autocorrelations of the SM, which lengths depended on the number and the distribution of points within the adopted grids. Directional analysis revealed a differentiated anisotropy of SM for different grids and numbers of measuring points. It can therefore be concluded that both the number of samples, as well as their layout on the experimental area, were reflected in the parameters characterizing the SM distribution. This suggests the need of using at least two variants of sampling, differing in the number and positioning of the measurement points, wherein the number of them must be at least 20. This is due to the value of the standard error and range of spatial variability, which show little change with the increase in the number of samples above this figure. Gravimetric method gives a more varied distribution of SM than those derived from TDR measurements. It should be noted that reducing the number of samples in the measuring grid leads to flattening the distribution of SM from both methods and increasing the estimation error at the same time. Grid of sensors for permanent measurement points should include points that have similar distributions of SM in the vicinity. Results of the analysis including number, the maximum correlation ranges and the acceptable estimation error should be taken into account when choosing of the measurement points. Adoption or possible adjustment of the distribution of the measurement points should be verified by performing additional measuring campaigns during the dry and wet periods. Presented approach seems to be appropriate for creation of regional-scale test (super) sites, to validate products of satellites equipped with SAR (Synthetic Aperture Radar), operating in C-band, with spatial resolution suited to single field scale, as for example: ERS-1, ERS-2, Radarsat and Sentinel-1, which is going to be launched in next few months. The work was partially funded by the Government of Poland through an ESA Contract under the PECS ELBARA_PD project No. 4000107897/13/NL/KML.

Comparison of spatial interpolation methods for soil moisture and its application for monitoring drought.

PubMed

Chen, Hui; Fan, Li; Wu, Wei; Liu, Hong-Bin

2017-09-26

Soil moisture data can reflect valuable information on soil properties, terrain features, and drought condition. The current study compared and assessed the performance of different interpolation methods for estimating soil moisture in an area with complex topography in southwest China. The approaches were inverse distance weighting, multifarious forms of kriging, regularized spline with tension, and thin plate spline. The 5-day soil moisture observed at 167 stations and daily temperature recorded at 33 stations during the period of 2010-2014 were used in the current work. Model performance was tested with accuracy indicators of determination coefficient (R 2 ), mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and modeling efficiency (ME). The results indicated that inverse distance weighting had the best performance with R 2 , MAPE, RMSE, RRMSE, and ME of 0.32, 14.37, 13.02%, 0.16, and 0.30, respectively. Based on the best method, a spatial database of soil moisture was developed and used to investigate drought condition over the study area. The results showed that the distribution of drought was characterized by evidently regional difference. Besides, drought mainly occurred in August and September in the 5 years and was prone to happening in the western and central parts rather than in the northeastern and southeastern areas.

Detection and quantification of soil-transmitted helminths in environmental samples: A review of current state-of-the-art and future perspectives.

PubMed

Amoah, Isaac Dennis; Singh, Gulshan; Stenström, Thor Axel; Reddy, Poovendhree

2017-05-01

It is estimated that over a billion people are infected with soil-transmitted helminths (STHs) globally with majority occurring in tropical and subtropical regions of the world. The roundworm (Ascaris lumbricoides), whipworm (Trichuris trichiura), and hookworms (Ancylostoma duodenale and Necator americanus) are the main species infecting people. These infections are mostly gained through exposure to faecally contaminated water, soil or contaminated food and with an increase in the risk of infections due to wastewater and sludge reuse in agriculture. Different methods have been developed for the detection and quantification of STHs eggs in environmental samples. However, there is a lack of a universally accepted technique which creates a challenge for comparative assessments of helminths egg concentrations both in different samples matrices as well as between locations. This review presents a comparison of reported methodologies for the detection of STHs eggs, an assessment of the relative performance of available detection methods and a discussion of new emerging techniques that could be applied for detection and quantification. It is based on a literature search using PubMed and Science Direct considering all geographical locations. Original research articles were selected based on their methodology and results sections. Methods reported in these articles were grouped into conventional, molecular and emerging techniques, the main steps in each method were then compared and discussed. The inclusion of a dissociation step aimed at detaching helminth eggs from particulate matter was found to improve the recovery of eggs. Additionally the selection and application of flotation solutions that take into account the relative densities of the eggs of different species of STHs also results in higher egg recovery. Generally the use of conventional methods was shown to be laborious and time consuming and prone to human error. The alternate use of nucleic acid-based techniques has improved the sensitivity of detection and made species specific identification possible. However, these nucleic acid based methods are expensive and less suitable in regions with limited resources and skill. The loop mediated isothermal amplification method shows promise for application in these settings due to its simplicity and use of basic equipment. In addition, the development of imaging soft-ware for the detection and quantification of STHs shows promise to further reduce human error associated with the analysis of environmental samples. It may be concluded that there is a need to comparatively assess the performance of different methods to determine their applicability in different settings as well as for use with different sample matrices (wastewater, sludge, compost, soil, vegetables etc.). Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Soil respiration and carbon loss relationship with temperature and land use conversion in freeze-thaw agricultural area.

PubMed

Ouyang, Wei; Lai, Xuehui; Li, Xia; Liu, Heying; Lin, Chunye; Hao, Fanghua

2015-11-15

Soil respiration (Rs) was hypothesized to have a special response pattern to soil temperature and land use conversion in the freeze-thaw area. The Rs differences of eight types of land use conversions during agricultural development were observed and the impacts of Rs on soil organic carbon (SOC) loss were assessed. The land use conversions during last three decades were categorized into eight types, and the 141 SOC sampling sites were grouped by conversion type. The typical soil sampling sites were subsequently selected for monitoring of soil temperature and Rs of each land use conversion types. The Rs correlations with temperature at difference depths and different conversion types were identified with statistical analysis. The empirical mean error model and the biophysical theoretical model with Arrhenius equation about the Rs sensitivity to temperature were both analyzed and shared the similar patterns. The temperature dependence of soil respiration (Q10) analysis further demonstrated that the averaged value of eight types of land use in this freeze-thaw agricultural area ranged from 1.15 to 1.73, which was lower than the other cold areas. The temperature dependence analysis demonstrated that the Rs in the top layer of natural land covers was more sensitive to temperature and experienced a large vertical difference. The natural land covers exhibited smaller Rs and the farmlands had the bigger value due to tillage practices. The positive relationships between SOC loss and Rs were identified, which demonstrated that Rs was the key chain for SOC loss during land use conversion. The spatial-vertical distributions of SOC concentration with the 1.5-km grid sampling showed that the more SOC loss in the farmland, which was coincided with the higher Rs in farmlands. The analysis of Rs dynamics provided an innovative explanation for SOC loss in the freeze-thaw agricultural area. The analysis of Rs dynamics provided an innovative explanation for SOC loss in the freeze-thaw agricultural area. Copyright © 2015 Elsevier B.V. All rights reserved.

Discrimination of plant-parasitic nematodes from complex soil communities using ecometagenetics.

PubMed

Porazinska, Dorota L; Morgan, Matthew J; Gaspar, John M; Court, Leon N; Hardy, Christopher M; Hodda, Mike

2014-07-01

Many plant pathogens are microscopic, cryptic, and difficult to diagnose. The new approach of ecometagenetics, involving ultrasequencing, bioinformatics, and biostatistics, has the potential to improve diagnoses of plant pathogens such as nematodes from the complex mixtures found in many agricultural and biosecurity situations. We tested this approach on a gradient of complexity ranging from a few individuals from a few species of known nematode pathogens in a relatively defined substrate to a complex and poorly known suite of nematode pathogens in a complex forest soil, including its associated biota of unknown protists, fungi, and other microscopic eukaryotes. We added three known but contrasting species (Pratylenchus neglectus, the closely related P. thornei, and Heterodera avenae) to half the set of substrates, leaving the other half without them. We then tested whether all nematode pathogens-known and unknown, indigenous, and experimentally added-were detected consistently present or absent. We always detected the Pratylenchus spp. correctly and with the number of sequence reads proportional to the numbers added. However, a single cyst of H. avenae was only identified approximately half the time it was present. Other plant-parasitic nematodes and nematodes from other trophic groups were detected well but other eukaryotes were detected less consistently. DNA sampling errors or informatic errors or both were involved in misidentification of H. avenae; however, the proportions of each varied in the different bioinformatic pipelines and with different parameters used. To a large extent, false-positive and false-negative errors were complementary: pipelines and parameters with the highest false-positive rates had the lowest false-negative rates and vice versa. Sources of error identified included assumptions in the bioinformatic pipelines, slight differences in primer regions, the number of sequence reads regarded as the minimum threshold for inclusion in analysis, and inaccessible DNA in resistant life stages. Identification of the sources of error allows us to suggest ways to improve identification using ecometagenetics.

Analysis of hydroxamate siderophores in soil solution using liquid chromatography with mass spectrometry and tandem mass spectrometry with on-line sample preconcentration.

PubMed

Olofsson, Madelen A; Bylund, Dan

2015-10-01

A liquid chromatography with electrospray ionization mass spectrometry method was developed to quantitatively and qualitatively analyze 13 hydroxamate siderophores (ferrichrome, ferrirubin, ferrirhodin, ferrichrysin, ferricrocin, ferrioxamine B, D1 , E and G, neocoprogen I and II, coprogen and triacetylfusarinine C). Samples were preconcentrated on-line by a switch-valve setup prior to analyte separation on a Kinetex C18 column. Gradient elution was performed using a mixture of an ammonium formate buffer and acetonitrile. Total analysis time including column conditioning was 20.5 min. Analytes were fragmented by applying collision-induced dissociation, enabling structural identification by tandem mass spectrometry. Limit of detection values for the selected ion monitoring method ranged from 71 pM to 1.5 nM with corresponding values of two to nine times higher for the multiple reaction monitoring method. The liquid chromatography with mass spectrometry method resulted in a robust and sensitive quantification of hydroxamate siderophores as indicated by retention time stability, linearity, sensitivity, precision and recovery. The analytical error of the methods, assessed through random-order, duplicate analysis of soil samples extracted with a mixture of 10 mM phosphate buffer and methanol, appears negligible in relation to between-sample variations. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Error characterization of microwave satellite soil moisture data sets using fourier analysis

USDA-ARS?s Scientific Manuscript database

Soil moisture is a key geophysical variable in hydrological and meteorological processes. Accurate and current observations of soil moisture over meso to global scales used as inputs to hydrological, weather and climate modelling will benefit the predictability and understanding of these processes. ...

Error characterization of microwave satellite soil moisture data sets using fourier analysis

USDA-ARS?s Scientific Manuscript database

Abstract: Soil moisture is a key geophysical variable in hydrological and meteorological processes. Accurate and current observations of soil moisture over mesoscale to global scales as inputs to hydrological, weather and climate modelling will benefit the predictability and understanding of these p...

10Be inventories in Alpine soils and their potentiality for dating land surfaces

NASA Astrophysics Data System (ADS)

Egli, Markus; Brandová, Dagmar; Böhlert, Ralph; Favilli, Filippo; Kubik, Peter W.

2010-05-01

To exploit natural archives and geomorphic objects it is necessary to date them first. Landscape evolution of Alpine areas is often strongly related to the activities of glaciers in the Pleistocene and Holocene. At sites where no organic matter for radiocarbon dating exists and where suitable boulders for surface exposure dating (using in situ produced cosmogenic nuclides) are absent, dating of soils could give information about the timing of landscape evolution. We explored the applicability of soil dating using the inventory of meteoric Be-10 in Alpine soils. For this purpose, a set of 6 soil profiles in the Swiss and Italian Alps was investigated. The surface at these sites had already been dated (using the radiocarbon technique or surface exposure dating using in situ produced Be-10). Consequently, a direct comparison of the ages of the soils using meteoric Be-10 and other dating techniques was made possible. The estimation of Be-10 deposition rates is subject to severe limitations and strongly influences the obtained results. We tested three scenarios using a) the meteoric Be-10 deposition rates as a function of the annual precipitation rate, b) a constant Be-10 input for the Central Alps and c) as b) but assuming a pre-exposure of the parent material. The obtained ages that are based on the Be-10 inventory in soils and on scenario a) for the Be-10 input agreed reasonably well with the expected age (obtained from surface exposure or radiocarbon dating). The ages obtained from soils using scenario b) produced mostly ages that were too old whereas the approach using scenario c) seemed to yield better results than scenario b). Erosion calculations can, in theory, be performed using the Be-10 inventory and Be-10 deposition rates. An erosion estimation was possible using scenario a) and c), but not using b). The estimated erosion rates are in a reasonable range. The dating of soils using Be-10 has several potential error sources. Analytical errors as well as errors from other parameters such as bulk soil density and soil skeleton content have to be taken into account. The error range was from 8 up to 21%. Furthermore, uncertainties in estimating Be-10 deposition rates substantially influence the calculated ages. Relative age estimates and, under optimal conditions, a numerical dating can be carried out. Age determination of Alpine soils using Be-10 gives another possibility to date surfaces when other methods fail or are not possible at all. It is, however, not straightforward, quite laborious and may consequently have some distinct limitations.

The potential for geostationary remote sensing of NO2 to improve weather prediction

NASA Astrophysics Data System (ADS)

Liu, X.; Mizzi, A. P.; Anderson, J. L.; Fung, I. Y.; Cohen, R. C.

2017-12-01

Observations of surface winds remain sparse making it challenging to simulate and predict the weather in circumstances of light winds that are most important for poor air quality. Direct measurements of short-lived chemicals from space might be a solution to this challenge. Here we investigate the application of data assimilation of NO­2 columns as will be observed from geostationary orbit to improve predictions and retrospective analysis of surface wind fields. Specifically, synthetic NO2 observations are sampled from a "nature run (NR)" regarded as the true atmosphere. Then NO2 observations are assimilated using EAKF methods into a "control run (CR)" which differs from the NR in the wind field. Wind errors are generated by introducing (1) errors in the initial conditions, (2) creating a model error by using two different formulations for the planetary boundary layer, (3) and by combining both of these effects. Assimilation of NO2 column observations succeeds in reducing wind errors, indicating the prospects for future geostationary atmospheric composition measurements to improve weather forecasting are substantial. We find that due to the temporal heterogeneity of wind errors, the success of this application favors chemical observations of high frequency, such as those from geostationary platform. We also show the potential to improve soil moisture field by assimilating NO­2 columns.

An improved Rosetta pedotransfer function and evaluation in earth system models

NASA Astrophysics Data System (ADS)

Zhang, Y.; Schaap, M. G.

2017-12-01

Soil hydraulic parameters are often difficult and expensive to measure, leading to the pedotransfer functions (PTFs) an alternative to predict those parameters. Rosetta (Schaap et al., 2001, denoted as Rosetta1) are widely used PTFs, which is based on artificial neural network (ANN) analysis coupled with the bootstrap re-sampling method, allowing the estimation of van Genuchten water retention parameters (van Genuchten, 1980, abbreviated here as VG), saturated hydraulic conductivity (Ks), as well as their uncertainties. We present an improved hierarchical pedotransfer functions (Rosetta3) that unify the VG water retention and Ks submodels into one, thus allowing the estimation of uni-variate and bi-variate probability distributions of estimated parameters. Results show that the estimation bias of moisture content was reduced significantly. Rosetta1 and Posetta3 were implemented in the python programming language, and the source code are available online. Based on different soil water retention equations, there are diverse PTFs used in different disciplines of earth system modelings. PTFs based on Campbell [1974] or Clapp and Hornberger [1978] are frequently used in land surface models and general circulation models, while van Genuchten [1980] based PTFs are more widely used in hydrology and soil sciences. We use an independent global scale soil database to evaluate the performance of diverse PTFs used in different disciplines of earth system modelings. PTFs are evaluated based on different soil characteristics and environmental characteristics, such as soil textural data, soil organic carbon, soil pH, as well as precipitation and soil temperature. This analysis provides more quantitative estimation error information for PTF predictions in different disciplines of earth system modelings.

Assimilation of SMOS Retrievals in the Land Information System

NASA Technical Reports Server (NTRS)

Blankenship, Clay B.; Case, Jonathan L.; Zavodsky, Bradley T.; Crosson, William L.

2016-01-01

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in the upper 5 cm with a 30-50 km resolution and a mission accuracy requirement of 0.04 cm(sub 3 cm(sub -3). These observations can be used to improve land surface model soil moisture states through data assimilation. In this paper, SMOS soil moisture retrievals are assimilated into the Noah land surface model via an Ensemble Kalman Filter within the NASA Land Information System. Bias correction is implemented using Cumulative Distribution Function (CDF) matching, with points aggregated by either land cover or soil type to reduce sampling error in generating the CDFs. An experiment was run for the warm season of 2011 to test SMOS data assimilation and to compare assimilation methods. Verification of soil moisture analyses in the 0-10 cm upper layer and root zone (0-1 m) was conducted using in situ measurements from several observing networks in the central and southeastern United States. This experiment showed that SMOS data assimilation significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10 cm layer. Time series at specific stations demonstrate the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared to using a simple uniform correction curve.

Assimilation of SMOS Retrievals in the Land Information System

PubMed Central

Blankenship, Clay B.; Case, Jonathan L.; Zavodsky, Bradley T.; Crosson, William L.

2018-01-01

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in the upper 5 cm with a 30-50 km resolution and a mission accuracy requirement of 0.04 cm3 cm−3. These observations can be used to improve land surface model soil moisture states through data assimilation. In this paper, SMOS soil moisture retrievals are assimilated into the Noah land surface model via an Ensemble Kalman Filter within the NASA Land Information System. Bias correction is implemented using Cumulative Distribution Function (CDF) matching, with points aggregated by either land cover or soil type to reduce sampling error in generating the CDFs. An experiment was run for the warm season of 2011 to test SMOS data assimilation and to compare assimilation methods. Verification of soil moisture analyses in the 0-10 cm upper layer and root zone (0-1 m) was conducted using in situ measurements from several observing networks in the central and southeastern United States. This experiment showed that SMOS data assimilation significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10 cm layer. Time series at specific stations demonstrate the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared to using a simple uniform correction curve. PMID:29367795

[Estimation of organic matter content of north fluvo-aquic soil based on the coupling model of wavelet transform and partial least squares].

PubMed

Wang, Yan-Cang; Yang, Gui-Jun; Zhu, Jin-Shan; Gu, Xiao-He; Xu, Peng; Liao, Qin-Hong

2014-07-01

For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0.722 and 0.221, respectively. The R2 and RMSE of the best model for detail coefficients are 0.670 and 0.255, respectively.

Oak soil-site relationships in northwestern West Virginia

Treesearch

L.R. Auchmoody; H. Clay Smith

1979-01-01

An oak soil-site productivity equation was developed for the well-drained, upland soils in the northwestern portion of West Virginia adjacent to the Ohio River. The equation uses five easily measured soil and topographic variables and average precipitation to predict site index. It accounts for 69 percent of the variation in oak site index and has a standard error of 4...

Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

NASA Astrophysics Data System (ADS)

Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

2018-03-01

Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

Potential of SENTINEL-2 images for predicting common topsoil properties over Temperate and Mediterranean agroecosystems

NASA Astrophysics Data System (ADS)

Vaudour, Emmanuelle; Gomez, Cécile; Fouad, Youssef; Gilliot, Jean-Marc; Lagacherie, Philippe

2017-04-01

This study aimed at exploring the potential of SENTINEL-2 (S2A) multispectral satellite images for predicting several topsoil properties in two contrasted environments: a temperate region marked by intensive annual crop cultivation and soils derived from either loess or colluvium and/or marine limestone or chalk for one part (Versailles Plain, 221 km2), and a Mediterranean region marked by vineyard cultivation and soils derived from either lacustrine limestone, calcareous sandstones, colluvium, or alluvial deposits (La Peyne catchment, 48 km2) for the other part. Two S2A images (acquired in mid-March 2016 over each site) were atmospherically corrected. Then NDVI was computed and thresholded (0.35) in order to extract bare soils. Prediction models of soil properties based on partial least squares regressions (PLSR) were built from S2A spectra of 72 and 143 sampling locations in the Versailles Plain and La Peyne catchment, respectively. Ten soil properties were investigated in both regions: pH, cation exchange capacity (CEC), five texture fractions (clay, coarse silt, fine silt, coarse sand and fine sand), iron, calcium carbonate and soil organic carbon (SOC) in the tilled horizon. Predictive abilities were studied according to R_cv2 and ratio of performance to deviation (RPD). Intermediate to near intermediate performances of prediction (R_cv2 and RPD between 0.28-0.70 and 1.19-1.85 respectively) were obtained for 6 topsoil properties: clay, iron, SOC, CEC, pH, coarse silt. In the Versailles Plain, 5 out of these properties could be predicted (by decreasing performance, CEC, SOC, pH, clay, coarse silt), while there were 4 predictable properties for La Peyne catchment (Iron, clay, CEC, coarse silt). The amount in coarse fragment content appeared to impact prediction error for iron content over La Peyne, while it influenced prediction error for SOC content over the Versailles Plain along with calcium carbonate content. A spatial structure of the estimated soil properties for bare soils pixels was highlighted, which promises further improvements in spatial prediction models for these properties. This work was carried out in the framework of both the TOSCA-CES "Cartographie Numérique des sols" and the PLEIADES-CO projects of the French Space Agency (CNES).

Estimating surface soil moisture from SMAP observations using a Neural Network technique.

PubMed

Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P

2018-01-01

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Surface Soil Moisture Retrieval Using SSM/I and Its Comparison with ESTAR: A Case Study Over a Grassland Region

NASA Technical Reports Server (NTRS)

Jackson, T.; Hsu, A. Y.; ONeill, P. E.

1999-01-01

This study extends a previous investigation on estimating surface soil moisture using the Special Sensor Microwave/Imager (SSM/I) over a grassland region. Although SSM/I is not optimal for soil moisture retrieval, it can under some conditions provide information. Rigorous analyses over land have been difficult due to the lack of good validation data sets. A scientific objective of the Southern Great Plains 1997 (SGP97) Hydrology Experiment was to investigate whether the retrieval algorithms for surface soil moisture developed at higher spatial resolution using truck-and aircraft-based passive microwave sensors can be extended to the coarser resolutions expected from satellite platform. With the data collected for the SGP97, the objective of this study is to compare the surface soil moisture estimated from the SSM/I data with those retrieved from the L-band Electronically Scanned Thinned Array Radiometer (ESTAR) data, the core sensor for the experiment, using the same retrieval algorithm. The results indicated that an error of estimate of 7.81% could be achieved with SSM/I data as contrasted to 2.82% with ESTAR data over three intensive sampling areas of different vegetation regimes. It confirms the results of previous study that SSM/I data can be used to retrieve surface soil moisture information at a regional scale under certain conditions.

Peak-flow frequency relations and evaluation of the peak-flow gaging network in Nebraska

USGS Publications Warehouse

Soenksen, Philip J.; Miller, Lisa D.; Sharpe, Jennifer B.; Watton, Jason R.

1999-01-01

Estimates of peak-flow magnitude and frequency are required for the efficient design of structures that convey flood flows or occupy floodways, such as bridges, culverts, and roads. The U.S. Geological Survey, in cooperation with the Nebraska Department of Roads, conducted a study to update peak-flow frequency analyses for selected streamflow-gaging stations, develop a new set of peak-flow frequency relations for ungaged streams, and evaluate the peak-flow gaging-station network for Nebraska. Data from stations located in or within about 50 miles of Nebraska were analyzed using guidelines of the Interagency Advisory Committee on Water Data in Bulletin 17B. New generalized skew relations were developed for use in frequency analyses of unregulated streams. Thirty-three drainage-basin characteristics related to morphology, soils, and precipitation were quantified using a geographic information system, related computer programs, and digital spatial data.For unregulated streams, eight sets of regional regression equations relating drainage-basin to peak-flow characteristics were developed for seven regions of the state using a generalized least squares procedure. Two sets of regional peak-flow frequency equations were developed for basins with average soil permeability greater than 4 inches per hour, and six sets of equations were developed for specific geographic areas, usually based on drainage-basin boundaries. Standard errors of estimate for the 100-year frequency equations (1percent probability) ranged from 12.1 to 63.8 percent. For regulated reaches of nine streams, graphs of peak flow for standard frequencies and distance upstream of the mouth were estimated.The regional networks of streamflow-gaging stations on unregulated streams were analyzed to evaluate how additional data might affect the average sampling errors of the newly developed peak-flow equations for the 100-year frequency occurrence. Results indicated that data from new stations, rather than more data from existing stations, probably would produce the greatest reduction in average sampling errors of the equations.

Natural transformation of chlordecone into 5b-hydrochlordecone in French West Indies soils: statistical evidence for investigating long-term persistence of organic pollutants.

PubMed

Devault, Damien A; Laplanche, Christophe; Pascaline, Hélène; Bristeau, Sébastien; Mouvet, Christophe; Macarie, Hervé

2016-01-01

Chlordecone (CLD) was an organochlorine insecticide whose previous use resulted in an extensive pollution of the environment with severe health effects and social consequences. A closely related compound, 5b-hydrochlordecone (5b-hydroCLD), has been searched for and often detected in environmental matrices from the geographical area where CLD was applied. The current consensus considered that its presence was not the result of a biotic or abiotic dechlorination of CLD in these matrices but rather the consequence of its presence as impurity (synthesis by-product) in the CLD released into the environment. The aim of the present study was to determine if and to what extent degradation of CLD into 5b-hydroCLD occurred in the field. To test this hypothesis, the ratios of 5b-hydroCLD and CLD concentrations in a dataset of 810 soils collected between 2006 and 2012 in Martinique were compared to the ratios measured in 3 samples of the CLD dust commercial formulations applied in the banana fields of French West Indies (FWI) and 1 sample of the technical-grade CLD corresponding to the active ingredient used in such formulations. Soil data were processed with a hierarchical Bayesian model to account for random measurement errors and data censoring. Any pathway of CLD transformation into 5b-hydroCLD occurring over the long term in FWI soils would indeed change the ratio of 5b-hydroCLD/CLD compared to what it was in the initially applied formulations. Results showed a significant increase of the 5b-hydroCLD/CLD ratio in the soils-25 times greater in soil than in commercial formulations-which suggested that natural CLD transformation into 5b-hydroCLD over the long term occurred in these soils. Results from this study may impact future decisions for the remediation of the polluted areas.

Water content determination of soil surface in an intensive apple orchard

NASA Astrophysics Data System (ADS)

Riczu, Péter; Nagy, Gábor; Tamás, János

2015-04-01

Currently in Hungary, less than 100,000 hectares of orchards can be found, from which cultivation of apple is one of the most dominant ones. Production of marketable horticulture products can be difficult without employing advanced and high quality horticulture practices, which, in turn, depends on appropriate management and irrigation systems, basically. The got out water amount depend on climatic, edafic factors and the water demand of plants as well. The soil water content can be determined by traditional and modern methods. In order to define soil moisture content, gravimetry measurement is one of the most accurate methods, but it is time consuming and sometimes soil sampling and given results are in different times. Today, IT provides the farmers such tools, like global positioning system (GPS), geographic information system (GIS) and remote sensing (RS). These tools develop in a great integration rapidly. RS methods are ideal to survey larger area quick and accurate. Laser scanning is a novel technique which analyses a real-world or object environment to collect structural and spectral data. In order to obtain soil moisture information, the Leica ScanStation C10 terrestrial 3D laser scanner was used on an intensive apple orchard on the Study and Regional Research Farm of the University of Debrecen, near Pallag. Previously, soil samples from the study area with different moisture content were used as reference points. Based on the return intensity values of the laser scanner can be distinguished the different moisture content areas of soil surface. Nevertheless, the error of laser distance echo were examined and statistically evaluated. This research was realized in the frames of TÁMOP 4.2.4. A/2-11-1-2012-0001 "National Excellence Program - Elaborating and operating an inland student and researcher personal support system". The project was subsidized by the European Union and co-financed by the European Social Fund.

Soil sampling kit and a method of sampling therewith

DOEpatents

Thompson, Cyril V.

1991-01-01

A soil sampling device and a sample containment device for containing a soil sample is disclosed. In addition, a method for taking a soil sample using the soil sampling device and soil sample containment device to minimize the loss of any volatile organic compounds contained in the soil sample prior to analysis is disclosed. The soil sampling device comprises two close fitting, longitudinal tubular members of suitable length, the inner tube having the outward end closed. With the inner closed tube withdrawn a selected distance, the outer tube can be inserted into the ground or other similar soft material to withdraw a sample of material for examination. The inner closed end tube controls the volume of the sample taken and also serves to eject the sample. The soil sample containment device has a sealing member which is adapted to attach to an analytical apparatus which analyzes the volatile organic compounds contained in the sample. The soil sampling device in combination with the soil sample containment device allow an operator to obtain a soil sample containing volatile organic compounds and minimizing the loss of the volatile organic compounds prior to analysis of the soil sample for the volatile organic compounds.

Soil sampling kit and a method of sampling therewith

DOEpatents

Thompson, C.V.

1991-02-05

A soil sampling device and a sample containment device for containing a soil sample is disclosed. In addition, a method for taking a soil sample using the soil sampling device and soil sample containment device to minimize the loss of any volatile organic compounds contained in the soil sample prior to analysis is disclosed. The soil sampling device comprises two close fitting, longitudinal tubular members of suitable length, the inner tube having the outward end closed. With the inner closed tube withdrawn a selected distance, the outer tube can be inserted into the ground or other similar soft material to withdraw a sample of material for examination. The inner closed end tube controls the volume of the sample taken and also serves to eject the sample. The soil sample containment device has a sealing member which is adapted to attach to an analytical apparatus which analyzes the volatile organic compounds contained in the sample. The soil sampling device in combination with the soil sample containment device allows an operator to obtain a soil sample containing volatile organic compounds and minimizing the loss of the volatile organic compounds prior to analysis of the soil sample for the volatile organic compounds. 11 figures.

Incorporation of globally available datasets into the roving cosmic-ray neutron probe method for estimating field-scale soil water content

NASA Astrophysics Data System (ADS)

Avery, William Alexander; Finkenbiner, Catherine; Franz, Trenton E.; Wang, Tiejun; Nguy-Robertson, Anthony L.; Suyker, Andrew; Arkebauer, Timothy; Muñoz-Arriola, Francisco

2016-09-01

The need for accurate, real-time, reliable, and multi-scale soil water content (SWC) monitoring is critical for a multitude of scientific disciplines trying to understand and predict the Earth's terrestrial energy, water, and nutrient cycles. One promising technique to help meet this demand is fixed and roving cosmic-ray neutron probes (CRNPs). However, the relationship between observed low-energy neutrons and SWC is affected by local soil and vegetation calibration parameters. This effect may be accounted for by a calibration equation based on local soil type and the amount of vegetation. However, determining the calibration parameters for this equation is labor- and time-intensive, thus limiting the full potential of the roving CRNP in large surveys and long transects, or its use in novel environments. In this work, our objective is to develop and test the accuracy of globally available datasets (clay weight percent, soil bulk density, and soil organic carbon) to support the operability of the roving CRNP. Here, we develop a 1 km product of soil lattice water over the continental United States (CONUS) using a database of in situ calibration samples and globally available soil taxonomy and soil texture data. We then test the accuracy of the global dataset in the CONUS using comparisons from 61 in situ samples of clay percent (RMSE = 5.45 wt %, R2 = 0.68), soil bulk density (RMSE = 0.173 g cm-3, R2 = 0.203), and soil organic carbon (RMSE = 1.47 wt %, R2 = 0.175). Next, we conduct an uncertainty analysis of the global soil calibration parameters using a Monte Carlo error propagation analysis (maximum RMSE ˜ 0.035 cm3 cm-3 at a SWC = 0.40 cm3 cm-3). In terms of vegetation, fast-growing crops (i.e., maize and soybeans), grasslands, and forests contribute to the CRNP signal primarily through the water within their biomass and this signal must be accounted for accurate estimation of SWC. We estimated the biomass water signal by using a vegetation index derived from MODIS imagery as a proxy for standing wet biomass (RMSE < 1 kg m-2). Lastly, we make recommendations on the design and validation of future roving CRNP experiments.

Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands

NASA Astrophysics Data System (ADS)

Anne, Naveen J. P.; Abd-Elrahman, Amr H.; Lewis, David B.; Hewitt, Nicole A.

2014-12-01

Developing spectral models of soil properties is an important frontier in remote sensing and soil science. Several studies have focused on modeling soil properties such as total pools of soil organic matter and carbon in bare soils. We extended this effort to model soil parameters in areas densely covered with coastal vegetation. Moreover, we investigated soil properties indicative of soil functions such as nutrient and organic matter turnover and storage. These properties include the partitioning of mineral and organic soil between particulate (>53 μm) and fine size classes, and the partitioning of soil carbon and nitrogen pools between stable and labile fractions. Soil samples were obtained from Avicennia germinans mangrove forest and Juncus roemerianus salt marsh plots on the west coast of central Florida. Spectra corresponding to field plot locations from Hyperion hyperspectral image were extracted and analyzed. The spectral information was regressed against the soil variables to determine the best single bands and optimal band combinations for the simple ratio (SR) and normalized difference index (NDI) indices. The regression analysis yielded levels of correlation for soil variables with R2 values ranging from 0.21 to 0.47 for best individual bands, 0.28 to 0.81 for two-band indices, and 0.53 to 0.96 for partial least-squares (PLS) regressions for the Hyperion image data. Spectral models using Hyperion data adequately (RPD > 1.4) predicted particulate organic matter (POM), silt + clay, labile carbon (C), and labile nitrogen (N) (where RPD = ratio of standard deviation to root mean square error of cross-validation [RMSECV]). The SR (0.53 μm, 2.11 μm) model of labile N with R2 = 0.81, RMSECV= 0.28, and RPD = 1.94 produced the best results in this study. Our results provide optimism that remote-sensing spectral models can successfully predict soil properties indicative of ecosystem nutrient and organic matter turnover and storage, and do so in areas with dense canopy cover.

Land surface dynamics monitoring using microwave passive satellite sensors

NASA Astrophysics Data System (ADS)

Guijarro, Lizbeth Noemi

Soil moisture, surface temperature and vegetation are variables that play an important role in our environment. There is growing demand for accurate estimation of these geophysical parameters for the research of global climate models (GCMs), weather, hydrological and flooding models, and for the application to agricultural assessment, land cover change, and a wide variety of other uses that meet the needs for the study of our environment. The different studies covered in this dissertation evaluate the capabilities and limitations of microwave passive sensors to monitor land surface dynamics. The first study evaluates the 19 GHz channel of the SSM/I instrument with a radiative transfer model and in situ datasets from the Illinois stations and the Oklahoma Mesonet to retrieve land surface temperature and surface soil moisture. The surface temperatures were retrieved with an average error of 5 K and the soil moisture with an average error of 6%. The results show that the 19 GHz channel can be used to qualitatively predict the spatial and temporal variability of surface soil moisture and surface temperature at regional scales. In the second study, in situ observations were compared with sensor observations to evaluate aspects of low and high spatial resolution at multiple frequencies with data collected from the Southern Great Plains Experiment (SGP99). The results showed that the sensitivity to soil moisture at each frequency is a function of wavelength and amount of vegetation. The results confirmed that L-band is more optimal for soil moisture, but each sensor can provide soil moisture information if the vegetation water content is low. The spatial variability of the emissivities reveals that resolution suffers considerably at higher frequencies. The third study evaluates C- and X-bands of the AMSR-E instrument. In situ datasets from the Soil Moisture Experiments (SMEX03) in South Central Georgia were utilized to validate the AMSR-E soil moisture product and to derive surface soil moisture with a radiative transfer model. The soil moisture was retrieved with an average error of 2.7% at X-band and 6.7% at C-band. The AMSR-E demonstrated its ability to successfully infer soil moisture during the SMEX03 experiment.

Evaluation of the performance of hydrological variables derived from GLDAS-2 and MERRA-2 in Mexico

NASA Astrophysics Data System (ADS)

Real-Rangel, R. A.; Pedrozo-Acuña, A.; Breña-Naranjo, J. A.

2017-12-01

Hydrological studies have found in data assimilation systems and global reanalysis of land surface variables (e.g soil moisture, streamflow) a wide range of applications, from drought monitoring to water balance and hydro-climatology variability assessment. Indeed, these hydrological data sources have led to an improvement in developing and testing monitoring and prediction systems in poorly gauged regions of the world. This work tests the accuracy and error of land surface variables (precipitation, soil moisture, runoff and temperature) derived from the data assimilation reanalysis products GLDAS-2 and MERRA-2. Validate the performance of these data platforms must be thoroughly evaluated in order to consider the error of hydrological variables (i.e., precipitation, soil moisture, runoff and temperature) derived from the reanalysis products. For such purpose, a quantitative assessment was performed at 2,892 climatological stations, 42 stream gauges and 44 soil moisture probes located in Mexico and across different climate regimes (hyper-arid to tropical humid). Results show comparisons between these gridded products against ground-based observational stations for 1979-2014. The results of this analysis display a spatial distribution of errors and accuracy over Mexico discussing differences between climates, enabling the informed use of these products.

Soil moisture retrieval in forest biomes: field experiment focus for SMAP 2018-2020 and beyond

USDA-ARS?s Scientific Manuscript database

The Soil Moisture Active Passive (SMAP) project has made excellent progress in addressing the requirements and science goals of the primary mission. The primary mission baseline requirement is estimates of global surface soil moisture with an error of no greater than 4% volumetric (one sigma) exclud...

SSDA code to apply data assimilation in soil water flow modeling: Documentation and user manual

USDA-ARS?s Scientific Manuscript database

Soil water flow models are based on simplified assumptions about the mechanisms, processes, and parameters of water retention and flow. That causes errors in soil water flow model predictions. Data assimilation (DA) with the ensemble Kalman filter (EnKF) corrects modeling results based on measured s...

Accessing the Soil Metagenome for Studies of Microbial Diversity▿ †

PubMed Central

Delmont, Tom O.; Robe, Patrick; Cecillon, Sébastien; Clark, Ian M.; Constancias, Florentin; Simonet, Pascal; Hirsch, Penny R.; Vogel, Timothy M.

2011-01-01

Soil microbial communities contain the highest level of prokaryotic diversity of any environment, and metagenomic approaches involving the extraction of DNA from soil can improve our access to these communities. Most analyses of soil biodiversity and function assume that the DNA extracted represents the microbial community in the soil, but subsequent interpretations are limited by the DNA recovered from the soil. Unfortunately, extraction methods do not provide a uniform and unbiased subsample of metagenomic DNA, and as a consequence, accurate species distributions cannot be determined. Moreover, any bias will propagate errors in estimations of overall microbial diversity and may exclude some microbial classes from study and exploitation. To improve metagenomic approaches, investigate DNA extraction biases, and provide tools for assessing the relative abundances of different groups, we explored the biodiversity of the accessible community DNA by fractioning the metagenomic DNA as a function of (i) vertical soil sampling, (ii) density gradients (cell separation), (iii) cell lysis stringency, and (iv) DNA fragment size distribution. Each fraction had a unique genetic diversity, with different predominant and rare species (based on ribosomal intergenic spacer analysis [RISA] fingerprinting and phylochips). All fractions contributed to the number of bacterial groups uncovered in the metagenome, thus increasing the DNA pool for further applications. Indeed, we were able to access a more genetically diverse proportion of the metagenome (a gain of more than 80% compared to the best single extraction method), limit the predominance of a few genomes, and increase the species richness per sequencing effort. This work stresses the difference between extracted DNA pools and the currently inaccessible complete soil metagenome. PMID:21183646

Some potential errors in the measurement of mercury gas exchange at the soil surface using a dynamic flux chamber.

PubMed

Gillis, A; Miller, D R

2000-10-09

A series of controlled environment experiments were conducted to examine the use of a dynamic flux chamber to measure soil emission and absorption of total gaseous mercury (TGM). Uncertainty about the appropriate airflow rates through the chamber and chamber exposure to ambient wind are shown to be major sources of potential error. Soil surface mercury flux measurements over a range of chamber airflow rates showed a positive linear relationship between flux rates and airflow rate through the chamber. Mercury flux measurements using the chamber in an environmental wind tunnel showed that exposure of the system to ambient winds decreased the measured flux rates by 40% at a wind speed of 1.0 m s(-1) and 90% at a wind speed of 2 m s(-1). Wind tunnel measurements also showed that the chamber footprint was limited to the area of soil inside the chamber and there is little uncertainty of the footprint size in dry soil.

Effects of climate change on soil moisture over China from 1960-2006

USGS Publications Warehouse

Zhu, Q.; Jiang, H.; Liu, J.

2009-01-01

Soil moisture is an important variable in the climate system and it has sensitive impact on the global climate. Obviously it is one of essential components in the climate change study. The Integrated Biosphere Simulator (IBIS) is used to evaluate the spatial and temporal patterns of soil moisture across China under the climate change conditions for the period 1960-2006. Results show that the model performed better in warm season than in cold season. Mean errors (ME) are within 10% for all the months and root mean squared errors (RMSE) are within 10% except winter season. The model captured the spatial variability higher than 50% in warm seasons. Trend analysis based on the Mann-Kendall method indicated that soil moisture in most area of China is decreased especially in the northern China. The areas with significant increasing trends in soil moisture mainly locate at northwestern China and small areas in southeastern China and eastern Tibet plateau. ?? 2009 IEEE.

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

NASA Technical Reports Server (NTRS)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

Evaluation of a surface/vegetation parameterization using satellite measurements of surface temperature

NASA Technical Reports Server (NTRS)

Taconet, O.; Carlson, T.; Bernard, R.; Vidal-Madjar, D.

1986-01-01

Ground measurements of surface-sensible heat flux and soil moisture for a wheat-growing area of Beauce in France were compared with the values derived by inverting two boundary layer models with a surface/vegetation formulation using surface temperature measurements made from NOAA-AVHRR. The results indicated that the trends in the surface heat fluxes and soil moisture observed during the 5 days of the field experiment were effectively captured by the inversion method using the remotely measured radiative temperatures and either of the two boundary layer methods, both of which contain nearly identical vegetation parameterizations described by Taconet et al. (1986). The sensitivity of the results to errors in the initial sounding values or measured surface temperature was tested by varying the initial sounding temperature, dewpoint, and wind speed and the measured surface temperature by amounts corresponding to typical measurement error. In general, the vegetation component was more sensitive to error than the bare soil model.

STIR Proposal For Research Area 2.1.2 Surface Energy Balance: Transient Soil Density Impacts Land Surface Characteristics and Characterization

DTIC Science & Technology

2015-12-22

not shown). The relatively small differences were likely associated with differences in surface albedo and longwave radiation from soil surface. Ground...SECURITY CLASSIFICATION OF: Soil density is commonly treated as static in studies on land surface property dynamics. Magnitudes of errors associated...with this assumption are largely unknown. Objectives of this preliminary investigation were to: i) quantify effects of soil density variation on soil

Carbon dioxide emissions from peat soils under potato cultivation in Uganda

NASA Astrophysics Data System (ADS)

Farmer, Jenny; Langan, Charlie; Smith, Jo

2017-04-01

Organic wetland soils in south western Uganda are found in valley bottom wetlands, surrounded by steep, mineral soil hill slopes. Land use change in these papyrus dominated wetlands has taken place over the past forty years, seeing wetland areas cleared of papyrus, rudimentary drainage channel systems dug, and soil cultivated and planted with crops, predominantly potatoes. There has been little research into the cultivation of organic wetlands soils in Uganda, or the impacts on soil carbon dynamics and associated carbon dioxide (CO2) emissions. This study used two rounds of farmer interviews to capture the land management practices on these soils and how they vary over the period of a year. Three potato fields were also randomly selected and sampled for CO2 emissions at four points in time during the year; 1) just after the potato beds had been dug, 2) during the potato growing period, 3) after the potato harvest, and 4) at the end of the fallow season. Carbon dioxide emissions, soil and air temperatures, water table depth, vegetation cover and land use were all recorded in situ in each field on each sampling occasion, from both the raised potato beds and the trenches in between them. There appeared to be a delay in the disturbance effect of digging the peat, with heterotrophic CO2 emissions from the raised beds not immediately increasing after being exposed to the air. Excluding these results, there was a significant linear relationship between mean emissions and water table depth from the raised beds and trenches in each field over time (p<0.001, r2=0.85), as well as between emissions and soil moisture content (p<0.001, r2=0.85). Temporal variability was observed, with significant differences in the means of emissions measured at the different sampling times (p<0.001, one-way ANOVA); this was the case in both raised beds and trenches in all fields studied, except for the trenches in one field which showed no significant difference between sampling times (p=0.55). Mean emissions from the raised beds were highest during the potato growing season (1.74 ± 0.07 g m-2 hr-1 (± shows standard error)) and lowest at the time when the beds had been freshly dug (0.67 ± 0.17 g m-2 hr-1). Mean emissions from the trenches were highest at the end of the fallow period (0.37 ± 0.02 g m-2 hr-1) and lowest at the time when the beds had been freshly dug (0.20 ± 0.05 g m-2 hr-1). As the first of its kind on Uganda's peat soils, this study has provided some insight into the use of these soils and impacts on CO2 emissions which can be used to inform Uganda's national emissions scenarios, whilst highlighting some of the fundamental data gaps which need to be addressed with future studies.

Reclamation of soils influenced by coal mining in Southern European Russia

NASA Astrophysics Data System (ADS)

Alekseenko, Vladimir; Bech, Jaume; Alekseenko, Alexey; Shvydkaya, Natalya; Roca, Núria

2016-04-01

In the recent decades, the concentrations of metals have increased in such media of biosphere as atmosphere, hydrosphere, pedosphere. The greatest geochemical changes have occurred in soils, which are the deposing medium where the high concentrations of metals are saved for years after their direct human use. Mining sites and beneficiation zones are the areas of the highest concentrations of metals in soils. Coal mining areas in the European part of Russia (Rostov region) were selected for a detailed consideration. Soil samples were taken from the uppermost soil horizons: layer of 0-30 cm. The soil samples were analysed for gross concentrations of Cu, Zn, Pb, Ag, Sn, Mo, Ba, Co, Ni, Mn, Ti, V, Cr, Ga, P, Li, Sr, Y, Yb, Nb, Sc, and Zr, using emission spectral analysis. All ordinary analyses were carried out in the certified and accredited laboratory. The external control was conducted by the X-ray fluorescence, gravimetric, and neutron activation analyses. Calculation of random and systematic errors showed high analyses repeatability and correctness. Several cases of self-purification of soils and restoration of landscapes were discussed. The way of remediation through the flooding of mining sites with water was investigated as well as filling of natural relief depressions with soils and dumps. The process of Technosols remediation at the sites occupied by tailings of waste heaps was considered separately. In conclusion: 1. The dominant contemporary way of remediation in Southern European Russia does not prevent the spread of metals through the decades. The modern underground coal mining leads to the destruction of soils in the area directly occupied by wastes and by rock dumps located nearby. 2. Soils have not formed yet as a result of self-restoration at the waste heaps at the age of 50 years, spontaneously combusted decades ago. The vegetation formed during this time virtually eliminates the occurrence of any significant soil-forming process. The ponds formed by the flooding of burning waste heaps, do not give possibility for the formation of soils and hardly contribute to plant growth. 3. The Technosols of waste heaps' surface layers are different from the surrounding steppe soils in geochemical features and mineralogical composition at every stage of their development. 4. The atmospheric and water inflow of material from the waste heaps changes (in the cases studied - worsens) the state of steppe soils within a radius of 1 km, and leads to the increase of heavy metals content in these soils. Keywords: Technosols, Technogenic Superficial Formations, self-purification, flooding

Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

NASA Astrophysics Data System (ADS)

Gruber, A.; Crow, W. T.; Dorigo, W. A.

2018-02-01

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.

Assessment the effect of homogenized soil on soil hydraulic properties and soil water transport

NASA Astrophysics Data System (ADS)

Mohawesh, O.; Janssen, M.; Maaitah, O.; Lennartz, B.

2017-09-01

Soil hydraulic properties play a crucial role in simulating water flow and contaminant transport. Soil hydraulic properties are commonly measured using homogenized soil samples. However, soil structure has a significant effect on the soil ability to retain and to conduct water, particularly in aggregated soils. In order to determine the effect of soil homogenization on soil hydraulic properties and soil water transport, undisturbed soil samples were carefully collected. Five different soil structures were identified: Angular-blocky, Crumble, Angular-blocky (different soil texture), Granular, and subangular-blocky. The soil hydraulic properties were determined for undisturbed and homogenized soil samples for each soil structure. The soil hydraulic properties were used to model soil water transport using HYDRUS-1D.The homogenized soil samples showed a significant increase in wide pores (wCP) and a decrease in narrow pores (nCP). The wCP increased by 95.6, 141.2, 391.6, 3.9, 261.3%, and nCP decreased by 69.5, 10.5, 33.8, 72.7, and 39.3% for homogenized soil samples compared to undisturbed soil samples. The soil water retention curves exhibited a significant decrease in water holding capacity for homogenized soil samples compared with the undisturbed soil samples. The homogenized soil samples showed also a decrease in soil hydraulic conductivity. The simulated results showed that water movement and distribution were affected by soil homogenizing. Moreover, soil homogenizing affected soil hydraulic properties and soil water transport. However, field studies are being needed to find the effect of these differences on water, chemical, and pollutant transport under several scenarios.

