Downscaling SMAP Soil Moisture Using Geoinformation Data and Geostatistics

NASA Astrophysics Data System (ADS)

Xu, Y.; Wang, L.

2017-12-01

Soil moisture is important for agricultural and hydrological studies. However, ground truth soil moisture data for wide area is difficult to achieve. Microwave remote sensing such as Soil Moisture Active Passive (SMAP) can offer a solution for wide coverage. However, existing global soil moisture products only provide observations at coarse spatial resolutions, which often limit their applications in regional agricultural and hydrological studies. This paper therefore aims to generate fine scale soil moisture information and extend soil moisture spatial availability. A statistical downscaling scheme is presented that incorporates multiple fine scale geoinformation data into the downscaling of coarse scale SMAP data in the absence of ground measurement data. Geoinformation data related to soil moisture patterns including digital elevation model (DEM), land surface temperature (LST), land use and normalized difference vegetation index (NDVI) at a fine scale are used as auxiliary environmental variables for downscaling SMAP data. Generalized additive model (GAM) and regression tree are first conducted to derive statistical relationships between SMAP data and auxiliary geoinformation data at an original coarse scale, and residuals are then downscaled to a finer scale via area-to-point kriging (ATPK) by accounting for the spatial correlation information of the input residuals. The results from standard validation scores as well as the triple collocation (TC) method against soil moisture in-situ measurements show that the downscaling method can significantly improve the spatial details of SMAP soil moisture while maintain the accuracy.

Spatial and temporal variability of throughfall and soil moisture in a deciduous forest in the low mountain ranges (Hesse, Germany)

NASA Astrophysics Data System (ADS)

Chifflard, Peter; Weishaupt, Philipp; Reiss, Martin

2017-04-01

Spatial and temporal patterns of throughfall can affect the heterogeneity of ecological, biogeochemical and hydrological processes at a forest floor and further the underlying soil. Previous research suggests different factors controlling the spatial and temporal patterns of throughfall, but most studies focus on coniferous forest, where the vegetation coverage is more or less constant over time. In deciduous forests the leaf area index varies due to the leaf fall in autumn which implicates a specific spatial and temporal variability of throughfall and furthermore of the soil moisture. Therefore, in the present study, the measurements of throughfall and soil moisture in a deciduous forest in the low mountain ranges focused especially on the period of leaf fall. The aims of this study were: 1) to detect the spatial and temporal variability of both the throughfall and the soil moisture, 2) to examine the temporal stability of the spatial patterns of the throughfall and soil moisture and 3) relate the soil moisture patterns to the throughfall patterns and further to the canopy characteristics. The study was carried out in a small catchment on middle Hesse (Germany) which is covered by beech forest. Annual mean air temperature is 9.4°C (48.9˚F) and annual mean precipitation is 650 mm. Base materials for soil genesis is greywacke and clay shale from Devonian deposits. The soil type at the study plot is a shallow cambisol. The study plot covers an area of about 150 m2 where 77 throughfall samplers where installed. The throughfall and the soil moisture (FDR-method, 20 cm depth) was measured immediately after every rainfall event at the 77 measurement points. During the period of October to December 2015 altogether 7 events were investigated. The geostatistical method kriging was used to interpolate between the measurements points to visualize the spatial patterns of each investigated parameter. Time-stability-plots were applied to examine temporal scatters of each investigated parameter. The spearmen and pearson correlation coefficients were applied to detect the relationship between the different investigated parameters. First results show that the spatial variability of throughfall decreases if the total amount of the throughfall increases. The soil moisture shows a similar behavior. It`s spatial variability decreases if higher soil moisture values were measured. Concerning the temporal stability of throughfall it can be shown that it is very high during the leaf-free period, although the rainfall events have different total througfall amounts. The soil moisture patterns consists of a low temporal stability and additionally only during one event a significant correlations between throughfall and soil moisture patterns exists. This implies that other factors than the throughfall patterns control the spatial patterns of soil moisture.

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.

Space-time modeling of soil moisture

NASA Astrophysics Data System (ADS)

Chen, Zijuan; Mohanty, Binayak P.; Rodriguez-Iturbe, Ignacio

2017-11-01

A physically derived space-time mathematical representation of the soil moisture field is carried out via the soil moisture balance equation driven by stochastic rainfall forcing. The model incorporates spatial diffusion and in its original version, it is shown to be unable to reproduce the relative fast decay in the spatial correlation functions observed in empirical data. This decay resulting from variations in local topography as well as in local soil and vegetation conditions is well reproduced via a jitter process acting multiplicatively over the space-time soil moisture field. The jitter is a multiplicative noise acting on the soil moisture dynamics with the objective to deflate its correlation structure at small spatial scales which are not embedded in the probabilistic structure of the rainfall process that drives the dynamics. These scales of order of several meters to several hundred meters are of great importance in ecohydrologic dynamics. Properties of space-time correlation functions and spectral densities of the model with jitter are explored analytically, and the influence of the jitter parameters, reflecting variabilities of soil moisture at different spatial and temporal scales, is investigated. A case study fitting the derived model to a soil moisture dataset is presented in detail.

Spatial variability of soil moisture retrieved by SMOS satellite

NASA Astrophysics Data System (ADS)

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

2015-04-01

Standard statistical methods assume that the analysed variables are independent. Since the majority of the processes observed in the nature are continuous in space and time, this assumption introduces a significant limitation for understanding the examined phenomena. In classical approach, valuable information about the locations of examined observations is completely lost. However, there is a branch of statistics, called geostatistics, which is the study of random variables, but taking into account the space where they occur. A common example of so-called "regionalized variable" is soil moisture. Using in situ methods it is difficult to estimate soil moisture distribution because it is often significantly diversified. Thanks to the geostatistical methods, by employing semivariance analysis, it is possible to get the information about the nature of spatial dependences and their lengths. Since the Soil Moisture and Ocean Salinity mission launch in 2009, the estimation of soil moisture spatial distribution for regional up to continental scale started to be much easier. In this study, the SMOS L2 data for Central and Eastern Europe were examined. The statistical and geostatistical features of moisture distributions of this area were studied for selected natural soil phenomena for 2010-2014 including: freezing, thawing, rainfalls (wetting), drying and drought. Those soil water "states" were recognized employing ground data from the agro-meteorological network of ground-based stations SWEX and SMUDP2 data from SMOS. After pixel regularization, without any upscaling, the geostatistical methods were applied directly on Discrete Global Grid (15-km resolution) in ISEA 4H9 projection, on which SMOS observations are reported. Analysis of spatial distribution of SMOS soil moisture, carried out for each data set, in most cases did not show significant trends. It was therefore assumed that each of the examined distributions of soil moisture in the adopted scale satisfies ergodicity and quasi-stationarity assumptions, required for geostatistical analysis. The semivariograms examinations revealed that spatial dependences occurring in the surface soil moisture distributions for the selected area were more or less 200 km. The exception was the driest of the studied days, when the spatial correlations of soil moisture were not disturbed for a long time by any rainfall. Spatial correlation length on that day was about 400 km. Because of zonal character of frost, the spatial dependences in the examined surface soil moisture distributions during freezing/thawing found to be disturbed. Probably, the amount of water remains the same, but it is not detected by SMOS, hence analysing dielectric constant instead of soil moisture would be more appropriate. Some spatial relations of soil moisture and freezing distribution with existing maps of soil granulometric fractions and soil specific surface area for Poland have also been found. The work was partially funded under the ELBARA_PD (Penetration Depth) project No. 4000107897/13/NL/KML. ELBARA_PD project is funded by the Government of Poland through an ESA (European Space Agency) Contract under the PECS (Plan for European Cooperating States).

Spatially enhanced passive microwave derived soil moisture: capabilities and opportunities

USDA-ARS?s Scientific Manuscript database

Low frequency passive microwave remote sensing is a proven technique for soil moisture retrieval, but its coarse resolution restricts the range of applications. Downscaling, otherwise known as disaggregation, has been proposed as the solution to spatially enhance these coarse resolution soil moistur...

Reconciling spatial and temporal soil moisture effects on afternoon rainfall

PubMed Central

Guillod, Benoit P.; Orlowsky, Boris; Miralles, Diego G.; Teuling, Adriaan J.; Seneviratne, Sonia I.

2015-01-01

Soil moisture impacts on precipitation have been strongly debated. Recent observational evidence of afternoon rain falling preferentially over land parcels that are drier than the surrounding areas (negative spatial effect), contrasts with previous reports of a predominant positive temporal effect. However, whether spatial effects relating to soil moisture heterogeneity translate into similar temporal effects remains unknown. Here we show that afternoon precipitation events tend to occur during wet and heterogeneous soil moisture conditions, while being located over comparatively drier patches. Using remote-sensing data and a common analysis framework, spatial and temporal correlations with opposite signs are shown to coexist within the same region and data set. Positive temporal coupling might enhance precipitation persistence, while negative spatial coupling tends to regionally homogenize land surface conditions. Although the apparent positive temporal coupling does not necessarily imply a causal relationship, these results reconcile the notions of moisture recycling with local, spatially negative feedbacks. PMID:25740589

Spatiotemporal Variability of Hillslope Soil Moisture Across Steep, Highly Dissected Topography

NASA Astrophysics Data System (ADS)

Jarecke, K. M.; Wondzell, S. M.; Bladon, K. D.

2016-12-01

Hillslope ecohydrological processes, including subsurface water flow and plant water uptake, are strongly influenced by soil moisture. However, the factors controlling spatial and temporal variability of soil moisture in steep, mountainous terrain are poorly understood. We asked: How do topography and soils interact to control the spatial and temporal variability of soil moisture in steep, Douglas-fir dominated hillslopes in the western Cascades? We will present a preliminary analysis of bimonthly soil moisture variability from July-November 2016 at 0-30 and 0-60 cm depth across spatially extensive convergent and divergent topographic positions in Watershed 1 of the H.J. Andrews Experimental Forest in central Oregon. Soil moisture monitoring locations were selected following a 5 m LIDAR analysis of topographic position, aspect, and slope. Topographic position index (TPI) was calculated as the difference in elevation to the mean elevation within a 30 m radius. Convergent (negative TPI values) and divergent (positive TPI values) monitoring locations were established along northwest to northeast-facing aspects and within 25-55 degree slopes. We hypothesized that topographic position (convergent vs. divergent), as well as soil physical properties (e.g., texture, bulk density), control variation in hillslope soil moisture at the sub-watershed scale. In addition, we expected the relative importance of hillslope topography to the spatial variability in soil moisture to differ seasonally. By comparing the spatiotemporal variability of hillslope soil moisture across topographic positions, our research provides a foundation for additional understanding of subsurface flow processes and plant-available soil-water in forests with steep, highly dissected terrain.

Application of spatial time domain reflectometry measurements in heterogeneous, rocky substrates

NASA Astrophysics Data System (ADS)

Gonzales, C.; Scheuermann, A.; Arnold, S.; Baumgartl, T.

2016-10-01

Measurement of soil moisture across depths using sensors is currently limited to point measurements or remote sensing technologies. Point measurements have limitations on spatial resolution, while the latter, although covering large areas may not represent real-time hydrologic processes, especially near the surface. The objective of the study was to determine the efficacy of elongated soil moisture probes—spatial time domain reflectometry (STDR)—and to describe transient soil moisture dynamics of unconsolidated mine waste rock materials. The probes were calibrated under controlled conditions in the glasshouse. Transient soil moisture content was measured using the gravimetric method and STDR. Volumetric soil moisture content derived from weighing was compared with values generated from a numerical model simulating the drying process. A calibration function was generated and applied to STDR field data sets. The use of elongated probes effectively assists in the real-time determination of the spatial distribution of soil moisture. It also allows hydrologic processes to be uncovered in the unsaturated zone, especially for water balance calculations that are commonly based on point measurements. The elongated soil moisture probes can potentially describe transient substrate processes and delineate heterogeneity in terms of the pore size distribution in a seasonally wet but otherwise arid environment.

New Physical Algorithms for Downscaling SMAP Soil Moisture

NASA Astrophysics Data System (ADS)

Sadeghi, M.; Ghafari, E.; Babaeian, E.; Davary, K.; Farid, A.; Jones, S. B.; Tuller, M.

2017-12-01

The NASA Soil Moisture Active Passive (SMAP) mission provides new means for estimation of surface soil moisture at the global scale. However, for many hydrological and agricultural applications the spatial SMAP resolution is too low. To address this scale issue we fused SMAP data with MODIS observations to generate soil moisture maps at 1-km spatial resolution. In course of this study we have improved several existing empirical algorithms and introduced a new physical approach for downscaling SMAP data. The universal triangle/trapezoid model was applied to relate soil moisture to optical/thermal observations such as NDVI, land surface temperature and surface reflectance. These algorithms were evaluated with in situ data measured at 5-cm depth. Our results demonstrate that downscaling SMAP soil moisture data based on physical indicators of soil moisture derived from the MODIS satellite leads to higher accuracy than that achievable with empirical downscaling algorithms. Keywords: Soil moisture, microwave data, downscaling, MODIS, triangle/trapezoid model.

Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method

DTIC Science & Technology

2010-01-25

2010 / Accepted: 19 January 2010 / Published: 25 January 2010 Abstract: Spatial and temporal soil moisture dynamics are critically needed to...scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial... dynamics is essential in the hydrological and meteorological modeling, improves our understanding of land surface–atmosphere interactions. Spatial and

Global response of the growing season to soil moisture and topography

NASA Astrophysics Data System (ADS)

Guevara, M.; Arroyo, C.; Warner, D. L.; Equihua, J.; Lule, A. V.; Schwartz, A.; Taufer, M.; Vargas, R.

2017-12-01

Soil moisture has a direct influence in plant productivity. Plant productivity and its greenness can be inferred by remote sensing with higher spatial detail than soil moisture. The objective was to improve the coarse scale of currently available satellite soil moisture estimates and identify areas of strong coupling between the interannual variability soil moisture and the maximum greenness vegetation fraction (MGVF) at the global scale. We modeled, cross-validated and downscaled remotely sensed soil moisture using machine learning and digital terrain analysis across 23 years (1991-2013) of available data. Improving the accuracy (0.69-0.87 % of cross-validated explained variance) and the spatial detail (from 27 to 15km) of satellite soil moisture, we filled temporal gaps of information across vegetated areas where satellite soil moisture does not work properly. We found that 7.57% of global vegetated area shows strong correlation with our downscaled product (R2>0.5, Fig. 1). We found a dominant positive response of vegetation greenness to topography-based soil moisture across water limited environments, however, the tropics and temperate environments of higher latitudes showed a sparse negative response. We conclude that topography can be used to effectively improve the spatial detail of globally available remotely sensed soil moisture, which is convenient to generate unbiased comparisons with global vegetation dynamics, and better inform land and crop modeling efforts.

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.

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

USDA-ARS?s Scientific Manuscript database

Many applications require soil moisture estimates over large spatial extents (30-300 km) and at fine-resolutions (10-30 m). Remote-sensing methods can provide soil moisture estimates over very large spatial extents (continental to global) at coarse resolutions (10-40 km), but their output must be d...

Integration of GIS, Geostatistics, and 3-D Technology to Assess the Spatial Distribution of Soil Moisture

NASA Technical Reports Server (NTRS)

Betts, M.; Tsegaye, T.; Tadesse, W.; Coleman, T. L.; Fahsi, A.

1998-01-01

The spatial and temporal distribution of near surface soil moisture is of fundamental importance to many physical, biological, biogeochemical, and hydrological processes. However, knowledge of these space-time dynamics and the processes which control them remains unclear. The integration of geographic information systems (GIS) and geostatistics together promise a simple mechanism to evaluate and display the spatial and temporal distribution of this vital hydrologic and physical variable. Therefore, this research demonstrates the use of geostatistics and GIS to predict and display soil moisture distribution under vegetated and non-vegetated plots. The research was conducted at the Winfred Thomas Agricultural Experiment Station (WTAES), Hazel Green, Alabama. Soil moisture measurement were done on a 10 by 10 m grid from tall fescue grass (GR), alfalfa (AA), bare rough (BR), and bare smooth (BS) plots. Results indicated that variance associated with soil moisture was higher for vegetated plots than non-vegetated plots. The presence of vegetation in general contributed to the spatial variability of soil moisture. Integration of geostatistics and GIS can improve the productivity of farm lands and the precision of farming.

A New Approach in Downscaling Microwave Soil Moisture Product using Machine Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, Peyman; Yan, Hongxiang; Moradkhani, Hamid

2016-04-01

Understating the soil moisture pattern has significant impact on flood modeling, drought monitoring, and irrigation management. Although satellite retrievals can provide an unprecedented spatial and temporal resolution of soil moisture at a global-scale, their soil moisture products (with a spatial resolution of 25-50 km) are inadequate for regional study, where a resolution of 1-10 km is needed. In this study, a downscaling approach using Genetic Programming (GP), a specialized version of Genetic Algorithm (GA), is proposed to improve the spatial resolution of satellite soil moisture products. The GP approach was applied over a test watershed in United States using the coarse resolution satellite data (25 km) from Advanced Microwave Scanning Radiometer - EOS (AMSR-E) soil moisture products, the fine resolution data (1 km) from Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index, and ground based data including land surface temperature, vegetation and other potential physical variables. The results indicated the great potential of this approach to derive the fine resolution soil moisture information applicable for data assimilation and other regional studies.

Modelling of Space-Time Soil Moisture in Savannas and its Relation to Vegetation Patterns

NASA Astrophysics Data System (ADS)

Rodriguez-Iturbe, I.; Mohanty, B.; Chen, Z.

2017-12-01

A physically derived space-time representation of the soil moisture field is presented. It includes the incorporation of a "jitter" process acting over the space-time soil moisture field and accounting for the short distance heterogeneities in topography, soil, and vegetation characteristics. The modelling scheme allows for the representation of spatial random fluctuations of soil moisture at small spatial scales and reproduces quite well the space-time correlation structure of soil moisture from a field study in Oklahoma. It is shown that the islands of soil moisture above different thresholds have sizes which follow power distributions over an extended range of scales. A discussion is provided about the possible links of this feature with the observed power law distributions of the clusters of trees in savannas.

Landscape complexity and soil moisture variation in south Georgia, USA, for remote sensing applications

NASA Astrophysics Data System (ADS)

Giraldo, Mario A.; Bosch, David; Madden, Marguerite; Usery, Lynn; Kvien, Craig

2008-08-01

SummaryThis research addressed the temporal and spatial variation of soil moisture (SM) in a heterogeneous landscape. The research objective was to investigate soil moisture variation in eight homogeneous 30 by 30 m plots, similar to the pixel size of a Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper plus (ETM+) image. The plots were adjacent to eight stations of an in situ soil moisture network operated by the United States Department of Agriculture-Agriculture Research Service USDA-ARS in Tifton, GA. We also studied five adjacent agricultural fields to examine the effect of different landuses/land covers (LULC) (grass, orchard, peanuts, cotton and bare soil) on the temporal and spatial variation of soil moisture. Soil moisture field data were collected on eight occasions throughout 2005 and January 2006 to establish comparisons within and among eight homogeneous plots. Consistently throughout time, analysis of variance (ANOVA) showed high variation in the soil moisture behavior among the plots and high homogeneity in the soil moisture behavior within them. A precipitation analysis for the eight sampling dates throughout the year 2005 showed similar rainfall conditions for the eight study plots. Therefore, soil moisture variation among locations was explained by in situ local conditions. Temporal stability geostatistical analysis showed that soil moisture has high temporal stability within the small plots and that a single point reading can be used to monitor soil moisture status for the plot within a maximum 3% volume/volume (v/v) soil moisture variation. Similarly, t-statistic analysis showed that soil moisture status in the upper soil layer changes within 24 h. We found statistical differences in the soil moisture between the different LULC in the agricultural fields as well as statistical differences between these fields and the adjacent 30 by 30 m plots. From this analysis, it was demonstrated that spatial proximity is not enough to produce similar soil moisture, since t-test's among adjacent plots with different LULCs showed significant differences. These results confirm that a remote sensing approach that considers homogeneous LULC landscape fragments can be used to identify landscape units of similar soil moisture behavior under heterogeneous landscapes. In addition, the in situ USDA-ARS network will serve better in remote sensing studies in which sensors with fine spatial resolution are evaluated. This study is a first step towards identifying landscape units that can be monitored using the single point reading of the USDA-ARS stations network.

Landscape complexity and soil moisture variation in south Georgia, USA, for remote sensing applications

USGS Publications Warehouse

Giraldo, M.A.; Bosch, D.; Madden, M.; Usery, L.; Kvien, Craig

2008-01-01

This research addressed the temporal and spatial variation of soil moisture (SM) in a heterogeneous landscape. The research objective was to investigate soil moisture variation in eight homogeneous 30 by 30 m plots, similar to the pixel size of a Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper plus (ETM+) image. The plots were adjacent to eight stations of an in situ soil moisture network operated by the United States Department of Agriculture-Agriculture Research Service USDA-ARS in Tifton, GA. We also studied five adjacent agricultural fields to examine the effect of different landuses/land covers (LULC) (grass, orchard, peanuts, cotton and bare soil) on the temporal and spatial variation of soil moisture. Soil moisture field data were collected on eight occasions throughout 2005 and January 2006 to establish comparisons within and among eight homogeneous plots. Consistently throughout time, analysis of variance (ANOVA) showed high variation in the soil moisture behavior among the plots and high homogeneity in the soil moisture behavior within them. A precipitation analysis for the eight sampling dates throughout the year 2005 showed similar rainfall conditions for the eight study plots. Therefore, soil moisture variation among locations was explained by in situ local conditions. Temporal stability geostatistical analysis showed that soil moisture has high temporal stability within the small plots and that a single point reading can be used to monitor soil moisture status for the plot within a maximum 3% volume/volume (v/v) soil moisture variation. Similarly, t-statistic analysis showed that soil moisture status in the upper soil layer changes within 24 h. We found statistical differences in the soil moisture between the different LULC in the agricultural fields as well as statistical differences between these fields and the adjacent 30 by 30 m plots. From this analysis, it was demonstrated that spatial proximity is not enough to produce similar soil moisture, since t-test's among adjacent plots with different LULCs showed significant differences. These results confirm that a remote sensing approach that considers homogeneous LULC landscape fragments can be used to identify landscape units of similar soil moisture behavior under heterogeneous landscapes. In addition, the in situ USDA-ARS network will serve better in remote sensing studies in which sensors with fine spatial resolution are evaluated. This study is a first step towards identifying landscape units that can be monitored using the single point reading of the USDA-ARS stations network. ?? 2008 Elsevier B.V.

Observing and modeling links between soil moisture, microbes and CH4 fluxes from forest soils

NASA Astrophysics Data System (ADS)

Christiansen, Jesper; Levy-Booth, David; Barker, Jason; Prescott, Cindy; Grayston, Sue

2017-04-01

Soil moisture is a key driver of methane (CH4) fluxes in forest soils, both of the net uptake of atmospheric CH4 and emission from the soil. Climate and land use change will alter spatial patterns of soil moisture as well as temporal variability impacting the net CH4 exchange. The impact on the resultant net CH4 exchange however is linked to the underlying spatial and temporal distribution of the soil microbial communities involved in CH4 cycling as well as the response of the soil microbial community to environmental changes. Significant progress has been made to target specific CH4 consuming and producing soil organisms, which is invaluable in order to understand the microbial regulation of the CH4 cycle in forest soils. However, it is not clear as to which extent soil moisture shapes the structure, function and abundance of CH4 specific microorganisms and how this is linked to observed net CH4 exchange under contrasting soil moisture regimes. Here we report on the results from a research project aiming to understand how the CH4 net exchange is shaped by the interactive effects soil moisture and the spatial distribution CH4 consuming (methanotrophs) and producing (methanogens). We studied the growing season variations of in situ CH4 fluxes, microbial gene abundances of methanotrophs and methanogens, soil hydrology, and nutrient availability in three typical forest types across a soil moisture gradient in a temperate rainforest on the Canadian Pacific coast. Furthermore, we conducted laboratory experiments to determine whether the net CH4 exchange from hydrologically contrasting forest soils responded differently to changes in soil moisture. Lastly, we modelled the microbial mediation of net CH4 exchange along the soil moisture gradient using structural equation modeling. Our study shows that it is possible to link spatial patterns of in situ net exchange of CH4 to microbial abundance of CH4 consuming and producing organisms. We also show that the microbial community responds different to environmental change dependent on the soil moisture regime. These results are important to include in future modeling efforts to predict changes in soil-atmosphere exchange of CH4 under global change.

A new Downscaling Approach for SMAP, SMOS and ASCAT by predicting sub-grid Soil Moisture Variability based on Soil Texture

NASA Astrophysics Data System (ADS)

Montzka, C.; Rötzer, K.; Bogena, H. R.; Vereecken, H.

2017-12-01

Improving the coarse spatial resolution of global soil moisture products from SMOS, SMAP and ASCAT is currently an up-to-date topic. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. A method has been developed that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. Here, we predict for each SMOS, SMAP and ASCAT grid cell the sub-grid soil moisture variability based on the SoilGrids1km data set. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean. The resulting data set provides important information for downscaling coarse soil moisture observations of the SMOS, SMAP and ASCAT missions. Downscaling SMAP data by a field capacity proxy indicates adequate accuracy of the sub-grid soil moisture patterns.

Disaggregation Of Passive Microwave Soil Moisture For Use In Watershed Hydrology Applications

NASA Astrophysics Data System (ADS)

Fang, Bin

In recent years the passive microwave remote sensing has been providing soil moisture products using instruments on board satellite/airborne platforms. Spatial resolution has been restricted by the diameter of antenna which is inversely proportional to resolution. As a result, typical products have a spatial resolution of tens of kilometers, which is not compatible for some hydrological research applications. For this reason, the dissertation explores three disaggregation algorithms that estimate L-band passive microwave soil moisture at the subpixel level by using high spatial resolution remote sensing products from other optical and radar instruments were proposed and implemented in this investigation. The first technique utilized a thermal inertia theory to establish a relationship between daily temperature change and average soil moisture modulated by the vegetation condition was developed by using NLDAS, AVHRR, SPOT and MODIS data were applied to disaggregate the 25 km AMSR-E soil moisture to 1 km in Oklahoma. The second algorithm was built on semi empirical physical models (NP89 and LP92) derived from numerical experiments between soil evaporation efficiency and soil moisture over the surface skin sensing depth (a few millimeters) by using simulated soil temperature derived from MODIS and NLDAS as well as AMSR-E soil moisture at 25 km to disaggregate the coarse resolution soil moisture to 1 km in Oklahoma. The third algorithm modeled the relationship between the change in co-polarized radar backscatter and the remotely sensed microwave change in soil moisture retrievals and assumed that change in soil moisture was a function of only the canopy opacity. The change detection algorithm was implemented using aircraft based the remote sensing data from PALS and UAVSAR that were collected in SMPAVEX12 in southern Manitoba, Canada. The PALS L-band h-polarization radiometer soil moisture retrievals were disaggregated by combining them with the PALS and UAVSAR L-band hh-polarization radar spatial resolutions of 1500 m and 5 m/800 m, respectively. All three algorithms were validated using ground measurements from network in situ stations or handheld hydra probes. The validation results demonstrate the practicability on coarse resolution passive microwave soil moisture products.

Spatial Estimation of Soil Moisture Using Synthetic Aperture Radar in Alaska

NASA Astrophysics Data System (ADS)

Meade, N. G.; Hinzman, L. D.; Kane, D. L.

1999-01-01

A spatially distributed Model of Arctic Thermal and Hydrologic processes (MATH) has been developed. One of the attributes of this model is the spatial and temporal prediction of soil moisture in the active layer. The spatially distributed output from this model required verification data obtained through remote sensing to assess performance at the watershed scale independently. Therefore, a neural network was trained to predict soil moisture contents near the ground surface. The input to train the neural network is synthetic aperture radar (SAR) pixel value, and field measurements of soil moisture, and vegetation, which were used as a surrogate for surface roughness. Once the network was trained, soil moisture predictions were made based on SAR pixel value and vegetation. These results were then used for comparison with results from the hydrologic model. The quality of neural network input was less than anticipated. Our digital elevation model (DEM) was not of high enough resolution to allow exact co-registration with soil moisture measurements; therefore, the statistical correlations were not as good as hoped. However, the spatial pattern of the SAR derived soil moisture contents compares favorably with the hydrologic MATH model results. Primary surface parameters that effect SAR include topography, surface roughness, vegetation cover and soil texture. Single parameters that are considered to influence SAR include incident angle of the radar, polarization of the radiation, signal strength and returning signal integration, to name a few. These factors influence the reflectance, but if one adequately quantifies the influences of terrain and roughness, it is considered possible to extract information on soil moisture from SAR imagery analysis and in turn use SAR imagery to validate hydrologic models

Downscaled soil moisture from SMAP evaluated using high density observations

USDA-ARS?s Scientific Manuscript database

Recently, a soil moisture downscaling algorithm based on a regression relationship between daily temperature changes and daily average soil moisture was developed to produce an enhanced spatial resolution on soil moisture product for the Advanced Microwave Scanning Radiometer–EOS (AMSR-E) satellite ...

Multiscale analysis of surface soil moisture dynamics in a mesoscale catchment utilizing an integrated ecohydrological model

NASA Astrophysics Data System (ADS)

Korres, W.; Reichenau, T. G.; Schneider, K.

2012-12-01

Soil moisture is one of the fundamental variables in hydrology, meteorology and agriculture, influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Numerous studies have shown that in addition to natural factors (rainfall, soil, topography etc.) agricultural management is one of the key drivers for spatio-temporal patterns of soil moisture in agricultural landscapes. Interactions between plant growth, soil hydrology and soil nitrogen transformation processes are modeled by using a dynamically coupled modeling approach. The process-based ecohydrological model components of the integrated decision support system DANUBIA are used to identify the important processes and feedbacks determining soil moisture patterns in agroecosystems. Integrative validation of plant growth and surface soil moisture dynamics serves as a basis for a spatially distributed modeling analysis of surface soil moisture patterns in the northern part of the Rur catchment (1100 sq km), Western Germany. An extensive three year dataset (2007-2009) of surface soil moisture-, plant- (LAI, organ specific biomass and N) and soil- (texture, N, C) measurements was collected. Plant measurements were carried out biweekly for winter wheat, maize, and sugar beet during the growing season. Soil moisture was measured with three FDR soil moisture stations. Meteorological data was measured with an eddy flux station. The results of the model validation showed a very good agreement between the modeled plant parameters (biomass, green LAI) and the measured parameters with values between 0.84 and 0.98 (Willmotts index of agreement). The modeled surface soil moisture (0 - 20 cm) showed also a very favorable agreement with the measurements for winter wheat and sugar beet with an RMSE between 1.68 and 3.45 Vol.-%. For maize, the RMSE was less favorable particularly in the 1.5 months prior to harvest. The modeled soil moisture remained in contrast to the measurements very responsive to precipitation with high soil moisture after precipitation events. This behavior indicates that the soil properties might have changed due to the formation of a surface crust or seal towards the end of the growing season. Spatial soil moisture patterns were investigated using a grid resolution of 150 meter. Spatial autocorrelation was computed on a daily basis using patterns of soil texture as well as transpiration and precipitation indices as co-variables. Spatial patterns of surface soil moisture are mostly determined by the structure of the soil properties (soil type) during winter, early growing season and after harvest of all crops. Later in the growing season, after establishment of a closed canopy the dependence of the soil moisture patterns on soil texture patterns becomes smaller and diminishes quickly after precipitation events, due to differences of the transpiration rate of the different crops. When changing the spatial scale of the analysis, the highest autocorrelation values can be found on a grid cell size between 450 and 1200 meters. Thus, small scale variability of transpiration induced by the land use pattern almost averages out, leaving the larger scale structure of soil properties to explain the soil moisture patterns.

Spatial and Temporal Soil Moisture Behavior in a Headwater Watershed of the Mantiqueira Range, Minas Gerais, Brazil

USDA-ARS?s Scientific Manuscript database

The characterization of temporal and spatial variability of soil moisture is highly relevant in watersheds for understanding the many hydrological and erosion processes, to better model the processes and apply them to conservation planning. The goal of this study was to map soil moisture of the surf...

Remotely sensed soil moisture input to a hydrologic model

NASA Technical Reports Server (NTRS)

Engman, E. T.; Kustas, W. P.; Wang, J. R.

1989-01-01

The possibility of using detailed spatial soil moisture maps as input to a runoff model was investigated. The water balance of a small drainage basin was simulated using a simple storage model. Aircraft microwave measurements of soil moisture were used to construct two-dimensional maps of the spatial distribution of the soil moisture. Data from overflights on different dates provided the temporal changes resulting from soil drainage and evapotranspiration. The study site and data collection are described, and the soil measurement data are given. The model selection is discussed, and the simulation results are summarized. It is concluded that a time series of soil moisture is a valuable new type of data for verifying model performance and for updating and correcting simulated streamflow.

Linking the soil moisture distribution pattern to dynamic processes along slope transects in the Loess Plateau, China.

PubMed

Wang, Shuai; Fu, Bojie; Gao, Guangyao; Zhou, Ji; Jiao, Lei; Liu, Jianbo

2015-12-01

Soil moisture pulses are a prerequisite for other land surface pulses at various spatiotemporal scales in arid and semi-arid areas. The temporal dynamics and profile variability of soil moisture in relation to land cover combinations were studied along five slopes transect on the Loess Plateau during the rainy season of 2011. Within the 3 months of the growing season coupled with the rainy season, all of the soil moisture was replenished in the area, proving that a type stability exists between different land cover soil moisture levels. Land cover combinations disturbed the trend determined by topography and increased soil moisture variability in space and time. The stability of soil moisture resulting from the dynamic processes could produce stable patterns on the slopes. The relationships between the mean soil moisture and vertical standard deviation (SD) and coefficient of variation (CV) were more complex, largely due to the fact that different land cover types had distinctive vertical patterns of soil moisture. The spatial SD of each layer had a positive correlation and the spatial CV exhibited a negative correlation with the increase in mean soil moisture. The soil moisture stability implies that sampling comparisons in this area can be conducted at different times to accurately compare different land use types.

Soil Tillage Management Affects Maize Grain Yield by Regulating Spatial Distribution Coordination of Roots, Soil Moisture and Nitrogen Status.

PubMed

Wang, Xinbing; Zhou, Baoyuan; Sun, Xuefang; Yue, Yang; Ma, Wei; Zhao, Ming

2015-01-01

The spatial distribution of the root system through the soil profile has an impact on moisture and nutrient uptake by plants, affecting growth and productivity. The spatial distribution of the roots, soil moisture, and fertility are affected by tillage practices. The combination of high soil density and the presence of a soil plow pan typically impede the growth of maize (Zea mays L.).We investigated the spatial distribution coordination of the root system, soil moisture, and N status in response to different soil tillage treatments (NT: no-tillage, RT: rotary-tillage, SS: subsoiling) and the subsequent impact on maize yield, and identify yield-increasing mechanisms and optimal soil tillage management practices. Field experiments were conducted on the Huang-Huai-Hai plain in China during 2011 and 2012. The SS and RT treatments significantly reduced soil bulk density in the top 0-20 cm layer of the soil profile, while SS significantly decreased soil bulk density in the 20-30 cm layer. Soil moisture in the 20-50 cm profile layer was significantly higher for the SS treatment compared to the RT and NT treatment. In the 0-20 cm topsoil layer, the NT treatment had higher soil moisture than the SS and RT treatments. Root length density of the SS treatment was significantly greater than density of the RT and NT treatments, as soil depth increased. Soil moisture was reduced in the soil profile where root concentration was high. SS had greater soil moisture depletion and a more concentration root system than RT and NT in deep soil. Our results suggest that the SS treatment improved the spatial distribution of root density, soil moisture and N states, thereby promoting the absorption of soil moisture and reducing N leaching via the root system in the 20-50 cm layer of the profile. Within the context of the SS treatment, a root architecture densely distributed deep into the soil profile, played a pivotal role in plants' ability to access nutrients and water. An optimal combination of deeper deployment of roots and resource (water and N) availability was realized where the soil was prone to leaching. The correlation between the depletion of resources and distribution of patchy roots endorsed the SS tillage practice. It resulted in significantly greater post-silking biomass and grain yield compared to the RT and NT treatments, for summer maize on the Huang-Huai-Hai plain.

Soil Tillage Management Affects Maize Grain Yield by Regulating Spatial Distribution Coordination of Roots, Soil Moisture and Nitrogen Status

PubMed Central

Wang, Xinbing; Zhou, Baoyuan; Sun, Xuefang; Yue, Yang; Ma, Wei; Zhao, Ming

2015-01-01

The spatial distribution of the root system through the soil profile has an impact on moisture and nutrient uptake by plants, affecting growth and productivity. The spatial distribution of the roots, soil moisture, and fertility are affected by tillage practices. The combination of high soil density and the presence of a soil plow pan typically impede the growth of maize (Zea mays L.).We investigated the spatial distribution coordination of the root system, soil moisture, and N status in response to different soil tillage treatments (NT: no-tillage, RT: rotary-tillage, SS: subsoiling) and the subsequent impact on maize yield, and identify yield-increasing mechanisms and optimal soil tillage management practices. Field experiments were conducted on the Huang-Huai-Hai plain in China during 2011 and 2012. The SS and RT treatments significantly reduced soil bulk density in the top 0–20 cm layer of the soil profile, while SS significantly decreased soil bulk density in the 20–30 cm layer. Soil moisture in the 20–50 cm profile layer was significantly higher for the SS treatment compared to the RT and NT treatment. In the 0-20 cm topsoil layer, the NT treatment had higher soil moisture than the SS and RT treatments. Root length density of the SS treatment was significantly greater than density of the RT and NT treatments, as soil depth increased. Soil moisture was reduced in the soil profile where root concentration was high. SS had greater soil moisture depletion and a more concentration root system than RT and NT in deep soil. Our results suggest that the SS treatment improved the spatial distribution of root density, soil moisture and N states, thereby promoting the absorption of soil moisture and reducing N leaching via the root system in the 20–50 cm layer of the profile. Within the context of the SS treatment, a root architecture densely distributed deep into the soil profile, played a pivotal role in plants’ ability to access nutrients and water. An optimal combination of deeper deployment of roots and resource (water and N) availability was realized where the soil was prone to leaching. The correlation between the depletion of resources and distribution of patchy roots endorsed the SS tillage practice. It resulted in significantly greater post-silking biomass and grain yield compared to the RT and NT treatments, for summer maize on the Huang-Huai-Hai plain. PMID:26098548

An integrated GIS application system for soil moisture data assimilation

NASA Astrophysics Data System (ADS)

Wang, Di; Shen, Runping; Huang, Xiaolong; Shi, Chunxiang

2014-11-01

The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive soil moisture data visually and mine the data for further research.This article introduces a comprehensive soil moisture assimilation data analysis system, which is instructed by tools of C#, IDL, ArcSDE, Visual Studio 2008 and SQL Server 2005. The system provides integrated service, management of efficient graphics visualization and analysis of land surface data assimilation. The system is not only able to improve the efficiency of data assimilation management, but also comprehensively integrate the data processing and analysis tools into GIS development environment. So analyzing the soil moisture assimilation data and accomplishing GIS spatial analysis can be realized in the same system. This system provides basic GIS map functions, massive data process and soil moisture products analysis etc. Besides,it takes full advantage of a spatial data engine called ArcSDE to effeciently manage, retrieve and store all kinds of data. In the system, characteristics of temporal and spatial pattern of soil moiture will be plotted. By analyzing the soil moisture impact factors, it is possible to acquire the correlation coefficients between soil moisture value and its every single impact factor. Daily and monthly comparative analysis of soil moisture products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, soil moisture map production function is realized for business application.

Assessment of multi-frequency electromagnetic induction for determining soil moisture patterns at the hillslope scale

NASA Astrophysics Data System (ADS)

Tromp-van Meerveld, H. J.; McDonnell, J. J.

2009-04-01

SummaryHillslopes are fundamental landscape units, yet represent a difficult scale for measurements as they are well-beyond our traditional point-scale techniques. Here we present an assessment of electromagnetic induction (EM) as a potential rapid and non-invasive method to map soil moisture patterns at the hillslope scale. We test the new multi-frequency GEM-300 for spatially distributed soil moisture measurements at the well-instrumented Panola hillslope. EM-based apparent conductivity measurements were linearly related to soil moisture measured with the Aqua-pro capacitance sensor below a threshold conductivity and represented the temporal patterns in soil moisture well. During spring rainfall events that wetted only the surface soil layers the apparent conductivity measurements explained the soil moisture dynamics at depth better than the surface soil moisture dynamics. All four EM frequencies (7.290, 9.090, 11.250, and 14.010 kHz) were highly correlated and linearly related to each other and could be used to predict soil moisture. This limited our ability to use the four different EM frequencies to obtain a soil moisture profile with depth. The apparent conductivity patterns represented the observed spatial soil moisture patterns well when the individually fitted relationships between measured soil moisture and apparent conductivity were used for each measurement point. However, when the same (master) relationship was used for all measurement locations, the soil moisture patterns were smoothed and did not resemble the observed soil moisture patterns very well. In addition the range in calculated soil moisture values was reduced compared to observed soil moisture. Part of the smoothing was likely due to the much larger measurement area of the GEM-300 compared to the soil moisture measurements.

AN ACTIVE-PASSIVE COMBINED ALGORITHM FOR HIGH SPATIAL RESOLUTION RETRIEVAL OF SOIL MOISTURE FROM SATELLITE SENSORS (Invited)

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Mladenova, I. E.; Narayan, U.

2009-12-01

Soil moisture is known to be an essential factor in controlling the partitioning of rainfall into surface runoff and infiltration and solar energy into latent and sensible heat fluxes. Remote sensing has long proven its capability to obtain soil moisture in near real-time. However, at the present time we have the Advanced Scanning Microwave Radiometer (AMSR-E) on board NASA’s AQUA platform is the only satellite sensor that supplies a soil moisture product. AMSR-E coarse spatial resolution (~ 50 km at 6.9 GHz) strongly limits its applicability for small scale studies. A very promising technique for spatial disaggregation by combining radar and radiometer observations has been demonstrated by the authors using a methodology is based on the assumption that any change in measured brightness temperature and backscatter from one to the next time step is due primarily to change in soil wetness. The approach uses radiometric estimates of soil moisture at a lower resolution to compute the sensitivity of radar to soil moisture at the lower resolution. This estimate of sensitivity is then disaggregated using vegetation water content, vegetation type and soil texture information, which are the variables on which determine the radar sensitivity to soil moisture and are generally available at a scale of radar observation. This change detection algorithm is applied to several locations. We have used aircraft observed active and passive data over Walnut Creek watershed in Central Iowa in 2002; the Little Washita Watershed in Oklahoma in 2003 and the Murrumbidgee Catchment in southeastern Australia for 2006. All of these locations have different soils and land cover conditions which leads to a rigorous test of the disaggregation algorithm. Furthermore, we compare the derived high spatial resolution soil moisture to in-situ sampling and ground observation networks

Evapotranspiration Controls Imposed by Soil Moisture: A Spatial Analysis across the United States

NASA Astrophysics Data System (ADS)

Rigden, A. J.; Tuttle, S. E.; Salvucci, G.

2014-12-01

We spatially analyze the control over evapotranspiration (ET) imposed by soil moisture across the United States using daily estimates of satellite-derived soil moisture and data-driven ET over a nine-year period (June 2002-June 2011) at 305 locations. The soil moisture data are developed using 0.25-degree resolution satellite observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), where the 9-year time series for each 0.25-degree pixel was selected from three potential algorithms (VUA-NASA, U. Montana, & NASA) based on the maximum mutual information between soil moisture and precipitation (Tuttle & Salvucci (2014), Remote Sens Environ, 114: 207-222). The ET data are developed independent of soil moisture using an emergent relationship between the diurnal cycle of the relative humidity profile and ET. The emergent relation is that the vertical variance of the relative humidity profile is less than what would occur for increased or decreased ET rates, suggesting that land-atmosphere feedback processes minimize this variance (Salvucci and Gentine (2013), PNAS, 110(16): 6287-6291). The key advantage of using this approach to estimate ET is that no measurements of surface limiting factors (soil moisture, leaf area, canopy conductance) are required; instead, ET is estimated from meteorological data measured at 305 common weather stations that are approximately uniformly distributed across the United States. The combination of these two independent datasets allows for a unique spatial analysis of the control on ET imposed by the availability of soil moisture. We fit evaporation efficiency curves across the United States at each of the 305 sites during the summertime (May-June-July-August-September). Spatial patterns are visualized by mapping optimal curve fitting coefficients across the Unites States. An analysis of efficiency curves and their spatial patterns will be presented.