Evaluation of hydrologic components of community land model 4 and bias identification

DOE PAGES

Du, Enhao; Vittorio, Alan Di; Collins, William D.

2015-04-01

Runoff and soil moisture are two key components of the global hydrologic cycle that should be validated at local to global scales in Earth System Models (ESMs) used for climate projection. Here, we have evaluated the runoff and surface soil moisture output by the Community Climate System Model (CCSM) along with 8 other models from the Coupled Model Intercomparison Project (CMIP5) repository using satellite soil moisture observations and stream gauge corrected runoff products. A series of Community Land Model (CLM) runs forced by reanalysis and coupled model outputs was also performed to identify atmospheric drivers of biases and uncertainties inmore » the CCSM. Results indicate that surface soil moisture simulations tend to be positively biased in high latitude areas by most selected CMIP5 models except CCSM, FGOALS, and BCC, which share similar land surface model code. With the exception of GISS, runoff simulations by all selected CMIP5 models were overestimated in mountain ranges and in most of the Arctic region. In general, positive biases in CCSM soil moisture and runoff due to precipitation input error were offset by negative biases induced by temperature input error. Excluding the impact from atmosphere modeling, the global mean of seasonal surface moisture oscillation was out of phase compared to observations in many years during 1985–2004. The CLM also underestimated runoff in the Amazon, central Africa, and south Asia, where soils all have high clay content. We hypothesize that lack of a macropore flow mechanism is partially responsible for this underestimation. However, runoff was overestimated in the areas covered by volcanic ash soils (i.e., Andisols), which might be associated with poor soil porosity representation in CLM. Finally, our results indicate that CCSM predictability of hydrology could be improved by addressing the compensating errors associated with precipitation and temperature and updating the CLM soil representation.« less

Sampling design and required sample size for evaluating contamination levels of 137Cs in Japanese fir needles in a mixed deciduous forest stand in Fukushima, Japan.

PubMed

Oba, Yurika; Yamada, Toshihiro

2017-05-01

We estimated the sample size (the number of samples) required to evaluate the concentration of radiocesium ( 137 Cs) in Japanese fir (Abies firma Sieb. & Zucc.), 5 years after the outbreak of the Fukushima Daiichi Nuclear Power Plant accident. We investigated the spatial structure of the contamination levels in this species growing in a mixed deciduous broadleaf and evergreen coniferous forest stand. We sampled 40 saplings with a tree height of 150 cm-250 cm in a Fukushima forest community. The results showed that: (1) there was no correlation between the 137 Cs concentration in needles and soil, and (2) the difference in the spatial distribution pattern of 137 Cs concentration between needles and soil suggest that the contribution of root uptake to 137 Cs in new needles of this species may be minor in the 5 years after the radionuclides were released into the atmosphere. The concentration of 137 Cs in needles showed a strong positive spatial autocorrelation in the distance class from 0 to 2.5 m, suggesting that the statistical analysis of data should consider spatial autocorrelation in the case of an assessment of the radioactive contamination of forest trees. According to our sample size analysis, a sample size of seven trees was required to determine the mean contamination level within an error in the means of no more than 10%. This required sample size may be feasible for most sites. Copyright © 2017 Elsevier Ltd. All rights reserved.

Environmental Monitoring Networks Optimization Using Advanced Active Learning Algorithms

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail; Volpi, Michele; Copa, Loris

2010-05-01

The problem of environmental monitoring networks optimization (MNO) belongs to one of the basic and fundamental tasks in spatio-temporal data collection, analysis, and modeling. There are several approaches to this problem, which can be considered as a design or redesign of monitoring network by applying some optimization criteria. The most developed and widespread methods are based on geostatistics (family of kriging models, conditional stochastic simulations). In geostatistics the variance is mainly used as an optimization criterion which has some advantages and drawbacks. In the present research we study an application of advanced techniques following from the statistical learning theory (SLT) - support vector machines (SVM) and the optimization of monitoring networks when dealing with a classification problem (data are discrete values/classes: hydrogeological units, soil types, pollution decision levels, etc.) is considered. SVM is a universal nonlinear modeling tool for classification problems in high dimensional spaces. The SVM solution is maximizing the decision boundary between classes and has a good generalization property for noisy data. The sparse solution of SVM is based on support vectors - data which contribute to the solution with nonzero weights. Fundamentally the MNO for classification problems can be considered as a task of selecting new measurement points which increase the quality of spatial classification and reduce the testing error (error on new independent measurements). In SLT this is a typical problem of active learning - a selection of the new unlabelled points which efficiently reduce the testing error. A classical approach (margin sampling) to active learning is to sample the points closest to the classification boundary. This solution is suboptimal when points (or generally the dataset) are redundant for the same class. In the present research we propose and study two new advanced methods of active learning adapted to the solution of MNO problem: 1) hierarchical top-down clustering in an input space in order to remove redundancy when data are clustered, and 2) a general method (independent on classifier) which gives posterior probabilities that can be used to define the classifier confidence and corresponding proposals for new measurement points. The basic ideas and procedures are explained by applying simulated data sets. The real case study deals with the analysis and mapping of soil types, which is a multi-class classification problem. Maps of soil types are important for the analysis and 3D modeling of heavy metals migration in soil and prediction risk mapping. The results obtained demonstrate the high quality of SVM mapping and efficiency of monitoring network optimization by using active learning approaches. The research was partly supported by SNSF projects No. 200021-126505 and 200020-121835.

Improved Uncertainty Quantification in Groundwater Flux Estimation Using GRACE

NASA Astrophysics Data System (ADS)

Reager, J. T., II; Rao, P.; Famiglietti, J. S.; Turmon, M.

2015-12-01

Groundwater change is difficult to monitor over large scales. One of the most successful approaches is in the remote sensing of time-variable gravity using NASA Gravity Recovery and Climate Experiment (GRACE) mission data, and successful case studies have created the opportunity to move towards a global groundwater monitoring framework for the world's largest aquifers. To achieve these estimates, several approximations are applied, including those in GRACE processing corrections, the formulation of the formal GRACE errors, destriping and signal recovery, and the numerical model estimation of snow water, surface water and soil moisture storage states used to isolate a groundwater component. A major weakness in these approaches is inconsistency: different studies have used different sources of primary and ancillary data, and may achieve different results based on alternative choices in these approximations. In this study, we present two cases of groundwater change estimation in California and the Colorado River basin, selected for their good data availability and varied climates. We achieve a robust numerical estimate of post-processing uncertainties resulting from land-surface model structural shortcomings and model resolution errors. Groundwater variations should demonstrate less variability than the overlying soil moisture state does, as groundwater has a longer memory of past events due to buffering by infiltration and drainage rate limits. We apply a model ensemble approach in a Bayesian framework constrained by the assumption of decreasing signal variability with depth in the soil column. We also discuss time variable errors vs. time constant errors, across-scale errors v. across-model errors, and error spectral content (across scales and across model). More robust uncertainty quantification for GRACE-based groundwater estimates would take all of these issues into account, allowing for more fair use in management applications and for better integration of GRACE-based measurements with observations from other sources.

10Be inventories in Alpine soils and their potential for dating land surfaces

NASA Astrophysics Data System (ADS)

Egli, Markus; Brandová, Dagmar; Böhlert, Ralph; Favilli, Filippo; Kubik, Peter W.

2010-07-01

To exploit natural sedimentary archives and geomorphic landforms it is necessary to date them first. Landscape evolution of Alpine areas is often strongly related to the activities of glaciers in the Pleistocene and Holocene. At sites where no organic matter for radiocarbon dating exists and where suitable boulders for surface exposure dating (using in situ produced cosmogenic nuclides) are absent, dating of soils could give information about the timing of landscape evolution. This paper explores the applicability of soil dating using the inventory of meteoric 10Be in Alpine soils. For this purpose, a set of 6 soil profiles in the Swiss and Italian Alps was investigated. The surface at these sites had already been dated (using the radiocarbon technique or the surface exposure determination using in situ produced 10Be). Consequently, a direct comparison of the ages of the soils using meteoric 10Be and other dating techniques was made possible. The estimation of 10Be deposition rates is subject to severe limitations and strongly influences the obtained results. We tested three scenarios using a) the meteoric 10Be deposition rates as a function of the annual precipitation rate, b) a constant 10Be input for the Central Alps, and c) as b) but assuming a pre-exposure of the parent material. The obtained ages that are based on the 10Be inventory in soils and on scenario a) for the 10Be input agreed reasonably well with the age using surface exposure or radiocarbon dating. The ages obtained from soils using scenario b) produced ages that were mostly too old whereas the approach using scenario c) seemed to yield better results than scenario b). Erosion calculations can, in theory, be performed using the 10Be inventory and 10Be deposition rates. An erosion estimation was possible using scenario a) and c), but not using b). The calculated erosion rates using these scenarios seemed to be plausible with values in the range of 0-57 mm/ky. The dating of soils using 10Be has several potential error sources. Analytical errors as well as errors from other parameters such as bulk soil density and soil skeleton content have to be taken into account. The error range was from 8 up to 21%. Furthermore, uncertainties in estimating 10Be deposition rates substantially influence the calculated ages. Relative age estimates and, under optimal conditions, absolute dating can be carried out. Age determination of Alpine soils using 10Be gives another possibility to date surfaces when other methods fail or are not possible at all. It is, however, not straightforward, quite laborious and may consequently have some distinct limitations.

Estimating soil zinc concentrations using reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Sun, Weichao; Zhang, Xia

2017-06-01

Soil contamination by heavy metals has been an increasingly severe threat to nature environment and human health. Efficiently investigation of contamination status is essential to soil protection and remediation. Visible and near-infrared reflectance spectroscopy (VNIRS) has been regarded as an alternative for monitoring soil contamination by heavy metals. Generally, the entire VNIR spectral bands are employed to estimate heavy metal concentration, which lacks interpretability and requires much calculation. In this study, 74 soil samples were collected from Hunan Province, China and their reflectance spectra were used to estimate zinc (Zn) concentration in soil. Organic matter and clay minerals have strong adsorption for Zn in soil. Spectral bands associated with organic matter and clay minerals were used for estimation with genetic algorithm based partial least square regression (GA-PLSR). The entire VNIR spectral bands, the bands associated with organic matter and the bands associated with clay minerals were incorporated as comparisons. Root mean square error of prediction, residual prediction deviation, and coefficient of determination (R2) for the model developed using combined bands of organic matter and clay minerals were 329.65 mg kg-1, 1.96 and 0.73, which is better than 341.88 mg kg-1, 1.89 and 0.71 for the entire VNIR spectral bands, 492.65 mg kg-1, 1.31 and 0.40 for the organic matter, and 430.26 mg kg-1, 1.50 and 0.54 for the clay minerals. Additionally, in consideration of atmospheric water vapor absorption in field spectra measurement, combined bands of organic matter and absorption around 2200 nm were used for estimation and achieved high prediction accuracy with R2 reached 0.640. The results indicate huge potential of soil reflectance spectroscopy in estimating Zn concentrations in soil.

Complementary-relationship-based 30 year normals (1981-2010) of monthly latent heat fluxes across the contiguous United States

NASA Astrophysics Data System (ADS)

Szilagyi, Jozsef

2015-11-01

Thirty year normal (1981-2010) monthly latent heat fluxes (ET) over the conterminous United States were estimated by a modified Advection-Aridity model from North American Regional Reanalysis (NARR) radiation and wind as well as Parameter-Elevation Regressions on Independent Slopes Model (PRISM) air and dew-point temperature data. Mean annual ET values were calibrated with PRISM precipitation (P) and validated against United States Geological Survey runoff (Q) data. At the six-digit Hydrologic Unit Code level (sample size of 334) the estimated 30 year normal runoff (P - ET) had a bias of 18 mm yr-1, a root-mean-square error of 96 mm yr-1, and a linear correlation coefficient value of 0.95, making the estimates on par with the latest Land Surface Model results but without the need for soil and vegetation information or any soil moisture budgeting.

Accumulation, Allocation, and Risk Assessment of Polycyclic Aromatic Hydrocarbons (PAHs) in Soil-Brassica chinensis System

PubMed Central

Zhang, Juan; Fan, Shukai; Du, Xiaoming; Yang, Juncheng; Wang, Wenyan; Hou, Hong

2015-01-01

Farmland soil and leafy vegetables accumulate more polycyclic aromatic hydrocarbons (PAHs) in suburban sites. In this study, 13 sampling areas were selected from vegetable fields in the outskirts of Xi’an, the largest city in northwestern China. The similarity of PAH composition in soil and vegetation was investigated through principal components analysis and redundancy analysis (RDA), rather than discrimination of PAH congeners from various sources. The toxic equivalent quantity of PAHs in soil ranged from 7 to 202 μg/kg d.w., with an average of 41 μg/kg d.w., which exceeded the agricultural/horticultural soil acceptance criteria for New Zealand. However, the cancer risk level posed by combined direct ingestion, dermal contact, inhalation of soil particles, and inhalation of surface soil vapor met the rigorous international criteria (1×10−6). The concentration of total PAHs was (1052±73) μg/kg d.w. in vegetation (mean±standard error). The cancer risks posed by ingestion of vegetation ranged from 2×10−5 to 2×10−4 with an average of 1.66×10−4, which was higher than international excess lifetime risk limits for carcinogens (1×10−4). The geochemical indices indicated that the PAHs in soil and vegetables were mainly from vehicle and crude oil combustion. Both the total PAHs in vegetation and bioconcentration factor for total PAHs (the ratio of total PAHs in vegetation to total PAHs in soil) increased with increasing pH as well as decreasing sand in soil. The total variation in distribution of PAHs in vegetation explained by those in soil reached 98% in RDA, which was statistically significant based on Monte Carlo permutation. Common pollution source and notable effects of soil contamination on vegetation would result in highly similar distribution of PAHs in soil and vegetation. PMID:25679782

A model based on Rock-Eval thermal analysis to quantify the size of the centennially persistent organic carbon pool in temperate soils

NASA Astrophysics Data System (ADS)

Cécillon, Lauric; Baudin, François; Chenu, Claire; Houot, Sabine; Jolivet, Romain; Kätterer, Thomas; Lutfalla, Suzanne; Macdonald, Andy; van Oort, Folkert; Plante, Alain F.; Savignac, Florence; Soucémarianadin, Laure N.; Barré, Pierre

2018-05-01

Changes in global soil carbon stocks have considerable potential to influence the course of future climate change. However, a portion of soil organic carbon (SOC) has a very long residence time ( > 100 years) and may not contribute significantly to terrestrial greenhouse gas emissions during the next century. The size of this persistent SOC reservoir is presumed to be large. Consequently, it is a key parameter required for the initialization of SOC dynamics in ecosystem and Earth system models, but there is considerable uncertainty in the methods used to quantify it. Thermal analysis methods provide cost-effective information on SOC thermal stability that has been shown to be qualitatively related to SOC biogeochemical stability. The objective of this work was to build the first quantitative model of the size of the centennially persistent SOC pool based on thermal analysis. We used a unique set of 118 archived soil samples from four agronomic experiments in northwestern Europe with long-term bare fallow and non-bare fallow treatments (e.g., manure amendment, cropland and grassland) as a sample set for which estimating the size of the centennially persistent SOC pool is relatively straightforward. At each experimental site, we estimated the average concentration of centennially persistent SOC and its uncertainty by applying a Bayesian curve-fitting method to the observed declining SOC concentration over the duration of the long-term bare fallow treatment. Overall, the estimated concentrations of centennially persistent SOC ranged from 5 to 11 g C kg-1 of soil (lowest and highest boundaries of four 95 % confidence intervals). Then, by dividing the site-specific concentrations of persistent SOC by the total SOC concentration, we could estimate the proportion of centennially persistent SOC in the 118 archived soil samples and the associated uncertainty. The proportion of centennially persistent SOC ranged from 0.14 (standard deviation of 0.01) to 1 (standard deviation of 0.15). Samples were subjected to thermal analysis by Rock-Eval 6 that generated a series of 30 parameters reflecting their SOC thermal stability and bulk chemistry. We trained a nonparametric machine-learning algorithm (random forests multivariate regression model) to predict the proportion of centennially persistent SOC in new soils using Rock-Eval 6 thermal parameters as predictors. We evaluated the model predictive performance with two different strategies. We first used a calibration set (n = 88) and a validation set (n = 30) with soils from all sites. Second, to test the sensitivity of the model to pedoclimate, we built a calibration set with soil samples from three out of the four sites (n = 84). The multivariate regression model accurately predicted the proportion of centennially persistent SOC in the validation set composed of soils from all sites (R2 = 0.92, RMSEP = 0.07, n = 30). The uncertainty of the model predictions was quantified by a Monte Carlo approach that produced conservative 95 % prediction intervals across the validation set. The predictive performance of the model decreased when predicting the proportion of centennially persistent SOC in soils from one fully independent site with a different pedoclimate, yet the mean error of prediction only slightly increased (R2 = 0.53, RMSEP = 0.10, n = 34). This model based on Rock-Eval 6 thermal analysis can thus be used to predict the proportion of centennially persistent SOC with known uncertainty in new soil samples from different pedoclimates, at least for sites that have similar Rock-Eval 6 thermal characteristics to those included in the calibration set. Our study reinforces the evidence that there is a link between the thermal and biogeochemical stability of soil organic matter and demonstrates that Rock-Eval 6 thermal analysis can be used to quantify the size of the centennially persistent organic carbon pool in temperate soils.

A Model of Thermal Conductivity for Planetary Soils: 1. Theory for Unconsolidated Soils

NASA Technical Reports Server (NTRS)

Piqueux, S.; Christensen, P. R.

2009-01-01

We present a model of heat conduction for mono-sized spherical particulate media under stagnant gases based on the kinetic theory of gases, numerical modeling of Fourier s law of heat conduction, theoretical constraints on the gas thermal conductivity at various Knudsen regimes, and laboratory measurements. Incorporating the effect of the temperature allows for the derivation of the pore-filling gas conductivity and bulk thermal conductivity of samples using additional parameters (pressure, gas composition, grain size, and porosity). The radiative and solid-to-solid conductivities are also accounted for. Our thermal model reproduces the well-established bulk thermal conductivity dependency of a sample with the grain size and pressure and also confirms laboratory measurements finding that higher porosities generally lead to lower conductivities. It predicts the existence of the plateau conductivity at high pressure, where the bulk conductivity does not depend on the grain size. The good agreement between the model predictions and published laboratory measurements under a variety of pressures, temperatures, gas compositions, and grain sizes provides additional confidence in our results. On Venus, Earth, and Titan, the pressure and temperature combinations are too high to observe a soil thermal conductivity dependency on the grain size, but each planet has a unique thermal inertia due to their different surface temperatures. On Mars, the temperature and pressure combination is ideal to observe the soil thermal conductivity dependency on the average grain size. Thermal conductivity models that do not take the temperature and the pore-filling gas composition into account may yield significant errors.

Testing the ISP method with the PARIO device: Accuracy of results and influence of homogenization technique

NASA Astrophysics Data System (ADS)

Durner, Wolfgang; Huber, Magdalena; Yangxu, Li; Steins, Andi; Pertassek, Thomas; Göttlein, Axel; Iden, Sascha C.; von Unold, Georg

2017-04-01

The particle-size distribution (PSD) is one of the main properties of soils. To determine the proportions of the fine fractions silt and clay, sedimentation experiments are used. Most common are the Pipette and Hydrometer method. Both need manual sampling at specific times. Both are thus time-demanding and rely on experienced operators. Durner et al. (Durner, W., S.C. Iden, and G. von Unold (2017): The integral suspension pressure method (ISP) for precise particle-size analysis by gravitational sedimentation, Water Resources Research, doi:10.1002/2016WR019830) recently developed the integral suspension method (ISP) method, which is implemented in the METER Group device PARIOTM. This new method estimates continuous PSD's from sedimentation experiments by recording the temporal evolution of the suspension pressure at a certain measurement depth in a sedimentation cylinder. It requires no manual interaction after start and thus no specialized training of the lab personnel. The aim of this study was to test the precision and accuracy of new method with a variety of materials, to answer the following research questions: (1) Are the results obtained by PARIO reliable and stable? (2) Are the results affected by the initial mixing technique to homogenize the suspension, or by the presence of sand in the experiment? (3) Are the results identical to the one that are obtained with the Pipette method as reference method? The experiments were performed with a pure quartz silt material and four real soil materials. PARIO measurements were done repetitively on the same samples in a temperature-controlled lab to characterize the repeatability of the measurements. Subsequently, the samples were investigated by the pipette method to validate the results. We found that the statistical error for silt fraction from replicate and repetitive measurements was in the range of 1% for the quartz material to 3% for soil materials. Since the sand fractions, as in any sedimentation method, must be measured explicitly and are used as fixed parameters in the PARIO evaluation, the error of the clay fraction is determined by error propagation from the sand and silt fraction. Homogenization of the suspension by overhead shaking gave lower reproducibility and smaller silt fractions than vertical stirring. However, it turned out that vertical stirring must be performed with sufficient rigour to obtain a fully homogeneous initial distribution. Analysis of material sieved to < 2000 μm and to < 200 μm gave equal results, i.e., there was no hint towards dragging effects of large particles. Complete removal of the sand fraction, i.e. sieving to < 63 μm lead to less silt, probably due to a loss of fine material by the sieving process. The PSD's obtained with the PARIO corresponded very well with the results of the Pipette method.

The potential for geostationary remote sensing of NO2 to improve weather prediction

NASA Astrophysics Data System (ADS)

Liu, X.; Mizzi, A. P.; Anderson, J. L.; Fung, I. Y.; Cohen, R. C.

2016-12-01

Observations of surface winds remain sparse making it challenging to simulate and predict the weather in circumstances of light winds that are most important for poor air quality. Direct measurements of short-lived chemicals from space might be a solution to this challenge. Here we investigate the application of data assimilation of NO­2 columns as will be observed from geostationary orbit to improve predictions and retrospective analysis of surface wind fields. Specifically, synthetic NO2 observations are sampled from a "nature run (NR)" regarded as the true atmosphere. Then NO2 observations are assimilated using EAKF methods into a "control run (CR)" which differs from the NR in the wind field. Wind errors are generated by introducing (1) errors in the initial conditions, (2) creating a model error by using two different formulations for the planetary boundary layer, (3) and by combining both of these effects. The assimilation reduces wind errors by up to 50%, indicating the prospects for future geostationary atmospheric composition measurements to improve weather forecasting are substantial. We also examine the assimilation sensitivity to the data assimilation window length. We find that due to the temporal heterogeneity of wind errors, the success of this application favors chemical observations of high frequency, such as those from geostationary platform. We also show the potential to improve soil moisture field by assimilating NO­2 columns.

Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing.

PubMed

Qiao, Pengwei; Lei, Mei; Yang, Sucai; Yang, Jun; Guo, Guanghui; Zhou, Xiaoyong

2018-06-01

Spatial interpolation method is the basis of soil heavy metal pollution assessment and remediation. The existing evaluation index for interpolation accuracy did not combine with actual situation. The selection of interpolation methods needs to be based on specific research purposes and research object characteristics. In this paper, As pollution in soils of Beijing was taken as an example. The prediction accuracy of ordinary kriging (OK) and inverse distance weighted (IDW) were evaluated based on the cross validation results and spatial distribution characteristics of influencing factors. The results showed that, under the condition of specific spatial correlation, the cross validation results of OK and IDW for every soil point and the prediction accuracy of spatial distribution trend are similar. But the prediction accuracy of OK for the maximum and minimum is less than IDW, while the number of high pollution areas identified by OK are less than IDW. It is difficult to identify the high pollution areas fully by OK, which shows that the smoothing effect of OK is obvious. In addition, with increasing of the spatial correlation of As concentration, the cross validation error of OK and IDW decreases, and the high pollution area identified by OK is approaching the result of IDW, which can identify the high pollution areas more comprehensively. However, because the semivariogram constructed by OK interpolation method is more subjective and requires larger number of soil samples, IDW is more suitable for spatial prediction of heavy metal pollution in soils.

A Proposed Extension to the Soil Moisture and Ocean Salinity Level 2 Algorithm for Mixed Forest and Moderate Vegetation Pixels

NASA Technical Reports Server (NTRS)

Panciera, Rocco; Walker, Jeffrey P.; Kalma, Jetse; Kim, Edward

2011-01-01

The Soil Moisture and Ocean Salinity (SMOS)mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4 GHz) emission of the Earth's surface with a spatial resolution of 40-50 km.Uncertainty in the retrieval of soilmoisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soilmoisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops),with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40-60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE,-3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, -0.3%v/v bias) in pixels characterised by a critical amount of forest (40-60%), at the limited cost of only a crude estimate of the optical depth of the forested area (better than 35% uncertainty). This study makes use of an unprecedented data set of airborne L-band observations and ground supporting data from the National Airborne Field Experiment 2005 (NAFE'05), which allowed accurate characterisation of the land surface heterogeneity over an area equivalent in size to a SMOS pixel.

Exfiltrometer apparatus and method for measuring unsaturated hydrologic properties in soil

DOEpatents

Hubbell, Joel M.; Sisson, James B.; Schafer, Annette L.

2006-01-17

Exfiltrometer apparatus includes a container for holding soil. A sample container for holding sample soil is positionable with respect to the container so that the sample soil contained in the sample container is in communication with soil contained in the container. A first tensiometer operatively associated with the sample container senses a surface water potential at about a surface of the sample soil contained in the sample container. A second tensiometer operatively associated with the sample container senses a first subsurface water potential below the surface of the sample soil. A water content sensor operatively associated with the sample container senses a water content in the sample soil. A water supply supplies water to the sample soil. A data logger operatively connected to the first and second tensiometers, and to the water content sensor receives and processes data provided by the first and second tensiometers and by the water content sensor.

Assimilation of SMOS Brightness Temperatures or Soil Moisture Retrievals into a Land Surface Model

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle J. M.; Reichle, Rolf H.

2016-01-01

Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40 degree incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

Validation and Scaling of Soil Moisture in a Semi-Arid Environment: SMAP Validation Experiment 2015 (SMAPVEX15)

NASA Technical Reports Server (NTRS)

Colliander, Andreas; Cosh, Michael H.; Misra, Sidharth; Jackson, Thomas J.; Crow, Wade T.; Chan, Steven; Bindlish, Rajat; Chae, Chun; Holifield Collins, Chandra; Yueh, Simon H.

2017-01-01

The NASA SMAP (Soil Moisture Active Passive) mission conducted the SMAP Validation Experiment 2015 (SMAPVEX15) in order to support the calibration and validation activities of SMAP soil moisture data products. The main goals of the experiment were to address issues regarding the spatial disaggregation methodologies for improvement of soil moisture products and validation of the in situ measurement upscaling techniques. To support these objectives high-resolution soil moisture maps were acquired with the airborne PALS (Passive Active L-band Sensor) instrument over an area in southeast Arizona that includes the Walnut Gulch Experimental Watershed (WGEW), and intensive ground sampling was carried out to augment the permanent in situ instrumentation. The objective of the paper was to establish the correspondence and relationship between the highly heterogeneous spatial distribution of soil moisture on the ground and the coarse resolution radiometer-based soil moisture retrievals of SMAP. The high-resolution mapping conducted with PALS provided the required connection between the in situ measurements and SMAP retrievals. The in situ measurements were used to validate the PALS soil moisture acquired at 1-km resolution. Based on the information from a dense network of rain gauges in the study area, the in situ soil moisture measurements did not capture all the precipitation events accurately. That is, the PALS and SMAP soil moisture estimates responded to precipitation events detected by rain gauges, which were in some cases not detected by the in situ soil moisture sensors. It was also concluded that the spatial distribution of the soil moisture resulted from the relatively small spatial extents of the typical convective storms in this region was not completely captured with the in situ stations. After removing those cases (approximately10 of the observations) the following metrics were obtained: RMSD (root mean square difference) of0.016m3m3 and correlation of 0.83. The PALS soil moisture was also compared to SMAP and in situ soil moisture at the 36-km scale, which is the SMAP grid size for the standard product. PALS and SMAP soil moistures were found to be very similar owing to the close match of the brightness temperature measurements and the use of a common soil moisture retrieval algorithm. Spatial heterogeneity, which was identified using the high-resolution PALS soil moisture and the intensive ground sampling, also contributed to differences between the soil moisture estimates. In general, discrepancies found between the L-band soil moisture estimates and the 5-cm depth in situ measurements require methodologies to mitigate the impact on their interpretations in soil moisture validation and algorithm development. Specifically, the metrics computed for the SMAP radiometer-based soil moisture product over WGEW will include errors resulting from rainfall, particularly during the monsoon season when the spatial distribution of soil moisture is especially heterogeneous.

Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

NASA Astrophysics Data System (ADS)

Alvarez-Garreton, C.; Ryu, D.; Western, A. W.; Su, C.-H.; Crow, W. T.; Robertson, D. E.; Leahy, C.

2014-09-01

Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool. Within this context, we assimilate active and passive satellite soil moisture (SSM) retrievals using an ensemble Kalman filter to improve operational flood prediction within a large semi-arid catchment in Australia (>40 000 km2). We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM-DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation and seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided more accurate streamflow prediction (Nash-Sutcliffe efficiency, NS = 0.77) than the lumped model (NS = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments. After SM-DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 27 and 31%, respectively; the NS of the ensemble mean increased by 7 and 38%, respectively; the false alarm ratio was reduced by 15 and 25%, respectively; and the ensemble prediction spread was reduced while its reliability was maintained. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed SSM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM-DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM-DA is effective at improving streamflow ensemble prediction, however, the updated prediction is still poor since SM-DA does not address systematic errors in the model.

Prediction of Soil pH Hyperspectral Spectrum in Guanzhong Area of Shaanxi Province Based on PLS

NASA Astrophysics Data System (ADS)

Liu, Jinbao; Zhang, Yang; Wang, Huanyuan; Cheng, Jie; Tong, Wei; Wei, Jing

2017-12-01

The soil pH of Fufeng County, Yangling County and Wugong County in Shaanxi Province was studied. The spectral reflectance was measured by ASD Field Spec HR portable terrain spectrum, and its spectral characteristics were analyzed. The first deviation of the original spectral reflectance of the soil, the second deviation, the logarithm of the reciprocal logarithm, the first order differential of the reciprocal logarithm and the second order differential of the reciprocal logarithm were used to establish the soil pH Spectral prediction model. The results showed that the correlation between the reflectance spectra after SNV pre-treatment and the soil pH was significantly improved. The optimal prediction model of soil pH established by partial least squares method was a prediction model based on the first order differential of the reciprocal logarithm of spectral reflectance. The principal component factor was 10, the decision coefficient Rc2 = 0.9959, the model root means square error RMSEC = 0.0076, the correction deviation SEC = 0.0077; the verification decision coefficient Rv2 = 0.9893, the predicted root mean square error RMSEP = 0.0157, The deviation of SEP = 0.0160, the model was stable, the fitting ability and the prediction ability were high, and the soil pH can be measured quickly.

Improving the Non-Hydrostatic Numerical Dust Model by Integrating Soil Moisture and Greenness Vegetation Fraction Data with Different Spatiotemporal Resolutions.

PubMed

Yu, Manzhu; Yang, Chaowei

2016-01-01

Dust storms are devastating natural disasters that cost billions of dollars and many human lives every year. Using the Non-Hydrostatic Mesoscale Dust Model (NMM-dust), this research studies how different spatiotemporal resolutions of two input parameters (soil moisture and greenness vegetation fraction) impact the sensitivity and accuracy of a dust model. Experiments are conducted by simulating dust concentration during July 1-7, 2014, for the target area covering part of Arizona and California (31, 37, -118, -112), with a resolution of ~ 3 km. Using ground-based and satellite observations, this research validates the temporal evolution and spatial distribution of dust storm output from the NMM-dust, and quantifies model error using measurements of four evaluation metrics (mean bias error, root mean square error, correlation coefficient and fractional gross error). Results showed that the default configuration of NMM-dust (with a low spatiotemporal resolution of both input parameters) generates an overestimation of Aerosol Optical Depth (AOD). Although it is able to qualitatively reproduce the temporal trend of the dust event, the default configuration of NMM-dust cannot fully capture its actual spatial distribution. Adjusting the spatiotemporal resolution of soil moisture and vegetation cover datasets showed that the model is sensitive to both parameters. Increasing the spatiotemporal resolution of soil moisture effectively reduces model's overestimation of AOD, while increasing the spatiotemporal resolution of vegetation cover changes the spatial distribution of reproduced dust storm. The adjustment of both parameters enables NMM-dust to capture the spatial distribution of dust storms, as well as reproducing more accurate dust concentration.

Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

NASA Technical Reports Server (NTRS)

Farhadi, Leila; Reichle, Rolf H.; DeLannoy, Gabrielle J. M.; Kimball, John S.

2014-01-01

The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission.

Comparative Viral Metagenomics of Environmental Samples from Korea

PubMed Central

Kim, Min-Soo; Whon, Tae Woong

2013-01-01

The introduction of metagenomics into the field of virology has facilitated the exploration of viral communities in various natural habitats. Understanding the viral ecology of a variety of sample types throughout the biosphere is important per se, but it also has potential applications in clinical and diagnostic virology. However, the procedures used by viral metagenomics may produce technical errors, such as amplification bias, while public viral databases are very limited, which may hamper the determination of the viral diversity in samples. This review considers the current state of viral metagenomics, based on examples from Korean viral metagenomic studies-i.e., rice paddy soil, fermented foods, human gut, seawater, and the near-surface atmosphere. Viral metagenomics has become widespread due to various methodological developments, and much attention has been focused on studies that consider the intrinsic role of viruses that interact with their hosts. PMID:24124407

Mapping global surface water inundation dynamics using synergistic information from SMAP, AMSR2 and Landsat

NASA Astrophysics Data System (ADS)

Du, J.; Kimball, J. S.; Galantowicz, J. F.; Kim, S.; Chan, S.; Reichle, R. H.; Jones, L. A.; Watts, J. D.

2017-12-01

A method to monitor global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fwLBand) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency Tb observations from AMSR2. The resulting fwLBand global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The fwLBand annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly fwLBand averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable fwLBand performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) fwLBand results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m fwLBand retrievals showed favourable classification accuracy for water (commission error 31.84%; omission error 28.08%) and land (commission error 0.82%; omission error 0.99%) and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new fwLBand algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics, potentially benefiting hydrological monitoring, flood assessments, and global climate and carbon modeling.

Assessment of cyanide contamination in soils with a handheld mid-infrared spectrometer.

PubMed

Soriano-Disla, José M; Janik, Leslie J; McLaughlin, Michael J

2018-02-01

We examined the feasibility of using handheld mid-infrared (MIR) Fourier-Transform infrared (FT-IR) instrumentation for detecting and analysing cyanide (CN) contamination in field contaminated soils. Cyanide spiking experiments were first carried out, in the laboratory, to test the sensitivity of infrared Fourier transform (DRIFT) spectrometry to ferro- and ferricyanide compounds across a range of reference soils and minerals. Both benchtop and handheld diffuse reflectance infrared spectrometers were tested. Excellent results were obtained for the reference soils and minerals, with the MIR outperforming the near-infrared (NIR) range. Spectral peaks characteristic of the -C≡N group were observed near 2062 and 2118cm -1 in the MIR region for the ferro- and ferricyanide compounds spiked into soils/minerals, respectively. In the NIR region such peaks were observed near 4134 and 4220cm -1 . Cyanide-contaminated samples were then collected in the field and analyzed with the two spectrometers to further test the applicability of the DRIFT technique for soils containing aged CN residues. The prediction of total CN in dry and ground contaminated soils using the handheld MIR instrument resulted in a coefficient of determination (R 2 ) of 0.88-0.98 and root mean square error of the cross-validation (RMSE) of 21-49mgkg -1 for a CN range of 0-611mgkg -1 . A major peak was observed in the MIR at about 2092cm -1 which was attributed to "Prussian Blue" (Fe 4 [Fe(CN) 6 ] 3 ·xH 2 O). These results demonstrate the potential of handheld DRIFT instrumentation as a promising alternative to the standard laboratory method to predict CN concentrations in contaminated field soils. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Within-field and regional-scale accuracies of topsoil organic carbon content prediction from an airborne visible near-infrared hyperspectral image combined with synchronous field spectra for temperate croplands

NASA Astrophysics Data System (ADS)

Vaudour, Emmanuelle; Gilliot, Jean-Marc; Bel, Liliane; Lefevre, Josias; Chehdi, Kacem

2016-04-01

This study was carried out in the framework of the TOSCA-PLEIADES-CO of the French Space Agency and benefited data from the earlier PROSTOCK-Gessol3 project supported by the French Environment and Energy Management Agency (ADEME). It aimed at identifying the potential of airborne hyperspectral visible near-infrared AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with intensive annual crop cultivation and both contrasted soils and SOC contents, located in the western region of Paris, France. Soils comprise hortic or glossic luvisols, calcaric, rendzic cambisols and colluvic cambisols. Airborne AISA-Eagle images (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks. Tracks were atmospherically corrected then mosaicked at a 2 m-resolution using a set of 24 synchronous field spectra of bare soils, black and white targets and impervious surfaces. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then calculation and thresholding of NDVI from an atmospherically corrected SPOT4 image acquired the same day enabled to map agricultural fields with bare soil. A total of 101 sites, which were sampled either at the regional scale or within one field, were identified as bare by means of this map. Predictions were made from the mosaic AISA spectra which were related to SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples, considering those 75 sites outside cloud shadows only, and different sampling strategies for selecting calibration samples. Validation root-mean-square errors (RMSE) were comprised between 3.73 and 4.49 g. Kg-1 and were ~4 g. Kg-1 in median. The most performing models in terms of coefficient of determination (R²) and Residual Prediction Deviation (RPD) values were the calibration models derived either from Kennard-Stone or conditioned Latin Hypercube sampling on smoothed spectra. However, the most generalizable model leading to lowest RMSE value of 3.73 g. Kg-1 at the regional scale and 1.44 g. Kg-1 at the within-field scale and low validation bias was the cross-validated leave-one-out PLSR model constructed with the 28 near-synchronous samples and raw spectra.

Evaluation of assumptions in soil moisture triple collocation analysis

USDA-ARS?s Scientific Manuscript database

Triple collocation analysis (TCA) enables estimation of error variances for three or more products that retrieve or estimate the same geophysical variable using mutually-independent methods. Several statistical assumptions regarding the statistical nature of errors (e.g., mutual independence and ort...

Beyond triple collocation: Applications to satellite soil moisture

USDA-ARS?s Scientific Manuscript database

Triple collocation is now routinely used to resolve the exact (linear) relationships between multiple measurements and/or representations of a geophysical variable that are subject to errors. It has been utilized in the context of calibration, rescaling and error characterisation to allow comparison...

Calibration and temperature correction of heat dissipation matric potential sensors

USGS Publications Warehouse

Flint, A.L.; Campbell, G.S.; Ellett, K.M.; Calissendorff, C.

2002-01-01

This paper describes how heat dissipation sensors, used to measure soil water matric potential, were analyzed to develop a normalized calibration equation and a temperature correction method. Inference of soil matric potential depends on a correlation between the variable thermal conductance of the sensor's porous ceramic and matric poten-tial. Although this correlation varies among sensors, we demonstrate a normalizing procedure that produces a single calibration relationship. Using sensors from three sources and different calibration methods, the normalized calibration resulted in a mean absolute error of 23% over a matric potential range of -0.01 to -35 MPa. Because the thermal conductivity of variably saturated porous media is temperature dependent, a temperature correction is required for application of heat dissipation sensors in field soils. A temperature correction procedure is outlined that reduces temperature dependent errors by 10 times, which reduces the matric potential measurement errors by more than 30%. The temperature dependence is well described by a thermal conductivity model that allows for the correction of measurements at any temperature to measurements at the calibration temperature.

Evaluation of structure from motion for soil microtopography measurement

USDA-ARS?s Scientific Manuscript database

Recent developments in low cost structure from motion (SFM) technologies offer new opportunities for geoscientists to acquire high resolution soil microtopography data at a fraction of the cost of conventional techniques. However, these new methodologies often lack easily accessible error metrics an...

Uranium-series dating of pedogenic silica and carbonate, Crater Flat, Nevada

NASA Astrophysics Data System (ADS)

Ludwig, K. R.; Paces, J. B.

2002-02-01

A 230Th-234U-238U dating study on pedogenic silica-carbonate clast rinds and matrix laminae from alluvium in Crater Flat, Nevada was conducted using small-sample thermal-ionization mass spectrometry (TIMS) analyses on a large suite of samples. Though the 232Th content of these soils is not particularly low (mostly 0.1-9 ppm), the high U content of the silica component (mostly 4-26 ppm) makes them particularly suitable for 230Th/U dating on single, 10 to 200 mg totally-digested samples using TIMS. We observed that (1) both micro- (within-rind) and macro-stratigraphic (mappable deposit) order of the 230Th/U ages were preserved in all cases; (2) back-calculated initial 234U/238U fall in a restricted range (typically 1.67±0.19), so that 234U/238U ages with errors of about 100 kyr (2σ) could be reliably determined for the oldest, 400 to 1000 ka rinds; and (3) though 13 of the samples were >350 ka, only three showed evidence for an open-system history, even though the sensitivity of such old samples to isotopic disruption is very high. An attempt to use leach-residue techniques to separate pedogenic from detrital U and Th failed, yielding corrupt 230Th/U ages. We conclude that 230Th/U ages determined from totally dissolved, multiple sub-mm size subsamples provide more reliable estimates of soil chronology than methods employing larger samples, chemical enhancement of 238U/232Th, or isochrons.

Effects of meteorological models on the solution of the surface energy balance and soil temperature variations in bare soils

NASA Astrophysics Data System (ADS)

Saito, Hirotaka; Šimůnek, Jiri

2009-07-01

SummaryA complete evaluation of the soil thermal regime can be obtained by evaluating the movement of liquid water, water vapor, and thermal energy in the subsurface. Such an evaluation requires the simultaneous solution of the system of equations for the surface water and energy balance, and subsurface heat transport and water flow. When only daily climatic data is available, one needs not only to estimate diurnal cycles of climatic data, but to calculate the continuous values of various components in the energy balance equation, using different parameterization methods. The objective of this study is to quantify the impact of the choice of different estimation and parameterization methods, referred together to as meteorological models in this paper, on soil temperature predictions in bare soils. A variety of widely accepted meteorological models were tested on the dataset collected at a proposed low-level radioactive-waste disposal site in the Chihuahua Desert in West Texas. As the soil surface was kept bare during the study, no vegetation effects were evaluated. A coupled liquid water, water vapor, and heat transport model, implemented in the HYDRUS-1D program, was used to simulate diurnal and seasonal soil temperature changes in the engineered cover installed at the site. The modified version of HYDRUS provides a flexible means for using various types of information and different models to evaluate surface mass and energy balance. Different meteorological models were compared in terms of their prediction errors for soil temperatures at seven observation depths. The results obtained indicate that although many available meteorological models can be used to solve the energy balance equation at the soil-atmosphere interface in coupled water, vapor, and heat transport models, their impact on overall simulation results varies. For example, using daily average climatic data led to greater prediction errors, while relatively simple meteorological models may significantly improve soil temperature predictions. On the other hand, while models for the albedo and soil emissivity had little impact on soil temperature predictions, the choice of the atmospheric emissivity models had a greater impact. A comparison of all the different models indicates that the error introduced at the soil atmosphere interface propagates to deeper layers. Therefore, attention needs to be paid not only to the precise determination of the soil hydraulic and thermal properties, but also to the selection of proper meteorological models for the components involved in the surface energy balance calculations.

Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

NASA Astrophysics Data System (ADS)

Baatz, Roland; Hendricks Franssen, Harrie-Jan; Han, Xujun; Hoar, Tim; Reemt Bogena, Heye; Vereecken, Harry

2017-05-01

In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm3 cm-3 for the assimilation period and 0.05 cm3 cm-3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm3 cm-3). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

Spatial variability of soil total and DTPA-extractable cadmium caused by long-term application of phosphate fertilizers, crop rotation, and soil characteristics.

PubMed

Jafarnejadi, A R; Sayyad, Gh; Homaee, M; Davamei, A H

2013-05-01

Increasing cadmium (Cd) accumulation in agricultural soils is undesirable due to its hazardous influences on human health. Thus, having more information on spatial variability of Cd and factors effective to increase its content on the cultivated soils is very important. Phosphate fertilizers are main contamination source of cadmium (Cd) in cultivated soils. Also, crop rotation is a critical management practice which can alter soil Cd content. This study was conducted to evaluate the effects of long-term consumption of the phosphate fertilizers, crop rotations, and soil characteristics on spatial variability of two soil Cd species (i.e., total and diethylene triamine pentaacetic acid (DTPA) extractable) in agricultural soils. The study was conducted in wheat farms of Khuzestan Province, Iran. Long-term (27-year period (1980 to 2006)) data including the rate and the type of phosphate fertilizers application, the respective area, and the rotation type of different regions were used. Afterwards, soil Cd content (total or DTPA extractable) and its spatial variability in study area (400,000 ha) were determined by sampling from soils of 255 fields. The results showed that the consumption rate of di-ammonium phosphate fertilizer have been varied enormously in the period study. The application rate of phosphorus fertilizers was very high in some subregions with have extensive agricultural activities (more than 95 kg/ha). The average and maximum contents of total Cd in the study region were obtained as 1.47 and 2.19 mg/kg and DTPA-extractable Cd as 0.084 and 0.35 mg/kg, respectively. The spatial variability of Cd indicated that total and DTPA-extractable Cd contents were over 0.8 and 0.1 mg/kg in 95 and 25 % of samples, respectively. The spherical model enjoys the best fitting and lowest error rate to appraise the Cd content. Comparing the phosphate fertilizer consumption rate with spatial variability of the soil cadmium (both total and DTPA extractable) revealed the high correlation between the consumption rate of P fertilizers and soil Cd content. Rotation type was likely the main effective factor on variations of the soil DTPA-extractable Cd contents in some parts (eastern part of study region) and could explain some Cd variation. Total Cd concentrations had significant correlation with the total neutralizing value (p < 0.01), available P (p < 0.01), cation exchange capacity (p < 0.05), and organic carbon (p < 0.05) variables. The DTPA-extractable Cd had significant correlation with OC (p < 0.01), pH, and clay content (p < 0.05). Therefore, consumption rate of the phosphate fertilizers and crop rotation are important factors on solubility and hence spatial variability of Cd content in agricultural soils.

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district, Haryana (India)

NASA Astrophysics Data System (ADS)

Rawat, Kishan Singh; Sehgal, Vinay Kumar; Pradhan, Sanatan; Ray, Shibendu S.

2018-03-01

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (σ o_{RH}), differences of circular vertical and horizontal σ o (σ o_{RV} {-} σ o_{RH}) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height ({RMS}_{height}). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., σ o. Near surface SM measurements were related to σ o_{RH}, σ o_{RV} {-} σ o_{RH} derived using 5.35 GHz (C-band) image of RISAT-1 and {RMS}_{height}. The roughness component derived in terms of {RMS}_{height} showed a good positive correlation with σ o_{RV} {-} σ o_{RH} (R2 = 0.65). By considering all the major influencing factors (σ o_{RH}, σ o_{RV} {-} σ o_{RH}, and {RMS}_{height}), an SEM was developed where SM (volumetric) predicted values depend on σ o_{RH}, σ o_{RV} {-} σ o_{RH}, and {RMS}_{height}. This SEM showed R2 of 0.87 and adjusted R2 of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement ({SM}_{Observed}) showed root mean square error (RMSE) = 0.06, relative-RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash-Sutcliffe efficiency (NSE) = 0.91 ({≈ } 1), index of agreement (d) = 1, coefficient of determination (R2) = 0.87, mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences ({S}d2) = 0.004. The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on σ o. By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.

Error in measuring radon in soil gas by means of passive detectors

USGS Publications Warehouse

Tanner, A.B.