Potential of bias correction for downscaling passive microwave and soil moisture data

USDA-ARS?s Scientific Manuscript database

Passive microwave satellites such as SMOS (Soil Moisture and Ocean Salinity) or SMAP (Soil Moisture Active Passive) observe brightness temperature (TB) and retrieve soil moisture at a spatial resolution greater than most hydrological processes. Bias correction is proposed as a simple method to disag...

Surface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active Passive satellite and evaluation at core validation sites

USDA-ARS?s Scientific Manuscript database

This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture ...

Studies and Application of Remote Sensing Retrieval Method of Soil Moisture Content in Land Parcel Units in Irrigation Area

NASA Astrophysics Data System (ADS)

Zhu, H.; Zhao, H. L.; Jiang, Y. Z.; Zang, W. B.

2018-05-01

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data caused by the research units of pixels, and doesn't involve compromises in the spatial scale and simulating precision like the grid simulation. When the application needs are met, the production efficiency of products can also be improved at a certain degree.

L-band Soil Moisture Mapping using Small UnManned Aerial Systems

NASA Astrophysics Data System (ADS)

Dai, E.

2015-12-01

Soil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, and impacts water resource management, agriculture, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 promises to provide global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions as low as 5 km for some products. However, there exists a need for measurements of soil moisture on smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture and evaporation and transpiration studies of boundary layer heat transport. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters (i.e., the height of the platform) .Compared with various other proposed methods of validation based on either situ measurements [1,2] or existing airborne sensors suitable for manned aircraft deployment [3], the integrated design of the LDCR on a lightweight small UAS (sUAS) is capable of providing sub-watershed (~km scale) coverage at very high spatial resolution (~15 m) suitable for scaling scale studies, and at comparatively low operator cost. The LDCR on Tempest unit can supply the soil moisture mapping with different resolution which is of order the Tempest altitude.

Variation in soil carbon dioxide efflux at two spatial scales in a topographically complex boreal forest

USGS Publications Warehouse

Kelsey, Katharine C.; Wickland, Kimberly P.; Striegl, Robert G.; Neff, Jason C.

2012-01-01

Carbon dynamics of high-latitude regions are an important and highly uncertain component of global carbon budgets, and efforts to constrain estimates of soil-atmosphere carbon exchange in these regions are contingent on accurate representations of spatial and temporal variability in carbon fluxes. This study explores spatial and temporal variability in soilatmosphere carbon dynamics at both fine and coarse spatial scales in a high-elevation, permafrost-dominated boreal black spruce forest. We evaluate the importance of landscape-level investigations of soil-atmosphere carbon dynamics by characterizing seasonal trends in soil-atmosphere carbon exchange, describing soil temperature-moisture-respiration relations, and quantifying temporal and spatial variability at two spatial scales: the plot scale (0–5 m) and the landscape scale (500–1000 m). Plot-scale spatial variability (average variation on a given measurement day) in soil CO2 efflux ranged from a coefficient of variation (CV) of 0.25 to 0.69, and plot-scale temporal variability (average variation of plots across measurement days) in efflux ranged from a CV of 0.19 to 0.36. Landscape-scale spatial and temporal variability in efflux was represented by a CV of 0.40 and 0.31, respectively, indicating that plot-scale spatial variability in soil respiration is as great as landscape-scale spatial variability at this site. While soil respiration was related to soil temperature at both the plot- and landscape scale, landscape-level descriptions of soil moisture were necessary to define soil respiration-moisture relations. Soil moisture variability was also integral to explaining temporal variability in soil respiration. Our results have important implications for research efforts in high-latitude regions where remote study sites make landscape-scale field campaigns challenging.

Application of Cosmic-ray Soil Moisture Sensing to Understand Land-atmosphere Interactions in Three North American Monsoon Ecosystems

NASA Astrophysics Data System (ADS)

Schreiner-McGraw, A.; Vivoni, E. R.; Franz, T. E.; Anderson, C.

2013-12-01

Human impacts on desert ecosystems have wide ranging effects on the hydrologic cycle which, in turn, influence interactions between the critical zone and the atmosphere. In this contribution, we utilize cosmic-ray soil moisture sensors at three human-modified semiarid ecosystems in the North American monsoon region: a buffelgrass pasture in Sonora, Mexico, a woody-plant encroached savanna ecosystem in Arizona, and a woody-plant encroached shrubland ecosystem in New Mexico. In each case, landscape heterogeneity in the form of bare soil and vegetation patches of different types leads to a complex mosaic of soil moisture and land-atmosphere interactions. Historically, the measurement of spatially-averaged soil moisture at the ecosystem scale (on the order of several hundred square meters) has been problematic. Thus, new advances in measuring cosmogenically-produced neutrons present an opportunity for observational and modeling studies in these ecosystems. We discuss the calibration of the cosmic-ray soil moisture sensors at each site, present comparisons to a distributed network of in-situ measurements, and verify the spatially-aggregated observations using the watershed water balance method at two sites. We focus our efforts on the summer season 2013 and its rainfall period during the North American monsoon. To compare neutron counts to the ground sensors, we utilized an aspect-elevation weighting algorithm to compute an appropriate spatial average for the in-situ measurements. Similarly, the water balance approach utilizes precipitation, runoff, and evapotranspiration measurements in the footprint of the cosmic-ray sensors to estimate a spatially-averaged soil moisture field. Based on these complementary approaches, we empirically determined a relationship between cosmogenically-produced neutrons and the spatially-aggregated soil moisture. This approach may improve upon existing methods used to calculate soil moisture from neutron counts that typically suffer from increasing errors for higher soil moisture content. We also examined the effects of sub-footprint variability in soil moisture on the neutron readings by comparing two of the sites with large variations in topographically-mediated surface flows. Our work also synthesizes seasonal soil moisture dynamics across the desert ecosystems and attempts to tease out differences due to land cover alterations, including the seasonal greening in each study site occurring during the North American monsoon.

Assessment of Multi-frequency Electromagnetic Induction for Determining Soil Moisture Patterns at the Hillslope Scale

NASA Astrophysics Data System (ADS)

Tromp-van Meerveld, I.; McDonnell, J.

2009-05-01

We present an assessment of electromagnetic induction (EM) as a potential rapid and non-invasive method to map soil moisture patterns at the Panola (GA, USA) hillslope. We address the following questions regarding the applicability of EM measurements for hillslope hydrological investigations: (1) Can EM be used for soil moisture measurements in areas with shallow soils?; (2) Can EM represent the temporal and spatial patterns of soil moisture throughout the year?; and (3) can multiple frequencies be used to extract additional information content from the EM approach and explain the depth profile of soil moisture? We found that the apparent conductivity measured with the multi-frequency GEM-300 was linearly related to soil moisture measured with an Aqua-pro capacitance sensor below a threshold conductivity and represented the temporal patterns in soil moisture well. During spring rainfall events that wetted only the surface soil layers the apparent conductivity measurements explained the soil moisture dynamics at depth better than the surface soil moisture dynamics. All four EM frequencies (7290, 9090, 11250, and 14010 Hz) were highly correlated and linearly related to each other and could be used to predict soil moisture. This limited our ability to use the four different EM frequencies to obtain a soil moisture profile with depth. The apparent conductivity patterns represented the observed spatial soil moisture patterns well when the individually fitted relationships between measured soil moisture and apparent conductivity were used for each measurement point. However, when the same (master) relationship was used for all measurement locations, the soil moisture patterns were smoothed and did not resemble the observed soil moisture patterns very well. In addition, the range in calculated soil moisture values was reduced compared to observed soil moisture. Part of the smoothing was likely due to the much larger measurement area of the GEM-300 compared to the Aqua-pro soil moisture measurements.

A review of spatial downscaling of satellite remotely sensed soil moisture

NASA Astrophysics Data System (ADS)

Peng, Jian; Loew, Alexander; Merlin, Olivier; Verhoest, Niko E. C.

2017-06-01

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.

Vegetation function and non-uniqueness of the hydrological response

NASA Astrophysics Data System (ADS)

Ivanov, V. Y.; Fatichi, S.; Kampf, S. K.; Caporali, E.

2012-04-01

Through local moisture uptake vegetation exerts seasonal and longer-term impacts on the watershed hydrological response. However, the role of vegetation may go beyond the conventionally implied and well-understood "sink" function in the basin soil moisture storage equation. We argue that vegetation function imposes a "homogenizing" effect on pre-event soil moisture spatial storage, decreasing the likelihood that a rainfall event will result in a topographically-driven redistribution of soil water and the consequent formation of variable source areas. In combination with vegetation temporal dynamics, this may lead to the non-uniqueness of the hydrological response with respect to the mean basin wetness. This study designs a set of relevant numerical experiments carried out with two physically-based models; one of the models, HYDRUS, resolves variably saturated subsurface flow using a fully three-dimensional formulation, while the other model, tRIBS+VEGGIE, uses a one-dimensional formulation applied in a quasi-three-dimensional framework in combination with the model of vegetation dynamics. We demonstrate that (1) vegetation function modifies spatial heterogeneity in moisture spatial storage by imposing different degrees of subsurface flow connectivity; explore mechanistically (2) how and why a basin with the same mean soil moisture can have distinctly different spatial soil moisture distributions; and demonstrate (2) how these distinct moisture distributions result in a hysteretic runoff response to precipitation. Furthermore, the study argues that near-surface soil moisture is an insufficient indicator of the initial moisture state of a catchment with the implication of its limited effect on hydrological predictability.

Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model

NASA Technical Reports Server (NTRS)

Orth, Rene; Koster, Randal Dean; Seneviratne, Sonia I.

2013-01-01

Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. We investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Our approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography.

Estimation of improved resolution soil moisture in vegetated areas using passive AMSR-E data

NASA Astrophysics Data System (ADS)

Moradizadeh, Mina; Saradjian, Mohammad R.

2018-03-01

Microwave remote sensing provides a unique capability for soil parameter retrievals. Therefore, various soil parameters estimation models have been developed using brightness temperature (BT) measured by passive microwave sensors. Due to the low resolution of satellite microwave radiometer data, the main goal of this study is to develop a downscaling approach to improve the spatial resolution of soil moisture estimates with the use of higher resolution visible/infrared sensor data. Accordingly, after the soil parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to the soil moisture estimations that have been validated against in situ soil moisture data. Advance Microwave Scanning Radiometer-EOS BT data in Soil Moisture Experiment 2003 region in the south and north of Oklahoma have been used to this end. Results illustrated that the soil moisture variability is effectively captured at 5 km spatial scales without a significant degradation of the accuracy.

Systems, methods, and software for determining spatially variable distributions of the dielectric properties of a heterogeneous material

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

Farrington, Stephen P.

Systems, methods, and software for measuring the spatially variable relative dielectric permittivity of materials along a linear or otherwise configured sensor element, and more specifically the spatial variability of soil moisture in one dimension as inferred from the dielectric profile of the soil matrix surrounding a linear sensor element. Various methods provided herein combine advances in the processing of time domain reflectometry data with innovations in physical sensing apparatuses. These advancements enable high temporal (and thus spatial) resolution of electrical reflectance continuously along an insulated waveguide that is permanently emplaced in contact with adjacent soils. The spatially resolved reflectance ismore » directly related to impedance changes along the waveguide that are dominated by electrical permittivity contrast due to variations in soil moisture. Various methods described herein are thus able to monitor soil moisture in profile with high spatial resolution.« less

Soil Moisture Project Evaluation Workshop

NASA Technical Reports Server (NTRS)

Gilbert, R. H. (Editor)

1980-01-01

Approaches planned or being developed for measuring and modeling soil moisture parameters are discussed. Topics cover analysis of spatial variability of soil moisture as a function of terrain; the value of soil moisture information in developing stream flow data; energy/scene interactions; applications of satellite data; verifying soil water budget models; soil water profile/soil temperature profile models; soil moisture sensitivity analysis; combinations of the thermal model and microwave; determing planetary roughness and field roughness; how crust or a soil layer effects microwave return; truck radar; and truck/aircraft radar comparison.

Southern Great Plains 1997 hydrology experiment: The spatial and temporal distribution of soil moisture within a quarter section pasture field

NASA Technical Reports Server (NTRS)

Tsegaye, T.; Coleman, T.; Tadesse, W.; Rajbhandari, N.; Senwo, Z.; Crosson, W.; Surrency, J.

1998-01-01

Understanding the spatial and temporal distribution of soil moisture near the soil surface is important to relate ground truth data to remotely sensed data using an electronically scanned thinned array radiometer (ESTAR). The research was conducted at the A-ARM EF site in the Little Washita Watershed in Chickasha Oklahoma. Soil moisture was measured on a 100 x 100-m grid on a quarter section (0.8 km by 0.8 km) size field where the DOE A-ARM SWATS is located. This site has several drainage channels and small ponds. The site is under four different land use practices, namely active pastureland, non-grazed pastureland covered with thick grass, forest area covered with trees, and a single residential area. Soil moisture was measured with a Time Domain Reflectometry (TDR) Delta-T 6-cm theta-probe and gravimetric soil moisture (GSM) technique for the top 6 cm of the soil depth. A fourth order polynomial equation was fitted to each probe calibration curve. The correlation between TDR and GSM measurement technique ranges from 0.81 to 0.91. Comparison of the spatial and temporal distribution of soil moisture measured by the TDR and GSM techniques showed very strong similarities. Such TDR probes can be used successfully to replace the GSM techniques to measure soil moisture content rapidly and accurately with site specific calibration.

On the temporal and spatial variability of near-surface soil moisture for the identification of representative in situ soil moisture monitoring stations

USDA-ARS?s Scientific Manuscript database

The high spatio-temporal variability of soil moisture complicates the validation of remotely sensed soil moisture products using in-situ monitoring stations. Therefore, a standard methodology for selecting the most repre- sentative stations for the purpose of validating satellites and land surface ...

Bayesian Hierarchical Modeling for Big Data Fusion in Soil Hydrology

NASA Astrophysics Data System (ADS)

Mohanty, B.; Kathuria, D.; Katzfuss, M.

2016-12-01

Soil moisture datasets from remote sensing (RS) platforms (such as SMOS and SMAP) and reanalysis products from land surface models are typically available on a coarse spatial granularity of several square km. Ground based sensors on the other hand provide observations on a finer spatial scale (meter scale or less) but are sparsely available. Soil moisture is affected by high variability due to complex interactions between geologic, topographic, vegetation and atmospheric variables. Hydrologic processes usually occur at a scale of 1 km or less and therefore spatially ubiquitous and temporally periodic soil moisture products at this scale are required to aid local decision makers in agriculture, weather prediction and reservoir operations. Past literature has largely focused on downscaling RS soil moisture for a small extent of a field or a watershed and hence the applicability of such products has been limited. The present study employs a spatial Bayesian Hierarchical Model (BHM) to derive soil moisture products at a spatial scale of 1 km for the state of Oklahoma by fusing point scale Mesonet data and coarse scale RS data for soil moisture and its auxiliary covariates such as precipitation, topography, soil texture and vegetation. It is seen that the BHM model handles change of support problems easily while performing accurate uncertainty quantification arising from measurement errors and imperfect retrieval algorithms. The computational challenge arising due to the large number of measurements is tackled by utilizing basis function approaches and likelihood approximations. The BHM model can be considered as a complex Bayesian extension of traditional geostatistical prediction methods (such as Kriging) for large datasets in the presence of uncertainties.

Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing

NASA Astrophysics Data System (ADS)

Gao, Shengguo; Zhu, Zhongli; Liu, Shaomin; Jin, Rui; Yang, Guangchao; Tan, Lei

2014-10-01

Soil moisture (SM) plays a fundamental role in the land-atmosphere exchange process. Spatial estimation based on multi in situ (network) data is a critical way to understand the spatial structure and variation of land surface soil moisture. Theoretically, integrating densely sampled auxiliary data spatially correlated with soil moisture into the procedure of spatial estimation can improve its accuracy. In this study, we present a novel approach to estimate the spatial pattern of soil moisture by using the BME method based on wireless sensor network data and auxiliary information from ASTER (Terra) land surface temperature measurements. For comparison, three traditional geostatistic methods were also applied: ordinary kriging (OK), which used the wireless sensor network data only, regression kriging (RK) and ordinary co-kriging (Co-OK) which both integrated the ASTER land surface temperature as a covariate. In Co-OK, LST was linearly contained in the estimator, in RK, estimator is expressed as the sum of the regression estimate and the kriged estimate of the spatially correlated residual, but in BME, the ASTER land surface temperature was first retrieved as soil moisture based on the linear regression, then, the t-distributed prediction interval (PI) of soil moisture was estimated and used as soft data in probability form. The results indicate that all three methods provide reasonable estimations. Co-OK, RK and BME can provide a more accurate spatial estimation by integrating the auxiliary information Compared to OK. RK and BME shows more obvious improvement compared to Co-OK, and even BME can perform slightly better than RK. The inherent issue of spatial estimation (overestimation in the range of low values and underestimation in the range of high values) can also be further improved in both RK and BME. We can conclude that integrating auxiliary data into spatial estimation can indeed improve the accuracy, BME and RK take better advantage of the auxiliary information compared to Co-OK, and BME outperforms RK by integrating the auxiliary data in a probability form.

Prediction of Root Zone Soil Moisture using Remote Sensing Products and In-Situ Observation under Climate Change Scenario

NASA Astrophysics Data System (ADS)

Singh, G.; Panda, R. K.; Mohanty, B.

2015-12-01

Prediction of root zone soil moisture status at field level is vital for developing efficient agricultural water management schemes. In this study, root zone soil moisture was estimated across the Rana watershed in Eastern India, by assimilation of near-surface soil moisture estimate from SMOS satellite into a physically-based Soil-Water-Atmosphere-Plant (SWAP) model. An ensemble Kalman filter (EnKF) technique coupled with SWAP model was used for assimilating the satellite soil moisture observation at different spatial scales. The universal triangle concept and artificial intelligence techniques were applied to disaggregate the SMOS satellite monitored near-surface soil moisture at a 40 km resolution to finer scale (1 km resolution), using higher spatial resolution of MODIS derived vegetation indices (NDVI) and land surface temperature (Ts). The disaggregated surface soil moisture were compared to ground-based measurements in diverse landscape using portable impedance probe and gravimetric samples. Simulated root zone soil moisture were compared with continuous soil moisture profile measurements at three monitoring stations. In addition, the impact of projected climate change on root zone soil moisture were also evaluated. The climate change projections of rainfall were analyzed for the Rana watershed from statistically downscaled Global Circulation Models (GCMs). The long-term root zone soil moisture dynamics were estimated by including a rainfall generator of likely scenarios. The predicted long term root zone soil moisture status at finer scale can help in developing efficient agricultural water management schemes to increase crop production, which lead to enhance the water use efficiency.

Spatial variability and its main controlling factors of the permafrost soil-moisture on the northern-slope of Bayan Har Mountains in Qinghai-Tibet Plateau

NASA Astrophysics Data System (ADS)

Cao, W.; Sheng, Y.

2017-12-01

The soil moisture movement is an important carrier of material cycle and energy flow among the various geo-spheres in the cold regions. It is very critical to protect the alpine ecology and hydrologic cycle in Qinghai-Tibet Plateau. Especially, it becomes one of the key problems to reveal the spatial-temporal variability of soil moisture movement and its main influence factors in earth system science. Thus, this research takes the north slope of Bayan Har Mountains in Qinghai-Tibet Plateau as a case study. The present study firstly investigates the change of permafrost moisture in different slope positions and depths. Based on this investigation, this article attempts to investigate the spatial variability of permafrost moisture and identifies the key influence factors in different terrain conditions. The method of classification and regression tree (CART) is adopted to identify the main controlling factors influencing the soil moisture movement. And the relationships between soil moisture and environmental factors are revealed by the use of the method of canonical correspondence analysis (CCA). The results show that: 1) the change of the soil moisture on the permafrost slope is divided into 4 stages, including the freezing stability phase, the rapid thawing phase, the thawing stability phase and the fast freezing phase; 2) this greatly enhances the horizontal flow in the freezing period due to the terrain slope and the freezing-thawing process. Vertical migration is the mainly form of the soil moisture movement. It leads to that the soil-moisture content in the up-slope is higher than that in the down-slope. On the contrary, the soil-moisture content in the up-slope is lower than that in the down-slope during the melting period; 3) the main environmental factors which affect the slope-permafrost soil-moisture are elevation, soil texture, soil temperature and vegetation coverage. But there are differences in the impact factors of the soil moisture in different freezing-thawing stages; 4) the main factors that affect the slope-permafrost soil-moisture at the shallow depth of 0-20cm are slope, elevation and vegetation coverage. And the main factors influencing the soil moisture at the middle and lower depth are complex.

Soil moisture and properties estimation by assimilating soil temperatures using particle batch smoother: A new perspective for DTS

NASA Astrophysics Data System (ADS)

Dong, J.; Steele-Dunne, S. C.; Ochsner, T. E.; Van De Giesen, N.

2015-12-01

Soil moisture, hydraulic and thermal properties are critical for understanding the soil surface energy balance and hydrological processes. Here, we will discuss the potential of using soil temperature observations from Distributed Temperature Sensing (DTS) to investigate the spatial variability of soil moisture and soil properties. With DTS soil temperature can be measured with high resolution (spatial <1m, and temporal < 1min) in cables up to kilometers in length. Soil temperature evolution is primarily controlled by the soil thermal properties, and the energy balance at the soil surface. Hence, soil moisture, which affects both soil thermal properties and the energy that participates the evaporation process, is strongly correlated to the soil temperatures. In addition, the dynamics of the soil moisture is determined by the soil hydraulic properties.Here we will demonstrate that soil moisture, hydraulic and thermal properties can be estimated by assimilating observed soil temperature at shallow depths using the Particle Batch Smoother (PBS). The PBS can be considered as an extension of the particle filter, which allows us to infer soil moisture and soil properties using the dynamics of soil temperature within a batch window. Both synthetic and real field data will be used to demonstrate the robustness of this approach. We will show that the proposed method is shown to be able to handle different sources of uncertainties, which may provide a new view of using DTS observations to estimate sub-meter resolution soil moisture and properties for remote sensing product validation.

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.

Surface soil moisture retrieval over a Mediterranean semi-arid region using X-band TerraSAR-X SAR data

NASA Astrophysics Data System (ADS)

Azza, Gorrab; Zribi, Mehrez; Baghdadi, Nicolas; Mougenot, Bernard; Boulet, Gilles; Lili-Chabaane, Zohra

2015-04-01

Mapping surface soil moisture with meter-scale spatial resolution is appropriate for multi- domains particularly hydrology and agronomy. It allows water resources and irrigation management decisions, drought monitoring and validation of multi-hydrological water balance models. In the last years, various studies have demonstrated the large potential of radar remote sensing data, mainly from C frequency band, to retrieve soil moisture. However, the accuracy of the soil moisture estimation, by inversing backscattering radar coefficients (σ°), is affected by the influence of surface roughness and vegetation biomass contributions. In recent years, different empirical, semi empirical and physical approaches are developed for bare soil conditions, to estimate accurately spatial soil moisture variability. In this study, we propose an approach based on the change detection method for the retrieval of surface soil moisture at a higher spatial resolution. The proposal algorithm combines multi-temporal X-band SAR images (TerraSAR-X) with different continuous thetaprobe measurements. Seven thetaprobe stations are installed at different depths over the central semi arid region of Tunisia (9°23' - 10°17' E, 35° 1'-35°55' N). They cover approximately the entire of our study site and provide regional scale information. Ground data were collected over agricultural bare soil fields simultaneously to various TerraSAR-X data acquired during 2013-2014 and 2014-2015. More than fourteen test fields were selected for each spatial acquisition campaign, with variations in soil texture and in surface soil roughness. For each date, we considered the volumetric water content with thetaprobe instrument and gravimetric sampling; we measured also the roughness parameters with pin profilor. To retrieve soil moisture from X-band SAR data, we analyzed statistically the sensitivity between radar measurements and ground soil moisture derived from permanent thetaprobe stations. Our analyses are applied over bare soil class identified from an optical image SPOT / HRV acquired in the same period of the measurements. Results have shown linear relationship for the radar signals as a function of volumetric soil moisture with high sensitivity about 0.21 dB/vol%. For estimation of change in soil moisture, we considered two options: On the first one, we applied the change detection approach between successive radar images (∆σ°) assuming unchanged soil roughness effects. Our soil moisture retrieval algorithm was validated on the basis of comparisons between estimated and in situ soil moisture measurements over test fields. Using this option, results have shown an accuracy (RMSE) of about 4.8 %. Secondly, we corrected the sensitivity of the radar backscatter images to the surface roughness variability. Results have shown a reduction of the difference between the retrieved soil moisture and ground measurements with an RMSE about 3.7%.

Spatio-temporal Root Zone Soil Moisture Estimation for Indo - Gangetic Basin from Satellite Derived (AMSR-2 and SMOS) Surface Soil Moisture

NASA Astrophysics Data System (ADS)

Sure, A.; Dikshit, O.

2017-12-01

Root zone soil moisture (RZSM) is an important element in hydrology and agriculture. The estimation of RZSM provides insight in selecting the appropriate crops for specific soil conditions (soil type, bulk density, etc.). RZSM governs various vadose zone phenomena and subsequently affects the groundwater processes. With various satellite sensors dedicated to estimating surface soil moisture at different spatial and temporal resolutions, estimation of soil moisture at root zone level for Indo - Gangetic basin which inherits complex heterogeneous environment, is quite challenging. This study aims at estimating RZSM and understand its variation at the level of Indo - Gangetic basin with changing land use/land cover, topography, crop cycles, soil properties, temperature and precipitation patterns using two satellite derived soil moisture datasets operating at distinct frequencies with different principles of acquisition. Two surface soil moisture datasets are derived from AMSR-2 (6.9 GHz - `C' Band) and SMOS (1.4 GHz - `L' band) passive microwave sensors with coarse spatial resolution. The Soil Water Index (SWI), accounting for soil moisture from the surface, is derived by considering a theoretical two-layered water balance model and contributes in ascertaining soil moisture at the vadose zone. This index is evaluated against the widely used modelled soil moisture dataset of GLDAS - NOAH, version 2.1. This research enhances the domain of utilising the modelled soil moisture dataset, wherever the ground dataset is unavailable. The coupling between the surface soil moisture and RZSM is analysed for two years (2015-16), by defining a parameter T, the characteristic time length. The study demonstrates that deriving an optimal value of T for estimating SWI at a certain location is a function of various factors such as land, meteorological, and agricultural characteristics.

Application of Multitemporal Remotely Sensed Soil Moisture for the Estimation of Soil Physical Properties

NASA Technical Reports Server (NTRS)

Mattikalli, N. M.; Engman, E. T.; Jackson, T. J.; Ahuja, L. R.

1997-01-01

This paper demonstrates the use of multitemporal soil moisture derived from microwave remote sensing to estimate soil physical properties. The passive microwave ESTAR instrument was employed during June 10-18, 1992, to obtain brightness temperature (TB) and surface soil moisture data in the Little Washita watershed, Oklahoma. Analyses of spatial and temporal variations of TB and soil moisture during the dry-down period revealed a direct relationship between changes in T and soil moisture and soil physical (viz. texture) and hydraulic (viz. saturated hydraulic conductivity, K(sat)) properties. Statistically significant regression relationships were developed for the ratio of percent sand to percent clay (RSC) and K(sat), in terms of change components of TB and surface soil moisture. Validation of results using field measured values and soil texture map indicated that both RSC and K(sat) can be estimated with reasonable accuracy. These findings have potential applications of microwave remote sensing to obtain quick estimates of the spatial distributions of K(sat), over large areas for input parameterization of hydrologic models.

Estimating Long Term Surface Soil Moisture in the GCIP Area From Satellite Microwave Observations

NASA Technical Reports Server (NTRS)

Owe, Manfred; deJeu, Vrije; VandeGriend, Adriaan A.

2000-01-01

Soil moisture is an important component of the water and energy balances of the Earth's surface. Furthermore, it has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, accurate estimates of surface soil moisture are often difficult to make, especially at large spatial scales. Soil moisture is a highly variable land surface parameter, and while point measurements are usually accurate, they are representative only of the immediate site which was sampled. Simple averaging of point values to obtain spatial means often leads to substantial errors. Since remotely sensed observations are already a spatially averaged or areally integrated value, they are ideally suited for measuring land surface parameters, and as such, are a logical input to regional or larger scale land process models. A nine-year database of surface soil moisture is being developed for the Central United States from satellite microwave observations. This region forms much of the GCIP study area, and contains most of the Mississippi, Rio Grande, and Red River drainages. Daytime and nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. The life of the SMMR instrument spanned from Nov. 1978 to Aug. 1987. At 6.6 GHz, the instrument provided a spatial resolution of approximately 150 km, and an orbital frequency over any pixel-sized area of about 2 daytime and 2 nighttime passes per week. Ground measurements of surface soil moisture from various locations throughout the study area are used to calibrate the microwave observations. Because ground measurements are usually only single point values, and since the time of satellite coverage does not always coincide with the ground measurements, the soil moisture data were used to calibrate a regional water balance for the top 1, 5, and 10 cm surface layers in order to interpolate daily surface moisture values. Such a climate-based approach is often more appropriate for estimating large-area spatially averaged soil moisture because meteorological data are generally more spatially representative than isolated point measurements of soil moisture. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. Real time estimates should improve weather and climate modelling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.

Using high-resolution soil moisture modelling to assess the uncertainty of microwave remotely sensed soil moisture products at the correct spatial and temporal support

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; Bierkens, M. F. P.; Van Dam, J. C.; De Jong, S. M.

2012-04-01

Soil moisture is a key variable in the hydrological cycle and important in hydrological modelling. When assimilating soil moisture into flood forecasting models, the improvement of forecasting skills depends on the ability to accurately estimate the spatial and temporal patterns of soil moisture content throughout the river basin. Space-borne remote sensing may provide this information with a high temporal and spatial resolution and with a global coverage. Currently three microwave soil moisture products are available: AMSR-E, ASCAT and SMOS. The quality of these satellite-based products is often assessed by comparing them with in-situ observations of soil moisture. This comparison is however hampered by the difference in spatial and temporal support (i.e., resolution, scale), because the spatial resolution of microwave satellites is rather low compared to in-situ field measurements. Thus, the aim of this study is to derive a method to assess the uncertainty of microwave satellite soil moisture products at the correct spatial support. To overcome the difference in support size between in-situ soil moisture observations and remote sensed soil moisture, we used a stochastic, distributed unsaturated zone model (SWAP, van Dam (2000)) that is upscaled to the support of different satellite products. A detailed assessment of the SWAP model uncertainty is included to ensure that the uncertainty in satellite soil moisture is not overestimated due to an underestimation of the model uncertainty. We simulated unsaturated water flow up to a depth of 1.5m with a vertical resolution of 1 to 10 cm and on a horizontal grid of 1 km2 for the period Jan 2010 - Jun 2011. The SWAP model was first calibrated and validated on in-situ data of the REMEDHUS soil moisture network (Spain). Next, to evaluate the satellite products, the model was run for areas in the proximity of 79 meteorological stations in Spain, where model results were aggregated to the correct support of the satellite product by averaging model results from the 1 km2 grid within the remote sensing footprint. Overall 440 (AMSR-E, SMOS) to 680 (ASCAT) timeseries were compared to the aggregated SWAP model results, providing valuable information on the uncertainty of satellite soil moisture at the proper support. Our results show that temporal dynamics are best captured by ASCAT resulting in an average correlation of 0.72 with the model, while ASMR-E (0.41) and SMOS (0.42) are less capable of representing these dynamics. Standard deviations found for ASCAT and SMOS are low, 0.049 and 0.051m3m-3 respectively, while AMSR-E has a higher value of 0.062m3m-3. All standard deviations are higher than the average model uncertainty of 0.017m3m-3. All satellite products show a negative bias compared to the model results, with the largest value for SMOS. Satellite uncertainty is not found to be significantly related to topography, but is found to increase in densely vegetated areas. In general AMSR-E has most difficulties capturing soil moisture dynamics in Spain, while SMOS and mainly ASCAT have a fair to good performance. However, all products contain valuable information about the near-surface soil moisture over Spain. Van Dam, J.C., 2000, Field scale water flow and solute transport. SWAP model concepts, parameter estimation and case studies. Ph.D. thesis, Wageningen University

Spatial Variability of Soil-Water Storage in the Southern Sierra Critical Zone Observatory: Measurement and Prediction

NASA Astrophysics Data System (ADS)

Oroza, C.; Bales, R. C.; Zheng, Z.; Glaser, S. D.

2017-12-01

Predicting the spatial distribution of soil moisture in mountain environments is confounded by multiple factors, including complex topography, spatial variably of soil texture, sub-surface flow paths, and snow-soil interactions. While remote-sensing tools such as passive-microwave monitoring can measure spatial variability of soil moisture, they only capture near-surface soil layers. Large-scale sensor networks are increasingly providing soil-moisture measurements at high temporal resolution across a broader range of depths than are accessible from remote sensing. It may be possible to combine these in-situ measurements with high-resolution LIDAR topography and canopy cover to estimate the spatial distribution of soil moisture at high spatial resolution at multiple depths. We study the feasibility of this approach using six years (2009-2014) of daily volumetric water content measurements at 10-, 30-, and 60-cm depths from the Southern Sierra Critical Zone Observatory. A non-parametric, multivariate regression algorithm, Random Forest, was used to predict the spatial distribution of depth-integrated soil-water storage, based on the in-situ measurements and a combination of node attributes (topographic wetness, northness, elevation, soil texture, and location with respect to canopy cover). We observe predictable patterns of predictor accuracy and independent variable ranking during the six-year study period. Predictor accuracy is highest during the snow-cover and early recession periods but declines during the dry period. Soil texture has consistently high feature importance. Other landscape attributes exhibit seasonal trends: northness peaks during the wet-up period, and elevation and topographic-wetness index peak during the recession and dry period, respectively.

Evaluation of Long-term Soil Moisture Proxies in the U.S. Great Plains

NASA Astrophysics Data System (ADS)

Yuan, S.; Quiring, S. M.

2016-12-01

Soil moisture plays an important role in land-atmosphere interactions through both surface energy and water balances. However, despite its importance, there are few long-term records of observed soil moisture for investigating long-term spatial and temporal variations of soil moisture. Hence, it is necessary to find suitable approximations of soil moisture observations. 5 drought indices will be compared with simulated and observed soil moisture over the U.S. Great Plains during two time periods (1980 - 2012 and 2003 - 2012). Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), Palmer Z Index (zindex) and Crop Moisture Index (CMI) will be calculated by PRISM data. The soil moisture simulations will be derived from NLDAS. In situ soil moisture will be obtained from North American Soil Moisture Database. The evaluation will focus on three main aspects: trends, variations and persistence. The results will support further research investigating long-term variations in soil moisture-climate interactions.

Remote sensing of soil moisture using airborne hyperspectral data

USGS Publications Warehouse

Finn, M.; Lewis, M.; Bosch, D.; Giraldo, Mario; Yamamoto, K.; Sullivan, D.; Kincaid, R.; Luna, R.; Allam, G.; Kvien, Craig; Williams, M.

2011-01-01

Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

Remote sensing of soil moisture using airborne hyperspectral data

USGS Publications Warehouse

Finn, Michael P.; Lewis, Mark (David); Bosch, David D.; Giraldo, Mario; Yamamoto, Kristina H.; Sullivan, Dana G.; Kincaid, Russell; Luna, Ronaldo; Allam, Gopala Krishna; Kvien, Craig; Williams, Michael S.

2011-01-01

Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R 2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

Advances in Assimilation of Satellite-Based Passive Microwave Observations for Soil-Moisture Estimation

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle J. M.; Pauwels, Valentijn; Reichle, Rolf H.; Draper, Clara; Koster, Randy; Liu, Qing

2012-01-01

Satellite-based microwave measurements have long shown potential to provide global information about soil moisture. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS, [1]) mission as well as the future National Aeronautics and Space Administration (NASA) Soil Moisture Active and Passive (SMAP, [2]) mission measure passive microwave emission at L-band frequencies, at a relatively coarse (40 km) spatial resolution. In addition, SMAP will measure active microwave signals at a higher spatial resolution (3 km). These new L-band missions have a greater sensing depth (of -5cm) compared with past and present C- and X-band microwave sensors. ESA currently also disseminates retrievals of SMOS surface soil moisture that are derived from SMOS brightness temperature observations and ancillary data. In this research, we address two major challenges with the assimilation of recent/future satellite-based microwave measurements: (i) assimilation of soil moisture retrievals versus brightness temperatures for surface and root-zone soil moisture estimation and (ii) scale-mismatches between satellite observations, models and in situ validation data.

Growing season soil moisture following restoration treatments of varying intensity in semi-arid ponderosa pine forests

NASA Astrophysics Data System (ADS)

O'Donnell, F. C.; Springer, A. E.; Sankey, T.; Masek Lopez, S.

2014-12-01

Forest restoration projects are being planned for large areas of overgrown semi-arid ponderosa pine forests of the Southwestern US. Restoration involves the thinning of smaller trees and prescribed or managed fire to reduce tree density, restore a more natural fire regime, and decrease the risk of catastrophic wildfire. The stated goals of these projects generally reduced plant water stress and improvements in hydrologic function. However, little is known about how to design restoration treatments to best meet these goals. As part of a larger project on snow cover, soil moisture, and groundwater recharge, we measured soil moisture, an indicator of plant water status, in four pairs of control and restored sites near Flagstaff, Arizona. The restoration strategies used at the sites range in both amount of open space created and degree of clustering of the remaining trees. We measured soil moisture using 30 cm vertical time domain reflectometry probes installed on 100 m transects at 5 m intervals so it would be possible to analyze the spatial pattern of soil moisture. Soil moisture was higher and more spatially variable in the restored sites than the control sites with differences in spatial pattern among the restoration types. Soil moisture monitoring will continue until the first snow fall, at which point measurements of snow depth and snow water equivalent will be made at the same locations.

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.

Topographic effects on denitrification in drained agricultural fields

USDA-ARS?s Scientific Manuscript database

Denitrification is affected by soil moisture, while soil moisture can be affected by topography. Therefore, denitrification can be spatially correlated to topographic gradients. Three prior converted fields on the Delmarva Peninsula were sampled spatially for denitrification enzyme activity. The up...

Mapping high-resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother

USDA-ARS?s Scientific Manuscript database

This study demonstrated a new method for mapping high-resolution (spatial: 1 m, and temporal: 1 h) soil moisture by assimilating distributed temperature sensing (DTS) observed soil temperatures at intermediate scales. In order to provide robust soil moisture and property estimates, we first proposed...

Spatial downscaling of SMAP soil moisture using MODIS land surface temperature and NDVI during SMAPVEX15

USDA-ARS?s Scientific Manuscript database

The SMAP (Soil Moisture Active Passive) mission provides global surface soil moisture product at 36 km resolution from its L-band radiometer. While the coarse resolution is satisfactory to many applications there are also a lot of applications which would benefit from a higher resolution soil moistu...

Agricultural Decision Support Through Robust Assimilation of Satellite Derived Soil Moisture Estimates

NASA Astrophysics Data System (ADS)

Mishra, V.; Cruise, J.; Mecikalski, J. R.

2012-12-01

Soil Moisture is a key component in the hydrological process, affects surface and boundary layer energy fluxes and is the driving factor in agricultural production. Multiple in situ soil moisture measuring instruments such as Time-domain Reflectrometry (TDR), Nuclear Probes etc. are in use along with remote sensing methods like Active and Passive Microwave (PM) sensors. In situ measurements, despite being more accurate, can only be obtained at discrete points over small spatial scales. Remote sensing estimates, on the other hand, can be obtained over larger spatial domains with varying spatial and temporal resolutions. Soil moisture profiles derived from satellite based thermal infrared (TIR) imagery can overcome many of the problems associated with laborious in-situ observations over large spatial domains. An area where soil moisture observation and assimilation is receiving increasing attention is agricultural crop modeling. This study revolves around the use of the Decision Support System for Agrotechnology Transfer (DSSAT) crop model to simulate corn yields under various forcing scenarios. First, the model was run and calibrated using observed precipitation and model generated soil moisture dynamics. Next, the modeled soil moisture was updated using estimates derived from satellite based TIR imagery and the Atmospheric Land Exchange Inverse (ALEXI) model. We selected three climatically different locations to test the concept. Test Locations were selected to represent varied climatology. Bell Mina, Alabama - South Eastern United States, representing humid subtropical climate. Nabb, Indiana - Mid Western United States, representing humid continental climate. Lubbok, Texas - Southern United States, representing semiarid steppe climate. A temporal (2000-2009) correlation analysis of the soil moisture values from both DSSAT and ALEXI were performed and validated against the Land Information System (LIS) soil moisture dataset. The results clearly show strong correlation (R = 73%) between ALEXI and DSSAT at Bell Mina. At Nabb and Lubbock the correlation was 50-60%. Further, multiple experiments were conducted for each location: a) a DSSAT rain-fed 10 year sequential run forced with daymet precipitation; b) a DSSAT sequential run with no precipitation data; and c) a DSSAT run forced with ALEXI soil moisture estimates alone. The preliminary results of all the experiments are quantified through soil moisture correlations and yield comparisons. In general, the preliminary results strongly suggest that DSSAT forced with ALEXI can provide significant information especially at locations where no significant precipitation data exists.

[Detecting the moisture content of forest surface soil based on the microwave remote sensing technology.

PubMed

Li, Ming Ze; Gao, Yuan Ke; Di, Xue Ying; Fan, Wen Yi

2016-03-01

The moisture content of forest surface soil is an important parameter in forest ecosystems. It is practically significant for forest ecosystem related research to use microwave remote sensing technology for rapid and accurate estimation of the moisture content of forest surface soil. With the aid of TDR-300 soil moisture content measuring instrument, the moisture contents of forest surface soils of 120 sample plots at Tahe Forestry Bureau of Daxing'anling region in Heilongjiang Province were measured. Taking the moisture content of forest surface soil as the dependent variable and the polarization decomposition parameters of C band Quad-pol SAR data as independent variables, two types of quantitative estimation models (multilinear regression model and BP-neural network model) for predicting moisture content of forest surface soils were developed. The spatial distribution of moisture content of forest surface soil on the regional scale was then derived with model inversion. Results showed that the model precision was 86.0% and 89.4% with RMSE of 3.0% and 2.7% for the multilinear regression model and the BP-neural network model, respectively. It indicated that the BP-neural network model had a better performance than the multilinear regression model in quantitative estimation of the moisture content of forest surface soil. The spatial distribution of forest surface soil moisture content in the study area was then obtained by using the BP neural network model simulation with the Quad-pol SAR data.

The spatial distribution and temporal variation of desert riparian forests and their influencing factors in the downstream Heihe River basin, China

NASA Astrophysics Data System (ADS)

Ding, Jingyi; Zhao, Wenwu; Daryanto, Stefani; Wang, Lixin; Fan, Hao; Feng, Qiang; Wang, Yaping

2017-05-01

Desert riparian forests are the main restored vegetation community in Heihe River basin. They provide critical habitats and a variety of ecosystem services in this arid environment. Since desert riparian forests are also sensitive to disturbance, examining the spatial distribution and temporal variation of these forests and their influencing factors is important to determine the limiting factors of vegetation recovery after long-term restoration. In this study, field experiment and remote sensing data were used to determine the spatial distribution and temporal variation of desert riparian forests and their relationship with the environmental factors. We classified five types of vegetation communities at different distances from the river channel. Community coverage and diversity formed a bimodal pattern, peaking at the distances of 1000 and 3000 m from the river channel. In general, the temporal normalized difference vegetation index (NDVI) trend from 2000 to 2014 was positive at different distances from the river channel, except for the region closest to the river bank (i.e. within 500 m from the river channel), which had been undergoing degradation since 2011. The spatial distribution of desert riparian forests was mainly influenced by the spatial heterogeneity of soil properties (e.g. soil moisture, bulk density and soil particle composition). Meanwhile, while the temporal variation of vegetation was affected by both the spatial heterogeneity of soil properties (e.g. soil moisture and soil particle composition) and to a lesser extent, the temporal variation of water availability (e.g. annual average and variability of groundwater, soil moisture and runoff). Since surface (0-30 cm) and deep (100-200 cm) soil moisture, bulk density and the annual average of soil moisture at 100 cm obtained from the remote sensing data were regarded as major determining factors of community distribution and temporal variation, conservation measures that protect the soil structure and prevent soil moisture depletion (e.g. artificial soil cover and water conveyance channels) were suggested to better protect desert riparian forests under climate change and intensive human disturbance.

Different rates of soil drying after rainfall are observed by the SMOS satellite and the South Fork In Situ Soil Moisture Network

USDA-ARS?s Scientific Manuscript database

Soil moisture affects the spatial variation of land–atmosphere interactions through its in'uence on the balance of latent and sensible heat 'ux. Wetter soils are more prone to 'ooding because a smaller fraction of rainfall can in'ltrate into the soil. The Soil Moisture and Oceanic Salinity (SMOS) sa...