1991-01-01

Passive detection of radon isotopes depends on diffusion of radon atoms from the sites of their generation to the location of the detecting or collecting device. Because some radon decays en route to a passive detector in soil, the radon concentration measured by the detector must be less than the concentration in those soil pores where it is undiminished by diffusion to the detector cavity. The true radon concentration may be significantly underestimated in moist soils. -Author

Evaluating the Utility of Remotely-Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring

NASA Technical Reports Server (NTRS)

Bolten, John D.; Crow, Wade T.; Zhan, Xiwu; Jackson, Thomas J.; Reynolds,Curt

2010-01-01

Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23degN - 50degN and 128degW - 65degW. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

On the interpolation of volumetric water content in research catchments

NASA Astrophysics Data System (ADS)

Dlamini, Phesheya; Chaplot, Vincent

Digital Soil Mapping (DSM) is widely used in the environmental sciences because of its accuracy and efficiency in producing soil maps compared to the traditional soil mapping. Numerous studies have investigated how the sampling density and the interpolation process of data points affect the prediction quality. While, the interpolation process is straight forward for primary attributes such as soil gravimetric water content (θg) and soil bulk density (ρb), the DSM of volumetric water content (θv), the product of θg by ρb, may either involve direct interpolations of θv (approach 1) or independent interpolation of ρb and θg data points and subsequent multiplication of ρb and θg maps (approach 2). The main objective of this study was to compare the accuracy of these two mapping approaches for θv. A 23 ha grassland catchment in KwaZulu-Natal, South Africa was selected for this study. A total of 317 data points were randomly selected and sampled during the dry season in the topsoil (0-0.05 m) for θg by ρb estimation. Data points were interpolated following approaches 1 and 2, and using inverse distance weighting with 3 or 12 neighboring points (IDW3; IDW12), regular spline with tension (RST) and ordinary kriging (OK). Based on an independent validation set of 70 data points, OK was the best interpolator for ρb (mean absolute error, MAE of 0.081 g cm-3), while θg was best estimated using IDW12 (MAE = 1.697%) and θv by IDW3 (MAE = 1.814%). It was found that approach 1 underestimated θv. Approach 2 tended to overestimate θv, but reduced the prediction bias by an average of 37% and only improved the prediction accuracy by 1.3% compared to approach 1. Such a great benefit of approach 2 (i.e., the subsequent multiplication of interpolated maps of primary variables) was unexpected considering that a higher sampling density (∼14 data point ha-1 in the present study) tends to minimize the differences between interpolations techniques and approaches. In the context of much lower sampling densities, as generally encountered in environmental studies, one can thus expect approach 2 to yield significantly greater accuracy than approach 1. This approach 2 seems promising and can be further tested for DSM of other secondary variables.

Robust geostatistical analysis of spatial data

NASA Astrophysics Data System (ADS)

Papritz, A.; Künsch, H. R.; Schwierz, C.; Stahel, W. A.

2012-04-01

Most of the geostatistical software tools rely on non-robust algorithms. This is unfortunate, because outlying observations are rather the rule than the exception, in particular in environmental data sets. Outlying observations may results from errors (e.g. in data transcription) or from local perturbations in the processes that are responsible for a given pattern of spatial variation. As an example, the spatial distribution of some trace metal in the soils of a region may be distorted by emissions of local anthropogenic sources. Outliers affect the modelling of the large-scale spatial variation, the so-called external drift or trend, the estimation of the spatial dependence of the residual variation and the predictions by kriging. Identifying outliers manually is cumbersome and requires expertise because one needs parameter estimates to decide which observation is a potential outlier. Moreover, inference after the rejection of some observations is problematic. A better approach is to use robust algorithms that prevent automatically that outlying observations have undue influence. Former studies on robust geostatistics focused on robust estimation of the sample variogram and ordinary kriging without external drift. Furthermore, Richardson and Welsh (1995) [2] proposed a robustified version of (restricted) maximum likelihood ([RE]ML) estimation for the variance components of a linear mixed model, which was later used by Marchant and Lark (2007) [1] for robust REML estimation of the variogram. We propose here a novel method for robust REML estimation of the variogram of a Gaussian random field that is possibly contaminated by independent errors from a long-tailed distribution. It is based on robustification of estimating equations for the Gaussian REML estimation. Besides robust estimates of the parameters of the external drift and of the variogram, the method also provides standard errors for the estimated parameters, robustified kriging predictions at both sampled and unsampled locations and kriging variances. The method has been implemented in an R package. Apart from presenting our modelling framework, we shall present selected simulation results by which we explored the properties of the new method. This will be complemented by an analysis of the Tarrawarra soil moisture data set [3].

Evaluation of two methods to determine glyphosate and AMPA in soils of Argentina

NASA Astrophysics Data System (ADS)

De Geronimo, Eduardo; Lorenzon, Claudio; Iwasita, Barbara; Faggioli, Valeria; Aparicio, Virginia; Costa, Jose Luis

2017-04-01

Argentine agricultural production is fundamentally based on a technological package combining no-tillage and the dependence of glyphosate applications to control weeds in transgenic crops (soybean, maize and cotton). Therefore, glyphosate is the most employed herbicide in the country, where 180 to 200 million liters are applied every year. Due to its widespread use, it is important to assess its impact on the environment and, therefore, reliable analytical methods are mandatory. Glyphosate molecule exhibits unique physical and chemical characteristics which difficult its quantification, especially in soils with high organic matter content, such as the central eastern Argentine soils, where strong interferences are normally observed. The objective of this work was to compare two methods for extraction and quantification of glyphosate and AMPA in samples of 8 representative soils of Argentina. The first analytical method (method 1) was based on the use of phosphate buffer as extracting solution and dichloromethane to minimize matrix organic content. In the second method (method 2), potassium hydroxide was used to extract the analytes followed by a clean-up step using solid phase extraction (SPE) to minimize strong interferences. Sensitivity, recoveries, matrix effects and robustness were evaluated. Both methodologies involved a derivatization with 9-fluorenyl-methyl-chloroformate (FMOC) in borate buffer and detection based on ultra-high-pressure liquid chromatography coupled to tandem mass spectrometry (UHPLC-MS/MS). Recoveries obtained from soil samples spiked at 0.1 and 1 mg kg-1 and were satisfactory in both methods (70% - 120%). However, there was a remarkable difference regarding the matrix effect, being the SPE clean-up step (method 2) insufficient to remove the interferences. Whereas the dilution and the clean-up with dichloromethane (method 1) were more effective minimizing the ionic suppression. Moreover, method 1 had fewer steps in the protocol of sample processing than method 2. This can be highly valuable in the routine lab work due to the reduction of potential undesired errors such as the loss of analyte or sample contamination. In addition, the substitution of SPE by another alternative involved a considerable reduction of analytical costs in method 1. We conclude that method 1 seemed to be simpler and cheaper than method 2, as well as reliable to quantify glyphosate in Argentinean soils. We hope that this experience can be useful to simplify the protocols of glyphosate quantification and contribute to the understanding of the fate of this herbicide in the environment.

Optimal averaging of soil moisture predictions from ensemble land surface model simulations

USDA-ARS?s Scientific Manuscript database

The correct interpretation of ensemble 3 soil moisture information obtained from the parallel implementation of multiple land surface models (LSMs) requires information concerning the LSM ensemble’s mutual error covariance. Here we propose a new technique for obtaining such information using an inst...

Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies.

PubMed

Liu, Geng; Niu, Junjie; Zhang, Chao; Guo, Guanlin

2015-12-01

Data distribution is usually skewed severely by the presence of hot spots in contaminated sites. This causes difficulties for accurate geostatistical data transformation. Three types of typical normal distribution transformation methods termed the normal score, Johnson, and Box-Cox transformations were applied to compare the effects of spatial interpolation with normal distribution transformation data of benzo(b)fluoranthene in a large-scale coking plant-contaminated site in north China. Three normal transformation methods decreased the skewness and kurtosis of the benzo(b)fluoranthene, and all the transformed data passed the Kolmogorov-Smirnov test threshold. Cross validation showed that Johnson ordinary kriging has a minimum root-mean-square error of 1.17 and a mean error of 0.19, which was more accurate than the other two models. The area with fewer sampling points and that with high levels of contamination showed the largest prediction standard errors based on the Johnson ordinary kriging prediction map. We introduce an ideal normal transformation method prior to geostatistical estimation for severely skewed data, which enhances the reliability of risk estimation and improves the accuracy for determination of remediation boundaries.

Hydrologic characterization of desert soils with varying degrees of pedogenesis: 2. Inverse modeling for eff ective properties

USGS Publications Warehouse

Mirus, B.B.; Perkins, K.S.; Nimmo, J.R.; Singha, K.

2009-01-01

To understand their relation to pedogenic development, soil hydraulic properties in the Mojave Desert were investi- gated for three deposit types: (i) recently deposited sediments in an active wash, (ii) a soil of early Holocene age, and (iii) a highly developed soil of late Pleistocene age. Eff ective parameter values were estimated for a simplifi ed model based on Richards' equation using a fl ow simulator (VS2D), an inverse algorithm (UCODE-2005), and matric pressure and water content data from three ponded infi ltration experiments. The inverse problem framework was designed to account for the eff ects of subsurface lateral spreading of infi ltrated water. Although none of the inverse problems converged on a unique, best-fi t parameter set, a minimum standard error of regression was reached for each deposit type. Parameter sets from the numerous inversions that reached the minimum error were used to develop probability distribu tions for each parameter and deposit type. Electrical resistance imaging obtained for two of the three infi ltration experiments was used to independently test fl ow model performance. Simulations for the active wash and Holocene soil successfully depicted the lateral and vertical fl uxes. Simulations of the more pedogenically developed Pleistocene soil did not adequately replicate the observed fl ow processes, which would require a more complex conceptual model to include smaller scale heterogeneities. The inverse-modeling results, however, indicate that with increasing age, the steep slope of the soil water retention curve shitis toward more negative matric pressures. Assigning eff ective soil hydraulic properties based on soil age provides a promising framework for future development of regional-scale models of soil moisture dynamics in arid environments for land-management applications. ?? Soil Science Society of America.

The integral suspension pressure method (ISP) for precise particle-size analysis by gravitational sedimentation

NASA Astrophysics Data System (ADS)

Durner, Wolfgang; Iden, Sascha C.; von Unold, Georg

2017-01-01

The particle-size distribution (PSD) of a soil expresses the mass fractions of various sizes of mineral particles which constitute the soil material. It is a fundamental soil property, closely related to most physical and chemical soil properties and it affects almost any soil function. The experimental determination of soil texture, i.e., the relative amounts of sand, silt, and clay-sized particles, is done in the laboratory by a combination of sieving (sand) and gravitational sedimentation (silt and clay). In the latter, Stokes' law is applied to derive the particle size from the settling velocity in an aqueous suspension. Traditionally, there are two methodologies for particle-size analysis from sedimentation experiments: the pipette method and the hydrometer method. Both techniques rely on measuring the temporal change of the particle concentration or density of the suspension at a certain depth within the suspension. In this paper, we propose a new method which is based on the pressure in the suspension at a selected depth, which is an integral measure of all particles in suspension above the measuring depth. We derive a mathematical model which predicts the pressure decrease due to settling of particles as function of the PSD. The PSD of the analyzed sample is identified by fitting the simulated time series of pressure to the observed one by inverse modeling using global optimization. The new method yields the PSD in very high resolution and its experimental realization completely avoids any disturbance by the measuring process. A sensitivity analysis of different soil textures demonstrates that the method yields unbiased estimates of the PSD with very small estimation variance and an absolute error in the clay and silt fraction of less than 0.5%.

The integral suspension pressure method (ISP) for precise particle-size analysis by gravitational sedimentation

NASA Astrophysics Data System (ADS)

Durner, Wolfgang; Iden, Sascha C.; von Unold, Georg

2017-04-01

The particle-size distribution (PSD) of a soil expresses the mass fractions of various sizes of mineral particles which constitute the soil material. It is a fundamental soil property, closely related to most physical and chemical soil properties and it affects almost any soil function. The experimental determination of soil texture, i.e., the relative amounts of sand, silt, and clay-sized particles, is done in the laboratory by a combination of sieving (sand) and gravitational sedimentation (silt and clay). In the latter, Stokes' law is applied to derive the particle size from the settling velocity in an aqueous suspension. Traditionally, there are two methodologies for particle-size analysis from sedimentation experiments: the pipette method and the hydrometer method. Both techniques rely on measuring the temporal change of the particle concentration or density of the suspension at a certain depth within the suspension. In this paper, we propose a new method which is based on the pressure in the suspension at a selected depth, which is an integral measure of all particles in suspension above the measuring depth. We derive a mathematical model which predicts the pressure decrease due to settling of particles as function of the PSD. The PSD of the analyzed sample is identified by fitting the simulated time series of pressure to the observed one by inverse modeling using global optimization. The new method yields the PSD in very high resolution and its experimental realization completely avoids any disturbance by the measuring process. A sensitivity analysis of different soil textures demonstrates that the method yields unbiased estimates of the PSD with very small estimation variance and an absolute error in the clay and silt fraction of less than 0.5%

Soil Carbon Stocks Decrease following Conversion of Secondary Forests to Rubber (Hevea brasiliensis) Plantations

PubMed Central

de Blécourt, Marleen; Brumme, Rainer; Xu, Jianchu; Corre, Marife D.; Veldkamp, Edzo

2013-01-01

Forest-to-rubber plantation conversion is an important land-use change in the tropical region, for which the impacts on soil carbon stocks have hardly been studied. In montane mainland southeast Asia, monoculture rubber plantations cover 1.5 million ha and the conversion from secondary forests to rubber plantations is predicted to cause a fourfold expansion by 2050. Our study, conducted in southern Yunnan province, China, aimed to quantify the changes in soil carbon stocks following the conversion from secondary forests to rubber plantations. We sampled 11 rubber plantations ranging in age from 5 to 46 years and seven secondary forest plots using a space-for-time substitution approach. We found that forest-to-rubber plantation conversion resulted in losses of soil carbon stocks by an average of 37.4±4.7 (SE) Mg C ha−1 in the entire 1.2-m depth over a time period of 46 years, which was equal to 19.3±2.7% of the initial soil carbon stocks in the secondary forests. This decline in soil carbon stocks was much larger than differences between published aboveground carbon stocks of rubber plantations and secondary forests, which range from a loss of 18 Mg C ha−1 to an increase of 8 Mg C ha−1. In the topsoil, carbon stocks declined exponentially with years since deforestation and reached a steady state at around 20 years. Although the IPCC tier 1 method assumes that soil carbon changes from forest-to-rubber plantation conversions are zero, our findings show that they need to be included to avoid errors in estimating overall ecosystem carbon fluxes. PMID:23894456

Global modeling of soil evaporation efficiency for a chosen soil type

NASA Astrophysics Data System (ADS)

Georgiana Stefan, Vivien; Mangiarotti, Sylvain; Merlin, Olivier; Chanzy, André

2016-04-01

One way of reproducing the dynamics of a system is by deriving a set of differential, difference or discrete equations directly from observational time series. A method for obtaining such a system is the global modeling technique [1]. The approach is here applied to the dynamics of soil evaporative efficiency (SEE), defined as the ratio of actual to potential evaporation. SEE is an interesting variable to study since it is directly linked to soil evaporation (LE) which plays an important role in the water cycle and since it can be easily derived from satellite measurements. One goal of the present work is to get a semi-empirical parameter that could account for the variety of the SEE dynamical behaviors resulting from different soil properties. Before trying to obtain such a semi-empirical parameter with the global modeling technique, it is first necessary to prove that this technique can be applied to the dynamics of SEE without any a priori information. The global modeling technique is thus applied here to a synthetic series of SEE, reconstructed from the TEC (Transfert Eau Chaleur) model [2]. It is found that an autonomous chaotic model can be retrieved for the dynamics of SEE. The obtained model is four-dimensional and exhibits a complex behavior. The comparison of the original and the model phase portraits shows a very good consistency that proves that the original dynamical behavior is well described by the model. To evaluate the model accuracy, the forecasting error growth is estimated. To get a robust estimate of this error growth, the forecasting error is computed for prediction horizons of 0 to 9 hours, starting from different initial conditions and statistics of the error growth are thus performed. Results show that, for a maximum error level of 40% of the signal variance, the horizon of predictability is close to 3 hours, approximately one third of the diurnal part of day. These results are interesting for various reasons. To the best of our knowledge, it is the very first time that a chaotic model is obtained for the SEE. It also shows that the SEE dynamics can be approximated by a low-dimensional autonomous model. From a theoretical point of view, it is also interesting to note that only very few low-dimensional models could be directly obtained for environmental dynamics, and that four-dimensional models are even rarer. Since a model could be obtained for the SEE, it can be expected, now, to adapt the global modeling technique and to apply it to a range of different soil conditions in order to get a global model that would account for the variability of soil properties. [1] MANGIAROTTI S., COUDRET R., DRAPEAU L., JARLAN L. Polynomial search and global modeling: two algorithms for modeling chaos. Physical Review E, 86(4), 046205, 2012. [2] CHANZY A., MUMEN M., RICHARD G. Accuracy of the top soil moisture simulation using a mechanistic model with limited soil characterization. Water Resources Research, 44, W03432, 2008.

Quantifying the uncertainty of regional and national estimates of soil carbon stocks

NASA Astrophysics Data System (ADS)

Papritz, Andreas

2013-04-01

At regional and national scales, carbon (C) stocks are frequently estimated by means of regression models. Such statistical models link measurements of carbons stocks, recorded for a set of soil profiles or soil cores, to covariates that characterize soil formation conditions and land management. A prerequisite is that these covariates are available for any location within a region of interest G because they are used along with the fitted regression coefficients to predict the carbon stocks at the nodes of a fine-meshed grid that is laid over G. The mean C stock in G is then estimated by the arithmetic mean of the stock predictions for the grid nodes. Apart from the mean stock, the precision of the estimate is often also of interest, for example to judge whether the mean C stock has changed significantly between two inventories. The standard error of the estimated mean stock in G can be computed from the regression results as well. Two issues are thereby important: (i) How large is the area of G relative to the support of the measurements? (ii) Are the residuals of the regression model spatially auto-correlated or is the assumption of statistical independence tenable? Both issues are correctly handled if one adopts a geostatistical block kriging approach for estimating the mean C stock within a region and its standard error. In the presentation I shall summarize the main ideas of external drift block kriging. To compute the standard error of the mean stock, one has in principle to sum the elements a potentially very large covariance matrix of point prediction errors, but I shall show that the required term can be approximated very well by Monte Carlo techniques. I shall further illustrated with a few examples how the standard error of the mean stock estimate changes with the size of G and with the strenght of the auto-correlation of the regression residuals. As an application a robust variant of block kriging is used to quantify the mean carbon stock stored in the soils of Swiss forests (Nussbaum et al., 2012). Nussbaum, M., Papritz, A., Baltensweiler, A., and Walthert, L. (2012). Organic carbon stocks of swiss forest soils. Final report, Institute of Terrestrial Ecosystems, ETH Zürich and Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), pp. 51, http://e-collection.library.ethz.ch/eserv/eth:6027/eth-6027-01.pdf

A simple analytical infiltration model for short-duration rainfall

NASA Astrophysics Data System (ADS)

Wang, Kaiwen; Yang, Xiaohua; Liu, Xiaomang; Liu, Changming

2017-12-01

Many infiltration models have been proposed to simulate infiltration process. Different initial soil conditions and non-uniform initial water content can lead to infiltration simulation errors, especially for short-duration rainfall (SHR). Few infiltration models are specifically derived to eliminate the errors caused by the complex initial soil conditions. We present a simple analytical infiltration model for SHR infiltration simulation, i.e., Short-duration Infiltration Process model (SHIP model). The infiltration simulated by 5 models (i.e., SHIP (high) model, SHIP (middle) model, SHIP (low) model, Philip model and Parlange model) were compared based on numerical experiments and soil column experiments. In numerical experiments, SHIP (middle) and Parlange models had robust solutions for SHR infiltration simulation of 12 typical soils under different initial soil conditions. The absolute values of percent bias were less than 12% and the values of Nash and Sutcliffe efficiency were greater than 0.83. Additionally, in soil column experiments, infiltration rate fluctuated in a range because of non-uniform initial water content. SHIP (high) and SHIP (low) models can simulate an infiltration range, which successfully covered the fluctuation range of the observed infiltration rate. According to the robustness of solutions and the coverage of fluctuation range of infiltration rate, SHIP model can be integrated into hydrologic models to simulate SHR infiltration process and benefit the flood forecast.

The consecutive dry days to trigger rainfall over West Africa

NASA Astrophysics Data System (ADS)

Lee, J. H.

2018-01-01

In order to resolve contradictions in addressing a soil moisture-precipitation feedback mechanism over West Africa and to clarify the impact of antecedent soil moisture on subsequent rainfall evolution, we first validated various data sets (SMOS satellite soil moisture observations, NOAH land surface model, TRMM rainfall, CMORPH rainfall and HadGEM climate models) with the Analyses Multidisciplinaires de la Mousson Africaine (AMMA) field campaign data. Based on this analysis, it was suggested that biases of data sets might cause contradictions in studying mechanisms. Thus, by taking into account uncertainties in data, it was found that the approach of consecutive dry days (i.e. a relative comparison of time-series) showed consistency across various data sets, while the direct comparison approach for soil moisture state and rainfall did not. Thus, it was discussed that it may be difficult to directly relate rain with soil moisture as the absolute value, however, it may be reasonable to compare a temporal progress of the variables. Based upon the results consistently showing a positive relationship between the consecutive dry days and rainfall, this study supports a negative feedback often neglected by climate model structure. This approach is less sensitive to interpretation errors arising from systematic errors in data sets, as this measures a temporal gradient of soil moisture state.

Normalized Rotational Multiple Yield Surface Framework (NRMYSF) stress-strain curve prediction method based on small strain triaxial test data on undisturbed Auckland residual clay soils

NASA Astrophysics Data System (ADS)

Noor, M. J. Md; Ibrahim, A.; Rahman, A. S. A.

2018-04-01

Small strain triaxial test measurement is considered to be significantly accurate compared to the external strain measurement using conventional method due to systematic errors normally associated with the test. Three submersible miniature linear variable differential transducer (LVDT) mounted on yokes which clamped directly onto the soil sample at equally 120° from the others. The device setup using 0.4 N resolution load cell and 16 bit AD converter was capable of consistently resolving displacement of less than 1µm and measuring axial strains ranging from less than 0.001% to 2.5%. Further analysis of small strain local measurement data was performed using new Normalized Multiple Yield Surface Framework (NRMYSF) method and compared with existing Rotational Multiple Yield Surface Framework (RMYSF) prediction method. The prediction of shear strength based on combined intrinsic curvilinear shear strength envelope using small strain triaxial test data confirmed the significant improvement and reliability of the measurement and analysis methods. Moreover, the NRMYSF method shows an excellent data prediction and significant improvement toward more reliable prediction of soil strength that can reduce the cost and time of experimental laboratory test.

Crop/weed discrimination using near-infrared reflectance spectroscopy (NIRS)

NASA Astrophysics Data System (ADS)

Zhang, Yun; He, Yong

2006-09-01

The traditional uniform herbicide application often results in an over chemical residues on soil, crop plants and agriculture produce, which have imperiled the environment and food security. Near-infrared reflectance spectroscopy (NIRS) offers a promising means for weed detection and site-specific herbicide application. In laboratory, a total of 90 samples (30 for each species) of the detached leaves of two weeds, i.e., threeseeded mercury (Acalypha australis L.) and fourleafed duckweed (Marsilea quadrfolia L.), and one crop soybean (Glycine max) was investigated for NIRS on 325- 1075 nm using a field spectroradiometer. 20 absorbance samples of each species after pretreatment were exported and the lacked Y variables were assigned independent values for partial least squares (PLS) analysis. During the combined principle component analysis (PCA) on 400-1000 nm, the PC1 and PC2 could together explain over 91% of the total variance and detect the three plant species with 98.3% accuracy. The full-cross validation results of PLS, i.e., standard error of prediction (SEP) 0.247, correlation coefficient (r) 0.954 and root mean square error of prediction (RMSEP) 0.245, indicated an optimum model for weed identification. By predicting the remaining 10 samples of each species in the PLS model, the results with deviation presented a 100% crop/weed detection rate. Thus, it could be concluded that PLS was an available alternative of for qualitative weed discrimination on NTRS.

Mobility of arsenic and its compounds in soil and soil solution: the effect of soil pretreatment and extraction methods.

PubMed

Száková, J; Tlustos, P; Goessler, W; Frková, Z; Najmanová, J

2009-12-30

The effect of soil extraction procedures and/or sample pretreatment (drying, freezing of the soil sample) on the extractability of arsenic and its compounds was tested. In the first part, five extraction procedures were compared with following order of extractable arsenic portions: 2M HNO(3)>0.43 M CH(3)COOH>or=0.05 M EDTA>or=Mehlich III (0.2M CH(3)COOH+0.25 M NH(4)NO(3)+0.013 M HNO(3)+0.015 M NH(4)F+0.001 M EDTA) extraction>water). Additionally, two methods of soil solution sampling were compared, centrifugation of saturated soil and the use of suction cups. The results showed that different sample pretreatments including soil solution sampling could lead to different absolute values of mobile arsenic content in soils. However, the interpretation of the data can lead to similar conclusions as apparent from the comparison of the soil solution sampling methods (r=0.79). For determination of arsenic compounds mild extraction procedures (0.05 M (NH(4))(2)SO(4), 0.01 M CaCl(2), and water) and soil solution sampling using suction cups were compared. Regarding the real soil conditions the extraction of fresh samples and/or in situ collection of soil solution are preferred among the sample pretreatments and/or soil extraction procedures. However, chemical stabilization of the solutions should be allowed and included in the analytical procedures for determination of individual arsenic compounds.

Influence of soil moisture on soil respiration

NASA Astrophysics Data System (ADS)

Fer, Miroslav; Kodesova, Radka; Nikodem, Antonin; Klement, Ales; Jelenova, Klara

2015-04-01

The aim of this work was to describe an impact of soil moisture on soil respiration. Study was performed on soil samples from morphologically diverse study site in loess region of Southern Moravia, Czech Republic. The original soil type is Haplic Chernozem, which was due to erosion changed into Regosol (steep parts) and Colluvial soil (base slope and the tributary valley). Soil samples were collected from topsoils at 5 points of the selected elevation transect and also from the parent material (loess). Grab soil samples, undisturbed soil samples (small - 100 cm3, and large - 713 cm3) and undisturbed soil blocks were taken. Basic soil properties were determined on grab soil samples. Small undisturbed soil samples were used to determine the soil water retention curves and the hydraulic conductivity functions using the multiple outflow tests in Tempe cells and a numerical inversion with HYDRUS 1-D. During experiments performed in greenhouse dry large undisturbed soil samples were wetted from below using a kaolin tank and cumulative water inflow due to capillary rise was measured. Simultaneously net CO2 exchange rate and net H2O exchange rate were measured using LCi-SD portable photosynthesis system with Soil Respiration Chamber. Numerical inversion of the measured cumulative capillary rise data using the HYDRUS-1D program was applied to modify selected soil hydraulic parameters for particular conditions and to simulate actual soil water distribution within each soil column in selected times. Undisturbed soil blocks were used to prepare thin soil sections to study soil-pore structure. Results for all soil samples showed that at the beginning of soil samples wetting the CO2 emission increased because of improving condition for microbes' activity. The maximum values were reached for soil column average soil water content between 0.10 and 0.15 cm3/cm3. Next CO2 emission decreased since the pore system starts filling by water (i.e. aggravated conditions for microbes, closing soil gas pathways etc.). In the case of H2O exchange rate, values increased with increasing soil water contents (up to 0.15-0.20 cm3/cm3) and then remained approximately constant. Acknowledgement: Authors acknowledge the financial support of the Ministry of Agriculture of the Czech Republic No. QJ1230319

Spatial distribution of soil moisture and hydrophobicity in the immediate period after a grassland fire in Lithuania

NASA Astrophysics Data System (ADS)

Pereira, P.; Pundyte, N.; Vaitkute, D.; Cepanko, V.; Pranskevicius, M.; Ubeda, X.; Mataix-Solera, J.; Cerda, A.

2012-04-01

Fire can affect significantly soil moisture (SM) and water repellency (WR) in the immediate period after the fire due the effect of the temperatures into soil profile and ash. This impact can be very heterogeneous, even in small distances, due to different conditions of combustion (e.g. fuel and soil moisture, fuel amount and type, distribution and connection, and geomorphological variables as aspect and slope) that influences fire temperature and severity. The aim of this work it is study the spatial distribution of SM and WR in a small plot (400 m2 with a sampling distance of 5 m) immediately after the a low severity grassland fire.. This was made in a burned but also in a control (unburned) plot as reference to can compare. In each plot we analyzed a total of 25 samples. SM was measured gravimetrically and WR with the water drop penetration time test (WDPT). Several interpolation methods were tested in order to identify the best predictor of SM and WR, as the Inverse Distance to a Weight (IDW) (with the power of 1,2,3,4 and 5), Local Polynomial with the first and second polynomial order, Polynomial Regression (PR), Radial Basis Functions (RBF) as Multilog (MTG), Natural Cubic Spline (NCS), Multiquadratic (MTQ), Inverse Multiquadratic (IMTQ) and Thin Plate Spline (TPS) and Ordinary Kriging. Interpolation accuracy was observed with the cross-validation method that is achieved by taking each observation in turn out of the sample and estimating from the remaining ones. The errors produced in each interpolation allowed us to calculate the Root Mean Square Error (RMSE). The best method is the one that showed the lower RMSE. The results showed that on average the SM in the control plot was 13.59 % (±2.83) and WR 2.9 (±1.3) seconds (s). The majority of the soils (88%) were hydrophilic (WDPT <5s). SM in the control plot showed a weak negative relationship with WR (r=-0.33, p<0.10). The coefficient of variation (CV%) of SM was 20.77% and SW of 44.62%. In the burned plot, SM was 14.17% (±2.83) and WR of 151 (±99) seconds (s). All the samples analysed were considered hydrophobic (WDPT >5s). We did not identify significant relationships among the variables (r=0.06, p>0.05) and the CV% was higher in WR (65.85%) than SM (19.96%). Overall we identified no significant changes in SM between plots, which means that fire did not had important implications on soil water content, contrary to observed in WR. The same dynamic was observed in the CV%. Among all tested methods the most accurate to interpolate SM, in the control plot IDW 1 and in the burned plot IDW 2, and this means that fire did not induce important inferences on the spatial distribution of SM. In WR, in the control plot, the best predictor was NCS and in the burned plot was IDW 1 and this means that spatial distribution WR was substantially affected by fire. In this case we observed an increase of the small scale variability in the burned area. Currently we are monitoring this burned area and observing the evaluation of the spatial variability of these two soil properties. It is important to observe their dynamic in the space and time and observe if fire will have medium and long term implications on SM and WR. Discussions about the results will be carried out during the poster session.

Assessment of soil biological quality index (QBS-ar) in different crop rotation systems in paddy soils

NASA Astrophysics Data System (ADS)

Nadimi-Goki, Mandana; Bini, Claudio; haefele, Stephan

2013-04-01

New methods, based on soil microarthropods for soil quality evaluation have been proposed by some Authors. Soil microarthropods demonstrated to respond sensitively to land management practices and to be correlated with beneficial soil functions. QBS Index (QBS-ar) is calculated on the basis of microarthropod groups present in a soil sample. Each biological form found in the sample receives a score from 1 to 20 (eco-morphological index, EMI), according to its adaptation to soil environment. The objective of this study was to evaluate the effect of various rotation systems and sampling periods on soil biological quality index, in paddy soils. For the purpose of this study surface soil samples (0-15 cm depth) were collected from different rotation systems (rice-rice-rice, soya-rice-rice, fallow-rice and pea-soya-rice) with three replications, and four sampling times in April (after field preparation), June (after seedling), August (after tillering stage) and October (after rice harvesting). The study area is located in paddy soils of Verona area, Northern Italy. Soil microarthropods from a total of 48 samples were extracted and classified according to the Biological Quality of Soil Index (QBS-ar) method. In addition soil moisture, Cumulative Soil Respiration and pH were measured in each site. More diversity of microarthropod groups was found in June and August sampling times. T-test results between different rotations did not show significant differences while the mean difference between rotation and different sampling times is statistically different. The highest QBS-ar value was found in the fallow-rice rotation in the forth soil sampling time. Similar value was found in soya-rice-rice rotation. Result of linear regression analysis indicated that there is significant correlation between QBS-ar values and Cumulative Soil Respiration. Keywords: soil biological quality index (QBS-ar), Crop Rotation System, paddy soils, Italy

Gap filling strategies and error in estimating annual soil respiration

USDA-ARS?s Scientific Manuscript database

Soil respiration (Rsoil) is one of the largest CO2 fluxes in the global carbon (C) cycle. Estimation of annual Rsoil requires extrapolation of survey measurements or gap-filling of automated records to produce a complete time series. While many gap-filling methodologies have been employed, there is ...

The potential of 2D Kalman filtering for soil moisture data assimilation

USDA-ARS?s Scientific Manuscript database

We examine the potential for parameterizing a two-dimensional (2D) land data assimilation system using spatial error auto-correlation statistics gleaned from a triple collocation analysis and the triplet of: (1) active microwave-, (2) passive microwave- and (3) land surface model-based surface soil ...

Estimation of available water capacity components of two-layered soils using crop model inversion: Effect of crop type and water regime

NASA Astrophysics Data System (ADS)

Sreelash, K.; Buis, Samuel; Sekhar, M.; Ruiz, Laurent; Kumar Tomer, Sat; Guérif, Martine

2017-03-01

Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the quality of estimation of soil hydraulic properties may vary depending on agro-environmental situations. The objective of this study was to evaluate this approach on an extensive field experiment. The dataset covered four crops (sunflower, sorghum, turmeric, maize) grown on different soils and several years in South India. The components of AWC (available water capacity) namely soil water content at field capacity and wilting point, and soil depth of two-layered soils were estimated by inversion of the crop model STICS with the GLUE (generalized likelihood uncertainty estimation) approach using observations of surface soil moisture (SSM; typically from 0 to 10 cm deep) and leaf area index (LAI), which are attainable from radar remote sensing in tropical regions with frequent cloudy conditions. The results showed that the quality of parameter estimation largely depends on the hydric regime and its interaction with crop type. A mean relative absolute error of 5% for field capacity of surface layer, 10% for field capacity of root zone, 15% for wilting point of surface layer and root zone, and 20% for soil depth can be obtained in favorable conditions. A few observations of SSM (during wet and dry soil moisture periods) and LAI (within water stress periods) were sufficient to significantly improve the estimation of AWC components. These results show the potential of crop model inversion for estimating the AWC components of two-layered soils and may guide the sampling of representative years and fields to use this technique for mapping soil properties that are relevant for distributed hydrological modelling.

Soil Sampling Techniques For Alabama Grain Fields

NASA Technical Reports Server (NTRS)

Thompson, A. N.; Shaw, J. N.; Mask, P. L.; Touchton, J. T.; Rickman, D.

2003-01-01

Characterizing the spatial variability of nutrients facilitates precision soil sampling. Questions exist regarding the best technique for directed soil sampling based on a priori knowledge of soil and crop patterns. The objective of this study was to evaluate zone delineation techniques for Alabama grain fields to determine which method best minimized the soil test variability. Site one (25.8 ha) and site three (20.0 ha) were located in the Tennessee Valley region, and site two (24.2 ha) was located in the Coastal Plain region of Alabama. Tennessee Valley soils ranged from well drained Rhodic and Typic Paleudults to somewhat poorly drained Aquic Paleudults and Fluventic Dystrudepts. Coastal Plain s o i l s ranged from coarse-loamy Rhodic Kandiudults to loamy Arenic Kandiudults. Soils were sampled by grid soil sampling methods (grid sizes of 0.40 ha and 1 ha) consisting of: 1) twenty composited cores collected randomly throughout each grid (grid-cell sampling) and, 2) six composited cores collected randomly from a -3x3 m area at the center of each grid (grid-point sampling). Zones were established from 1) an Order 1 Soil Survey, 2) corn (Zea mays L.) yield maps, and 3) airborne remote sensing images. All soil properties were moderately to strongly spatially dependent as per semivariogram analyses. Differences in grid-point and grid-cell soil test values suggested grid-point sampling does not accurately represent grid values. Zones created by soil survey, yield data, and remote sensing images displayed lower coefficient of variations (8CV) for soil test values than overall field values, suggesting these techniques group soil test variability. However, few differences were observed between the three zone delineation techniques. Results suggest directed sampling using zone delineation techniques outlined in this paper would result in more efficient soil sampling for these Alabama grain fields.

Simulating the fate of water in field soil crop environment

NASA Astrophysics Data System (ADS)

Cameira, M. R.; Fernando, R. M.; Ahuja, L.; Pereira, L.

2005-12-01

This paper presents an evaluation of the Root Zone Water Quality Model(RZWQM) for assessing the fate of water in the soil-crop environment at the field scale under the particular conditions of a Mediterranean region. The RZWQM model is a one-dimensional dual porosity model that allows flow in macropores. It integrates the physical, biological and chemical processes occurring in the root zone, allowing the simulation of a wide spectrum of agricultural management practices. This study involved the evaluation of the soil, hydrologic and crop development sub-models within the RZWQM for two distinct agricultural systems, one consisting of a grain corn planted in a silty loam soil, irrigated by level basins and the other a forage corn planted in a sandy soil, irrigated by sprinklers. Evaluation was performed at two distinct levels. At the first level the model capability to fit the measured data was analyzed (calibration). At the second level the model's capability to extrapolate and predict the system behavior for conditions different than those used when fitting the model was assessed (validation). In a subsequent paper the same type of evaluation is presented for the nitrogen transformation and transport model. At the first level a change in the crop evapotranspiration (ETc) formulation was introduced, based upon the definition of the effective leaf area, resulting in a 51% decrease in the root mean square error of the ETc simulations. As a result the simulation of the root water uptake was greatly improved. A new bottom boundary condition was implemented to account for the presence of a shallow water table. This improved the simulation of the water table depths and consequently the soil water evolution within the root zone. The soil hydraulic parameters and the crop variety specific parameters were calibrated in order to minimize the simulation errors of soil water and crop development. At the second level crop yield was predicted with an error of 1.1 and 2.8% for grain and forage corn, respectively. Soil water was predicted with an efficiency ranging from 50 to 95% for the silty loam soil and between 56 and 72% for the sandy soil. The purposed calibration procedure allowed the model to predict crop development, yield and the water balance terms, with accuracy that is acceptable in practical applications for complex and spatially variable field conditions. An iterative method was required to account for the strong interaction between the different model components, based upon detailed experimental data on soils and crops.

Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation Downscaling

NASA Technical Reports Server (NTRS)

Koster, Randal; Walker, Greg; Mahanama, Sarith; Reichle, Rolf

2012-01-01

Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which the downscaling of seasonal precipitation forecasts, if it could be done accurately, would improve streamflow forecasts. The reduction in streamflow forecast skill (with forecasted streamflow measured against observations) associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself. This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized ("perfect model") analysis, precipitation downscaling is found to have an impact on large-scale streamflow forecasts only if two conditions are met: (i) evaporation variance is significant relative to the precipitation variance, and (ii) the subgrid spatial variance of precipitation is adequately large. In the large-scale continental region studied (the conterminous United States), these two conditions are met in only a somewhat limited area.

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS

PubMed Central

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data. PMID:26090852

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

PubMed

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

An empirical understanding of triple collocation evaluation measure

NASA Astrophysics Data System (ADS)

Scipal, Klaus; Doubkova, Marcela; Hegyova, Alena; Dorigo, Wouter; Wagner, Wolfgang

2013-04-01

Triple collocation method is an advanced evaluation method that has been used in the soil moisture field for only about half a decade. The method requires three datasets with an independent error structure that represent an identical phenomenon. The main advantages of the method are that it a) doesn't require a reference dataset that has to be considered to represent the truth, b) limits the effect of random and systematic errors of other two datasets, and c) simultaneously assesses the error of three datasets. The objective of this presentation is to assess the triple collocation error (Tc) of the ASAR Global Mode Surface Soil Moisture (GM SSM 1) km dataset and highlight problems of the method related to its ability to cancel the effect of error of ancillary datasets. In particular, the goal is to a) investigate trends in Tc related to the change in spatial resolution from 5 to 25 km, b) to investigate trends in Tc related to the choice of a hydrological model, and c) to study the relationship between Tc and other absolute evaluation methods (namely RMSE and Error Propagation EP). The triple collocation method is implemented using ASAR GM, AMSR-E, and a model (either AWRA-L, GLDAS-NOAH, or ERA-Interim). First, the significance of the relationship between the three soil moisture datasets was tested that is a prerequisite for the triple collocation method. Second, the trends in Tc related to the choice of the third reference dataset and scale were assessed. For this purpose the triple collocation is repeated replacing AWRA-L with two different globally available model reanalysis dataset operating at different spatial resolution (ERA-Interim and GLDAS-NOAH). Finally, the retrieved results were compared to the results of the RMSE and EP evaluation measures. Our results demonstrate that the Tc method does not eliminate the random and time-variant systematic errors of the second and the third dataset used in the Tc. The possible reasons include the fact a) that the TC method could not fully function with datasets acting at very different spatial resolutions, or b) that the errors were not fully independent as initially assumed.

BOREAS TE-2 NSA Soil Lab Data

NASA Technical Reports Server (NTRS)

Veldhuis, Hugo; Hall, Forrest G. (Editor); Knapp, David E. (Editor)

2000-01-01

This data set contains the major soil properties of soil samples collected in 1994 at the tower flux sites in the Northern Study Area (NSA). The soil samples were collected by Hugo Veldhuis and his staff from the University of Manitoba. The mineral soil samples were largely analyzed by Barry Goetz, under the supervision of Dr. Harold Rostad at the University of Saskatchewan. The organic soil samples were largely analyzed by Peter Haluschak, under the supervision of Hugo Veldhuis at the Centre for Land and Biological Resources Research in Winnipeg, Manitoba. During the course of field investigation and mapping, selected surface and subsurface soil samples were collected for laboratory analysis. These samples were used as benchmark references for specific soil attributes in general soil characterization. Detailed soil sampling, description, and laboratory analysis were performed on selected modal soils to provide examples of common soil physical and chemical characteristics in the study area. The soil properties that were determined include soil horizon; dry soil color; pH; bulk density; total, organic, and inorganic carbon; electric conductivity; cation exchange capacity; exchangeable sodium, potassium, calcium, magnesium, and hydrogen; water content at 0.01, 0.033, and 1.5 MPascals; nitrogen; phosphorus: particle size distribution; texture; pH of the mineral soil and of the organic soil; extractable acid; and sulfur. These data are stored in ASCII text files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Field reconnaissance and estimation of petroleum hydrocarbon and heavy metal contents of soils affected by the Ebocha-8 oil spillage in Niger Delta, Nigeria.

PubMed

Osuji, Leo C; Onojake, Chukunedum M

2006-04-01

Field reconnaissance of the Ebocha-8 oil spill-affected site at Obiobi/Obrikom in the Niger Delta region of Nigeria was carried out to assess the extent of damage to the terrestrial ecosystem and delimit the epicenter of oil spillage. Following three successive reconnaissance surveys, the area to be sampled was delimited (200 x 200 m2), and soil samples were collected using the grid method from three replicate quadrats at two depths, surface (0-15 cm) and subsurface (15-30 cm). A geographically similar area located 50 m adjacent to the oil-polluted area was used as a reference (control) site. Total hydrocarbon content (THC) and heavy metal concentrations were later determined in the laboratory by extraction and spetrophotemetric techniques. Generally, the THC of soils at surface and subsurface depths of the oil-polluted plots was 2.06 x 10(4) +/- 4.97 x 10(3) mg/kg and 1.67 x 10(3) +/- 3.61 x 10(2) mg/kg soil, respectively, (no overlap in standard errors at 95% confidence limit) while concentrations of heavy metals(Pb, Cd, V, Cu and Ni) were enhanced, especially at the surface. The high levels of THC and heavy metals may predispose the site, which hitherto served as arable agricultural land, to impaired fertility and possible conflagration. When concentrations of heavy metals reach the levels obtained in this study, they may become toxic to plants or possibly bio-accumulate, thus leading to toxic reactions along the food chain. While the spilled-oil may have contributed to the enhanced levels of the metals in the affected soils, physico-chemical properties of the soils, mobility of metals, and the intense rainfall and flooding that preceded the period of study may have also contributed in part to their enhanced concentrations. The presence of high hydrocarbon content may cause oxygen deprivation, which may result in the death of soil fauna by asphyxiation. There is, therefore, an urgent need to clear the affected site of these excess hydrocarbon deposits so as to enhance the rehabilitation process of the affected mat layer of soils. Other appropriate mitigating measures, such as subsequent monitoring of hydrocarbon levels at suitable intervals after the clean up activities, are also recommended, with reference to the findings of this study, for effective management of the affected area.

Regional Climate Simulations over North America: Interaction of Local Processes with Improved Large-Scale Flow.

NASA Astrophysics Data System (ADS)

Miguez-Macho, Gonzalo; Stenchikov, Georgiy L.; Robock, Alan

2005-04-01

The reasons for biases in regional climate simulations were investigated in an attempt to discern whether they arise from deficiencies in the model parameterizations or are due to dynamical problems. Using the Regional Atmospheric Modeling System (RAMS) forced by the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis, the detailed climate over North America at 50-km resolution for June 2000 was simulated. First, the RAMS equations were modified to make them applicable to a large region, and its turbulence parameterization was corrected. The initial simulations showed large biases in the location of precipitation patterns and surface air temperatures. By implementing higher-resolution soil data, soil moisture and soil temperature initialization, and corrections to the Kain-Fritch convective scheme, the temperature biases and precipitation amount errors could be removed, but the precipitation location errors remained. The precipitation location biases could only be improved by implementing spectral nudging of the large-scale (wavelength of 2500 km) dynamics in RAMS. This corrected for circulation errors produced by interactions and reflection of the internal domain dynamics with the lateral boundaries where the model was forced by the reanalysis.

Automatic variable selection method and a comparison for quantitative analysis in laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Duan, Fajie; Fu, Xiao; Jiang, Jiajia; Huang, Tingting; Ma, Ling; Zhang, Cong

2018-05-01

In this work, an automatic variable selection method for quantitative analysis of soil samples using laser-induced breakdown spectroscopy (LIBS) is proposed, which is based on full spectrum correction (FSC) and modified iterative predictor weighting-partial least squares (mIPW-PLS). The method features automatic selection without artificial processes. To illustrate the feasibility and effectiveness of the method, a comparison with genetic algorithm (GA) and successive projections algorithm (SPA) for different elements (copper, barium and chromium) detection in soil was implemented. The experimental results showed that all the three methods could accomplish variable selection effectively, among which FSC-mIPW-PLS required significantly shorter computation time (12 s approximately for 40,000 initial variables) than the others. Moreover, improved quantification models were got with variable selection approaches. The root mean square errors of prediction (RMSEP) of models utilizing the new method were 27.47 (copper), 37.15 (barium) and 39.70 (chromium) mg/kg, which showed comparable prediction effect with GA and SPA.

High resolution change estimation of soil moisture and its assimilation into a land surface model

NASA Astrophysics Data System (ADS)

Narayan, Ujjwal

Near surface soil moisture plays an important role in hydrological processes including infiltration, evapotranspiration and runoff. These processes depend non-linearly on soil moisture and hence sub-pixel scale soil moisture variability characterization is important for accurate modeling of water and energy fluxes at the pixel scale. Microwave remote sensing has evolved as an attractive technique for global monitoring of near surface soil moisture. A radiative transfer model has been tested and validated for soil moisture retrieval from passive microwave remote sensing data under a full range of vegetation water content conditions. It was demonstrated that soil moisture retrieval errors of approximately 0.04 g/g gravimetric soil moisture are attainable with vegetation water content as high as 5 kg/m2. Recognizing the limitation of low spatial resolution associated with passive sensors, an algorithm that uses low resolution passive microwave (radiometer) and high resolution active microwave (radar) data to estimate soil moisture change at the spatial resolution of radar operation has been developed and applied to coincident Passive and Active L and S band (PALS) and Airborne Synthetic Aperture Radar (AIRSAR) datasets acquired during the Soil Moisture Experiments in 2002 (SMEX02) campaign with root mean square error of 10% and a 4 times enhancement in spatial resolution. The change estimation algorithm has also been used to estimate soil moisture change at 5 km resolution using AMSR-E soil moisture product (50 km) in conjunction with the TRMM-PR data (5 km) for a 3 month period demonstrating the possibility of high resolution soil moisture change estimation using satellite based data. Soil moisture change is closely related to precipitation and soil hydraulic properties. A simple assimilation framework has been implemented to investigate whether assimilation of surface layer soil moisture change observations into a hydrologic model will potentially improve it performance. Results indicate an improvement in model prediction of near surface and deep layer soil moisture content when the update is performed to the model state as compared to free model runs. It is also seen that soil moisture change assimilation is able to mitigate the effect of erroneous precipitation input data.

Hydrologic data assimilation with a hillslope-scale-resolving model and L band radar observations: Synthetic experiments with the ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Flores, Alejandro N.; Bras, Rafael L.; Entekhabi, Dara

2012-08-01

Soil moisture information is critical for applications like landslide susceptibility analysis and military trafficability assessment. Existing technologies cannot observe soil moisture at spatial scales of hillslopes (e.g., 100 to 102 m) and over large areas (e.g., 102 to 105 km2) with sufficiently high temporal coverage (e.g., days). Physics-based hydrologic models can simulate soil moisture at the necessary spatial and temporal scales, albeit with error. We develop and test a data assimilation framework based on the ensemble Kalman filter for constraining uncertain simulated high-resolution soil moisture fields to anticipated remote sensing products, specifically NASA's Soil Moisture Active-Passive (SMAP) mission, which will provide global L band microwave observation approximately every 2-3 days. The framework directly assimilates SMAP synthetic 3 km radar backscatter observations to update hillslope-scale bare soil moisture estimates from a physics-based model. Downscaling from 3 km observations to hillslope scales is achieved through the data assimilation algorithm. Assimilation reduces bias in near-surface soil moisture (e.g., top 10 cm) by approximately 0.05 m3/m3and expected root-mean-square errors by at least 60% in much of the watershed, relative to an open loop simulation. However, near-surface moisture estimates in channel and valley bottoms do not improve, and estimates of profile-integrated moisture throughout the watershed do not substantially improve. We discuss the implications of this work, focusing on ongoing efforts to improve soil moisture estimation in the entire soil profile through joint assimilation of other satellite (e.g., vegetation) and in situ soil moisture measurements.

Soil maps as data input for soil erosion models: errors related to map scales

NASA Astrophysics Data System (ADS)

van Dijk, Paul; Sauter, Joëlle; Hofstetter, Elodie

2010-05-01

Soil erosion rates depend in many ways on soil and soil surface characteristics which vary in space and in time. To account for spatial variations of soil features, most distributed soil erosion models require data input derived from soil maps. Ideally, the level of spatial detail contained in the applied soil map should correspond to the objective of the modelling study. However, often the model user has only one soil map available which is then applied without questioning its suitability. The present study seeks to determine in how far soil map scale can be a source of error in erosion model output. The study was conducted on two different spatial scales, with for each of them a convenient soil erosion model: a) the catchment scale using the physically-based Limbourg Soil Erosion Model (LISEM), and b) the regional scale using the decision-tree expert model MESALES. The suitability of the applied soil map was evaluated with respect to an imaginary though realistic study objective for both models: the definition of erosion control measures at strategic locations at the catchment scale; the identification of target areas for the definition of control measures strategies at the regional scale. Two catchments were selected to test the sensitivity of LISEM to the spatial detail contained in soil maps: one catchment with relatively little contrast in soil texture, dominated by loess-derived soil (south of the Alsace), and one catchment with strongly contrasted soils at the limit between the Alsatian piedmont and the loess-covered hills of the Kochersberg. LISEM was run for both catchments using different soil maps ranging in scale from 1/25 000 to 1/100 000 to derive soil related input parameters. The comparison of the output differences was used to quantify the map scale impact on the quality of the model output. The sensitivity of MESALES was tested on the Haut-Rhin county for which two soil maps are available for comparison: 1/50 000 and 1/100 000. The order of resulting target areas (communes) was compared to evaluate the error induced by using the coarser soil data at 1/100 000. Results shows that both models are sensitive to the soil map scale used for model data input. A low sensitivity was found for the catchment with relatively homogeneous soil textures and the use of 1/100 000 soil maps seems allowed. The results for the catchment with strong soil texture variations showed significant differences depending on soil map scale on 75% of the catchment area. Here, the use of 1/100 000 soil map will indeed lead to wrong erosion diagnostics and will hamper the definition of a sound erosion control strategy. The regional scale model MESALES proved to be very sensitive to soil information. The two soil related model parameters (crusting sensitivity, and soil erodibility) reacted very often in the same direction therewith amplifying the change in the final erosion hazard class. The 1/100 000 soil map yielded different results on 40% of the sloping area compared to the 1/50 000 map. Significant differences in the order of target areas were found as well. The present study shows that the degree of sensitivity of the model output to soil map scale is rather variable and depends partly on the spatial variability of soil texture within the study area. Soil (textural) diversity needs to be accounted for to assure a fruitful use of soil erosion models. In some situations this might imply that additional soil data need to be collected in the field to refine the available soil map.

Worldwide Organic Soil Carbon and Nitrogen Data (1986) (NDP-018)

DOE Data Explorer

Zinke, P. J. [Univ. of California, Berkeley, CA (United States); Stangenberger, A. G. [Univ. of California, Berkeley, CA (United States); Post, W. M. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Emanuel, W. R. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Olson, J. S. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Millemann, R. E. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Boden, T. A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

1986-01-01

This data base was begun with the collection and analysis of soil samples from California. Additional data came from soil surveys of Italy, Greece, Iran, Thailand, Vietnam, various tropical Amazonian areas, and U.S. forests and from the soil-survey literature. The analyzed samples were collected at uniform soil-depth increments and included bulk-density determinations. The data on each sample are soil profile number; soil profile carbon content; soil profile nitrogen content; sampling site latitude and longitude; site elevation; profile literature reference source; and soil profile codes for Holdridge life zone, Olson ecosystem type, and parent material. These data may be used to estimate the size of the soil organic carbon and nitrogen pools at equilibrium with natural soil-forming factors.

Rapid Carbon Assessment Project: Data Summary and Availability

NASA Astrophysics Data System (ADS)

Wills, Skye; Loecke, Terry; Roecker, Stephen; Beaudette, Dylan; Libohova, Zamir; Monger, Curtis; Lindbo, David

2017-04-01

The Rapid Carbon Assessment (RaCA) project was undertaken to estimate regional soil organic carbon (SOC) stocks across the conterminous United States (CONUS) as a one-time event. Sample locations were selected randomly using the NRI (National Resource Inventory) sampling framework covering all areas in CONUS with SSURGO certified maps as of Dec 2012. Within each of 17 regions, sites were selected by a combination of soil and land use/cover groups (LUGR). At each of more than 6,000 sites five pedons were described and sampled to a depth of 100cm (one central and 4 satellites 30m in each cardinal direction). There were 144,833 samples described from 32,084 pedons at 6, 017 sites. A combination of measurement and modeled bulk density was used for all samples. A visible near-infrared (VNIR) spectrophotometer was used to scan each sample for prediction of soil carbon contents. The samples of each central pedon were analyzed by the Kellogg Soil Survey Laboratory for combustion carbon and calcimeter inorganic carbon. SOC stocks were calculated for each pedon using a standard fixed depth technique to depths of 5, 30 and 100cm. Pedon SOC stocks were transformed to better approach normality before LUGR, regional and land use/cover summaries were calculated. The values reported are geometric means. A detailed spatial map can be produced using LUGR mean assignment to correlated pixels. LUGR values range from 1 to 3,000 Mg ha-1. While some artifacts are visible due to the stratified nature of sampling and extrapolation, the predictions are generally smooth and highlight some distinct geomorphic features including the sandhills in the Great Plains in the central US, mountainous regions in the West and coastal wetlands in the East. Regional averages range from 46 Mg ha-1 in the desert Southwest to 182 Mg ha-1 in the Northeast. Regional trends correlate to climate variables such as precipitation and potential evapotranspiration. While land use/cover classes vary in mean values, the range within each class overlap and they are not significantly different. As expected, wetlands have the highest SOC stocks, 261 Mg ha-1, and range lands the lowest, 51 Mg ha-1. This is due primarily to the great stocks between 30 and 100cm in wetlands. Ongoing work includes incorporating measurement error into uncertainties and using Bayesian inference to test differences between land use/cover classes. Project information and raw data including sample descriptions, sample data, processing scripts, VNIR scans, and maps are available via web and R based packages. Future work will be done to map carbon across landscapes using environmental covariates and produce probabilities of C concentrations and stocks across multiple land use and management scenarios

The effects of sampling frequency on the climate statistics of the European Centre for Medium-Range Weather Forecasts

NASA Astrophysics Data System (ADS)

Phillips, Thomas J.; Gates, W. Lawrence; Arpe, Klaus

1992-12-01

The effects of sampling frequency on the first- and second-moment statistics of selected European Centre for Medium-Range Weather Forecasts (ECMWF) model variables are investigated in a simulation of "perpetual July" with a diurnal cycle included and with surface and atmospheric fields saved at hourly intervals. The shortest characteristic time scales (as determined by the e-folding time of lagged autocorrelation functions) are those of ground heat fluxes and temperatures, precipitation and runoff, convective processes, cloud properties, and atmospheric vertical motion, while the longest time scales are exhibited by soil temperature and moisture, surface pressure, and atmospheric specific humidity, temperature, and wind. The time scales of surface heat and momentum fluxes and of convective processes are substantially shorter over land than over oceans. An appropriate sampling frequency for each model variable is obtained by comparing the estimates of first- and second-moment statistics determined at intervals ranging from 2 to 24 hours with the "best" estimates obtained from hourly sampling. Relatively accurate estimation of first- and second-moment climate statistics (10% errors in means, 20% errors in variances) can be achieved by sampling a model variable at intervals that usually are longer than the bandwidth of its time series but that often are shorter than its characteristic time scale. For the surface variables, sampling at intervals that are nonintegral divisors of a 24-hour day yields relatively more accurate time-mean statistics because of a reduction in errors associated with aliasing of the diurnal cycle and higher-frequency harmonics. The superior estimates of first-moment statistics are accompanied by inferior estimates of the variance of the daily means due to the presence of systematic biases, but these probably can be avoided by defining a different measure of low-frequency variability. Estimates of the intradiurnal variance of accumulated precipitation and surface runoff also are strongly impacted by the length of the storage interval. In light of these results, several alternative strategies for storage of the EMWF model variables are recommended.

Soil forensics: How far can soil clay analysis distinguish between soil vestiges?

PubMed

Corrêa, R S; Melo, V F; Abreu, G G F; Sousa, M H; Chaker, J A; Gomes, J A

2018-03-01

Soil traces are useful as forensic evidences because they frequently adhere to individuals and objects associated with crimes and can place or discard a suspect at/from a crime scene. Soil is a mixture of organic and inorganic components and among them soil clay contains signatures that make it reliable as forensic evidence. In this study, we hypothesized that soils can be forensically distinguished through the analysis of their clay fraction alone, and that samples of the same soil type can be consistently distinguished according to the distance they were collected from each other. To test these hypotheses 16 Oxisol samples were collected at distances of between 2m and 1.000m, and 16 Inceptisol samples were collected at distances of between 2m and 300m from each other. Clay fractions were extracted from soil samples and analyzed for hyperspectral color reflectance (HSI), X-ray diffraction crystallographic (XRD), and for contents of iron oxides, kaolinite and gibbsite. The dataset was submitted to multivariate analysis and results were from 65% to 100% effective to distinguish between samples from the two soil types. Both soil types could be consistently distinguished for forensic purposes according to the distance that samples were collected from each other: 1000m for Oxisol and 10m for Inceptisol. Clay color and XRD analysis were the most effective techniques to distinguish clay samples, and Inceptisol samples were more easily distinguished than Oxisol samples. Soil forensics seems a promising field for soil scientists as soil clay can be useful as forensic evidence by using routine analytical techniques from soil science. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

Visible-near infrared spectroscopy as a tool to improve mapping of soil properties

NASA Astrophysics Data System (ADS)

Evgrafova, Alevtina; Kühnel, Anna; Bogner, Christina; Haase, Ina; Shibistova, Olga; Guggenberger, Georg; Tananaev, Nikita; Sauheitl, Leopold; Spielvogel, Sandra

2017-04-01

Spectroscopic measurements, which are non-destructive, precise and rapid, can be used to predict soil properties and help estimate the spatial variability of soil properties at the pedon scale. These estimations are required for quantifying soil properties with higher precision, identifying the changes in soil properties and ecosystem response to climate change as well as increasing the estimation accuracy of soil-related models. Our objectives were to (i) predict soil properties for nested samples (n = 296) using the laboratory-based visible-near infrared (vis-NIR) spectra of air-dried (<2 mm) soil samples and values of measured soil properties for gridded samples (n = 174) as calibration and validation sets; (ii) estimate the precision and predictive accuracy of an empirical spectral model using (a) our own spectral library and (b) the global spectral library; (iii) support the global spectral library with obtained vis-NIR spectral data on permafrost-affected soils. The soil samples were collected from three permafrost-affected soil profiles underlain by permafrost at various depths between 23 cm to 57.5 cm below the surface (Cryosols) and one soil profile with no presence of permafrost within the upper 100 cm layer (Cambisol) in order to characterize the spatial distribution and variability of soil properties. The gridded soil samples (n = 174) were collected using an 80 cm wide grid with a mesh size of 10 cm on both axes. In addition, 300 nested soil samples were collected using a grid of 12 cm by 12 cm (25 samples per grid) from a hole of 1 cm in a diameter with a distance from the next sample of 1 cm. Due to a small amount of available soil material (< 1.5 g), 296 nested soil samples were analyzed only using vis-NIR spectroscopy. The air-dried mineral gridded soil samples (n = 174) were sieved through a 2-mm sieve and ground with an agate mortar prior to the elemental analysis. The soil organic carbon and total nitrogen concentrations (in %) were determined using a dry combustion method on the Vario EL cube analyzer (Elementar Analysensysteme GmbH, Germany). Inorganic C was removed from the mineral soil samples with pH values higher than 7 prior to the elemental analysis using the volatilization method (HCl, 6 hours). The pH of soil samples was measured in 0.01 M CaCl2 using a 1:2 soil:solution ratio. However, for soil sample with a high in organic matter content, a 1:10 ratio was applied. We also measured oxalate and dithionite extracted iron, aluminum and manganese oxides and hydroxides using inductively coupled plasma optical emission spectroscopy (Varian Vista MPX ICP-OES, Agilent Technologies, USA). We predicted the above-mentioned soil properties for all nested samples using partial least squares regression, which was performed using R program. We can conclude that vis-NIR spectroscopy can be used effectively in order to describe, estimate and further map the spatial patterns of soil properties using geostatistical methods. This research could also help to improve the global soil spectral library taking into account that only few previous applications of vis-NIR spectroscopy were conducted on permafrost-affected soils of Northern Siberia. Keywords: Visible-near infrared spectroscopy, vis-NIR, permafrost-affected soils, Siberia, partial least squares regression.

76 FR 50133 - National Oil and Hazardous Substances Pollution Contingency Plan; National Priorities List...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-12

... (NCDOH) collected soil samples from the Site. Analyses of the samples indicated that the soils were... Metcalf and Eddy, Inc. for Commander in 1990. During the RI subsurface soil samples, ground water samples and surface soil samples were collected and analyzed. As part of the ground water investigation...

Contribution of uranium to gross alpha radioactivity in some environmental samples in Kuwait

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bou-Rabee, F.; Bakir, Y.; Bem, H.

1995-08-01

This study was done in connection with the use of uranium-tipped antitank shells during the Gulf War and possible contamination of the environment of Kuwait. It was found that uranium concentrations in the soil samples ranged from 0.3 {mu}g/g to 1.85 {mu}g/g. The average value of 0.7 {mu}g/g was lower than the world average value of 2.1 {mu}g/g for surface soils. Its contribution to the total natural alpha radioactivity (excluding Rn and its short-lived daughters) varied from 1.1% to 14%. The solid fall-out samples showed higher uranium concentration which varied from 0.35 {mu}g/g to 1.73 {mu}/g (average 1.47 {mu}g/g) butmore » its contribution to the gross alpha radioactivity was in the same range, from 1.1 to 13.2%. The difference in the concentration of uranium in suspended air matter samples during the summer of 1993 and the winter of 1994 was found to be 2.0 {mu}g/g and 1.0 {mu}g/g, respectively. The uranium contribution to the natural alpha radioactivity in these samples was in the same range but lower for the winter period. The isotopic ratio of {sup 235}U to {sup 238}U for the measured samples was basically within an experimental error of {+-}0.001, close to the theoretical value of 0.007. The calculated total annual intake of uranium via inhalation for the Kuwait population was 0.07 Bq, e.g., 0.2% of the annual limit on intake. 13 refs., 1 fig., 3 tabs.« less

Assessing NIR & MIR Spectral Analysis as a Method for Soil C Estimation Across a Network of Sampling Sites

NASA Astrophysics Data System (ADS)

Spencer, S.; Ogle, S.; Borch, T.; Rock, B.

2008-12-01

Monitoring soil C stocks is critical to assess the impact of future climate and land use change on carbon sinks and sources in agricultural lands. A benchmark network for soil carbon monitoring of stock changes is being designed for US agricultural lands with 3000-5000 sites anticipated and re-sampling on a 5- to10-year basis. Approximately 1000 sites would be sampled per year producing around 15,000 soil samples to be processed for total, organic, and inorganic carbon, as well as bulk density and nitrogen. Laboratory processing of soil samples is cost and time intensive, therefore we are testing the efficacy of using near-infrared (NIR) and mid-infrared (MIR) spectral methods for estimating soil carbon. As part of an initial implementation of national soil carbon monitoring, we collected over 1800 soil samples from 45 cropland sites in the mid-continental region of the U.S. Samples were processed using standard laboratory methods to determine the variables above. Carbon and nitrogen were determined by dry combustion and inorganic carbon was estimated with an acid-pressure test. 600 samples are being scanned using a bench- top NIR reflectance spectrometer (30 g of 2 mm oven-dried soil and 30 g of 8 mm air-dried soil) and 500 samples using a MIR Fourier-Transform Infrared Spectrometer (FTIR) with a DRIFT reflectance accessory (0.2 g oven-dried ground soil). Lab-measured carbon will be compared to spectrally-estimated carbon contents using Partial Least Squares (PLS) multivariate statistical approach. PLS attempts to develop a soil C predictive model that can then be used to estimate C in soil samples not lab-processed. The spectral analysis of soil samples either whole or partially processed can potentially save both funding resources and time to process samples. This is particularly relevant for the implementation of a national monitoring network for soil carbon. This poster will discuss our methods, initial results and potential for using NIR and MIR spectral approaches to either replace or augment traditional lab-based carbon analyses of soils.

Evaluation and simulation of nitrogen mineralization of paddy soils in Mollisols area of Northeast China under waterlogged incubation

PubMed Central

Zhang, Yuling; Xu, Wenjing; Duan, Pengpeng; Cong, Yaohui; An, Tingting; Yu, Na; Zou, Hongtao; Dang, Xiuli; An, Jing; Fan, Qingfeng; Zhang, Yulong

2017-01-01

Background Understanding the nitrogen (N) mineralization process and applying appropriate model simulation are key factors in evaluating N mineralization. However, there are few studies of the N mineralization characteristics of paddy soils in Mollisols area of Northeast China. Materials and methods The soils were sampled from the counties of Qingan and Huachuan, which were located in Mollisols area of Northeast China. The sample soil was incubated under waterlogged at 30°C in a controlled temperature cabinet for 161 days (a 2: 1 water: soil ratio was maintained during incubation). Three models, i.e. the single first-order kinetics model, the double first-order kinetics model and the mixed first-order and zero-order kinetics model were used to simulate the cumulative mineralised N (NH4+-N and TSN) in the laboratory and waterlogged incubation. Principal results During 161 days of waterlogged incubation, the average cumulative total soluble N (TSN), ammonium N (NH4+-N), and soluble organic N (SON) was 122.2 mg kg-1, 85.9 mg kg-1, and 36.3 mg kg-1, respectively. Cumulative NH4+-N was significantly (P < 0.05) positively correlated with organic carbon (OC), total N (TN), pH, and exchangeable calcium (Ca), and cumulative TSN was significantly (P < 0.05) positively correlated with OC, TN, and exchangeable Ca, but was not significantly (P > 0.05) correlated with C/N ratio, cation exchange capacity (CEC), extractable iron (Fe), clay, and sand. When the cumulative NH4+-N and TSN were simulated, the single first-order kinetics model provided the least accurate simulation. The parameter of the double first-order kinetics model also did not represent the actual data well, but the mixed first-order and zero-order kinetics model provided the most accurate simulation, as demonstrated by the estimated standard error, F statistic values, parameter accuracy, and fitting effect. Conclusions Overall, the results showed that SON was involved with N mineralization process, and the mixed first-order and zero-order kinetics model accurately simulates the N mineralization process of paddy soil in Mollisols area of Northeast China under waterlogged incubation. PMID:28170409

Evaluation and simulation of nitrogen mineralization of paddy soils in Mollisols area of Northeast China under waterlogged incubation.

PubMed

Zhang, Yuling; Xu, Wenjing; Duan, Pengpeng; Cong, Yaohui; An, Tingting; Yu, Na; Zou, Hongtao; Dang, Xiuli; An, Jing; Fan, Qingfeng; Zhang, Yulong

2017-01-01

Understanding the nitrogen (N) mineralization process and applying appropriate model simulation are key factors in evaluating N mineralization. However, there are few studies of the N mineralization characteristics of paddy soils in Mollisols area of Northeast China. The soils were sampled from the counties of Qingan and Huachuan, which were located in Mollisols area of Northeast China. The sample soil was incubated under waterlogged at 30°C in a controlled temperature cabinet for 161 days (a 2: 1 water: soil ratio was maintained during incubation). Three models, i.e. the single first-order kinetics model, the double first-order kinetics model and the mixed first-order and zero-order kinetics model were used to simulate the cumulative mineralised N (NH4+-N and TSN) in the laboratory and waterlogged incubation. During 161 days of waterlogged incubation, the average cumulative total soluble N (TSN), ammonium N (NH4+-N), and soluble organic N (SON) was 122.2 mg kg-1, 85.9 mg kg-1, and 36.3 mg kg-1, respectively. Cumulative NH4+-N was significantly (P < 0.05) positively correlated with organic carbon (OC), total N (TN), pH, and exchangeable calcium (Ca), and cumulative TSN was significantly (P < 0.05) positively correlated with OC, TN, and exchangeable Ca, but was not significantly (P > 0.05) correlated with C/N ratio, cation exchange capacity (CEC), extractable iron (Fe), clay, and sand. When the cumulative NH4+-N and TSN were simulated, the single first-order kinetics model provided the least accurate simulation. The parameter of the double first-order kinetics model also did not represent the actual data well, but the mixed first-order and zero-order kinetics model provided the most accurate simulation, as demonstrated by the estimated standard error, F statistic values, parameter accuracy, and fitting effect. Overall, the results showed that SON was involved with N mineralization process, and the mixed first-order and zero-order kinetics model accurately simulates the N mineralization process of paddy soil in Mollisols area of Northeast China under waterlogged incubation.

Linearised and non-linearised isotherm models optimization analysis by error functions and statistical means

PubMed Central

2014-01-01

In adsorption study, to describe sorption process and evaluation of best-fitting isotherm model is a key analysis to investigate the theoretical hypothesis. Hence, numerous statistically analysis have been extensively used to estimate validity of the experimental equilibrium adsorption values with the predicted equilibrium values. Several statistical error analysis were carried out. In the present study, the following statistical analysis were carried out to evaluate the adsorption isotherm model fitness, like the Pearson correlation, the coefficient of determination and the Chi-square test, have been used. The ANOVA test was carried out for evaluating significance of various error functions and also coefficient of dispersion were evaluated for linearised and non-linearised models. The adsorption of phenol onto natural soil (Local name Kalathur soil) was carried out, in batch mode at 30 ± 20 C. For estimating the isotherm parameters, to get a holistic view of the analysis the models were compared between linear and non-linear isotherm models. The result reveled that, among above mentioned error functions and statistical functions were designed to determine the best fitting isotherm. PMID:25018878

Global-scale assessment and combination of SMAP with ASCAT (Active) and AMSR2 (Passive) soil moisture products

USDA-ARS?s Scientific Manuscript database

Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and comb...

Comparative assessment of the methods for exchangeable acidity measuring

NASA Astrophysics Data System (ADS)

Vanchikova, E. V.; Shamrikova, E. V.; Bespyatykh, N. V.; Zaboeva, G. A.; Bobrova, Yu. I.; Kyz"yurova, E. V.; Grishchenko, N. V.

2016-05-01

A comparative assessment of the results of measuring the exchangeable acidity and its components by different methods was performed for the main mineral genetic horizons of texturally-differentiated gleyed and nongleyed soddy-podzolic and gley-podzolic soils of the Komi Republic. It was shown that the contents of all the components of exchangeable soil acidity determined by the Russian method (with potassium chloride solution as extractant, c(KCl) = 1 mol/dm3) were significantly higher than those obtained by the international method (with barium chloride solution as extractant, c(BaCl2) = 0.1 mol/dm3). The error of the estimate of the concentration of H+ ions extracted with barium chloride solution equaled 100%, and this allowed only qualitative description of this component of the soil acidity. In the case of the extraction with potassium chloride, the error of measurements was 50%. It was also shown that the use of potentiometric titration suggested by the Russian method overestimates the results of soil acidity measurement caused by the exchangeable metal ions (Al(III), Fe(III), and Mn(II)) in comparison with the atomic emission method.

Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Nearing, Grey S.; Crow, Wade T.; Thorp, Kelly R.; Moran, Mary S.; Reichle, Rolf H.; Gupta, Hoshin V.

2012-01-01

Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.

Intrinsic and induced isoproturon catabolic activity in dissimilar soils and soils under dissimilar land use.

PubMed

Reid, Brian J; Papanikolaou, Niki D; Wilcox, Ronah K

2005-02-01

The catabolic activity with respect to the systemic herbicide isoproturon was determined in soil samples by (14)C-radiorespirometry. The first experiment assessed levels of intrinsic catabolic activity in soil samples that represented three dissimilar soil series under arable cultivation. Results showed average extents of isoproturon mineralisation (after 240 h assay time) in the three soil series to be low. A second experiment assessed the impact of addition of isoproturon (0.05 microg kg(-1)) into these soils on the levels of catabolic activity following 28 days of incubation. Increased catabolic activity was observed in all three soils. A third experiment assessed levels of intrinsic catabolic activity in soil samples representing a single soil series managed under either conventional agricultural practice (including the use of isoproturon) or organic farming practice (with no use of isoproturon). Results showed higher (and more consistent) levels of isoproturon mineralisation in the soil samples collected from conventional land use. The final experiment assessed the impact of isoproturon addition on the levels of inducible catabolic activity in these soils. The results showed no significant difference in the case of the conventional farm soil samples while the induction of catabolic activity in the organic farm soil samples was significant.

Method for ultra-trace cesium isotope ratio measurements from environmental samples using thermal ionization mass spectrometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Snow, Mathew S.; Snyder, Darin C.; Mann, Nick R.

2015-05-01

135Cs/ 137Cs isotope ratios can provide the age, origin and history of environmental Cs contamination. Relatively high precision 135Cs/ 137Cs isotope ratio measurements from samples containing femtogram quantities of 137Cs are needed to accurately track contamination resuspension and redistribution following environmental 137Cs releases; however, mass spectrometric analyses of environmental samples are limited by the large quantities of ionization inhibitors and isobaric interferences which are present at relatively high concentrations in the environment. We report a new approach for Cs purification from environmental samples. An initial ammonium molybdophosphate-polyacrylonitrile (AMP-PAN) column provides a robust method for extracting Cs under a wide varietymore » of sample matrices and mass loads. Cation exchange separations using a second AMP-PAN column result in more than two orders of magnitude greater Cs/Rb separation factors than commercially available strong cation exchangers. Coupling an AMP-PAN cation exchanging step to a microcation column (AG50W resin) enables consistent 2-4% (2σ) measurement errors for samples containing 3-6,000 fg 137Cs, representing the highest precision 135Cs/ 137Cs ratio measurements currently reported for soil samples at the femtogram level.« less

Estimation of Soil Moisture Content from the Spectral Reflectance of Bare Soils in the 0.4–2.5 μm Domain

PubMed Central

Fabre, Sophie; Briottet, Xavier; Lesaignoux, Audrey

2015-01-01

This work aims to compare the performance of new methods to estimate the Soil Moisture Content (SMC) of bare soils from their spectral signatures in the reflective domain (0.4–2.5 μm) in comparison with widely used spectral indices like Normalized Soil Moisture Index (NSMI) and Water Index SOIL (WISOIL). Indeed, these reference spectral indices use wavelengths located in the water vapour absorption bands and their performance are thus very sensitive to the quality of the atmospheric compensation. To reduce these limitations, two new spectral indices are proposed which wavelengths are defined using the determination matrix tool by taking into account the atmospheric transmission: Normalized Index of Nswir domain for Smc estimatiOn from Linear correlation (NINSOL) and Normalized Index of Nswir domain for Smc estimatiOn from Non linear correlation (NINSON). These spectral indices are completed by two new methods based on the global shape of the soil spectral signatures. These methods are the Inverse Soil semi-Empirical Reflectance model (ISER), using the inversion of an existing empirical soil model simulating the soil spectral reflectance according to soil moisture content for a given soil class, and the convex envelope model, linking the area between the envelope and the spectral signature to the SMC. All these methods are compared using a reference database built with 32 soil samples and composed of 190 spectral signatures with five or six soil moisture contents. Half of the database is used for the calibration stage and the remaining to evaluate the performance of the SMC estimation methods. The results show that the four new methods lead to similar or better performance than the one obtained by the reference indices. The RMSE is ranging from 3.8% to 6.2% and the coefficient of determination R2 varies between 0.74 and 0.91 with the best performance obtained with the ISER model. In a second step, simulated spectral radiances at the sensor level are used to analyse the sensitivity of these methods to the sensor spectral resolution and the water vapour content knowledge. The spectral signatures of the database are then used to simulate the signal at the top of atmosphere with a radiative transfer model and to compute the integrated incident signal representing the spectral radiance measurements of the HYMAP airborne hyperspectral instrument. The sensor radiances are then corrected from the atmosphere by an atmospheric compensation tool to retrieve the surface reflectances. The SMC estimation methods are then applied on the retrieve spectral reflectances. The adaptation of the spectral index wavelengths to the HyMap sensor spectral bands and the application of the convex envelope and ISER models to boarder spectral bands lead to an error on the SMC estimation. The best performance is then obtained with the ISER model (RMSE of 2.9% and R2 of 0.96) while the four other methods lead to quite similar RMSE (from 6.4% to 7.8%) and R2 (between 0.79 and 0.83) values. In the atmosphere compensation processing, an error on the water vapour content is introduced. The most robust methods to water vapour content variations are WISOIL, NINSON, NINSOL and ISER model. The convex envelope model and NSMI index require an accurate estimation of the water vapour content in the atmosphere. PMID:25648710

Soil moisture variability across different scales in an Indian watershed for satellite soil moisture product validation

NASA Astrophysics Data System (ADS)

Singh, Gurjeet; Panda, Rabindra K.; Mohanty, Binayak P.; Jana, Raghavendra B.

2016-05-01

Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate remotely sensed quantities such as NASA's Soil Moisture Active Passive (SMAP) product. In the present study, in-situ soil moisture data were collected at two nested scale extents (0.5 km and 3 km) to understand the trend of soil moisture variability across these scales. This ground-based soil moisture sampling was conducted in the 500 km2 Rana watershed situated in eastern India. The study area is characterized as sub-humid, sub-tropical climate with average annual rainfall of about 1456 mm. Three 3x3 km square grids were sampled intensively once a day at 49 locations each, at a spacing of 0.5 km. These intensive sampling locations were selected on the basis of different topography, soil properties and vegetation characteristics. In addition, measurements were also made at 9 locations around each intensive sampling grid at 3 km spacing to cover a 9x9 km square grid. Intensive fine scale soil moisture sampling as well as coarser scale samplings were made using both impedance probes and gravimetric analyses in the study watershed. The ground-based soil moisture samplings were conducted during the day, concurrent with the SMAP descending overpass. Analysis of soil moisture spatial variability in terms of areal mean soil moisture and the statistics of higher-order moments, i.e., the standard deviation, and the coefficient of variation are presented. Results showed that the standard deviation and coefficient of variation of measured soil moisture decreased with extent scale by increasing mean soil moisture.

The use of Vacutainer tubes for collection of soil samples for helium analysis

USGS Publications Warehouse

Hinkle, Margaret E.; Kilburn, James E.

1979-01-01

Measurements of the helium concentration of soil samples collected and stored in Vacutainer-brand evacuated glass tubes show that Vacutainers are reliable containers for soil collection. Within the limits of reproducibility, helium content of soils appears to be independent of variations in soil temperature, barometric pressure, and quantity of soil moisture present in the sample.

Perturbations in the initial soil moisture conditions: Impacts on hydrologic simulation in a large river basin

NASA Astrophysics Data System (ADS)

Niroula, Sundar; Halder, Subhadeep; Ghosh, Subimal

2018-06-01

Real time hydrologic forecasting requires near accurate initial condition of soil moisture; however, continuous monitoring of soil moisture is not operational in many regions, such as, in Ganga basin, extended in Nepal, India and Bangladesh. Here, we examine the impacts of perturbation/error in the initial soil moisture conditions on simulated soil moisture and streamflow in Ganga basin and its propagation, during the summer monsoon season (June to September). This provides information regarding the required minimum duration of model simulation for attaining the model stability. We use the Variable Infiltration Capacity model for hydrological simulations after validation. Multiple hydrologic simulations are performed, each of 21 days, initialized on every 5th day of the monsoon season for deficit, surplus and normal monsoon years. Each of these simulations is performed with the initial soil moisture condition obtained from long term runs along with positive and negative perturbations. The time required for the convergence of initial errors is obtained for all the cases. We find a quick convergence for the year with high rainfall as well as for the wet spells within a season. We further find high spatial variations in the time required for convergence; the region with high precipitation such as Lower Ganga basin attains convergence at a faster rate. Furthermore, deeper soil layers need more time for convergence. Our analysis is the first attempt on understanding the sensitivity of hydrological simulations of Ganga basin on initial soil moisture conditions. The results obtained here may be useful in understanding the spin-up requirements for operational hydrologic forecasts.

The Soil Sink for Nitrous Oxide: Trivial Amount but Challenging Question

NASA Astrophysics Data System (ADS)

Davidson, E. A.; Savage, K. E.; Sihi, D.

2015-12-01

Net uptake of atmospheric nitrous oxide (N2O) has been observed sporadically for many years. Such observations have often been discounted as measurement error or noise, but they were reported frequently enough to gain some acceptance as valid. The advent of fast response field instruments with good sensitivity and precision has permitted confirmation that some soils can be small sinks of N2O. With regards to "closing the global N2O budget" the soil sink is trivial, because it is smaller than the error terms of most other budget components. Although not important from a global budget perspective, the existence of a soil sink for atmospheric N2O presents a fascinating challenge for understanding the physical, chemical, and biological processes that explain the sink. Reduction of N2O by classical biological denitrification requires reducing conditions generally found in wet soil, and yet we have measured the N2O sink in well drained soils, where we also simultaneously measure a sink for atmospheric methane (CH4). Co-occurrence of N2O reduction and CH4 oxidation would require a broad range of microsite conditions within the soil, spanning high and low oxygen concentrations. Abiotic sinks for N2O or other biological processes that consume N2O could exist, but have not yet been identified. We are attempting to simulate processes of diffusion of N2O, CH4, and O2 from the atmosphere and within a soil profile to determine if classical biological N2O reduction and CH4 oxidation at rates consistent with measured fluxes are plausible.

Integration of multi-sensor data to measure soil surface changes

NASA Astrophysics Data System (ADS)

Eltner, Anette; Schneider, Danilo

2016-04-01

Digital elevation models (DEM) of high resolution and accuracy covering a suitable sized area of interest can be a promising approach to help understanding the processes of soil erosion. Thereby, the plot under investigation should remain undisturbed. The fragile marl landscape in Andalusia (Spain) is especially prone to soil detachment and transport with unique sediment connectivity characteristics due to the soil properties and climatic conditions. A 600 m² field plot is established and monitored during three field campaigns (Sep. 2013, Nov. 2013 and Feb. 2014). Unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are suitable tools to generate high resolution topography data that describe soil surface changes at large field plots. Thereby, the advantages of both methods are utilised in a synergetic manner. On the one hand, TLS data is assumed to comprise a higher reliability regarding consistent error behaviour than DEMs derived from overlapping UAV images. Therefore, global errors (e.g. dome effect) and local errors (e.g. DEM blunders due to erroneous image matching) within the UAV data are assessed with the DEMs produced by TLS. Furthermore, TLS point clouds allow for fast and reliable filtering of vegetation spots, which is not as straightforward within the UAV data due to known image matching problems in areas displaying plant cover. On the other hand, systematic DEM errors linked to TLS are detected and possibly corrected utilising the DEMs reconstructed from overlapping UAV images. Furthermore, TLS point clouds are filtered corresponding to the degree of point quality, which is estimated from parameters of the scan geometry (i.e. incidence angle and footprint size). This is especially relevant for this study because the area of interest is located at gentle hillslopes that are prone to soil erosion. Thus, the view of the scanning device onto the surface results in an adverse angle, which is solely slightly improved by the usage of a 4 m high tripod. Surface roughness is considered as a further parameter to evaluate the TLS point quality. The filtering tool allows for choosing each data point either from the TLS or UAV data corresponding to the data acquisition geometry and surface properties. The filtered points are merged into one point cloud, which is finally processed to reduce remaining data noise. DEM analysis reveals a continuous decrease of soil surface roughness after tillage, the reappearance of former wheel tracks and local patterns of erosion as well as accumulation.

Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Miller, B. A.; Koszinski, S.; Wehrhan, M.; Sommer, M.

2015-03-01

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m-2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Miller, B. A.; Koszinski, S.; Wehrhan, M.; Sommer, M.

2014-11-01

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which are to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m-2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Partial least squares methods for spectrally estimating lunar soil FeO abundance: A stratified approach to revealing nonlinear effect and qualitative interpretation

NASA Astrophysics Data System (ADS)

Li, Lin

2008-12-01

Partial least squares (PLS) regressions were applied to lunar highland and mare soil data characterized by the Lunar Soil Characterization Consortium (LSCC) for spectral estimation of the abundance of lunar soil chemical constituents FeO and Al2O3. The LSCC data set was split into a number of subsets including the total highland, Apollo 16, Apollo 14, and total mare soils, and then PLS was applied to each to investigate the effect of nonlinearity on the performance of the PLS method. The weight-loading vectors resulting from PLS were analyzed to identify mineral species responsible for spectral estimation of the soil chemicals. The results from PLS modeling indicate that the PLS performance depends on the correlation of constituents of interest to their major mineral carriers, and the Apollo 16 soils are responsible for the large errors of FeO and Al2O3 estimates when the soils were modeled along with other types of soils. These large errors are primarily attributed to the degraded correlation FeO to pyroxene for the relatively mature Apollo 16 soils as a result of space weathering and secondary to the interference of olivine. PLS consistently yields very accurate fits to the two soil chemicals when applied to mare soils. Although Al2O3 has no spectrally diagnostic characteristics, this chemical can be predicted for all subset data by PLS modeling at high accuracies because of its correlation to FeO. This correlation is reflected in the symmetry of the PLS weight-loading vectors for FeO and Al2O3, which prove to be very useful for qualitative interpretation of the PLS results. However, this qualitative interpretation of PLS modeling cannot be achieved using principal component regression loading vectors.

Effects of soil water saturation on sampling equilibrium and kinetics of selected polycyclic aromatic hydrocarbons.

PubMed

Kim, Pil-Gon; Roh, Ji-Yeon; Hong, Yongseok; Kwon, Jung-Hwan

2017-10-01

Passive sampling can be applied for measuring the freely dissolved concentration of hydrophobic organic chemicals (HOCs) in soil pore water. When using passive samplers under field conditions, however, there are factors that might affect passive sampling equilibrium and kinetics, such as soil water saturation. To determine the effects of soil water saturation on passive sampling, the equilibrium and kinetics of passive sampling were evaluated by observing changes in the distribution coefficient between sampler and soil (K sampler/soil ) and the uptake rate constant (k u ) at various soil water saturations. Polydimethylsiloxane (PDMS) passive samplers were deployed into artificial soils spiked with seven selected polycyclic aromatic hydrocarbons (PAHs). In dry soil (0% water saturation), both K sampler/soil and k u values were much lower than those in wet soils likely due to the contribution of adsorption of PAHs onto soil mineral surfaces and the conformational changes in soil organic matter. For high molecular weight PAHs (chrysene, benzo[a]pyrene, and dibenzo[a,h]anthracene), both K sampler/soil and k u values increased with increasing soil water saturation, whereas they decreased with increasing soil water saturation for low molecular weight PAHs (phenanthrene, anthracene, fluoranthene, and pyrene). Changes in the sorption capacity of soil organic matter with soil water content would be the main cause of the changes in passive sampling equilibrium. Henry's law constant could explain the different behaviors in uptake kinetics of the selected PAHs. The results of this study would be helpful when passive samplers are deployed under various soil water saturations. Copyright © 2017 Elsevier Ltd. All rights reserved.

A mathematical model for the transfer of soil solutes to runoff under water scouring.

PubMed

Yang, Ting; Wang, Quanjiu; Wu, Laosheng; Zhang, Pengyu; Zhao, Guangxu; Liu, Yanli

2016-11-01

The transfer of nutrients from soil to runoff often causes unexpected pollution in water bodies. In this study, a mathematical model that relates to the detachment of soil particles by water flow and the degree of mixing between overland flow and soil nutrients was proposed. The model assumes that the mixing depth is an integral of average water flow depth, and it was evaluated by experiments with three water inflow rates to bare soil surfaces and to surfaces with eight treatments of different stone coverages. The model predicted outflow rates were compared with the experimentally observed data to test the accuracy of the infiltration parameters obtained by curve fitting the models to the data. Further analysis showed that the comprehensive mixing coefficient (ke) was linearly correlated with Reynolds' number Re (R(2)>0.9), and this relationship was verified by comparing the simulated potassium concentration and cumulative mass with observed data, respectively. The best performance with the bias error analysis (Nash Sutcliffe coefficient of efficiency (NS), relative error (RE) and the coefficient of determination (R(2))) showed that the predicted data by the proposed model was in good agreement with the measured data. Thus the model can be used to guide soil-water and fertilization management to minimize nutrient runoff from cropland. Copyright © 2016 Elsevier B.V. All rights reserved.

An Inverse Method to Estimate the Root Water Uptake Source-Sink Term in Soil Water Transport Equation under the Effect of Superabsorbent Polymer

PubMed Central

Liao, Renkuan; Yang, Peiling; Wu, Wenyong; Ren, Shumei

2016-01-01

The widespread use of superabsorbent polymers (SAPs) in arid regions improves the efficiency of local land and water use. However, SAPs’ repeated absorption and release of water has periodic and unstable effects on both soil’s physical and chemical properties and on the growth of plant roots, which complicates modeling of water movement in SAP-treated soils. In this paper, we proposea model of soil water movement for SAP-treated soils. The residence time of SAP in the soil and the duration of the experiment were considered as the same parameter t. This simplifies previously proposed models in which the residence time of SAP in the soil and the experiment’s duration were considered as two independent parameters. Numerical testing was carried out on the inverse method of estimating the source/sink term of root water uptake in the model of soil water movement under the effect of SAP. The test results show that time interval, hydraulic parameters, test error, and instrument precision had a significant influence on the stability of the inverse method, while time step, layering of soil, and boundary conditions had relatively smaller effects. A comprehensive analysis of the method’s stability, calculation, and accuracy suggests that the proposed inverse method applies if the following conditions are satisfied: the time interval is between 5 d and 17 d; the time step is between 1000 and 10000; the test error is ≥ 0.9; the instrument precision is ≤ 0.03; and the rate of soil surface evaporation is ≤ 0.6 mm/d. PMID:27505000

Near infrared spectroscopy to estimate the temperature reached on burned soils: strategies to develop robust models.

NASA Astrophysics Data System (ADS)

Guerrero, César; Pedrosa, Elisabete T.; Pérez-Bejarano, Andrea; Keizer, Jan Jacob

2014-05-01

The temperature reached on soils is an important parameter needed to describe the wildfire effects. However, the methods for measure the temperature reached on burned soils have been poorly developed. Recently, the use of the near-infrared (NIR) spectroscopy has been pointed as a valuable tool for this purpose. The NIR spectrum of a soil sample contains information of the organic matter (quantity and quality), clay (quantity and quality), minerals (such as carbonates and iron oxides) and water contents. Some of these components are modified by the heat, and each temperature causes a group of changes, leaving a typical fingerprint on the NIR spectrum. This technique needs the use of a model (or calibration) where the changes in the NIR spectra are related with the temperature reached. For the development of the model, several aliquots are heated at known temperatures, and used as standards in the calibration set. This model offers the possibility to make estimations of the temperature reached on a burned sample from its NIR spectrum. However, the estimation of the temperature reached using NIR spectroscopy is due to changes in several components, and cannot be attributed to changes in a unique soil component. Thus, we can estimate the temperature reached by the interaction between temperature and the thermo-sensible soil components. In addition, we cannot expect the uniform distribution of these components, even at small scale. Consequently, the proportion of these soil components can vary spatially across the site. This variation will be present in the samples used to construct the model and also in the samples affected by the wildfire. Therefore, the strategies followed to develop robust models should be focused to manage this expected variation. In this work we compared the prediction accuracy of models constructed with different approaches. These approaches were designed to provide insights about how to distribute the efforts needed for the development of robust models, since this step is the bottle-neck of this technique. In the first approach, a plot-scale model was used to predict the temperature reached in samples collected in other plots from the same site. In a plot-scale model, all the heated aliquots come from a unique plot-scale sample. As expected, the results obtained with this approach were deceptive, because this approach was assuming that a plot-scale model would be enough to represent the whole variability of the site. The accuracy (measured as the root mean square error of prediction, thereinafter RMSEP) was 86ºC, and the bias was also high (>30ºC). In the second approach, the temperatures predicted through several plot-scale models were averaged. The accuracy was improved (RMSEP=65ºC) respect the first approach, because the variability from several plots was considered and biased predictions were partially counterbalanced. However, this approach implies more efforts, since several plot-scale models are needed. In the third approach, the predictions were obtained with site-scale models. These models were constructed with aliquots from several plots. In this case, the results were accurate, since the RMSEP was around 40ºC, the bias was very small (<1ºC) and the R2 was 0.92. As expected, this approach clearly outperformed the second approach, in spite of the fact that the same efforts were needed. In a plot-scale model, only one interaction between temperature and soil components was modelled. However, several different interactions between temperature and soil components were present in the calibration matrix of a site-scale model. Consequently, the site-scale models were able to model the temperature reached excluding the influence of the differences in soil composition, resulting in more robust models respect that variation. Summarizing, the results were highlighting the importance of an adequate strategy to develop robust and accurate models with moderate efforts, and how a wrong strategy can result in deceptive predictions.

Evaluation of Porcelain Cup Soil Water Samplers for Bacteriological Sampling1

PubMed Central

Dazzo, Frank B.; Rothwell, Donald F.

1974-01-01

The validity of obtaining soil water for fecal coliform analyses by porcelain cup soil water samplers was examined. Numbers from samples of manure slurry drawn through porcelain cups were reduced 100- to 10,000,000-fold compared to numbers obtained from the external manure slurry, and 65% of the cups yielded coliform-free samples. Fecal coliforms adsorbed to cups apparently were released, thus influencing the counts of subsequent samples. Fecal coliforms persisted in soil water samplers buried in soil and thus could significantly influence the coliform counts of water samples obtained a month later. These studies indicate that porcelain cup soil water samplers do not yield valid water samples for fecal coliform analyses. Images PMID:16349998

Viscoelastic Properties of Soil with Different Ammonium Nitrate Addition

NASA Astrophysics Data System (ADS)

Kawecka-Radomska, M.; Tomczyńska-Mleko, M.; Muszyńskic, S.; Wesołowska-Trojanowska, M.; Mleko, S.