Does the stress-gradient hypothesis hold water? Disentangling spatial and temporal variation in plant effects on soil moisture in dryland systems

USGS Publications Warehouse

Butterfield, Bradley J.; Bradford, John B.; Armas, Cristina; Prieto, Ivan; Pugnaire, Francisco I.

2016-01-01

Taken together, the results of this simulation study suggest that plant effects on soil moisture are predictable based on relatively general relationships between precipitation inputs and differential evaporation and transpiration rates between plant and interspace microsites that are largely driven by temperature. In particular, this study highlights the importance of differentiating between temporal and spatial variation in weather and climate, respectively, in determining plant effects on available soil moisture. Rather than focusing on the somewhat coarse-scale predictions of the SGH, it may be more beneficial to explicitly incorporate plant effects on soil moisture into predictive models of plant-plant interaction outcomes in drylands.

SoilSCAPE in-Situ Observations of Soil Moisture for SMAP Validation: Pushing the Envelopes of Spatial Coverage and Energy Efficiency in Sparse Wireless Sensor Networks (Invited)

NASA Astrophysics Data System (ADS)

Moghaddam, M.; Silva, A.; Clewley, D.; Akbar, R.; Entekhabi, D.

2013-12-01

Soil Moisture Sensing Controller and oPtimal Estimator (SoilSCAPE) is a wireless in-situ sensor network technology, developed under the support of NASA ESTO/AIST program, for multi-scale validation of soil moisture retrievals from the Soil Moisture Active and Passive (SMAP) mission. The SMAP sensor suite is expected to produce soil moisture retrievals at 3 km scale from the radar instrument, at 36 km from the radiometer, and at 10 km from the combination of the two sensors. To validate the retrieved soil moisture maps at any of these scales, it is necessary to perform in-situ observations at multiple scales (ten, hundreds, and thousands of meters), representative of the true spatial variability of soil moisture fields. The most recent SoilSCAPE network, deployed in the California central valley, has been designed, built, and deployed to accomplish this goal, and is expected to become a core validation site for SMAP. The network consists of up to 150 sensor nodes, each comprised of 3-4 soil moisture sensors at various depths, deployed over a spatial extent of 36 km by 36 km. The network contains multiple sub-networks, each having up to 30 nodes, whose location is selected in part based on maximizing the land cover diversity within the 36 km cell. The network has achieved unprecedented energy efficiency, longevity, and spatial coverage using custom-designed hardware and software protocols. The network architecture utilizes a nested strategy, where a number of end devices (EDs) communicate to a local coordinator (LC) using our recently developed hardware with ultra-efficient circuitry and best-effort-timeslot allocation communication protocol. The LCs in turn communicates with the base station (BS) via text messages and a new compression scheme. The hardware and software technologies required to implement this latest deployment of the SoilSCAPE network will be presented in this paper, and several data sets resulting from the measurements will be shown. The data are available publicly in near-real-time from the project web site, and are also available and searchable via an extensive set of metadata fields through the ORNL-DAAC.

Spatial and temporal variability of soil moisture on the field with and without plants*

NASA Astrophysics Data System (ADS)

Usowicz, B.; Marczewski, W.; Usowicz, J. B.

2012-04-01

Spatial and temporal variability of the natural environment is its inherent and unavoidable feature. Every element of the environment is characterized by its own variability. One of the kinds of variability in the natural environment is the variability of the soil environment. To acquire better and deeper knowledge and understanding of the temporal and spatial variability of the physical, chemical and biological features of the soil environment, we should determine the causes that induce a given variability. Relatively stable features of soil include its texture and mineral composition; examples of those variables in time are the soil pH or organic matter content; an example of a feature with strong dynamics is the soil temperature and moisture content. The aim of this study was to identify the variability of soil moisture on the field with and without plants using geostatistical methods. The soil moisture measurements were taken on the object with plant canopy and without plants (as reference). The measurements of soil moisture and meteorological components were taken within the period of April-July. The TDR moisture sensors covered 5 cm soil layers and were installed in the plots in the soil layers of 0-0.05, 0.05-0.1, 0.1-0.15, 0.2-0.25, 0.3-0.35, 0.4-0.45, 0.5-0.55, 0.8-0.85 m. Measurements of soil moisture were taken once a day, in the afternoon hours. For the determination of reciprocal correlation, precipitation data and data from soil moisture measurements with the TDR meter were used. Calculations of reciprocal correlation of precipitation and soil moisture at various depths were made for three objects - spring barley, rye, and bare soil, at the level of significance of p<0.05. No significant reciprocal correlation was found between the precipitation and soil moisture in the soil profile for any of the objects studied. Although the correlation analysis indicates a lack of correlation between the variables under consideration, observation of the soil moisture runs in particular objects and of precipitation distribution shows clearly that rainfall has an effect on the soil moisture. The amount of precipitation water that increased the soil moisture depended on the strength of the rainfall, on the hydrological properties of the soil (primarily the soil density), the status of the plant cover, and surface runoff. Basing on the precipitation distribution and on the soil moisture runs, an attempt was made at finding a temporal and spatial relationship between those variables, employing for the purpose the geostatistical methods which permit time and space to be included in the analysis. The geostatistical parameters determined showed the temporal dependence of moisture distribution in the soil profile, with the autocorrelation radius increasing with increasing depth in the profile. The highest values of the radius were observed in the plots with plant cover below the arable horizon, and the lowest in the arable horizon on the barley and fallow plots. The fractal dimensions showed a clear decrease in values with increasing depth in the plots with plant cover, while in the bare plots they were relatively constant within the soil profile under study. Therefore, they indicated that the temporal distribution of soil moisture within the soil profile in the bare field was more random in character than in the plots with plants. The results obtained and the analyses indicate that the moisture in the soil profile, its variability and determination, are significantly affected by the type and condition of plant canopy. The differentiation in moisture content between the plots studied resulted from different precipitation interception and different intensity of water uptake by the roots. * The work was financially supported in part by the ESA Programme for European Cooperating States (PECS), No.98084 "SWEX-R, Soil Water and Energy Exchange/Research", AO-3275.

Evaluation of SMAP Level 2 Soil Moisture Algorithms Using SMOS Data

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann; Shi, J. C.

2011-01-01

The objectives of the SMAP (Soil Moisture Active Passive) mission are global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolution, respectively. SMAP will provide soil moisture with a spatial resolution of 10 km with a 3-day revisit time at an accuracy of 0.04 m3/m3 [1]. In this paper we contribute to the development of the Level 2 soil moisture algorithm that is based on passive microwave observations by exploiting Soil Moisture Ocean Salinity (SMOS) satellite observations and products. SMOS brightness temperatures provide a global real-world, rather than simulated, test input for the SMAP radiometer-only soil moisture algorithm. Output of the potential SMAP algorithms will be compared to both in situ measurements and SMOS soil moisture products. The investigation will result in enhanced SMAP pre-launch algorithms for soil moisture.

Closing the water balance with cosmic-ray soil moisture measurements and assessing their spatial variability within two semiarid watersheds

NASA Astrophysics Data System (ADS)

Schreiner-McGraw, A. P.; Vivoni, E. R.; Mascaro, G.; Franz, T. E.

2015-06-01

Soil moisture dynamics reflect the complex interactions of meteorological conditions with soil, vegetation and terrain properties. In this study, intermediate scale soil moisture estimates from the cosmic-ray sensing (CRS) method are evaluated for two semiarid ecosystems in the southwestern United States: a mesquite savanna at the Santa Rita Experimental Range (SRER) and a mixed shrubland at the Jornada Experimental Range (JER). Evaluations of the CRS method are performed for small watersheds instrumented with a distributed sensor network consisting of soil moisture sensor profiles, an eddy covariance tower and runoff flumes used to close the water balance. We found an excellent agreement between the CRS method and the distributed sensor network (RMSE of 0.009 and 0.013 m3 m-3 at SRER and JER) at the hourly time scale over the 19-month study period, primarily due to the inclusion of 5 cm observations of shallow soil moisture. Good agreement was obtained in soil moisture changes estimated from the CRS and watershed water balance methods (RMSE = 0.001 and 0.038 m3 m-3 at SRER and JER), with deviations due to bypassing of the CRS measurement depth during large rainfall events. This limitation, however, was used to show that drier-than-average conditions at SRER promoted plant water uptake from deeper layers, while the wetter-than-average period at JER resulted in leakage towards deeper soils. Using the distributed sensor network, we quantified the spatial variability of soil moisture in the CRS footprint and the relation between evapotranspiration and soil moisture, in both cases finding similar predictive relations at both sites that are applicable to other semiarid ecosystems in the southwestern US. Furthermore, soil moisture spatial variability was related to evapotranspiration in a manner consistent with analytical relations derived using the CRS method, opening up new possibilities for understanding land-atmosphere interactions.

The Temporal Dynamics of Spatial Patterns of Observed Soil Moisture Interpreted Using the Hydrus 1-D Model

NASA Astrophysics Data System (ADS)

Chen, M.; Willgoose, G. R.; Saco, P. M.

2009-12-01

This paper investigates the soil moisture dynamics over two subcatchments (Stanley and Krui) in the Goulburn River in NSW during a three year period (2005-2007) using the Hydrus 1-D unsaturated soil water flow model. The model was calibrated to the seven Stanley microcatchment sites (1 sqkm site) using continuous time surface 30cm and full profile soil moisture measurements. Soil type, leaf area index and soil depth were found to be the key parameters changing model fit to the soil moisture time series. They either shifted the time series up or down, changed the steepness of dry-down recessions or determined the lowest point of soil moisture dry-down respectively. Good correlations were obtained between observed and simulated soil water storage (R=0.8-0.9) when calibrated parameters for one site were applied to the other sites. Soil type was also found to be the main determinant (after rainfall) of the mean of modelled soil moisture time series. Simulations of top 30cm were better than those of the whole soil profile. Within the Stanley microcatchment excellent soil moisture matches could be generated simply by adjusting the mean of soil moisture up or down slightly. Only minor modification of soil properties from site to site enable good fits for all of the Stanley sites. We extended the predictions of soil moisture to a larger spatial scale of the Krui catchment (sites up to 30km distant from Stanley) using soil and vegetation parameters from Stanley but the locally recorded rainfall at the soil moisture measurement site. The results were encouraging (R=0.7~0.8). These results show that it is possible to use a calibrated soil moisture model to extrapolate the soil moisture to other sites for a catchment with an area of up to 1000km2. This paper demonstrates the potential usefulness of continuous time, point scale soil moisture (typical of that measured by permanently installed TDR probes) in predicting the soil wetness status over a catchment of significant size.

Passive microwave soil moisture downscaling using vegetation index and skin surface temperature

USDA-ARS?s Scientific Manuscript database

Soil moisture satellite estimates are available from a variety of passive microwave satellite sensors, but their spatial resolution is frequently too coarse for use by land managers and other decision makers. In this paper, a soil moisture downscaling algorithm based on a regression relationship bet...

Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model

USDA-ARS?s Scientific Manuscript database

This paper aims to investigate how surface soil moisture data assimilation affects each hydrologic process and how spatially varying inputs affect the potential capability of surface soil moisture assimilation at the watershed scale. The Ensemble Kalman Filter (EnKF) is coupled with a watershed scal...

Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

USDA-ARS?s Scientific Manuscript database

As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote sensing methods have the spatial and temporal resolution required to...

Merging thermal and microwave satellite observations for a high-resolution soil moisture data product

USDA-ARS?s Scientific Manuscript database

Many societal applications of soil moisture data products require high spatial resolution and numerical accuracy. Current thermal geostationary satellite sensors (GOES Imager and GOES-R ABI) could produce 2-16km resolution soil moisture proxy data. Passive microwave satellite radiometers (e.g. AMSR...

Role of subsurface physics in the assimilation of surface soil moisture observations

USDA-ARS?s Scientific Manuscript database

Soil moisture controls the exchange of water and energy between the land surface and the atmosphere and exhibits memory that may be useful for climate prediction at monthly time scales. Though spatially distributed observations of soil moisture are increasingly becoming available from remotely sense...

The Effects of Fine-scale Soil Moisture and Canopy Heterogeneities on Energy and Soil Water Fluxes in a Temperate Mixed Deciduous Forest

NASA Astrophysics Data System (ADS)

He, L.; Ivanov, V. Y.; Bohrer, G.; Maurer, K.; Vogel, C. S.; Moghaddam, M.

2011-12-01

Vegetation is heterogeneous at different scales, influencing spatially variable energy and water exchanges between land-surface and atmosphere. Current land surface parameterizations of large-scale models consider spatial variability at a scale of a few kilometers and treat vegetation cover as aggregated patches with uniform properties. However, the coupling mechanisms between fine-scale soil moisture, vegetation, and energy fluxes such as evapotranspiration are strongly nonlinear; the aggregation of surface variations may produce biased energy fluxes. This study aims to improve the understanding of the scale impact in atmosphere-biosphere-hydrosphere interactions, which affects predictive capabilities of land surface models. The study uses a high-resolution, physically-based ecohydrological model tRIBS + VEGGIE as a data integration tool to upscale the heterogeneity of canopy distribution resolved at a few meters to the watershed scale. The study was carried out for a spatially heterogeneous, temperate mixed forest environment of Northern Michigan located near the University of Michigan Biological Station (UMBS). Energy and soil water dynamics were simulated at the tree-canopy resolution in the horizontal plane for a small domain (~2 sq. km) located within a footprint of the AmeriFlux tower. A variety of observational data were used to constrain and confirm the model, including a 3-m profile continuous soil moisture dataset and energy flux data (measured at the AmeriFlux tower footprint). A scenario with a spatially uniform canopy, corresponding to the commonly used 'big-leaf' scheme in land surface parameterizations was used to infer the effects of coarse-scale averaging. To gain insights on how heterogeneous canopy and soil moisture interact and contribute to the domain-averaged transpiration, several scenarios of tree-scale leaf area and soil moisture spatial variability were designed. Specifically, for the same mean states, the scenarios of variability of canopy biomass account for the spatial distribution of photosynthesis (and thus the stomatal resistance), the aerodynamic and leaf boundary layer resistances as well as the differential radiation forcing due to tall tree exposure and lateral shading of short trees. The numerical experiments show that by transpiring spatially varying amounts of water, heterogeneous canopies adjust the spatial soil water state to the scaled inverse of the canopy biomass regardless of the initial moisture state. Such a spatial distribution can be further wiped out because of the differential water stress. The aggregation of canopy-scale atmosphere-biosphere-hydrosphere interactions demonstrates non-linear relationship between soil moisture and evapotranspiration, influencing domain-averaged energy fluxes.

Aspect-related Vegetation Differences Amplify Soil Moisture Variability in Semiarid Landscapes

NASA Astrophysics Data System (ADS)

Yetemen, O.; Srivastava, A.; Kumari, N.; Saco, P. M.

2017-12-01

Soil moisture variability (SMV) in semiarid landscapes is affected by vegetation, soil texture, climate, aspect, and topography. The heterogeneity in vegetation cover that results from the effects of microclimate, terrain attributes (slope gradient, aspect, drainage area etc.), soil properties, and spatial variability in precipitation have been reported to act as the dominant factors modulating SMV in semiarid ecosystems. However, the role of hillslope aspect in SMV, though reported in many field studies, has not received the same degree of attention probably due to the lack of extensive large datasets. Numerical simulations can then be used to elucidate the contribution of aspect-driven vegetation patterns to this variability. In this work, we perform a sensitivity analysis to study on variables driving SMV using the CHILD landscape evolution model equipped with a spatially-distributed solar-radiation component that couples vegetation dynamics and surface hydrology. To explore how aspect-driven vegetation heterogeneity contributes to the SMV, CHILD was run using a range of parameters selected to reflect different scenarios (from uniform to heterogeneous vegetation cover). Throughout the simulations, the spatial distribution of soil moisture and vegetation cover are computed to estimate the corresponding coefficients of variation. Under the uniform spatial precipitation forcing and uniform soil properties, the factors affecting the spatial distribution of solar insolation are found to play a key role in the SMV through the emergence of aspect-driven vegetation patterns. Hence, factors such as catchment gradient, aspect, and latitude, define water stress and vegetation growth, and in turn affect the available soil moisture content. Interestingly, changes in soil properties (porosity, root depth, and pore-size distribution) over the domain are not as effective as the other factors. These findings show that the factors associated to aspect-related vegetation differences amplify the soil moisture variability of semi-arid landscapes.

Surface Soil Moisture Estimates Across China Based on Multi-satellite Observations and A Soil Moisture Model

NASA Astrophysics Data System (ADS)

Zhang, Ke; Yang, Tao; Ye, Jinyin; Li, Zhijia; Yu, Zhongbo

2017-04-01

Soil moisture is a key variable that regulates exchanges of water and energy between land surface and atmosphere. Soil moisture retrievals based on microwave satellite remote sensing have made it possible to estimate global surface (up to about 10 cm in depth) soil moisture routinely. Although there are many satellites operating, including NASA's Soil Moisture Acitive Passive mission (SMAP), ESA's Soil Moisture and Ocean Salinity mission (SMOS), JAXA's Advanced Microwave Scanning Radiometer 2 mission (AMSR2), and China's Fengyun (FY) missions, key differences exist between different satellite-based soil moisture products. In this study, we applied a single-channel soil moisture retrieval model forced by multiple sources of satellite brightness temperature observations to estimate consistent daily surface soil moisture across China at a spatial resolution of 25 km. By utilizing observations from multiple satellites, we are able to estimate daily soil moisture across the whole domain of China. We further developed a daily soil moisture accounting model and applied it to downscale the 25-km satellite-based soil moisture to 5 km. By comparing our estimated soil moisture with observations from a dense observation network implemented in Anhui Province, China, our estimated soil moisture results show a reasonably good agreement with the observations (RMSE < 0.1 and r > 0.8).

L-band Soil Moisture Mapping using Small UnManned Aerial Systems

NASA Astrophysics Data System (ADS)

Dai, E.; Gasiewski, A. J.; Stachura, M.; Elston, J.; Venkitasubramony, A.

2016-12-01

1. IntroductionSoil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, and impacts water resource management, agriculture, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 promises to provide global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions as low as 5 km for some products. However, there exists a need for measurements of soil moisture on smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture and evaporation and transpiration studies of boundary layer heat transport. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters (i.e., the height of the platform). Compared with various other proposed methods of validation based on either in-situ measurements [1,2] or existing airborne sensors suitable for manned aircraft deployment [3], the integrated design of the LDCR on a lightweight small UAS (sUAS) is capable of providing sub-watershed ( km scale) coverage at very high spatial resolution ( 15 m) suitable for scaling scale studies, and at comparatively low operator cost. To demonstrate the LDCR several flights had been performed during field experiments at the Canton Oklahoma Soilscape site on September 8th and 9th, 2015 and Yuma Colorado Irrigation Research Foundation (IRF) site from June to August, 2016. These tests were flown at 25-50 m altitude to obtain differing spatial resolutions. The scientific intercomparisons of LDCR retrieved soil moisture and in-situ measurements will be presented. 2. References[1] McIntyre, E.M., A.J. Gasiewski, and D. Manda D, "Near Real-Time Passive C-Band Microwave Soil Moisture Retrieval During CLASIC 2007," Proc. IGARSS, 2008. [2] Robock, A., S. Steele-Dunne, J. Basara, W. Crow, and M. Moghaddam M, "In Situ Network and Scaling," SMAP Algorithm and Cal/Val Workshop, 2009. [3] Walker, A., "Airborne Microwave Radiometer Measurements During CanEx-SM10," Second SMAP Cal/Val Workshop, 2011.

High-Resolution Soil Moisture Retrieval using SMAP-L Band Radiometer and RISAT-C band Radar Data for the Indian Subcontinent

NASA Astrophysics Data System (ADS)

Singh, G.; Das, N. N.; Panda, R. K.; Mohanty, B.; Entekhabi, D.; Bhattacharya, B. K.

2016-12-01

Soil moisture status at high resolution (1-10 km) is vital for hydrological, agricultural and hydro-metrological applications. The NASA Soil Moisture Active Passive (SMAP) mission had potential to provide reliable soil moisture estimate at finer spatial resolutions (3 km and 9 km) at the global extent, but suffered a malfunction of its radar, consequently making the SMAP mission observations only from radiometer that are of coarse spatial resolution. At present, the availability of high-resolution soil moisture product is limited, especially in developing countries like India, which greatly depends on agriculture for sustaining a huge population. Therefore, an attempt has been made in the reported study to combine the C-band synthetic aperture radar (SAR) data from Radar Imaging Satellite (RISAT) of the Indian Space Research Organization (ISRO) with the SMAP mission L-band radiometer data to obtain high-resolution (1 km and 3 km) soil moisture estimates. In this study, a downscaling approach (Active-Passive Algorithm) implemented for the SMAP mission was used to disaggregate the SMAP radiometer brightness temperature (Tb) using the fine resolution SAR backscatter (σ0) from RISAT. The downscaled high-resolution Tb was then subjected to tau-omega model in conjunction with high-resolution ancillary data to retrieve soil moisture at 1 and 3 km scale. The retrieved high-resolution soil moisture estimates were then validated with ground based soil moisture measurement under different hydro-climatic regions of India. Initial results show tremendous potential and reasonable accuracy for the retrieved soil moisture at 1 km and 3 km. It is expected that ISRO will implement this approach to produce high-resolution soil moisture estimates for the Indian subcontinent.

Improving Soil Moisture Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations

NASA Technical Reports Server (NTRS)

Girotto, Manuela

2018-01-01

Observations from recent soil moisture dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide global high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) soil moisture profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface soil moisture 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of soil moisture (i.e., surface and deeper water storages).

Spatial variation and driving factors of soil moisture at multi-scales: a case study in Loess Plateau of China

NASA Astrophysics Data System (ADS)

Zhao, W.; Zhang, X.; Liu, Y.; Fang, X.

2017-12-01

Currently, the ecological restoration of the Loess Plateau has led to significant achievements such as increases in vegetation coverage, decreases in soil erosion, and enhancement of ecosystem services. Soil moisture shortages, however, commonly occur as a result of limited rainfall and strong evaporation in this semiarid region of China. Since soil moisture is critical in regulating plant growth in these semiarid regions, it is crucial to identify the spatial variation and factors affecting soil moisture at multi-scales in the Loess Plateau of China. In the last several years, extensive studies on soil moisture have been carried out by our research group at the plot, small watershed, watershed, and regional scale in the Loess Plateau, providing some information for vegetation restoration in the region. The main research results are as follows: (1) the highest soil moisture content was in the 0-0.1 m layer with a large coefficient of variation; (2) in the 0-0.1m layer, soil moisture content was negatively correlated with relative elevation, slope and vegetation cover, the correlations among slope, aspect and soil moisture increased with depth increased; (3) as for the deep soil moisture content, the higher spatial variation of deep SMC occurred at 1.2-1.4 m and 4.8-5.0m; (4) the deep soil moisture content in native grassland and farmland were significant higher than that of introduced vegetation; (5) at regional scale, the soil water content under different precipitation zones increased following the increase of precipitation, while, the influencing factors of deep SMC at watershed scale varied with land management types; (6) in the areas with multi-year precipitation of 370 - 440mm, natural grass is more suitable for restoration, and this should be treated as the key areas in vegetation restoration; (7) appropriate planting density and species selection should be taken into account for introduced vegetation management; (8) it is imperative to take the local reality into account and to balance the economic and ecological benefits so that the ratio of artificial vegetation and natural restoration can be optimized to realize sustainability of vegetation restoration

Topographic Controls on Spatial Patterns of Soil Texture and Moisture in a Semi-arid Montane Catchment with Aspect-Dependent Vegetation

NASA Astrophysics Data System (ADS)

Lehman, B. M.; Niemann, J. D.

2008-12-01

Soil moisture exerts significant control over the partitioning of latent and sensible energy fluxes, the magnitude of both vertical and lateral water fluxes, the physiological and water-use characteristics of vegetation, and nutrient cycling. Considerable progress has been made in determining how soil characteristics, topography, and vegetation influence spatial patterns of soil moisture in humid environments at the catchment, hillslope, and plant scales. However, understanding of the controls on soil moisture patterns beyond the plant scale in semi-arid environments remains more limited. This study examines the relationships between the spatial patterns of near surface soil moisture (upper 5 cm), terrain indices, and soil properties in a small, semi-arid, montane catchment. The 8 ha catchment, located in the Cache La Poudre River Canyon in north-central Colorado, has a total relief of 115 m and an average elevation of 2193 m. It is characterized by steep slopes and shallow, gravelly/sandy soils with scattered granite outcroppings. Depth to bedrock ranges from 0 m to greater than 1 m. Vegetation in the catchment is highly correlated with topographic aspect. In particular, north-facing hillslopes are predominately vegetated by ponderosa pines, while south-facing slopes are mostly vegetated by several shrub species. Soil samples were collected at a 30 m resolution to characterize soil texture and bulk density, and several datasets consisting of more than 300 point measurements of soil moisture were collected using time domain reflectometry (TDR) between Fall 2007 and Summer 2008 at a 15 m resolution. Results from soil textural analysis performed with sieving and the ASTM standard hydrometer method show that soil texture is finer on the north-facing hillslope than on the south-facing hillslope. Cos(aspect) is the best univariate predictor of silts, while slope is the best predictor of coarser fractions up to fine gravel. Bulk density increases with depth but shows no significant relationship with topographic indices. When the catchment average soil moisture is low, the variance of soil moisture increases with the average. When the average is high, the variance remains relatively constant. Little of the variation in soil moisture is explained by topographic indices when the catchment is either very wet or dry; however, when the average soil moisture takes on intermediate values, cos(aspect) is consistently the best predictor among the terrain indices considered.

Spatial and temporal variation of moisture content in the soil profiles of two different agricultural fields of semi-arid region.

PubMed

Baskan, Oguz; Kosker, Yakup; Erpul, Gunay

2013-12-01

Modeling spatio-temporal variation of soil moisture with depth in the soil profile plays an important role for semi-arid crop production from an agro-hydrological perspective. This study was performed in Guvenc Catchment. Two soil series that were called Tabyabayir (TaS) and Kervanpinari (KeS) and classified as Leptosol and Vertisol Soil Groups were used in this research. The TeS has a much shallower (0-34 cm) than the KeS (0-134 cm). At every sampling time, a total of geo-referenced 100 soil moisture samples were taken based on horizon depths. The results indicated that soil moisture content changed spatially and temporally with soil texture and profile depth significantly. In addition, land use was to be important factor when soil was shallow. When the soil conditions were towards to dry, higher values for the coefficient of variation (CV) were observed for TaS (58 and 43% for A and C horizons, respectively); however, the profile CV values were rather stable at the KeS. Spatial variability range of TaS was always higher at both dry and wet soil conditions when compared to that of KeS. Excessive drying of soil prevented to describe any spatial model for surface horizon, additionally resulting in a high nugget variance in the subsurface horizon for the TaS. On the contrary to TaS, distribution maps were formed all horizons for the KeS at any measurement times. These maps, depicting both dry and wet soil conditions through the profile depth, are highly expected to reduce the uncertainty associated with spatially and temporally determining the hydraulic responses of the catchment soils.

Passive Microwave Remote Sensing of Soil Moisture

NASA Technical Reports Server (NTRS)

Njoku, Eni G.; Entekhabi, Dara

1996-01-01

Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive Microwave soil moisture sensors currently considered for space operation are in the range 10-20 km. The most useful frequency range for soil moisture sensing is 1-5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations.

Soil moisture remote sensing: State of the science

USDA-ARS?s Scientific Manuscript database

Satellites (e.g., SMAP, SMOS) using passive microwave techniques, in particular at L band frequency, have shown good promise for global mapping of near-surface (0-5 cm) soil moisture at a spatial resolution of 25-40 km and temporal resolution of 2-3 days. C- and X-band soil moisture records date bac...

Validating the BERMS in situ soil moisture network with a large scale temporary network

USDA-ARS?s Scientific Manuscript database

Calibration and validation of soil moisture satellite products requires data records of large spatial and temporal extent, but obtaining this data can be challenging. These challenges can include remote locations, and expense of equipment. One location with a long record of soil moisture data is th...

Validation and scaling of soil moisture in a semi-arid environment: SMAP Validation Experiment 2015 (SMAPVEX15)

USDA-ARS?s Scientific Manuscript database

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 product.The main goals of the experiment were to address issues regarding the spatial disaggregation...

Validation of SMAP Root Zone Soil Moisture Estimates with Improved Cosmic-Ray Neutron Probe Observations

NASA Astrophysics Data System (ADS)

Babaeian, E.; Tuller, M.; Sadeghi, M.; Franz, T.; Jones, S. B.

2017-12-01

Soil Moisture Active Passive (SMAP) soil moisture products are commonly validated based on point-scale reference measurements, despite the exorbitant spatial scale disparity. The difference between the measurement depth of point-scale sensors and the penetration depth of SMAP further complicates evaluation efforts. Cosmic-ray neutron probes (CRNP) with an approximately 500-m radius footprint provide an appealing alternative for SMAP validation. This study is focused on the validation of SMAP level-4 root zone soil moisture products with 9-km spatial resolution based on CRNP observations at twenty U.S. reference sites with climatic conditions ranging from semiarid to humid. The CRNP measurements are often biased by additional hydrogen sources such as surface water, atmospheric vapor, or mineral lattice water, which sometimes yield unrealistic moisture values in excess of the soil water storage capacity. These effects were removed during CRNP data analysis. Comparison of SMAP data with corrected CRNP observations revealed a very high correlation for most of the investigated sites, which opens new avenues for validation of current and future satellite soil moisture products.

An evaluation of the spatial resolution of soil moisture information

NASA Technical Reports Server (NTRS)

Hardy, K. R.; Cohen, S. H.; Rogers, L. K.; Burke, H. H. K.; Leupold, R. C.; Smallwood, M. D.

1981-01-01

Rainfall-amount patterns in the central regions of the U.S. were assessed. The spatial scales of surface features and their corresponding microwave responses in the mid western U.S. were investigated. The usefulness for U.S. government agencies of soil moisture information at scales of 10 km and 1 km. was ascertained. From an investigation of 494 storms, it was found that the rainfall resulting from the passage of most types of storms produces patterns which can be resolved on a 10 km scale. The land features causing the greatest problem in the sensing of soil moisture over large agricultural areas with a radiometer are bodies of water. Over the mid-western portions of the U.S., water occupies less than 2% of the total area, the consequently, the water bodies will not have a significant impact on the mapping of soil moisture. Over most of the areas, measurements at a 10-km resolution would adequately define the distribution of soil moisture. Crop yield models and hydrological models would give improved results if soil moisture information at scales of 10 km was available.

New DEMs may stimulate significant advancements in remote sensing of soil moisture

NASA Astrophysics Data System (ADS)

Nolan, Matt; Fatland, Dennis R.

From Napoleon's defeat at Waterloo to increasing corn yields in Kansas to greenhouse gas flux in the Arctic, the importance of soil moisture is endemic to world affairs and merits the considerable attention it receives from the scientific community. This importance can hardly be overstated, though it often goes unstated.Soil moisture is one of the key variables in a variety of broad areas critical to the conduct of societies' economic and political affairs and their well-being; these include the health of agricultural crops, global climate dynamics, military trafficability planning, and hazards such as flooding and forest fires. Unfortunately the in situ measurement of the spatial distribution of soil moisture on a watershed-scale is practically impossible. And despite decades of international effort, a satellite remote sensing technique that can reliably measure soil moisture with a spatial resolution of meters has not yet been identified or implemented. Due to the lack of suitable measurement techniques and, until recently digital elevation models (DEMs), our ability to understand and predict soil moisture dynamics through modeling has largely remained crippled from birth [Grayson and Bloschl, 200l].

In Situ Validation of the Soil Moisture Active Passive (SMAP) Satellite Mission

NASA Technical Reports Server (NTRS)

Jackson, T.; Cosh, M.; Crow, W.; Colliander, A.; Walker, J.

2011-01-01

SMAP is a new NASA mission proposed for 2014 that would provide a number of soil moisture and freeze/thaw products. The soil moisture products span spatial resolutions from 3 to 40 km. In situ soil moisture observations will be one of the key elements of the validation program for SMAP. Data from the currently available set of soil moisture observing sites and networks need improvement if they are to be useful. Problems include a lack of standardization of instrumentation and installation and the disparity in spatial scale between the point-scale in situ data (a few centimeters) and the coarser satellite products. SMAP has initiated activities to resolve these issues for some of the existing resources. The other challenge to soil moisture validation is the need to expand the number of sites and their geographic distribution. SMAP is attempting to increase the number of sites and their value in validation through collaboration. The issues and solutions involving in situ validation being investigated will be described along with recent results from SMAP validation projects.

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.

Can the normalized soil moisture index improve the prediction of soil organic carbon based on hyperspectral remote sensing data?

NASA Astrophysics Data System (ADS)

van Wesemael, Bas; Nocita, Marco

2016-04-01

One of the problems for mapping of soil organic carbon (SOC) at large-scale based on visible - near and short wave infrared (VIS-NIR-SWIR) remote sensing techniques is the spatial variation of topsoil moisture when the images are collected. Soil moisture is certainly an aspect causing biased SOC estimations, due to the problems in discriminating reflectance differences due to either variations in organic matter or soil moisture, or their combination. In addition, the difficult validation procedures make the accurate estimation of soil moisture from optical airborne a major challenge. After all, the first millimeters of the soil surface reflect the signal to the airborne sensor and show a large spatial, vertical and temporal variation in soil moisture. Hence, the difficulty of assessing the soil moisture of this thin layer at the same moment of the flight. The creation of a soil moisture proxy, directly retrievable from the hyperspectral data is a priority to improve the large-scale prediction of SOC. This paper aims to verify if the application of the normalized soil moisture index (NSMI) to Airborne Prima Experiment (APEX) hyperspectral images could improve the prediction of SOC. The study area was located in the loam region of Wallonia, Belgium. About 40 samples were collected from bare fields covered by the flight lines, and analyzed in the laboratory. Soil spectra, corresponding to the sample locations, were extracted from the images. Once the NSMI was calculated for the bare fields' pixels, spatial patterns, presumably related to within field soil moisture variations, were revealed. SOC prediction models, built using raw and pre-treated spectra, were generated from either the full dataset (general model), or pixels belonging to one of the two classes of NSMI values (NSMI models). The best result, with a RMSE after validation of 1.24 g C kg-1, was achieved with a NSMI model, compared to the best general model, characterized by a RMSE of 2.11 g C kg-1. These results confirmed the advantage to controlling the effect of soil moisture on the detection of SOC. The NSMI proved to be a flexible concept, due to the possible use of different SWIR wavelengths, and ease of use, because measurements of soil moisture by other techniques are not needed. However, in the future, it will be important to assess the effectiveness of the NSMI for different soil types, and other hyperspectral sensors.

Downscaling SMAP Radiometer Soil Moisture over the CONUS using Soil-Climate Information and Ensemble Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.

2017-12-01

Soil moisture contributes significantly towards the improvement of weather and climate forecast and understanding terrestrial ecosystem processes. It is known as a key hydrologic variable in the agricultural drought monitoring, flood modeling and irrigation management. While satellite retrievals can provide an unprecedented information on soil moisture at global-scale, the products are generally at coarse spatial resolutions (25-50 km2). This often hampers their use in regional or local studies, which normally require a finer resolution of the data set. This work presents a new framework based on an ensemble learning method while using soil-climate information derived from remote-sensing and ground-based observations to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer soil moisture over the Continental U.S. (CONUS) at 1 km spatial resolution. In the proposed method, a suite of remotely sensed and in situ data sets in addition to soil texture information and topography data among others were used. The downscaled product was validated against in situ soil moisture measurements collected from a limited number of core validation sites and several hundred sparse soil moisture networks throughout the CONUS. The obtained results indicated a great potential of the proposed methodology to derive the fine resolution soil moisture information applicable for fine resolution hydrologic modeling, data assimilation and other regional studies.

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.

Application of IEM model on soil moisture and surface roughness estimation

NASA Technical Reports Server (NTRS)

Shi, Jiancheng; Wang, J. R.; Oneill, P. E.; Hsu, A. Y.; Engman, E. T.

1995-01-01

Monitoring spatial and temporal changes of soil moisture are of importance to hydrology, meteorology, and agriculture. This paper reports a result on study of using L-band SAR imagery to estimate soil moisture and surface roughness for bare fields. Due to limitations of the Small Perturbation Model, it is difficult to apply this model on estimation of soil moisture and surface roughness directly. In this study, we show a simplified model derived from the Integral Equation Model for estimation of soil moisture and surface roughness. We show a test of this model using JPL L-band AIRSAR data.

Spatial structure and scaling of macropores in hydrological process at small catchment scale

NASA Astrophysics Data System (ADS)

Silasari, Rasmiaditya; Broer, Martine; Blöschl, Günter

2013-04-01

During rainfall events, the formation of overland flow can occur under the circumstances of saturation excess and/or infiltration excess. These conditions are affected by the soil moisture state which represents the soil water content in micropores and macropores. Macropores act as pathway for the preferential flows and have been widely studied locally. However, very little is known about their spatial structure and conductivity of macropores and other flow characteristic at the catchment scale. This study will analyze these characteristics to better understand its importance in hydrological processes. The research will be conducted in Petzenkirchen Hydrological Open Air Laboratory (HOAL), a 64 ha catchment located 100 km west of Vienna. The land use is divided between arable land (87%), pasture (5%), forest (6%) and paved surfaces (2%). Video cameras will be installed on an agricultural field to monitor the overland flow pattern during rainfall events. A wireless soil moisture network is also installed within the monitored area. These field data will be combined to analyze the soil moisture state and the responding surface runoff occurrence. The variability of the macropores spatial structure of the observed area (field scale) then will be assessed based on the topography and soil data. Soil characteristics will be supported with laboratory experiments on soil matrix flow to obtain proper definitions of the spatial structure of macropores and its variability. A coupled physically based distributed model of surface and subsurface flow will be used to simulate the variability of macropores spatial structure and its effect on the flow behaviour. This model will be validated by simulating the observed rainfall events. Upscaling from field scale to catchment scale will be done to understand the effect of macropores variability on larger scales by applying spatial stochastic methods. The first phase in this study is the installation and monitoring configuration of video cameras and soil moisture monitoring equipment to obtain the initial data of overland flow occurrence and soil moisture state relationships.

Impacts of precipitation and potential evapotranspiration patterns on downscaling soil moisture in regions with large topographic relief

NASA Astrophysics Data System (ADS)

Cowley, Garret S.; Niemann, Jeffrey D.; Green, Timothy R.; Seyfried, Mark S.; Jones, Andrew S.; Grazaitis, Peter J.

2017-02-01

Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing, but that resolution is poorly suited for many applications. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution soil moisture using fine-resolution topographic, vegetation, and soil data to produce fine-resolution (10-30 m) estimates of soil moisture. The EMT+VS model performs well at catchments with low topographic relief (≤124 m), but it has not been applied to regions with larger ranges of elevation. Large relief can produce substantial variations in precipitation and potential evapotranspiration (PET), which might affect the fine-resolution patterns of soil moisture. In this research, simple methods to downscale temporal average precipitation and PET are developed and included in the EMT+VS model, and the effects of spatial variations in these variables on the surface soil moisture estimates are investigated. The methods are tested against ground truth data at the 239 km2 Reynolds Creek watershed in southern Idaho, which has 1145 m of relief. The precipitation and PET downscaling methods are able to capture the main features in the spatial patterns of both variables. The space-time Nash-Sutcliffe coefficients of efficiency of the fine-resolution soil moisture estimates improve from 0.33 to 0.36 and 0.41 when the precipitation and PET downscaling methods are included, respectively. PET downscaling provides a larger improvement in the soil moisture estimates than precipitation downscaling likely because the PET pattern is more persistent through time, and thus more predictable, than the precipitation pattern.

Root Water Uptake and Soil Moisture Pattern Dynamics - Capturing Connections, Controls and Causalities

NASA Astrophysics Data System (ADS)

Blume, T.; Heidbuechel, I.; Hassler, S. K.; Simard, S.; Guntner, A.; Stewart, R. D.; Weiler, M.

2015-12-01

We hypothesize that there is a shift in controls on landscape scale soil moisture patterns when plants become active during the growing season. Especially during the summer soil moisture patterns are not only controlled by soils, topography and related abiotic site characteristics but also by root water uptake. Root water uptake influences soil moisture patterns both in the lateral and vertical direction. Plant water uptake from different soil depths is estimated based on diurnal fluctuations in soil moisture content and was investigated with a unique setup of 46 field sites in Luxemburg and 15 field sites in Germany. These sites cover a range of geologies, soils, topographic positions and types of vegetation. Vegetation types include pasture, pine forest (young and old) and different deciduous forest stands. Available data at all sites includes information at high temporal resolution from 3-5 soil moisture and soil temperature profiles, matrix potential, piezometers and sapflow sensors as well as standard climate data. At sites with access to a stream, discharge or water level is also recorded. The analysis of soil moisture patterns over time indicates a shift in regime depending on season. Depth profiles of root water uptake show strong differences between different forest stands, with maximum depths ranging between 50 and 200 cm. Temporal dynamics of signal strength within the profile furthermore suggest a locally shifting spatial distribution of root water uptake depending on water availability. We will investigate temporal thresholds (under which conditions spatial patterns of root water uptake become most distinct) as well as landscape controls on soil moisture and root water uptake dynamics.

The Australian National Airborne Field Experiment 2005: Soil Moisture Remote Sensing at 60 Meter Resolution and Up

NASA Technical Reports Server (NTRS)

Kim, E. J.; Walker, J. P.; Panciera, R.; Kalma, J. D.

2006-01-01

Spatially-distributed soil moisture observations have applications spanning a wide range of spatial resolutions from the very local needs of individual farmers to the progressively larger areas of interest to weather forecasters, water resource managers, and global climate modelers. To date, the most promising approach for space-based remote sensing of soil moisture makes use of passive microwave emission radiometers at L-band frequencies (1-2 GHz). Several soil moisture-sensing satellites have been proposed in recent years, with the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission scheduled to be launched first in a couple years. While such a microwave-based approach has the advantage of essentially allweather operation, satellite size limits spatial resolution to 10's of km. Whether used at this native resolution or in conjunction with some type of downscaling technique to generate soil moisture estimates on a finer-scale grid, the effects of subpixel spatial variability play a critical role. The soil moisture variability is typically affected by factors such as vegetation, topography, surface roughness, and soil texture. Understanding and these factors is the key to achieving accurate soil moisture retrievals at any scale. Indeed, the ability to compensate for these factors ultimately limits the achievable spatial resolution and/or accuracy of the retrieval. Over the last 20 years, a series of airborne campaigns in the USA have supported the development of algorithms for spaceborne soil moisture retrieval. The most important observations involved imagery from passive microwave radiometers. The early campaigns proved that the retrieval worked for larger and larger footprints, up to satellite-scale footprints. These provided the solid basis for proposing the satellite missions. More recent campaigns have explored other aspects such as retrieval performance through greater amounts of vegetation. All of these campaigns featured extensive ground truth collection over a range of grid spacings, to provide a basis for examining the effects of subpixel variability. However, the native footprint size of the airborne L-band radiometers was always a few hundred meters. During the recently completed (November, 2005) National Airborne Field Experiment (NAFE) campaign in Australia, a compact L-band radiometer was deployed on a small aircraft. This new combination permitted routine observations at native resolutions as high as 60 meters, substantially finer than in previous airborne soil moisture campaigns, as well as satellite footprint areal coverage. The radiometer, the Polarimetric L-band Microwave Radiometer (PLMR) performed extremely well and operations included extensive calibration-related observations. Thus, along with the extensive fine-scale ground truth, the NAFE dataset includes all the ingredients for the first scaling studies involving very-high-native resolution soil moisture observations and the effects of vegetation, roughness, etc. A brief overview of the NAFE will be presented, then examples of the airborne observations with resolutions from 60 m to 1 km will be shown, and early results from scaling studies will be discussed.

Disaggregation of remotely sensed soil moisture under all sky condition using machine learning approach in Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, S.; Kim, H.; Choi, M.; Kim, K.