2017-12-01

Four different soils samples were taken from not cultivated recreational places. Particle-size distribution and pH (in water and in 1 M KCl) of the soil samples were measured. Soil samples were saturated with deionized water and solution of ammonium nitrate with the concentration of 5, 50 or 500 mM for 3 days. The samples were analyzed using dynamic oscillatory rheometer by frequency and strain sweeps. Soil samples were similar to physical gels, as they presented rheological properties between those of a concentrated biopolymer and a true gel. 50 mM concentration of the salt was enough to make changes in the elasticity of the soils. Small concentration of the fertilizer caused weakening of the soil samples structure. Higher concentration of ammonium nitrate caused the increase in the moduli crossover strain value. For the loam sample taken from a playground, with the highest content of the particles <0.002 mm (clay aluminosilicates), the lowest value of strain was observed at the moduli intersection. Lower strain value was necessary for the sliding shear effect of soil A sample effecting transgression to the "flowing" state. Strain sweep moduli crossover point can be used as a determinant of the rheological properties of soil.

Comparison of different tree sap flow up-scaling procedures using Monte-Carlo simulations

NASA Astrophysics Data System (ADS)

Tatarinov, Fyodor; Preisler, Yakir; Roahtyn, Shani; Yakir, Dan

2015-04-01

An important task in determining forest ecosystem water balance is the estimation of stand transpiration, allowing separating evapotranspiration into transpiration and soil evaporation. This can be based on up-scaling measurements of sap flow in representative trees (SF), which can be done by different mathematical algorithms. The aim of the present study was to evaluate the error associated with different up-scaling algorithms under different conditions. Other types of errors (such as, measurement error, within tree SF variability, choice of sample plot etc.) were not considered here. A set of simulation experiments using Monte-Carlo technique was carried out and three up-scaling procedures were tested. (1) Multiplying mean stand sap flux density based on unit sapwood cross-section area (SFD) by total sapwood area (Klein et al, 2014); (2) deriving of linear dependence of tree sap flow on tree DBH and calculating SFstand using predicted SF by DBH classes and stand DBH distribution (Cermak et al., 2004); (3) same as method 2 but using non-linear dependency. Simulations were performed under different SFD(DBH) slope (bs, positive, negative, zero); different DBH and SFD standard deviations (Δd and Δs, respectively) and DBH class size. It was assumed that all trees in a unit area are measured and the total SF of all trees in the experimental plot was taken as the reference SFstand value. Under negative bs all models tend to overestimate SFstand and the error increases exponentially with decreasing bs. Under bs >0 all models tend to underestimate SFstand, but the error is much smaller than for bs

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2013-11-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model for flood predictions with lead times up to 10 days. For this study, satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF, are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2014-06-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

Natural radioactivity of riverbank sediments of the Maritza and Tundja Rivers in Turkey.

PubMed

Aytas, Sule; Yusan, Sabriye; Aslani, Mahmoud A A; Karali, Turgay; Turkozu, D Alkim; Gok, Cem; Erenturk, Sema; Gokce, Melis; Oguz, K Firat

2012-01-01

This article represents the first results of the natural radionuclides in the Maritza and Tundja river sediments, in the vicinity of Edirne city, Turkey. The aim of the article is to describe the natural radioactivity concentrations as a baseline for further studies and to obtain the distribution patterns of radioactivity in trans-boundary river sediments of the Maritza and Tundja, which are shared by Turkey, Bulgaria and Greece. Sediment samples were collected during the period of August 2007-April 2010. The riverbank sediment samples were analyzed firstly for their pH, organic matter content and soil texture. The gross alpha/beta and (238)U, (232)Th and (40)K activity concentrations were then investigated in the collected sediment samples. The mean and standard error of mean values of gross alpha and gross beta activity concentrations were found as 91 ± 11, 410 ± 69 Bq/kg and 86 ± 11, 583 ± 109 Bq/kg for the Maritza and Tundja river sediments, respectively. Moreover, the mean and standard error of mean values of (238)U, (232)Th and (40)K activity concentrations were determined as 219 ± 68, 128 ± 55, 298 ± 13 and as 186 ± 98, 121 ± 68, 222 ± 30 Bq/kg for the Maritza and Tundja River, respectively. Absorbed dose rates (D) and annual effective dose equivalent s have been calculated for each sampling point. The average value of adsorbed dose rate and effective dose equivalent were found as 191 and 169 nGy/h; 2 and 2 mSv/y for the Maritza and the Tundja river sediments, respectively.

Prediction of soil urea conversion and quantification of the importance degrees of influencing factors through a new combinatorial model based on cluster method and artificial neural network.

PubMed

Lei, Tao; Guo, Xianghong; Sun, Xihuan; Ma, Juanjuan; Zhang, Shaowen; Zhang, Yong

2018-05-01

Quantitative prediction of soil urea conversion is crucial in determining the mechanism of nitrogen transformation and understanding the dynamics of soil nutrients. This study aimed to establish a combinatorial prediction model (MCA-F-ANN) for soil urea conversion and quantify the relative importance degrees (RIDs) of influencing factors with the MCA-F-ANN method. Data samples were obtained from laboratory culture experiments, and soil nitrogen content and physicochemical properties were measured every other day. Results showed that when MCA-F-ANN was used, the mean-absolute-percent error values of NH 4 + -N, NO 3 - -N, and NH 3 contents were 3.180%, 2.756%, and 3.656%, respectively. MCA-F-ANN predicted urea transformation under multi-factor coupling conditions more accurately than traditional models did. The RIDs of reaction time (RT), electrical conductivity (EC), temperature (T), pH, nitrogen application rate (F), and moisture content (W) were 32.2%-36.5%, 24.0%-28.9%, 12.8%-15.2%, 9.8%-12.5%, 7.8%-11.0%, and 3.5%-6.0%, respectively. The RIDs of the influencing factors in a descending order showed the pattern RT > EC > T > pH > F > W. RT and EC were the key factors in the urea conversion process. The prediction accuracy of urea transformation process was improved, and the RIDs of the influencing factors were quantified. Copyright © 2018 Elsevier Ltd. All rights reserved.

Land use surveys by means of automatic interpretation of LANDSAT system data

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Lombardo, M. A.; Novo, E. M. L. D.; Niero, M.; Foresti, C.

1981-01-01

Analyses for seven land-use classes are presented. The classes are: urban area, industrial area, bare soil, cultivated area, pastureland, reforestation, and natural vegetation. The automatic classification of LANDSAT MSS data using a maximum likelihood algorithm shows a 39% average error of emission and a 3.45 error of commission for the seven classes.

Effects of Subsetting by Parent Materials on Prediction of Soil Organic Matter Content in a Hilly Area Using Vis–NIR Spectroscopy

PubMed Central

Xu, Shengxiang; Shi, Xuezheng; Wang, Meiyan; Zhao, Yongcun

2016-01-01

Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis–NIR) diffuse reflectance spectroscopy (350–2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis–NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km2 and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0–20 cm) samples were collected for SOM analysis and scanned with a Vis–NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23–4.69 g kg−1; R2 (coefficient of determination) = 0.80–0.84; RPD (ratio of standard deviation to RMSE) = 2.19–2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88–3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55–3.49 g kg−1; R2 = 0.87–0.93; RPD = 2.67–3.12; RPIQ = 3.15–4.02). Among the four different parent materials, the largest R2 and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis–NIR spectroscopy. PMID:26974821

Automated soil gas monitoring chamber

DOEpatents

Edwards, Nelson T.; Riggs, Jeffery S.

2003-07-29

A chamber for trapping soil gases as they evolve from the soil without disturbance to the soil and to the natural microclimate within the chamber has been invented. The chamber opens between measurements and therefore does not alter the metabolic processes that influence soil gas efflux rates. A multiple chamber system provides for repetitive multi-point sampling, undisturbed metabolic soil processes between sampling, and an essentially airtight sampling chamber operating at ambient pressure.

Development of internal forest soil reference samples and testing of digestion methods

Treesearch

J.E. Hislop; J.W. Hornbeck; S.W. Bailey; R.A. Hallett

1998-01-01

Our research requires determinations of total elemental concentrations of forest soils. The lack of certified forest soil reference materials led us to develop internal reference samples. Samples were collected from three soil horizons (Oa, B, and C) at three locations having forested, acidic soils similar to those we commonly analyze. A shatterbox was used to...

`VIS/NIR mapping of TOC and extent of organic soils in the Nørre Å valley

NASA Astrophysics Data System (ADS)

Knadel, M.; Greve, M. H.; Thomsen, A.

2009-04-01

Organic soils represent a substantial pool of carbon in Denmark. The need for carbon stock assessment calls for more rapid and effective mapping methods to be developed. The aim of this study was to compare traditional soil mapping with maps produced from the results of a mobile VIS/NIR system and to evaluate the ability to estimate TOC and map the area of organic soils. The Veris mobile VIS/NIR spectroscopy system was compared to traditional manual sampling. The system is developed for in-situ near surface measurements of soil carbon content. It measures diffuse reflectance in the 350 nm-2200 nm region. The system consists of two spectrophotometers mounted on a toolbar and pulled by a tractor. Optical measurements are made through a sapphire window at the bottom of the shank. The shank was pulled at a depth of 5-7 cm at a speed of 4-5 km/hr. 20-25 spectra per second with 8 nm resolution were acquired by the spectrometers. Measurements were made on 10-12 m spaced transects. The system also acquired soil electrical conductivity (EC) for two soil depths: shallow EC-SH (0- 31 cm) and deep conductivity EC-DP (0- 91 cm). The conductivity was recorded together with GPS coordinates and spectral data for further construction of the calibration models. Two maps of organic soils in the Nørre Å valley (Central Jutland) were generated: (i) based on a conventional 25 m grid with 162 sampling points and laboratory analysis of TOC, (ii) based on in-situ VIS/NIR measurements supported by chemometrics. Before regression analysis, spectral information was compressed by calculating principal components. The outliers were determined by a mahalanobis distance equation and removed. Clustering using a fuzzy c- means algorithm was conducted. Within each cluster a location with the minimal spatial variability was selected. A map of 15 representative sample locations was proposed. The interpolation of the spectra into a single spectrum was performed using a Gaussian kernel weighting function. Spectra obtained near a sampled location were averaged. The collected spectra were correlated to TOC of the 15 representative samples using multivariate regression techniques (Unscrambler 9.7; Camo ASA, Oslo, Norway). Two types of calibrations were performed: using only spectra and using spectra together with the auxiliary data (EC-SH and EC-DP). These calibration equations were computed using PLS regression, segmented cross-validation method on centred data (using the raw spectral data, log 1/R). Six different spectra pre-treatments were conducted: (1) only spectra, (2) Savitsky-Golay smoothing over 11 wavelength points and transformation to a (3) 1'st and (4) 2'nd Savitzky and Golay derivative algorithm with a derivative interval of 21 wavelength points, (5) with or (6) without smoothing. The best treatment was considered to be the one with the lowest Root Mean Square Error of Prediction (RMSEP), the highest r2 between the VIS/NIR-predicted and measured values in the calibration model and the lowest mean deviation of predicted TOC values. The best calibration model was obtained with the mathematical pre-treatment's including smoothing, calculating the 2'nd derivative and outlier removal. The two TOC maps were compared after interpolation using kriging. They showed a similar pattern in the TOC distribution. Despite the unfavourable field conditions the VIS/NIR system performed well in both low and high TOC areas. Water content in places exceeding field capacity in the lower parts of the investigated field did not seriously degrade measurements. The present study represents the first attempt to apply the mobile Veris VIS/NIR system to the mapping of TOC of peat soils in Denmark. The result from this study show that a mobile VIS/NIR system can be applied to cost effective TOC mapping of mineral and organic soils with highly varying water content. Key words: VIS/NIR spectroscopy, organic soils, TOC

Calculating sediment discharge from a highway construction site in central Pennsylvania

USGS Publications Warehouse

Reed, L.A.; Ward, J.R.; Wetzel, K.L.

1985-01-01

The Pennsylvania Department of Transportation, the Federal Highway Administration, and the U.S. Geological Survey have cooperated in a study to evaluate two methods of predicting sediment yields during highway construction. Sediment yields were calculated using the Universal Soil Loss and the Younkin Sediment Prediction Equations. Results were compared to the actual measured values, and standard errors and coefficients of correlation were calculated. Sediment discharge from the construction area was determined for storms that occurred during construction of Interstate 81 in a 0.38-square mile basin near Harrisburg, Pennsylvania. Precipitation data tabulated included total rainfall, maximum 30-minute rainfall, kinetic energy, and the erosive index of the precipitation. Highway construction data tabulated included the area disturbed by clearing and grubbing, the area in cuts and fills, the average depths of cuts and fills, the area seeded and mulched, and the area paved. Using the Universal Soil Loss Equation, sediment discharge from the construction area was calculated for storms. The standard error of estimate was 0.40 (about 105 percent), and the coefficient of correlation was 0.79. Sediment discharge from the construction area was also calculated using the Younkin Equation. The standard error of estimate of 0.42 (about 110 percent), and the coefficient of correlation of 0.77 are comparable to those from the Universal Soil Loss Equation.

76 FR 11334 - Safety Zone; Soil Sampling; Chicago River, Chicago, IL

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-02

...-AA00 Safety Zone; Soil Sampling; Chicago River, Chicago, IL AGENCY: Coast Guard, DHS. ACTION: Temporary... North Branch of the Chicago River due to soil sampling in this area. This temporary safety zone is... soil sampling efforts. DATES: This rule is effective from 7 a.m. on March 1, 2011, until 5 p.m. on...

How soil organic matter composition controls hexachlorobenzene-soil-interactions: adsorption isotherms and quantum chemical modeling.

PubMed

Ahmed, Ashour A; Kühn, Oliver; Aziz, Saadullah G; Hilal, Rifaat H; Leinweber, Peter

2014-04-01

Hazardous persistent organic pollutants (POPs) interact in soil with the soil organic matter (SOM) but this interaction is insufficiently understood at the molecular level. We investigated the adsorption of hexachlorobenzene (HCB) on soil samples with systematically modified SOM. These samples included the original soil, the soil modified by adding a hot water extract (HWE) fraction (soil+3 HWE and soil+6 HWE), and the pyrolyzed soil. The SOM contents increased in the order pyrolyzed soil

Quantitative Determination of Noa (Naturally Occurring Asbestos) in Rocks : Comparison Between Pcom and SEM Analysis

NASA Astrophysics Data System (ADS)

Baietto, Oliviero; Amodeo, Francesco; Giorgis, Ilaria; Vitaliti, Martina

2017-04-01

The quantification of NOA (Naturally Occurring Asbestos) in a rock or soil matrix is complex and subject to numerous errors. The purpose of this study is to compare two fundamental methodologies used for the analysis: the first one uses Phase Contrast Optical Microscope (PCOM) while the second one uses Scanning Electron Microscope (SEM). The two methods, although they provide the same result, which is the asbestos mass to total mass ratio, have completely different characteristics and both present pros and cons. The current legislation in Italy involves the use of SEM, DRX, FTIR, PCOM (DM 6/9/94) for the quantification of asbestos in bulk materials and soils and the threshold beyond which the material is considered as hazardous waste is a concentration of asbestos fiber of 1000 mg/kg.(DM 161/2012). The most used technology is the SEM which is the one among these with the better analytical sensitivity.(120mg/Kg DM 6 /9/94) The fundamental differences among the analyses are mainly: - Amount of analyzed sample portion - Representativeness of the sample - Resolution - Analytical precision - Uncertainty of the methodology - Operator errors Due to the problem of quantification of DRX and FTIR (1% DM 6/9/94) our Asbestos Laboratory (DIATI POLITO) since more than twenty years apply the PCOM methodology and in the last years the SEM methodology for quantification of asbestos content. The aim of our research is to compare the results obtained from a PCOM analysis with the results provided by SEM analysis on the base of more than 100 natural samples both from cores (tunnel-boring or explorative-drilling) and from tunnelling excavation . The results obtained show, in most cases, a good correlation between the two techniques. Of particular relevance is the fact that both techniques are reliable for very low quantities of asbestos, even lower than the analytical sensitivity. This work highlights the comparison between the two techniques emphasizing strengths and weaknesses of the two procedures and suggests how an integrated approach, together with the skills and experience of the operator may be the best way forward in order to obtain a constructive improvement of analysis techniques.

Synergistic Utilization of Microwave Satellite Data and GRACE-Total Water Storage Anomaly for Improving Available Water Capacity Prediction in Lower Mekong Basin

NASA Astrophysics Data System (ADS)

Gupta, M.; Bolten, J. D.; Lakshmi, V.

2015-12-01

The Mekong River is the longest river in Southeast Asia and the world's eighth largest in discharge with draining an area of 795,000 km² from the eastern watershed of the Tibetan Plateau to the Mekong Delta including three provinces of China, Myanmar, Lao PDR, Thailand, Cambodia and Viet Nam. This makes the life of people highly vulnerable to availability of the water resources as soil moisture is one of the major fundamental variables in global hydrological cycles. The day-to-day variability in soil moisture on field to global scales is an important quantity for early warning systems for events like flooding and drought. In addition to the extreme situations the accurate soil moisture retrieval are important for agricultural irrigation scheduling and water resource management. The present study proposes a method to determine the effective soil hydraulic parameters directly from information available for the soil moisture state from the recently launched SMAP (L-band) microwave remote sensing observations. Since the optimized parameters are based on the near surface soil moisture information, further constraints are applied during the numerical simulation through the assimilation of GRACE Total Water Storage (TWS) within the physically based land surface model. This work addresses the improvement of available water capacity as the soil hydraulic parameters are optimized through the utilization of satellite-retrieved near surface soil moisture. The initial ranges of soil hydraulic parameters are taken in correspondence with the values available from the literature based on FAO. The optimization process is divided into two steps: the state variable are optimized and the optimal parameter values are then transferred for retrieving soil moisture and streamflow. A homogeneous soil system is considered as the soil moisture from sensors such as AMSR-E/SMAP can only be retrieved for the top few centimeters of soil. To evaluate the performance of the system in helping improve simulation accuracy and whether they can be used to obtain soil moisture profiles at poorly gauged catchments the root mean square error (RMSE) and Mean Bias error (MBE) are used to measure the performance of the simulations.

Combination of a Fast Cleanup Procedure and a DR-CALUX® Bioassay for Dioxin Surveillance in Taiwanese Soils

PubMed Central

Lin, Ding-Yan; Lee, Yi-Pin; Li, Chiu-Ping; Chi, Kai-Hsien; Liang, Bo-Wei P.; Liu, Wen-Yao; Wang, Chih-Cheng; Lin, Susana; Chen, Ting-Chien; Yeh, Kuei-Jyum C.; Hsu, Ping-Chi; Hsu, Yi-Chyun; Chao, How-Ran; Tsou, Tsui-Chun

2014-01-01

Our goal was to determine dioxin levels in 800 soil samples collected from Taiwan. An in vitro DR-CALUX® assay was carried out with the help of an automated Soxhlet system and fast cleanup column. The mean dioxin level of 800 soil samples was 36.0 pg-bioanalytical equivalents (BEQs)/g dry weight (d.w.). Soil dioxin-BEQs were higher in northern Taiwan (61.8 pg-BEQ/g d.w.) than in central, southern, and eastern Taiwan (22.2, 24.9, and 7.80 pg-BEQ/g d.w., respectively). Analysis of multiple linear regression models identified four major predictors of dioxin-BEQs including soil sampling location (β = 0.097, p < 0.001), land use (β = 0.065, p < 0.001), soil brightness (β = 0.170, p < 0.001), and soil moisture (β = 0.051, p = 0.020), with adjusted R2 = 0.947 (p < 0.001) (n = 662). An univariate logistic regression analysis with the cut-off point of 33.4 pg-BEQ/g d.w. showed significant odds ratios (ORs) for soil sampling location (OR = 2.43, p < 0.001), land use (OR = 1.47, p < 0.001), and soil brightness (OR = 2.83, p = 0.009). In conclusion, four variables, including soil sampling location, land use, soil brightness, and soil moisture, may be related to soil-dioxin contamination. Soil samples collected in northern Taiwan, and especially in Bade City, soils near industrial areas, and soils with darker color may contain higher dioxin-BEQ levels. PMID:24806195

Identification and correction of spectral contamination in 2H/1H and 18O/16O measured in leaf, stem, and soil water.

PubMed

Schultz, Natalie M; Griffis, Timothy J; Lee, Xuhui; Baker, John M

2011-11-15

Plant water extracts typically contain organic materials that may cause spectral interference when using isotope ratio infrared spectroscopy (IRIS), resulting in errors in the measured isotope ratios. Manufacturers of IRIS instruments have developed post-processing software to identify the degree of contamination in water samples, and potentially correct the isotope ratios of water with known contaminants. Here, the correction method proposed by an IRIS manufacturer, Los Gatos Research, Inc., was employed and the results were compared with those obtained from isotope ratio mass spectrometry (IRMS). Deionized water was spiked with methanol and ethanol to create correction curves for δ(18)O and δ(2)H. The contamination effects of different sample types (leaf, stem, soil) and different species from agricultural fields, grasslands, and forests were compared. The average corrections in leaf samples ranged from 0.35 to 15.73‰ for δ(2)H and 0.28 to 9.27‰ for δ(18)O. The average corrections in stem samples ranged from 1.17 to 13.70‰ for δ(2)H and 0.47 to 7.97‰ for δ(18)O. There was no contamination observed in soil water. Cleaning plant samples with activated charcoal had minimal effects on the degree of spectral contamination, reducing the corrections, by on average, 0.44‰ for δ(2)H and 0.25‰ for δ(18)O. The correction method eliminated the discrepancies between IRMS and IRIS for δ(18)O, and greatly reduced the discrepancies for δ(2)H. The mean differences in isotope ratios between IRMS and the corrected IRIS method were 0.18‰ for δ(18)O, and -3.39‰ for δ(2)H. The inability to create an ethanol correction curve for δ(2)H probably caused the larger discrepancies. We conclude that ethanol and methanol are the primary compounds causing interference in IRIS analyzers, and that each individual analyzer will probably require customized correction curves. Copyright © 2011 John Wiley & Sons, Ltd.

Detection of Toxoplasma gondii oocysts in soils in northwestern China using a new semi-nested PCR assay.

PubMed

Wang, Meng; Meng, Peng; Ye, Qiang; Pu, Yuan-Hua; Yang, Xiao-Yu; Luo, Jian-Xun; Zhang, Nian-Zhang; Zhang, De-Lin

2014-09-28

Toxoplasma gondii is a zoonotic pathogen that can infect a range of animals and humans. Ingestion of T. gondii oocysts in soil is a significant transmission route for humans and animals acquiring toxoplasmosis. In the present study, we developed a new semi-nested PCR method to determine T. gondii oocysts distribution in soils in northwestern China. The one tube semi-nested PCR assay was developed to detect the oocysts of T. gondii in soil, targeting the repetitive 529 bp fragment of T. gondii genomic DNA. Then a total of 268 soil samples, including 148 samples from Gansu Province and 120 samples from Qinghai Province, northwestern China, were examined by the semi-nested PCR method. One third of the positive samples were sequenced. The sensitivity of the semi-nested PCR assay was 10(2)  T. gondii oocysts in 5 g soil sample. Investigation of soil samples from northwestern China showed that 34 out of 268 soil samples (12.69%) were T. gondii positive. Sequences of the partial 529 bp fragments varied from 0-1.2% among the sequenced samples. The prevalence of T. gondii oocysts in soil from cities (24/163) was slightly higher than that in soils from pasturing areas (10/105) (P = 0.21). Among the different regions in cities, the prevalence of T. gondii oocysts in soils from parks was 14.15%, whereas that in soils from schools was 19.05%. The present study firstly reported the prevalence of T. gondii oocysts in soils in northwest China using a novel semi-nested PCR assay, which provided baseline data for the effective prevention and control of toxoplasmosis in this region.

Changes in the enzymatic activity of soil samples upon their storage

NASA Astrophysics Data System (ADS)

Dadenko, E. V.; Kazeev, K. Sh.; Kolesnikov, S. I.; Val'Kov, V. F.

2009-12-01

The influence of the duration and conditions of storage of soil samples on the activity of soil enzymes (catalase, β-fructofuranosidase, and dehydrogenase) was studied for the main soils of southern Russia (different subtypes of chernozems, chestnut soils, brown forest soils, gray forest soils, solonetzes, and solonchaks). The following soil storage conditions were tested: (1) the air-dry state at room temperature, (2) the airdry state at a low positive (in a refrigerator, +4°C) temperature, (3) naturally moist samples at a low positive temperature, and (4) naturally moist samples at a negative (in a freezer, -5°C) temperature. It was found that the sample storing caused significant changes in the enzymatic activities, which depended on the soil type, the land use, the type of enzyme, and the duration and conditions of the sample storage. In the course of the storage, the changes in the enzymatic activity had a nonlinear character. The maximum changes were observed in the initial period (up to 12 weeks). Then, a very gradual decrease in the activity of the studied enzymes was observed. Upon the long-term (>12 weeks) storage under the different conditions, the difference in the activities of the soil enzymes became less pronounced. The storage of soil samples in the air-dried state at room temperature can be recommended for mass investigations.

Using soil test results to determine fertilizer applications

Treesearch

C. B. Davey

2002-01-01

Using soil test results is a very useful practice IF the sample(s) of soil are good representations of the nursery soil. The lab results can be no more accurate than the samples submitted, and IF you know the texture of the nursery soil, and IF you know which soil extractant was used by the lab, and IF you know what crop is to be grown, and IF, for trees, which species...

Organochlorine insecticide residues in soil and earthworms in the Delhi area, India, August-October 1974

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yadav, D.V.; Mittal, P.K.; Agarwal, H.C.

1981-09-01

DDT residues in soil and earthworms from 50 sites in Delhi were monitored. DDT was detected in all but two samples each of soil and earthworms. Among DDT residues, p,p'-DDE was most common and was found in 48 samples each of soil and earthworms; p,p'-DDT was detected in only 43 soil samples and 46 earthworm samples. p,p'-TDE and o,p'-DDT were also present in smaller concentrations in 29 and 15 soil samples and in 43 and 25 earthworm samples, respectively. Maximum total DDT concentration of 2.6 ppm was detected in the soil from Durga Nagar in the vicinity of a DDTmore » factory. The highest concentration of 37.7 ppm total DDT in earthworms was also obtained from the same site. The maximum concentration factor found in the earthworms was 551. The total DDT concentration in the earthworms and soil showed significant correlation.« less

Major and trace element geochemistry and background concentrations for soils in Connecticut

USGS Publications Warehouse

Brown, Craig; Thomas, Margaret A.

2014-01-01

Soil samples were collected throughout Connecticut (CT) to determine the relationship of soil chemistry with the underlying geology and to better understand background concentrations of major and trace elements in soils. Soil samples were collected (1) from the upper 5 cm of surficial soil at 100 sites, (2) from the A horizon at 86 of these sites, and (3) from the deeper horizon, typically the C horizon, at 79 of these sites. The <2-millimeter fraction of each sample was analyzed for 44 elements by methods that yield the total or near-total elemental content. Sample sites were characterized by glacial setting, underlying bedrock geology, and soil type. These spatial data were used with element concentrations in the C-horizon to relate geologic factors to soil chemistry. Concentrations of elements in C-horizon soils varied with grain size in surficial glacial materials and with underlying rock types, as determined using nonparametric statistical procedures. Concentrations of most elements in C-horizon soils showed a positive correlation with silt and (or) clay content and were higher in surficial materials mapped as till, thick till, and (or) fines. Element concentrations in C-horizon soils showed significant differences among the underlying geologic provinces and were highest overlying the Grenville Belt and (or) the Grenville Shelf Sequence Provinces in western CT. These rocks consist mainly of carbonates and the relatively high element concentrations in overlying soils likely result from less influence of dilution by quartz compared to other provinces. Element concentrations in C-horizon soils in CT were compared with those in samples from other New England states overlying similar lithologic bedrock types. The upper range of As concentrations in C-horizon soils overlying the New Hampshire-Maine (NH-ME) Sequence in CT was 15 mg/kg, lower than the upper range of 24 mg/kg in C-horizon soils overlying the same sequence in ME. In CT, U concentration means were significantly higher in C-horizon soils overlying Avalonian granites, and U concentrations ranged as high as 14 mg/kg, compared to those in C-horizon soil samples collected from other New England states, which ranged as high as 6.1 mg/kg in a sample in NH overlying the NH-ME Sequence. Element concentrations in C-horizon soils in CT were compared with those in samples collected from shallower depths. Concentrations of most major elements were highest in C-horizon soil samples, including Al, Ca, Fe, K, Na, and Ti, but element concentrations showed a relatively similar pattern in A-horizon and surficial soil samples among the underlying geologic provinces. Trace element concentrations, including Ba, W, Ga, Ni, Cs, Rb, Sr, Th, Sc, and U, also were higher in C-horizon soil samples than in overlying soil samples. Concentrations of Mg, and several trace elements, including Mn, P, As, Nb, Sn, Be, Bi, Hg, Se, Sb, La, Co, Cr, Pb, V, Y, Cu, Pb, and Zn were highest in some A-horizon or surficial soils, and indicate possible contributions from anthropogenic sources. Because element concentrations in soils above the C horizon are more likely to be affected by anthropogenic factors, concentration ranges in C-horizon soils and their spatially varying geologic associations should be considered when estimating background concentrations of elements in CT soils.

A soil sampling intercomparison exercise for the ALMERA network.

PubMed

Belli, Maria; de Zorzi, Paolo; Sansone, Umberto; Shakhashiro, Abduhlghani; Gondin da Fonseca, Adelaide; Trinkl, Alexander; Benesch, Thomas

2009-11-01

Soil sampling and analysis for radionuclides after an accidental or routine release is a key factor for the dose calculation to members of the public, and for the establishment of possible countermeasures. The IAEA organized for selected laboratories of the ALMERA (Analytical Laboratories for the Measurement of Environmental Radioactivity) network a Soil Sampling Intercomparison Exercise (IAEA/SIE/01) with the objective of comparing soil sampling procedures used by different laboratories. The ALMERA network is a world-wide network of analytical laboratories located in IAEA member states capable of providing reliable and timely analysis of environmental samples in the event of an accidental or intentional release of radioactivity. Ten ALMERA laboratories were selected to participate in the sampling exercise. The soil sampling intercomparison exercise took place in November 2005 in an agricultural area qualified as a "reference site", aimed at assessing the uncertainties associated with soil sampling in agricultural, semi-natural, urban and contaminated environments and suitable for performing sampling intercomparison. In this paper, the laboratories sampling performance were evaluated.

High resolution analysis of soil elements with laser-induced breakdown

DOEpatents

Ebinger, Michael H [Santa Fe, NM; Harris, Ronny D [Los Alamos, NM

2010-04-06

The invention is a system and method of detecting a concentration of an element in a soil sample wherein an opening or slot is formed in a container that supports a soil sample that was extracted from the ground whereupon at least a length of the soil sample is exposed via the opening. At each of a plurality of points along the exposed length thereof, the soil sample is ablated whereupon a plasma is formed that emits light characteristic of the elemental composition of the ablated soil sample. Each instance of emitted light is separated according to its wavelength and for at least one of the wavelengths a corresponding data value related to the intensity of the light is determined. As a function of each data value a concentration of an element at the corresponding point along the length of the soil core sample is determined.

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI AMSR-E, SSM/I, AMSU-B and the TRMM PR

NASA Technical Reports Server (NTRS)

Fisher, Brad; Wolff, David B.

2010-01-01

Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.

Prevalence and types of preanalytical error in hematology laboratory of a tertiary care hospital in South India.

PubMed

Arul, Pitchaikaran; Pushparaj, Magesh; Pandian, Kanmani; Chennimalai, Lingasamy; Rajendran, Karthika; Selvaraj, Eniya; Masilamani, Suresh

2018-01-01

An important component of laboratory medicine is preanalytical phase. Since laboratory report plays a major role in patient management, more importance should be given to the quality of laboratory tests. The present study was undertaken to find the prevalence and types of preanalytical errors at a tertiary care hospital in South India. In this cross-sectional study, a total of 118,732 samples ([62,474 outpatient department [OPD] and 56,258 inpatient department [IPD]) were received in hematology laboratory. These samples were analyzed for preanalytical errors such as misidentification, incorrect vials, inadequate samples, clotted samples, diluted samples, and hemolyzed samples. The overall prevalence of preanalytical errors found was 513 samples, which is 0.43% of the total number of samples received. The most common preanalytical error observed was inadequate samples followed by clotted samples. Overall frequencies (both OPD and IPD) of preanalytical errors such as misidentification, incorrect vials, inadequate samples, clotted samples, diluted samples, and hemolyzed samples were 0.02%, 0.05%, 0.2%, 0.12%, 0.02%, and 0.03%, respectively. The present study concluded that incorrect phlebotomy techniques due to lack of awareness is the main reason for preanalytical errors. This can be avoided by proper communication and coordination between laboratory and wards, proper training and continuing medical education programs for laboratory and paramedical staffs, and knowledge of the intervening factors that can influence laboratory results.

Procedures for sampling radium-contaminated soils

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fleischhauer, H.L.

Two procedures for sampling the surface layer (0 to 15 centimeters) of radium-contaminated soil are recommended for use in remedial action projects. Both procedures adhere to the philosophy that soil samples should have constant geometry and constant volume in order to ensure uniformity. In the first procedure, a ''cookie cutter'' fashioned from pipe or steel plate, is driven to the desired depth by means of a slide hammer, and the sample extracted as a core or plug. The second procedure requires use of a template to outline the sampling area, from which the sample is obtained using a trowel ormore » spoon. Sampling to the desired depth must then be performed incrementally. Selection of one procedure over the other is governed primarily by soil conditions, the cookie cutter being effective in nongravelly soils, and the template procedure appropriate for use in both gravelly and nongravelly soils. In any event, a minimum sample volume of 1000 cubic centimeters is recommended. The step-by-step procedures are accompanied by a description of the minimum requirements for sample documentation. Transport of the soil samples from the field is then addressed in a discussion of the federal regulations for shipping radioactive materials. Interpretation of those regulations, particularly in light of their application to remedial action soil-sampling programs, is provided in the form of guidance and suggested procedures. Due to the complex nature of the regulations, however, there is no guarantee that our interpretations of them are complete or entirely accurate. Preparation of soil samples for radium-226 analysis by means of gamma-ray spectroscopy is described.« less

Bacterial Community Diversity of Oil-Contaminated Soils Assessed by High Throughput Sequencing of 16S rRNA Genes.

PubMed

Peng, Mu; Zi, Xiaoxue; Wang, Qiuyu

2015-09-24

Soil bacteria play a major role in ecological and biodegradable function processes in oil-contaminated soils. Here, we assessed the bacterial diversity and changes therein in oil-contaminated soils exposed to different periods of oil pollution using 454 pyrosequencing of 16S rRNA genes. No less than 24,953 valid reads and 6246 operational taxonomic units (OTUs) were obtained from all five studied samples. OTU richness was relatively higher in contaminated soils than clean samples. Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Planctomycetes and Proteobacteria were the dominant phyla among all the soil samples. The heatmap plot depicted the relative percentage of each bacterial family within each sample and clustered five samples into two groups. For the samples, bacteria in the soils varied at different periods of oil exposure. The oil pollution exerted strong selective pressure to propagate many potentially petroleum degrading bacteria. Redundancy analysis (RDA) indicated that organic matter was the highest determinant factor for explaining the variations in community compositions. This suggests that compared to clean soils, oil-polluted soils support more diverse bacterial communities and soil bacterial community shifts were mainly controlled by organic matter and exposure time. These results provide some useful information for bioremediation of petroleum contaminated soil in the future.

Sampling Errors in Monthly Rainfall Totals for TRMM and SSM/I, Based on Statistics of Retrieved Rain Rates and Simple Models

NASA Technical Reports Server (NTRS)

Bell, Thomas L.; Kundu, Prasun K.; Einaudi, Franco (Technical Monitor)

2000-01-01

Estimates from TRMM satellite data of monthly total rainfall over an area are subject to substantial sampling errors due to the limited number of visits to the area by the satellite during the month. Quantitative comparisons of TRMM averages with data collected by other satellites and by ground-based systems require some estimate of the size of this sampling error. A method of estimating this sampling error based on the actual statistics of the TRMM observations and on some modeling work has been developed. "Sampling error" in TRMM monthly averages is defined here relative to the monthly total a hypothetical satellite permanently stationed above the area would have reported. "Sampling error" therefore includes contributions from the random and systematic errors introduced by the satellite remote sensing system. As part of our long-term goal of providing error estimates for each grid point accessible to the TRMM instruments, sampling error estimates for TRMM based on rain retrievals from TRMM microwave (TMI) data are compared for different times of the year and different oceanic areas (to minimize changes in the statistics due to algorithmic differences over land and ocean). Changes in sampling error estimates due to changes in rain statistics due 1) to evolution of the official algorithms used to process the data, and 2) differences from other remote sensing systems such as the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I), are analyzed.

Contamination characteristics and source apportionment of trace metals in soils around Miyun Reservoir.

PubMed

Chen, Haiyang; Teng, Yanguo; Chen, Ruihui; Li, Jiao; Wang, Jinsheng

2016-08-01

Due to their toxicity and bioaccumulation, trace metals in soils can result in a wide range of toxic effects on animals, plants, microbes, and even humans. Recognizing the contamination characteristics of soil metals and especially apportioning their potential sources are the necessary preconditions for pollution prevention and control. Over the past decades, several receptor models have been developed for source apportionment. Among them, positive matrix factorization (PMF) has gained popularity and was recommended by the US Environmental Protection Agency as a general modeling tool. In this study, an extended chemometrics model, multivariate curve resolution-alternating least squares based on maximum likelihood principal component analysis (MCR-ALS/MLPCA), was proposed for source apportionment of soil metals and applied to identify the potential sources of trace metals in soils around Miyun Reservoir. Similar to PMF, the MCR-ALS/MLPCA model can incorporate measurement error information and non-negativity constraints in its calculation procedures. Model validation with synthetic dataset suggested that the MCR-ALS/MLPCA could extract acceptable recovered source profiles even considering relatively larger error levels. When applying to identify the sources of trace metals in soils around Miyun Reservoir, the MCR-ALS/MLPCA model obtained the highly similar profiles with PMF. On the other hand, the assessment results of contamination status showed that the soils around reservoir were polluted by trace metals in slightly moderate degree but potentially posed acceptable risks to the public. Mining activities, fertilizers and agrochemicals, and atmospheric deposition were identified as the potential anthropogenic sources with contributions of 24.8, 14.6, and 13.3 %, respectively. In order to protect the drinking water source of Beijing, special attention should be paid to the metal inputs to soils from mining and agricultural activities.

Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants

NASA Astrophysics Data System (ADS)

Shi, Tiezhu; Wang, Junjie; Chen, Yiyun; Wu, Guofeng

2016-10-01

Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350-2500 nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n = 62) and validation (n = 32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, Rv2 = 0.54; root mean square error in validation, RMSEv = 12.99 mg kg-1; and residual prediction deviation in validation, RPDv = 1.35) was slightly better than using the soil spectra (Rv2 = 0.42, RMSEv = 13.35 mg kg-1, and RPDv = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (Rv2 = 0.63, RMSEv = 11.94 mg kg-1, RPDv = 1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and 2290 nm, leaf spectral bands near 700, 890 and 900 nm in PLSR models were important wavelengths for soil arsenic prediction. Moreover, soil arsenic showed significantly positive correlations with soil organic matter (r = 0.62, p < 0.01) and leaf arsenic (r = 0.77, p < 0.01), and a significantly negative correlation with leaf chlorophyll (r = -0.67, p < 0.01). The results showed that the prediction of arsenic contents using soil and leaf spectra may be based on their relationships with soil organic matter and leaf chlorophyll contents, respectively. Although RPD of 1.47 was below the recommended RPD of >2 for soil analysis, arsenic prediction in agricultural soils can be improved by combining the leaf and soil spectra.

Improved Saturated Hydraulic Conductivity Pedotransfer Functions Using Machine Learning Methods

NASA Astrophysics Data System (ADS)

Araya, S. N.; Ghezzehei, T. A.

2017-12-01

Saturated hydraulic conductivity (Ks) is one of the fundamental hydraulic properties of soils. Its measurement, however, is cumbersome and instead pedotransfer functions (PTFs) are often used to estimate it. Despite a lot of progress over the years, generic PTFs that estimate hydraulic conductivity generally don't have a good performance. We develop significantly improved PTFs by applying state of the art machine learning techniques coupled with high-performance computing on a large database of over 20,000 soils—USKSAT and the Florida Soil Characterization databases. We compared the performance of four machine learning algorithms (k-nearest neighbors, gradient boosted model, support vector machine, and relevance vector machine) and evaluated the relative importance of several soil properties in explaining Ks. An attempt is also made to better account for soil structural properties; we evaluated the importance of variables derived from transformations of soil water retention characteristics and other soil properties. The gradient boosted models gave the best performance with root mean square errors less than 0.7 and mean errors in the order of 0.01 on a log scale of Ks [cm/h]. The effective particle size, D10, was found to be the single most important predictor. Other important predictors included percent clay, bulk density, organic carbon percent, coefficient of uniformity and values derived from water retention characteristics. Model performances were consistently better for Ks values greater than 10 cm/h. This study maximizes the extraction of information from a large database to develop generic machine learning based PTFs to estimate Ks. The study also evaluates the importance of various soil properties and their transformations in explaining Ks.

Integrated Processing of High Resolution Topographic Data for Soil Erosion Assessment Considering Data Acquisition Schemes and Surface Properties

NASA Astrophysics Data System (ADS)

Eltner, A.; Schneider, D.; Maas, H.-G.

2016-06-01

Soil erosion is a decisive earth surface process strongly influencing the fertility of arable land. Several options exist to detect soil erosion at the scale of large field plots (here 600 m²), which comprise different advantages and disadvantages depending on the applied method. In this study, the benefits of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are exploited to quantify soil surface changes. Beforehand data combination, TLS data is co-registered to the DEMs generated with UAV photogrammetry. TLS data is used to detect global as well as local errors in the DEMs calculated from UAV images. Additionally, TLS data is considered for vegetation filtering. Complimentary, DEMs from UAV photogrammetry are utilised to detect systematic TLS errors and to further filter TLS point clouds in regard to unfavourable scan geometry (i.e. incidence angle and footprint) on gentle hillslopes. In addition, surface roughness is integrated as an important parameter to evaluate TLS point reliability because of the increasing footprints and thus area of signal reflection with increasing distance to the scanning device. The developed fusion tool allows for the estimation of reliable data points from each data source, considering the data acquisition geometry and surface properties, to finally merge both data sets into a single soil surface model. Data fusion is performed for three different field campaigns at a Mediterranean field plot. Successive DEM evaluation reveals continuous decrease of soil surface roughness, reappearance of former wheel tracks and local soil particle relocation patterns.

Effect of anatomical fractionation on the enzymatic hydrolysis of acid and alkaline pretreated corn stover.

PubMed

Duguid, K B; Montross, M D; Radtke, C W; Crofcheck, C L; Wendt, L M; Shearer, S A

2009-11-01

Due to concerns with biomass collection systems and soil sustainability there are opportunities to investigate the optimal plant fractions to collect for conversion. An ideal feedstock would require a low severity pretreatment to release a maximum amount of sugar during enzymatic hydrolysis. Corn stover fractions were separated manually and analyzed for glucan, xylan, acid soluble lignin, acid insoluble lignin, and ash composition. The stover fractions were also pretreated with either 0%, 0.4%, or 0.8% NaOH for 2 h at room temperature, washed, autoclaved and saccharified. In addition, dilute sulfuric acid pretreated samples underwent simultaneous saccharification and fermentation (SSF) to ethanol. In general, the two pretreatments produced similar trends with cobs, husks, and leaves responding best to the pretreatments, the tops of stalks responding slightly less, and the bottom of the stalks responding the least. For example, corn husks pretreated with 0.8% NaOH released over 90% (standard error of 3.8%) of the available glucan, while only 45% (standard error of 1.1%) of the glucan was produced from identically treated stalk bottoms. Estimates of the theoretical ethanol yield using acid pretreatment followed by SSF were 65% (standard error of 15.9%) for husks and 29% (standard error of 1.8%) for stalk bottoms. This suggests that integration of biomass collection systems to remove sustainable feedstocks could be integrated with the processes within a biorefinery to minimize overall ethanol production costs.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Wade C.

Oak Ridge Institute for Science and Education (ORISE) personnel visited the United Nuclear Corporation (UNC) Naval Products site on three separate occasions during the months of October and November 2011. The purpose of these visits was to conduct confirmatory surveys of soils associated with the Argyle Street sewer line that was being removed. Soil samples were collected from six different, judgmentally determined locations in the Argyle Street sewer trench. In addition to the six soil samples collected by ORISE, four replicate soil samples were collected by Cabrera Services, Inc. (CSI) for analysis by the ORISE laboratory. Replicate samples S0010 andmore » S0011 were final status survey (FSS) bias samples; S0012 was an FSS systematic sample; and S0015 was a waste characterization sample. Six soil samples were also collected for background determination. Uranium-235 and uranium-238 concentrations were determined via gamma spectroscopy; the spectra were also reviewed for other identifiable photopeaks. Radionuclide concentrations for these soil samples are provided. In addition to the replicate samples and the samples collected by ORISE, CSI submitted three soil samples for inter-laboratory comparison analyses. One sample was from the background reference area, one was from waste characterization efforts (material inside the sewer line), and one was a FSS sample. The inter-laboratory comparison analyses results between ORISE and CSI were in agreement, except for one sample collected in the reference area. Smear results For Argyle Street sewer pipes are tabulated.« less

Physical Validation of TRMM TMI and PR Monthly Rain Products Over Oklahoma

NASA Technical Reports Server (NTRS)

Fisher, Brad L.

2004-01-01

The Tropical Rainfall Measuring Mission (TRMM) provides monthly rainfall estimates using data collected by the TRMM satellite. These estimates cover a substantial fraction of the earth's surface. The physical validation of TRMM estimates involves corroborating the accuracy of spaceborne estimates of areal rainfall by inferring errors and biases from ground-based rain estimates. The TRMM error budget consists of two major sources of error: retrieval and sampling. Sampling errors are intrinsic to the process of estimating monthly rainfall and occur because the satellite extrapolates monthly rainfall from a small subset of measurements collected only during satellite overpasses. Retrieval errors, on the other hand, are related to the process of collecting measurements while the satellite is overhead. One of the big challenges confronting the TRMM validation effort is how to best estimate these two main components of the TRMM error budget, which are not easily decoupled. This four-year study computed bulk sampling and retrieval errors for the TRMM microwave imager (TMI) and the precipitation radar (PR) by applying a technique that sub-samples gauge data at TRMM overpass times. Gridded monthly rain estimates are then computed from the monthly bulk statistics of the collected samples, providing a sensor-dependent gauge rain estimate that is assumed to include a TRMM equivalent sampling error. The sub-sampled gauge rain estimates are then used in conjunction with the monthly satellite and gauge (without sub- sampling) estimates to decouple retrieval and sampling errors. The computed mean sampling errors for the TMI and PR were 5.9% and 7.796, respectively, in good agreement with theoretical predictions. The PR year-to-year retrieval biases exceeded corresponding TMI biases, but it was found that these differences were partially due to negative TMI biases during cold months and positive TMI biases during warm months.

Modeling forest site productivity using mapped geospatial attributes within a South Carolina Landscape, USA

DOE PAGES

Parresol, B. R.; Scott, D. A.; Zarnoch, S. J.; ...

2017-12-15

Spatially explicit mapping of forest productivity is important to assess many forest management alternatives. We assessed the relationship between mapped variables and site index of forests ranging from southern pine plantations to natural hardwoods on a 74,000-ha landscape in South Carolina, USA. Mapped features used in the analysis were soil association, land use condition in 1951, depth to groundwater, slope and aspect. Basal area, species composition, age and height were the tree variables measured. Linear modelling identified that plot basal area, depth to groundwater, soils association and the interactions between depth to groundwater and forest group, and between land usemore » in 1951 and forest group were related to site index (SI) (R 2 =0.37), but this model had regression attenuation. We then used structural equation modeling to incorporate error-in-measurement corrections for basal area and groundwater to remove bias in the model. We validated this model using 89 independent observations and found the 95% confidence intervals for the slope and intercept of an observed vs. predicted site index error-corrected regression included zero and one, respectively, indicating a good fit. With error in measurement incorporated, only basal area, soil association, and the interaction between forest groups and land use were important predictors (R2 =0.57). Thus, we were able to develop an unbiased model of SI that could be applied to create a spatially explicit map based primarily on soils as modified by past (land use and forest type) and recent forest management (basal area).« less

Modeling forest site productivity using mapped geospatial attributes within a South Carolina Landscape, USA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parresol, B. R.; Scott, D. A.; Zarnoch, S. J.