2016-12-01

Estimating spatiotemporal variation of soil moisture is crucial to hydrological applications such as flood, drought, and near real-time climate forecasting. Recent advances in space-based passive microwave measurements allow the frequent monitoring of the surface soil moisture at a global scale and downscaling approaches have been applied to improve the spatial resolution of passive microwave products available at local scale applications. However, most downscaling methods using optical and thermal dataset, are valid only in cloud-free conditions; thus renewed downscaling method under all sky condition is necessary for the establishment of spatiotemporal continuity of datasets at fine resolution. In present study Support Vector Machine (SVM) technique was utilized to downscale a satellite-based soil moisture retrievals. The 0.1 and 0.25-degree resolution of daily Land Parameter Retrieval Model (LPRM) L3 soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) were disaggregated over Northeast Asia in 2015. Optically derived estimates of surface temperature (LST), normalized difference vegetation index (NDVI), and its cloud products were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) for the purpose of downscaling soil moisture in finer resolution under all sky condition. Furthermore, a comparison analysis between in situ and downscaled soil moisture products was also conducted for quantitatively assessing its accuracy. Results showed that downscaled soil moisture under all sky condition not only preserves the quality of AMSR2 LPRM soil moisture at 1km resolution, but also attains higher spatial data coverage. From this research we expect that time continuous monitoring of soil moisture at fine scale regardless of weather conditions would be available.

Remote Sensing Soil Moisture Analysis by Unmanned Aerial Vehicles Digital Imaging

NASA Astrophysics Data System (ADS)

Yeh, C. Y.; Lin, H. R.; Chen, Y. L.; Huang, S. Y.; Wen, J. C.

2017-12-01

In recent years, remote sensing analysis has been able to apply to the research of climate change, environment monitoring, geology, hydro-meteorological, and so on. However, the traditional methods for analyzing wide ranges of surface soil moisture of spatial distribution surveys may require plenty resources besides the high cost. In the past, remote sensing analysis performed soil moisture estimates through shortwave, thermal infrared ray, or infrared satellite, which requires lots of resources, labor, and money. Therefore, the digital image color was used to establish the multiple linear regression model. Finally, we can find out the relationship between surface soil color and soil moisture. In this study, we use the Unmanned Aerial Vehicle (UAV) to take an aerial photo of the fallow farmland. Simultaneously, we take the surface soil sample from 0-5 cm of the surface. The soil will be baking by 110° C and 24 hr. And the software ImageJ 1.48 is applied for the analysis of the digital images and the hue analysis into Red, Green, and Blue (R, G, B) hue values. The correlation analysis is the result from the data obtained from the image hue and the surface soil moisture at each sampling point. After image and soil moisture analysis, we use the R, G, B and soil moisture to establish the multiple regression to estimate the spatial distributions of surface soil moisture. In the result, we compare the real soil moisture and the estimated soil moisture. The coefficient of determination (R2) can achieve 0.5-0.7. The uncertainties in the field test, such as the sun illumination, the sun exposure angle, even the shadow, will affect the result; therefore, R2 can achieve 0.5-0.7 reflects good effect for the in-suit test by using the digital image to estimate the soil moisture. Based on the outcomes of the research, using digital images from UAV to estimate the surface soil moisture is acceptable. However, further investigations need to be collected more than ten days (four times a day) data to verify the relation between the image hue and the soil moisture for reliable moisture estimated model. And it is better to use the digital single lens reflex camera to prevent the deformation of the image and to have a better auto exposure. Keywords: soil, moisture, remote sensing

The SMAP mission combined active-passive soil moisture product at 9 km and 3km spatial resolutions

USDA-ARS?s Scientific Manuscript database

The NASA Soil Moisture Active Passive (SMAP) mission with onboard L-band radiometer and radar was launched on January 31st, 2015. The spacecraft provided high-resolution (3 km and 9 km) global soil moisture estimates at regular intervals by combining radiometer and radar observations for ~2.5 months...

Soil moisture response to snowmelt and rainfall in a Sierra Nevada mixed-conifer forest

Treesearch

Roger C. Bales; Jan W. Hopmans; Anthony T. O’Geen; Matthew Meadows; Peter C. Hartsough; Peter Kirchner; Carolyn T. Hunsaker; Dylan Beaudette

2011-01-01

Using data from a water-balance instrument cluster with spatially distributed sensors we determined the magnitude and within-catchment variability of components of the catchment-scale water balance, focusing on the relationship of seasonal evapotranspiration to changes in snowpack and soil moisture storage. Co-located, continuous snow depth and soil moisture...

An initial assessment of SMAP soil moisture disaggregation scheme using TIR surface evaporation data over the continental United States

USDA-ARS?s Scientific Manuscript database

The Soil Moisture Active Passive (SMAP) mission is dedicated toward global soil moisture mapping. Typically, an L-band microwave radiometer has a spatial resolution on the order of 36-40 km, which is too coarse for many specific hydro-meteorological and agricultural applications. With the failure of...

Trends in Soil Moisture Reflect More Than Slope Position: Soils on San Cristóbal Island, Galápagos as a Case Study

NASA Astrophysics Data System (ADS)

Percy, M.; Singha, K.; Benninger, L. K.; Riveros-Iregui, D. A.; Mirus, B. B.

2015-12-01

The spatial and temporal distribution of soil moisture in tropical critical zones depends upon a number of variables including topographic position, soil texture, overlying vegetation, and local microclimates. We investigate the influences on soil moisture on a tropical basaltic island (San Cristóbal, Galápagos) across a variety of microclimates during the transition from the wetter to the drier season. We used multiple approaches to characterize spatial and temporal patterns in soil moisture at four sites across microclimates ranging from arid to very humid. The microclimates on San Cristóbal vary with elevation, so our monitoring includes two sites in the transitional zone at lower elevations, one in the humid zone at moderate elevations, and one in the very humid zone in higher elevations. We made over 250 near-surface point measurements per site using a Hydrosense II probe, and estimated the lateral variability in soil moisture across each site with an EM-31 electrical conductivity meter. We also monitored continuous time-series of in-situ soil moisture dynamics using three nested TDR probes collocated with meteorological stations at each of the sites. Preliminary analysis indicates that soils in the very humid zone have lower electrical conductivities across all the hillslopes as compared to the humid and transitional zones, which suggests that additional factors beyond climate and slope position are important. While soil texture across the very humid site is fairly uniform, variations in vegetation have a strong control on soil moisture patterns. At the remaining sites the vegetation patterns also have a very strong local influence on soil moisture, but correlation between the depth to clay layers and soil moisture patterns suggests that mineralogy is also important. Our findings suggest that the microclimatic setting is a crucial consideration for understanding relations between vegetation, soil texture, and soil-moisture dynamics in tropical critical zones.

Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network

NASA Astrophysics Data System (ADS)

Martini, Edoardo; Werban, Ulrike; Zacharias, Steffen; Pohle, Marco; Dietrich, Peter; Wollschläger, Ute

2017-01-01

Electromagnetic induction (EMI) measurements are widely used for soil mapping, as they allow fast and relatively low-cost surveys of soil apparent electrical conductivity (ECa). Although the use of non-invasive EMI for imaging spatial soil properties is very attractive, the dependence of ECa on several factors challenges any interpretation with respect to individual soil properties or states such as soil moisture (θ). The major aim of this study was to further investigate the potential of repeated EMI measurements to map θ, with particular focus on the temporal variability of the spatial patterns of ECa and θ. To this end, we compared repeated EMI measurements with high-resolution θ data from a wireless soil moisture and soil temperature monitoring network for an extensively managed hillslope area for which soil properties and θ dynamics are known. For the investigated site, (i) ECa showed small temporal variations whereas θ varied from very dry to almost saturation, (ii) temporal changes of the spatial pattern of ECa differed from those of the spatial pattern of θ, and (iii) the ECa-θ relationship varied with time. Results suggest that (i) depending upon site characteristics, stable soil properties can be the major control of ECa measured with EMI, and (ii) for soils with low clay content, the influence of θ on ECa may be confounded by changes of the electrical conductivity of the soil solution. Further, this study discusses the complex interplay between factors controlling ECa and θ, and the use of EMI-based ECa data with respect to hydrological applications.

An intercomparison of remotely sensed soil moisture products at various spatial scales over the Iberian penisula

USDA-ARS?s Scientific Manuscript database

Soil moisture (SM) can be retrieved from active microwave (AM)-, passive microwave (PM)- and thermal infrared (TIR)-observations, each having their unique spatial- and temporal-coverage. A limitation of TIR-based SM retrievals is its dependency on cloud-free conditions, while microwave retrievals ar...

Multi-time scale analysis of the spatial representativeness of in situ soil moisture data within satellite footprints

USDA-ARS?s Scientific Manuscript database

We conduct a novel comprehensive investigation that seeks to prove the connection between spatial and time scales in surface soil moisture (SM) within the satellite footprint (~50 km). Modeled and measured point series at Yanco and Little Washita in situ networks are first decomposed into anomalies ...

Integration of soil moisture and geophysical datasets for improved water resource management in irrigated systems

NASA Astrophysics Data System (ADS)

Finkenbiner, Catherine; Franz, Trenton E.; Avery, William Alexander; Heeren, Derek M.

2016-04-01

Global trends in consumptive water use indicate a growing and unsustainable reliance on water resources. Approximately 40% of total food production originates from irrigated agriculture. With increasing crop yield demands, water use efficiency must increase to maintain a stable food and water trade. This work aims to increase our understanding of soil hydrologic fluxes at intermediate spatial scales. Fixed and roving cosmic-ray neutron probes were combined in order to characterize the spatial and temporal patterns of soil moisture at three study sites across an East-West precipitation gradient in the state of Nebraska, USA. A coarse scale map was generated for the entire domain (122 km2) at each study site. We used a simplistic data merging technique to produce a statistical daily soil moisture product at a range of key spatial scales in support of current irrigation technologies: the individual sprinkler (˜102m2) for variable rate irrigation, the individual wedge (˜103m2) for variable speed irrigation, and the quarter section (0.82 km2) for uniform rate irrigation. Additionally, we were able to generate a daily soil moisture product over the entire study area at various key modeling and remote sensing scales 12, 32, and 122 km2. Our soil moisture products and derived soil properties were then compared against spatial datasets (i.e. field capacity and wilting point) from the US Department of Agriculture Web Soil Survey. The results show that our "observed" field capacity was higher compared to the Web Soil Survey products. We hypothesize that our results, when provided to irrigators, will decrease water losses due to runoff and deep percolation as sprinkler managers can better estimate irrigation application depth and times in relation to soil moisture depletion below field capacity and above maximum allowable depletion. The incorporation of this non-contact and pragmatic geophysical method into current irrigation practices across the state and globe has the potential to greatly increase agricultural water use efficiency at scale.

Regional co-variability of spatial and temporal soil moisture-precipitation coupling in North Africa: an observational perspective

NASA Astrophysics Data System (ADS)

Petrova, Irina Y.; van Heerwaarden, Chiel C.; Hohenegger, Cathy; Guichard, Françoise

2018-06-01

The magnitude and sign of soil moisture-precipitation coupling (SMPC) is investigated using a probability-based approach and 10 years of daily microwave satellite data across North Africa at a 1° horizontal scale. Specifically, the co-existence and co-variability of spatial (i.e. using soil moisture gradients) and temporal (i.e. using soil moisture anomaly) soil moisture effects on afternoon rainfall is explored. The analysis shows that in the semi-arid environment of the Sahel, the negative spatial and the negative temporal coupling relationships do not only co-exist, but are also dependent on one another. Hence, if afternoon rain falls over temporally drier soils, it is likely to be surrounded by a wetter environment. Two regions are identified as SMPC hot spots. These are the south-western part of the domain (7-15° N, 10° W-7° E), with the most robust negative SMPC signal, and the South Sudanese region (5-13° N, 24-34° E). The sign and significance of the coupling in the latter region is found to be largely modulated by the presence of wetlands and is susceptible to the number of long-lived propagating convective systems. The presence of wetlands and an irrigated land area is found to account for about 30 % of strong and significant spatial SMPC in the North African domain. This study provides the first insight into regional variability of SMPC in North Africa, and supports the potential relevance of mechanisms associated with enhanced sensible heat flux and mesoscale variability in surface soil moisture for deep convection development.

Assimilating soil moisture into an Earth System Model

NASA Astrophysics Data System (ADS)

Stacke, Tobias; Hagemann, Stefan

2017-04-01

Several modelling studies reported potential impacts of soil moisture anomalies on regional climate. In particular for short prediction periods, perturbations of the soil moisture state may result in significant alteration of surface temperature in the following season. However, it is not clear yet whether or not soil moisture anomalies affect climate also on larger temporal and spatial scales. In an earlier study, we showed that soil moisture anomalies can persist for several seasons in the deeper soil layers of a land surface model. Additionally, those anomalies can influence root zone moisture, in particular during explicitly dry or wet periods. Thus, one prerequisite for predictability, namely the existence of long term memory, is evident for simulated soil moisture and might be exploited to improve climate predictions. The second prerequisite is the sensitivity of the climate system to soil moisture. In order to investigate this sensitivity for decadal simulations, we implemented a soil moisture assimilation scheme into the Max-Planck Institute for Meteorology's Earth System Model (MPI-ESM). The assimilation scheme is based on a simple nudging algorithm and updates the surface soil moisture state once per day. In our experiments, the MPI-ESM is used which includes model components for the interactive simulation of atmosphere, land and ocean. Artificial assimilation data is created from a control simulation to nudge the MPI-ESM towards predominantly dry and wet states. First analyses are focused on the impact of the assimilation on land surface variables and reveal distinct differences in the long-term mean values between wet and dry state simulations. Precipitation, evapotranspiration and runoff are larger in the wet state compared to the dry state, resulting in an increased moisture transport from the land to atmosphere and ocean. Consequently, surface temperatures are lower in the wet state simulations by more than one Kelvin. In terms of spatial pattern, the largest differences between both simulations are seen for continental areas, while regions with a maritime climate are least sensitive to soil moisture assimilation.

An underestimated role of precipitation frequency in regulating summer soil moisture

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

Wu, Chaoyang; Chen, Jing M.; Pumpanen, Jukka

2012-04-26

Soil moisture induced droughts are expected to become more frequent under future global climate change. Precipitation has been previously assumed to be mainly responsible for variability in summer soil moisture. However, little is known about the impacts of precipitation frequency on summer soil moisture, either interannually or spatially. To better understand the temporal and spatial drivers of summer drought, 415 site yr measurements observed at 75 flux sites world wide were used to analyze the temporal and spatial relationships between summer soil water content (SWC) and the precipitation frequencies at various temporal scales, i.e., from half-hourly, 3, 6, 12 andmore » 24 h measurements. Summer precipitation was found to be an indicator of interannual SWC variability with r of 0.49 (p < 0.001) for the overall dataset. However, interannual variability in summer SWC was also significantly correlated with the five precipitation frequencies and the sub-daily precipitation frequencies seemed to explain the interannual SWC variability better than the total of precipitation. Spatially, all these precipitation frequencies were better indicators of summer SWC than precipitation totals, but these better performances were only observed in non-forest ecosystems. Our results demonstrate that precipitation frequency may play an important role in regulating both interannual and spatial variations of summer SWC, which has probably been overlooked or underestimated. However, the spatial interpretation should carefully consider other factors, such as the plant functional types and soil characteristics of diverse ecoregions.« less

Reconstructions of Soil Moisture for the Upper Colorado River Basin Using Tree-Ring Chronologies

NASA Astrophysics Data System (ADS)

Tootle, G.; Anderson, S.; Grissino-Mayer, H.

2012-12-01

Soil moisture is an important factor in the global hydrologic cycle, but existing reconstructions of historic soil moisture are limited. Tree-ring chronologies (TRCs) were used to reconstruct annual soil moisture in the Upper Colorado River Basin (UCRB). Gridded soil moisture data were spatially regionalized using principal components analysis and k-nearest neighbor techniques. Moisture sensitive tree-ring chronologies in and adjacent to the UCRB were correlated with regional soil moisture and tested for temporal stability. TRCs that were positively correlated and stable for the calibration period were retained. Stepwise linear regression was applied to identify the best predictor combinations for each soil moisture region. The regressions explained 42-78% of the variability in soil moisture data. We performed reconstructions for individual soil moisture grid cells to enhance understanding of the disparity in reconstructive skill across the regions. Reconstructions that used chronologies based on ponderosa pines (Pinus ponderosa) and pinyon pines (Pinus edulis) explained increased variance in the datasets. Reconstructed soil moisture was standardized and compared with standardized reconstructed streamflow and snow water equivalent from the same region. Soil moisture reconstructions were highly correlated with streamflow and snow water equivalent reconstructions, indicating reconstructions of soil moisture in the UCRB using TRCs successfully represent hydrologic trends, including the identification of periods of prolonged drought.

Soil Moisture (SMAP) and Vapor Pressure Deficit Controls on Evaporative Fraction over the Continental U.S.

NASA Astrophysics Data System (ADS)

Salvucci, G.; Rigden, A. J.; Gianotti, D.; Entekhabi, D.

2017-12-01

We analyze the control over evapotranspiration (ET) imposed by soil moisture limitations and stomatal closure due to vapor pressure deficit (VPD) across the United States using estimates of satellite-derived soil moisture from SMAP and a meteorological, data-driven ET estimate over a two year period at over 1000 locations. The ET data are developed independent of soil moisture using the emergent relationship between the diurnal cycle of the relative humidity profile and ET based on ETRHEQ (Salvucci and Gentine (2013), PNAS, 110(16): 6287-6291, Rigden and Salvucci, 2015, WRR, 51(4): 2951-2973; Rigden and Salvucci, 2017, GCB, 23(3) 1140-1151). The key advantage of using this approach to estimate ET is that no measurements of surface limiting factors (soil moisture, leaf area, canopy conductance) are required; instead, ET is estimated from only meteorological data. The combination of these two independent datasets allows for a unique spatial analysis of the control on ET imposed by the availability of soil moisture vs. VPD. Spatial patterns of limitations are inferred by fitting the ETRHEQ-inferred surface conductance to a weighted sum of a Jarvis type stomatal conductance model and bare soil evaporation conductance model, with separate moisture-dependent evaporation efficiency relations for bare soil and vegetation. Spatial patterns are visualized by mapping the optimal curve fitting coefficients and by conducting sensitivity analyses of the resulting fitted model across the Unites States. Results indicate regional variations in rate-limiting factors, and suggest that in some areas the VPD effect on stomatal closure is strong enough to induce a decrease in ET under projected climate change, despite an increase in atmospheric drying (and thus evaporative demand).

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.

Screening variability and change of soil moisture under wide-ranging climate conditions: Snow dynamics effects.

PubMed

Verrot, Lucile; Destouni, Georgia

2015-01-01

Soil moisture influences and is influenced by water, climate, and ecosystem conditions, affecting associated ecosystem services in the landscape. This paper couples snow storage-melting dynamics with an analytical modeling approach to screening basin-scale, long-term soil moisture variability and change in a changing climate. This coupling enables assessment of both spatial differences and temporal changes across a wide range of hydro-climatic conditions. Model application is exemplified for two major Swedish hydrological basins, Norrström and Piteälven. These are located along a steep temperature gradient and have experienced different hydro-climatic changes over the time period of study, 1950-2009. Spatially, average intra-annual variability of soil moisture differs considerably between the basins due to their temperature-related differences in snow dynamics. With regard to temporal change, the long-term average state and intra-annual variability of soil moisture have not changed much, while inter-annual variability has changed considerably in response to hydro-climatic changes experienced so far in each basin.

Using a spatially-distributed hydrologic biogeochemistry model to study the spatial variation of carbon processes in a Critical Zone Observatory

NASA Astrophysics Data System (ADS)

Shi, Y.; Eissenstat, D. M.; Davis, K. J.; He, Y.

2015-12-01

Forest carbon processes are affected by soil moisture, soil temperature and solar radiation. Most of the current biogeochemical models are 1-D and represent one point in space. Therefore they can neither resolve topographically driven hill-slope soil moisture patterns, nor simulate the nonlinear effects of soil moisture on carbon processes. A spatially-distributed biogeochemistry model, Flux-PIHM-BGC, has been developed by coupling the Biome-BGC (BBGC) model with a coupled physically-based land surface hydrologic model, Flux-PIHM. Flux-PIHM incorporates a land-surface scheme (adapted from the Noah land surface model) into the Penn State Integrated Hydrologic Model (PIHM). Because PIHM is capable of simulating lateral water flow and deep groundwater, Flux-PIHM is able to represent the link between groundwater and the surface energy balance, as well as the land surface heterogeneities caused by topography. Flux-PIHM-BGC model was tested at the Susquehanna/Shale Hills critical zone observatory (SSHCZO). The abundant observations at the SSHCZO, including eddy covariance fluxes, soil moisture, groundwater level, sap flux, stream discharge, litterfall, leaf area index, aboveground carbon stock, and soil carbon efflux, provided an ideal test bed for the coupled model. Model results show that when uniform solar radiation is used, vegetation carbon and soil carbon are positively correlated with soil moisture in space, which agrees with the observations within the watershed. When topographically-driven solar radiation is used, however, the wetter valley floor becomes radiation limited, and produces less vegetation and soil carbon than the drier hillslope due to the assumption that canopy height is uniform in the watershed. This contradicts with the observations, and suggests that a tree height model with dynamic allocation model are needed to reproduce the spatial variation of carbon processes within a watershed.

A review of the methods available for estimating soil moisture and its implications for water resource management

NASA Astrophysics Data System (ADS)

Dobriyal, Pariva; Qureshi, Ashi; Badola, Ruchi; Hussain, Syed Ainul

2012-08-01

SummaryThe maintenance of elevated soil moisture is an important ecosystem service of the natural ecosystems. Understanding the patterns of soil moisture distribution is useful to a wide range of agencies concerned with the weather and climate, soil conservation, agricultural production and landscape management. However, the great heterogeneity in the spatial and temporal distribution of soil moisture and the lack of standard methods to estimate this property limit its quantification and use in research. This literature based review aims to (i) compile the available knowledge on the methods used to estimate soil moisture at the landscape level, (ii) compare and evaluate the available methods on the basis of common parameters such as resource efficiency, accuracy of results and spatial coverage and (iii) identify the method that will be most useful for forested landscapes in developing countries. On the basis of the strengths and weaknesses of each of the methods reviewed we conclude that the direct method (gravimetric method) is accurate and inexpensive but is destructive, slow and time consuming and does not allow replications thereby having limited spatial coverage. The suitability of indirect methods depends on the cost, accuracy, response time, effort involved in installation, management and durability of the equipment. Our review concludes that measurements of soil moisture using the Time Domain Reflectometry (TDR) and Ground Penetrating Radar (GPR) methods are instantaneously obtained and accurate. GPR may be used over larger areas (up to 500 × 500 m a day) but is not cost-effective and difficult to use in forested landscapes in comparison to TDR. This review will be helpful to researchers, foresters, natural resource managers and agricultural scientists in selecting the appropriate method for estimation of soil moisture keeping in view the time and resources available to them and to generate information for efficient allocation of water resources and maintenance of soil moisture regime.

On the relationship between land surface infrared emissivity and soil moisture

NASA Astrophysics Data System (ADS)

Zhou, Daniel K.; Larar, Allen M.; Liu, Xu

2018-01-01

The relationship between surface infrared (IR) emissivity and soil moisture content has been investigated based on satellite measurements. Surface soil moisture content can be estimated by IR remote sensing, namely using the surface parameters of IR emissivity, temperature, vegetation coverage, and soil texture. It is possible to separate IR emissivity from other parameters affecting surface soil moisture estimation. The main objective of this paper is to examine the correlation between land surface IR emissivity and soil moisture. To this end, we have developed a simple yet effective scheme to estimate volumetric soil moisture (VSM) using IR land surface emissivity retrieved from satellite IR spectral radiance measurements, assuming those other parameters impacting the radiative transfer (e.g., temperature, vegetation coverage, and surface roughness) are known for an acceptable time and space reference location. This scheme is applied to a decade of global IR emissivity data retrieved from MetOp-A infrared atmospheric sounding interferometer measurements. The VSM estimated from these IR emissivity data (denoted as IR-VSM) is used to demonstrate its measurement-to-measurement variations. Representative 0.25-deg spatially-gridded monthly-mean IR-VSM global datasets are then assembled to compare with those routinely provided from satellite microwave (MW) multisensor measurements (denoted as MW-VSM), demonstrating VSM spatial variations as well as seasonal-cycles and interannual variability. Initial positive agreement is shown to exist between IR- and MW-VSM (i.e., R2 = 0.85). IR land surface emissivity contains surface water content information. So, when IR measurements are used to estimate soil moisture, this correlation produces results that correspond with those customarily achievable from MW measurements. A decade-long monthly-gridded emissivity atlas is used to estimate IR-VSM, to demonstrate its seasonal-cycle and interannual variation, which is spatially coherent and consistent with that from MW measurements, and, moreover, to achieve our objective of investigating the relationship between land surface IR emissivity and soil moisture.

Distributed Soil Moisture Estimation in a Mountainous Semiarid Basin: Constraining Soil Parameter Uncertainty through Field Studies

NASA Astrophysics Data System (ADS)

Yatheendradas, S.; Vivoni, E.

2007-12-01

A common practice in distributed hydrological modeling is to assign soil hydraulic properties based on coarse textural datasets. For semiarid regions with poor soil information, the performance of a model can be severely constrained due to the high model sensitivity to near-surface soil characteristics. Neglecting the uncertainty in soil hydraulic properties, their spatial variation and their naturally-occurring horizonation can potentially affect the modeled hydrological response. In this study, we investigate such effects using the TIN-based Real-time Integrated Basin Simulator (tRIBS) applied to the mid-sized (100 km2) Sierra Los Locos watershed in northern Sonora, Mexico. The Sierra Los Locos basin is characterized by complex mountainous terrain leading to topographic organization of soil characteristics and ecosystem distributions. We focus on simulations during the 2004 North American Monsoon Experiment (NAME) when intensive soil moisture measurements and aircraft- based soil moisture retrievals are available in the basin. Our experiments focus on soil moisture comparisons at the point, topographic transect and basin scales using a range of different soil characterizations. We compare the distributed soil moisture estimates obtained using (1) a deterministic simulation based on soil texture from coarse soil maps, (2) a set of ensemble simulations that capture soil parameter uncertainty and their spatial distribution, and (3) a set of simulations that conditions the ensemble on recent soil profile measurements. Uncertainties considered in near-surface soil characterization provide insights into their influence on the modeled uncertainty, into the value of soil profile observations, and into effective use of on-going field observations for constraining the soil moisture response uncertainty.

Joint Assimilation of SMOS Brightness Temperature and GRACE Terrestrial Water Storage Observations for Improved Soil Moisture Estimation

NASA Technical Reports Server (NTRS)

Girotto, Manuela; Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Rodell, Matthew

2017-01-01

Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0 - 5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle.

Joint assimilation of SMOS brightness temperature and GRACE terrestrial water storage observations for improved soil moisture estimation

NASA Astrophysics Data System (ADS)

Girotto, M.; Reichle, R. H.; De Lannoy, G.; Rodell, M.

2017-12-01

Observations from recent soil moisture missions (e.g. SMOS) have been used in innovative data assimilation studies to provide global high spatial (i.e. 40 km) and temporal resolution (i.e. 3-days) soil moisture profile estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0-5 cm), the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage column but, it is characterized by low spatial (i.e. 150,000 km2) and temporal (i.e. monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). This work hypothesizes that unprecedented soil water profile accuracy can be obtained through the joint assimilation of GRACE terrestrial water storage and SMOS brightness temperature observations. A particular challenge of the joint assimilation is the use of the two different types of measurements that are relevant for hydrologic processes representing different temporal and spatial scales. The performance of the joint assimilation strongly depends on the chosen assimilation methods, measurement and model error spatial structures. The optimization of the assimilation technique constitutes a fundamental step toward a multi-variate multi-resolution integrative assimilation system aiming to improve our understanding of the global terrestrial water cycle.

A multiyear study of soil moisture patterns across agricultural and forested landscapes

NASA Astrophysics Data System (ADS)

Georgakakos, C. B.; Hofmeister, K.; O'Connor, C.; Buchanan, B.; Walter, T.

2017-12-01

This work compares varying spatial and temporal soil moisture patterns in wet and dry years between forested and agricultural landscapes. This data set spans 6 years (2012-2017) of snow-free soil moisture measurements across multiple watersheds and land covers in New York State's Finger Lakes region. Due to the relatively long sampling period, we have captured fluctuations in soil moisture dynamics across wetter, dryer, and average precipitation years. We can therefore analyze response of land cover types to precipitation under varying climatic and hydrologic conditions. Across the study period, mean soil moisture in forest soils was significantly drier than in agricultural soils, and exhibited a smaller range of moisture conditions. In the drought year of 2016, soil moisture at all sites was significantly drier compared to the other years. When comparing the effects of land cover and year on soil moisture, we found that land cover had a more significant influence. Understanding the difference in landscape soil moisture dynamics between forested and agricultural land will help predict watershed responses to changing precipitation patterns in the future.

Near Surface Soil Moisture Estimation Using SAR Images: A Case Study in the Mediterranean Area of Catalonia

NASA Astrophysics Data System (ADS)

Reppucci, Antonio; Moreno, Laura

2010-12-01

Information on Soil moisture spatial and temporal evolution is of great importance for managing the utilization of soils and vegetation, in particular in environments where the water resources are scarce. In-situ measurement of soil moisture are costly and not able to sample the spatial behaviour of a whole region. Thanks to their all weather capability and wide coverage, Synthetic Aperture Radar (SAR) images offer the opportunity to monitor large area with high resolution. This study presents the results of a project, partially founded by the Catalan government, to improve the monitoring of soil moisture using Earth Observation data. In particular the project is focused on the calibration of existing semi-empirical algorithm in the area of study. This will be done using co-located SAR and in-situ measurements acquired during several field campaigns. Observed deviations between SAR measurements and in-situ measurement are discussed.

Spatial Dependence of Physical Attributes and Mechanical Properties of Ultisol in a Sugarcane Field.

PubMed

Tavares, Uilka Elisa; Rolim, Mário Monteiro; de Oliveira, Veronildo Souza; Pedrosa, Elvira Maria Regis; Siqueira, Glécio Machado; Magalhães, Adriana Guedes

2015-01-01

This study investigates the effect of conventional tillage and application of the monoculture of sugar cane on soil health. Variables like density, moisture, texture, consistency limits, and preconsolidation stress were taken as indicators of soil quality. The measurements were made at a 120 × 120 m field cropped with sugar cane under conventional tillage. The objective of this work was to characterize the soil and to study the spatial dependence of the physical and mechanical attributes. Then, undisturbed soil samples were collected to measure bulk density, moisture content and preconsolidation stress and disturbed soil samples for classification of soil texture, and consistency limits. The soil texture indicated that soil can be characterized as sandy clay soil and a sandy clay loam soil, and the consistency limits indicated that the soil presents an inorganic low plasticity clay. The preconsolidation tests tillage in soil moisture content around 19% should be avoided or should be chosen a management of soil with lighter vehicles in this moisture content, to avoid risk of compaction. Using geostatistical techniques mapping was possible to identify areas of greatest conservation soil and greater disturbance of the ground.

Spatial Dependence of Physical Attributes and Mechanical Properties of Ultisol in a Sugarcane Field

PubMed Central

Tavares, Uilka Elisa; Monteiro Rolim, Mário; Souza de Oliveira, Veronildo; Maria Regis Pedrosa, Elvira; Siqueira, Glécio Machado; Guedes Magalhães, Adriana

2015-01-01

This study investigates the effect of conventional tillage and application of the monoculture of sugar cane on soil health. Variables like density, moisture, texture, consistency limits, and preconsolidation stress were taken as indicators of soil quality. The measurements were made at a 120 × 120 m field cropped with sugar cane under conventional tillage. The objective of this work was to characterize the soil and to study the spatial dependence of the physical and mechanical attributes. Then, undisturbed soil samples were collected to measure bulk density, moisture content and preconsolidation stress and disturbed soil samples for classification of soil texture, and consistency limits. The soil texture indicated that soil can be characterized as sandy clay soil and a sandy clay loam soil, and the consistency limits indicated that the soil presents an inorganic low plasticity clay. The preconsolidation tests tillage in soil moisture content around 19% should be avoided or should be chosen a management of soil with lighter vehicles in this moisture content, to avoid risk of compaction. Using geostatistical techniques mapping was possible to identify areas of greatest conservation soil and greater disturbance of the ground. PMID:26167528

L-band HIgh Spatial Resolution Soil Moisture Mapping using SMALL UnManned Aerial Systems

NASA Astrophysics Data System (ADS)

Dai, E.; Venkitasubramony, A.; Gasiewski, A. J.; Stachura, M.; Elston, J. S.; Walter, B.; Lankford, D.; Corey, C.

2017-12-01

Soil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, water resource management, agriculture, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 provided new passive global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions of 36 km. However, there exists a need for measurements of soil moisture on much smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture and evaporation and transpiration studies of boundary layer heat transport. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters. Compared with other methods of validation based on either in-situ measurements [1,2] or existing airborne sensors suitable for manned aircraft deployment [3], the integrated design of the LDCR on a lightweight small UAS (sUAS) is capable of providing sub-watershed ( km scale) coverage at very high spatial resolution ( 15 m) suitable for scaling studies, and at comparatively low operator cost. To demonstrate the LDCR several flights had been performed during field experiments at the Canton Oklahoma Soilscape site and Yuma Colorado Irrigation Research Foundation (IRF) site in 2015 and 2016, respectively, using LDCR Revision A and Tempest sUAS. The scientific intercomparisons of LDCR retrieved soil moisture and in-situ measurements will be presented. LDCR Revision B has been built and integrated into SuperSwift sUAS and additional field experiments will be performed at IRF in 2017. In Revision B the IF signal is sampled at 80 MS/s to enable digital correlation and RFI mitigation capabilities, in addition to analog correlation. [1] McIntyre, E.M., A.J. Gasiewski, and D. Manda D, "Near Real-Time Passive C-Band Microwave Soil Moisture Retrieval During CLASIC 2007," Proc. IGARSS, 2008. [2] Robock, A., S. Steele-Dunne, J. Basara, W. Crow, and M. Moghaddam M, "In Situ Network and Scaling," SMAP Algorithm and Cal/Val Workshop, 2009. [3] Walker, A., "Airborne Microwave Radiometer Measurements During CanEx-SM10," Second SMAP Cal/Val Workshop, 2011.

Testing FASST a One-Dimensional Hydrological Model for Soil Moisture Studies at the Little River Watershed, Tifton, Georgia

USDA-ARS?s Scientific Manuscript database

The FASST (Fast All Season Strength model, US Army Corps of Engineers), one-dimensional hydrologic model was used to evaluate soil moisture across the USDA-ARS-SEWRL Little River Watershed in south Georgia US. The ultimate goal of this research is to assess the spatial variation of soil moisture acr...

Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China

NASA Astrophysics Data System (ADS)

Wang, Hui-Lin; An, Ru; You, Jia-jun; Wang, Ying; Chen, Yuehong; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballard, Jonathan Arthur

2017-10-01

Soil moisture plays an important role in the water cycle within the surface ecosystem, and it is the basic condition for the growth of plants. Currently, the spatial resolutions of most soil moisture data from remote sensing range from ten to several tens of km, while those observed in-situ and simulated for watershed hydrology, ecology, agriculture, weather, and drought research are generally <1 km. Therefore, the existing coarse-resolution remotely sensed soil moisture data need to be downscaled. This paper proposes a universal and multitemporal soil moisture downscaling method suitable for large areas. The datasets comprise land surface, brightness temperature, precipitation, and soil and topographic parameters from high-resolution data and active/passive microwave remotely sensed essential climate variable soil moisture (ECV_SM) data with a spatial resolution of 25 km. Using this method, a total of 288 soil moisture maps of 1-km resolution from the first 10-day period of January 2003 to the last 10-day period of December 2010 were derived. The in-situ observations were used to validate the downscaled ECV_SM. In general, the downscaled soil moisture values for different land cover and land use types are consistent with the in-situ observations. Mean square root error is reduced from 0.070 to 0.061 using 1970 in-situ time series observation data from 28 sites distributed over different land uses and land cover types. The performance was also assessed using the GDOWN metric, a measure of the overall performance of the downscaling methods based on the same dataset. It was positive in 71.429% of cases, indicating that the suggested method in the paper generally improves the representation of soil moisture at 1-km resolution.

Benchmarking a soil moisture data assimilation system for agricultural drought monitoring

USDA-ARS?s Scientific Manuscript database

Agricultural drought is defined as a shortage of moisture in the root zone of plants. Recently available satellite-based remote sensing data have accelerated development of drought early warning system by providing spatially continuous soil moisture information repeatedly at short-term interval. Non...

Measurement of seasonal changes and spatial variations in pavement unbound base and subgrade properties.

DOT National Transportation Integrated Search

2009-01-01

Soils often undergo cyclic wetting/drying, but there is very limited research on unsaturated : soils subjected to variations in moisture content. More specifically, field moisture variation : over time in highway unbound bases and subgrade soils is a...

Ground-Based Passive Microwave Remote Sensing Observations of Soil Moisture at S and L Band with Insight into Measurement Accuracy

NASA Technical Reports Server (NTRS)

Laymon, Charles A.; Crosson, William L.; Jackson, Thomas J.; Manu, Andrew; Tsegaye, Teferi D.; Soman, V.; Arnold, James E. (Technical Monitor)

2001-01-01

Accurate estimates of spatially heterogeneous algorithm variables and parameters are required in determining the spatial distribution of soil moisture using radiometer data from aircraft and satellites. A ground-based experiment in passive microwave remote sensing of soil moisture was conducted in Huntsville, Alabama from July 1-14, 1996 to study retrieval algorithms and their sensitivity to variable and parameter specification. With high temporal frequency observations at S and L band, we were able to observe large scale moisture changes following irrigation and rainfall events, as well as diurnal behavior of surface moisture among three plots, one bare, one covered with short grass and another covered with alfalfa. The L band emitting depth was determined to be on the order of 0-3 or 0-5 cm below 0.30 cubic centimeter/cubic centimeter with an indication of a shallower emitting depth at higher moisture values. Surface moisture behavior was less apparent on the vegetated plots than it was on the bare plot because there was less moisture gradient and because of difficulty in determining vegetation water content and estimating the vegetation b parameter. Discrepancies between remotely sensed and gravimetric, soil moisture estimates on the vegetated plots point to an incomplete understanding of the requirements needed to correct for the effects of vegetation attenuation. Quantifying the uncertainty in moisture estimates is vital if applications are to utilize remotely-sensed soil moisture data. Computations based only on the real part of the complex dielectric constant and/or an alternative dielectric mixing model contribute a relatively insignificant amount of uncertainty to estimates of soil moisture. Rather, the retrieval algorithm is much more sensitive to soil properties, surface roughness and biomass.

An Evaluation of Soil Moisture Retrievals Using Aircraft and Satellite Passive Microwave Observations during SMEX02

NASA Technical Reports Server (NTRS)

Bolten, John D.; Lakshmi, Venkat

2009-01-01

The Soil Moisture Experiments conducted in Iowa in the summer of 2002 (SMEX02) had many remote sensing instruments that were used to study the spatial and temporal variability of soil moisture. The sensors used in this paper (a subset of the suite of sensors) are the AQUA satellite-based AMSR-E (Advanced Microwave Scanning Radiometer- Earth Observing System) and the aircraft-based PSR (Polarimetric Scanning Radiometer). The SMEX02 design focused on the collection of near simultaneous brightness temperature observations from each of these instruments and in situ soil moisture measurements at field- and domain- scale. This methodology provided a basis for a quantitative analysis of the soil moisture remote sensing potential of each instrument using in situ comparisons and retrieved soil moisture estimates through the application of a radiative transfer model. To this end, the two sensors are compared with respect to their estimation of soil moisture.

Soil Moisture Retrieval with Airborne PALS Instrument over Agricultural Areas in SMAPVEX16

NASA Technical Reports Server (NTRS)

Colliander, Andreas; Jackson, Thomas J.; Cosh, Mike; Misra, Sidharth; Bindlish, Rajat; Powers, Jarrett; McNairn, Heather; Bullock, P.; Berg, A.; Magagi, A.;

A high resolution method for soil moisture mapping at large spatial and temporal scales

NASA Astrophysics Data System (ADS)

moreno, D.; Sayde, C.; Ochsner, T. E.; Sorin, C.; Selker, J. S.

2013-12-01

Soil moisture is a critical component of the planet's water budget, yet precise measurement of its dynamics across the critical scales of 0.1-1,000 m continues to be an area of great uncertainty. Here we present the preliminary results for a large scale installation of soil moisture quantification based on the work of Sayde et al. (2010) using actively heated fiber optic with a DTS system capable of soil moisture measurements at high spatial (reporting every 0.125 m) and temporal resolution (read as frequently as each 15 min)). The fiber optic (FO) sensing cables were installed in 2 sections: 1) a highly resolved multi-scale spiral 75m x 65m in size, 530 m total path length, and 2) a 770 m transect in the foot print of the cosmos cosmic ray probe installed at the site. In each of those 2 sections, the FO cables were deployed at 3 depths: 5, 10, and 15 cm. In this system the FO sensing system provides measurements of soil moisture at >39,000 locations simultaneously for each heat pulse. In addition, six soil monitoring stations along the fiber optic path were installed to provide additional validation and calibration of the DTS data. Finally, gravimetric soil moisture and soil thermal samplings were performed periodically to provide additional distributed validation and calibration of the DTS data. The ability of this DTS FO system to provide soil moisture measurements over four orders of magnitude in spatial scale (0.1 - 1,000m) will allow better understanding of the spatio-temporal variability in soil moisture in the field, which is essential to develop protocols for calibration and validation of large scale soil moisture remote sensing data (such as NASA airMOSS soil moisture air flights). The material is based upon work supported by NASA under award NNX12AP58G, with equipment and assistance also provided by CTEMPs.org with support from the National Science Foundation under Grant Number 1129003. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NASA or the National Science Foundation.. Sayde, C., C. Gregory, M. Gil-Rodriguez, N. Tufillaro, S. Tyler, N. van de Giesen, M. English, R. Cuenca, and J.S. Selker (2010), Feasibility of soil moisture monitoring with heated fiber optics, Water Resour. Res., 46, W06201, doi:10.1029/2009WR007846.

Using satellite image data to estimate soil moisture

NASA Astrophysics Data System (ADS)

Chuang, Chi-Hung; Yu, Hwa-Lung

2017-04-01

Soil moisture is considered as an important parameter in various study fields, such as hydrology, phenology, and agriculture. In hydrology, soil moisture is an significant parameter to decide how much rainfall that will infiltrate into permeable layer and become groundwater resource. Although soil moisture is a critical role in many environmental studies, so far the measurement of soil moisture is using ground instrument such as electromagnetic soil moisture sensor. Use of ground instrumentation can directly obtain the information, but the instrument needs maintenance and consume manpower to operation. If we need wide range region information, ground instrumentation probably is not suitable. To measure wide region soil moisture information, we need other method to achieve this purpose. Satellite remote sensing techniques can obtain satellite image on Earth, this can be a way to solve the spatial restriction on instrument measurement. In this study, we used MODIS data to retrieve daily soil moisture pattern estimation, i.e., crop water stress index (cwsi), over the year of 2015. The estimations are compared with the observations at the soil moisture stations from Taiwan Bureau of soil and water conservation. Results show that the satellite remote sensing data can be helpful to the soil moisture estimation. Further analysis can be required to obtain the optimal parameters for soil moisture estimation in Taiwan.

Representing soil moisture - precipitation feedbacks in the Sahel: spatial scale and parameterisation

NASA Astrophysics Data System (ADS)

Taylor, C.; Birch, C.; Parker, D.; Guichard, F.; Nikulin, G.; Dixon, N.

2013-12-01

Land surface properties influence the life cycle of convective systems across West Africa via space-time variability in sensible and latent heat fluxes. Previous observational and modelling studies have shown that areas with strong mesoscale variability in vegetation cover or soil moisture induce coherent structures in the daytime planetary boundary layer. In particular, horizontal gradients in sensible heat flux can induce convergence zones which favour the initiation of deep convection. A recent study based on satellite data (Taylor et al. 2011), illustrated the climatological importance of soil moisture gradients in the initiation of long-lived Mesoscale Convective Systems (MCS) in the Sahel. Here we provide a unique assessment of how models of different spatial resolutions represent soil moisture - precipitation feedbacks in the region, and compare their behaviour to observations. Specifically we examine whether the inability of large-scale models to capture the observed preference for afternoon rain over drier soil in semi-arid regions [Taylor et al., 2012] is due to inadequate spatial resolution and/or systematic bias in convective parameterisations. Firstly, we use a convection-permitting simulation at 4km resolution to explore the underlying mechanisms responsible for soil moisture controls on daytime convective initiation in the Sahel. The model reproduces very similar spatial structure as the observations in terms of antecedent soil moisture in the vicinity of a large sample of convective initiations. We then examine how this same model, run at coarser resolution, simulates the feedback of soil moisture on daily rainfall. In particular we examine the impact of switching on the convective parameterisation on rainfall persistence, and compare the findings with 10 regional climate models (RCMs). Finally, we quantify the impact of the feedback on dry-spell return times using a simple statistical model. The results highlight important weaknesses in convective parameterisations which are likely to impact land surface sensitivity studies and hydroclimatic variability on certain time and space scales. Taylor, C.M., Gounou, A., Guichard, F., Harris, P.P., Ellis, R.J.,Couvreux, F., and M. De Kauwe. 2011, Frequency of Sahelian storm initiation enhanced over mesoscale soil-moisture patterns, Nature Geoscience, 4, 430-433, doi:10.1038/ngeo1173 Taylor, C.M., de Jeu, R.A.M., Guichard, F., Harris, P.P, and W.A. Dorigo. 2012, Afternoon rain more likely over drier soils, Nature, 489, 423-426, doi:10.1038/nature11377

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model. Part 1; Overview

NASA Technical Reports Server (NTRS)

Crosson, William L.; Laymon, Charles A.; Inguva, Ramarao; Schamschula, Marius; Caulfield, John

1998-01-01

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Spatiotemporal characterization of soil moisture fields in agricultural areas using cosmic-ray neutron probes and data fusion

NASA Astrophysics Data System (ADS)

Franz, Trenton; Wang, Tiejun

2015-04-01

Approximately 40% of global food production comes from irrigated agriculture. With the increasing demand for food even greater pressures will be placed on water resources within these systems. In this work we aimed to characterize the spatial and temporal patterns of soil moisture at the field-scale (~500 m) using the newly developed cosmic-ray neutron rover near Waco, NE USA. Here we mapped soil moisture of 144 quarter section fields (a mix of maize, soybean, and natural areas) each week during the 2014 growing season (May to September). The 12 by 12 km study domain also contained three stationary cosmic-ray neutron probes for independent validation of the rover surveys. Basic statistical analysis of the domain indicated a strong relationship between the mean and variance of soil moisture at several averaging scales. The relationships between the mean and higher order moments were not significant. Scaling analysis indicated strong power law behavior between the variance of soil moisture and averaging area with minimal dependence of mean soil moisture on the slope of the power law function. In addition, we combined the data from the three stationary cosmic-ray neutron probes and mobile surveys using linear regression to derive a daily soil moisture product at 1, 3, and 12 km spatial resolutions for the entire growing season. The statistical relationships derived from the rover dataset offer a novel set of observations that will be useful in: 1) calibrating and validating land surface models, 2) calibrating and validating crop models, 3) soil moisture covariance estimates for statistical downscaling of remote sensing products such as SMOS and SMAP, and 4) provide daily center-pivot scale mean soil moisture data for optimal irrigation timing and volume amounts.

Mapping patterns of soil properties and soil moisture using electromagnetic induction to investigate the impact of land use changes on soil processes

NASA Astrophysics Data System (ADS)

Robinet, Jérémy; von Hebel, Christian; van der Kruk, Jan; Govers, Gerard; Vanderborght, Jan

2016-04-01

As highlighted by many authors, classical or geophysical techniques for measuring soil moisture such as destructive soil sampling, neutron probes or Time Domain Reflectometry (TDR) have some major drawbacks. Among other things, they provide point scale information, are often intrusive and time-consuming. ElectroMagnetic Induction (EMI) instruments are often cited as a promising alternative hydrogeophysical methods providing more efficiently soil moisture measurements ranging from hillslope to catchment scale. The overall objective of our research project is to investigate whether a combination of geophysical techniques at various scales can be used to study the impact of land use change on temporal and spatial variations of soil moisture and soil properties. In our work, apparent electrical conductivity (ECa) patterns are obtained with an EM multiconfiguration system. Depth profiles of ECa were subsequently inferred through a calibration-inversion procedure based on TDR data. The obtained spatial patterns of these profiles were linked to soil profile and soil water content distributions. Two catchments with contrasting land use (agriculture vs. natural forest) were selected in a subtropical region in the south of Brazil. On selected slopes within the catchments, combined EMI and TDR measurements were carried out simultaneously, under different atmospheric and soil moisture conditions. Ground-truth data for soil properties were obtained through soil sampling and auger profiles. The comparison of these data provided information about the potential of the EMI technique to deliver qualitative and quantitative information about the variability of soil moisture and soil properties.

Gravity changes, soil moisture and data assimilation

NASA Astrophysics Data System (ADS)

Walker, J.; Grayson, R.; Rodell, M.; Ellet, K.

2003-04-01

Remote sensing holds promise for near-surface soil moisture and snow mapping, but current techniques do not directly resolve the deeper soil moisture or groundwater. The benefits that would arise from improved monitoring of variations in terrestrial water storage are numerous. The year 2002 saw the launch of NASA's Gravity Recovery And Climate Experiment (GRACE) satellites, which are mapping the Earth's gravity field at such a high level of precision that we expect to be able to infer changes in terrestrial water storage (soil moisture, groundwater, snow, ice, lake, river and vegetation). The project described here has three distinct yet inter-linked components that all leverage off the same ground-based monitoring and land surface modelling framework. These components are: (i) field validation of a relationship between soil moisture and changes in the Earth's gravity field, from ground- and satellite-based measurements of changes in gravity; (ii) development of a modelling framework for the assimilation of gravity data to constrain land surface model predictions of soil moisture content (such a framework enables the downscaling and disaggregation of low spatial (500 km) and temporal (monthly) resolution measurements of gravity change to finer spatial and temporal resolutions); and (iii) further refining the downscaling and disaggregation of space-borne gravity measurements by making use of other remotely sensed information, such as the higher spatial (25 km) and temporal (daily) resolution remotely sensed near-surface soil moisture measurements from the Advanced Microwave Scanning Radiometer (AMSR) instruments on Aqua and ADEOS II. The important field work required by this project will be in the Murrumbidgee Catchment, Australia, where an extensive soil moisture monitoring program by the University of Melbourne is already in place. We will further enhance the current monitoring network by the addition of groundwater wells and additional soil moisture sites. Ground-based gravity measurements will also be made on a monthly basis at each monitoring site. There will be two levels of modelling and monitoring; regional across the entire Murrumbidgee Catchment (100,000 km2), and local across a small sub-catchment (150 km2).

[Three-dimension temporal and spatial dynamics of soil water for the artificial vegetation in the center of Taklimakan desert under saline water drip-irrigation].

PubMed

Ding, Xin-yuan; Zhou, Zhi-bin; Xu, Xin-wen; Lei, Jia-qiang; Lu, Jing-jing; Ma, Xue-xi; Feng, Xiao

2015-09-01

Three-dimension temporal and spatial dynamics of the soil water characteristics during four irrigating cycles of months from April to July for the artificial vegetation in the center of Taklimakan Desert under saline water drip-irrigation had been analyzed by timely measuring the soil water content in horizontal and vertical distances 60 cm and 120 cm away from the irrigating drips, respectively. Periodic spatial and temporal variations of soil water content were observed. When the precipitation effect was not considered, there were no significant differences in the characteristics of soil water among the irrigation intervals in different months, while discrepancies were obvious in the temporal and spatial changes of soil moisture content under the conditions of rainfall and non-rainfall. When it referred to the temporal changes of soil water, it was a little higher in April but a bit lower in July, and the soil water content in June was the highest among four months because some remarkable events of precipitation happened in this month. However, as a whole, the content of soil moisture was reduced as months (from April to July) went on and it took a decreasing tendency along with days (1-15 d) following a power function. Meanwhile, the characteristics of soil water content displayed three changeable stages in an irrigation interval. When it referred to the spatial distributions of soil water, the average content of soil moisture was reduced along with the horizontal distance following a linear regression function, and varied with double peaks along with the vertical distance. In addition, the spatial distribution characteristics of the soil water were not influenced by the factors of precipitation and irrigating time but the physical properties of soil.

Long-term SMOS soil moisture products: A comprehensive evaluation across scales and methods in the Duero Basin (Spain)

NASA Astrophysics Data System (ADS)

González-Zamora, Ángel; Sánchez, Nilda; Martínez-Fernández, José; Gumuzzio, Ángela; Piles, María; Olmedo, Estrella

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) Level 2 soil moisture and the new L3 product from the Barcelona Expert Center (BEC) were validated from January 2010 to June 2014 using two in situ networks in Spain. The first network is the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), which has been extensively used for validating remotely sensed observations of soil moisture. REMEDHUS can be considered a small-scale network that covers a 1300 km2 region. The second network is a large-scale network that covers the main part of the Duero Basin (65,000 km2). At an existing meteorological network in the Castilla y Leon region (Inforiego), soil moisture probes were installed in 2012 to provide data until 2014. Comparisons of the temporal series using different strategies (total average, land use, and soil type) as well as using the collocated data at each location were performed. Additionally, spatial correlations on each date were computed for specific days. Finally, an improved version of the Triple Collocation (TC) method, i.e., the Extended Triple Collocation (ETC), was used to compare satellite and in situ soil moisture estimates with outputs of the Soil Water Balance Model Green-Ampt (SWBM-GA). The results of this work showed that SMOS estimates were consistent with in situ measurements in the time series comparisons, with Pearson correlation coefficients (R) and an Agreement Index (AI) higher than 0.8 for the total average and the land-use averages and higher than 0.85 for the soil-texture averages. The results obtained at the Inforiego network showed slightly better results than REMEDHUS, which may be related to the larger scale of the former network. Moreover, the best results were obtained when all networks were jointly considered. In contrast, the spatial matching produced worse results for all the cases studied. These results showed that the recent reprocessing of the L2 products (v5.51) improved the accuracy of soil moisture retrievals such that they are now suitable for developing new L3 products, such as the presented in this work. Additionally, the validation based on comparisons between dense/sparse networks and satellite retrievals at a coarse resolution showed that temporal patterns in the soil moisture are better reproduced than spatial patterns.

An inversion method for retrieving soil moisture information from satellite altimetry observations

NASA Astrophysics Data System (ADS)

Uebbing, Bernd; Forootan, Ehsan; Kusche, Jürgen; Braakmann-Folgmann, Anne

2016-04-01

Soil moisture represents an important component of the terrestrial water cycle that controls., evapotranspiration and vegetation growth. Consequently, knowledge on soil moisture variability is essential to understand the interactions between land and atmosphere. Yet, terrestrial measurements are sparse and their information content is limited due to the large spatial variability of soil moisture. Therefore, over the last two decades, several active and passive radar and satellite missions such as ERS/SCAT, AMSR, SMOS or SMAP have been providing backscatter information that can be used to estimate surface conditions including soil moisture which is proportional to the dielectric constant of the upper (few cm) soil layers . Another source of soil moisture information are satellite radar altimeters, originally designed to measure sea surface height over the oceans. Measurements of Jason-1/2 (Ku- and C-Band) or Envisat (Ku- and S-Band) nadir radar backscatter provide high-resolution along-track information (~ 300m along-track resolution) on backscatter every ~10 days (Jason-1/2) or ~35 days (Envisat). Recent studies found good correlation between backscatter and soil moisture in upper layers, especially in arid and semi-arid regions, indicating the potential of satellite altimetry both to reconstruct and to monitor soil moisture variability. However, measuring soil moisture using altimetry has some drawbacks that include: (1) the noisy behavior of the altimetry-derived backscatter (due to e.g., existence of surface water in the radar foot-print), (2) the strong assumptions for converting altimetry backscatters to the soil moisture storage changes, and (3) the need for interpolating between the tracks. In this study, we suggest a new inversion framework that allows to retrieve soil moisture information from along-track Jason-2 and Envisat satellite altimetry data, and we test this scheme over the Australian arid and semi-arid regions. Our method consists of: (i) deriving time-invariant spatial patterns (base-functions) by applying principal component analysis (PCA) to simulated soil moisture from a large-scale land surface model. (ii) Estimating time-variable soil moisture evolution by fitting these base functions of (i) to the along-track retracked backscatter coefficients in a least squares sense. (iii) Combining the estimated time-variable amplitudes and the pre-computed base-functions, which results in reconstructed (spatio-temporal) soil moisture information. We will show preliminary results that are compared to available high-resolution soil moisture model data over the region (the Australian Water Resource Assessment, AWRA model). We discuss the possibility of using altimetry-derived soil moisture estimations to improve the simulation skill of soil moisture in the Global Land Data Assimilation System (GLDAS) over Australia.

Synergistic soil moisture observation - an interdisciplinary multi-sensor approach to yield improved estimates across scales

NASA Astrophysics Data System (ADS)

Schrön, M.; Fersch, B.; Jagdhuber, T.

2017-12-01

The representative determination of soil moisture across different spatial ranges and scales is still an important challenge in hydrology. While in situ measurements are trusted methods at the profile- or point-scale, cosmic-ray neutron sensors (CRNS) are renowned for providing volume averages for several hectares and tens of decimeters depth. On the other hand, airborne remote-sensing enables the coverage of regional scales, however limited to the top few centimeters of the soil.Common to all of these methods is a challenging data processing part, often requiring calibration with independent data. We investigated the performance and potential of three complementary observational methods for the determination of soil moisture below grassland in an alpine front-range river catchment (Rott, 55 km2) of southern Germany.We employ the TERENO preAlpine soil moisture monitoring network, along with additional soil samples taken throughout the catchment. Spatial soil moisture products have been generated using surveys of a car-mounted mobile CRNS (rover), and an aerial acquisition of the polarimetric synthetic aperture radar (F-SAR) of DLR.The study assesses (1) the viability of the different methods to estimate soil moisture for their respective scales and extents, and (2) how either method could support an improvement of the others. We found that in situ data can provide valuable information to calibrate the CRNS rover and to train the vegetation removal part of the polarimetric SAR (PolSAR) retrieval algorithm. Vegetation correction is mandatory to obtain the sub-canopy soil moisture patterns. While CRNS rover surveys can be used to evaluate the F-SAR product across scales, vegetation-related PolSAR products in turn can support the spatial correction of CRNS products for biomass water. Despite the different physical principles, the synthesis of the methods can provide reasonable soil moisture information by integrating from the plot to the landscape scale. The combination of in situ, CRNS, and remote-sensing data leads to substantial improvement, especially for the latter two. The study shows how interdisciplinary research can greatly advance the methodology and processing algorithms for individual geoscientific instruments and their hydrologically relevant products.

Fractal scaling of apparent soil moisture estimated from vertical planes of Vertisol pit images

NASA Astrophysics Data System (ADS)

Cumbrera, Ramiro; Tarquis, Ana M.; Gascó, Gabriel; Millán, Humberto

2012-07-01

SummaryImage analysis could be a useful tool for investigating the spatial patterns of apparent soil moisture at multiple resolutions. The objectives of the present work were (i) to define apparent soil moisture patterns from vertical planes of Vertisol pit images and (ii) to describe the scaling of apparent soil moisture distribution using fractal parameters. Twelve soil pits (0.70 m long × 0.60 m width × 0.30 m depth) were excavated on a bare Mazic Pellic Vertisol. Six of them were excavated in April/2011 and six pits were established in May/2011 after 3 days of a moderate rainfall event. Digital photographs were taken from each Vertisol pit using a Kodak™ digital camera. The mean image size was 1600 × 945 pixels with one physical pixel ≈373 μm of the photographed soil pit. Each soil image was analyzed using two fractal scaling exponents, box counting (capacity) dimension (DBC) and interface fractal dimension (Di), and three prefractal scaling coefficients, the total number of boxes intercepting the foreground pattern at a unit scale (A), fractal lacunarity at the unit scale (Λ1) and Shannon entropy at the unit scale (S1). All the scaling parameters identified significant differences between both sets of spatial patterns. Fractal lacunarity was the best discriminator between apparent soil moisture patterns. Soil image interpretation with fractal exponents and prefractal coefficients can be incorporated within a site-specific agriculture toolbox. While fractal exponents convey information on space filling characteristics of the pattern, prefractal coefficients represent the investigated soil property as seen through a higher resolution microscope. In spite of some computational and practical limitations, image analysis of apparent soil moisture patterns could be used in connection with traditional soil moisture sampling, which always renders punctual estimates.

Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons

NASA Astrophysics Data System (ADS)

Karthikeyan, L.; Pan, Ming; Wanders, Niko; Kumar, D. Nagesh; Wood, Eric F.

2017-11-01

Soil moisture is widely recognized as an important land surface variable that provides a deeper knowledge of land-atmosphere interactions and climate change. Space-borne passive and active microwave sensors have become valuable and essential sources of soil moisture observations at global scales. Over the past four decades, several active and passive microwave sensors have been deployed, along with the recent launch of two fully dedicated missions (SMOS and SMAP). Signifying the four decades of microwave remote sensing of soil moisture, this Part 2 of the two-part review series aims to present an overview of how our knowledge in this field has improved in terms of the design of sensors and their accuracy for retrieving soil moisture. The first part discusses the developments made in active and passive microwave soil moisture retrieval algorithms. We assess the evolution of the products of various sensors over the last four decades, in terms of daily coverage, temporal performance, and spatial performance, by comparing the products of eight passive sensors (SMMR, SSM/I, TMI, AMSR-E, WindSAT, AMSR2, SMOS and SMAP), two active sensors (ERS-Scatterometer, MetOp-ASCAT), and one active/passive merged soil moisture product (ESA-CCI combined product) with the International Soil Moisture Network (ISMN) in-situ stations and the Variable Infiltration Capacity (VIC) land surface model simulations over the Contiguous United States (CONUS). In the process, the regional impacts of vegetation conditions on the spatial and temporal performance of soil moisture products are investigated. We also carried out inter-satellite comparisons to study the roles of sensor design and algorithms on the retrieval accuracy. We find that substantial improvements have been made over recent years in this field in terms of daily coverage, retrieval accuracy, and temporal dynamics. We conclude that the microwave soil moisture products have significantly evolved in the last four decades and will continue to make key contributions to the progress of hydro-meteorological and climate sciences.

The Utility of the Real-Time NASA Land Information System Data for Drought Monitoring Applications

NASA Technical Reports Server (NTRS)

White, Kristopher D.; Case, Jonathan L.

2013-01-01

Measurements of soil moisture are a crucial component for the proper monitoring of drought conditions. The large spatial variability of soil moisture complicates the problem. Unfortunately, in situ soil moisture observing networks typically consist of sparse point observations, and conventional numerical model analyses of soil moisture used to diagnose drought are of coarse spatial resolution. Decision support systems such as the U.S. Drought Monitor contain drought impact resolution on sub-county scales, which may not be supported by the existing soil moisture networks or analyses. The NASA Land Information System, which is run with 3 km grid spacing over the eastern United States, has demonstrated utility for monitoring soil moisture. Some of the more useful output fields from the Land Information System are volumetric soil moisture in the 0-10 cm and 40-100 cm layers, column-integrated relative soil moisture, and the real-time green vegetation fraction derived from MODIS (Moderate Resolution Imaging Spectroradiometer) swath data that are run within the Land Information System in place of the monthly climatological vegetation fraction. While these and other variables have primarily been used in local weather models and other operational forecasting applications at National Weather Service offices, the use of the Land Information System for drought monitoring has demonstrated utility for feedback to the Drought Monitor. Output from the Land Information System is currently being used at NWS Huntsville to assess soil moisture, and to provide input to the Drought Monitor. Since feedback to the Drought Monitor takes place on a weekly basis, weekly difference plots of column-integrated relative soil moisture are being produced by the NASA Short-term Prediction Research and Transition Center and analyzed to facilitate the process. In addition to the Drought Monitor, these data are used to assess drought conditions for monthly feedback to the Alabama Drought Monitoring and Impact Group and the Tennessee Drought Task Force, which are comprised of federal, state, and local agencies and other water resources professionals.

Moment Analysis Characterizing Water Flow in Repellent Soils from On- and Sub-Surface Point Sources

NASA Astrophysics Data System (ADS)

Xiong, Yunwu; Furman, Alex; Wallach, Rony

2010-05-01

Water repellency has a significant impact on water flow patterns in the soil profile. Flow tends to become unstable in such soils, which affects the water availability to plants and subsurface hydrology. In this paper, water flow in repellent soils was experimentally studied using the light reflection method. The transient 2D moisture profiles were monitored by CCD camera for tested soils packed in a transparent flow chamber. Water infiltration experiments and subsequent redistribution from on-surface and subsurface point sources with different flow rates were conducted for two soils of different repellency degrees as well as for wettable soil. We used spatio-statistical analysis (moments) to characterize the flow patterns. The zeroth moment is related to the total volume of water inside the moisture plume, and the first and second moments are affinitive to the center of mass and spatial variances of the moisture plume, respectively. The experimental results demonstrate that both the general shape and size of the wetting plume and the moisture distribution within the plume for the repellent soils are significantly different from that for the wettable soil. The wetting plume of the repellent soils is smaller, narrower, and longer (finger-like) than that of the wettable soil compared with that for the wettable soil that tended to roundness. Compared to the wettable soil, where the soil water content decreases radially from the source, moisture content for the water-repellent soils is higher, relatively uniform horizontally and gradually increases with depth (saturation overshoot), indicating that flow tends to become unstable. Ellipses, defined around the mass center and whose semi-axes represented a particular number of spatial variances, were successfully used to simulate the spatial and temporal variation of the moisture distribution in the soil profiles. Cumulative probability functions were defined for the water enclosed in these ellipses. Practically identical cumulative probability functions (beta distribution) were obtained for all soils, all source types, and flow rates. Further, same distributions were obtained for the infiltration and redistribution processes. This attractive result demonstrates the competence and advantage of the moment analysis method.

Definition of SMOS Level 3 Land Products for the Villafranca del Castillo Data Processing Centre (CP34)

NASA Astrophysics Data System (ADS)

Lopez-Baeza, E.; Monsoriu Torres, A.; Font, J.; Alonso, O.

2009-04-01

The ESA SMOS (Soil Moisture and Ocean Salinity) Mission is planned to be launched in July 2009. The satellite will measure soil moisture over the continents and surface salinity of the oceans at resolutions that are sufficient for climatological-type studies. This paper describes the procedure to be used at the Spanish SMOS Level 3 and 4 Data Processing Centre (CP34) to generate Soil Moisture and other Land Surface Product maps from SMOS Level 2 data. This procedure can be used to map Soil Moisture, Vegetation Water Content and Soil Dielectric Constant data into different pre-defined spatial grids with fixed temporal frequency. The L3 standard Land Surface Products to be generated at CP34 are: Soil Moisture products: maximum spatial resolution with no spatial averaging, temporal averaging of 3 days, daily generation maximum spatial resolution with no spatial averaging, temporal averaging of 10 days, generation frequency of once every 10 days. b': maximum spatial resolution with no spatial averaging, temporal averaging of monthly decades (1st to 10th of the month, 11th to 20th of the month, 21st to last day of the month), generation frequency of once every decade monthly average, temporal averaging from L3 decade averages, monthly generation Seasonal average, temporal averaging from L3 monthly averages, seasonally generation yearly average, temporal averaging from L3 monthly averages, yearly generation Vegetation Water Content products: maximum spatial resolution with no spatial averaging, temporal averaging of 10 days, generation frequency of once every 10 days. a': maximum spatial resolution with no spatial averaging, temporal averaging of monthly decades (1st to 10th of the month, 11th to 20th of the month, 21st to last day of the month) using simple averaging method over the L2 products in ISEA grid, generation frequency of once every decade monthly average, temporal averaging from L3 decade averages, monthly generation seasonal average, temporal averaging from L3 monthly averages, seasonally generation yearly average, temporal averaging from L3 monthly averages, yearly generation Dielectric Constant products: (the dielectric constant products are delivered together with soil moisture products, with the same averaging periods and generation frequency): maximum spatial resolution with no spatial averaging, temporal averaging of 3 days, daily generation maximum spatial resolution with no spatial averaging, temporal averaging of 10 days, generation frequency of once every 10 days. b': maximum spatial resolution with no spatial averaging, temporal averaging of monthly decades (1st to 10th of the month, 11th to 20th of the month, 21st to last day of the month), generation frequency of once every decade monthly average, temporal averaging from L3 decade averages, monthly generation seasonal average, temporal averaging from L3 monthly averages, seasonally generation yearly average, temporal averaging from L3 monthly averages, yearly generation.

Soil Moisture Processes in the Near Surface Unsaturated Zone: Experimental Investigations in Multi-scale Test Systems

NASA Astrophysics Data System (ADS)

Illangasekare, T. H.; Sakaki, T.; Smits, K. M.; Limsuwat, A.; Terrés-Nícoli, J. M.

2008-12-01

Understanding the dynamics of soil moisture distribution near the ground surface is of interest in various applications involving land-atmospheric interaction, evaporation from soils, CO2 leakage from carbon sequestration, vapor intrusion into buildings, and land mine detection. Natural soil heterogeneity in combination with water and energy fluxes at the soil surface creates complex spatial and temporal distributions of soil moisture. Even though considerable knowledge exists on how soil moisture conditions change in response to flux and energy boundary conditions, emerging problems involving land atmospheric interactions require the quantification of soil moisture variability both at high spatial and temporal resolutions. The issue of up-scaling becomes critical in all applications, as in general, field measurements are taken at sparsely distributed spatial locations that require assimilation with measurements taken using remote sensing technologies. It is our contention that the knowledge that will contribute to both improving our understanding of the fundamental processes and practical problem solution cannot be obtained easily in the field due to a number of constraints. One of these basic constraints is the inability to make measurements at very fine spatial scales at high temporal resolutions in naturally heterogeneous field systems. Also, as the natural boundary conditions at the land/atmospheric interface are not controllable in the field, even in pilot scale studies, the developed theories and tools cannot be validated for the diversity of conditions that could be expected in the field. Intermediate scale testing using soil tanks packed to represent different heterogeneous test configurations provides an attractive and cost effective alternative to investigate a class of problems involving the shallow unsaturated zone. In this presentation, we will discuss the advantages and limitations of studies conducted in both two and three dimensional intermediate scale test systems together with instrumentation and measuring techniques. The features and capabilities of a new coupled porous media/climate wind tunnel test system that allows for the study of near surface unsaturated soil moisture conditions under climate boundary conditions will also be presented with the goal of exploring opportunities to use such a facility to study some of the multi-scale problems in the near surface unsaturated zone.

Evaluating the spatial distribution of water balance in a small watershed, Pennsylvania

NASA Astrophysics Data System (ADS)

Yu, Zhongbo; Gburek, W. J.; Schwartz, F. W.

2000-04-01

A conceptual water-balance model was modified from a point application to be distributed for evaluating the spatial distribution of watershed water balance based on daily precipitation, temperature and other hydrological parameters. The model was calibrated by comparing simulated daily variation in soil moisture with field observed data and results of another model that simulates the vertical soil moisture flow by numerically solving Richards' equation. The impacts of soil and land use on the hydrological components of the water balance, such as evapotranspiration, soil moisture deficit, runoff and subsurface drainage, were evaluated with the calibrated model in this study. Given the same meteorological conditions and land use, the soil moisture deficit, evapotranspiration and surface runoff increase, and subsurface drainage decreases, as the available water capacity of soil increases. Among various land uses, alfalfa produced high soil moisture deficit and evapotranspiration and lower surface runoff and subsurface drainage, whereas soybeans produced an opposite trend. The simulated distribution of various hydrological components shows the combined effect of soil and land use. Simulated hydrological components compare well with observed data. The study demonstrated that the distributed water balance approach is efficient and has advantages over the use of single average value of hydrological variables and the application at a single point in the traditional practice.

Agriculture, forestry, range, and soils, chapter 2, part C

NASA Technical Reports Server (NTRS)

1975-01-01

The feasibility of using microwave systems in agriculture, forestry, range, and soil moisture measurements was studied. Theory and preliminary results show the feasibility of measuring moisture status in the soil. For vegetational resources, crop identification for inventory and for yield and production estimates is most feasible. Apart from moisture- and water-related phenomena, microwave systems are also used to record structural and spatial data related to crops and forests.

Estimating Soil Moisture Using Polsar Data: a Machine Learning Approach

NASA Astrophysics Data System (ADS)

Khedri, E.; Hasanlou, M.; Tabatabaeenejad, A.

2017-09-01

Soil moisture is an important parameter that affects several environmental processes. This parameter has many important functions in numerous sciences including agriculture, hydrology, aerology, flood prediction, and drought occurrence. However, field procedures for moisture calculations are not feasible in a vast agricultural region territory. This is due to the difficulty in calculating soil moisture in vast territories and high-cost nature as well as spatial and local variability of soil moisture. Polarimetric synthetic aperture radar (PolSAR) imaging is a powerful tool for estimating soil moisture. These images provide a wide field of view and high spatial resolution. For estimating soil moisture, in this study, a model of support vector regression (SVR) is proposed based on obtained data from AIRSAR in 2003 in C, L, and P channels. In this endeavor, sequential forward selection (SFS) and sequential backward selection (SBS) are evaluated to select suitable features of polarized image dataset for high efficient modeling. We compare the obtained data with in-situ data. Output results show that the SBS-SVR method results in higher modeling accuracy compared to SFS-SVR model. Statistical parameters obtained from this method show an R2 of 97% and an RMSE of lower than 0.00041 (m3/m3) for P, L, and C channels, which has provided better accuracy compared to other feature selection algorithms.

Incorporating water table dynamics in climate modeling: 1. Water table observations and equilibrium water table simulations

NASA Astrophysics Data System (ADS)

Fan, Ying; Miguez-Macho, Gonzalo; Weaver, Christopher P.; Walko, Robert; Robock, Alan

2007-05-01

Soil moisture is a key participant in land-atmosphere interactions and an important determinant of terrestrial climate. In regions where the water table is shallow, soil moisture is coupled to the water table. This paper is the first of a two-part study to quantify this coupling and explore its implications in the context of climate modeling. We examine the observed water table depth in the lower 48 states of the United States in search of salient spatial and temporal features that are relevant to climate dynamics. As a means to interpolate and synthesize the scattered observations, we use a simple two-dimensional groundwater flow model to construct an equilibrium water table as a result of long-term climatic and geologic forcing. Model simulations suggest that the water table depth exhibits spatial organization at watershed, regional, and continental scales, which may have implications for the spatial organization of soil moisture at similar scales. The observations suggest that water table depth varies at diurnal, event, seasonal, and interannual scales, which may have implications for soil moisture memory at these scales.

Seasonal soil moisture and drought occurrence in Europe in CMIP5 projections for the 21st century

NASA Astrophysics Data System (ADS)

Ruosteenoja, Kimmo; Markkanen, Tiina; Venäläinen, Ari; Räisänen, Petri; Peltola, Heli

2018-02-01

Projections for near-surface soil moisture content in Europe for the 21st century were derived from simulations performed with 26 CMIP5 global climate models (GCMs). Two Representative Concentration Pathways, RCP4.5 and RCP8.5, were considered. Unlike in previous research in general, projections were calculated separately for all four calendar seasons. To make the moisture contents simulated by the various GCMs commensurate, the moisture data were normalized by the corresponding local maxima found in the output of each individual GCM. A majority of the GCMs proved to perform satisfactorily in simulating the geographical distribution of recent soil moisture in the warm season, the spatial correlation with an satellite-derived estimate varying between 0.4 and 0.8. In southern Europe, long-term mean soil moisture is projected to decline substantially in all seasons. In summer and autumn, pronounced soil drying also afflicts western and central Europe. In northern Europe, drying mainly occurs in spring, in correspondence with an earlier melt of snow and soil frost. The spatial pattern of drying is qualitatively similar for both RCP scenarios, but weaker in magnitude under RCP4.5. In general, those GCMs that simulate the largest decreases in precipitation and increases in temperature and solar radiation tend to produce the most severe soil drying. Concurrently with the reduction of time-mean soil moisture, episodes with an anomalously low soil moisture, occurring once in 10 years in the recent past simulations, become far more common. In southern Europe by the late 21st century under RCP8.5, such events would be experienced about every second year.

Developing a Soil Moisture Index for California Grasslands from Airborne Hyperspectral Imagery

NASA Astrophysics Data System (ADS)

Flamme, H. E.; Roberts, D. A.; Miller, D. L.

2016-12-01

Soil moisture is a key environmental factor controlling vegetation diversity and productivity, evaporation, transpiration, and rainfall runoff. Despite the contribution of soil moisture to ecological productivity, the hydrologic cycle, and erosion, it is currently not being monitored as accurately or as frequently as other environmental factors. Traditional soil moisture monitoring techniques rely on in situ measurements, which become costly when evaluating areas of unevenly distributed soil characteristics and varying topography. Alternatively, satellite remote sensing, such as passive microwave from SMAP, can provide soil moisture but only at very coarse spatial resolutions. Imagery from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) has the potential to allow better spatial and temporal monitoring of soil moisture. This study established a relationship between plant available water and hyperspectral reflectance via linear regressions of data from 2013-2015 for two grassland field sites: 1) near Santa Barbara, California, at Coal Oil Point Reserve (COPR) and 2) Airstrip station (AIRS) at UC Santa Barbara's Sedgwick Reserve near Santa Ynez, California. Volumetric soil moisture measurements at 10 cm and 20 cm depths were provided by meteorological stations situated in COPR and AIRS while reflectance data were extracted from AVIRIS. We found strong correlations between plant available water and bands centered at wavelengths 704 nm and 831 nm, which we used to create Hyperspectral Soil Moisture Index (HSMI): 0.38((ρ831-ρ704)/(ρ831+ρ704))-0.02. HSMI demonstrated a coefficient of determination (R2) of 0.71 for linear regressions of reflectance versus plant available water with a lag time of 28 days. We applied HSMI to the AIRS and COPR grasslands for 2011 AVIRIS scenes. Plant available water values predicted by HSMI were 0.039 higher at AIRS and 0.048 higher at COPR than the field measurements at the sites. Differences in grass species, soil composition, and climate between COPR and AIRS likely contributed to the errors in the soil moisture predicted by HSMI.

Land surface hydrology parameterization for atmospheric general circulation models including subgrid scale spatial variability

NASA Technical Reports Server (NTRS)

Entekhabi, D.; Eagleson, P. S.

1989-01-01

Parameterizations are developed for the representation of subgrid hydrologic processes in atmospheric general circulation models. Reasonable a priori probability density functions of the spatial variability of soil moisture and of precipitation are introduced. These are used in conjunction with the deterministic equations describing basic soil moisture physics to derive expressions for the hydrologic processes that include subgrid scale variation in parameters. The major model sensitivities to soil type and to climatic forcing are explored.

Influence of spatial and temporal variability of subsurface soil moisture and temperature on vapour intrusion

NASA Astrophysics Data System (ADS)

Bekele, Dawit N.; Naidu, Ravi; Chadalavada, Sreenivasulu

2014-05-01

A comprehensive field study was conducted at a site contaminated with chlorinated solvents, mainly trichloroethylene (TCE), to investigate the influence of subsurface soil moisture and temperature on vapour intrusion (VI) into built structures. Existing approaches to predict the risk of VI intrusion into buildings assume homogeneous or discrete layers in the vadose zone through which TCE migrates from an underlying source zone. In reality, the subsurface of the majority of contaminated sites will be subject to significant variations in moisture and temperature. Detailed site-specific data were measured contemporaneously to evaluate the impact of spatial and temporal variability of subsurface soil properties on VI exposure assessment. The results revealed that indoor air vapour concentrations would be affected by spatial and temporal variability of subsurface soil moisture and temperature. The monthly monitoring of soil-gas concentrations over a period of one year at a depth of 3 m across the study site demonstrated significant variation in TCE vapour concentrations, which ranged from 480 to 629,308 μg/m3. Soil-gas wells at 1 m depth exhibited high seasonal variability in TCE vapour concentrations with a coefficient of variation 1.02 in comparison with values of 0.88 and 0.74 in 2 m and 3 m wells, respectively. Contour plots of the soil-gas TCE plume during wet and dry seasons showed that the plume moved across the site, hence locations of soil-gas monitoring wells for human risk assessment is a site specific decision. Subsurface soil-gas vapour plume characterisation at the study site demonstrates that assessment for VI is greatly influenced by subsurface soil properties such as temperature and moisture that fluctuate with the seasons of the year.

The Temperature Sensitivity (Q10) of Soil Respiration: Controlling Factors and Spatial Prediction at Regional Scale Based on Environmental Soil Classes

NASA Astrophysics Data System (ADS)

Meyer, N.; Welp, G.; Amelung, W.

2018-02-01

The temperature sensitivity of heterotrophic soil respiration is crucial for modeling carbon dynamics but it is variable. Presently, however, most models employ a fixed value of 1.5 or 2.0 for the increase of soil respiration per 10°C increase in temperature (Q10). Here we identified the variability of Q10 at a regional scale (Rur catchment, Germany/Belgium/Netherlands). We divided the study catchment into environmental soil classes (ESCs), which we define as unique combinations of land use, aggregated soil groups, and texture. We took nine soil samples from each ESC (108 samples) and incubated them at four soil moisture levels and five temperatures (5-25°C). We hypothesized that Q10 variability is controlled by soil organic carbon (SOC) degradability and soil moisture and that ESC can be used as a widely available proxy for Q10, owing to differences in SOC degradability. Measured Q10 values ranged from 1.2 to 2.8 and were correlated with indicators of SOC degradability (e.g., pH, r = -0.52). The effect of soil moisture on Q10 was variable: Q10 increased with moisture in croplands but decreased in forests. The ESC captured significant parts of Q10 variability under dry (R2 = 0.44) and intermediate (R2 = 0.36) moisture conditions, where Q10 increased in the order cropland

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

Soil Moisture: The Hydrologic Interface Between Surface and Ground Waters

NASA Technical Reports Server (NTRS)

Engman, Edwin T.

1997-01-01

A hypothesis is presented that many hydrologic processes display a unique signature that is detectable with microwave remote sensing. These signatures are in the form of the spatial and temporal distributions of surface soil moisture. The specific hydrologic processes that may be detected include groundwater recharge and discharge zones, storm runoff contributing areas, regions of potential and less than potential evapotranspiration (ET), and information about the hydrologic properties of soils. In basin and hillslope hydrology, soil moisture is the interface between surface and ground waters.

On the non-uniqueness of the hydro-geomorphic responses in a zero-order catchment with respect to soil moisture

NASA Astrophysics Data System (ADS)

Kim, Jongho; Dwelle, M. Chase; Kampf, Stephanie K.; Fatichi, Simone; Ivanov, Valeriy Y.

2016-06-01

This study advances mechanistic interpretation of predictability challenges in hydro-geomorphology related to the role of soil moisture spatial variability. Using model formulations describing the physics of overland flow, variably saturated subsurface flow, and erosion and sediment transport, this study explores (1) why a basin with the same mean soil moisture can exhibit distinctly different spatial moisture distributions, (2) whether these varying distributions lead to non-unique hydro-geomorphic responses, and (3) what controls non-uniqueness in relation to the response type. Two sets of numerical experiments are carried out with two physically-based models, HYDRUS and tRIBS+VEGGIE+FEaST, and their outputs are analyzed with respect to pre-storm moisture state. The results demonstrate that distinct spatial moisture distributions for the same mean wetness arise because near-surface soil moisture dynamics exhibit different degrees of coupling with deeper-soil moisture and the process of subsurface drainage. The consequences of such variations are different depending on the type of hydrological response. Specifically, if the predominant runoff response is of infiltration excess type, the degree of non-uniqueness is related to the spatial distribution of near-surface moisture. If runoff is governed by subsurface stormflow, the extent of deep moisture contributing area and its "readiness to drain" determine the response characteristics. Because the processes of erosion and sediment transport superimpose additional controls over factors governing runoff generation and overland flow, non-uniqueness of the geomorphic response can be highly dampened or enhanced. The explanation is sediment composed by multi-size particles can alternate states of mobilization or surface shielding and the transient behavior is inherently intertwined with the availability of mobile particles. We conclude that complex nonlinear dynamics of hydro-geomorphic processes are inherent expressions of physical interactions. As complete knowledge of watershed properties, states, or forcings will always present the ultimate, if ever resolvable, challenge, deterministic predictability will remain handicapped. Coupling of uncertainty quantification methods and space-time physics-based approaches will need to evolve to facilitate mechanistic interpretations and informed practical applications.

Investigation of remote sensing techniques of measuring soil moisture

NASA Technical Reports Server (NTRS)

Newton, R. W. (Principal Investigator); Blanchard, A. J.; Nieber, J. L.; Lascano, R.; Tsang, L.; Vanbavel, C. H. M.

1981-01-01

Major activities described include development and evaluation of theoretical models that describe both active and passive microwave sensing of soil moisture, the evaluation of these models for their applicability, the execution of a controlled field experiment during which passive microwave measurements were acquired to validate these models, and evaluation of previously acquired aircraft microwave measurements. The development of a root zone soil water and soil temperature profile model and the calibration and evaluation of gamma ray attenuation probes for measuring soil moisture profiles are considered. The analysis of spatial variability of soil information as related to remote sensing is discussed as well as the implementation of an instrumented field site for acquisition of soil moisture and meteorologic information for use in validating the soil water profile and soil temperature profile models.

SMAP Soil Moisture Disaggregation using Land Surface Temperature and Vegetation Data

NASA Astrophysics Data System (ADS)

Fang, B.; Lakshmi, V.

2016-12-01

Soil moisture (SM) is a key parameter in agriculture, hydrology and ecology studies. The global SM retrievals have been providing by microwave remote sensing technology since late 1970s and many SM retrieval algorithms have been developed, calibrated and applied on satellite sensors such as AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System), AMSR-2 (Advanced Microwave Scanning Radiometer 2) and SMOS (Soil Moisture and Ocean Salinity). Particularly, SMAP (Soil Moisture Active/Passive) satellite, which was developed by NASA, was launched in January 2015. SMAP provides soil moisture products of 9 km and 36 km spatial resolutions which are not capable for research and applications of finer scale. Toward this issue, this study applied a SM disaggregation algorithm to disaggregate SMAP passive microwave soil moisture 36 km product. This algorithm was developed based on the thermal inertial relationship between daily surface temperature variation and daily average soil moisture which is modulated by vegetation condition, by using remote sensing retrievals from AVHRR (Advanced Very High Resolution Radiometer, MODIS (Moderate Resolution Imaging Spectroradiometer), SPOT (Satellite Pour l'Observation de la Terre), as well as Land Surface Model (LSM) output from NLDAS (North American Land Data Assimilation System). The disaggregation model was built at 1/8o spatial resolution on monthly basis and was implemented to calculate and disaggregate SMAP 36 km SM retrievals to 1 km resolution in Oklahoma. The SM disaggregation results were also validated using MESONET (Mesoscale Network) and MICRONET (Microscale Network) ground SM measurements.

Relation Between the Rainfall and Soil Moisture During Different Phases of Indian Monsoon

NASA Astrophysics Data System (ADS)

Varikoden, Hamza; Revadekar, J. V.

2018-03-01

Soil moisture is a key parameter in the prediction of southwest monsoon rainfall, hydrological modelling, and many other environmental studies. The studies on relationship between the soil moisture and rainfall in the Indian subcontinent are very limited; hence, the present study focuses the association between rainfall and soil moisture during different monsoon seasons. The soil moisture data used for this study are the ESA (European Space Agency) merged product derived from four passive and two active microwave sensors spanning over the period 1979-2013. The rainfall data used are India Meteorological Department gridded daily data. Both of these data sets are having a spatial resolution of 0.25° latitude-longitude grid. The study revealed that the soil moisture is higher during the southwest monsoon period similar to rainfall and during the pre-monsoon period, the soil moisture is lower. The annual cycle of both the soil moisture and rainfall has the similitude of monomodal variation with a peak during the month of August. The interannual variability of soil moisture and rainfall shows that they are linearly related with each other, even though they are not matched exactly for individual years. The study of extremes also exhibits the surplus amount of soil moisture during wet monsoon years and also the regions of surplus soil moisture are well coherent with the areas of high rainfall.

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.

Spatial distribution of soil moisture obtained from gravimetric and TDR methods for SMOS validation, at the Polesie test site SVRT 3275, in Poland

NASA Astrophysics Data System (ADS)

Usowicz, B.; Marczewski, W.; Lipiec, J.; Usowicz, J. B.; Sokolowska, Z.; Dabkowska-Naskret, H.; Hajnos, M.; Lukowski, M. I.