Spatially explicit mapping of forest productivity is important to assess many forest management alternatives. We assessed the relationship between mapped variables and site index of forests ranging from southern pine plantations to natural hardwoods on a 74,000-ha landscape in South Carolina, USA. Mapped features used in the analysis were soil association, land use condition in 1951, depth to groundwater, slope and aspect. Basal area, species composition, age and height were the tree variables measured. Linear modelling identified that plot basal area, depth to groundwater, soils association and the interactions between depth to groundwater and forest group, and between land usemore » in 1951 and forest group were related to site index (SI) (R 2 =0.37), but this model had regression attenuation. We then used structural equation modeling to incorporate error-in-measurement corrections for basal area and groundwater to remove bias in the model. We validated this model using 89 independent observations and found the 95% confidence intervals for the slope and intercept of an observed vs. predicted site index error-corrected regression included zero and one, respectively, indicating a good fit. With error in measurement incorporated, only basal area, soil association, and the interaction between forest groups and land use were important predictors (R2 =0.57). Thus, we were able to develop an unbiased model of SI that could be applied to create a spatially explicit map based primarily on soils as modified by past (land use and forest type) and recent forest management (basal area).« less

Comparative Efficiency of the Fenwick Can and Schuiling Centrifuge in Extracting Nematode Cysts from Different Soil Types

PubMed Central

Bellvert, Joaquim; Crombie, Kieran; Horgan, Finbarr G.

2008-01-01

The Fenwick can and Schuiling centrifuge are widely used to extract nematode cysts from soil samples. The comparative efficiencies of these two methods during cyst extraction have not been determined for different soil types under different cyst densities. Such information is vital for statutory laboratories that must choose a method for routine, high-throughput soil monitoring. In this study, samples of different soil types seeded with varying densities of potato cyst nematode (Globodera rostochiensis) cysts were processed using both methods. In one experiment, with 200 ml samples, recovery was similar between methods. In a second experiment with 500 ml samples, cyst recovery was higher using the Schuiling centrifuge. For each method and soil type, cyst extraction efficiency was similar across all densities tested. Extraction was efficient from pure sand (Fenwick 72%, Schuiling 84%) and naturally sandy soils (Fenwick 62%, Schuiling 73%), but was significantly less efficient from clay-soil (Fenwick 42%, Schuiling 44%) and peat-soil with high organic matter content (Fenwick 35%, Schuiling 33%). Residual moisture (<10% w/w) in samples prior to analyses reduced extraction efficiency, particularly for sand and sandy soils. For each soil type and method, there were significant linear relationships between the number of cysts extracted and the numbers of cysts in the samples. We discuss the advantages and disadvantages of each extraction method for cyst extraction in statutory soil laboratories. PMID:19259516

Detection of Soil Nitrogen Using Near Infrared Sensors Based on Soil Pretreatment and Algorithms

PubMed Central

Nie, Pengcheng; Dong, Tao; He, Yong; Qu, Fangfang

2017-01-01

Soil nitrogen content is one of the important growth nutrient parameters of crops. It is a prerequisite for scientific fertilization to accurately grasp soil nutrient information in precision agriculture. The information about nutrients such as nitrogen in the soil can be obtained quickly by using a near-infrared sensor. The data can be analyzed in the detection process, which is nondestructive and non-polluting. In order to investigate the effect of soil pretreatment on nitrogen content by near infrared sensor, 16 nitrogen concentrations were mixed with soil and the soil samples were divided into three groups with different pretreatment. The first group of soil samples with strict pretreatment were dried, ground, sieved and pressed. The second group of soil samples were dried and ground. The third group of soil samples were simply dried. Three linear different modeling methods are used to analyze the spectrum, including partial least squares (PLS), uninformative variable elimination (UVE), competitive adaptive reweighted algorithm (CARS). The model of nonlinear partial least squares which supports vector machine (LS-SVM) is also used to analyze the soil reflectance spectrum. The results show that the soil samples with strict pretreatment have the best accuracy in predicting nitrogen content by near-infrared sensor, and the pretreatment method is suitable for practical application. PMID:28492480

Metal Load of the Crops Depending on Land Use, Land Management and Soil Characteristics

NASA Astrophysics Data System (ADS)

Oeztan, Sezin; Duering, Rolf-Alexander

2010-05-01

The increase of pollutant concentrations in soil and in the food chain became very important in the past few decades. Metals of different toxicities (Cd, Zn, As, Cr, Cu, Pb, Ni, Co, V, Tl) occur in soils as a result of weathering, industrial processes, fertilization and atmospheric deposition. Some of them can be absorbed by the plants due to their mobility. The transfer of metals from soil into the plants can be explained by the physicochemical characteristics of the soil such as pH-value, organic matter and clay content. Badly adapted cultivation of the agricultural soils (declining pH-value, application of unsuitable fertilizers) can enhance the mobility of the metals and by the way increase their concentrations in agricultural products. With this study, a field experiment was established and the aim is to test the relations between available metal concentrations in the soil and metal load of the plants depending on the fertilization techniques. The plants and soil samples of the reference sites were taken, heavy metal contents of the soil samples identified by Microwave Assisted Extraction (MAE) and compared to the Aqua Regia Digestion Method for confirming the methodology. For the determination of the metal content in plants, MAE was executed to the selected plant samples and for that procedure, the samples were digested with HNO3 and H2O2 in the microwave oven. Quantation of the metals in soil and in plants was done by ICP-OES Methodology. The evaluation of the first results confirmed that the metal content of the soil is strongly dependent on the properties of different fertilization variants (N,P,K) used and physicochemical characteristics of the soils. According to the fertilization variants, total metal contents of the soil are increased in the soil samples which have high amounts of N, P, K fertilization. Soils which were enforced with high P fertilization degrees had significantly higher total Cd content. Results on the Cd content of the plant samples also revealed that transition of metals from soil to plants depend heavily on the fertilizer since plant samples and soil samples treated with the same fertilizer showed similar results.

Stability of mercury concentration measurements in archived soil and peat samples

USGS Publications Warehouse

Navrátil, Tomáš; Burns, Douglas; Nováková, Tereza; Kaňa, Jiří; Rohovec, Jan; Roll, Michal; Ettler, Vojtěch

2018-01-01

Archived soil samples can provide important information on the history of environmental contamination and by comparison with recently collected samples, temporal trends can be inferred. Little previous work has addressed whether mercury (Hg) concentrations in soil samples are stable with long-term storage under standard laboratory conditions. In this study, we have re-analyzed using cold vapor atomic adsorption spectroscopy a set of archived soil samples that ranged from relatively pristine mountainous sites to a polluted site near a non-ferrous metal smelter with a wide range of Hg concentrations (6 - 6485 µg kg-1). Samples included organic and mineral soils and peats with a carbon content that ranged from 0.2 to 47.7%. Soil samples were stored in polyethylene bags or bottles and held in laboratory rooms where temperature was not kept to a constant value. Mercury concentrations in four subsets of samples were originally measured in 2000, 2005, 2006 and 2007, and re-analyzed in 2017, i.e. after 17, 12, 11 and 10 years of storage. Statistical analyses of either separated or lumped data yielded no significant differences between the original and current Hg concentrations. Based on these analyses, we show that archived soil and peat samples can be used to evaluate historical soil mercury contamination.

Detection of environmental sources of Histoplasma capsulatum in Chiang Mai, Thailand, by nested PCR.

PubMed

Norkaew, Treepradab; Ohno, Hideaki; Sriburee, Pojana; Tanabe, Koichi; Tharavichitkul, Prasit; Takarn, Piyawan; Puengchan, Tanpalang; Bumrungsri, Sara; Miyazaki, Yoshitsugu

2013-12-01

Histoplasmosis is a systemic mycosis caused by inhaling spores of Histoplasma capsulatum, a dimorphic fungus. This fungus grows in soil contaminated with bat and avian excreta. Each year, patients with disseminated histoplasmosis have been diagnosed in Chiang Mai, northern Thailand. No published information is currently available on the environmental sources of this fungus in Chiang Mai or anywhere else in Thailand. The aim of this study was to detect H. capsulatum in soil samples contaminated with bat guano and avian droppings by nested PCR. Two hundred and sixty-five samples were collected from the following three sources: soil contaminated with bat guano, 88 samples; soil contaminated with bird droppings, 86 samples; and soil contaminated with chicken droppings, 91 samples. Genomic DNA was directly extracted from each sample, and H. capsulatum was detected by nested PCR using a primer set specific to a gene encoding 100-kDa-like protein (HcI, HcII and HcIII, HcIV). Histoplasma capsulatum was detected in seven of 88 soil samples contaminated with bat guano, one of 21 soil samples contaminated with pigeon droppings and 10 of 91 soil samples contaminated with chicken droppings. The results indicate the possibility of the association of bat guano and chicken droppings with H. capsulatum in this area of Thailand.

Preanalytical Errors in Hematology Laboratory- an Avoidable Incompetence.

PubMed

HarsimranKaur, Vikram Narang; Selhi, Pavneet Kaur; Sood, Neena; Singh, Aminder

2016-01-01

Quality assurance in the hematology laboratory is a must to ensure laboratory users of reliable test results with high degree of precision and accuracy. Even after so many advances in hematology laboratory practice, pre-analytical errors remain a challenge for practicing pathologists. This study was undertaken with an objective to evaluate the types and frequency of preanalytical errors in hematology laboratory of our center. All the samples received in the Hematology Laboratory of Dayanand Medical College and Hospital, Ludhiana, India over a period of one year (July 2013-July 2014) were included in the study and preanalytical variables like clotted samples, quantity not sufficient, wrong sample, without label, wrong label were studied. Of 471,006 samples received in the laboratory, preanalytical errors, as per the above mentioned categories was found in 1802 samples. The most common error was clotted samples (1332 samples, 0.28% of the total samples) followed by quantity not sufficient (328 sample, 0.06%), wrong sample (96 samples, 0.02%), without label (24 samples, 0.005%) and wrong label (22 samples, 0.005%). Preanalytical errors are frequent in laboratories and can be corrected by regular analysis of the variables involved. Rectification can be done by regular education of the staff.

Can next-generation soil data products improve soil moisture modelling at the continental scale? An assessment using a new microclimate package for the R programming environment

NASA Astrophysics Data System (ADS)

Kearney, Michael R.; Maino, James L.

2018-06-01

Accurate models of soil moisture are vital for solving core problems in meteorology, hydrology, agriculture and ecology. The capacity for soil moisture modelling is growing rapidly with the development of high-resolution, continent-scale gridded weather and soil data together with advances in modelling methods. In particular, the GlobalSoilMap.net initiative represents next-generation, depth-specific gridded soil products that may substantially increase soil moisture modelling capacity. Here we present an implementation of Campbell's infiltration and redistribution model within the NicheMapR microclimate modelling package for the R environment, and use it to assess the predictive power provided by the GlobalSoilMap.net product Soil and Landscape Grid of Australia (SLGA, ∼100 m) as well as the coarser resolution global product SoilGrids (SG, ∼250 m). Predictions were tested in detail against 3 years of root-zone (3-75 cm) soil moisture observation data from 35 monitoring sites within the OzNet project in Australia, with additional tests of the finalised modelling approach against cosmic-ray neutron (CosmOz, 0-50 cm, 9 sites from 2011 to 2017) and satellite (ASCAT, 0-2 cm, continent-wide from 2007 to 2009) observations. The model was forced by daily 0.05° (∼5 km) gridded meteorological data. The NicheMapR system predicted soil moisture to within experimental error for all data sets. Using the SLGA or the SG soil database, the OzNet soil moisture could be predicted with a root mean square error (rmse) of ∼0.075 m3 m-3 and a correlation coefficient (r) of 0.65 consistently through the soil profile without any parameter tuning. Soil moisture predictions based on the SLGA and SG datasets were ≈ 17% closer to the observations than when using a chloropleth-derived soil data set (Digital Atlas of Australian Soils), with the greatest improvements occurring for deeper layers. The CosmOz observations were predicted with similar accuracy (r = 0.76 and rmse of ∼0.085 m3 m-3). Comparisons at the continental scale to 0-2 cm satellite data (ASCAT) showed that the SLGA/SG datasets increased model fit over simulations using the DAAS soil properties (r ∼ 0.63 &rmse 15% vs. r 0.48 &rmse 18%, respectively). Overall, our results demonstrate the advantages of using GlobalSoilMap.net products in combination with gridded weather data for modelling soil moisture at fine spatial and temporal resolution at the continental scale.

A model of the 0.4-GHz scatterometer. [used for agriculture soil moisture program

NASA Technical Reports Server (NTRS)

Wu, S. T.

1978-01-01

The 0.4 GHz aircraft scatterometer system used for the agricultural soil moisture estimation program is analyzed for the antenna pattern, the signal flow in the receiver data channels, and the errors in the signal outputs. The operational principal, system sensitivity, data handling, and resolution cell length requirements are also described. The backscattering characteristics of the agriculture scenes are contained in the form of the functional dependence of the backscattering coefficient on the incidence angle. The substantial gains of the cross-polarization term of the horizontal and vertical antennas have profound effects on the cross-polarized backscattered signals. If these signals are not corrected properly, large errors could result in the estimate of the cross-polarized backscattering coefficient. It is also necessary to correct the variations of the aircraft parameters during data processing to minimize the error in the 0 degree estimation. Recommendations are made to improve the overall performance of the scatterometer system.

Non-Nuclear Alternatives to Monitoring Moisture-Density Response in Soils

DTIC Science & Technology

2013-03-01

devices can be done pretest or posttest , as they all provide a means to correct the raw field data readings. Moisture Density Indicator (M+DI) The...obtained from the soil nuclear density gauge. The devices and techniques that were tested are grouped into four broad families: nuclear, electrical...43  Details of device rejection based on errors .............................................................................. 43  Accuracy of

Assessment of grass root effects on soil piping in sandy soils using the pinhole test

NASA Astrophysics Data System (ADS)

Bernatek-Jakiel, Anita; Vannoppen, Wouter; Poesen, Jean

2017-04-01

Soil piping is a complex land degradation process, which involves the hydraulic removal of soil particles by subsurface flow. This process is frequently underestimated and omitted in most soil erosion studies. However, during the last decades several studies reported the importance of soil piping in various climatic zones and for a wide range of soil types. Compared to sheet, rill and gully erosion, very few studies focused on the factors controlling piping and, so far, there is no research study dealing with the effects of plant roots on piping susceptibility of soils having a low cohesion. The objective of this study is therefore to assess the impact of grass root density (RD) on soil piping in sandy soils using the pinhole test. The pinhole test involves a water flow passing through a hole of 1 mm diameter in a soil specimen (sampled using a metal ring with a diameter of 5 cm and a length of 8 cm), under varying hydraulic heads (50 mm, 180 mm, 380 mm and 1020 mm; Nadal-Romero et al., 2011). To provide a quantitative assessment piping susceptibility of the soil sample, the pipeflow discharge (cm3 s-1) and the sediment discharge (g s-1) were measured every minute during a five minute test. Bare and root-permeated samples were tested, using a sandy soil with a sand, silt, clay content of respectively, 94%, 4% and 2%. The root-permeated topsoil samples were taken in field plots sown with a mixture of grasses with fibrous roots. All soil samples were placed on a sandbox with a 100 mm head for 24 hours to ensure a similar water content for all samples. In total, 67 pinhole tests (lasting 5 minutes each) were conducted, i.e. 43 root-permeated soil samples with RD ranging from 0.01 to 0.93 kg m-3 and 24 root-free soil samples as a reference. Clear piping erosion could be observed in 65% of the root-free soil samples, whereas only 17% of rooted soil samples revealed clear piping erosion during the tests. Statistical analyses show that there is a negative correlation (-0.41, p < 0.05) between RD and sediment discharge. Mean pipeflow discharge was 1.4 times larger for the root-free samples compared to the root-permeated samples, while mean sediment discharge was 3 times higher for the root-free samples compared to the rooted samples. This indicates that the presence of fibrous roots in topsoils decreases the susceptibility to soil piping significantly. Furthermore, a positive correlation between the hydraulic head (50-1020 mm) and sediment discharge was observed. Overall, our results suggest that root density is a highly relevant factor for decreasing the soil piping erosion rates in the sandy topsoils. The presence of even very low root densities (< 1 kg m3) decrease pipeflow and sediment discharge. A. Bernatek-Jakiel is supported by the ETIUDA doctoral scholarship (UMO-2015/16/T/ST10/00505) financed by the National Science Centre of Poland. Reference: Nadal-Romero, E., Verachtert, E., Maes, R., Poesen, J., 2011. Quantitative assessment of the piping erosion susceptibility of loess-derived soil horizons using the pinhole test. Geomorphology 135, 66-79.

Decoupling pipeline influences in soil resistivity measurements with finite element techniques

NASA Astrophysics Data System (ADS)

Deo, R. N.; Azoor, R. M.; Zhang, C.; Kodikara, J. K.

2018-03-01

Periodic inspection of pipeline conditions is an important asset management strategy conducted by water and sewer utilities for efficient and economical operations of their assets in field. The Level 1 pipeline condition assessment involving resistivity profiling along the pipeline right-of-way is a common technique for delineating pipe sections that might be installed in highly corrosive soil environment. However, the technique can suffer from significant perturbations arising from the buried pipe itself, resulting in errors in native soil characterisation. To address this problem, a finite element model was developed to investigate the degree to which pipes of different a) diameters, b) burial depths, and c) surface conditions (bare or coated) can influence in-situ soil resistivity measurements using Wenner methods. It was found that the greatest errors can arise when conducting measurements over a bare pipe with the array aligned parallel to the pipe. Depending upon the pipe surface conditions, in-situ resistivity measurements can either be underestimated or overestimated from true soil resistivities. Following results based on simulations and decoupling equations, a guiding framework for removing pipe influences in soil resistivity measurements were developed that can be easily used to perform corrections on measurements. The equations require simple a-prior information on the pipe diameter, burial depth, surface condition, and the array length and orientation used. Findings from this study have immediate application and is envisaged to be useful for critical civil infrastructure monitoring and assessment.

A new model integrating short- and long-term aging of copper added to soils

PubMed Central

Zeng, Saiqi; Li, Jumei; Wei, Dongpu

2017-01-01

Aging refers to the processes by which the bioavailability/toxicity, isotopic exchangeability, and extractability of metals added to soils decline overtime. We studied the characteristics of the aging process in copper (Cu) added to soils and the factors that affect this process. Then we developed a semi-mechanistic model to predict the lability of Cu during the aging process with descriptions of the diffusion process using complementary error function. In the previous studies, two semi-mechanistic models to separately predict short-term and long-term aging of Cu added to soils were developed with individual descriptions of the diffusion process. In the short-term model, the diffusion process was linearly related to the square root of incubation time (t1/2), and in the long-term model, the diffusion process was linearly related to the natural logarithm of incubation time (lnt). Both models could predict short-term or long-term aging processes separately, but could not predict the short- and long-term aging processes by one model. By analyzing and combining the two models, we found that the short- and long-term behaviors of the diffusion process could be described adequately using the complementary error function. The effect of temperature on the diffusion process was obtained in this model as well. The model can predict the aging process continuously based on four factors—soil pH, incubation time, soil organic matter content and temperature. PMID:28820888

Soil analyses for 1,3-dichloropropene (1,3-DCP), sodium n-methyldithiocarbamate (metam-sodium), and their degradation products near Fort Hall Idaho, September 1999 through March 2000

USGS Publications Warehouse

Parliman, D.J.

2001-01-01

Between September 1999 and March 2000, soil samples from the Fort Hall, Idaho, area were analyzed for two soil fumigants, 1,3-dichloropropene (1,3-DCP) and sodium n-methyldithiocarbamate (metam-sodium), and their degradation products. Ground water is the only source of drinking water at Fort Hall, and the purpose of the investigation was to determine potential risk of ground-water contamination from persistence and movement of these pesticides in cropland soils. 1,3-DCP, metam-sodium, or their degradation products were detected in 42 of 104 soil samples. The samples were collected from 1-, 2-, and 3-foot depths in multiple backhoe trenches during four sampling events—before pesticide application in September; after application in October; before soil freeze in December; and after soil thaw in March. In most cases, concentrations of the pesticide compounds were at or near their laboratory minimum reporting limits. U.S. Environmental Protection Agency Method 5035 was used as the guideline for soil sample preparation and analyses, and either sodium bisulfate (NaHSO4), an acidic preservative, or pesticide-free water was added to samples prior to analyses. Addition of NaHSO4 to the samples resulted in a greater number of compound detections, but pesticide-free water was added to most samples to avoid the strong reactions of soil carbonate minerals with the NaHSO4. As a result, nondetection of compounds in samples containing pesticide-free water did not necessarily indicate that the compounds were absent. Detections of these compounds were inconsistent among trenches with similar soil characteristics and histories of soil fumigant use. Compounds were detected at different depths and different trench locations during each sampling event. Overall results of this study showed that the original compounds or their degradation products can persist in soil 6 months or more after their application and are present to at least 3 feet below land surface in some areas. A few of the soil analyses results were unexpected. Degradation products of metam-sodium were detected in samples from croplands with a history of 1,3-DCP applications only, and were not detected in samples from croplands with a history of metam-sodium applications. Although 1,2-dibromoethane (EDB) has not been used in the area for many years, EDB was detected in a few soil samples. The presence of EDB in soil could be caused by irrigation of croplands with EDBcontaminated ground water. Analyses of these soil samples resulted in many unanswered questions, and further studies are needed. One potential study to determine vertical extent of pesticide compound migration in sediments, for example, would include analysis of one or more columns of soil and sediments (land surface to ground water, about 35 to 50 feet below land surface) in areas with known soil contamination. Another study would expand the scope of soil contamination to include broader types of cropland conditions and compound analyses.

A new in-situ method to determine the apparent gas diffusion coefficient of soils

NASA Astrophysics Data System (ADS)

Laemmel, Thomas; Paulus, Sinikka; Schack-Kirchner, Helmer; Maier, Martin

2015-04-01

Soil aeration is an important factor for the biological activity in the soil and soil respiration. Generally, gas exchange between soil and atmosphere is assumed to be governed by diffusion and Fick's Law is used to describe the fluxes in the soil. The "apparent soil gas diffusion coefficient" represents the proportional factor between the flux and the gas concentration gradient in the soil and reflects the ability of the soil to "transport passively" gases through the soil. One common way to determine this coefficient is to take core samples in the field and determine it in the lab. Unfortunately this method is destructive and needs laborious field work and can only reflect a small fraction of the whole soil. As a consequence insecurity about the resulting effective diffusivity on the profile scale must remain. We developed a new in-situ method using new gas sampling device, tracer gas and inverse soil gas modelling. The gas sampling device contains several sampling depths and can be easily installed into vertical holes of an auger, which allows for fast installation of the system. At the lower end of the device inert tracer gas is injected continuously. The tracer gas diffuses into the surrounding soil. The resulting distribution of the tracer gas concentrations is used to deduce the diffusivity profile of the soil. For Finite Element Modeling of the gas sampling device/soil system the program COMSOL is used. We will present the results of a field campaign comparing the new in-situ method with lab measurements on soil cores. The new sampling pole has several interesting advantages: it can be used in-situ and over a long time; so it allows following modifications of diffusion coefficients in interaction with rain but also vegetation cycle and wind.

Using Data Assimilation Diagnostics to Assess the SMAP Level-4 Soil Moisture Product

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Liu, Qing; De Lannoy, Gabrielle; Crow, Wade; Kimball, John; Koster, Randy; Ardizzone, Joe

2018-01-01

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx.2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

Global Assessment of the SMAP Level-4 Soil Moisture Product Using Assimilation Diagnostics

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Liu, Qing; De Lannoy, Gabrielle; Crow, Wade; Kimball, John; Koster, Randy; Ardizzone, Joe

2018-01-01

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx. 2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

The estimation of soil water fluxes using lysimeter data

NASA Astrophysics Data System (ADS)

Wegehenkel, M.

2009-04-01

The validation of soil water balance models regarding soil water fluxes in the field is still a problem. This requires time series of measured model outputs. In our study, a soil water balance model was validated using lysimeter time series of measured model outputs. The soil water balance model used in our study was the Hydrus-1D-model. This model was tested by a comparison of simulated with measured daily rates of actual evapotranspiration, soil water storage, groundwater recharge and capillary rise. These rates were obtained from twelve weighable lysimeters with three different soils and two different lower boundary conditions for the time period from January 1, 1996 to December 31, 1998. In that period, grass vegetation was grown on all lysimeters. These lysimeters are located in Berlin, Germany. One potential source of error in lysimeter experiments is preferential flow caused by an artificial channeling of water due to the occurrence of air space between the soil monolith and the inside wall of the lysimeters. To analyse such sources of errors, Hydrus-1D was applied with different modelling procedures. The first procedure consists of a general uncalibrated appli-cation of Hydrus-1D. The second one includes a calibration of soil hydraulic parameters via inverse modelling of different percolation events with Hydrus-1D. In the third procedure, the model DUALP_1D was applied with the optimized hydraulic parameter set to test the hy-pothesis of the existence of preferential flow paths in the lysimeters. The results of the different modelling procedures indicated that, in addition to a precise determination of the soil water retention functions, vegetation parameters such as rooting depth should also be taken into account. Without such information, the rooting depth is a calibration parameter. However, in some cases, the uncalibrated application of both models also led to an acceptable fit between measured and simulated model outputs.

Hyperspectral imaging to investigate the distribution of organic matter and iron down the soil profile

NASA Astrophysics Data System (ADS)

Hobley, Eleanor; Kriegs, Stefanie; Steffens, Markus

2017-04-01

Obtaining reliable and accurate data regarding the spatial distribution of different soil components is difficult due to issues related with sampling scale and resolution on the one hand and laboratory analysis on the other. When investigating the chemical composition of soil, studies frequently limit themselves to two dimensional characterisations, e.g. spatial variability near the surface or depth distribution down the profile, but rarely combine both approaches due to limitations to sampling and analytical capacities. Furthermore, when assessing depth distributions, samples are taken according to horizon or depth increments, resulting in a mixed sample across the sampling depth. Whilst this facilitates mean content estimation per depth increment and therefore reduces analytical costs, the sample information content with regards to heterogeneity within the profile is lost. Hyperspectral imaging can overcome these sampling limitations, yielding high resolution spectral data of down the soil profile, greatly enhancing the information content of the samples. This can then be used to augment horizontal spatial characterisation of a site, yielding three dimensional information into the distribution of spectral characteristics across a site and down the profile. Soil spectral characteristics are associated with specific chemical components of soil, such as soil organic matter or iron contents. By correlating the content of these soil components with their spectral behaviour, high resolution multi-dimensional analysis of soil chemical composition can be obtained. Here we present a hyperspectral approach to the characterisation of soil organic matter and iron down different soil profiles, outlining advantages and issues associated with the methodology.

3D-Digital soil property mapping by geoadditive models

NASA Astrophysics Data System (ADS)

Papritz, Andreas

2016-04-01

In many digital soil mapping (DSM) applications, soil properties must be predicted not only for a single but for multiple soil depth intervals. In the GlobalSoilMap project, as an example, predictions are computed for the 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, 100-200 cm depth intervals (Arrouays et al., 2014). Legacy soil data are often used for DSM. It is common for such datasets that soil properties were measured for soil horizons or for layers at varying soil depth and with non-constant thickness (support). This poses problems for DSM: One strategy is to harmonize the soil data to common depth prior to the analyses (e.g. Bishop et al., 1999) and conduct the statistical analyses for each depth interval independently. The disadvantage of this approach is that the predictions for different depths are computed independently from each other so that the predicted depth profiles may be unrealistic. Furthermore, the error induced by the harmonization to common depth is ignored in this approach (Orton et al. 2016). A better strategy is therefore to process all soil data jointly without prior harmonization by a 3D-analysis that takes soil depth and geographical position explicitly into account. Usually, the non-constant support of the data is then ignored, but Orton et al. (2016) presented recently a geostatistical approach that accounts for non-constant support of soil data and relies on restricted maximum likelihood estimation (REML) of a linear geostatistical model with a separable, heteroscedastic, zonal anisotropic auto-covariance function and area-to-point kriging (Kyriakidis, 2004.) Although this model is theoretically coherent and elegant, estimating its many parameters by REML and selecting covariates for the spatial mean function is a formidable task. A simpler approach might be to use geoadditive models (Kammann and Wand, 2003; Wand, 2003) for 3D-analyses of soil data. geoAM extend the scope of the linear model with spatially correlated errors to account for nonlinear effects of covariates by fitting componentwise smooth, nonlinear functions to the covariates (additive terms). REML estimation of model parameters and computing best linear unbiased predictions (BLUP) builds in the geoAM framework on the fact that both geostatistical and additive models can be parametrized as linear mixed models Wand, 2003. For 3D-DSM analysis of soil data, it is natural to model depth profiles of soil properties by additive terms of soil depth. Including interactions between these additive terms and covariates of the spatial mean function allows to model spatially varying depth profiles. Furthermore, with suitable choice of the basis functions of the additive term (e.g. polynomial regression splines), non-constant support of the soil data can be taken into account. Finally, boosting (Bühlmann and Hothorn, 2007) can be used for selecting covariates for the spatial mean function. The presentation will detail the geoAM approach and present an example of geoAM for 3D-analysis of legacy soil data. Arrouays, D., McBratney, A. B., Minasny, B., Hempel, J. W., Heuvelink, G. B. M., MacMillan, R. A., Hartemink, A. E., Lagacherie, P., and McKenzie, N. J. (2014). The GlobalSoilMap project specifications. In GlobalSoilMap Basis of the global spatial soil information system, pages 9-12. CRC Press. Bishop, T., McBratney, A., and Laslett, G. (1999). Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma, 91(1-2), 27-45. Bühlmann, P. and Hothorn, T. (2007). Boosting algorithms: Regularization, prediction and model fitting. Statistical Science, 22(4), 477-505. Kammann, E. E. and Wand, M. P. (2003). Geoadditive models. Journal of the Royal Statistical Society. Series C: Applied Statistics, 52(1), 1-18. Kyriakidis, P. (2004). A geostatistical framework for area-to-point spatial interpolation. Geographical Analysis, 36(3), 259-289. Orton, T., Pringle, M., and Bishop, T. (2016). A one-step approach for modelling and mapping soil properties based on profile data sampled over varying depth intervals. Geoderma, 262, 174-186. Wand, M. P. (2003). Smoothing and mixed models. Computational Statistics, 18(2), 223-249.

Biogenic NO emission from a spruce forest soil in the Fichtelgebirge (Germany) under the influence of different understorey vegetation cover

NASA Astrophysics Data System (ADS)

Bargsten, A.; Andreae, M. O.; Meixner, F. X.

2009-04-01

Within the framework of the EGER project (ExchanGE processes in mountainous Regions) soil samples have been taken from the spruce forest site "Weidenbrunnen" (Fichtelgebirge, Germany) in September 2008 to determine the NO exchange in the laboratory and for a series of soil analyses. The soil was sampled below different understorey vegetation covers: young Norway spruce, moss/litter, blueberries and grass. We investigated the net NO release rate from corresponding organic layers as well as from the A horizon of respective soils. Additionally we measured pH, C/N ratio, contents of ammonium, nitrate, and organic C, bulk density, the thickness of the organic layer and the quality of the organic matter. Net NO release rates (as well as the NO production and NO consumption rates) from the soil samples were determined by a fully automated laboratory incubation & fumigation system. Purified dry air passed five dynamic incubation chambers, four containing water saturated soil samples and one reference chamber. By this procedure, the soil samples dried out slowly (within 2-6 days), covering the full range of soil moisture (0-300% gravimetric soil moisture). To quantify NO production and NO consumption rates separately, soil samples were fumigated with zero-air (approx. 0 ppb NO) and air of 133 ppb NO. The chambers were placed in a thermostatted cabinet for incubation at 10 an 20˚ C. NO and H2O concentrations at the outlet of the five dynamic chambers were measured sequentially by chemiluminescence and IR-absorption based analyzers, switching corresponding valves every two minutes. Net NO release rates were determined from the NO concentration difference between soil containing and reference chambers. Corresponding measurements of H2O mixing ratio yielded the evaporation loss of the soil samples, which (referenced to the gravimetric soil water content before and after the incubation experiment) provided the individual soil moisture contents of each soil samples during the incubation experiment. Our contribution focus net NO release rates, NO production and NO consumption rates of spruce forest soils sampled under different understorey vegetation covers. Generally, organic layers show significant higher NO production and NO consumption rates than the soils from the corresponding A horizons. Soils under the understorey vegetation cover "moos/litter" revealed the lowest NO production and NO consumption rates. Net NO release rates, NO production and NO consumption rates of soil samples obtained below the four different under- storey vegetation covers will be discussed in terms of pH, C/N ratio, contents of ammonium, nitrate, and organic C, bulk density, thickness of organic layer, as well as quality of the organic matter.

New Temperature Calibrations and Validation Tests of 5- and 6-Methyl brGDGTs in Lake Sediment

NASA Astrophysics Data System (ADS)

Russell, J. M.; Williams, J. W.; Jackson, S. T.; S Sinninghe Damsté, J.; Watson, B. I.

2017-12-01

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are increasingly used to reconstruct changes in temperature and other environmental variables. There are now multiple methods to measure brGDGTs, many different brGDGT calibrations for different environments, and many applications of the brGDGT proxy, yet brGDGT-based temperature reconstructions have rarely been tested against independent paleoclimate data to evaluate and validate the proxy. We present new temperature calibrations of brGDGTs preserved in 65 lake sediment samples determined using new, improved chromatographic methods that separate 5- and 6-methyl brGDGT isomers. We test these new calibrations, as well as calibrations using older methods that do not separate brGDGT isomers, in a sediment core spanning the last deglaciation from a classic North American site (Silver Lake, USA) against independent pollen-derived temperature estimates. The distributions of and environmental controls on 5- and 6-methyl brGDGTs differs significantly in lake sediments versus soils, suggesting different controls on bacterial membrane lipid compositions in the two environments. This results in different calibrations in soils and lake sediments; however, like soils, separation of 5- and 6-methyl isomers significantly improves the errors statistics of some brGDGT-temperature calibrations, with calibration errors of 2-2.5 ºC. Applying these calibrations to sediments from Silver Lake, we observe warming from the last glacial maximum to the Holocene of 10.5 ºC as well as clear Bolling-Allerod and Younger Dryas responses. The amplitude and structure of temperature changes inferred from brGDGTs match well with estimates from pollen, with correlations (r2) as high as 0.88, indicating GDGTs can provide accurate temperature reconstructions. We further observe relationships between brGDGT- and pollen-inferred temperature estimates that suggest GDGT proxies can provide information on vegetation responses to climate changes in the past.

Probability density of spatially distributed soil moisture inferred from crosshole georadar traveltime measurements

NASA Astrophysics Data System (ADS)

Linde, N.; Vrugt, J. A.

2009-04-01

Geophysical models are increasingly used in hydrological simulations and inversions, where they are typically treated as an artificial data source with known uncorrelated "data errors". The model appraisal problem in classical deterministic linear and non-linear inversion approaches based on linearization is often addressed by calculating model resolution and model covariance matrices. These measures offer only a limited potential to assign a more appropriate "data covariance matrix" for future hydrological applications, simply because the regularization operators used to construct a stable inverse solution bear a strong imprint on such estimates and because the non-linearity of the geophysical inverse problem is not explored. We present a parallelized Markov Chain Monte Carlo (MCMC) scheme to efficiently derive the posterior spatially distributed radar slowness and water content between boreholes given first-arrival traveltimes. This method is called DiffeRential Evolution Adaptive Metropolis (DREAM_ZS) with snooker updater and sampling from past states. Our inverse scheme does not impose any smoothness on the final solution, and uses uniform prior ranges of the parameters. The posterior distribution of radar slowness is converted into spatially distributed soil moisture values using a petrophysical relationship. To benchmark the performance of DREAM_ZS, we first apply our inverse method to a synthetic two-dimensional infiltration experiment using 9421 traveltimes contaminated with Gaussian errors and 80 different model parameters, corresponding to a model discretization of 0.3 m × 0.3 m. After this, the method is applied to field data acquired in the vadose zone during snowmelt. This work demonstrates that fully non-linear stochastic inversion can be applied with few limiting assumptions to a range of common two-dimensional tomographic geophysical problems. The main advantage of DREAM_ZS is that it provides a full view of the posterior distribution of spatially distributed soil moisture, which is key to appropriately treat geophysical parameter uncertainty and infer hydrologic models.

Rapid analysis of 2,4-D in soil samples by modified Soxhlet apparatus using HPLC with UV detection.

PubMed

Kashyap, Sanjay M; Pandya, Girish H; Kondawar, Vivek K; Gabhane, Sanjay S

2005-02-01

The 2,4-dichlorophenoxy acetic acid (2,4-D) is used as a systemic herbicide to control broadleaf weeds in wheat, corn, range land/pasture land, sorghum, and barley. In this study, a fast and efficient method is developed by selection of modified extraction apparatus and high-performance liquid chromatography (HPLC)-UV conditions for the determination of 2,4-D in soil samples. The method is applied to the study of soil samples collected from the agricultural field. The herbicide is extracted from soil samples by acetonitrile in a modified Soxhlet apparatus. The advantages of the apparatus are that it uses small volume of organic solvent, reduced time of extraction, and better recovery of the analyte. The extract is filtered using a very fine microfiber paper. The total extract is concentrated in a rotatory evaporator, dried under ultrahigh pure N2, and finally reconstituted in 1 mL of acetonitrile. HPLC-UV at 228 nm is used for analysis. The herbicide is identified and quantitated using the HPLC system. The method is validated by the analysis of spiked soil samples. Recoveries obtained varied from 85% to 100% for spiked soil samples. The limit of quantitation (LOQ) and the limit of detection (LOD) are 0.010 and 0.005 parts per million (ppm), respectively, for spiked soil samples. The LOQ and LOD are 0.006 and 0.003 ppm for unspiked soil samples. The measured concentrations of 2,4-D in spiked soil samples are between 0.010 and 0.020 ppm with an average of 0.016 +/- 0.003 ppm. For unspiked soil samples it is between 0.006 ppm and 0.012 ppm with an average of 0.009 +/- 0.002 ppm. The measured concentrations of 2,4-D in soil samples are generally low and do not exceed the regulatory agencies guidelines.

Assessment of Soil-Gas, Surface-Water, and Soil Contamination at the Installation Railhead, Fort Gordon, Georgia, 2008-2009

USGS Publications Warehouse

Landmeyer, James E.; Harrelson, Larry G.; Ratliff, W. Hagan; Wellborn, John B.

2010-01-01

The U.S. Geological Survey, in cooperation with the U.S. Department of the Army Environmental and Natural Resources Management Office of the U.S. Army Signal Center and Fort Gordon, assessed soil gas, surface water, and soil for contaminants at the Installation Railhead (IR) at Fort Gordon, Georgia, from October 2008 to September 2009. The assessment included delineation of organic contaminants present in soil-gas samples beneath the IR, and in a surface-water sample collected from an unnamed tributary to Marcum Branch in the western part of the IR. Inorganic contaminants were determined in a surface-water sample and in soil samples. This assessment was conducted to provide environmental contamination data to Fort Gordon personnel pursuant to requirements of the Resource Conservation and Recovery Act Part B Hazardous Waste Permit process. Soil-gas samples collected within a localized area on the western part of the IR contained total petroleum hydrocarbons; benzene, toluene, ethylbenzene, and total xylenes (referred to as BTEX); and naphthalene above the method detection level. These soil-gas samples were collected where buildings had previously stood. Soil-gas samples collected within a localized area contained perchloroethylene (PCE). These samples were collected where buildings 2410 and 2405 had been. Chloroform and toluene were detected in a surface-water sample collected from an unnamed tributary to Marcum Branch but at concentrations below the National Primary Drinking Water Standard maximum contaminant level (MCL) for each compound. Iron was detected in the surface-water sample at 686 micrograms per liter (ug/L) and exceeded the National Secondary Drinking Water Standard MCL for iron. Metal concentrations in composite soil samples collected at three locations from land surface to a depth of 6 inches did not exceed the U.S. Environmental Protection Agency Regional Screening Levels for industrial soil.

Evaluating mixed samples as a source of error in non-invasive genetic studies using microsatellites

USGS Publications Warehouse

Roon, David A.; Thomas, M.E.; Kendall, K.C.; Waits, L.P.

2005-01-01

The use of noninvasive genetic sampling (NGS) for surveying wild populations is increasing rapidly. Currently, only a limited number of studies have evaluated potential biases associated with NGS. This paper evaluates the potential errors associated with analysing mixed samples drawn from multiple animals. Most NGS studies assume that mixed samples will be identified and removed during the genotyping process. We evaluated this assumption by creating 128 mixed samples of extracted DNA from brown bear (Ursus arctos) hair samples. These mixed samples were genotyped and screened for errors at six microsatellite loci according to protocols consistent with those used in other NGS studies. Five mixed samples produced acceptable genotypes after the first screening. However, all mixed samples produced multiple alleles at one or more loci, amplified as only one of the source samples, or yielded inconsistent electropherograms by the final stage of the error-checking process. These processes could potentially reduce the number of individuals observed in NGS studies, but errors should be conservative within demographic estimates. Researchers should be aware of the potential for mixed samples and carefully design gel analysis criteria and error checking protocols to detect mixed samples.

Metal pollution (Cd, Pb, Zn, and As) in agricultural soils and soybean, Glycine max, in southern China.

PubMed

Zhao, Yunyun; Fang, Xiaolong; Mu, Yinghui; Cheng, Yanbo; Ma, Qibin; Nian, Hai; Yang, Cunyi

2014-04-01

Crops produced on metal-polluted agricultural soils may lead to chronic toxicity to humans via the food chain. To assess metal pollution in agricultural soils and soybean in southern China, 30 soybean grain samples and 17 soybean-field soil samples were collected from 17 sites in southern China, and metal concentrations of samples were analyzed by graphite furnace atomic absorption spectrophotometer. The integrated pollution index was used to evaluate if the samples were contaminated by Cd, Pb, Zn and As. Results showed that Cd concentration of 12 samples, Pb concentration of 2 samples, Zn concentration of 2 samples, and As concentrations of 2 samples were above the maximum permissible levels in soils. The integrated pollution index indicated that 11 of 17 soil samples were polluted by metals. Metal concentrations in soybean grain samples ranged from 0.11 to 0.91 mg kg(-1) for Cd; 0.34 to 2.83 mg kg(-1) for Pb; 42 to 88 mg kg(-1) for Zn; and 0.26 to 5.07 mg kg(-1) for As, which means all 30 soybean grain samples were polluted by Pb, Pb/Cd, Cd/Pb/As or Pb/As. Taken together, our study provides evidence that metal pollution is an important concern in agricultural soils and soybeans in southern China.

Soil sampling and analytical strategies for mapping fallout in nuclear emergencies based on the Fukushima Dai-ichi Nuclear Power Plant accident.

PubMed

Onda, Yuichi; Kato, Hiroaki; Hoshi, Masaharu; Takahashi, Yoshio; Nguyen, Minh-Long

2015-01-01

The Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident resulted in extensive radioactive contamination of the environment via deposited radionuclides such as radiocesium and (131)I. Evaluating the extent and level of environmental contamination is critical to protecting citizens in affected areas and to planning decontamination efforts. However, a standardized soil sampling protocol is needed in such emergencies to facilitate the collection of large, tractable samples for measuring gamma-emitting radionuclides. In this study, we developed an emergency soil sampling protocol based on preliminary sampling from the FDNPP accident-affected area. We also present the results of a preliminary experiment aimed to evaluate the influence of various procedures (e.g., mixing, number of samples) on measured radioactivity. Results show that sample mixing strongly affects measured radioactivity in soil samples. Furthermore, for homogenization, shaking the plastic sample container at least 150 times or disaggregating soil by hand-rolling in a disposable plastic bag is required. Finally, we determined that five soil samples within a 3 m × 3-m area are the minimum number required for reducing measurement uncertainty in the emergency soil sampling protocol proposed here. Copyright © 2014 Elsevier Ltd. All rights reserved.

Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias

PubMed Central

Chambers, David A.; Glasgow, Russell E.

2014-01-01

Abstract A number of commentaries have suggested that large studies are more reliable than smaller studies and there is a growing interest in the analysis of “big data” that integrates information from many thousands of persons and/or different data sources. We consider a variety of biases that are likely in the era of big data, including sampling error, measurement error, multiple comparisons errors, aggregation error, and errors associated with the systematic exclusion of information. Using examples from epidemiology, health services research, studies on determinants of health, and clinical trials, we conclude that it is necessary to exercise greater caution to be sure that big sample size does not lead to big inferential errors. Despite the advantages of big studies, large sample size can magnify the bias associated with error resulting from sampling or study design. Clin Trans Sci 2014; Volume #: 1–5 PMID:25043853

ANALYZING NUMERICAL ERRORS IN DOMAIN HEAT TRANSPORT MODELS USING THE CVBEM.

USGS Publications Warehouse

Hromadka, T.V.

1987-01-01

Besides providing an exact solution for steady-state heat conduction processes (Laplace-Poisson equations), the CVBEM (complex variable boundary element method) can be used for the numerical error analysis of domain model solutions. For problems where soil-water phase change latent heat effects dominate the thermal regime, heat transport can be approximately modeled as a time-stepped steady-state condition in the thawed and frozen regions, respectively. The CVBEM provides an exact solution of the two-dimensional steady-state heat transport problem, and also provides the error in matching the prescribed boundary conditions by the development of a modeling error distribution or an approximate boundary generation.

Contamination valuation of soil and groundwater source at anaerobic municipal solid waste landfill site.

PubMed

Aziz, Shuokr Qarani; Maulood, Yousif Ismael

2015-12-01

The present work aimed to determine the risks that formed landfill leachate from anaerobic Erbil Landfill Site (ELS) poses on groundwater source and to observe the effects of disposed municipal solid waste (MSW) on soil properties. The study further aims to fill the gap in studies on the effects of disposed MSW and produced leachate on the groundwater characteristics and soil quality at ELS, Iraq. Soil, leachate, and groundwater samples were collected from ELS for use as samples in this study. Unpolluted groundwater samples were collected from an area outside of the landfill. Field and laboratory experiments for the soil samples were conducted. Chemical analyses for the soil samples such as organic matter, total salts, and SO4 (=) were also performed. Raw leachate and groundwater samples were analyzed using physical and chemical experiments. The yields for sorptivity, steady-state infiltration rate, and hydraulic conductivity of the soil samples were 0.0006 m/√s, 0.00004 m/s, and 2.17 × 10(-5) m/s, respectively. The soil at ELS was found to be light brown clayey gravel with sand and light brown gravely lean clay layers with low permeability. Unprocessed leachate analysis identified the leachate as stabilized. Findings showed that the soil and groundwater at the anaerobic ELS were contaminated.

Quantifying Soil Carbon Change from Wildfires in Peatland Ecosystems of the Eastern United States Using Repeat LiDAR

NASA Astrophysics Data System (ADS)

Reddy, A.; Hawbaker, T. J.; Zhu, Z.; Ward, S.; Wurster, F.; Newcomb, D.

2013-12-01

Wildfires are an increasing concern in peatland ecosystems along the coastal plains of the Eastern US. Human- and climate-induced changes to the ecosystems' hydrology can leave the soils, heavy with organic matter, susceptible to combustion in wildfires. This results in large losses of carbon that took many years to accumulate. However, accurately quantifying carbon losses in peatlands from wildfires is challenging because field data collection over extensive areas is difficult. For this study, our first objective was to evaluate the use of pre- and post-fire LiDAR data to quantify changes in surface elevations and soil carbon stocks for the 2011 Lateral West fire, which occurred in the Great Dismal Swamp National Wildlife Refuge (GDSNWR), Virginia, USA. Our second objective was to use a Monte Carlo approach to estimate how the vertical error in LiDAR points affected our calculation of soil carbon emissions. Bare-earth LiDAR points from 2010 and 2012 were obtained for GDSNWR with densities of 2 pulses/m2 and vertical elevation RMSE of 9 and 7 cm, respectively. Monte Carlo replicates were used to perturb individual bare-earth LiDAR points and generate probability distributions of elevation change within 10 m grid cells. Change in soil carbon were calculated within the Monte Carlo replicates by multiplying the LiDAR-derived volume of soil loss by depth-specific published values of soil bulk density, organic matter content, and carbon content. The 5th, 50th and 95th percentiles of the elevation and carbon change distributions were outputted as raster layers. Loss in soil volume ranged from 10,820,000 to 13,190,000 m3 based on vertical error. Carbon loss within the entire area burned by the Lateral West fire perimeter (32.1 km2), based on the 5th, 50th and 95th percentiles was 0.64, 0.96, and 1.33 Tg C, respectively. Our study demonstrated a method to use LiDAR data to quantify carbon loss following fires in peatland ecosystems and incorporate elevation errors to calculate uncertainties in the estimates. The results of our analysis showed that soil carbon lost from the 2011 Lateral West fire were not insignificant, and suggest that the hydrology of these ecosystems should be managed to preserve and restore peatlands and their carbon stocks where possible, as they can play an important role in mitigating and offsetting fossil-fuel emissions.