2009-04-01

The purpose is obtaining trustful ground based measurement data of SM (Soil Moisture) for validating SMOS, respectively to spatial and temporal distribution and variations. A use of Time Domain Reflectometric (TDR) method is fast, simple and less destructive, to the soil matter, than a usual standard gravimetric method. TDR tools operate efficiently, enable nearly instant measurements, and allow on collecting many measurements from numerous sites, even when operated manually in short time intervals. The method enables also very frequent sampling of SM at few selected fixed sites, when long terms of temporal variations are needed. In effect one obtains reasonably large data base for determining spatial and temporal distributions of SM. The study is devoted to determining a plan on collecting TDR data, in the scales of small and large field areas, and checking their relevance to those available from gravimetric methods. Finally, the ground based SM distributions are needed for validating other SM distributions, available remotely in larger scales, from the satellite data of ENVISAT-ASAR, and from SMOS (Soil Moisture and Ocean Salinity Mission) when it becomes operational. The ground based evaluations are served mainly by geo-statistical analysis. The space borne estimations are retrieved by image processing and physical models, proper to relevant Remote Sensing (RS) instruments on the orbit. Finally, validation must engage again the geo-statistical evaluations, to assess the agreement between direct and remote sensing means, and provide a measure of trust for extending the limited scales of the ground based data, on concluding the agreement in scales proper to the satellite data. The study is focused mainly on trustful evaluating data from the ground, provided independently on satellite data sources. SM ground based data are collected permanently at 2 selected tests sites, and temporary in areas around the tests sites, in one day sessions, repeated several times per vegetation season. Permanent measurements are provided in profiles, down to 50 cm below surface. Temporary SM measurements are collected by hand held TDR (FOM/mts type, Easy Test Ltd., Lublin, Poland) from the top surface layer (1-6 cm), in a grid covering small and large areas, containing few hundred sites. The same places are served by collecting soil samples for the gravimetric analysis of SM, bulk density, other physical and textural characteristics. Sessions on measurement in large areas on the scale of community are repeated for separate days. The two methods used were compared with correlation coefficient, regression equation and differences of values. The spatial variability of soil moisture from gravimetric and TDR measurements were analyzed using geostatistical methods. The semivariogram parameters were determined and mathematical functions were fitted to empirically derived semivariograms. These functions were used for estimation of spatial distribution of soil moisture in cultivated fields by the kriging method. The results showed that spatial distribution patterns of topsoil soil moisture in the investigated areas obtained from TDR and gravimetric methods were in general similar to each other. The TDR soil moisture contents were dependent on bulk density and texture of soil. In areas with fine-textured soils of lower soil bulk densities (approximately below 1.35 Mg m^-3) we observed that TDR soil moisture and spatial differentiation were greater compared to those with gravimetric method. However at higher bulk densities the inverse was true. The spatial patterns were further modified in areas with domination of coarse-textured soils. Decrease of measurement points results in smoothing soil moisture pattern and at the same time in a greater estimation error. The TDR method can be useful tool for ground moisture measurements and validation of satellite data. The use of specific calibration or correction for soil bulk density and texture with respect to the reflectometric method is recommended. The study is a contribution to the project SWEX (AO-3275) and funded by the Polish Ministry of Science and Higher Education (in part by Grant No. N305 046 31/1707 and in part by Grant No. N305 107 32/3865).

Can we quantify the variability of soil moisture across scales using Electromagnetic Induction ?

NASA Astrophysics Data System (ADS)

Robinet, Jérémy; von Hebel, Christian; van der Kruk, Jan; Govers, Gerard; Vanderborght, Jan

2017-04-01

Soil moisture is a key variable in many natural processes. Therefore, technological and methodological advancements are of primary importance to provide accurate measurements of spatial and temporal variability of soil moisture. In that context, ElectroMagnetic Induction (EMI) instruments are often cited as a hydrogeophysical method with a large potential, through the measurement of the soil apparent electrical conductivity (ECa). To our knowledge, no studies have evaluated the potential of EMI to characterize variability of soil moisture on both agricultural and forested land covers in a (sub-) tropical environment. These differences in land use could be critical as differences in temperature, transpiration and root water uptake can have significant effect, notably on the electrical conductivity of the pore water. In this study, we used an EMI instrument to carry out a first assessment of the impact of deforestation and agriculture on soil moisture in a subtropical region in the south of Brazil. We selected slopes of different topographies (gentle vs. steep) and contrasting land uses (natural forest vs. agriculture) within two nearby catchments. At selected locations on the slopes, we measured simultaneously ECa using EMI and a depth-weighted average of the soil moisture using TDR probes installed within soil pits. We found that the temporal variability of the soil moisture could not be measured accurately with EMI, probably because of important temporal variations of the pore water electrical conductivity and the relatively small temporal variations in soil moisture content. However, we found that its spatial variability could be effectively quantified using a non-linear relationship, for both intra- and inter-slopes variations. Within slopes, the ECa could explained between 67 and 90% of the variability of the soil moisture, while a single non-linear model for all the slopes could explain 55% of the soil moisture variability. We eventually showed that combining a specific relationship for the most degraded slope (steep slope under agriculture) and a single relationship for all the other slopes, both non-linear relations, yielded the best results with an overall explained variance of 90%. We applied the latter model to measurements of the ECa along transects at the different slopes, which allowed us to highlight the strong control of topography on the soil moisture content. We also observed a significant impact of the land use with higher moisture content on the agricultural slopes, probably due to a reduced evapotranspiration.

Plan of research for integrated soil moisture studies. Recommendations of the Soil Moisture Working Group

NASA Technical Reports Server (NTRS)

1980-01-01

Soil moisture information is a potentially powerful tool for applications in agriculture, water resources, and climate. At present, it is difficult for users of this information to clearly define their needs in terms of accuracy, resolution and frequency because of the current sparsity of data. A plan is described for defining and conducting an integrated and coordinated research effort to develop and refine remote sensing techniques which will determine spatial and temporal variations of soil moisture and to utilize soil moisture information in support of agricultural, water resources, and climate applications. The soil moisture requirements of these three different application areas were reviewed in relation to each other so that one plan covering the three areas could be formulated. Four subgroups were established to write and compile the plan, namely models, ground-based studies, aircraft experiments, and spacecraft missions.

Using Actively Heated Fibre Optics (AHFO) to determine soil thermal conductivity and soil moisture content at high spatial and temporal resolution

NASA Astrophysics Data System (ADS)

Ciocca, Francesco; Abesser, Corinna; Hannah, David; Blaen, Philip; Chalari, Athena; Mondanos, Michael; Krause, Stefan

2017-04-01

Optical fibre distributed temperature sensing (DTS) is increasingly used in environmental monitoring and for subsurface characterisation, e.g. to obtain precise measurements of soil temperature at high spatio-temporal resolution, over several kilometres of optical fibre cable. When combined with active heating of metal elements embedded in the optical fibre cable (active-DTS), the temperature response of the soil to heating provides valuable information from which other important soil parameters, such as thermal conductivity and soil moisture content, can be inferred. In this presentation, we report the development of an Actively Heated Fibre Optics (AHFO) method for the characterisation of soil thermal conductivity and soil moisture dynamics at high temporal and spatial resolutions at a vegetated hillslope site in central England. The study site is located within a juvenile forest adjacent to the Birmingham Institute of Forest Research (BIFoR) experimental site. It is instrumented with three loops of a 500m hybrid-optical cable installed at 10cm, 25cm and 40cm depths. Active DTS surveys were undertaken in June and October 2016, collecting soil temperature data at 0.25m intervals along the cable, prior to, during and after the 900s heating phase. Soil thermal conductivity and soil moisture were determined according to Ciocca et al. 2012, applied to both the cooling and the heating phase. Independent measurements of soil thermal conductivity and soil moisture content were collected using thermal needle probes, calibrated capacitance-based probes and laboratory methods. Results from both the active DTS survey and independent in-situ and laboratory measurements will be presented, including the observed relationship between thermal conductivity and moisture content at the study site and how it compares against theoretical curves used by the AHFO methods. The spatial variability of soil thermal conductivity and soil moisture content, as observed using the different methods, will be shown and an outlook will be provided of how the AHFO method can benefit soil sciences, ground source heat pump applications and groundwater recharge estimations. This research is part of the Distributed intelligent Heat Pulse System (DiHPS) project which is funded by the UK Natural Environmental Research Council (NERC). The project is supported by BIFoR, the European Space Agency (ESA), CarbonZero Ltd, the UK Forestry Commission and the UK Soil Moisture Observation Network (COSMOS-UK). This work is distributed under the Creative Commons Attribution 3.0 Unported Licence together with an author copyright. This licence does not conflict with the regulations of the Crown Copyright. Ciocca F., Lunati I., van de Giesen N., and Parlange M.B. 2012. Heated optical fiber for distributed soil-moisture measurements: A lysimeter experiment. Vadose Zone J. 11. doi:10.2136/vzj2011.0177

Estimating Surface Soil Moisture in a Mixed-Landscape using SMAP and MODIS/VIIRS Data

NASA Astrophysics Data System (ADS)

Tang, J.; Di, L.; Xiao, J.

2017-12-01

Soil moisture, a critical parameter of earth ecosystem linking land surface and atmosphere, has been widely applied in many application (Di, 1991; Njoku et al. 2003; Western 2002; Zhao et al. 2014; McColl et al. 2017) from regional to continental or even global scale. The advent of satellite-based remote sensing, particular in the last two decades, has proven successful for mapping the surface soil moisture (SSM) from space (Petropoulos et al. 2015; Kim et al. 2015; Molero et al. 2016). The current soil moisture products, however, is not able to fully characterize the spatial and temporal variability of soil moisture at mixed landscape types (Albergel et al. 2013; Zeng et al. 2015). In this research, we derived the SSM at 1-km spatial resolution by using sensor observation and high-level products from SMAP and MODIS/VIIRS as well as metrorological, landcover, and soil data. Specifically, we proposed a practicable method to produce the originally planned SMAP L3_SM_A with comparable quality by downscaling the SMAP L3_SM_P product through a proved method, the geographically weighted regression method at mixed landscape in southern New Hampshire. This estimated SSM was validated using the Soil Climate Analysis Network (SCAN) from Natural Resources Conservation Service (NRCS) of United States Department of Agriculture (USDA).

Effects of spatial variations of soil moisture and vegetation on the evolution of a prestorm environment - A numerical case study

NASA Technical Reports Server (NTRS)

Chang, Jy-Tai; Wetzel, Peter J.

1991-01-01

To examine the effects of spatial variations of soil moisture and vegetation coverage on the evolution of a prestorm environment, the Goddard mesoscale model is modified to incorporate a simple evapotranspiration model that requires these two parameters. The case study of 3-4 June 1980 is of special interest due to the development of a tornado producing convective complex near Grand Island, Nebraska during a period of comparatively weak synoptic-scale forcing. It is shown that the observed stationary front was strongly enhanced by differential heating created by observed gradients of soil moisture, as acted upon by the vegetation cover.

Investigating local controls on soil moisture temporal stability using an inverse modeling approach

NASA Astrophysics Data System (ADS)

Bogena, Heye; Qu, Wei; Huisman, Sander; Vereecken, Harry

2013-04-01

A better understanding of the temporal stability of soil moisture and its relation to local and nonlocal controls is a major challenge in modern hydrology. Both local controls, such as soil and vegetation properties, and non-local controls, such as topography and climate variability, affect soil moisture dynamics. Wireless sensor networks are becoming more readily available, which opens up opportunities to investigate spatial and temporal variability of soil moisture with unprecedented resolution. In this study, we employed the wireless sensor network SoilNet developed by the Forschungszentrum Jülich to investigate soil moisture variability of a grassland headwater catchment in Western Germany within the framework of the TERENO initiative. In particular, we investigated the effect of soil hydraulic parameters on the temporal stability of soil moisture. For this, the HYDRUS-1D code coupled with a global optimizer (DREAM) was used to inversely estimate Mualem-van Genuchten parameters from soil moisture observations at three depths under natural (transient) boundary conditions for 83 locations in the headwater catchment. On the basis of the optimized parameter sets, we then evaluated to which extent the variability in soil hydraulic conductivity, pore size distribution, air entry suction and soil depth between these 83 locations controlled the temporal stability of soil moisture, which was independently determined from the observed soil moisture data. It was found that the saturated hydraulic conductivity (Ks) was the most significant attribute to explain temporal stability of soil moisture as expressed by the mean relative difference (MRD).

Integrating Multi-Sensor Remote Sensing and In-situ Measurements for Africa Drought Monitoring and Food Security Assessment

NASA Astrophysics Data System (ADS)

Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.

2014-12-01

Drought is one of the most complicated natural hazards, and causes serious environmental, economic and social consequences. Agricultural production systems, which are highly susceptible to weather and climate extremes, are often the first and most vulnerable sector to be affected by drought events. In Africa, crop yield potential and grazing quality are already nearing their limit of temperature sensitivity, and, rapid population growth and frequent drought episodes pose serious complications for food security. It is critical to promote sustainable agriculture development in Africa under conditions of climate extremes. Soil moisture is one of the most important indicators for agriculture drought, and is a fundamentally critical parameter for decision support in crop management, including planting, water use efficiency and irrigation. While very significant technological advances have been introduced for remote sensing of surface soil moisture from space, in-situ measurements are still critical for calibration and validation of soil moisture estimation algorithms. For operational applications, synergistic collaboration is needed to integrate measurements from different sensors at different spatial and temporal scales. In this presentation, a collaborative effort is demonstrated for drought monitoring in Africa, supported and coordinated by WMO, including surface soil moisture and crop status monitoring. In-situ measurements of soil moisture, precipitation and temperature at selected sites are provided by local partners in Africa. Measurements from the Soil Moisture and Ocean Salinity (SMOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are integrated with in-situ observations to derive surface soil moisture at high spatial resolution. Crop status is estimated through temporal analysis of current and historical MODIS measurements. Integrated analysis of soil moisture data and crop status provides both in-depth understanding of drought conditions and potential impacts on crop yield. This information is extremely useful in local decision support for agricultural management.

Integrating Multi-Sensor Remote Sensing and In-situ Measurements for Africa Drought Monitoring and Food Security Assessment

NASA Astrophysics Data System (ADS)

Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.

2015-12-01

Drought is one of the most complicated natural hazards, and causes serious environmental, economic and social consequences. Agricultural production systems, which are highly susceptible to weather and climate extremes, are often the first and most vulnerable sector to be affected by drought events. In Africa, crop yield potential and grazing quality are already nearing their limit of temperature sensitivity, and, rapid population growth and frequent drought episodes pose serious complications for food security. It is critical to promote sustainable agriculture development in Africa under conditions of climate extremes. Soil moisture is one of the most important indicators for agriculture drought, and is a fundamentally critical parameter for decision support in crop management, including planting, water use efficiency and irrigation. While very significant technological advances have been introduced for remote sensing of surface soil moisture from space, in-situ measurements are still critical for calibration and validation of soil moisture estimation algorithms. For operational applications, synergistic collaboration is needed to integrate measurements from different sensors at different spatial and temporal scales. In this presentation, a collaborative effort is demonstrated for drought monitoring in Africa, supported and coordinated by WMO, including surface soil moisture and crop status monitoring. In-situ measurements of soil moisture, precipitation and temperature at selected sites are provided by local partners in Africa. Measurements from the Soil Moisture and Ocean Salinity (SMOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are integrated with in-situ observations to derive surface soil moisture at high spatial resolution. Crop status is estimated through temporal analysis of current and historical MODIS measurements. Integrated analysis of soil moisture data and crop status provides both in-depth understanding of drought conditions and potential impacts on crop yield. This information is extremely useful in local decision support for agricultural management.

Lodgepole pine site index in relation to synoptic measures of climate, soil moisture and soil nutrients.

Treesearch

G. Geoff Wang; Shongming Huang; Robert A. Monserud; Ryan J. Klos

2004-01-01

Lodgepole pine site index was examined in relation to synoptic measures of topography, soil moisture, and soil nutrients in Alberta. Data came from 214 lodgepole pine-dominated stands sampled as a part of the provincial permanent sample plot program. Spatial location (elevation, latitude, and longitude) and natural subregions (NSRs) were topographic variables that...

Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons 1986

USDA-ARS?s Scientific Manuscript database

Soil moisture content on a horizontal scale of hectometers and at depths of decimeters can be inferred from measurements of low-energy cosmic-ray neutrons that are generated within soil, moderated mainly by hydrogen atoms, and diffused back to the atmosphere. These neutrons are sensitive to water co...

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

NASA Astrophysics Data System (ADS)

Carranza, Coleen D. U.; van der Ploeg, Martine J.; Torfs, Paul J. J. F.

2018-04-01

Recent advances in radar remote sensing popularized the mapping of surface soil moisture at different spatial scales. Surface soil moisture measurements are used in combination with hydrological models to determine subsurface soil moisture values. However, variability of soil moisture across the soil column is important for estimating depth-integrated values, as decoupling between surface and subsurface can occur. In this study, we employ new methods to investigate the occurrence of (de)coupling between surface and subsurface soil moisture. Using time series datasets, lagged dependence was incorporated in assessing (de)coupling with the idea that surface soil moisture conditions will be reflected at the subsurface after a certain delay. The main approach involves the application of a distributed-lag nonlinear model (DLNM) to simultaneously represent both the functional relation and the lag structure in the time series. The results of an exploratory analysis using residuals from a fitted loess function serve as a posteriori information to determine (de)coupled values. Both methods allow for a range of (de)coupled soil moisture values to be quantified. Results provide new insights into the decoupled range as its occurrence among the sites investigated is not limited to dry conditions.

Confronting weather and climate models with observational data from soil moisture networks over the United States

PubMed Central

Dirmeyer, Paul A.; Wu, Jiexia; Norton, Holly E.; Dorigo, Wouter A.; Quiring, Steven M.; Ford, Trenton W.; Santanello, Joseph A.; Bosilovich, Michael G.; Ek, Michael B.; Koster, Randal D.; Balsamo, Gianpaolo; Lawrence, David M.

2018-01-01

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those we find that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely due to differences in instrumentation, calibration and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory) and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison. PMID:29645013

Confronting Weather and Climate Models with Observational Data from Soil Moisture Networks over the United States

NASA Technical Reports Server (NTRS)

Dirmeyer, Paul A.; Wu, Jiexia; Norton, Holly E.; Dorigo, Wouter A.; Quiring, Steven M.; Ford, Trenton W.; Santanello, Joseph A., Jr.; Bosilovich, Michael G.; Ek, Michael B.; Koster, Randal Dean;

Confronting weather and climate models with observational data from soil moisture networks over the United States.

PubMed

Dirmeyer, Paul A; Wu, Jiexia; Norton, Holly E; Dorigo, Wouter A; Quiring, Steven M; Ford, Trenton W; Santanello, Joseph A; Bosilovich, Michael G; Ek, Michael B; Koster, Randal D; Balsamo, Gianpaolo; Lawrence, David M

2016-04-01

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those we find that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely due to differences in instrumentation, calibration and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory) and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.

The NASA Soil Moisture Active Passive (SMAP) Mission: Overview

NASA Technical Reports Server (NTRS)

O'Neill, Peggy; Entekhabi, Dara; Njoku, Eni; Kellogg, Kent

2011-01-01

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council?s Decadal Survey [1]. Its mission design consists of L-band radiometer and radar instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every 2-3 days. The combined active/passive microwave soil moisture product will have a spatial resolution of 10 km and a mean latency of 24 hours. In addition, the SMAP surface observations will be combined with advanced modeling and data assimilation to provide deeper root zone soil moisture and net ecosystem exchange of carbon. SMAP is expected to launch in the late 2014 - early 2015 time frame.

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.

Development of an Objective High Spatial Resolution Soil Moisture Index

NASA Astrophysics Data System (ADS)

Zavodsky, B.; Case, J.; White, K.; Bell, J. R.

2015-12-01

Drought detection, analysis, and mitigation has become a key challenge for a diverse set of decision makers, including but not limited to operational weather forecasters, climatologists, agricultural interests, and water resource management. One tool that is heavily used is the United States Drought Monitor (USDM), which is derived from a complex blend of objective data and subjective analysis on a state-by-state basis using a variety of modeled and observed precipitation, soil moisture, hydrologic, and vegetation and crop health data. The NASA Short-term Prediction Research and Transition (SPoRT) Center currently runs a real-time configuration of the Noah land surface model (LSM) within the NASA Land Information System (LIS) framework. The LIS-Noah is run at 3-km resolution for local numerical weather prediction (NWP) and situational awareness applications at select NOAA/National Weather Service (NWS) forecast offices over the Continental U.S. (CONUS). To enhance the practicality of the LIS-Noah output for drought monitoring and assessing flood potential, a 30+-year soil moisture climatology has been developed in an attempt to place near real-time soil moisture values in historical context at county- and/or watershed-scale resolutions. This LIS-Noah soil moisture climatology and accompanying anomalies is intended to complement the current suite of operational products, such as the North American Land Data Assimilation System phase 2 (NLDAS-2), which are generated on a coarser-resolution grid that may not capture localized, yet important soil moisture features. Daily soil moisture histograms are used to identify the real-time soil moisture percentiles at each grid point according to the county or watershed in which the grid point resides. Spatial plots are then produced that map the percentiles as proxies to the different USDM categories. This presentation will highlight recent developments of this gridded, objective soil moisture index, comparison to subjective analyses, and application examples.

Global Soil Moisture from the Aquarius/SAC-D Satellite: Description and Initial Assessment

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas; Cosh, Michael; Zhao, Tianjie; O'Neil, Peggy

2015-01-01

Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology and with soil moisture from other satellite missions (Advanced Microwave Scanning Radiometer for the Earth Observing System and Soil Moisture Ocean Salinity). Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m(exp 3)/m(exp 3), Bias = -0.007 m(exp 3)/m(exp 3), and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.

Soil moisture needs in earth sciences

NASA Technical Reports Server (NTRS)

Engman, Edwin T.

1992-01-01

The author reviews the development of passive and active microwave techniques for measuring soil moisture with respect to how the data may be used. New science programs such as the EOS, the GEWEX Continental-Scale International Project (GCIP) and STORM, a mesoscale meteorology and hydrology project, will have to account for soil moisture either as a storage in water balance computations or as a state variable in-process modeling. The author discusses future soil moisture needs such as frequency of measurement, accuracy, depth, and spatial resolution, as well as the concomitant model development that must proceed concurrently if the development in microwave technology is to have a major impact in these areas.

Implementation of a global-scale operational data assimilation system for satellite-based soil moisture retrievals

NASA Astrophysics Data System (ADS)

Bolten, J.; Crow, W.; Zhan, X.; Reynolds, C.

2008-08-01

Timely and accurate monitoring of global weather anomalies and drought conditions is essential for assessing global crop conditions. Soil moisture observations are particularly important for crop yield fluctuations provided by the US Department of Agriculture (USDA) Production Estimation and Crop Assessment Division (PECAD). The current system utilized by PECAD estimates soil moisture from a 2-layer water balance model based on precipitation and temperature data from World Meteorological Organization (WMO) and US Air Force Weather Agency (AFWA). The accuracy of this system is highly dependent on the data sources used; particularly the accuracy, consistency, and spatial and temporal coverage of the land and climatic data input into the models. However, many regions of the globe lack observations at the temporal and spatial resolutions required by PECAD. This study incorporates NASA's soil moisture remote sensing product provided by the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the U.S. Department of Agriculture Crop Assessment and Data Retrieval (CADRE) decision support system. A quasi-global-scale operational data assimilation system has been designed and implemented to provide CADRE a daily product of integrated AMSR-E soil moisture observations with the PECAD two-layer soil moisture model forecasts. A methodology of the system design and a brief evaluation of the system performance over the Conterminous United States (CONUS) is presented.

Potential for Remotely Sensed Soil Moisture Data in Hydrologic Modeling

NASA Technical Reports Server (NTRS)

Engman, Edwin T.

1997-01-01

Many hydrologic processes display a unique signature that is detectable with microwave remote sensing. These signatures are in the form of the spatial and temporal distributions of surface soil moisture and portray the spatial heterogeneity of hydrologic processes and properties that one encounters in drainage basins. The hydrologic processes that may be detected include ground water recharge and discharge zones, storm runoff contributing areas, regions of potential and less than potential ET, and information about the hydrologic properties of soils and heterogeneity of hydrologic parameters. Microwave remote sensing has the potential to detect these signatures within a basin in the form of volumetric soil moisture measurements in the top few cm. These signatures should provide information on how and where to apply soil physical parameters in distributed and lumped parameter models and how to subdivide drainage basins into hydrologically similar sub-basins.

Soil moisture variability over Odra watershed: Comparison between SMOS and GLDAS data

NASA Astrophysics Data System (ADS)

Zawadzki, Jaroslaw; Kędzior, Mateusz

2016-03-01

Monitoring of temporal and spatial soil moisture variability is an important issue, both from practical and scientific point of view. It is well known that passive, L-band, radiometric measurements provide best soil moisture estimates. Unfortunately as it was observed during Soil Moisture and Ocean Salinity (SMOS) mission, which was specially dedicated to measure soil moisture, these measurements suffer significant data loss. It is caused mainly by radio frequency interference (RFI) which strongly contaminates Central Europe and even in particularly unfavorable conditions, might prevent these data from being used for regional or watershed scale analysis. Nevertheless, it is highly awaited by researchers to receive statistically significant information on soil moisture over the area of a big watershed. One of such watersheds, the Odra (Oder) river watershed, lies in three European countries - Poland, Germany and the Czech Republic. The area of the Odra river watershed is equal to 118,861 km2 making it the second most important river in Poland as well as one of the most significant one in Central Europe. This paper examines the SMOS soil moisture data in the Odra river watershed in the period from 2010 to 2012. This attempt was made to check the possibility of assessing, from the low spatial resolution observations of SMOS, useful information that could be exploited for practical aims in watershed scale, for example, in water storage models even while moderate RFI takes place. Such studies, performed over the area of a large watershed, were recommended by researchers in order to obtain statistically significant results. To meet these expectations, Centre Aval de Traitement des Donnes SMOS (CATDS), 3-days averaged data, together with Global Land Data Assimilation System (GLDAS) National Centers for Environmental Prediction/Oregon State University/Air Force/Hydrologic Research Lab (NOAH) model 0.25 soil moisture values were used for statistical analyses and mutual comparisons. The results obtained using various statistical tools unveil high scientific potential of CATDS SMOS data to study soil moisture over the Odra river watershed. This was also confirmed by reasonable agreement between results derived from CATDS SMOS Ascending and GLDAS data sets. This agreement was achieved mainly by using these data spatially averaged over the whole watershed area, and for observations performed in the period longer than three-day averaging time. Comparisons of separate three-day data in a given pixel position, or at smaller areas would be difficult because of data gaps. Hence, the results of the work suggest that despite of RFI interferences, SMOS observations can provide effective input for analysis of soil moisture at regional scales. Moreover, it was shown that CATDS SMOS soil moisture data are better correlated with rainfall rate than GLDAS ones.

Complementary effects of surface water and groundwater on soil moisture dynamics in a degraded coastal floodplain forest

NASA Astrophysics Data System (ADS)

Kaplan, D.; Muñoz-Carpena, R.

2011-02-01

SummaryRestoration of degraded floodplain forests requires a robust understanding of surface water, groundwater, and vadose zone hydrology. Soil moisture is of particular importance for seed germination and seedling survival, but is difficult to monitor and often overlooked in wetland restoration studies. This research hypothesizes that the complex effects of surface water and shallow groundwater on the soil moisture dynamics of floodplain wetlands are spatially complementary. To test this hypothesis, 31 long-term (4-year) hydrological time series were collected in the floodplain of the Loxahatchee River (Florida, USA), where watershed modifications have led to reduced freshwater flow, altered hydroperiod and salinity, and a degraded ecosystem. Dynamic factor analysis (DFA), a time series dimension reduction technique, was applied to model temporal and spatial variation in 12 soil moisture time series as linear combinations of common trends (representing shared, but unexplained, variability) and explanatory variables (selected from 19 additional candidate hydrological time series). The resulting dynamic factor models yielded good predictions of observed soil moisture series (overall coefficient of efficiency = 0.90) by identifying surface water elevation, groundwater elevation, and net recharge (cumulative rainfall-cumulative evapotranspiration) as important explanatory variables. Strong and complementary linear relationships were found between floodplain elevation and surface water effects (slope = 0.72, R2 = 0.86, p < 0.001), and between elevation and groundwater effects (slope = -0.71, R2 = 0.71, p = 0.001), while the effect of net recharge was homogenous across the experimental transect (slope = 0.03, R2 = 0.05, p = 0.242). This study provides a quantitative insight into the spatial structure of groundwater and surface water effects on soil moisture that will be useful for refining monitoring plans and developing ecosystem restoration and management scenarios in degraded coastal floodplains.

Multi-Scale Soil Moisture Monitoring and Modeling at ARS Watersheds for NASA's Soil Moisture Active Passive (SMAP) Calibration/Validation Mission

NASA Astrophysics Data System (ADS)

Coopersmith, E. J.; Cosh, M. H.

2014-12-01

NASA's SMAP satellite, launched in November of 2014, produces estimates of average volumetric soil moisture at 3, 9, and 36-kilometer scales. The calibration and validation process of these estimates requires the generation of an identically-scaled soil moisture product from existing in-situ networks. This can be achieved via the integration of NLDAS precipitation data to perform calibration of models at each ­in-situ gauge. In turn, these models and the gauges' volumetric estimations are used to generate soil moisture estimates at a 500m scale throughout a given test watershed by leveraging, at each location, the gauge-calibrated models deemed most appropriate in terms of proximity, calibration efficacy, soil-textural similarity, and topography. Four ARS watersheds, located in Iowa, Oklahoma, Georgia, and Arizona are employed to demonstrate the utility of this approach. The South Fork watershed in Iowa represents the simplest case - the soil textures and topography are relative constants and the variability of soil moisture is simply tied to the spatial variability of precipitation. The Little Washita watershed in Oklahoma adds soil textural variability (but remains topographically simple), while the Little River watershed in Georgia incorporates topographic classification. Finally, the Walnut Gulch watershed in Arizona adds a dense precipitation network to be employed for even finer-scale modeling estimates. Results suggest RMSE values at or below the 4% volumetric standard adopted for the SMAP mission are attainable over the desired spatial scales via this integration of modeling efforts and existing in-situ networks.

Inter-Comparison of SMAP, SMOS and GCOM-W Soil Moisture Products

NASA Astrophysics Data System (ADS)

Bindlish, R.; Jackson, T. J.; Chan, S.; Burgin, M. S.; Colliander, A.; Cosh, M. H.

2016-12-01

The Soil Moisture Active Passive (SMAP) mission was launched on Jan 31, 2015. The goal of the SMAP mission is to produce soil moisture with accuracy better than 0.04 m3/m3 with a revisit frequency of 2-3 days. The validated standard SMAP passive soil moisture product (L2SMP) with a spatial resolution of 36 km was released in May 2016. Soil moisture observations from in situ sensors are typically used to validate the satellite estimates. But, in situ observations provide ground truth for limited amount of landcover and climatic conditions. Although each mission will have its own issues, observations by other satellite instruments can be play a role in the calibration and validation of SMAP. SMAP, SMOS and GCOM-W missions share some commonnalities because they are currently providing operational brightness temperature and soil moisture products. SMAP and SMOS operate at L-band but GCOM-W uses X-band observations for soil moisture estimation. All these missions use different ancillary data sources, parameterization and algorithm to retrieve soil moisture. Therefore, it is important to validate and to compare the consistency of these products. Soil moisture products from the different missions will be compared with the in situ observations. SMAP soil moisture products will be inter-compared at global scales with SMOS and GCOM-W soil moisture products. The major contribution of satellite product inter-comparison is that it allows the assessment of the quality of the products over wider geographical and climate domains. Rigorous assessment will lead to a more reliable and accurate soil moisture product from all the missions.

The SWEX at the area of Eastern Poland: Comparison of soil moisture obtained from ground measurements and SMOS satellite data*

NASA Astrophysics Data System (ADS)

Usowicz, J. B.; Marczewski, W.; Usowicz, B.; Lukowski, M. I.; Lipiec, J.; Slominski, J.

2012-04-01

Soil moisture, together with soil and vegetation characteristics, plays an important role in exchange of water and energy between the land surface and the atmospheric boundary layer. Accurate knowledge of current and future spatial and temporal variation in soil moisture is not well known, nor easy to measure or predict. Knowledge of soil moisture in surface and root zone soil moisture is critical for achieving sustainable land and water management. The importance of SM is so high that this ECV is recommended by GCOS (Global Climate Observing System) to any attempts of evaluating of effects the climate change, and therefore it is one of the goals for observing the Earth by the ESA SMOS Mission (Soil Moisture and Ocean Salinity), globally. SMOS provides its observations by means of the interferometric radiometry method (1.4 GHz) from the orbit. In parallel, ten ground based stations are kept by IA PAN, in area of the Eastern Wall in Poland, in order to validate SMOS data and for other ground based agrophysical purposes. Soil moisture measurements obtained from ground and satellite measurements from SMOS were compared using Bland-Altman method of agreement, concordance correlation coefficient (CCC) and total deviation index (TDI). Observed similar changes in soil moisture, but the values obtained from satellite measurements were lower. Minor differences between the compared data are at higher moisture contents of soil and they grow with decreasing soil moisture. Soil moisture trends are maintained in the individual stations. Such distributions of soil moisture were mainly related to soil type. * The work was financially supported in part by the ESA Programme for European Cooperating States (PECS), No.98084 "SWEX-R, Soil Water and Energy Exchange/Research", AO3275.

Challenges in Ecohydrological Monitoring at Soil-Vegetation Interfaces: Exploiting the Potential for Fibre Optic Technologies

NASA Astrophysics Data System (ADS)

Chalari, A.; Ciocca, F.; Krause, S.; Hannah, D. M.; Blaen, P.; Coleman, T. I.; Mondanos, M.

2015-12-01

The Birmingham Institute of Forestry Research (BIFoR) is using Free-Air Carbon Enrichment (FACE) experiments to quantify the long-term impact and resilience of forests into rising atmospheric CO2 concentrations. The FACE campaign critically relies on a successful monitoring and understanding of the large variety of ecohydrological processes occurring across many interfaces, from deep soil to above the tree canopy. At the land-atmosphere interface, soil moisture and temperature are key variables to determine the heat and water exchanges, crucial to the vegetation dynamics as well as to groundwater recharge. Traditional solutions for monitoring soil moisture and temperature such as remote techniques and point sensors show limitations in fast acquisition rates and spatial coverage, respectively. Hence, spatial patterns and temporal dynamics of heat and water fluxes at this interface can only be monitored to a certain degree, limiting deeper knowledge in dynamically evolving systems (e.g. in impact of growing vegetation). Fibre optics Distributed Temperature Sensors (DTS) can measure soil temperatures at high spatiotemporal resolutions and accuracy, along kilometers of optical cable buried in the soil. Heat pulse methods applied to electrical elements embedded in the optical cable can be used to obtain the soil moisture. In July 2015 a monitoring system based on DTS has been installed in a recently forested hillslope at BIFoR in order to quantify high-resolution spatial patterns and high-frequency temporal dynamics of soil heat fluxes and soil moisture conditions. Therefore, 1500m of optical cables have been carefully deployed in three overlapped loops at 0.05m, 0.25m and 0.4m from the soil surface and an electrical system to send heat pulses along the optical cable has been developed. This paper discussed both, installation and design details along with first results of the soil moisture and temperature monitoring carried out since July 2015. Moreover, interpretations of the collected data to investigate the impact on soil moisture dynamics of i) forest evolution (long timescale), (ii) seasonality and, (iii) high-frequency forcing, are discussed.

Analysis of spatiotemporal soil moisture patterns at the catchment scale using a wireless sensor network

NASA Astrophysics Data System (ADS)

Bogena, Heye R.; Huisman, Johan A.; Rosenbaum, Ulrike; Weuthen, Ansgar; Vereecken, Harry

2010-05-01

Soil water content plays a key role in partitioning water and energy fluxes and controlling the pattern of groundwater recharge. Despite the importance of soil water content, it is not yet measured in an operational way at larger scales. The aim of this paper is to present the potential of real-time monitoring for the analysis of soil moisture patterns at the catchment scale using the recently developed wireless sensor network SoilNet [1], [2]. SoilNet is designed to measure soil moisture, salinity and temperature in several depths (e.g. 5, 20 and 50 cm). Recently, a small forest catchment Wüstebach (~27 ha) has been instrumented with 150 sensor nodes and more than 1200 soil sensors in the framework of the Transregio32 and the Helmholtz initiative TERENO (Terrestrial Environmental Observatories). From August to November 2009, more than 6 million soil moisture measurements have been performed. We will present first results from a statistical and geostatistical analysis of the data. The observed spatial variability of soil moisture corresponds well with the 800-m scale variability described in [3]. The very low scattering of the standard deviation versus mean soil moisture plots indicates that sensor network data shows less artificial soil moisture variations than soil moisture data originated from measurement campaigns. The variograms showed more or less the same nugget effect, which indicates that the sum of the sub-scale variability and the measurement error is rather time-invariant. Wet situations showed smaller spatial variability, which is attributed to saturated soil water content, which poses an upper limit and is typically not strongly variable in headwater catchments with relatively homogeneous soil. The spatiotemporal variability in soil moisture at 50 cm depth was significantly lower than at 5 and 20 cm. This finding indicates that the considerable variability of the top soil is buffered deeper in the soil due to lateral and vertical water fluxes. Topographic features showed the strongest correlation with soil moisture during dry periods, indicating that the control of topography on the soil moisture pattern depends on the soil water status. Interpolation using the external drift kriging method demonstrated that the high sampling density allows capturing the key patterns of soil moisture variation in the Wüstebach catchment. References: [1] Bogena, H.R., J.A. Huisman, C. Oberdörster, H. Vereecken (2007): Evaluation of a low-cost soil water content sensor for wireless network applications. Journal of Hydrology: 344, 32- 42. [2] Rosenbaum, U., Huisman, J.A., Weuthen, A., Vereecken, H. and Bogena, H.R. (2010): Quantification of sensor-to-sensor variability of the ECH2O EC-5, TE and 5TE sensors in dielectric liquids. Accepted for publication in Vadose Zone Journal (09/2009). [3] Famiglietti J.S., D. Ryu, A. A. Berg, M. Rodell and T. J. Jackson (2008), Field observations of soil moisture variability across scales, Water Resour. Res. 44, W01423, doi:10.1029/2006WR005804.

Plant hydraulics improves and topography mediates prediction of aspen mortality in southwestern USA.

PubMed

Tai, Xiaonan; Mackay, D Scott; Anderegg, William R L; Sperry, John S; Brooks, Paul D

2017-01-01

Elevated forest mortality has been attributed to climate change-induced droughts, but prediction of spatial mortality patterns remains challenging. We evaluated whether introducing plant hydraulics and topographic convergence-induced soil moisture variation to land surface models (LSM) can help explain spatial patterns of mortality. A scheme predicting plant hydraulic safety loss from soil moisture was developed using field measurements and a plant physiology-hydraulics model, TREES. The scheme was upscaled to Populus tremuloides forests across Colorado, USA, using LSM-modeled and topography-mediated soil moisture, respectively. The spatial patterns of hydraulic safety loss were compared against aerial surveyed mortality. Incorporating hydraulic safety loss raised the explanatory power of mortality by 40% compared to LSM-modeled soil moisture. Topographic convergence was mostly influential in suppressing mortality in low and concave areas, explaining an additional 10% of the variations in mortality for those regions. Plant hydraulics integrated water stress along the soil-plant continuum and was more closely tied to plant physiological response to drought. In addition to the well-recognized topo-climate influence due to elevation and aspect, we found evidence that topographic convergence mediates tree mortality in certain parts of the landscape that are low and convergent, likely through influences on plant-available water. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

A comparison between evapotranspiration estimates based on remotely sensed surface energy balance and ground-based soil water balance analyses

USDA-ARS?s Scientific Manuscript database

Remotely sensed and in-situ data were used to investigate dynamics of root zone soil moisture and evapotranspiration (ET) at four Mesonet stations in north-central Oklahoma over an 11-year period (2000-2010). Two moisture deficit indicators based on soil matric potential had spatial and temporal pat...

Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed

NASA Technical Reports Server (NTRS)

Santanello, Joseph A.; Peters-Lidard, Christa D.; Garcia, Matthew E.; Mocko, David M.; Tischler, Michael A.; Moran, M. Susan; Thoma, D. P.

2007-01-01

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.

Topographic soil wetness index derived from combined Alaska-British Columbia datasets for the Gulf of Alaska region

NASA Astrophysics Data System (ADS)

D'Amore, D. V.; Biles, F. E.

2016-12-01

The flow of water is often highlighted as a priority in land management planning and assessments related to climate change. Improved measurement and modeling of soil moisture is required to develop predictive estimates for plant distributions, soil moisture, and snowpack, which all play important roles in ecosystem planning in the face of climate change. Drainage indexes are commonly derived from GIS tools with digital elevation models. Soil moisture classes derived from these tools are useful digital proxies for ecosystem functions associated with the concentration of water on the landscape. We developed a spatially explicit topographically derived soil wetness index (TWI) across the perhumid coastal temperate rainforest (PCTR) of Alaska and British Columbia. Developing applicable drainage indexes in complex terrain and across broad areas required careful application of the appropriate DEM, caution with artifacts in GIS covers and mapping realistic zones of wetlands with the indicator. The large spatial extent of the model has facilitated the mapping of forest habitat and the development of water table depth mapping in the region. A key element of the TWI is the merging of elevation datasets across the US-Canada border where major rivers transect the international boundary. The unified TWI allows for seemless mapping across the international border and unified ecological applications. A python program combined with the unified DEM allows end users to quickly apply the TWI to all areas of the PCTR. This common platform can facilitate model comparison and improvements to local soil moisture conditions, generation of streamflow, and ecological site conditions. In this presentation we highlight the application of the TWI for mapping risk factors related to forest decline and the development of a regional water table depth map. Improved soil moisture maps are critical for deriving spatial models of changes in soil moisture for both plant growth and streamflow across future climate conditions.

Impact of microwave derived soil moisture on hydrologic simulations using a spatially distributed water balance model

NASA Technical Reports Server (NTRS)

Lin, D. S.; Wood, E. F.; Famiglietti, J. S.; Mancini, M.

1994-01-01

Spatial distributions of soil moisture over an agricultural watershed with a drainage area of 60 ha were derived from two NASA microwave remote sensors, and then used as a feedback to determine the initial condition for a distributed water balance model. Simulated hydrologic fluxes over a period of twelve days were compared with field observations and with model predictions based on a streamflow derived initial condition. The results indicated that even the low resolution remotely sensed data can improve the hydrologic model's performance in simulating the dynamics of unsaturated zone soil moisture. For the particular watershed under study, the simulated water budget was not sensitive to the resolutions of the microwave sensors.

Historical climate controls soil respiration responses to current soil moisture.

PubMed

Hawkes, Christine V; Waring, Bonnie G; Rocca, Jennifer D; Kivlin, Stephanie N

2017-06-13

Ecosystem carbon losses from soil microbial respiration are a key component of global carbon cycling, resulting in the transfer of 40-70 Pg carbon from soil to the atmosphere each year. Because these microbial processes can feed back to climate change, understanding respiration responses to environmental factors is necessary for improved projections. We focus on respiration responses to soil moisture, which remain unresolved in ecosystem models. A common assumption of large-scale models is that soil microorganisms respond to moisture in the same way, regardless of location or climate. Here, we show that soil respiration is constrained by historical climate. We find that historical rainfall controls both the moisture dependence and sensitivity of respiration. Moisture sensitivity, defined as the slope of respiration vs. moisture, increased fourfold across a 480-mm rainfall gradient, resulting in twofold greater carbon loss on average in historically wetter soils compared with historically drier soils. The respiration-moisture relationship was resistant to environmental change in field common gardens and field rainfall manipulations, supporting a persistent effect of historical climate on microbial respiration. Based on these results, predicting future carbon cycling with climate change will require an understanding of the spatial variation and temporal lags in microbial responses created by historical rainfall.

Evaluation of a Soil Moisture Data Assimilation System Over West Africa

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Crow, W.; Zhan, X.; Jackson, T.; Reynolds, C.

2009-05-01

A crucial requirement of global crop yield forecasts by the U.S. Department of Agriculture (USDA) International Production Assessment Division (IPAD) is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogeneity and dynamic nature of precipitation events and resulting soil moisture, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult. IPAD estimates global soil moisture using daily estimates of minimum and maximum temperature and precipitation applied to a modified Palmer two-layer soil moisture model which calculates the daily amount of soil moisture withdrawn by evapotranspiration and replenished by precipitation. We attempt to improve upon the existing system by 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 soil moisture model. This work aims at evaluating the utility of merging satellite-retrieved soil moisture estimates with the IPAD two-layer soil moisture model used within the DBMS. We present a quantitative analysis of the assimilated soil moisture product over West Africa (9°N- 20°N; 20°W-20°E). This region contains many key agricultural areas and has a high agro- meteorological gradient from desert and semi-arid vegetation in the North, to grassland, trees and crops in the South, thus providing an ideal location for evaluating the assimilated soil moisture product over multiple land cover types and conditions. A data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing assimilated soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline 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.

Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

NASA Astrophysics Data System (ADS)

Akbar, Ruzbeh; Short Gianotti, Daniel; McColl, Kaighin A.; Haghighi, Erfan; Salvucci, Guido D.; Entekhabi, Dara

2018-03-01

The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface-only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface-only soil moisture observations. To proceed, first an observation-based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry-downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root-mean-squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation-driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge-corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east-west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.