Development of a rapid soil water content detection technique using active infrared thermal methods for in-field applications.

PubMed

Antonucci, Francesca; Pallottino, Federico; Costa, Corrado; Rimatori, Valentina; Giorgi, Stefano; Papetti, Patrizia; Menesatti, Paolo

2011-01-01

The aim of this study was to investigate the suitability of active infrared thermography and thermometry in combination with multivariate statistical partial least squares analysis as rapid soil water content detection techniques both in the laboratory and the field. Such techniques allow fast soil water content measurements helpful in both agricultural and environmental fields. These techniques, based on the theory of heat dissipation, were tested by directly measuring temperature dynamic variation of samples after heating. For the assessment of temperature dynamic variations data were collected during three intervals (3, 6 and 10 s). To account for the presence of specific heats differences between water and soil, the analyses were regulated using slopes to linearly describe their trends. For all analyses, the best model was achieved for a 10 s slope. Three different approaches were considered, two in the laboratory and one in the field. The first laboratory-based one was centred on active infrared thermography, considered measurement of temperature variation as independent variable and reported r = 0.74. The second laboratory-based one was focused on active infrared thermometry, added irradiation as independent variable and reported r = 0.76. The in-field experiment was performed by active infrared thermometry, heating bare soil by solar irradiance after exposure due to primary tillage. Some meteorological parameters were inserted as independent variables in the prediction model, which presented r = 0.61. In order to obtain more general and wide estimations in-field a Partial Least Squares Discriminant Analysis on three classes of percentage of soil water content was performed obtaining a high correct classification in the test (88.89%). The prediction error values were lower in the field with respect to laboratory analyses. Both techniques could be used in conjunction with a Geographic Information System for obtaining detailed information on soil heterogeneity.

Development of a Rapid Soil Water Content Detection Technique Using Active Infrared Thermal Methods for In-Field Applications

PubMed Central

Antonucci, Francesca; Pallottino, Federico; Costa, Corrado; Rimatori, Valentina; Giorgi, Stefano; Papetti, Patrizia; Menesatti, Paolo

2011-01-01

The aim of this study was to investigate the suitability of active infrared thermography and thermometry in combination with multivariate statistical partial least squares analysis as rapid soil water content detection techniques both in the laboratory and the field. Such techniques allow fast soil water content measurements helpful in both agricultural and environmental fields. These techniques, based on the theory of heat dissipation, were tested by directly measuring temperature dynamic variation of samples after heating. For the assessment of temperature dynamic variations data were collected during three intervals (3, 6 and 10 s). To account for the presence of specific heats differences between water and soil, the analyses were regulated using slopes to linearly describe their trends. For all analyses, the best model was achieved for a 10 s slope. Three different approaches were considered, two in the laboratory and one in the field. The first laboratory-based one was centred on active infrared thermography, considered measurement of temperature variation as independent variable and reported r = 0.74. The second laboratory–based one was focused on active infrared thermometry, added irradiation as independent variable and reported r = 0.76. The in-field experiment was performed by active infrared thermometry, heating bare soil by solar irradiance after exposure due to primary tillage. Some meteorological parameters were inserted as independent variables in the prediction model, which presented r = 0.61. In order to obtain more general and wide estimations in-field a Partial Least Squares Discriminant Analysis on three classes of percentage of soil water content was performed obtaining a high correct classification in the test (88.89%). The prediction error values were lower in the field with respect to laboratory analyses. Both techniques could be used in conjunction with a Geographic Information System for obtaining detailed information on soil heterogeneity. PMID:22346632

Hg Storage and Mobility in Tundra Soils of Northern Alaska

NASA Astrophysics Data System (ADS)

Olson, C.; Obrist, D.

2017-12-01

Atmospheric mercury (Hg) can be transported over long distances to remote regions such as the Arctic where it can then deposit and temporarily be stored in soils. This research aims to improve the understanding of terrestrial Hg storage and mobility in the arctic tundra, a large receptor area for atmospheric deposition and a major source of Hg to the Arctic Ocean. We aim to characterize spatial Hg pool sizes across various tundra sites and to quantify the mobility of Hg from thawing tundra soils using laboratory mobility experiments. Active layer and permafrost soil samples were collected in the summer of 2014 and 2015 at the Toolik Field Station in northern Alaska (68° 38' N) and along a 200 km transect extending from Toolik to the Arctic Ocean. Soil samples were analyzed for total Hg concentration, bulk density, and major and trace elements. Hg pool sizes were estimated by scaling up Hg soil concentrations using soil bulk density measurements. Mobility of Hg in tundra soils was quantified by shaking soil samples with ultrapure Milli-Q® water as an extracting solution for 24 and 72 hours. Additionally, meltwater samples were collected for analysis when present. The extracted supernatant was analyzed for total Hg, dissolved organic carbon, cations and anions, redox, and ph. Mobility of Hg from soil was calculated using Hg concentrations determined in solid soil samples and in supernatant of soil solution samples. Results of this study show Hg levels in tundra mineral soils that are 2-5 times higher than those observed at temperate sites closer to pollution sources. Most of the soil Hg was located in mineral horizons where Hg mass accounted for 72% of the total soil pool. Soil Hg pool sizes across the tundra sites were highly variable (166 - 1,365 g ha-1; avg. 419 g ha-1) due to the heterogeneity in soil type, bulk density, depth to frozen layer, and soil Hg concentration. Preliminary results from the laboratory experiment show higher mobility of Hg in mineral soils of active layer samples (0.062%) than in permafrost soils (0.026%) where soil Hg concentrations were lower. Mobilization of Hg stored in thawing permafrost soils could lead to accelerated export of Hg to aquatic systems, with major implications to Arctic wildlife and human health.

Are There Dangerous Levels of Lead in Local Soil?

NASA Astrophysics Data System (ADS)

Pita, I.

2017-12-01

The purpose of this experiment was to show that comparing random soil samples from areas in New Orleans; the Garden District will have the highest levels of lead in soil. My Independent variable was the soil samples collected from locations in the Garden District area of New Orleans, and other locations throughout New Orleans. The control was the soil samples collected from the local playground in the New Orleans area. My dependent variable was the lead soil test kit, using ppm (parts per million) of lead to show concentration. 400 ppm + in bare soil where children play is considered dangerous hazard levels. 1,000 + ppm in all other areas is considered dangerous hazard levels. The first step to my experiment, I collected soil samples from different locations throughout the Garden District area of New Orleans. The second step to my experiment, I conducted the lead soil testing in a controlled area at home in a well ventilated room, using all the necessary safety equipment needed, I began testing a 24 hour test period and a 48 hour test period. I then collected the data from both test. The results showed that soil samples from the Garden District area compared to the other sample locations had higher lead concentrations in the soil. This backed my hypothesis when comparing soil samples from areas in New Orleans, the Garden District will have the highest lead levels. In conclusion these experiments showed that with the soil samples collected, there were higher concentrations of lead in the soil from the Garden District area compared to the other areas where soil was collected. Reconstruction and renovations, from the devastation that Hurricane Katrina created, are evident of the lead in paint of older homes which now show the lead concentration in the soil. Lead is a lethal element if consumed or inhaled in high doses, which can damage key organs in our body, which can be deadly. Better awareness through social media, television, radio, doctors, studies, pamphlets, environmental agencies, and other forms to address the steps in protecting your family and home for a lead free environment.

Tests on the centrifugal flotation technique and its use in estimating the prevalence of Toxocara in soil samples from urban and suburban areas of Malaysia.

PubMed

Loh, A G; Israf, D A

1998-03-01

The influence of soil texture (silt, sand and laterite) and flotation solutions (saturated NaCl, sucrose, NaNO3, and ZnSO4) upon the recovery of Toxocara ova from seeded soil samples with the centrifugal flotation technique was investigated. Soil samples of different texture were artificially seeded with Toxocara spp. ova and subjected to a centrifugal flotation technique which used various flotation solutions. The results showed significant (P < 0.001) interactions between the soil types and the flotation solutions. The highest percentage of ova recovery was obtained with silty soil (34.9-100.8%) with saturated NaCl as the flotation solution (45.3-100.8%). A combination of washing of soil samples with 0.1% Tween 80, and flotation using saturated NaCl and a 30 min coverslip recovery period was used to study the prevalence of contamination of soil samples. Forty-six soil samples were collected from up to 24 public parks/playgrounds in urban areas of Petaling Jaya and suburban areas of Serdang. The prevalence of Toxocara species in the urban and suburban areas was 54.5% and 45.8% respectively.

Analysis of problems and failures in the measurement of soil-gas radon concentration.

PubMed

Neznal, Martin; Neznal, Matěj

2014-07-01

Long-term experience in the field of soil-gas radon concentration measurements allows to describe and explain the most frequent causes of failures, which can appear in practice when various types of measurement methods and soil-gas sampling techniques are used. The concept of minimal sampling depth, which depends on the volume of the soil-gas sample and on the soil properties, is shown in detail. Consideration of minimal sampling depth at the time of measurement planning allows to avoid the most common mistakes. The ways how to identify influencing parameters, how to avoid a dilution of soil-gas samples by the atmospheric air, as well as how to recognise inappropriate sampling methods are discussed. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Rapid and sensitive determination of tellurium in soil and plant samples by sector-field inductively coupled plasma mass spectrometry.

PubMed

Yang, Guosheng; Zheng, Jian; Tagami, Keiko; Uchida, Shigeo

2013-11-15

In this work, we report a rapid and highly sensitive analytical method for the determination of tellurium in soil and plant samples using sector field inductively coupled plasma mass spectrometry (SF-ICP-MS). Soil and plant samples were digested using Aqua regia. After appropriate dilution, Te in soil and plant samples was directly analyzed without any separation and preconcentration. This simple sample preparation approach avoided to a maximum extent any contamination and loss of Te prior to the analysis. The developed analytical method was validated by the analysis of soil/sediment and plant reference materials. Satisfactory detection limits of 0.17 ng g(-1) for soil and 0.02 ng g(-1) for plant samples were achieved, which meant that the developed method was applicable to studying the soil-to-plant transfer factor of Te. Our work represents for the first time that data on the soil-to-plant transfer factor of Te were obtained for Japanese samples which can be used for the estimation of internal radiation dose of radioactive tellurium due to the Fukushima Daiichi Nuclear Power Plant accident. Copyright © 2013 Elsevier B.V. All rights reserved.

77 FR 67777 - National Oil and Hazardous Substance Pollution Contingency Plan; National Priorities List...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-14

... subsequent soil samples showed levels of metals at or below generic residential criteria or background values... 1994- 1996 and additional sampling between 1998 and 2007. Area A--Site Entrance: Soil boring samples... verification samples. Additional soil samples were collected from the same location as the previous collection...

Anthropogenic impact on the presence of L. monocytogenes in soil, fruits, and vegetables.

PubMed

Szymczak, Barbara; Szymczak, Mariusz; Sawicki, Wojciech; Dąbrowski, Waldemar

2014-01-01

The aim of this study was to determine the prevalence of Listeria sp. and Listeria monocytogenes in soil samples with reference to type of fertilizers (natural and artificial) and distance from places intensively exploited by men, as well as to determine the relationship between the presence of L. monocytogenes in the soil and in fruits and vegetables. The examined 1,000 soil samples originated from 15 different areas, whilst 140 samples of fruits and 210 samples of vegetables were collected from those areas. L. monocytogenes was isolated only from 5.5 % of all soil samples coming exclusively from meadows intensively grazed by cattle (27.8 %) and areas near food processing plants (25 %) and wild animal forests (24 %). Listeria sp. and L. monocytogenes were not present on artificially fertilized areas and wastelands. L. monocytogenes was detected in 10 % of samples of strawberry, 15 % of potato samples, and 5 % of parsley samples. Our data indicate that Listeria spp. and particularly L. monocytogenes were found in the soil from (1) arable lands fertilized with manure, (2) pasture (the land fertilized with feces of domestic animals), and (3) forests (again, the land fertilized with feces of animals, not domestic but wild). The bacteria were not detected in the soil samples collected at (1) artificially fertilized arable lands and (2) wastelands (the lands that were not fertilized with manure or animal feces). Moreover, a correlation was determined in the presence of L. monocytogenes between soil samples and samples of the examined fruits and vegetables.

Evaluation of soil quality indicators in paddy soils under different crop rotation systems

NASA Astrophysics Data System (ADS)

Nadimi-Goki, Mandana; Bini, Claudio; Haefele, Stephan; Abooei, Monireh

2013-04-01

Evaluation of soil quality indicators in paddy soils under different crop rotation systems Soil quality, by definition, reflects the capacity to sustain plant and animal productivity, maintain or enhance water and air quality, and promote plant and animal health. Soil quality assessment is an essential issue in soil management for agriculture and natural resource protection. This study was conducted to detect the effects of four crop rotation systems (rice-rice-rice, soya-rice-rice, fallow-rice and pea-soya-rice) on soil quality indicators (soil moisture, porosity, bulk density, water-filled pore space, pH, extractable P, CEC, OC, OM, microbial respiration, active carbon) in paddy soils of Verona area, Northern Italy. Four adjacent plots which managed almost similarly, over five years were selected. Surface soil samples were collected from each four rotation systems in four times, during growing season. Each soil sample was a composite of sub-samples taken from 3 points within 350 m2 of agricultural land. A total of 48 samples were air-dried and passed through 2mm sieve, for some chemical, biological, and physical measurements. Statistical analysis was done using SPSS. Statistical results revealed that frequency distribution of most data was normal. The lowest CV% was related to pH. Analysis of variance (ANOVA) and comparison test showed that there are significant differences in soil quality indicators among crop rotation systems and sampling times. Results of multivariable regression analysis revealed that soil respiration had positively correlation coefficient with soil organic matter, soil moisture and cation exchange capacity. Overall results indicated that the rice rotation with legumes such as bean and soybean improved soil quality over a long time in comparison to rice-fallow rotation, and this is reflected in rice yield. Keywords: Soil quality, Crop Rotation System, Paddy Soils, Italy

FIELD-SCALE STUDIES: HOW DOES SOIL SAMPLE PRETREATMENT AFFECT REPRESENTATIVENESS ? (ABSTRACT)

EPA Science Inventory

Samples from field-scale studies are very heterogeneous and can contain large soil and rock particles. Oversize materials are often removed before chemical analysis of the soil samples because it is not practical to include these materials. Is the extracted sample representativ...

FIELD-SCALE STUDIES: HOW DOES SOIL SAMPLE PRETREATMENT AFFECT REPRESENTATIVENESS?

EPA Science Inventory

Samples from field-scale studies are very heterogeneous and can contain large soil and rock particles. Oversize materials are often removed before chemical analysis of the soil samples because it is not practical to include these materials. Is the extracted sample representativ...

An error criterion for determining sampling rates in closed-loop control systems

NASA Technical Reports Server (NTRS)

Brecher, S. M.

1972-01-01

The determination of an error criterion which will give a sampling rate for adequate performance of linear, time-invariant closed-loop, discrete-data control systems was studied. The proper modelling of the closed-loop control system for characterization of the error behavior, and the determination of an absolute error definition for performance of the two commonly used holding devices are discussed. The definition of an adequate relative error criterion as a function of the sampling rate and the parameters characterizing the system is established along with the determination of sampling rates. The validity of the expressions for the sampling interval was confirmed by computer simulations. Their application solves the problem of making a first choice in the selection of sampling rates.

Heavy metals and hydrocarbons contents in soils of urban areas of Yamal autonomous region (Russia)

NASA Astrophysics Data System (ADS)

Alekseev, Ivan; Abakumov, Evgeny; Shamilishvili, George

2016-04-01

This investigation is devoted to evaluation of heavy metals and hydrocarbons contents in soils of different functional localities within the Yamalo-Nenets autonomous region (YaNAR, North-Western Siberia, Russia). Geo-accumulation indices Igeo (Müller 1988) were calculated in order to assess soil contamination levels with heavy metals (Cu, Pb, Cd, Zn, Ni, As, Hg) in the studied settlements: Harsaim, Aksarka, Labytnangy, Harp and Salekhard. The degree of soil pollution was assessed according to seven contamination classes (Förstner et al. 1990) in order of increasing numerical value of the index. Cd's regional soil background concentrations of the Yamal peninsula (Moskovchenko 2010), Hg's Earth crust clarke (Greenwood & Earnshaw 2008) and concentrations of the rest trace elements in natural sandy soil from the Beliy island, YaNAR (Tomashunas & Abakumov, 2014) were used in calculations. In general terms, obtained Igeo values in all samples were under or slightly above the 0 level, indicating low to moderate pollution of the studied soils. However, considerable Igeo values of Zn, Pb and Ni were revealed in several samples, suggesting different soil pollution levels, namely: Zn Igeo in Harsaim soil sample of 2.22 - moderate polluted to highly polluted soil; Pb Igeo in Aksarka soil sample of 4.04 - highly polluted to extremely polluted soil; Ni Igeo in Harp soil sample of 4.34 - highly polluted to extremely polluted soil. Soil contamination level was additionally evaluated, comparing with the maximal permissible concentrations (MPCs) of the trace elements in soil (SANPIN 4266-87), established by the national legislation. Almost all samples exceeded the MPC for As in soils (2 mg•kg-1). Concentrations of Ni in several soil samples taken in Harp were 19 times higher than recommended level (20 mg•kg-1). Moderate excess of Zn, Pb and Cu MPCs was also noted. Data obtained will be used in further environmental researches and environmental management purposes in this key oil and gas exploration region. This study was supported by Russian president's grant for Young Doctors of Science № MD 3615-2015-4.

Evaluation and characterization of anti-estrogenic and anti-androgenic activities in soil samples along the Second Songhua River, China.

PubMed

Li, Jian; Wang, Yafei; Kong, Dongdong; Wang, Jinsheng; Teng, Yanguo; Li, Na

2015-11-01

In the present study, re-combined estrogen receptor (ER) and androgen receptor (AR) gene yeast assays combined with a novel approach based on Monte Carlo simulation were used for evaluation and characterization of soil samples collected from Jilin along the Second Songhua River to assess their antagonist/agonist properties for ER and AR. The results showed that estrogenic activity only occurred in the soil samples collected in the agriculture area, but most soil samples showed anti-estrogenic activities, and the bioassay-derived 4-hydroxytamoxifen equivalents ranged from N.D. to 23.51 μg/g. Hydrophilic substance fractions were determined as potential contributors associated with anti-estrogenic activity in these soil samples. Moreover, none of the soil samples exhibited AR agonistic potency, whereas 54% of the soil samples exhibited AR antagonistic potency. The flutamide equivalents varied between N.D. and 178.05 μg/g. Based on Monte Carlo simulation-related mass balance analysis, the AR antagonistic activities were significantly correlated with the media polar and polar fractions. All of these results support that this novel calculation method can be adopted effectively to quantify and characterize the ER/AR agonists and antagonists of the soil samples, and these data could help provide useful information for future management and remediation efforts.

Soil and surface layer type affect non-rainfall water inputs

NASA Astrophysics Data System (ADS)

Agam, Nurit; Berliner, Pedro; Jiang, Anxia

2017-04-01

Non-rainfall water inputs (NRWIs), which include fog deposition, dew formation, and direct water vapor adsorption by the soil, play a vital role in arid and semiarid regions. Environmental conditions, namely radiation, air temperature, air humidity, and wind speed, largely affect the water cycle driven by NRWIs. The substrate type (soil type and the existence/absence of a crust layer) may as well play a major role. Our objective was to quantify the effects of soil type (loess vs. sand) and surface layer (bare vs. crusted) on the gain and posterior evaporation of NRWIs in the Negev Highlands throughout the dry summer season. Four undisturbed soil samples (20 cm diameter and 50 cm depth) were excavated and simultaneously introduced into a PVC tube. Two samples were obtained in the Negev's Boker plain (loess soil) and two in the Nizzana sand dunes in the Western Negev. On one sample from each site the crust was removed while on the remaining one the natural crust was left in place. The samples were brought to the research site at the Jacob Bluestein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel (31˚08' N, 34˚53' E, 400 meter above the sea level) where they were exposed to the same environmental conditions. The four samples in their PVC tubes were placed on top of scales and the samples mass was continuously monitored. Soil temperatures were monitored at depths of 1, 2, 3, 5 and10 cm in each microlysimeter (ML) using Copper-Constantan thermocouples. The results of particle size distribution indicated that the crust of the loess soil is probably a physical crust, i.e., a crust that forms due to raindroplets impact; while the crust on the sand soil is biological. On most days, the loess soils adsorbed more water than their corresponding sand soil samples. For both soils, the samples for which the crust was removed adsorbed more water than the samples for which it was intact. The difference in daily water adsorption amount between crusted and non-crusted sandy soils often exceeded that between crusted and non-crusted loess soils.

Investigating the Relationship Between Soil Water Mobility and Stable Isotope Composition with Implications for the Ecohydrologic Separation Hypothesis

NASA Astrophysics Data System (ADS)

Shuler, J.; McNamara, J. P.; Benner, S. G.; Kohn, M. J.; Evans, S.

2017-12-01

The ecohydrologic separation (ES) hypothesis states that streams and plants return different soil water compartments to the atmosphere and that these compartments bear distinct isotopic compositions that can be used to infer soil water mobility. Recent studies have found isotopic evidence for ES in a variety of ecosystems, though interpretations of these data vary. ES investigations frequently suffer from low sampling frequencies as well as incomplete or missing soil moisture and matric potential data to support assumptions of soil water mobility. We sampled bulk soil water every 2-3 weeks in the upper 1 m of a hillslope profile from May 2016 to July 2017 in a semi-arid watershed outside Boise, ID. Twig samples of three plant species were also collected concurrently. Plant and soil water samples extracted via cryogenic vacuum distillation were analyzed for δ2H and δ18O composition. Soil moisture and soil matric potential sensors were installed at five and four depths in the profile, respectively. Shallow bulk soil water was progressively enriched in both isotopes over the growing season and plotted along a soil evaporation line in a plot of δ2H versus δ18O. Plant water during the growing season plotted below both the Local Meteoric Water Line and soil evaporation line. Plant water isotopic composition could not be traced to any source sampled in this study. Additionally, soil moisture and matric potential data revealed that soils were well-drained and that mobile soil water was unavailable throughout most of the growing season at the depths sampled. Soil water isotopic composition alone failed to predict mobility as observed in soil moisture and matric potential data. These results underscore the need for standard hydrologic definitions for the mobile and immobile compartments of soil water in future studies of the ES hypothesis and ecohydrologic processes in general.

Fine Increment Soil Collector (FISC): A new device to support high resolution soil and sediment sampling for agri-environmental assessments

NASA Astrophysics Data System (ADS)

Mabit, Lionel; Meusburger, Katrin; Iurian, Andra-Rada; Owens, Philip N.; Toloza, Arsenio; Alewell, Christine

2014-05-01

Soil and sediment related research for terrestrial agri-environmental assessments requires accurate depth incremental sampling of soil and exposed sediment profiles. Existing coring equipment does not allow collecting soil/sediment increments at millimetre resolution. Therefore, the authors have designed an economic, portable, hand-operated surface soil/sediment sampler - the Fine Increment Soil Collector (FISC) - which allows extensive control of soil/sediment sampling process and easy recovery of the material collected by using a simple screw-thread extraction system. In comparison with existing sampling tools, the FISC has the following advantages and benefits: (i) it permits sampling of soil/sediment samples at the top of the profile; (ii) it is easy to adjust so as to collect soil/sediment at mm resolution; (iii) it is simple to operate by one single person; (iv) incremental samples can be performed in the field or at the laboratory; (v) it permits precise evaluation of bulk density at millimetre vertical resolution; and (vi) sample size can be tailored to analytical requirements. To illustrate the usefulness of the FISC in sampling soil and sediments for 7Be - a well-known cosmogenic soil tracer and fingerprinting tool - measurements, the sampler was tested in a forested soil located 45 km southeast of Vienna in Austria. The fine resolution increments of 7Be (i.e. 2.5 mm) affects directly the measurement of the 7Be total inventory but above all impacts the shape of the 7Be exponential profile which is needed to assess soil movement rates. The FISC can improve the determination of the depth distributions of other Fallout Radionuclides (FRN) - such as 137Cs, 210Pbexand239+240Pu - which are frequently used for soil erosion and sediment transport studies and/or sediment fingerprinting. Such a device also offers great potential to investigate FRN depth distributions associated with fallout events such as that associated with nuclear emergencies. Furthermore, prior to remediation activities - such as topsoil removal - in contaminated soils and sediments (e.g. by heavy metals, pesticides or nuclear power plant accident releases), basic environmental assessment often requires the determination of the extent and the depth penetration of the different contaminants, precision that can be provided by using the FISC.

Visual assessment of soil structure quality in an agroextractivist system in Southeastern Amazonia

NASA Astrophysics Data System (ADS)

Fernanda Simões da Silva, Laura; Stuchi Boschi, Raquel; Ortega Gomes, Matheus; Cooper, Miguel

2016-04-01

Soil structure is considered a key factor in the functioning of soil, affecting its ability to support plant and animal life, and moderate environmental quality. Numerous methods are available to evaluate soil structure based on physical, chemical and biological indicators. Among the physical indicators, the attributes most commonly used are soil bulk density, porosity, soil resistance to penetration, tensile strength of aggregates, soil water infiltration, and available water. However, these methods are expensive and generally time costly for sampling and laboratorial procedures. Recently, evaluations using qualitative and semi-quantitative indicators of soil structure quality have gained importance. Among these methods, the method known as Visual Evaluation of Soil Structure (VESS) (Ball et al., 2007; Guimarães et al., 2011) can supply this necessity in temperate and tropical regions. The study area is located in the Piranheira Praialta Agroextrativist Settlement Project in the county of Nova Ipixuna, Pará, Brazil. Two toposequences were chosen, one under native forest and the other under pasture. Pits were opened in different landscape positions (upslope, midslope and downslope) for soil morphological, micromorphological and physical characterization. The use of the soil visual evaluation method (SVE) consisted in collecting an undisturbed soil sample of approximately 25 cm in length, 20 cm in width and 10 cm in depth. 12 soil samples were taken for each land use. The samples were manually fragmented, respecting the fracture planes between the aggregates. The SVE was done comparing the fragmented sample with a visual chart and scores were given to the soil structure. The categories that define the soil structure quality (Qe) vary from 1 to 5. Lower scores mean better soil structure. The final score calculation was done using the classification key of Ball et al. (2007) adapted by Guimarães (2011). A change in soil structure was observed between forest and pasture. The presence of layers of different depths, and size and shape of aggregates resulted in a lower Qe in the forest soils (Qe= 2,04 ±0,4), followed by the pasture (Qe= 3,09 ± 1,3). These results indicate certain degradation in the soil structure in the pasture. The variability of the soil structure in the forest samples was lower. The pasture samples presented a worse soil structure when compared to the forest, although their Qe values can be considered good.

Reconnaissance soil geochemistry at the Riverton Uranium Mill Tailings Remedial Action Site, Fremont County, Wyoming

USGS Publications Warehouse

Smith, David B.; Sweat, Michael J.

2012-01-01

Soil samples were collected and chemically analyzed from the Riverton Uranium Mill Tailings Remedial Action Site, which lies within the Wind River Indian Reservation in Fremont County, Wyoming. Nineteen soil samples from a depth of 0 to 5 centimeters were collected in August 2011 from the site. The samples were sieved to less than 2 millimeters and analyzed for 44 major and trace elements following a near-total multi-acid extraction. Soil pH was also determined. The geochemical data were compared to a background dataset consisting of 160 soil samples previously collected from the same depth throughout the State of Wyoming as part of another ongoing study by the U.S. Geological Survey. Risk from potentially toxic elements in soil from the site to biologic receptors and humans was estimated by comparing the concentration of these elements with soil screening values established by the U.S. Environmental Protection Agency. All 19 samples exceeded the carcinogenic human health screening level for arsenic in residential soils of 0.39 milligrams per kilogram (mg/kg), which represents a one-in-one-million cancer risk (median arsenic concentration in the study area is 2.7 mg/kg). All 19 samples also exceeded the lead and vanadium screening levels for birds. Eighteen of the 19 samples exceeded the manganese screening level for plants, 13 of the 19 samples exceeded the antimony screening level for mammals, and 10 of 19 samples exceeded the zinc screening level for birds. However, these exceedances are also found in soils at most locations in the Wyoming Statewide soil database, and elevated concentrations alone are not necessarily cause for alarm. Uranium and thorium, two other elements of environmental concern, are elevated in soils at the site as compared to the Wyoming dataset, but no human or ecological soil screening levels have been established for these elements.

Rapid fusion method for the determination of Pu, Np, and Am in large soil samples

DOE PAGES

Maxwell, Sherrod L.; Culligan, Brian; Hutchison, Jay B.; ...

2015-02-14

A new rapid sodium hydroxide fusion method for the preparation of 10-20 g soil samples has been developed by the Savannah River National Laboratory (SRNL). The method enables lower detection limits for plutonium, neptunium, and americium in environmental soil samples. The method also significantly reduces sample processing time and acid fume generation compared to traditional soil digestion techniques using hydrofluoric acid. Ten gram soil aliquots can be ashed and fused using the new method in 1-2 hours, completely dissolving samples, including refractory particles. Pu, Np and Am are separated using stacked 2mL cartridges of TEVA and DGA Resin and measuredmore » using alpha spectrometry. The method can be adapted for measurement by inductively-coupled plasma mass spectrometry (ICP-MS). Two 10 g soil aliquots of fused soil may be combined prior to chromatographic separations to further improve detection limits. Total sample preparation time, including chromatographic separations and alpha spectrometry source preparation, is less than 8 hours.« less

Nutrient Budgets in Successional Northern Hardwood Forests: Uncertainty in soil, root, and tree concentrations and pools (Invited)

NASA Astrophysics Data System (ADS)

Yanai, R. D.; Bae, K.; Levine, C. R.; Lilly, P.; Vadeboncoeur, M. A.; Fatemi, F. R.; Blum, J. D.; Arthur, M.; Hamburg, S.

2013-12-01

Ecosystem nutrient budgets are difficult to construct and even more difficult to replicate. As a result, uncertainty in the estimates of pools and fluxes are rarely reported, and opportunities to assess confidence through replicated measurements are rare. In this study, we report nutrient concentrations and contents of soil and biomass pools in northern hardwood stands in replicate plots within replicate stands in 3 age classes (14-19 yr, 26-29 yr, and > 100 yr) at the Bartlett Experimental Forest, USA. Soils were described by quantitative soil pits in three plots per stand, excavated by depth increment to the C horizon and analyzed by a sequential extraction procedure. Variation in soil mass among pits within stands averaged 28% (coefficient of variation); variation among stands within an age class ranged from 9-25%. Variation in nutrient concentrations were higher still (averaging 38%, within element, depth increment, and extraction type), perhaps because the depth increments contained varying proportions of genetic horizons. To estimate nutrient contents of aboveground biomass, we propagated model uncertainty through allometric equations, and found errors ranging from 3-7%, depending on the stand. The variation in biomass among plots within stands (6-19%) was always larger than the allometric uncertainties. Variability in measured nutrient concentrations of tree tissues were more variable than the uncertainty in biomass. Foliage had the lowest variability (averaging 16% for Ca, Mg, K, N and P within age class and species), and wood had the highest (averaging 30%), when reported in proportion to the mean, because concentrations in wood are low. For Ca content of aboveground biomass, sampling variation was the greatest source of uncertainty. Coefficients of variation among plots within a stand averaged 16%; stands within an age class ranged from 5-25% CV, including uncertainties in tree allometry and tissue chemistry. Uncertainty analysis can help direct research effort to areas most in need of improvement. In systems such as the one we studied, more intensive sampling would be the best approach to reducing uncertainty, as natural spatial variation was higher than model or measurement uncertainties.

Data processing for the Active Particle-induced X-ray Spectrometer and initial scientific results from Chang'e-3 mission

NASA Astrophysics Data System (ADS)

Fu, Xiao-Hui; Li, Chun-Lai; Zhang, Guang-Liang; Zou, Yong-Liao; Liu, Jian-Jun; Ren, Xin; Tan, Xu; Zhang, Xiao-Xia; Zuo, Wei; Wen, Wei-Bin; Peng, Wen-Xi; Cui, Xing-Zhu; Zhang, Cheng-Mo; Wang, Huan-Yu

2014-12-01

The Active Particle-induced X-ray Spectrometer (APXS) is an important payload mounted on the Yutu rover, which is part of the Chang'e-3 mission. The scientific objective of APXS is to perform in-situ analysis of the chemical composition of lunar soil and rock samples. The radioactive sources, 55Fe and 109Cd, decay and produce α-particles and X-rays. When X-rays and α-particles interact with atoms in the surface material, they knock electrons out of their orbits, which release energy by emitting X-rays that can be measured by a silicon drift detector (SDD). The elements and their concentrations can be determined by analyzing their peak energies and intensities. APXS has analyzed both the calibration target and lunar soil once during the first lunar day and again during the second lunar day. The total detection time lasted about 266 min and more than 2000 frames of data records have been acquired. APXS has three operating modes: calibration mode, distance sensing mode and detection mode. In detection mode, work distance can be calculated from the X-ray counting rate collected by SDD. Correction for the effect of temperature has been performed to convert the channel number for each spectrum to X-ray energy. Dead time correction is used to eliminate the systematic error in quantifying the activity of an X-ray pulse in a sample and derive the real count rate. We report APXS data and initial results during the first and second lunar days for the Yutu rover. In this study, we analyze the data from the calibration target and lunar soil on the first lunar day. Seven major elements, including Mg, Al, Si, K, Ca, Ti and Fe, have been identified. Comparing the peak areas and ratios of calibration basalt and lunar soil the landing site was found to be depleted in K, and have lower Mg and Al but higher Ca, Ti, and Fe. In the future, we will obtain the elemental concentrations of lunar soil at the Chang'e-3 landing site using APXS data.

Relationships between soil moisture-holding properties and soil texture, organic matter content, and bulk density

NASA Technical Reports Server (NTRS)

Riley, H. C. F.

1981-01-01

Specimens from the surface horizon and the subsoil of 62 soil horizons in Hedmark and Oppland were investigated to study how the mechanical composition of the soil, the organic matter content and the bulk density affect their porosity and air capacity and their total and available water content. Most of the specimens belonged to the loam group, and a smaller number was from sandy and silty types of soil. Equations were established to make it possible to calculate the water retention curves and the amount of available water from the above mentioned parameters. As a rule, errors derived from the equations are no greater than those which are found in similar research in other countries.

Prevalence of Pre-Analytical Errors in Clinical Chemistry Diagnostic Labs in Sulaimani City of Iraqi Kurdistan

PubMed Central

2017-01-01

Background Laboratory testing is roughly divided into three phases: a pre-analytical phase, an analytical phase and a post-analytical phase. Most analytical errors have been attributed to the analytical phase. However, recent studies have shown that up to 70% of analytical errors reflect the pre-analytical phase. The pre-analytical phase comprises all processes from the time a laboratory request is made by a physician until the specimen is analyzed at the lab. Generally, the pre-analytical phase includes patient preparation, specimen transportation, specimen collection and storage. In the present study, we report the first comprehensive assessment of the frequency and types of pre-analytical errors at the Sulaimani diagnostic labs in Iraqi Kurdistan. Materials and Methods Over 2 months, 5500 venous blood samples were observed in 10 public diagnostic labs of Sulaimani City. The percentages of rejected samples and types of sample inappropriateness were evaluated. The percentage of each of the following pre-analytical errors were recorded: delay in sample transportation, clotted samples, expired reagents, hemolyzed samples, samples not on ice, incorrect sample identification, insufficient sample, tube broken in centrifuge, request procedure errors, sample mix-ups, communication conflicts, misinterpreted orders, lipemic samples, contaminated samples and missed physician’s request orders. The difference between the relative frequencies of errors observed in the hospitals considered was tested using a proportional Z test. In particular, the survey aimed to discover whether analytical errors were recorded and examine the types of platforms used in the selected diagnostic labs. Results The analysis showed a high prevalence of improper sample handling during the pre-analytical phase. In appropriate samples, the percentage error was as high as 39%. The major reasons for rejection were hemolyzed samples (9%), incorrect sample identification (8%) and clotted samples (6%). Most quality control schemes at Sulaimani hospitals focus only on the analytical phase, and none of the pre-analytical errors were recorded. Interestingly, none of the labs were internationally accredited; therefore, corrective actions are needed at these hospitals to ensure better health outcomes. Internal and External Quality Assessment Schemes (EQAS) for the pre-analytical phase at Sulaimani clinical laboratories should be implemented at public hospitals. Furthermore, lab personnel, particularly phlebotomists, need continuous training on the importance of sample quality to obtain accurate test results. PMID:28107395

Prevalence of Pre-Analytical Errors in Clinical Chemistry Diagnostic Labs in Sulaimani City of Iraqi Kurdistan.

PubMed

Najat, Dereen

2017-01-01

Laboratory testing is roughly divided into three phases: a pre-analytical phase, an analytical phase and a post-analytical phase. Most analytical errors have been attributed to the analytical phase. However, recent studies have shown that up to 70% of analytical errors reflect the pre-analytical phase. The pre-analytical phase comprises all processes from the time a laboratory request is made by a physician until the specimen is analyzed at the lab. Generally, the pre-analytical phase includes patient preparation, specimen transportation, specimen collection and storage. In the present study, we report the first comprehensive assessment of the frequency and types of pre-analytical errors at the Sulaimani diagnostic labs in Iraqi Kurdistan. Over 2 months, 5500 venous blood samples were observed in 10 public diagnostic labs of Sulaimani City. The percentages of rejected samples and types of sample inappropriateness were evaluated. The percentage of each of the following pre-analytical errors were recorded: delay in sample transportation, clotted samples, expired reagents, hemolyzed samples, samples not on ice, incorrect sample identification, insufficient sample, tube broken in centrifuge, request procedure errors, sample mix-ups, communication conflicts, misinterpreted orders, lipemic samples, contaminated samples and missed physician's request orders. The difference between the relative frequencies of errors observed in the hospitals considered was tested using a proportional Z test. In particular, the survey aimed to discover whether analytical errors were recorded and examine the types of platforms used in the selected diagnostic labs. The analysis showed a high prevalence of improper sample handling during the pre-analytical phase. In appropriate samples, the percentage error was as high as 39%. The major reasons for rejection were hemolyzed samples (9%), incorrect sample identification (8%) and clotted samples (6%). Most quality control schemes at Sulaimani hospitals focus only on the analytical phase, and none of the pre-analytical errors were recorded. Interestingly, none of the labs were internationally accredited; therefore, corrective actions are needed at these hospitals to ensure better health outcomes. Internal and External Quality Assessment Schemes (EQAS) for the pre-analytical phase at Sulaimani clinical laboratories should be implemented at public hospitals. Furthermore, lab personnel, particularly phlebotomists, need continuous training on the importance of sample quality to obtain accurate test results.

Calibration of micromechanical parameters for DEM simulations by using the particle filter

NASA Astrophysics Data System (ADS)

Cheng, Hongyang; Shuku, Takayuki; Thoeni, Klaus; Yamamoto, Haruyuki

2017-06-01

The calibration of DEM models is typically accomplished by trail and error. However, the procedure lacks of objectivity and has several uncertainties. To deal with these issues, the particle filter is employed as a novel approach to calibrate DEM models of granular soils. The posterior probability distribution of the microparameters that give numerical results in good agreement with the experimental response of a Toyoura sand specimen is approximated by independent model trajectories, referred as `particles', based on Monte Carlo sampling. The soil specimen is modeled by polydisperse packings with different numbers of spherical grains. Prepared in `stress-free' states, the packings are subjected to triaxial quasistatic loading. Given the experimental data, the posterior probability distribution is incrementally updated, until convergence is reached. The resulting `particles' with higher weights are identified as the calibration results. The evolutions of the weighted averages and posterior probability distribution of the micro-parameters are plotted to show the advantage of using a particle filter, i.e., multiple solutions are identified for each parameter with known probabilities of reproducing the experimental response.

REPRESENTATIVE SAMPLING AND ANALYSIS OF HETEROGENEOUS SOILS

EPA Science Inventory

Standard sampling and analysis methods for hazardous substances in contaminated soils currently are available and routinely employed. Standard methods inherently assume a homogeneous soil matrix and contaminant distribution; therefore only small sample quantities typically are p...

Evaluation and characterization of thyroid-disrupting activities in soil samples along the Second Songhua River, China.

PubMed

Kong, Dongdong; Wang, Yafei; Wang, Jinsheng; Teng, Yanguo; Li, Na; Li, Jian

2016-11-01

In this study, a recombinant thyroid receptor (TR) gene yeast assay combined with Monte Carlo simulation were used to evaluate and characterize soil samples collected from Jilin (China) along the Second Songhua River, for their ant/agonist effect on TR. No TR agonistic activity was found in soils, but many soil samples exhibited TR antagonistic activities, and the bioassay-derived amiodarone hydrochloride equivalents, which was calculated based on Monte Carlo simulation, ranged from not detected (N.D.) to 35.5μg/g. Hydrophilic substance fractions were determined to be the contributors to TR antagonistic activity in these soil samples. Our results indicate that the novel calculation method is effective for the quantification and characterization of TR antagonists in soil samples, and these data could provide useful information for future management and remediation efforts for contaminated soils. Copyright © 2016 Elsevier Inc. All rights reserved.

Impact of climate, vegetation, soil and crop management variables on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site

NASA Astrophysics Data System (ADS)

Garrigues, S.; Olioso, A.; Carrer, D.; Decharme, B.; Calvet, J.-C.; Martin, E.; Moulin, S.; Marloie, O.

2015-10-01

Generic land surface models are generally driven by large-scale data sets to describe the climate, the soil properties, the vegetation dynamic and the cropland management (irrigation). This paper investigates the uncertainties in these drivers and their impacts on the evapotranspiration (ET) simulated from the Interactions between Soil, Biosphere, and Atmosphere (ISBA-A-gs) land surface model over a 12-year Mediterranean crop succession. We evaluate the forcing data sets used in the standard implementation of ISBA over France where the model is driven by the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) high spatial resolution atmospheric reanalysis, the leaf area index (LAI) time courses derived from the ECOCLIMAP-II land surface parameter database and the soil texture derived from the French soil database. For climate, we focus on the radiations and rainfall variables and we test additional data sets which include the ERA-Interim (ERA-I) low spatial resolution reanalysis, the Global Precipitation Climatology Centre data set (GPCC) and the MeteoSat Second Generation (MSG) satellite estimate of downwelling shortwave radiations. The evaluation of the drivers indicates very low bias in daily downwelling shortwave radiation for ERA-I (2.5 W m-2) compared to the negative biases found for SAFRAN (-10 W m-2) and the MSG satellite (-12 W m-2). Both SAFRAN and ERA-I underestimate downwelling longwave radiations by -12 and -16 W m-2, respectively. The SAFRAN and ERA-I/GPCC rainfall are slightly biased at daily and longer timescales (1 and 0.5 % of the mean rainfall measurement). The SAFRAN rainfall is more precise than the ERA-I/GPCC estimate which shows larger inter-annual variability in yearly rainfall error (up to 100 mm). The ECOCLIMAP-II LAI climatology does not properly resolve Mediterranean crop phenology and underestimates the bare soil period which leads to an overall overestimation of LAI over the crop succession. The simulation of irrigation by the model provides an accurate irrigation amount over the crop cycle but the timing of irrigation occurrences is frequently unrealistic. Errors in the soil hydrodynamic parameters and the lack of irrigation in the simulation have the largest influence on ET compared to uncertainties in the large-scale climate reanalysis and the LAI climatology. Among climate variables, the errors in yearly ET are mainly related to the errors in yearly rainfall. The underestimation of the available water capacity and the soil hydraulic diffusivity induce a large underestimation of ET over 12 years. The underestimation of radiations by the reanalyses and the absence of irrigation in the simulation lead to the underestimation of ET while the overall overestimation of LAI by the ECOCLIMAP-II climatology induces an overestimation of ET over 12 years. This work shows that the key challenges to monitor the water balance of cropland at regional scale concern the representation of the spatial distribution of the soil hydrodynamic parameters, the variability of the irrigation practices, the seasonal and inter-annual dynamics of vegetation and the spatiotemporal heterogeneity of rainfall.

Soil pollution in the railway junction Niš (Serbia) and possibility of bioremediation of hydrocarbon-contaminated soil

NASA Astrophysics Data System (ADS)

Jovanovic, Larisa; Aleksic, Gorica; Radosavljevic, Milan; Onjia, Antonije

2015-04-01

Mineral oil leaking from vehicles or released during accidents is an important source of soil and ground water pollution. In the railway junction Niš (Serbia) total 90 soil samples polluted with mineral oil derivatives were investigated. Field work at the railway Niš sites included the opening of soil profiles and soil sampling. The aim of this work is the determination of petroleum hydrocarbons concentration in the soil samples and the investigation of the bioremediation technique for treatment heavily contaminated soil. For determination of petroleum hydrocarbons in the soil samples method of gas-chromatography was carried out. On the basis of measured concentrations of petroleum hydrocarbons in the soil it can be concluded that: Obtained concentrations of petroleum hydrocarbons in 60% of soil samples exceed the permissible values (5000 mg/kg). The heavily contaminated soils, according the Regulation on the program of systematic monitoring of soil quality indicators for assessing the risk of soil degradation and methodology for development of remediation programs, Annex 3 (Official Gazette of RS, No.88 / 2010), must be treated using some of remediation technologies. Between many types of phytoremediation of soil contaminated with mineral oils and their derivatives, the most suitable are phytovolatalisation and phytostimulation. During phytovolatalisation plants (poplar, willow, aspen, sorgum, and rye) absorb organic pollutants through the root, and then transported them to the leaves where the reduced pollutants are released into the atmosphere. In the case of phytostimulation plants (mulberry, apple, rye, Bermuda) secrete from the roots enzymes that stimulates the growth of bacteria in the soil. The increase in microbial activity in soil promotes the degradation of pollutants. Bioremediation is performed by composting the contaminated soil with addition of composting materials (straw, manure, sawdust, and shavings), moisture components, oligotrophs and heterotrophs bacteria.

Information Requirements for Integrating Spatially Discrete, Feature-Based Earth Observations

NASA Astrophysics Data System (ADS)

Horsburgh, J. S.; Aufdenkampe, A. K.; Lehnert, K. A.; Mayorga, E.; Hsu, L.; Song, L.; Zaslavsky, I.; Valentine, D. L.

2014-12-01

Several cyberinfrastructures have emerged for sharing observational data collected at densely sampled and/or highly instrumented field sites. These include the CUAHSI Hydrologic Information System (HIS), the Critical Zone Observatory Integrated Data Management System (CZOData), the Integrated Earth Data Applications (IEDA) and EarthChem system, and the Integrated Ocean Observing System (IOOS). These systems rely on standard data encodings and, in some cases, standard semantics for classes of geoscience data. Their focus is on sharing data on the Internet via web services in domain specific encodings or markup languages. While they have made progress in making data available, it still takes investigators significant effort to discover and access datasets from multiple repositories because of inconsistencies in the way domain systems describe, encode, and share data. Yet, there are many scenarios that require efficient integration of these data types across different domains. For example, understanding a soil profile's geochemical response to extreme weather events requires integration of hydrologic and atmospheric time series with geochemical data from soil samples collected over various depth intervals from soil cores or pits at different positions on a landscape. Integrated access to and analysis of data for such studies are hindered because common characteristics of data, including time, location, provenance, methods, and units are described differently within different systems. Integration requires syntactic and semantic translations that can be manual, error-prone, and lossy. We report information requirements identified as part of our work to define an information model for a broad class of earth science data - i.e., spatially-discrete, feature-based earth observations resulting from in-situ sensors and environmental samples. We sought to answer the question: "What information must accompany observational data for them to be archivable and discoverable within a publication system as well as interpretable once retrieved from such a system for analysis and (re)use?" We also describe development of multiple functional schemas (i.e., physical implementations for data storage, transfer, and archival) for the information model that capture the requirements reported here.