Empirical relationships between soil moisture, albedo, and the planetary boundary layer height: a two-layer bucket model approach

NASA Astrophysics Data System (ADS)

Sanchez-Mejia, Z. M.; Papuga, S. A.

2013-12-01

In semiarid regions, where water resources are limited and precipitation dynamics are changing, understanding land surface-atmosphere interactions that regulate the coupled soil moisture-precipitation system is key for resource management and planning. We present a modeling approach to study soil moisture and albedo controls on planetary boundary layer height (PBLh). We used data from the Santa Rita Creosote Ameriflux site and Tucson Airport atmospheric sounding to generate empirical relationships between soil moisture, albedo and PBLh. We developed empirical relationships and show that at least 50% of the variation in PBLh can be explained by soil moisture and albedo. Then, we used a stochastically driven two-layer bucket model of soil moisture dynamics and our empirical relationships to model PBLh. We explored soil moisture dynamics under three different mean annual precipitation regimes: current, increase, and decrease, to evaluate at the influence on soil moisture on land surface-atmospheric processes. While our precipitation regimes are simple, they represent future precipitation regimes that can influence the two soil layers in our conceptual framework. For instance, an increase in annual precipitation, could impact on deep soil moisture and atmospheric processes if precipitation events remain intense. We observed that the response of soil moisture, albedo, and the PBLh will depend not only on changes in annual precipitation, but also on the frequency and intensity of this change. We argue that because albedo and soil moisture data are readily available at multiple temporal and spatial scales, developing empirical relationships that can be used in land surface - atmosphere applications are of great value.

Improving soil moisture simulation to support Agricultural Water Resource Management using Satellite-based water cycle observations

NASA Astrophysics Data System (ADS)

Gupta, Manika; Bolten, John; Lakshmi, Venkat

2016-04-01

Efficient and sustainable irrigation systems require optimization of operational parameters such as irrigation amount which are dependent on the soil hydraulic parameters that affect the model's accuracy in simulating soil water content. However, it is a scientific challenge to provide reliable estimates of soil hydraulic parameters and irrigation estimates, given the absence of continuously operating soil moisture and rain gauge network. For agricultural water resource management, the in-situ measurements of soil moisture are currently limited to discrete measurements at specific locations, and such point-based measurements do not represent the spatial distribution at a larger scale accurately, as soil moisture is highly variable both spatially and temporally (Wang and Qu 2009). In the current study, flood irrigation scheme within the land surface model is triggered when the root-zone soil moisture deficit reaches below a threshold of 25%, 50% and 75% with respect to the maximum available water capacity (difference between field capacity and wilting point) and applied until the top layer is saturated. An additional important criterion needed to activate the irrigation scheme is to ensure that it is irrigation season by assuming that the greenness vegetation fraction (GVF) of the pixel exceed 0.40 of the climatological annual range of GVF (Ozdogan et al. 2010). The main hypothesis used in this study is that near-surface remote sensing soil moisture data contain useful information that can describe the effective hydrological conditions of the basin such that when appropriately inverted, it would provide field capacity and wilting point soil moisture, which may be representative of that basin. Thus, genetic algorithm inverse method is employed to derive the effective parameters and derive the soil moisture deficit for the root zone by coupling of AMSR-E soil moisture with the physically based hydrological model. Model performance is evaluated using MODIS-evapotranspiration (ET) and MODIS land surface temperature (LST) products. The soil moisture estimates for the root zone are also validated with the in-situ field data, for three sites (2- irrigated and 1- rainfed) located at the University of Nebraska Agricultural Research and Development Center near Mead, NE and monitored by three AmeriFlux installations (Verma et al., 2005) by evaluating the root mean square error (RMSE) and Mean Bias error (MBE).

Hydrologic responses to restored wildfire regimes revealed by soil moisture-vegetation relationships

NASA Astrophysics Data System (ADS)

Boisramé, Gabrielle; Thompson, Sally; Stephens, Scott

2018-02-01

Many forested mountain watersheds worldwide evolved with frequent fire, which Twentieth Century fire suppression activities eliminated, resulting in unnaturally dense forests with high water demand. Restoration of pre-suppression forest composition and structure through a variety of management activities could improve forest resilience and water yields. This study explores the potential for "managed wildfire", whereby naturally ignited fires are allowed to burn, to alter the water balance. Interest in this type of managed wildfire is increasing, yet its long-term effects on water balance are uncertain. We use soil moisture as a spatially-distributed hydrologic indicator to assess the influence of vegetation, fire history and landscape position on water availability in the Illilouette Creek Basin in Yosemite National Park. Over 6000 manual surface soil moisture measurements were made over a period of three years, and supplemented with continuous soil moisture measurements over the top 1m of soil in three sites. Random forest and linear mixed effects models showed a dominant effect of vegetation type and history of vegetation change on measured soil moisture. Contemporary and historical vegetation maps were used to upscale the soil moisture observations to the basin and infer soil moisture under fire-suppressed conditions. Little change in basin-averaged soil moisture was inferred due to managed wildfire, but the results indicated that large localized increases in soil moisture had occurred, which could have important impacts on local ecology or downstream flows.

Downscaling Soil Moisture in the Southern Great Plains Through a Calibrated Multifractal Model for Land Surface Modeling Applications

NASA Technical Reports Server (NTRS)

Mascaro, Giuseppe; Vivoni, Enrique R.; Deidda, Roberto

2010-01-01

Accounting for small-scale spatial heterogeneity of soil moisture (theta) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft!based (800 m) theta estimates collected during the southern Great Plains experiment in 1997 (SGP97).We first demonstrate the presence of scale invariance and multifractality in theta fields of nine square domains of size 25.6 x 25.6 sq km, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale theta distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the theta fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional downscaling relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale theta distribution required by land surface models and data assimilation systems.

Water content estimated from point scale to plot scale

NASA Astrophysics Data System (ADS)

Akyurek, Z.; Binley, A. M.; Demir, G.; Abgarmi, B.

2017-12-01

Soil moisture controls the portioning of rainfall into infiltration and runoff. Here we investigate measurements of soil moisture using a range of techniques spanning different spatial scales. In order to understand soil water content in a test basin, 512 km2 in area, in the south of Turkey, a Cosmic Ray CRS200B soil moisture probe was installed at elevation of 1459 m and an ML3 ThetaProbe (CS 616) soil moisture sensor was established at 5cm depth used to get continuous soil moisture. Neutron count measurements were corrected for the changes in atmospheric pressure, atmospheric water vapour and intensity of incoming neutron flux. The calibration of the volumetric soil moisture was performed, from the laboratory analysis, the bulk density varies between 1.719 (g/cm3) -1.390 (g/cm3), and the dominant soil texture is silty clay loam and silt loamThe water content reflectometer was calibrated for soil-specific conditions and soil moisture estimates were also corrected with respect to soil temperature. In order to characterize the subsurface, soil electrical resistivity tomography was used. Wenner and Schlumberger array geometries were used with electrode spacing varied from 1m- 5 m along 40 m and 200 m profiles. From the inversions of ERT data it is apparent that within 50 m distance from the CRS200B, the soil is moderately resistive to a depth of 2m and more conductive at greater depths. At greater distances from the CRS200B, the ERT results indicate more resistive soils. In addition to the ERT surveys, ground penetrating radar surveys using a common mid-point configuration was used with 200MHz antennas. The volumetric soil moisture obtained from GPR appears to overestimate those based on TDR observations. The values obtained from CS616 (at a point scale) and CRS200B (at a mesoscale) are compared with the values obtained at a plot scale. For the field study dates (20-22.06.2017) the volumetric moisture content obtained from CS616 were 25.14%, 25.22% and 25.96% respectively. The values obtained from CRS200B were 23.23%, 22.81% and 23.26% for the same dates. Whereas the values obtained from GPR were between 32%-44%. Soil moisture observed by CRS200B is promising to monitor the water content in the soil at the mesoscale and ERT surveys help to understand the spatial variability of the soil water content within the footprint of CRS200B.

Validation of Distributed Soil Moisture: Airborne Polarimetric SAR vs. Ground-based Sensor Networks

NASA Astrophysics Data System (ADS)

Jagdhuber, T.; Kohling, M.; Hajnsek, I.; Montzka, C.; Papathanassiou, K. P.

2012-04-01

The knowledge of spatially distributed soil moisture is highly desirable for an enhanced hydrological modeling in terms of flood prevention and for yield optimization in combination with precision farming. Especially in mid-latitudes, the growing agricultural vegetation results in an increasing soil coverage along the crop cycle. For a remote sensing approach, this vegetation influence has to be separated from the soil contribution within the resolution cell to extract the actual soil moisture. Therefore a hybrid decomposition was developed for estimation of soil moisture under vegetation cover using fully polarimetric SAR data. The novel polarimetric decomposition combines a model-based decomposition, separating the volume component from the ground components, with an eigen-based decomposition of the two ground components into a surface and a dihedral scattering contribution. Hence, this hybrid decomposition, which is based on [1,2], establishes an innovative way to retrieve soil moisture under vegetation. The developed inversion algorithm for soil moisture under vegetation cover is applied on fully polarimetric data of the TERENO campaign, conducted in May and June 2011 for the Rur catchment within the Eifel/Lower Rhine Valley Observatory. The fully polarimetric SAR data were acquired in high spatial resolution (range: 1.92m, azimuth: 0.6m) by DLR's novel F-SAR sensor at L-band. The inverted soil moisture product from the airborne SAR data is validated with corresponding distributed ground measurements for a quality assessment of the developed algorithm. The in situ measurements were obtained on the one hand by mobile FDR probes from agricultural fields near the towns of Merzenhausen and Selhausen incorporating different crop types and on the other hand by distributed wireless sensor networks (SoilNet clusters) from a grassland test site (near the town of Rollesbroich) and from a forest stand (within the Wüstebach sub-catchment). Each SoilNet cluster incorporates around 150 wireless measuring devices on a grid of approximately 30ha for distributed soil moisture sensing. Finally, the comparison of both distributed soil moisture products results in a discussion on potentials and limitations for obtaining soil moisture under vegetation cover with high resolution fully polarimetric SAR. [1] S.R. Cloude, Polarisation: applications in remote sensing. Oxford, Oxford University Press, 2010. [2] Jagdhuber, T., Hajnsek, I., Papathanassiou, K.P. and Bronstert, A.: A Hybrid Decomposition for Soil Moisture Estimation under Vegetation Cover Using Polarimetric SAR. Proc. of the 5th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, ESA-ESRIN, Frascati, Italy, January 24-28, 2011, p.1-6.

The Soil Moisture Active and Passive Mission (SMAP): Science and Applications

NASA Technical Reports Server (NTRS)

Entekhabi, Dara; O'Neill, Peggy; Njoku, Eni

2009-01-01

The Soil Moisture Active and Passive mission (SMAP) will provide global maps of soil moisture content and surface freeze/thaw state. Global measurements of these variables are critical for terrestrial water and carbon cycle applications. The SMAP observatory consists of two multipolarization L-band sensors, a radar and radiometer, that share a deployable-mesh reflector antenna. The combined observations from the two sensors will allow accurate estimation of soil moisture at hydrometeorological (10 km) and hydroclimatological (40 km) spatial scales. The rotating antenna configuration provides conical scans of the Earth surface at a constant look angle. The wide-swath (1000 km) measurements will allow global mapping of soil moisture and its freeze/thaw state with 2-3 days revisit. Freeze/thaw in boreal latitudes will be mapped using the radar at 3 km resolution with 1-2 days revisit. The synergy of active and passive observations enables measurements of soil moisture and freeze/thaw state with unprecedented resolution, sensitivity, area coverage and revisit.

PALS (Passive Active L-band System) Radiometer-Based Soil Moisture Retrieval for the SMAP Validation Experiment 2012 (SMAPVEX12)

NASA Astrophysics Data System (ADS)

Colliander, A.; Jackson, T. J.; Chan, S.; Bindlish, R.; O'Neill, P. E.; Chazanoff, S. L.; McNairn, H.; Bullock, P.; Powers, J.; Wiseman, G.; Berg, A. A.; Magagi, R.; Njoku, E. G.

2014-12-01

NASA's (National Aeronautics and Space Administration) Soil Moisture Active Passive (SMAP) mission is scheduled for launch in early January 2015. For pre-launch soil moisture algorithm development and validation, the SMAP project and NASA coordinated a SMAP Validation Experiment 2012 (SMAPVEX12) together with Agriculture and Agri-Food Canada in the vicinity of Winnipeg, Canada in June 7-July 19, 2012. Coincident active and passive airborne L-band data were acquired using the Passive Active L-band System (PALS) on 17 days during the experiment. Simultaneously with the PALS measurements, soil moisture ground truth data were collected manually. The vegetation and surface roughness were sampled on non-flight days. The SMAP mission will produce surface (top 5 cm) soil moisture products a) using a combination of its L-band radiometer and SAR (Synthetic Aperture Radar) measurements, b) using the radiometer measurement only, and c) using the SAR measurements only. The SMAPVEX12 data are being utilized for the development and testing of the algorithms applied for generating these soil moisture products. This talk will focus on presenting results of retrieving surface soil moisture using the PALS radiometer. The issues that this retrieval faces are very similar to those faced by the global algorithm using the SMAP radiometer. However, the different spatial resolution of the two observations has to be accounted for in the analysis. The PALS 3 dB footprint in the experiment was on the order of 1 km, whereas the SMAP radiometer has a footprint of about 40 km. In this talk forward modeled brightness temperature over the manually sampled fields and the retrieved soil moisture over the entire experiment domain are presented and discussed. In order to provide a retrieval product similar to that of the SMAP passive algorithm, various ancillary information had to be obtained for the SMAPVEX12 domain. In many cases there are multiple options on how to choose and reprocess these data. The derivation of these data elements and their impact on the retrieval and the spatial scales of the different observations are also discussed. In particular, land cover and soil type heterogeneity have a dramatic impact on parameterization of the algorithm when going from finer to coarser spatial resolutions.

Use of soil moisture dynamics and patterns for the investigation of runoff generation processes with emphasis on preferential flow

NASA Astrophysics Data System (ADS)

Blume, T.; Zehe, E.; Bronstert, A.

2007-08-01

Spatial patterns as well as temporal dynamics of soil moisture have a major influence on runoff generation. The investigation of these dynamics and patterns can thus yield valuable information on hydrological processes, especially in data scarce or previously ungauged catchments. The combination of spatially scarce but temporally high resolution soil moisture profiles with episodic and thus temporally scarce moisture profiles at additional locations provides information on spatial as well as temporal patterns of soil moisture at the hillslope transect scale. This approach is better suited to difficult terrain (dense forest, steep slopes) than geophysical techniques and at the same time less cost-intensive than a high resolution grid of continuously measuring sensors. Rainfall simulation experiments with dye tracers while continuously monitoring soil moisture response allows for visualization of flow processes in the unsaturated zone at these locations. Data was analyzed at different spacio-temporal scales using various graphical methods, such as space-time colour maps (for the event and plot scale) and indicator maps (for the long-term and hillslope scale). Annual dynamics of soil moisture and decimeter-scale variability were also investigated. The proposed approach proved to be successful in the investigation of flow processes in the unsaturated zone and showed the importance of preferential flow in the Malalcahuello Catchment, a data-scarce catchment in the Andes of Southern Chile. Fast response times of stream flow indicate that preferential flow observed at the plot scale might also be of importance at the hillslope or catchment scale. Flow patterns were highly variable in space but persistent in time. The most likely explanation for preferential flow in this catchment is a combination of hydrophobicity, small scale heterogeneity in rainfall due to redistribution in the canopy and strong gradients in unsaturated conductivities leading to self-reinforcing flow paths.

Use of soil moisture dynamics and patterns at different spatio-temporal scales for the investigation of subsurface flow processes

NASA Astrophysics Data System (ADS)

Blume, T.; Zehe, E.; Bronstert, A.

2009-07-01

Spatial patterns as well as temporal dynamics of soil moisture have a major influence on runoff generation. The investigation of these dynamics and patterns can thus yield valuable information on hydrological processes, especially in data scarce or previously ungauged catchments. The combination of spatially scarce but temporally high resolution soil moisture profiles with episodic and thus temporally scarce moisture profiles at additional locations provides information on spatial as well as temporal patterns of soil moisture at the hillslope transect scale. This approach is better suited to difficult terrain (dense forest, steep slopes) than geophysical techniques and at the same time less cost-intensive than a high resolution grid of continuously measuring sensors. Rainfall simulation experiments with dye tracers while continuously monitoring soil moisture response allows for visualization of flow processes in the unsaturated zone at these locations. Data was analyzed at different spacio-temporal scales using various graphical methods, such as space-time colour maps (for the event and plot scale) and binary indicator maps (for the long-term and hillslope scale). Annual dynamics of soil moisture and decimeter-scale variability were also investigated. The proposed approach proved to be successful in the investigation of flow processes in the unsaturated zone and showed the importance of preferential flow in the Malalcahuello Catchment, a data-scarce catchment in the Andes of Southern Chile. Fast response times of stream flow indicate that preferential flow observed at the plot scale might also be of importance at the hillslope or catchment scale. Flow patterns were highly variable in space but persistent in time. The most likely explanation for preferential flow in this catchment is a combination of hydrophobicity, small scale heterogeneity in rainfall due to redistribution in the canopy and strong gradients in unsaturated conductivities leading to self-reinforcing flow paths.

Soil moisture mapping at Bubnow Wetland using L-band radiometer (ELBARA III)

NASA Astrophysics Data System (ADS)

Łukowski, Mateusz; Schwank, Mike; Szlązak, Radosław; Wiesmann, Andreas; Marczewski, Wojciech; Usowicz, Bogusław; Usowicz, Jerzy; Rojek, Edyta; Werner, Charles

2016-04-01

The study of soil moisture is a scientific challenge. Not only because of large diversity of soils and differences in their water content, but also due to the difficulty of measuring, especially in large scale. On this field of interest several methods to determine the content of water in soil exists. The basic and referential is gravimetric method, which is accurate, but suitable only for small spatial scales and time-consuming. Indirect methods are faster, but need to be validated, for example those based on dielectric properties of materials (e.g. time domain reflectometry - TDR) or made from distance (remote), like brightness temperature measurements. Remote sensing of soil moisture can be performed locally (from towers, drones, planes etc.) or globally (satellites). These techniques can complement and help to verify different models and assumptions. In our studies, we applied spatial statistics to local soil moisture mapping using ELBARA III (ESA L-band radiometer, 1.4 GHz) mounted on tower (6.5 meter height). Our measurements were carried out in natural Bubnow Wetland, near Polesie National Park (Eastern Poland), during spring time. This test-site had been selected because it is representative for one of the biggest wetlands in Europe (1400 km2), called "Western Polesie", localized in Ukraine, Poland and Belarus. We have investigated Bubnow for almost decade, using meteorological and soil moisture stations, conducting campaigns of hand-held measurements and collecting soil samples. Now, due to the possibility of rotation at different incidence angles (as in previous ELBARA systems) and the new azimuth tracking capabilities, we obtained brightness temperature data not only at different distances from the tower, but also around it, in footprints containing different vegetation and soil types. During experiment we collected data at area about 450 m2 by rotating ELBARA's antenna 5-175° in horizontal and 30-70° in vertical plane. This type of approach allows combining multiple independent measurements (performed nearly simultaneously) to one consistent soil moisture map. Spatial statistics helps with correcting blind spots or distortions causes by assembly elements, especially on corners of ELBARA's tower. Moreover, using this technique we can observe distribution of soil moisture with time dependency. In order to validate our data, the results were compared with measurements obtained by means of the TDR method. The presented approach enables better understanding the soil moisture spatial distribution over a particular local area of interests, before extending soil water assessments on larger areas. The work was partially funded under two ESA projects: 1) "ELBARA_PD (Penetration Depth)" No. 4000107897/13/NL/KML, funded by the Government of Poland through an ESA-PECS contract (Plan for European Cooperating States) 2) "Technical Support for the fabrication and deployment of the radiometer ELBARA-III in Bubnow, Poland" No. 4000113360/15/NL/FF/gp

The impact of soil moisture extremes and their spatiotemporal variability on Zambian maize yields

NASA Astrophysics Data System (ADS)

Zhao, Y.; Estes, L. D.; Vergopolan, N.

2017-12-01

Food security in sub-Saharan Africa is highly sensitive to climate variability. While it is well understood that extreme heat has substantial negative impacts on crop yield, the impacts of precipitation extremes, particularly over large spatial extents, are harder to quantify. There are three primary reasons for this difficulty, which are (1) lack of high quality, high resolution precipitation data, (2) rainfall data provide incomplete information on plant water availability, the variable that most directly affects crop performance, and (3) the type of rainfall extreme that most affects crop yields varies throughout the crop development stage. With respect to the first reason, the spatial and temporal variation of precipitation is much greater than that of temperature, yet the spatial resolution of rainfall data is typically even coarser than it is for temperature, particularly within Africa. Even if there were high-resolution rainfall data, the amount of water available to crops also depends on other physical factors that affect evapotranspiration, which are strongly influenced by heterogeneity in the land surface related to topography, soil properties, and land cover. In this context, soil moisture provides a better measure of crop water availability than rainfall. Furthermore, soil moisture has significantly different influences on crop yield depending on the crop's growth stage. The goal of this study is to understand how the spatiotemporal scales of soil moisture extremes interact with crops, more specifically, the timing and the spatial scales of extreme events like droughts and flooding. In this study, we simulate daily-1km soil moisture using HydroBlocks - a physically based land surface model - and compare it with precipitation and remote sensing derived maize yields between 2000 and 2016 in Zambia. We use a novel combination of the SCYM (scalable satellite-based yield mapper) method with DSSAT crop model, which is a mechanistic model responsive to water stress. Understanding the relationships between soil moisture spatiotemporal variability and yields can help to improve agricultural drought risk assessment and seasonal crop yield forecasting as well as early season warning of potential famines.

Measuring the spatial variation in surface moisture on a coastal beach with an infra-red terrestrial laser scanner

NASA Astrophysics Data System (ADS)

Smit, Yvonne; Donker, Jasper; Ruessink, Gerben

2016-04-01

Coastal sand dunes provide essential protection against marine flooding. Consequently, dune erosion during severe storms has been studied intensively, resulting in well-developed erosion models for use in scientific and applied projects. Nowadays there is growing awareness that similarly advanced knowledge on dune recovery and growth is needed to predict future dune development. For this reason, aeolian sand transport from the beach into the dunes has to be investigated thoroughly. Surface moisture is a major factor limiting aeolian transport on sandy beaches. By increasing the velocity threshold for sediment entrainment, pick-up rates reduce and the fetch length increases. Conventional measurement techniques cannot adequately characterize the spatial and temporal distribution of surface moisture content required to study the effects on aeolian transport. Here we present a new method for detecting surface moisture at high temporal and spatial resolution using the RIEGL VZ-400 terrestrial laser scanner (TLS). Because this TLS operates at a wavelength near a water absorption band (1550 nm), TLS reflectance is an accurate parameter to measure surface soil moisture over its full range. Three days of intensive laser scanning were performed on a Dutch beach to illustrate the applicability of the TLS. Gravimetric soil moisture samples were used to calibrate the relation between reflectance and surface moisture. Results reveal a robust negative relation for the full range of possible surface moisture contents (0% - 25%). This relation holds to about 80 m from the TLS. Within this distance the TLS typically produces O(106-107) data points, which we averaged into soil moisture maps with a 0.25x0.25 m resolution. This grid size largely removes small moisture disturbances induced by, for example, footprints or tire tracks, while retaining larger scale trends. As the next step in our research, we will analyze the obtained maps to determine which processes affect the spatial and temporal surface-moisture variability.

Integrating effective drought index (EDI) and remote sensing derived parameters for agricultural drought assessment and prediction in Bundelkhand region of India

NASA Astrophysics Data System (ADS)

Padhee, S. K.; Nikam, B. R.; Aggarwal, S. P.; Garg, V.

2014-11-01

Drought is an extreme condition due to moisture deficiency and has adverse effect on society. Agricultural drought occurs when restraining soil moisture produces serious crop stress and affects the crop productivity. The soil moisture regime of rain-fed agriculture and irrigated agriculture behaves differently on both temporal and spatial scale, which means the impact of meteorologically and/or hydrological induced agriculture drought will be different in rain-fed and irrigated areas. However, there is a lack of agricultural drought assessment system in Indian conditions, which considers irrigated and rain-fed agriculture spheres as separate entities. On the other hand recent advancements in the field of earth observation through different satellite based remote sensing have provided researchers a continuous monitoring of soil moisture, land surface temperature and vegetation indices at global scale, which can aid in agricultural drought assessment/monitoring. Keeping this in mind, the present study has been envisaged with the objective to develop agricultural drought assessment and prediction technique by spatially and temporally assimilating effective drought index (EDI) with remote sensing derived parameters. The proposed technique takes in to account the difference in response of rain-fed and irrigated agricultural system towards agricultural drought in the Bundelkhand region (The study area). The key idea was to achieve the goal by utilizing the integrated scenarios from meteorological observations and soil moisture distribution. EDI condition maps were prepared from daily precipitation data recorded by Indian Meteorological Department (IMD), distributed within the study area. With the aid of frequent MODIS products viz. vegetation indices (VIs), and land surface temperature (LST), the coarse resolution soil moisture product from European Space Agency (ESA) were downscaled using linking model based on Triangle method to a finer resolution soil moisture product. EDI and spatially downscaled soil moisture products were later used with MODIS 16 days NDVI product as key elements to assess and predict agricultural drought in irrigated and rain-fed agricultural systems in Bundelkhand region of India. Meteorological drought, soil moisture deficiency and NDVI degradation were inhabited for each and every pixel of the image in GIS environment, for agricultural impact assessment at a 16 day temporal scale for Rabi seasons (October-April) between years 2000 to 2009. Based on the statistical analysis, good correlations were found among the parameters EDI and soil moisture anomaly; NDVI anomaly and soil moisture anomaly lagged to 16 days and these results were exploited for the development of a linear prediction model. The predictive capability of the developed model was validated on the basis of spatial distribution of predicted NDVI which was compared with MODIS NDVI product in the beginning of preceding Rabi season (Oct-Dec of 2010).The predictions of the model were based on future meteorological data (year 2010) and were found to be yielding good results. The developed model have good predictive capability based on future meteorological data (rainfall data) availability, which enhances its utility in analyzing future Agricultural conditions if meteorological data is available.

Evaluation of Remote Sensing and Hydrological Model Based Soil Moisture Datasets in Drought Perspective

NASA Astrophysics Data System (ADS)

Hüsami Afşar, M.; Bulut, B.; Yilmaz, M. T.

2017-12-01

Soil moisture is one of the fundamental parameters of the environment that plays a major role in carbon, energy, and water cycles. Spatial distribution and temporal changes of soil moisture is one of the important components in climatic, ecological and natural hazards at global, regional and local levels scales. Therefore retrieval of soil moisture datasets has a great importance in these studies. Given soil moisture can be retrieved through different platforms (i.e., in-situ measurements, numerical modeling, and remote sensing) for the same location and time period, it is often desirable to evaluate these different datasets to assign the most accurate estimates for different purposes. During last decades, efforts have been given to provide evaluations about different soil moisture products based on various statistical analysis of the soil moisture time series (i.e., comparison of correlation, bias, and their error standard deviation). On the other hand, there is still need for the comparisons of the soil moisture products in drought analysis context. In this study, LPRM and NOAH Land Surface Model soil moisture datasets are investigated in drought analysis context using station-based watershed average datasets obtained over four USDA ARS watersheds as ground truth. Here, the drought analysis are performed using the standardized soil moisture datasets (i.e., zero mean and one standard deviation) while the droughts are defined as consecutive negative anomalies less than -1 for longer than 3 months duration. Accordingly, the drought characteristics (duration and severity) and false alarm and hit/miss ratios of LPRM and NOAH datasets are validated using station-based datasets as ground truth. Results showed that although the NOAH soil moisture products have better correlations, LPRM based soil moisture retrievals show better consistency in drought analysis. This project is supported by TUBITAK Project number 114Y676.

Evaluation of Crop-Water Consumption Simulation to Support Agricultural Water Resource Management using Satellite-based Water Cycle Observations

NASA Astrophysics Data System (ADS)

Gupta, M.; Bolten, J. D.; Lakshmi, V.

2016-12-01

Water scarcity is one of the main factors limiting agricultural development. Numerical models integrated with remote sensing datasets are increasingly being used operationally as inputs for crop water balance models and agricultural forecasting due to increasing availability of high temporal and spatial resolution datasets. However, the model accuracy in simulating soil water content is affected by the accuracy of the soil hydraulic parameters used in the model, which are used in the governing equations. However, soil databases are known to have a high uncertainty across scales. Also, for agricultural sites, the in-situ measurements of soil moisture are currently limited to discrete measurements at specific locations, and such point-based measurements do not represent the spatial distribution at a larger scale accurately, as soil moisture is highly variable both spatially and temporally. The present study utilizes effective soil hydraulic parameters obtained using a 1-km downscaled microwave remote sensing soil moisture product based on the NASA Advanced Microwave Scanning Radiometer (AMSR-E) using the genetic algorithm inverse method within the Catchment Land Surface Model (CLSM). Secondly, to provide realistic irrigation estimates for agricultural sites, an irrigation scheme within the land surface model is triggered when the root-zone soil moisture deficit reaches the threshold, 50% with respect to the maximum available water capacity obtained from the effective soil hydraulic parameters. An additional important criterion utilized is the estimation of crop water consumption based on dynamic root growth and uptake in root zone layer. Model performance is evaluated using MODIS land surface temperature (LST) product. The soil moisture estimates for the root zone are also validated with the in situ field data, for three sites (2- irrigated and 1- rainfed) located at the University of Nebraska Agricultural Research and Development Center near Mead, NE and monitored by three AmeriFlux installations (Verma et al., 2005).

Geophysical characterization of soil moisture spatial patterns in a tillage experiment

NASA Astrophysics Data System (ADS)

Martinez, G.; Vanderlinden, K.; Giráldez, J. V.; Muriel, J. L.

2009-04-01

Knowledge on the spatial soil moisture pattern can improve the characterisation of the hydrological response of either field-plots or small watersheds. Near-surface geophysical methods, such as electromagnetic induction (EMI), provide a means to map such patterns using non-invasive and non-destructive measurements of the soil apparent electrical conductivity (ECa. In this study ECa was measured using an EMI sensor and used to characterize spatially the hydrologic response of a cropped field to an intense shower. The study site is part of a long-term tillage experiment in Southern Spain in which Conventional Tillage (CT), Direct Drilling (DD) and Minimum Tillage (MT) are being evaluated since 1982. Soil ECa was measured before and after a rain event of 115 mm, near the soil surface and at deeper depth (ECas and ECad, respectively) using the EM38-DD EMI sensor. Simultaneously, elevation data were collected at each sampling point to generate a Digital Elevation Model (DEM). Soil moisture during the first survey was close to permanent wilting point and near field capacity during the second survey. For the first survey, both ECas and ECad, were higher in the CT and MT than in the DD plots. After the rain event, rill erosion appeared only in CT and MT plots were soil was uncovered, matching the drainage lines obtained from the DEM. Apparent electrical conductivity increased all over the field plot with higher increments in the DD plots. These plots showed the highest ECas and ECad values, in contrast to the spatial pattern found during the first sampling. Difference maps obtained from the two ECas and ECad samplings showed a clear difference between DD plots and CT and MT plots due to their distinct hydrologic response. Water infiltration was higher in the soil of the DD plots than in the MT and CT plots, as reflected by their ECad increment. Higher ECa increments were observed in the depressions of the terrain, where water and sediments accumulated. On the contrary, the most elevated places of the field showed lower ECa increments. When soil is wet topography dominates the hydrologic response of the field, while under drier conditions, hydraulic conductivity controls the soil water dynamics. These results show that when static soil properties, e.g. clay content, are spatially uniform, ECa can detect changes in dynamic properties like soil moisture content, characterizing their spatial pattern.

An analysis of soil moisture and vegetation conditions during a period of rapid subseasonal oscillations between drought and pluvials over Texas during 2015

NASA Astrophysics Data System (ADS)

Hunt, E. D.; Otkin, J.; Zhong, Y.

2017-12-01

Flash drought, characterized by the rapid onset of abnormally warm and dry weather conditions that leads to the rapid depletion of soil moisture and rapid deteriorations in vegetation health. Flash recovery, on the other hand, is characterized by a period(s) of intense precipitation where drought conditions are quickly eradicated and may be replaced by saturated soils and flooding. Both flash drought and flash recovery are closely tied to the rapid depletion or recharge of root zone soil moisture; therefore, soil moisture observations are very useful for monitoring their evolution. However, in-situ soil moisture observations tend to be concentrated over small regions and thus other methods are needed to provide a spatially continuous depiction of soil moisture conditions. One option is to use top soil moisture retrievals from the Soil Moisture Active Passive (SMAP) sensor. SMAP provides routine coverage of surface soil moisture (0-5 cm) over most of the globe, including the timespan (2015) and region of interest (Texas) that are the focus of our study. This region had an unusual sequence of flash recovery-flash drought-flash recovery during an six-month period during 2015 that provides a valuable case study of rapid transitions between extreme soil moisture conditions. During this project, SMAP soil moisture retrievals are being used in combination with in-situ soil moisture observations and assimilated into the Land Information System (LIS) to provide information about soil moisture content. LIS also provides greenness vegetation fraction data over large regions. The relationship between soil moisture and vegetation conditions and the response of the vegetation to the rapidly changing conditions are also assessed using the satellite thermal infrared based Evaporative Stress Index (ESI) that depicts anomalies in evapotranspiration, along with other vegetation datasets (leaf area index, greenness fraction) derived using MODIS observations. Preliminary results with the Noah land surface model (inside of LIS) shows that it broadly captured the soil moisture evolution during the 2015 sequence but tended to underestimate the magnitude of soil moisture anomalies. The ESI also showed negative anomalies during the drought. These and other results will be presented at the annual meeting.

Development and Validation of The SMAP Enhanced Passive Soil Moisture Product

NASA Technical Reports Server (NTRS)

Chan, S.; Bindlish, R.; O'Neill, P.; Jackson, T.; Chaubell, J.; Piepmeier, J.; Dunbar, S.; Colliander, A.; Chen, F.; Entekhabi, D.;

Evaluating ESA CCI soil moisture in East Africa.

PubMed

McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R; Wang, Shugong; Peters-Lidard, Christa D; Verdin, James P

2016-06-01

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R>0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

Soil moisture dynamics and dominant controls at different spatial scales over semiarid and semi-humid areas

NASA Astrophysics Data System (ADS)

Suo, Lizhu; Huang, Mingbin; Zhang, Yongkun; Duan, Liangxia; Shan, Yan

2018-07-01

Soil moisture dynamics plays an active role in ecological and hydrological processes, and it depends on a large number of environmental factors, such as topographic attributes, soil properties, land use types, and precipitation. However, studies must still clarify the relative significance of these environmental factors at different soil depths and at different spatial scales. This study aimed: (1) to characterize temporal and spatial variations in soil moisture content (SMC) at four soil layers (0-40, 40-100, 100-200, and 200-500 cm) and three spatial scales (plot, hillslope, and region); and (2) to determine their dominant controls in diverse soil layers at different spatial scales over semiarid and semi-humid areas of the Loess Plateau, China. Given the high co-dependence of environmental factors, partial least squares regression (PLSR) was used to detect relative significance among 15 selected environmental factors that affect SMC. Temporal variation in SMC decreased with increasing soil depth, and vertical changes in the 0-500 cm soil profile were divided into a fast-changing layer (0-40 cm), an active layer (40-100 cm), a sub-active layer (100-200 cm), and a relatively stable layer (200-500 cm). PLSR models simulated SMC accurately in diverse soil layers at different scales; almost all values for variation in response (R2) and goodness of prediction (Q2) were >0.5 and >0.0975, respectively. Upper and lower layer SMCs were the two most important factors that influenced diverse soil layers at three scales, and these SMC variables exhibited the highest importance in projection (VIP) values. The 7-day antecedent precipitation and 7-day antecedent potential evapotranspiration contributed significantly to SMC only at the 0-40 cm soil layer. VIP of soil properties, especially sand and silt content, which influenced SMC strongly, increased significantly after increasing the measured scale. Mean annual precipitation and potential evapotranspiration also influenced SMC at the regional scale significantly. Overall, this study indicated that dominant controls of SMC varied among three spatial scales on the Loess Plateau, and VIP was a function of spatial scale and soil depth.

Understanding tree growth in response to moisture variability: Linking 32 years of satellite based soil moisture observations with tree rings

NASA Astrophysics Data System (ADS)

Albrecht, Franziska; Dorigo, Wouter; Gruber, Alexander; Wagner, Wolfgang; Kainz, Wolfgang

2014-05-01

Climate change induced drought variability impacts global forest ecosystems and forest carbon cycle dynamics. Physiological drought stress might even become an issue in regions generally not considered water-limited. The water balance at the soil surface is essential for forest growth. Soil moisture is a key driver linking precipitation and tree development. Tree ring based analyses are a potential approach to study the driving role of hydrological parameters for tree growth. However, at present two major research gaps are apparent: i) soil moisture records are hardly considered and ii) only a few studies are linking tree ring chronologies and satellite observations. Here we used tree ring chronologies obtained from the International Tree ring Data Bank (ITRDB) and remotely sensed soil moisture observations (ECV_SM) to analyze the moisture-tree growth relationship. The ECV_SM dataset, which is being distributed through ESA's Climate Change Initiative for soil moisture covers the period 1979 to 2010 at a spatial resolution of 0.25°. First analyses were performed for Mongolia, a country characterized by a continental arid climate. We extracted 13 tree ring chronologies suitable for our analysis from the ITRDB. Using monthly satellite based soil moisture observations we confirmed previous studies on the seasonality of soil moisture in Mongolia. Further, we investigated the relationship between tree growth (as reflected by tree ring width index) and remotely sensed soil moisture records by applying correlation analysis. In terms of correlation coefficient a strong response of tree growth to soil moisture conditions of current April to August was observed, confirming a strong linkage between tree growth and soil water storage. The highest correlation was found for current April (R=0.44), indicating that sufficient water supply is vital for trees at the beginning of the growing season. To verify these results, we related the chronologies to reanalysis precipitation and temperature datasets. Precipitation was important during both the current and previous growth season. Temperature showed the strongest correlation for previous (R=0.12) and current October (R=0.21). Hence, our results demonstrated that water supply is most likely limiting tree growth during the growing season, while temperature is determining its length. We are confident that long-term satellite based soil moisture observations can bridge spatial and temporal limitations that are inherent to in situ measurements, which are traditionally used for tree ring research. Our preliminary results are a foundation for further studies linking remotely sensed datasets and tree ring chronologies, an approach that has not been widely investigated among the scientific community.

Multi-profile analysis of soil moisture within the U.S. Climate Reference Network

USDA-ARS?s Scientific Manuscript database

Soil moisture estimates are crucial for hydrologic modeling and agricultural decision-support efforts. These measurements are also pivotal for long-term inquiries regarding the impacts of climate change and the resulting droughts over large spatial and temporal scales. However, it has only been t...

Microwave Soil Moisture Retrieval Under Trees Using a Modified Tau-Omega Model

USDA-ARS?s Scientific Manuscript database

IPAD is to provide timely and accurate estimates of global crop conditions for use in up-to-date commodity intelligence reports. A crucial requirement of these global crop yield forecasts is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogen...

Factors affecting temporal and spatial soil moisture variation in and adjacent to group selection openings

Treesearch

W.H. McNab

1991-01-01

Soil moisture content was intensively sampled in three, 1-acre blocks containing an opening and surrounding mature upland hardwoods. Openings covering 0.19-0.26 ac were created by group-selection cutting, and they were occupied by 1-year-old trees and shrubs.

Scaling Properties and Spatial Interpolation of Soil Moisture

DTIC Science & Technology

2004-08-24

the sensitivities is useful not only for characterizing soil moisture but also for forecasting the vulnerability of a region’s water cycle to climate...regional water balance was presented that can be used to assess the impact of climatic fluctuations and changes on the water cycle of a region. In

Fine scale climatic and soil variability effects on plant species cover along the Front Range of Colorado, USA

NASA Astrophysics Data System (ADS)

Cumming, William Frank Preston

Fine scale studies are rarely performed to address landscape level responses to microclimatic variability. Is it the timing, distribution, and magnitude of soil temperature and moisture that affects what species emerge each season and, in turn, their resilience to fluctuations in microclimate. For this dissertation research, I evaluated the response of vegetation change to microclimatic variability within two communities over a three year period (2009-2012) utilizing 25 meter transects at two locations along the Front Range of Colorado near Boulder, CO and Golden, CO respectively. To assess microclimatic variability, spatial and temporal autocorrelation analyses were performed with soil temperature and moisture. Species cover was assessed along several line transects and correlated with microclimatic variability. Spatial and temporal autocorrelograms are useful tools in identifying the degree of dependency of soil temperature and moisture on the distance and time between pairs of measurements. With this analysis I found that a meter spatial resolution and two-hour measurements are sufficient to capture the fine scale variability in soil properties throughout the year. By comparing this to in situ measurements of soil properties and species percent cover I found that there are several plant functional types and/or species origin in particular that are more sensitive to variations in temperature and moisture than others. When all seasons, locations, correlations, and regional climate are looked at, it is the month of March that stands out in terms of significance. Additionally, of all of the vegetation types represented at these two sites C4, C3, native, non-native, and forb species seem to be the most sensitive to fluctuations in soil temperature, moisture, and regional climate in the spring season. The steady decline in percent species cover the study period and subsequent decrease in percent species cover and size at both locations may indicate that certain are unable to respond to continually higher temperatures and lower moisture availability that is inevitable with future climatic variability.

Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates

NASA Astrophysics Data System (ADS)

Sánchez-Ruiz, Sergio; Piles, María; Sánchez, Nilda; Martínez-Fernández, José; Vall-llossera, Mercè; Camps, Adriano

2014-08-01

Sensors in the range of visible and near-shortwave-thermal infrared regions can be used in combination with passive microwave observations to provide soil moisture maps at much higher spatial resolution than the original resolution of current radiometers. To do so, a new downscaling algorithm ultimately based on the land surface temperature (LST) - Normalized Difference Vegetation Index (NDVI) - Brightness Temperature (TB) relationship is used, in which shortwave infrared indices are used as vegetation descriptors, instead of the more common near infrared ones. The theoretical basis of those indices, calculated as the normalized ratio of the 1240, 1640 and 2130 nm shortwave infrared (SWIR) bands and the 858 nm near infrared (NIR) band indicate that they are able to provide estimates of the vegetation water content. These so-called water indices extracted from MODIS products, have been used together with MODIS LST, and SMOS TB to improve the spatial resolution of ∼40 km SMOS soil moisture estimates. The aim was to retrieve soil moisture maps with the same accuracy as SMOS, but at the same resolution of the MODIS dataset, i.e., 500 m, which were then compared against in situ measurements from the REMEDHUS network in Spain. Results using two years of SMOS and MODIS data showed a similar performance for the four indices, with slightly better results when using the index derived from the first SWIR band. For the areal-average, a coefficient of correlation (R) of ∼0.61 and ∼0.72 for the morning and afternoon orbits, respectively, and a centered root mean square difference (cRMSD) of ∼0.04 m3 m-3 for both orbits was obtained. A twofold improvement of the current versions of this downscaling approach has been achieved by using more frequent and higher spatial resolution water indexes as vegetation descriptors: (1) the spatial resolution of the resulting soil moisture maps can be enhanced from ∼40 km up to 500 m, and (2) more accurate soil moisture maps (in terms of R and cRMSD) can be obtained, especially in periods of high vegetation activity. The results of this study support the use of high resolution LST and SWIR-based vegetation indices to disaggregate SMOS observations down to 500 m soil moisture maps, meeting the needs of fine-scale hydrological applications.

[Spatial heterogeneity of soil moisture of mountain apple orchards with rainwater collection and infiltration (RWCI) system in the Loess Plateau, China].