Consumption of methane by soils.

PubMed

Dueñas, C; Fernández, M C; Carretero, J; Pérez, M; Liger, E

1994-05-01

Measurements of the methane flux and methane concentration profiles in soil air are presented. The flux of methane from the soil is calculated by two methods: a) Direct by placing a static open chamber at the soil surface. b) Indirect, using the (222)Rn concentrations profile and the (222)Rn flux in the soil surface in parallel with the methane concentration ((222)Rn calibrated fluxes). The methane flux has been determined in two kinds of soils (sandy and loamy) in the surroundings of Málaga (SPAIN). The directly measured methane fluxes at all investigated sites is higher than methane fluxes derived from "Rn calibrated fluxes". Atmospheric methane is consumed by soils, mean direct flux to the atmosphere were - 0.33 g m(-2)yr-1. The direct methane flux is the same within the measuring error in sandy and loamy soils. The influence of the soil parameters on the methane flux indicates that microbial decomposition of methane is primarily controlled by the transport of methane.

On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar

PubMed Central

Verhoest, Niko E.C; Lievens, Hans; Wagner, Wolfgang; Álvarez-Mozos, Jesús; Moran, M. Susan; Mattia, Francesco

2008-01-01

Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale. PMID:27879932

Distribution and Diversity of Soil Microfauna from East Antarctica: Assessing the Link between Biotic and Abiotic Factors

PubMed Central

Velasco-Castrillón, Alejandro; Schultz, Mark B.; Colombo, Federica; Gibson, John A. E.; Davies, Kerrie A.; Austin, Andrew D.; Stevens, Mark I.

2014-01-01

Terrestrial life in Antarctica has been described as some of the simplest on the planet, and mainly confined to soil microfaunal communities. Studies have suggested that the lack of diversity is due to extreme environmental conditions and thought to be driven by abiotic factors. In this study we investigated soil microfauna composition, abundance, and distribution in East Antarctica, and assessed correlations with soil geochemistry and environmental variables. We examined 109 soil samples from a wide range of ice-free habitats, spanning 2000 km from Framnes Mountains to Bailey Peninsula. Microfauna across all samples were patchily distributed, from complete absence of invertebrates to over 1600 specimens/gram of dry weight of soil (gdw), with highest microfauna abundance observed in samples with visible vegetation. Bdelloid rotifers were on average the most widespread found in 87% of sampled sites and the most abundant (44 specimens/gdw). Tardigrades occurred in 57% of the sampled sites with an abundance of 12 specimens/gdw. Nematodes occurred in 71% of samples with a total abundance of 3 specimens/gdw. Ciliates and mites were rarely found in soil samples, with an average abundance of 1.3 and 0.04 specimens/gdw, respectively. We found that microfaunal composition and abundance were mostly correlated with the soil geochemical parameters; phosphorus, NO3 − and salinity, and likely to be the result of soil properties and historic landscape formation and alteration, rather than the geographic region they were sampled from. Studies focusing on Antarctic biodiversity must take into account soil geochemical and environmental factors that influence population and species heterogeneity. PMID:24498126

Distribution of pesticide residues in soil and uncertainty of sampling.

PubMed

Suszter, Gabriela K; Ambrus, Árpád

2017-08-03

Pesticide residues were determined in about 120 soil cores taken randomly from the top 15 cm layer of two sunflower fields about 30 days after preemergence herbicide treatments. Samples were extracted with acetone-ethyl acetate mixture and the residues were determined with GC-TSD. Residues of dimethenamid, pendimethalin, and prometryn ranged from 0.005 to 2.97 mg/kg. Their relative standard deviations (CV) were between 0.66 and 1.13. The relative frequency distributions of residues in soil cores were very similar to those observed in root and tuber vegetables grown in pesticide treated soils. Based on all available information, a typical CV of 1.00 was estimated for pesticide residues in primary soil samples (soil cores). The corresponding expectable relative uncertainty of sampling is 20% when composite samples of size 25 are taken. To obtain a reliable estimate of the average residues in the top 15 cm layer of soil of a field up to 8 independent replicate random samples should be taken. To obtain better estimate of the actual residue level of the sampled filed would be marginal if larger number of samples were taken.

[Biodegradation of landfill leachate in soil].

PubMed

Fu, Mei-yun; Zhou, Li-xiang

2007-01-01

With aerobic and anaerobic incubation tests, this paper studied the biodegradation of three kind landfill leachates in acidic and calcareous soils. The leachates were collected from a landfill just receiving refuse (fresh sample) and the landfills having received refuse for 4-5 years (Tianjingwa sample) and 12 years (Shuige sample). The results showed that in the first seven days of incubation, these three landfill leachates degraded more quickly. Under aerobic condition, the apparent degradation rate of fresh sample, Tianjingwa sample and Shuige sample was 88.9%, 60.5% and 25.0% in acidic soil, and 96.6%, 80.4%, and 65.0% in calcareous soil, respectively. Seven days after, a lower degradation rate was observed. In same test soils, the shorter the landfilling age, the higher apparent degradation rate of the leachates was. Similar results were obtained under anaerobic condition, but the degradation rates were lower. The degradation of test landfill leachates fitted first-order kinetics model well, with a half-life of 12-16 days for fresh sample, and 20-30 days for Tianjingwa and Shuige samples. Once the leachates penetrated into soil, their degradation quickened greatly, suggesting that soil treatment of landfill leachate could have definite efficacy.

78 FR 28597 - State Median Income Estimates for a Four-Person Household: Notice of the Federal Fiscal Year (FFY...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-15

....gov/acs/www/ or contact the Census Bureau's Social, Economic, and Housing Statistics Division at (301...) Sampling Error, which consists of the error that arises from the use of probability sampling to create the... direction; and (2) Sampling Error, which consists of the error that arises from the use of probability...

Comparing soil moisture anomalies from multiple independent sources over different regions across the globe

NASA Astrophysics Data System (ADS)

Cammalleri, Carmelo; Vogt, Jürgen V.; Bisselink, Bernard; de Roo, Ad

2017-12-01

Agricultural drought events can affect large regions across the world, implying the need for a suitable global tool for an accurate monitoring of this phenomenon. Soil moisture anomalies are considered a good metric to capture the occurrence of agricultural drought events, and they have become an important component of several operational drought monitoring systems. In the framework of the JRC Global Drought Observatory (GDO, http://edo.jrc.ec.europa.eu/gdo/), the suitability of three datasets as possible representations of root zone soil moisture anomalies has been evaluated: (1) the soil moisture from the Lisflood distributed hydrological model (namely LIS), (2) the remotely sensed Land Surface Temperature data from the MODIS satellite (namely LST), and (3) the ESA Climate Change Initiative combined passive/active microwave skin soil moisture dataset (namely CCI). Due to the independency of these three datasets, the triple collocation (TC) technique has been applied, aiming at quantifying the likely error associated with each dataset in comparison to the unknown true status of the system. TC analysis was performed on five macro-regions (namely North America, Europe, India, southern Africa and Australia) detected as suitable for the experiment, providing insight into the mutual relationship between these datasets as well as an assessment of the accuracy of each method. Even if no definitive statement on the spatial distribution of errors can be provided, a clear outcome of the TC analysis is the good performance of the remote sensing datasets, especially CCI, over dry regions such as Australia and southern Africa, whereas the outputs of LIS seem to be more reliable over areas that are well monitored through meteorological ground station networks, such as North America and Europe. In a global drought monitoring system, the results of the error analysis are used to design a weighted-average ensemble system that exploits the advantages of each dataset.

[Distribution Characteristics of Heavy Metals in Environmental Samples Around Electroplating Factories and the Health Risk Assessment].

PubMed

Guo, Peng-ran; Lei, Yong-qian; Zhou, Qiao-li; Wang, Chang; Pan, Jia-chuan

2015-09-01

This study aimed to investigate the pollution degree and human health risk of heavy metals in soil and air samples around electroplating factories. Soil, air and waste gas samples were collected to measure 8 heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn) in two electroplating factories, located in Baiyun district of Guangzhou city. Geoaccumulation index and USEPA Risk Assessment Guidance for Superfund (RAGS) were respectively carried out. Results showed that concentrations of Hg and Pb in waste gas and Cr in air samples were higher than limits of the corresponding quality standards, and concentrations of Cd, Hg and Zn in soil samples reached the moderate pollution level. The HQ and HI of exposure by heavy metals in air and soil samples were both lower than 1, indicating that there was no non-carcinogen risk. CRAs and CRCr in soil samples were beyond the maximum acceptable level of carcinogen risk (10(-4)), and the contribution rate of CRCr to TCR was over 81%. CRCr, CRNi and TCR in air samples were in range of 10(-6) - 10(-4), indicating there was possibly carcinogen risk but was acceptable risk. CR values for children were higher than adults in soils, but were higher for adults in air samples. Correlation analysis revealed that concentrations of heavy metals in soils were significantly correlated with these in waste gas samples, and PCA data showed pollution sources of Cd, Hg and Zn in soils were different from other metals.

Pesticides in soils and ground water in selected irrigated agricultural areas near Havre, Ronan, and Huntley, Montana

USGS Publications Warehouse

Clark, D.W.

1990-01-01

Three areas in Montana representing a range of agricultural practices and applied pesticides, were studied to document whether agricultural pesticides are being transported into the soil and shallow groundwater in irrigated areas. Analytical scans for triazine herbicides, organic-acid herbicides, and carbamate insecticides were performed on soil and shallow groundwater samples. The results indicate pesticide residue in both types of samples. The concentrations of pesticides in the groundwater were less than Federal health-advisory limits. At the Havre Agricultural Experiment Station, eight wells were installed at two sites. All four soil samples and two of four water samples collected after application of pesticides contained detectable concentrations of atrazine or dicamba. In an area where seed potatoes are grown near Ronan, eight wells were installed at two sites. Pesticides were not detected after initial application of pesticides and irrigation water. The site was resampled after irrigation water was reapplied, and aldicarb metabolities were detected in four of five soil samples and one of five water samples. At the Huntley Agricultural Experiment Station, five wells were installed in a no-tillage corn field where atrazine was applied in 1987. Soil and water samples were collected in June and July 1988; pesticides were not detected in any samples. Results indicate residue of two pesticides in soil samples and three soluble pesticides in groundwater samples. Therefore, irrigated agricultural areas in Montana might be susceptible to transport of soluble pesticides through permeable soil to the shallow groundwater system. (USGS)

Soil Gas Sample Handling: Evaluation of Water Removal and Sample Ganging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fritz, Brad G.; Abrecht, David G.; Hayes, James C.

2016-10-31

Soil gas sampling is currently conducted in support of Nuclear Test Ban treaty verification. Soil gas samples are collected and analyzed for isotopes of interest. Some issues that can impact sampling and analysis of these samples are excess moisture and sample processing time. Here we discuss three potential improvements to the current sampling protocol; a desiccant for water removal, use of molecular sieve to remove CO 2 from the sample during collection, and a ganging manifold to allow composite analysis of multiple samples.

Microbial soil community analyses for forensic science: Application to a blind test.

PubMed

Demanèche, Sandrine; Schauser, Leif; Dawson, Lorna; Franqueville, Laure; Simonet, Pascal

2017-01-01

Soil complexity, heterogeneity and transferability make it valuable in forensic investigations to help obtain clues as to the origin of an unknown sample, or to compare samples from a suspect or object with samples collected at a crime scene. In a few countries, soil analysis is used in matters from site verification to estimates of time after death. However, up to date the application or use of soil information in criminal investigations has been limited. In particular, comparing bacterial communities in soil samples could be a useful tool for forensic science. To evaluate the relevance of this approach, a blind test was performed to determine the origin of two questioned samples (one from the mock crime scene and the other from a 50:50 mixture of the crime scene and the alibi site) compared to three control samples (soil samples from the crime scene, from a context site 25m away from the crime scene and from the alibi site which was the suspect's home). Two biological methods were used, Ribosomal Intergenic Spacer Analysis (RISA), and 16S rRNA gene sequencing with Illumina Miseq, to evaluate the discriminating power of soil bacterial communities. Both techniques discriminated well between soils from a single source, but a combination of both techniques was necessary to show that the origin was a mixture of soils. This study illustrates the potential of applying microbial ecology methodologies in soil as an evaluative forensic tool. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Dry heat effects on survival of indigenous soil particle microflora and particle viability studies of Kennedy Space Center soil

NASA Technical Reports Server (NTRS)

Ruschmeyer, O. R.; Pflug, I. J.; Gove, R.; Heisserer, Y.

1975-01-01

Research efforts were concentrated on attempts to obtain data concerning the dry heat resistance of particle microflora in Kennedy Space Center soil samples. The in situ dry heat resistance profiles at selected temperatures for the aggregate microflora on soil particles of certain size ranges were determined. Viability profiles of older soil samples were compared with more recently stored soil samples. The effect of increased particle numbers on viability profiles after dry heat treatment was investigated. These soil particle viability data for various temperatures and times provide information on the soil microflora response to heat treatment and are useful in making selections for spacecraft sterilization cycles.

Mapping Copper and Lead Concentrations at Abandoned Mine Areas Using Element Analysis Data from ICP–AES and Portable XRF Instruments: A Comparative Study

PubMed Central

Lee, Hyeongyu; Choi, Yosoon; Suh, Jangwon; Lee, Seung-Ho

2016-01-01

Understanding spatial variation of potentially toxic trace elements (PTEs) in soil is necessary to identify the proper measures for preventing soil contamination at both operating and abandoned mining areas. Many studies have been conducted worldwide to explore the spatial variation of PTEs and to create soil contamination maps using geostatistical methods. However, they generally depend only on inductively coupled plasma atomic emission spectrometry (ICP–AES) analysis data, therefore such studies are limited by insufficient input data owing to the disadvantages of ICP–AES analysis such as its costly operation and lengthy period required for analysis. To overcome this limitation, this study used both ICP–AES and portable X-ray fluorescence (PXRF) analysis data, with relatively low accuracy, for mapping copper and lead concentrations at a section of the Busan abandoned mine in Korea and compared the prediction performances of four different approaches: the application of ordinary kriging to ICP–AES analysis data, PXRF analysis data, both ICP–AES and transformed PXRF analysis data by considering the correlation between the ICP–AES and PXRF analysis data, and co-kriging to both the ICP–AES (primary variable) and PXRF analysis data (secondary variable). Their results were compared using an independent validation data set. The results obtained in this case study showed that the application of ordinary kriging to both ICP–AES and transformed PXRF analysis data is the most accurate approach when considers the spatial distribution of copper and lead contaminants in the soil and the estimation errors at 11 sampling points for validation. Therefore, when generating soil contamination maps for an abandoned mine, it is beneficial to use the proposed approach that incorporates the advantageous aspects of both ICP–AES and PXRF analysis data. PMID:27043594

Mapping Copper and Lead Concentrations at Abandoned Mine Areas Using Element Analysis Data from ICP-AES and Portable XRF Instruments: A Comparative Study.

PubMed

Lee, Hyeongyu; Choi, Yosoon; Suh, Jangwon; Lee, Seung-Ho

2016-03-30

Understanding spatial variation of potentially toxic trace elements (PTEs) in soil is necessary to identify the proper measures for preventing soil contamination at both operating and abandoned mining areas. Many studies have been conducted worldwide to explore the spatial variation of PTEs and to create soil contamination maps using geostatistical methods. However, they generally depend only on inductively coupled plasma atomic emission spectrometry (ICP-AES) analysis data, therefore such studies are limited by insufficient input data owing to the disadvantages of ICP-AES analysis such as its costly operation and lengthy period required for analysis. To overcome this limitation, this study used both ICP-AES and portable X-ray fluorescence (PXRF) analysis data, with relatively low accuracy, for mapping copper and lead concentrations at a section of the Busan abandoned mine in Korea and compared the prediction performances of four different approaches: the application of ordinary kriging to ICP-AES analysis data, PXRF analysis data, both ICP-AES and transformed PXRF analysis data by considering the correlation between the ICP-AES and PXRF analysis data, and co-kriging to both the ICP-AES (primary variable) and PXRF analysis data (secondary variable). Their results were compared using an independent validation data set. The results obtained in this case study showed that the application of ordinary kriging to both ICP-AES and transformed PXRF analysis data is the most accurate approach when considers the spatial distribution of copper and lead contaminants in the soil and the estimation errors at 11 sampling points for validation. Therefore, when generating soil contamination maps for an abandoned mine, it is beneficial to use the proposed approach that incorporates the advantageous aspects of both ICP-AES and PXRF analysis data.

L-Band H Polarized Microwave Emission During the Corn Growth Cycle

NASA Technical Reports Server (NTRS)

Joseph, A. T.; va der Velde, R.; O'Neill, P. E.; Kim, E.; Lang, R. H.; Gish, T.

2012-01-01

Hourly L-band (1.4 GHz) horizontally (H) polarized brightness temperatures (T(sub B))'s measured during five episodes (more than two days of continuous measurements) of the 2002 corn growth cycle are analyzed. These T(sub B)'s measurements were acquired as a part of a combined active/passive microwave field campaign, and were obtained at five incidence and three azimuth angles relative to the row direction. In support of this microwave data collection, intensive ground sampling took place once a week. Moreover, the interpretation of the hourly T(sub B)'s could also rely on the data obtained using the various automated instruments installed in the same field. In this paper, the soil moisture and temperature measured at fixed time intervals have been employed as input for the tau-omega model to reproduce the hourly T(sub B). Through the calibration of the vegetation and surface roughness parameterizations, the impact of the vegetation morphological changes on the microwave emission and the dependence of the soil surface roughness parameter, h(sub r), on soil moisture are investigated. This analysis demonstrates that the b parameter, appearing in the representation of the canopy opacity, has an angular dependence that varies throughout the growing period and also that the parameter hr increases as the soil dries in a portion of the dry-down cycle. The angular dependence of the b parameter imposes the largest uncertainty on T(sub B) simulations near senescence as the response of b to the incidence is also affected by the crop row orientation. On the other hand, the incorporation of a soil moisture dependent h(sub r) parameterization was responsible for the largest error reduction of T(sub B) simulations in the early growth cycle.

The potential of remotely sensed soil moisture for operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S.; Bierkens, M. F.

2013-12-01

Nowadays, remotely sensed soil moisture is readily available from multiple space born sensors. The high temporal resolution and global coverage make these products very suitable for large-scale land-surface applications. The potential to use these products in operational flood forecasting has thus far not been extensively studied. In this study, we evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the timing and height of the flood peak and low flows. EFAS is used for operational flood forecasting in Europe and uses a distributed hydrological model for flood predictions for lead times up to 10 days. Satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of only discharge observations. Discharge observations are available at the outlet and at six additional locations throughout the catchment. To assimilate soil moisture data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, derived from a detailed model-satellite soil moisture comparison study, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are used in that the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 10-15% on average, compared to assimilation of discharge only. The rank histograms show that the forecast is not biased. The timing errors in the flood predictions are decreased when soil moisture data is used and imminent floods can be forecasted with skill one day earlier. In conclusion, our study shows that assimilation of satellite soil moisture increases the performance of flood forecasting systems for large catchments, like the Upper Danube. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of future soil moisture missions with a higher spatial resolution like SMAP to improve near-real time flood forecasting in large catchments.

Comparison of soil sampling and analytical methods for asbestos at the Sumas Mountain Asbestos Site-Working towards a toolbox for better assessment.

PubMed

Wroble, Julie; Frederick, Timothy; Frame, Alicia; Vallero, Daniel

2017-01-01

Established soil sampling methods for asbestos are inadequate to support risk assessment and risk-based decision making at Superfund sites due to difficulties in detecting asbestos at low concentrations and difficulty in extrapolating soil concentrations to air concentrations. Environmental Protection Agency (EPA)'s Office of Land and Emergency Management (OLEM) currently recommends the rigorous process of Activity Based Sampling (ABS) to characterize site exposures. The purpose of this study was to compare three soil analytical methods and two soil sampling methods to determine whether one method, or combination of methods, would yield more reliable soil asbestos data than other methods. Samples were collected using both traditional discrete ("grab") samples and incremental sampling methodology (ISM). Analyses were conducted using polarized light microscopy (PLM), transmission electron microscopy (TEM) methods or a combination of these two methods. Data show that the fluidized bed asbestos segregator (FBAS) followed by TEM analysis could detect asbestos at locations that were not detected using other analytical methods; however, this method exhibited high relative standard deviations, indicating the results may be more variable than other soil asbestos methods. The comparison of samples collected using ISM versus discrete techniques for asbestos resulted in no clear conclusions regarding preferred sampling method. However, analytical results for metals clearly showed that measured concentrations in ISM samples were less variable than discrete samples.

Comparison of soil sampling and analytical methods for asbestos at the Sumas Mountain Asbestos Site—Working towards a toolbox for better assessment

PubMed Central

2017-01-01

Established soil sampling methods for asbestos are inadequate to support risk assessment and risk-based decision making at Superfund sites due to difficulties in detecting asbestos at low concentrations and difficulty in extrapolating soil concentrations to air concentrations. Environmental Protection Agency (EPA)’s Office of Land and Emergency Management (OLEM) currently recommends the rigorous process of Activity Based Sampling (ABS) to characterize site exposures. The purpose of this study was to compare three soil analytical methods and two soil sampling methods to determine whether one method, or combination of methods, would yield more reliable soil asbestos data than other methods. Samples were collected using both traditional discrete (“grab”) samples and incremental sampling methodology (ISM). Analyses were conducted using polarized light microscopy (PLM), transmission electron microscopy (TEM) methods or a combination of these two methods. Data show that the fluidized bed asbestos segregator (FBAS) followed by TEM analysis could detect asbestos at locations that were not detected using other analytical methods; however, this method exhibited high relative standard deviations, indicating the results may be more variable than other soil asbestos methods. The comparison of samples collected using ISM versus discrete techniques for asbestos resulted in no clear conclusions regarding preferred sampling method. However, analytical results for metals clearly showed that measured concentrations in ISM samples were less variable than discrete samples. PMID:28759607

[Development of an analyzing system for soil parameters based on NIR spectroscopy].

PubMed

Zheng, Li-Hua; Li, Min-Zan; Sun, Hong

2009-10-01

A rapid estimation system for soil parameters based on spectral analysis was developed by using object-oriented (OO) technology. A class of SOIL was designed. The instance of the SOIL class is the object of the soil samples with the particular type, specific physical properties and spectral characteristics. Through extracting the effective information from the modeling spectral data of soil object, a map model was established between the soil parameters and its spectral data, while it was possible to save the mapping model parameters in the database of the model. When forecasting the content of any soil parameter, the corresponding prediction model of this parameter can be selected with the same soil type and the similar soil physical properties of objects. And after the object of target soil samples was carried into the prediction model and processed by the system, the accurate forecasting content of the target soil samples could be obtained. The system includes modules such as file operations, spectra pretreatment, sample analysis, calibrating and validating, and samples content forecasting. The system was designed to run out of equipment. The parameters and spectral data files (*.xls) of the known soil samples can be input into the system. Due to various data pretreatment being selected according to the concrete conditions, the results of predicting content will appear in the terminal and the forecasting model can be stored in the model database. The system reads the predicting models and their parameters are saved in the model database from the module interface, and then the data of the tested samples are transferred into the selected model. Finally the content of soil parameters can be predicted by the developed system. The system was programmed with Visual C++6.0 and Matlab 7.0. And the Access XP was used to create and manage the model database.

Assessment of groundwater and soil quality for agricultural purposes in Kopruoren basin, Kutahya, Turkey

NASA Astrophysics Data System (ADS)

Arslan, Sebnem

2017-07-01

This research evaluated the irrigation water and agricultural soil quality in the Kopruoren Basin by using hierarchical cluster analysis. Physico-chemical properties and major ion chemistry of 19 groundwater samples were used to determine the irrigation water quality indices. The results revealed out that the groundwaters are in general suitable for irrigation and have low sodium hazard, although they are very hard in nature due to the dominant presence of Ca+2, Mg+2 and HCO3- ions. Water samples contain arsenic in concentrations below the recommended guidelines for irrigation (59.7 ± 14.7 μg/l), however, arsenic concentrations in 89% of the 9 soil samples exceed the maximum allowable concentrations set for agricultural soils (81 ± 24.3 mg/kg). Nickel element, albeit not present in high concentrations in water samples, is enriched in all of the agricultural soil samples (390 ± 118.2 mg/kg). Hierarchical cluster analysis studies conducted to identify the sources of chemical constituents in water and soil samples elicited that the chemistry of the soils in the study area are highly impacted by the soil parent material and both geogenic and anthropogenic pollution sources are responsible for the metal contents of the soil samples. On the other hand, water chemistry in the area is affected by water-rock interactions, anthropogenic and agricultural pollution.

Using Large-Scale Precipitation to Validate AMSR-E Satellite Soil Moisture Estimates by Means of Mutual Information

NASA Astrophysics Data System (ADS)

Tuttle, S. E.; Salvucci, G.

2013-12-01

Validation of remotely sensed soil moisture is complicated by the difference in scale between remote sensing footprints and traditional ground-based soil moisture measurements. To address this issue, a new method was developed to evaluate the useful information content of remotely sensed soil moisture data using only large-scale precipitation (i.e. without modeling). Under statistically stationary conditions [Salvucci, 2001], precipitation conditionally averaged according to soil moisture (denoted E[P|S]) results in a sigmoidal shape in a manner that reflects the dependence of drainage, runoff, and evapotranspiration on soil moisture. However, errors in satellite measurement and algorithmic conversion of satellite data to soil moisture can degrade this relationship. Thus, remotely sensed soil moisture products can be assessed by the degree to which the natural sigmoidal relationship is preserved. The metric of mutual information was used as an error-dependent measure of the strength of the sigmoidal relationship, calculated from a two-dimensional histogram of soil moisture versus precipitation estimated using Gaussian mixture models. Three AMSR-E algorithms (VUA-NASA [Owe et al., 2001], NASA [Njoku et al., 2003], and U. Montana [Jones & Kimball, 2010]) were evaluated with the method for a nine-year period (2002-2011) over the contiguous United States at ¼° latitude-longitude resolution, using precipitation from the North American Land Data Assimilation System (NLDAS). The U. Montana product resulted in the highest mutual information for 57% of the region, followed by VUA-NASA and NASA at 40% and 3%, respectively. Areas where the U. Montana product yielded the maximum mutual information generally coincided with low vegetation biomass and flatter terrain, while the VUA-NASA product contained more useful information in more rugged and highly vegetated areas. Additionally, E[P|S] curves resulting from the Gaussian mixture method can potentially be decomposed into their conditional evapotranspiration and drainage plus runoff components using matrix factorization methods, allowing for time-averaged mapping of these fluxes over the study area.

Characterization of Soil Samples of Enzyme Activity

ERIC Educational Resources Information Center

Freeland, P. W.

1977-01-01

Described are nine enzyme essays for distinguishing soil samples. Colorimetric methods are used to compare enzyme levels in soils from different sites. Each soil tested had its own spectrum of activity. Attention is drawn to applications of this technique in forensic science and in studies of soil fertility. (Author/AJ)

SOLVENT EXTRACTION AND SOIL WASHING TREATMENT OF CONTAMINATED SOILS FROM WOOD PRESERVING SITES: BENCH SCALE STUDIES

EPA Science Inventory

Bench-scale solvent extraction and soil washing studies were performed on soil samples obtained from three abandoned wood preserving sites that included in the NPL. The soil samples from these sites were contaminated with high levels of polyaromatic hydrocarbons (PAHs), pentachlo...

Blood transfusion sampling and a greater role for error recovery.

PubMed

Oldham, Jane

Patient identification errors in pre-transfusion blood sampling ('wrong blood in tube') are a persistent area of risk. These errors can potentially result in life-threatening complications. Current measures to address root causes of incidents and near misses have not resolved this problem and there is a need to look afresh at this issue. PROJECT PURPOSE: This narrative review of the literature is part of a wider system-improvement project designed to explore and seek a better understanding of the factors that contribute to transfusion sampling error as a prerequisite to examining current and potential approaches to error reduction. A broad search of the literature was undertaken to identify themes relating to this phenomenon. KEY DISCOVERIES: Two key themes emerged from the literature. Firstly, despite multi-faceted causes of error, the consistent element is the ever-present potential for human error. Secondly, current focus on error prevention could potentially be augmented with greater attention to error recovery. Exploring ways in which clinical staff taking samples might learn how to better identify their own errors is proposed to add to current safety initiatives.

Study on a pattern classification method of soil quality based on simplified learning sample dataset

USGS Publications Warehouse

Zhang, Jiahua; Liu, S.; Hu, Y.; Tian, Y.

2011-01-01

Based on the massive soil information in current soil quality grade evaluation, this paper constructed an intelligent classification approach of soil quality grade depending on classical sampling techniques and disordered multiclassification Logistic regression model. As a case study to determine the learning sample capacity under certain confidence level and estimation accuracy, and use c-means algorithm to automatically extract the simplified learning sample dataset from the cultivated soil quality grade evaluation database for the study area, Long chuan county in Guangdong province, a disordered Logistic classifier model was then built and the calculation analysis steps of soil quality grade intelligent classification were given. The result indicated that the soil quality grade can be effectively learned and predicted by the extracted simplified dataset through this method, which changed the traditional method for soil quality grade evaluation. ?? 2011 IEEE.

[Prediction models of soil organic matter based on spectral curve in the upstream of Heihe basin].

PubMed

Liu, Jiao; Li, Yi; Liu, Shi-Bin

2013-12-01

Benefiting from the high spectral resolution, ground hyperspectral remote sensing technology can express the ground surface feature in detail, meanwhile, multispectral remote sensing has more advantages in studying the features in a large space time region, because of its long time-series images and wide coverage. Investigating the prediction models between the soil organic matter (SOM) content and the hyperspectral data and the sensitive bands based on different indices mathematically obtained from reflectance could combine the advantages of both kinds of spectral data, and provide a new method to search the spatio-temporal characteristics of SOM. Two hundred twenty three soil samples were chosen from the upper reaches of Heihe Basin to measure the SOM content and hyperspectral curve. Taking 181 of them, the stepwise linear regression methods were used to establish models between the SOM and five indices, including reflectance (lambda), reciprocal (REC), logarithm of the reciprocal (LR), continuum-removal (CR) and the first derivative reflectance (FDR). After then, the left 42 samples were used for model validation: firstly, the best model of the same index was chosen by the values of Pearson correlation coefficient (r) and Root mean squared error (RMSE) between the measured value and predicted value; secondly, the best models of different indices were compared. As a result, the model built by reflectance has a better estimation of SOM with the r: 0.863 and RMSE: 4.79. And the sensitive bands of the reflectance model contain 474 nm during TM1, 636 nm during TM3 and 1 632 nm during TM5. This result could be a reference for the retrieval of SOM content of the upper reaches by using the TM remote sensing data.

Evaluation of multidimensional transport through a field-scale compacted soil liner

USGS Publications Warehouse

Willingham, T.W.; Werth, C.J.; Valocchi, A.J.; Krapac, I.G.; Toupiol, C.; Stark, T.D.; Daniel, D.E.

2004-01-01

A field-scale compacted soil liner was constructed at the University of Illinois at Urbana-Champaign by the U.S. Environmental Protection Agency (USEPA) and Illinois State Geological Survey in 1988 to investigate chemical transport rates through low permeability compacted clay liners (CCLs). Four tracers (bromide and three benzoic acid tracers) were each added to one of four large ring infiltrometers (LRIs) while tritium was added to the pond water (excluding the infiltrometers). Results from the long-term transport of Br- from the localized source zone of LRI are presented in this paper. Core samples were taken radially outward from the center of the Br- LRI and concentration depth profiles were obtained. Transport properties were evaluated using an axially symmetric transport model. Results indicate that (1) transport was diffusion controlled; (2) transport due to advection was negligible and well within the regulatory limits of ksat???1 ?? 10-7 cm/s; (3) diffusion rates in the horizontal and vertical directions were the same; and (4) small positioning errors due to compression during soil sampling did not affect the best fit advection and diffusion values. The best-fit diffusion coefficient for bromide was equal to the molecular diffusion coefficient multiplied by a tortuosity factor of 0.27, which is within 8% of the tortuosity factor (0.25) found in a related study where tritium transport through the same liner was evaluated. This suggests that the governing mechanisms for the transport of tritium and bromide through the CCL were similar. These results are significant because they address transport through a composite liner from a localized source zone which occurs when defects or punctures in the geomembrane of a composite system are present. ?? ASCE.

Soil Moisture and Temperature Measuring Networks in the Tibetan Plateau and Their Hydrological Applications

NASA Astrophysics Data System (ADS)

Yang, Kun; Chen, Yingying; Qin, Jun; Lu, Hui

2017-04-01

Multi-sphere interactions over the Tibetan Plateau directly impact its surrounding climate and environment at a variety of spatiotemporal scales. Remote sensing and modeling are expected to provide hydro-meteorological data needed for these process studies, but in situ observations are required to support their calibration and validation. For this purpose, we have established two networks on the Tibetan Plateau to measure densely two state variables (soil moisture and temperature) and four soil depths (0 5, 10, 20, and 40 cm). The experimental area is characterized by low biomass, high soil moisture dynamic range, and typical freeze-thaw cycle. As auxiliary parameters of these networks, soil texture and soil organic carbon content are measured at each station to support further studies. In order to guarantee continuous and high-quality data, tremendous efforts have been made to protect the data logger from soil water intrusion, to calibrate soil moisture sensors, and to upscale the point measurements. One soil moisture network is located in a semi-humid area in central Tibetan Plateau (Naqu), which consists of 56 stations with their elevation varying over 4470 4950 m and covers three spatial scales (1.0, 0.3, 0.1 degree). The other is located in a semi-arid area in southern Tibetan Plateau (Pali), which consists of 25 stations and covers an area of 0.25 degree. The spatiotemporal characteristics of the former network were analyzed, and a new spatial upscaling method was developed to obtain the regional mean soil moisture truth from the point measurements. Our networks meet the requirement for evaluating a variety of soil moisture products, developing new algorithms, and analyzing soil moisture scaling. Three applications with the network data are presented in this paper. 1. Evaluation of Current remote sensing and LSM products. The in situ data have been used to evaluate AMSR-E, AMSR2, SMOS and SMAP products and four modeled outputs by the Global Land Data Assimilation System (GLDAS). 2. Development of New Products. We developed a dual-pass land data assimilation system. The essential idea of the system is to calibrate a land data assimilation system before a normal data assimilation. The calibration is based on satellite data rather than in situ data. Through this way, we may alleviate the impact of uncertainties in determining the error covariance of both observation operator and model operation, as it is always tough to determine the covariance. The performance of the data assimilation system is presented through comparison against the Tibetan Plateau soil moisture measuring networks. And the results are encouraging. 3. Estimation of Soil Parameter Values in a Land Surface Model. We explored the possibility to estimate soil parameter values by assimilating AMSR-E brightness temperature (TB) data. In the assimilation system, the TB is simulated by the coupled system of a land surface model (LSM) and a radiative transfer model (RTM), and the simulation errors highly depend on parameters in both the LSM and the RTM. Thus, sensitive soil parameters may be inversely estimated through minimizing the TB errors. The effectiveness of the estimated parameter values is evaluated against intensive measurements of soil parameters and soil moisture in three grasslands of the Tibetan Plateau and the Mongolian Plateau. The results indicate that this satellite data-based approach can improve the data quality of soil porosity, a key parameter for soil moisture modeling, and LSM simulations with the estimated parameter values reasonably reproduce the measured soil moisture. This demonstrates it is feasible to calibrate LSMs for soil moisture simulations at grid scale by assimilating microwave satellite data, although more efforts are expected to improve the robustness of the model calibration.

Multivariate analysis and visualization of soil quality data for no-till systems.

PubMed

Villamil, M B; Miguez, F E; Bollero, G A

2008-01-01

To evidence the multidimensionality of the soil quality concept, we propose the use of data visualization as a tool for exploratory data analyses, model building, and diagnostics. Our objective was to establish the best edaphic indicators for assessing soil quality in four no-till systems with regard to functioning as a medium for crop production and nutrient cycling across two Illinois locations. The compared situations were no-till corn-soybean rotations including either winter fallowing (C/S) or cover crops of rye (Secale cereale; C-R/S-R), hairy vetch (Vicia villosa; C-R/S-V), or their mixture (C-R/S-VR). The dataset included the variables bulk density (BD), penetration resistance (PR), water aggregate stability (WAS), soil reaction (pH), and the contents of soil organic matter (SOM), total nitrogen (TN), soil nitrates (NO(3)-N), and available phosphorus (P). Interactive data visualization along with canonical discriminant analysis (CDA) allowed us to show that WAS, BD, and the contents of P, TN, and SOM have the greatest potential as soil quality indicators in no-till systems in Illinois. It was more difficult to discriminate among WCC rotations than to separate these from C/S, considerably inflating the error rate associated with CDA. We predict that observations of no-till C/S will be classified correctly 51% of the time, while observations of no-till WCC rotations will be classified correctly 74% of the time. High error rates in CDA underscore the complexity of no-till systems and the need in this area for more long-term studies with larger datasets to increase accuracy to acceptable levels.

Assessment of soil and water contaminants from selected locations in and near the Idaho Army National Guard Orchard Training Area, Ada County, Idaho, 2001-2003

USGS Publications Warehouse

Parliman, D.J.

2004-01-01

In 2001, the National Guard Bureau and the U.S. Geological Survey began a project to compile hydrogeologic data and determine presence or absence of soil, surface-water, and ground-water contamination at the Idaho Army National Guard Orchard Training Area in southwestern Idaho. Between June 2002 and April 2003, a total of 114 soil, surface-water, ground-water, precipitation, or dust samples were collected from 68 sample sites (65 different locations) in the Orchard Training Area (OTA) or along the vehicle corridor to the OTA. Soil and water samples were analyzed for concentrations of selected total trace metals, major ions, nutrients, explosive compounds, semivolatile organics, and petroleum hydrocarbons. Water samples also were analyzed for concentrations of selected dissolved trace metals and major ions. Distinguishing naturally occurring large concentrations of trace metals, major ions, and nutrients from contamination related to land and water uses at the OTA was difficult. There were no historical analyses for this area to compare with modern data, and although samples were collected from 65 locations in and near the OTA, sampled areas represented only a small part of the complex OTA land-use areas and soil types. For naturally occurring compounds, several assumptions were made?anomalously large concentrations, when tied to known land uses, may indicate presence of contamination; naturally occurring concentrations cannot be separated from contamination concentrations in mid- and lower ranges of data; and smallest concentrations may represent the lowest naturally occurring range of concentrations and (or) the absence of contaminants related to land and water uses. Presence of explosive, semivolatile organic (SVOC), and petroleum hydrocarbon compounds in samples indicates contamination from land and water uses. In areas along the vehicle corridor and major access roads within the OTA, most trace metal, major ion, and nutrient concentrations in soil samples were not in the upper 10th percentile of data, but concentrations of 25 metals, ions, or nutrients were in the upper 10th percentile in a puddle sample near the heavy equipment maneuvering area, MPRC-H. The largest concentrations of tin, ammonia, and nitrite plus nitrate (as nitrogen) in water from the OTA were detected in a sample from this puddle. Petroleum hydrocarbons were the most common contaminant, detected in all soil and surface-water samples. An SVOC, bis (2-ethylhexyl) phthalate, a plasticizer, was detected at a site along the vehicle corridor. In Maneuver Areas within the OTA, many soil samples contained at least one trace metal, major ion, or nutrient in the upper 10th percentile of data, and the largest concentrations of cobalt, iron, mercury, titanium, sodium, ammonia, or total phosphorus were detected in 6 of 13 soil samples outside the Tadpole Lake area. The largest concentrations of aluminum, arsenic, beryllium, nickel, selenium, silver, strontium, thallium, vanadium, chloride, potassium, sulfate, and nitrite plus nitrate were detected in soil samples from the Tadpole Lake area. Water from Tadpole Lake contained the largest total concentrations of 19 trace metals, 4 major ions, and 1 nutrient. Petroleum hydrocarbons were detected in 5 soil samples and water from Tadpole Lake. SVOCs related to combustion of fuel or plasticizers were detected in 1 soil sample. Explosive compounds were detected in 1 precipitation sample.In the Impact Area within the OTA, most soil samples contained at least one trace metal, major ion, or nutrient in the upper 10th percentile of data, and the largest concentrations of barium, chromium, copper, manganese, lead, or orthophosphate were detected in 6 of the 18 soil samples. Petroleum hydrocarbons were detected in 4 soil samples, SVOCs in 6 samples, and explosive compounds in 4 samples. In the mobilization and training equipment site (MATES) compound adjacent to the OTA, all soil and water samples contained at lea

[Evaluation and source analysis of the mercury pollution in soils and vegetables around a large-scale zinc smelting plant].

PubMed

Liu, Fang; Wang, Shu-Xiao; Wu, Qing-Ru; Lin, Hai

2013-02-01

The farming soil and vegetable samples around a large-scale zinc smelter were collected for mercury content analyses, and the single pollution index method with relevant regulations was used to evaluate the pollution status of sampled soils and vegetables. The results indicated that the surface soil and vegetables were polluted with mercury to different extent. Of the soil samples, 78% exceeded the national standard. The mercury concentration in the most severely contaminated area was 29 times higher than the background concentration, reaching the severe pollution degree. The mercury concentration in all vegetable samples exceeded the standard of non-pollution vegetables. Mercury concentration, in the most severely polluted vegetables were 64.5 times of the standard, and averagely the mercury concentration in the vegetable samples was 25.4 times of the standard. For 85% of the vegetable samples, the mercury concentration, of leaves were significantly higher than that of roots, which implies that the mercury in leaves mainly came from the atmosphere. The mercury concentrations in vegetable roots were significantly correlated with that in soils, indicating the mercury in roots was mainly from soil. The mercury emissions from the zinc smelter have obvious impacts on the surrounding soils and vegetables. Key words:zinc smelting; mercury pollution; soil; vegetable; mercury content

Soil separator and sampler and method of sampling

DOEpatents

O'Brien, Barry H [Idaho Falls, ID; Ritter, Paul D [Idaho Falls, ID

2010-02-16

A soil sampler includes a fluidized bed for receiving a soil sample. The fluidized bed may be in communication with a vacuum for drawing air through the fluidized bed and suspending particulate matter of the soil sample in the air. In a method of sampling, the air may be drawn across a filter, separating the particulate matter. Optionally, a baffle or a cyclone may be included within the fluidized bed for disentrainment, or dedusting, so only the finest particulate matter, including asbestos, will be trapped on the filter. The filter may be removable, and may be tested to determine the content of asbestos and other hazardous particulate matter in the soil sample.

Thermal Conductivity Prediction of Soil in Complex Plant Soil System using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Wardani, A. K.; Purqon, A.

2016-08-01

Thermal conductivity is one of thermal properties of soil in seed germination and plants growth. Different soil types have different thermal conductivity. One of soft-computing promising method to predict thermal conductivity of soil types is Artificial Neural Network (ANN). In this study, we estimate the thermal conductivity of soil prediction in a soil-plant complex systems using ANN. With a feed-forward multilayer trained with back-propagation with 4, 10 and 1 on the input, hidden and output layers respectively. Our input are heating time, temperature and thermal resistance with thermal conductivity of soil as a target. ANN prediction demonstrates a good agreement with Mean Squared Error-testing (MSEte) of 9.56 x 10-7 for soils with green beans and those of bare soils is 7.00 × 10-7 respectively Green beans grow only on black-clay soil with a thermal conductivity of 0.7 W/m K with a sufficient water content. Our results demonstrate that temperature, moisture content, colour, texture and structure of soil are greatly affect to the thermal conductivity of soil in seed germination and plant growth. In future, it is potentially applied to estimate more complex compositions of plant-soil systems.

Spatial distributions and seasonal variations of organochlorine pesticides in water and soil samples in Bolu, Turkey.

PubMed

Karadeniz, Hatice; Yenisoy-Karakaş, Serpil

2015-03-01

In this study, a total of 75 water samples (38 groundwater and 37 surface water samples) and 54 surface soil samples were collected from the five districts of Bolu, which is located in the Western Black Sea Region of Turkey in the summer season of 2009. In the autumn season, 17 water samples (surface water and groundwater samples) and 17 soil samples were collected within the city center to observe the seasonal changes of organochlorine pesticides (OCPs). Groundwater and surface water samples were extracted using solid phase extraction. Soil samples were extracted ultrasonically. Sixteen OCP compounds in the standard solution were detected by a gas chromatography-electron capture detector (GC-ECD). Therefore, the method validation was performed for those 16 OCP compounds. However, 13 OCP compounds could be observed in the samples. The concentrations of most OCPs were higher in samples collected in the summer than those in the autumn. The most frequently observed pesticides were endosulfan sulfate and 4,4'-dichlorodiphenyltrichloroethane (DDT) in groundwater samples, α-HCH in surface water samples, and endosulfan sulfate in soil samples. The average concentration of endosulfan sulfate was the highest in water and soil samples. Compared to the literature values, the average concentrations in this study were lower values. Spatial distribution of OCPs was evaluated with the aid of contour maps for the five districts of Bolu. Generally, agricultural processes affected the water and soil quality in the region. However, non-agricultural areas were also affected by pesticides. The concentrations of pesticides were below the legal limits of European directives for each pesticide.

Mixture and method for simulating soiling and weathering of surfaces

DOEpatents

Sleiman, Mohamad; Kirchstetter, Thomas; Destaillats, Hugo; Levinson, Ronnen; Berdahl, Paul; Akbari, Hashem

2018-01-02

This disclosure provides systems, methods, and apparatus related to simulated soiling and weathering of materials. In one aspect, a soiling mixture may include an aqueous suspension of various amounts of salt, soot, dust, and humic acid. In another aspect, a method may include weathering a sample of material in a first exposure of the sample to ultraviolet light, water vapor, and elevated temperatures, depositing a soiling mixture on the sample, and weathering the sample in a second exposure of the sample to ultraviolet light, water vapor, and elevated temperatures.

Effect of heavy metals on pH buffering capacity and solubility of Ca, Mg, K, and P in non-spiked and heavy metal-spiked soils.

PubMed

Najafi, Sarvenaz; Jalali, Mohsen

2016-06-01

In many parts of the world, soil acidification and heavy metal contamination has become a serious concern due to the adverse effects on chemical properties of soil and crop yield. The aim of this study was to investigate the effect of pH (in the range of 1 to 3 units above and below the native pH of soils) on calcium (Ca), magnesium (Mg), potassium (K), and phosphorus (P) solubility in non-spiked and heavy metal-spiked soil samples. Spiked samples were prepared by cadmium (Cd), copper (Cu), nickel (Ni), and zinc (Zn) as chloride salts and incubating soils for 40 days. The pH buffering capacity (pHBC) of each sample was determined by plotting the amount of H(+) or OH(-) added (mmol kg(-1)) versus the related pH value. The pHBC of soils ranged from 47.1 to 1302.5 mmol kg(-1) for non-spiked samples and from 45.0 to 1187.4 mmol kg(-1) for spiked soil samples. The pHBC values were higher in soil 2 (non-spiked and spiked) which had higher calcium carbonate content. The results indicated the presence of heavy metals in soils generally decreased the solution pH and pHBC values in spiked samples. In general, solubility of Ca, Mg, and K decreased with increasing equilibrium pH of non-spiked and spiked soil samples. In the case of P, increasing the pH to about 7, decreased the solubility in all soils but further increase of pH from 7, enhanced P solubility. The solubility trends and values for Ca, Mg, and K did not differed significantly in non-spiked and spiked samples. But in the case of P, a reduction in solubility was observed in heavy metal-spiked soils. The information obtained in this study can be useful to make better estimation of the effects of soil pollutants on anion and cation solubility from agricultural and environmental viewpoints.

Analyses and description of soil samples from Mountain Lake and Peters Mountain Wilderness Study areas, Virginia and West Virginia

USGS Publications Warehouse

Motooka, J.M.; Curtis, Craig A.; Lesure, Frank Gardner

1978-01-01

Semiquantitative emission spectrographic analyses for 30 elements and atomic absorption analysis for zinc on 98 soil samples are reported here in detail. Location for all samples are in Universal Transverse Mercator (UTM) coordinates. A few samples of soil developed on Lower Devonian sandstone and chert contain more barium and zinc than soils on other formations but do not suggest the occurrence of economic concentrations of either element.