PubMed

Song, Xiao Lin; Zhao, Xi Ning; Gao, Xiao Dong; Wu, Pu Te; Ma, Wen; Yao, Jie; Jiang, Xiao Li; Zhang, Wei

2017-11-01

Water scarcity is a critical factor influencing rain-fed agricultural production on the Loess Plateau, and the exploitation of rainwater is an effective avenue to alleviate water scarcity in this area. This study was conducted to investigate the spatial and temporal distribution of soil moisture in the 0-300 cm under a 21-year-old apple orchard with the rainwater collection and infiltration (RWCI) system by using a time domain reflectometer (TDR) probe on the Loess Plateau. The results showed that there was a low soil moisture zone in the 40-80 cm under the CK, and the RWCI system significantly increased soil moisture in this depth interval. Over this depth, the annual average soil moisture under RWCI 40 , RWCI 60 and RWCI 80 was 39.2%, 47.2% and 29.1% higher than that of bare slope (BS) and 75.3%, 85.4% and 62.7% higher than that of CK, respectively. The maximum infiltration depth of water under RWCI 40 , RWCI 60 and RWCI 80 was 80 cm, 120 cm and 180 cm, respectively, and the soil moisture in the 0-60, 0-100 and 0-120 cm was more affected by RWCI 40 , RWCI 60 and RWCI 80 , respectively. Over the whole growth period of apple tree, the maximum value of soil moisture content in the 0-300 cm existed in the RWCI 80 treatment, followed by the RWCI 40 and RWCI 60 treatments. Overall, the RWCI system is an effective meaning of transforming rainwater to available water resources and realizing efficient use of agricultural water on the Loess Plateau.

Trends in soil moisture and real evapotranspiration in Douro River for the period 1980-2010

NASA Astrophysics Data System (ADS)

García-Valdecasas-Ojeda, Matilde; de Franciscis, Sebastiano; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

This study analyzes the evolution of different hydrological variables, such as soil moisture and real evapotranspiration, for the last 30 years, in the Douro Basin, the most extensive basin in the Iberian Peninsula. The different components of the real evaporation, connected to the soil moisture content, can be important when analyzing the intensity of droughts and heat waves, and particularly relevant for the study of the climate change impacts. The real evapotranspiration and soil moisture data are provided by simulations obtained using the Variable Infiltration Capacity (VIC) hydrological model. This model is a large-scale hydrologic model and allows estimates of different variables in the hydrological system of a basin. Land surface is modeled as a grid of large and uniform cells with sub-grid heterogeneity (e.g. land cover), while water influx is local, only depending from the interaction between grid cells and local atmosphere environment. Observational data of temperature and precipitation from Spain02 dataset are used as input variables for VIC model. The simulations have a spatial resolution of about 9 km, and the analysis is carried out on a seasonal time-scale. Additionally, we compare these results with those obtained from a dynamical downscaling driven by ERA-Interim data using the Weather Research and Forecasting (WRF) model, with the same spatial resolution. The results obtained from Spain02 data show a decrease in soil moisture at different parts of the basin during spring and summer, meanwhile soil moisture seems to be increased for autumn. No significant changes are found for real evapotranspiration. Keywords: real evapotranspiration, soil moisture, Douro Basin, trends, VIC, WRF. Acknowledgements: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates

NASA Astrophysics Data System (ADS)

Lievens, H.; Reichle, R. H.; Liu, Q.; De Lannoy, G.; Dunbar, R. S.; Kim, S.; Das, N. N.; Cosh, M. H.; Walker, J. P.; Wagner, W.

2017-12-01

SMAP (Soil Moisture Active and Passive) radiometer observations at 40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model (CLSM) to generate the SMAP Level 4 Soil Moisture product. The use of C-band radar backscatter observations from Sentinel-1 has the potential to add value to the radiance assimilation by increasing the level of spatial detail. The specifications of Sentinel-1 are appealing, particularly its high spatial resolution (5 by 20 m in interferometric wide swath mode) and frequent revisit time (6 day repeat cycle for the Sentinel-1A and Sentinel-1B constellation). However, the shorter wavelength of Sentinel-1 observations implies less sensitivity to soil moisture. This study investigates the value of Sentinel-1 data for hydrologic simulations by assimilating the radar observations into CLSM, either separately from or simultaneously with SMAP radiometer observations. To facilitate the assimilation of the radar observations, CLSM is coupled to the water cloud model, simulating the radar backscatter as observed by Sentinel-1. The innovations, i.e. differences between observations and simulations, are converted into increments to the model soil moisture state through an Ensemble Kalman Filter. The assimilation impact is assessed by comparing 3-hourly, 9 km surface and root-zone soil moisture simulations with in situ measurements from 9 km SMAP core validation sites and sparse networks, from May 2015 to 2017. The Sentinel-1 assimilation consistently improves surface soil moisture, whereas root-zone impacts are mostly neutral. Relatively larger improvements are obtained from SMAP assimilation. The joint assimilation of SMAP and Sentinel-1 observations performs best, demonstrating the complementary value of radar and radiometer observations.

Assessing heterogeneity in soil nitrogen cycling: a plot-scale approach

Treesearch

Peter Baas; Jacqueline E. Mohan; David Markewitz; Jennifer D. Knoepp

2014-01-01

The high level of spatial and temporal heterogeneity in soil N cycling processes hinders our ability to develop an ecosystem-wide understanding of this cycle. This study examined how incorporating an intensive assessment of spatial variability for soil moisture, C, nutrients, and soil texture can better explain ecosystem N cycling at the plot scale. Five sites...

Instrumenting an upland research catchment in Canterbury, New Zealand to study controls on variability of soil moisture, shallow groundwater and streamflow

NASA Astrophysics Data System (ADS)

McMillan, Hilary; Srinivasan, Ms

2015-04-01

Hydrologists recognise the importance of vertical drainage and deep flow paths in runoff generation, even in headwater catchments. Both soil and groundwater stores are highly variable over multiple scales, and the distribution of water has a strong control on flow rates and timing. In this study, we instrumented an upland headwater catchment in New Zealand to measure the temporal and spatial variation in unsaturated and saturated-zone responses. In NZ, upland catchments are the source of much of the water used in lowland agriculture, but the hydrology of such catchments and their role in water partitioning, storage and transport is poorly understood. The study area is the Langs Gully catchment in the North Branch of the Waipara River, Canterbury: this catchment was chosen to be representative of the foothills environment, with lightly managed dryland pasture and native Matagouri shrub vegetation cover. Over a period of 16 months we measured continuous soil moisture at 32 locations and near-surface water table (< 2 m) at 14 locations, as well as measuring flow at 3 stream gauges. The distributed measurement sites were located to allow comparisons between North and South facing locations, near-stream versus hillslope locations, and convergent versus divergent hillslopes. We found that temporal variability is strongly controlled by the climatic seasonal cycle, for both soil moisture and water table, and for both the mean and extremes of their distributions. Groundwater is a larger water storage component than soil moisture, and the difference increases with catchment wetness. The spatial standard deviation of both soil moisture and groundwater is larger in winter than in summer. It peaks during rainfall events due to partial saturation of the catchment, and also rises in spring as different locations dry out at different rates. The most important controls on spatial variability are aspect and distance from stream. South-facing and near-stream locations have higher water tables and more, larger soil moisture wetting events. Typical hydrological models do not explicitly account for aspect, but our results suggest that it is an important factor in hillslope runoff generation. Co-measurement of soil moisture and water table level allowed us to identify interrelationships between the two. Locations where water tables peaked closest to the surface had consistently wetter soils and higher water tables. These wetter sites were the same across seasons. However, temporary patterns of strong soil moisture response to summer storms did not correspond to the wetter sites. Total catchment spatial variability is composed of multiple variability sources, and the dominant type is sensitive to those stores that are close to a threshold such as field capacity or saturation. Therefore, we classified spatial variability as 'summer mode' or 'winter mode'. In summer mode, variability is controlled by shallow processes e.g. interactions of water with soils and vegetation. In winter mode, variability is controlled by deeper processes e.g. groundwater movement and bypass flow. Double flow peaks observed during some events show the direct impact of groundwater variability on runoff generation. Our results suggest that emergent catchment behaviour depends on the combination of these multiple, time varying components of variability.

Remote Sensing for Agriculture, Ecosystems and Hydrology III

NASA Technical Reports Server (NTRS)

Engman, Edwin T.

1999-01-01

The science need for remotely sensed soil moisture has been well established in the hydrologic, climate change and weather forecasting communities. In spite of this well documented science need there are currently no satellite missions flying or funded to make this very important geophysical measurement. There have been a number of experimental aircraft programs that have demonstrated the feasibility of using long wave microwave sensors for estimating soil moisture. Unfortunately, this science driver, soil moisture, imposes very difficult technical requirements for a satellite sensor system. Global soil moisture is driven by a spatial resolution on the order of 20 to 30 km and measurements need to be taken every two to three days to be useful to the science community. The principal sensor to accomplish the soil moisture measurements is an L- band passive microwave radiometer and achieving the spatial and temporal requirements requires a very large antenna. This paper describes the several alternatives to solve the very large antenna challenge and still meet the radiometer sensitivity requirement. The paper also discusses the alternatives considered to obtain the necessary ancillary data for characterizing the surface roughness, the surface temperature and the attenuation affects of vegetation needed to derive the geophysical parameter. Finally, the paper discusses proposed missions and how well they will meet the science requirements.

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

PubMed Central

Zhang, Jialin; Li, Xiuhong; Yang, Rongjin; Liu, Qiang; Zhao, Long; Dou, Baocheng

2017-01-01

In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Kriging method to interpolate with the aid of remote sensing images. The underlying idea is to extend the traditional Kriging by introducing spectral variables, and operating on spatial and spectral combined space. The algorithm has been applied to WSN-measured soil moisture data in HiWATER campaign to generate daily maps from 10 June to 15 July 2012. For comparison, three traditional Kriging methods are applied: Ordinary Kriging (OK), which used WSN data only, Co-kriging and KED, both of which integrated remote sensing data as covariate. Visual inspections indicate that the result from Extended Kriging shows more spatial details than that of OK, Co-kriging, and KED. The Root Mean Square Error (RMSE) of Extended Kriging was found to be the smallest among the four interpolation results. This indicates that the proposed method has advantages in combining remote sensing information and ground measurements in soil moisture interpolation. PMID:28617351

Sensitivity of Land Surface Parameters on Thunderstorm Simulation through HRLDAS-WRF Coupling Mode

NASA Astrophysics Data System (ADS)

Kumar, Dinesh; Kumar, Krishan; Mohanty, U. C.; Kisore Osuri, Krishna

2016-07-01

Land surface characteristics play an important role in large scale, regional and mesoscale atmospheric process. Representation of land surface characteristics can be improved through coupling of mesoscale atmospheric models with land surface models. Mesoscale atmospheric models depend on Land Surface Models (LSM) to provide land surface variables such as fluxes of heat, moisture, and momentum for lower boundary layer evolution. Studies have shown that land surface properties such as soil moisture, soil temperature, soil roughness, vegetation cover, have considerable effect on lower boundary layer. Although, the necessity to initialize soil moisture accurately in NWP models is widely acknowledged, monitoring soil moisture at regional and global scale is a very tough task due to high spatial and temporal variability. As a result, the available observation network is unable to provide the required spatial and temporal data for the most part of the globe. Therefore, model for land surface initializations rely on updated land surface properties from LSM. The solution for NWP land-state initialization can be found by combining data assimilation techniques, satellite-derived soil data, and land surface models. Further, it requires an intermediate step to use observed rainfall, satellite derived surface insolation, and meteorological analyses to run an uncoupled (offline) integration of LSM, so that the evolution of modeled soil moisture can be forced by observed forcing conditions. Therefore, for accurate land-state initialization, high resolution land data assimilation system (HRLDAS) is used to provide the essential land surface parameters. Offline-coupling of HRLDAS-WRF has shown much improved results over Delhi, India for four thunder storm events. The evolution of land surface variables particularly soil moisture, soil temperature and surface fluxes have provided more realistic condition. Results have shown that most of domain part became wetter and warmer after assimilation of soil moisture and soil temperature at the initial condition which helped to improve the exchange fluxes at lower atmospheric level. Mixing ratio were increased along with elevated theta-e at lower level giving a signature of improvement in LDAS experiment leading to a suitable condition for convection. In the analysis, moisture convergence, mixing ratio and vertical velocities have improved significantly in terms of intensity and time lag. Surface variables like soil moisture, soil temperature, sensible heat flux and latent heat flux have progressed in a possible realistic pattern. Above discussion suggests that assimilation of soil moisture and soil temperature improves the overall simulations significantly.

Towards soil property retrieval from space: Proof of concept using in situ observations

NASA Astrophysics Data System (ADS)

Bandara, Ranmalee; Walker, Jeffrey P.; Rüdiger, Christoph

2014-05-01

Soil moisture is a key variable that controls the exchange of water and energy fluxes between the land surface and the atmosphere. However, the temporal evolution of soil moisture is neither easy to measure nor monitor at large scales because of its high spatial variability. This is mainly a result of the local variation in soil properties and vegetation cover. Thus, land surface models are normally used to predict the evolution of soil moisture and yet, despite their importance, these models are based on low-resolution soil property information or typical values. Therefore, the availability of more accurate and detailed soil parameter data than are currently available is vital, if regional or global soil moisture predictions are to be made with the accuracy required for environmental applications. The proposed solution is to estimate the soil hydraulic properties via model calibration to remotely sensed soil moisture observation, with in situ observations used as a proxy in this proof of concept study. Consequently, the feasibility is assessed, and the level of accuracy that can be expected determined, for soil hydraulic property estimation of duplex soil profiles in a semi-arid environment using near-surface soil moisture observations under naturally occurring conditions. The retrieved soil hydraulic parameters were then assessed by their reliability to predict the root zone soil moisture using the Joint UK Land Environment Simulator model. When using parameters that were retrieved using soil moisture observations, the root zone soil moisture was predicted to within an accuracy of 0.04 m3/m3, which is an improvement of ∼0.025 m3/m3 on predictions that used published values or pedo-transfer functions.

Spatial prediction of near surface soil water retention functions using hydrogeophysics

NASA Astrophysics Data System (ADS)

Gibson, J. P.; Franz, T. E.

2017-12-01

The hydrological community often turns to widely available spatial datasets such as SSURGO to characterize the spatial variability of soil across a landscape of interest. This has served as a reasonable first approximation when lacking localized soil data. However, previous work has shown that information loss within land surface models primarily stems from parameterization. Localized soil sampling is both expensive and time intense, and thus a need exists in connecting spatial datasets with ground observations. Given that hydrogeophysics is data-dense, rapid, and relatively easy to adopt, it is a promising technique to help dovetail localized soil sampling with larger spatial datasets. In this work, we utilize 2 geophysical techniques; cosmic ray neutron probe and electromagnetic induction, to identify temporally stable soil moisture patterns. This is achieved by measuring numerous times over a range of wet to dry field conditions in order to apply an empirical orthogonal function. We then present measured water retention functions of shallow cores extracted within each temporally stable zone. Lastly, we use soil moisture patterns as a covariate to predict soil hydraulic properties in areas without measurement and validate using a leave-one-out cross validation analysis. Using these approaches to better constrain soil hydraulic property variability, we speculate that further research can better estimate hydrologic fluxes in areas of interest.

Prediction of near-surface soil moisture at large scale by digital terrain modeling and neural networks.

PubMed

Lavado Contador, J F; Maneta, M; Schnabel, S

2006-10-01

The capability of Artificial Neural Network models to forecast near-surface soil moisture at fine spatial scale resolution has been tested for a 99.5 ha watershed located in SW Spain using several easy to achieve digital models of topographic and land cover variables as inputs and a series of soil moisture measurements as training data set. The study methods were designed in order to determining the potentials of the neural network model as a tool to gain insight into soil moisture distribution factors and also in order to optimize the data sampling scheme finding the optimum size of the training data set. Results suggest the efficiency of the methods in forecasting soil moisture, as a tool to assess the optimum number of field samples, and the importance of the variables selected in explaining the final map obtained.

Aircraft active microwave measurements for estimating soil moisture

NASA Technical Reports Server (NTRS)

Jackson, T. J.; Chang, A.; Schmugge, T. J.

1981-01-01

Both active and passive microwave sensors are sensitive to variations in near-surface soil moisture. The principal advantage of active microwave systems for soil moisture applications is that high spatial resolution can be retained even at satellite attitudes. The considered investigation is concerned with the use of active microwave scatterometers for estimating near-surface soil moisture. Microwave scatterometer data were obtained during a series of three aircraft flights over a group of Oklahoma research watersheds during May 1978. Data were obtained for the C, L, and P bands at angles of incidence between 5 and 50 degrees. The best results were obtained using C band data at incidence angles of 10 and 15 degrees and soil moisture depth of 0 to 15 cm. These results were in excellent agreement with the conclusions of the truck-mounted scatterometer measurement program reported by Ulaby et al. (1978, 1979).

Electrical resistivity surveys to understand vegetation-water interlinkages in a northern latitude headwater catchment

NASA Astrophysics Data System (ADS)

Soulsby, C.; Dick, J.; Tetzlaff, D.; Bradford, J.

2016-12-01

The role of vegetation on the partitioning of precipitation, and the subsequent storage and release of water within the landscape is poorly understood. In particular, the relationship between vegetation and soil moisture is complex and reciprocal. The role of soil moisture as the primary source of water to plants may affect vegetation distribution. In turn, the structure of vegetation canopies may regulate water partitioning into interception, throughfall and steam flow. Such spatial differences in the inputs, together with complex patterns of water uptake from highly distributed root networks can create marked heterogeneity in soil moisture dynamics at small scales. Here, we present a study combining 3D and 2D ERT surveys with soil moisture measurements in a 3.2km upland catchment in the Scottish Highlands to understand influences of different vegetation types on spatio-temporal dynamics in soil moisture. The study focussed on one year of fortnightly ERT surveys to investigate plant-soil-water interactions within the root zone in podzolic soils. Locations were selected in both forest stands of 15m high Scots pine (Pinus sylvestris) and non-forest locations dominated by heather (Calluna vulgaris) shrubs (<0.5m high). These dominant species are typical of forest and non-forest vegetation communities in the Scottish Highlands. Results showed differences in the soil moisture dynamics under the different vegetation types, with heterogeneous patterns in the forested site mainly correlated with canopy cover and mirroring interception losses, with pronounced wetting cycles of the soil surrounding the bole of trees as a consequence of stem flow. Temporal variability in the forested site was greater, probably due to the interception, and increased evapotranspiration losses relative to the heather site, with drying typically being focussed on the areas around the trees, and reflecting the amount of water uptake. Moisture changes in the heather site were fairly heterogeneous are related to micro-topographic affects, lower interception ( 30% compared with 45%) and a smaller microclimatic effect of the canopy which serves to create greater fluctuations in soil moisture. Our results confirm the value in using geophysics to spatially elucidate subsurface plant-soil-water interactions.

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.

Using Historical Precipitation, Temperature, and Runoff Observations to Evaluate Evaporation Formulations in Land Surface Models

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Mahanama, P. P.

2012-01-01

Key to translating soil moisture memory into subseasonal precipitation and air temperature forecast skill is a realistic treatment of evaporation in the forecast system used - in particular, a realistic treatment of how evaporation responds to variations in soil moisture. The inherent soil moisture-evaporation relationships used in today's land surface models (LSMs), however, arguably reflect little more than guesswork given the lack of evaporation and soil moisture data at the spatial scales represented by regional and global models. Here we present a new approach for evaluating this critical aspect of LSMs. Seasonally averaged precipitation is used as a proxy for seasonally-averaged soil moisture, and seasonally-averaged air temperature is used as a proxy for seasonally-averaged evaporation (e.g., more evaporative cooling leads to cooler temperatures) the relationship between historical precipitation and temperature measurements accordingly mimics in certain important ways nature's relationship between soil moisture and evaporation. Additional information on the relationship is gleaned from joint analysis of precipitation and streamflow measurements. An experimental framework that utilizes these ideas to guide the development of an improved soil moisture-evaporation relationship is described and demonstrated.

Historical climate controls soil respiration responses to current soil moisture

PubMed Central

Waring, Bonnie G.; Rocca, Jennifer D.; Kivlin, Stephanie N.

2017-01-01

Ecosystem carbon losses from soil microbial respiration are a key component of global carbon cycling, resulting in the transfer of 40–70 Pg carbon from soil to the atmosphere each year. Because these microbial processes can feed back to climate change, understanding respiration responses to environmental factors is necessary for improved projections. We focus on respiration responses to soil moisture, which remain unresolved in ecosystem models. A common assumption of large-scale models is that soil microorganisms respond to moisture in the same way, regardless of location or climate. Here, we show that soil respiration is constrained by historical climate. We find that historical rainfall controls both the moisture dependence and sensitivity of respiration. Moisture sensitivity, defined as the slope of respiration vs. moisture, increased fourfold across a 480-mm rainfall gradient, resulting in twofold greater carbon loss on average in historically wetter soils compared with historically drier soils. The respiration–moisture relationship was resistant to environmental change in field common gardens and field rainfall manipulations, supporting a persistent effect of historical climate on microbial respiration. Based on these results, predicting future carbon cycling with climate change will require an understanding of the spatial variation and temporal lags in microbial responses created by historical rainfall. PMID:28559315

CO2 efflux from soils with seasonal water repellency

NASA Astrophysics Data System (ADS)

Urbanek, Emilia; Doerr, Stefan H.

2017-10-01

Soil carbon dioxide (CO2) emissions are strongly dependent on pore water distribution, which in turn can be modified by reduced wettability. Many soils around the world are affected by soil water repellency (SWR), which reduces infiltration and results in diverse moisture distribution. SWR is temporally variable and soils can change from wettable to water-repellent and vice versa throughout the year. Effects of SWR on soil carbon (C) dynamics, and specifically on CO2 efflux, have only been studied in a few laboratory experiments and hence remain poorly understood. Existing studies suggest soil respiration is reduced with increasing severity of SWR, but the responses of soil CO2 efflux to varying water distribution created by SWR are not yet known.Here we report on the first field-based study that tests whether SWR indeed reduces soil CO2 efflux, based on in situ measurements carried out over three consecutive years at a grassland and pine forest sites under the humid temperate climate of the UK.Soil CO2 efflux was indeed very low on occasions when soil exhibited consistently high SWR and low soil moisture following long dry spells. Low CO2 efflux was also observed when SWR was absent, in spring and late autumn when soil temperatures were low, but also in summer when SWR was reduced by frequent rainfall events. The highest CO2 efflux occurred not when soil was wettable, but when SWR, and thus soil moisture, was spatially patchy, a pattern observed for the majority of the measurement period. Patchiness of SWR is likely to have created zones with two different characteristics related to CO2 production and transport. Zones with wettable soil or low persistence of SWR with higher proportion of water-filled pores are expected to provide water with high nutrient concentration resulting in higher microbial activity and CO2 production. Soil zones with high SWR persistence, on the other hand, are dominated by air-filled pores with low microbial activity, but facilitating O2 supply and CO2 exchange between the soil and the atmosphere.The effects of soil moisture and SWR on soil CO2 efflux are strongly co-correlated, but the results of this study support the notion that SWR indirectly affects soil CO2 efflux by affecting soil moisture distribution. The appearance of SWR is influenced by moisture and temperature, but once present, SWR influences subsequent infiltration patterns and resulting soil water distribution, which in turn affects respiration. This study demonstrates that SWR can have contrasting effects on CO2 efflux. It can reduce it in dry soil zones by preventing their re-wetting, but, at the field soil scale and when spatially variable, it can also enhance overall CO2 efflux. Spatial variability in SWR and associated soil moisture distribution therefore need to be considered when evaluating the effects of SWR on soil C dynamics under current and predicted future climatic conditions.

Soil carbon content and CO2 flux along a hydrologic gradient in a High-Arctic tundra lake basin, Northwest Greenland

NASA Astrophysics Data System (ADS)

McKnight, J.; Klein, E. S.; Welker, J. M.; Schaeffer, S. M.; Franklin, M.

2015-12-01

High Arctic landscapes are composed of watershed basins that vary in size and ecohydrology, but typically have a plant community complex that ranges from dry tundra to moist tundra to wet sedge systems along water body shorelines. The spatial extent of these plant communities reflects mean annual soil moisture and temperature, and is vulnerable to changes in climate conditions. Soil moisture and temperature significantly influence organic matter microbial activity and decomposition, and can affect the fate of soil carbon in tundra soils. Consequently, due to the unique soil carbon differences between tundra plant communities, shifts in their spatial extent may drive future High Arctic biosphere-atmosphere interactions. Understanding this terrestrial-atmosphere trace gas feedback, however, requires quantification of the rates and patterns of CO2 exchange along soil moisture gradients and the associated soil properties. In summer of 2015, soil CO2 flux rate, soil moisture and temperature were measured along a soil moisture gradient spanning three vegetation zones (dry tundra, wet tundra, and wet grassland) in a snow melt-fed lake basin near Thule Greenland. Mean soil temperature during the 2015 growing season was greater in dry tundra than in wet tundra and wet grassland (13.0 ± 1.2, 7.8 ± 0.8, and 5.5 ± 0.9°C, respectively). Mean volumetric soil moisture differed among all three vegetation zones where the soil moisture gradient ranged from 9 % (dry tundra) to 34 % (wet tundra) to 51 % (wet grassland). Mean soil CO2 flux was significantly greater in the wet grassland (1.7 ± 0.1 μmol m-2 s-1) compared to wet tundra (0.9 ± 0.2 μmol m-2 s-1) and dry tundra (1.2 ± 0.2 μmol m-2 s-1). Soil CO2 flux increased and decreased with seasonal warming and cooling of soil temperature. Although soil temperature was an important seasonal driver of soil CO2 flux rates, differences in mean seasonal soil CO2 flux rates among vegetation zones appeared to be a function of the combined effects of soil temperature and soil moisture conditions. These results suggest that the response of vegetation distribution to shifts in precipitation and warmer climate conditions may have significant implications for release of soil carbon as CO2 in High Arctic tundra ecosystems in Northwest Greenland.

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.

Influence of Soil Heterogeneity on Mesoscale Land Surface Fluxes During Washita '92

NASA Technical Reports Server (NTRS)

Jasinski, Michael F.; Jin, Hao

1998-01-01

The influence of soil heterogeneity on the partitioning of mesoscale land surface energy fluxes at diurnal time scales is investigated over a 10(exp 6) sq km domain centered on the Little Washita Basin, Oklahoma, for the period June 10 - 18, 1992. The sensitivity study is carried out using MM5/PLACE, the Penn State/NCAR MM5 model enhanced with the Parameterization for Land-Atmosphere-Cloud Exchange or PLACE. PLACE is a one-dimensional land surface model possessing detailed plant and soil water physics algorithms, multiple soil layers, and the capacity to model subgrid heterogeneity. A series of 12-hour simulations were conducted with identical atmospheric initialization and land surface characterization but with different initial soil moisture and texture. A comparison then was made of the simulated land surface energy flux fields, the partitioning of net radiation into latent and sensible heat, and the soil moisture fields. Results indicate that heterogeneity in both soil moisture and texture affects the spatial distribution and partitioning of mesoscale energy balance. Spatial averaging results in an overprediction of latent heat flux, and an underestimation of sensible heat flux. In addition to the primary focus on the partitioning of the land surface energy, the modeling effort provided an opportunity to examine the issue of initializing the soil moisture fields for coupled three-dimensional models. For the present case, the initial soil moisture and temperature were determined from off-line modeling using PLACE at each grid box, driven with a combination of observed and assimilated data fields.

Assimilation of Sentinel-1 and SMAP observations to improve GEOS-5 soil moisture

NASA Astrophysics Data System (ADS)

Lievens, Hans; Reichle, Rolf; Wagner, Wolfgang; De Lannoy, Gabrielle; Liu, Qing; Verhoest, Niko

2017-04-01

The SMAP (Soil Moisture Active and Passive) mission carries an L-band radiometer that provides brightness temperature observations at a nominal resolution of 40 km. These radiance observations are routinely assimilated into GEOS-5 (Goddard Earth Observing System version 5) to generate the SMAP Level 4 Soil Moisture product. The use of C-band radar backscatter observations from Sentinel-1 has the potential to add value to the radiance assimilation by increasing the level of spatial detail. The specifications of Sentinel-1 are appealing, particularly its high spatial resolution (5 by 20 m in interferometric wide swath mode) and frequent revisit time (potentially every 3 days for the Sentinel-1A and Sentinel-1B constellation). However, the shorter wavelength of Sentinel-1 observations implies less sensitivity to soil moisture. This study investigates the value of Sentinel-1 data for hydrologic simulations by assimilating the radar observations into GEOS-5, either separately from or simultaneously with SMAP radiometer observations. The assimilation can be performed if either or both Sentinel-1 or SMAP observations are available, and is thus not restricted to synchronised overpasses. To facilitate the assimilation of the radar observations, GEOS-5 is coupled to the water cloud model, simulating the radar backscatter as observed by Sentinel-1. The innovations, i.e. differences between observations and simulations, are converted into increments to the model soil moisture state through an Ensemble Kalman Filter. The model runs are performed at 9-km spatial and 3-hourly temporal resolution, over the period from May 2015 to October 2016. The impact of the assimilation on surface and root-zone soil moisture simulations is assessed using in situ measurements from SMAP core validation sites and sparse networks. The assimilation of Sentinel-1 backscatter is found to consistently improve surface and root-zone soil moisture, relative to the open loop (no assimilation). However, the improvements are less pronounced than those with the assimilation of SMAP observations, likely because of less frequent observations. The best performance was obtained with the simultaneous assimilation of Sentinel-1 and SMAP data, indicating the complementary value of both types of observations for improving hydrologic simulations.

Reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Cui, Y.; Long, D.; Hong, Y.; Zeng, C.; Han, Z.

2016-12-01

Reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau Yaokui Cui, Di Long, Yang Hong, Chao Zeng, and Zhongying Han State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China Abstract: Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the world's third pole. Large-scale consistent and continuous soil moisture datasets are of importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is one of relatively new passive microwave products. The FY-3B/MWRI soil moisture product is reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo using different gap-filling methods. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and the NDVI, LST, and albedo, but also the relationship between the soil moisture and the four-dimensional variation using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R2 larger than 0.63, and RMSE less than 0.1 cm3 cm-3 and bias less than 0.07 cm3 cm-3 for both frozen and unfrozen periods, compared with in-situ measurements in the central TP. The reconstruction method is subsequently applied to generate spatially consistent and temporally continuous surface soil moisture over the TP. The reconstructed FY-3B/MWRI soil moisture product could be valuable in studying meteorology, hydrology, and agriculture over the TP. Keywords: FY-3B/MWRI; Soil moisture; Reconstruction; Tibetan Plateau

Analyzing and Visualizing Precipitation and Soil Moisture in ArcGIS

NASA Technical Reports Server (NTRS)

Yang, Wenli; Pham, Long; Zhao, Peisheng; Kempler, Steve; Wei, Jennifer

2016-01-01

Precipitation and soil moisture are among the most important parameters in many land GIS (Geographic Information System) research and applications. These data are available globally from NASA GES DISC (Goddard Earth Science Data and Information Services Center) in GIS-ready format at 10-kilometer spatial resolution and 24-hour or less temporal resolutions. In this presentation, well demonstrate how rainfall and soil moisture data are used in ArcGIS to analyze and visualize spatiotemporal patterns of droughts and their impacts on natural vegetation and agriculture in different parts of the world.

Data Assimilation of SMAP Observations and the Impact on Weather Forecasts and Heat Stress

NASA Technical Reports Server (NTRS)

Zavodsky, Bradley; Case, Jonathan; Blankenship, Clay; Crosson, William; White, Khristopher

2014-01-01

SPoRT produces real-time LIS soil moisture products for situational awareness and local numerical weather prediction over CONUS, Mesoamerica, and East Africa ?Currently interact/collaborate with operational partners on evaluation of soil moisture products ?Drought/fire ?Extreme heat ?Convective initiation ?Flood and water borne diseases ?Initial efforts to assimilate L2 soil moisture observations from SMOS (as a precursor for SMAP) have been successful ?Active/passive blended product from SMAP will be assimilated similarly and higher spatial resolution should improve on local-scale processes

Large-area Soil Moisture Surveys Using a Cosmic-ray Rover: Approaches and Results from Australia

NASA Astrophysics Data System (ADS)

Hawdon, A. A.; McJannet, D. L.; Renzullo, L. J.; Baker, B.; Searle, R.

2017-12-01

Recent improvements in satellite instrumentation has increased the resolution and frequency of soil moisture observations, and this in turn has supported the development of higher resolution land surface process models. Calibration and validation of these products is restricted by the mismatch of scales between remotely sensed and contemporary ground based observations. Although the cosmic ray neutron soil moisture probe can provide estimates soil moisture at a scale useful for the calibration and validation purposes, it is spatially limited to a single, fixed location. This scaling issue has been addressed with the development of mobile soil moisture monitoring systems that utilizes the cosmic ray neutron method, typically referred to as a `rover'. This manuscript describes a project designed to develop approaches for undertaking rover surveys to produce soil moisture estimates at scales comparable to satellite observations and land surface process models. A custom designed, trailer-mounted rover was used to conduct repeat surveys at two scales in the Mallee region of Victoria, Australia. A broad scale survey was conducted at 36 x 36 km covering an area of a standard SMAP pixel and an intensive scale survey was conducted over a 10 x 10 km portion of the broad scale survey, which is at a scale equivalent to that used for national water balance modelling. We will describe the design of the rover, the methods used for converting neutron counts into soil moisture and discuss factors controlling soil moisture variability. We found that the intensive scale rover surveys produced reliable soil moisture estimates at 1 km resolution and the broad scale at 9 km resolution. We conclude that these products are well suited for future analysis of satellite soil moisture retrievals and finer scale soil moisture models.

Using a spatially-distributed hydrologic biogeochemistry model with nitrogen transport to study the spatial variation of carbon stocks and fluxes in a Critical Zone Observatory

NASA Astrophysics Data System (ADS)

Shi, Y.; Eissenstat, D. M.; He, Y.; Davis, K. J.

2017-12-01

Most current biogeochemical models are 1-D and represent one point in space. Therefore, they cannot resolve topographically driven land surface heterogeneity (e.g., lateral water flow, soil moisture, soil temperature, solar radiation) or the spatial pattern of nutrient availability. A spatially distributed forest biogeochemical model with nitrogen transport, Flux-PIHM-BGC, has been developed by coupling a 1-D mechanistic biogeochemical model Biome-BGC (BBGC) with a spatially distributed land surface hydrologic model, Flux-PIHM, and adding an advection dominated nitrogen transport module. Flux-PIHM is a coupled physically based model, which incorporates a land-surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model, and is augmented by adding a topographic solar radiation module. Flux-PIHM is able to represent the link between groundwater and the surface energy balance, as well as land surface heterogeneities caused by topography. In the coupled Flux-PIHM-BGC model, each Flux-PIHM model grid couples a 1-D BBGC model, while nitrogen is transported among model grids via surface and subsurface water flow. In each grid, Flux-PIHM provides BBGC with soil moisture, soil temperature, and solar radiation, while BBGC provides Flux-PIHM with spatially-distributed leaf area index. The coupled Flux-PIHM-BGC model has been implemented at the Susquehanna/Shale Hills Critical Zone Observatory. The model-predicted aboveground vegetation carbon and soil carbon distributions generally agree with the macro patterns observed within the watershed. The importance of abiotic variables (including soil moisture, soil temperature, solar radiation, and soil mineral nitrogen) in predicting aboveground carbon distribution is calculated using a random forest. The result suggests that the spatial pattern of aboveground carbon is controlled by the distribution of soil mineral nitrogen. A Flux-PIHM-BGC simulation without the nitrogen transport module is also executed. The model without nitrogen transport fails in predicting the spatial patterns of vegetation carbon, which indicates the importance of having a nitrogen transport module in spatially distributed ecohydrologic modeling.

On the Fidelity of Semi-distributed Hydrologic Model Simulations for Large Scale Catchment Applications

NASA Astrophysics Data System (ADS)

Ajami, H.; Sharma, A.; Lakshmi, V.

2017-12-01

Application of semi-distributed hydrologic modeling frameworks is a viable alternative to fully distributed hyper-resolution hydrologic models due to computational efficiency and resolving fine-scale spatial structure of hydrologic fluxes and states. However, fidelity of semi-distributed model simulations is impacted by (1) formulation of hydrologic response units (HRUs), and (2) aggregation of catchment properties for formulating simulation elements. Here, we evaluate the performance of a recently developed Soil Moisture and Runoff simulation Toolkit (SMART) for large catchment scale simulations. In SMART, topologically connected HRUs are delineated using thresholds obtained from topographic and geomorphic analysis of a catchment, and simulation elements are equivalent cross sections (ECS) representative of a hillslope in first order sub-basins. Earlier investigations have shown that formulation of ECSs at the scale of a first order sub-basin reduces computational time significantly without compromising simulation accuracy. However, the implementation of this approach has not been fully explored for catchment scale simulations. To assess SMART performance, we set-up the model over the Little Washita watershed in Oklahoma. Model evaluations using in-situ soil moisture observations show satisfactory model performance. In addition, we evaluated the performance of a number of soil moisture disaggregation schemes recently developed to provide spatially explicit soil moisture outputs at fine scale resolution. Our results illustrate that the statistical disaggregation scheme performs significantly better than the methods based on topographic data. Future work is focused on assessing the performance of SMART using remotely sensed soil moisture observations using spatially based model evaluation metrics.

Exploring the Role of Soil Moisture Conditions for Rainfall Triggered Landslides on Catchment Scale: the case of the Ialomita Sub Carpathians, Romania

NASA Astrophysics Data System (ADS)

Chitu, Zenaida; Bogaard, Thom; Adler, Mary-Jeanne; Steele-Dunne, Susan; Hrachowitz, Markus; Busuioc, Aristita; Sandric, Ionut; Istrate, Alexandru

2014-05-01

Like in many parts of the world, landslides represent in Romania recurrent phenomena that produce numerous damages to the infrastructure every few years. The high frequency of landslide events over the world has resulted to the development of many early warning systems that are based on the definition of rainfall thresholds triggering landslides. In Romania in particular, recent studies exploring the temporal occurrence of landslides have revealed that rainfall represents the most important triggering factor for landslides. The presence of low permeability soils and gentle slope degrees in the Ialomita Subcarpathians of Romania makes that cumulated precipitation over variable time interval and the hydraulic response of the soil plays a key role in landslides triggering. In order to identify the slope responses to rainfall events in this particular area we investigate the variability of soil moisture and its relationship to landslide events in three Subcarpathians catchments (Cricovul Dulce, Bizididel and Vulcana) by combining in situ measurements, satellite-based radiometry and hydrological modelling. For the current study, hourly soil moisture measurements from six soil moisture monitoring stations that are fitted with volumetric soil moisture sensors, temperature soil sensors and rain gauges sensors are used. Pedotransfer functions will be applied in order to infer hydraulic soil properties from soil texture sampled from 50 soil profiles. The information about spatial and temporal variability of soil moisture content will be completed with the Level 2 soil moisture products from the Soil Moisture and Ocean Salinity (SMOS) mission. A time series analysis of soil moisture is planned to be integrated to landslide and rainfall time series in order to determine a preliminary rainfall threshold triggering landslides in Ialomita Subcarpathians.

COSMOS: COsmic-ray Soil Moisture Observing System planned for the United States

NASA Astrophysics Data System (ADS)

Zweck, C.; Zreda, M.; Shuttleworth, J.; Zeng, X.

2008-12-01

Because soil water exerts a critical control on weather, climate, ecosystem, and water cycle, understanding soil moisture changes in time and space is crucial for many fields within natural sciences. A serious handicap in soil moisture measurements is the mismatch between limited point measurements using contact methods and remote sensing estimates over large areas. We present a novel method to measure soil moisture non- invasively at an intermediate spatial scale that will alleviate this problem. The method takes advantage of the dependence of cosmic-ray neutron intensity on the hydrogen content of soils (Zreda et al., Geophysical Research Letters, accepted). Low-energy cosmic-ray neutrons are produced and moderated in the soil, transported from the soil into the atmosphere where they are measured with a cosmic-ray neutron probe to provide integrated soil moisture content over a footprint of several hundred meters and a depth of a few decimeters. The method and the instrument are intended for deployment in the continental-scale COSMOS network that is designed to cover the contiguous region of the USA. Fully deployed, the COSMOS network will consist of up to 500 probes, and will provide continuous soil moisture content (together with atmospheric pressure, temperature and relative humidity) measured and reported hourly. These data will be used for initialization and assimilation of soil moisture conditions in weather and short-term (seasonal) climate forecasting, and for other land-surface applications.

High-resolution soil moisture mapping in Afghanistan

NASA Astrophysics Data System (ADS)

Hendrickx, Jan M. H.; Harrison, J. Bruce J.; Borchers, Brian; Kelley, Julie R.; Howington, Stacy; Ballard, Jerry

2011-06-01

Soil moisture conditions have an impact upon virtually all aspects of Army activities and are increasingly affecting its systems and operations. Soil moisture conditions affect operational mobility, detection of landmines and unexploded ordinance, natural material penetration/excavation, military engineering activities, blowing dust and sand, watershed responses, and flooding. This study further explores a method for high-resolution (2.7 m) soil moisture mapping using remote satellite optical imagery that is readily available from Landsat and QuickBird. The soil moisture estimations are needed for the evaluation of IED sensors using the Countermine Simulation Testbed in regions where access is difficult or impossible. The method has been tested in Helmand Province, Afghanistan, using a Landsat7 image and a QuickBird image of April 23 and 24, 2009, respectively. In previous work it was found that Landsat soil moisture can be predicted from the visual and near infra-red Landsat bands1-4. Since QuickBird bands 1-4 are almost identical to Landsat bands 1- 4, a Landsat soil moisture map can be downscaled using QuickBird bands 1-4. However, using this global approach for downscaling from Landsat to QuickBird scale yielded a small number of pixels with erroneous soil moisture values. Therefore, the objective of this study is to examine how the quality of the downscaled soil moisture maps can be improved by using a data stratification approach for the development of downscaling regression equations for each landscape class. It was found that stratification results in a reliable downscaled soil moisture map with a spatial resolution of 2.7 m.

An assessment of the differences between spatial resolution and grid size for the SMAP enhanced soil moisture product over homogeneous sites

USDA-ARS?s Scientific Manuscript database

Satellite-based passive microwave remote sensing typically involves a scanning antenna that makes measurements at irregularly spaced locations. These locations can change on a day to day basis. Soil moisture products derived from satellite-based passive microwave remote sensing are usually resampled...

Multispectral Passive Microwave Correlations with an Antecedent Precipitation Index Using the Nimbus 7 SMMR.

DTIC Science & Technology

1984-08-01

M.D. Smallwood , 1981: An evaluation of the spatial resolution of soil moisture information. NASA CR-166724, Goddard Space Flight Center, Greenbelt, MD...Thesis, Dept of Meteor- ology, Univ. of Okla., Norman , 59 pp. Theis, S.W., 1979: Surface soil moisture estimation with the Electri- cally Scanning

Assimilation of spatially sparse in situ soil moisture networks into a continuous model domain

USDA-ARS?s Scientific Manuscript database

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 ...

Playa Soil Moisture and Evaporation Dynamics During the MATERHORN Field Program

NASA Astrophysics Data System (ADS)

Hang, Chaoxun; Nadeau, Daniel F.; Jensen, Derek D.; Hoch, Sebastian W.; Pardyjak, Eric R.

2016-06-01

We present an analysis of field data collected over a desert playa in western Utah, USA in May 2013, the most synoptically active month of the year, as part of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) program. The results show that decreasing surface albedo, decreasing Bowen ratio and increasing net radiation with increasing soil moisture sustained a powerful positive feedback mechanism promoting large evaporation rates immediately following rain events. Additionally, it was found that, while nocturnal evaporation was negligible during dry periods, it was quite significant (up to 30 % of the daily cumulative flux) during nights following rain events. Our results further show that the highest spatial variability in surface soil moisture is found under dry conditions. Finally, we report strong spatial heterogeneities in evaporation rates following a rain event. The cumulative evaporation for the different sampling sites over a five-day period varied from ≈ 0.1 to ≈ 6.6 mm. Overall, this study allows us to better understand the mechanisms underlying soil moisture dynamics of desert playas as well as evaporation following occasional rain events.

Drought monitoring with soil moisture active passive (SMAP) measurements

NASA Astrophysics Data System (ADS)

Mishra, Ashok; Vu, Tue; Veettil, Anoop Valiya; Entekhabi, Dara

2017-09-01

Recent launch of space-borne systems to estimate surface soil moisture may expand the capability to map soil moisture deficit and drought with global coverage. In this study, we use Soil Moisture Active Passive (SMAP) soil moisture geophysical retrieval products from passive L-band radiometer to evaluate its applicability to forming agricultural drought indices. Agricultural drought is quantified using the Soil Water Deficit Index (SWDI) based on SMAP and soil properties (field capacity and available water content) information. The soil properties are computed using pedo-transfer function with soil characteristics derived from Harmonized World Soil Database. The SMAP soil moisture product needs to be rescaled to be compatible with the soil parameters derived from the in situ stations. In most locations, the rescaled SMAP information captured the dynamics of in situ soil moisture well and shows the expected lag between accumulations of precipitation and delayed increased in surface soil moisture. However, the SMAP soil moisture itself does not reveal the drought information. Therefore, the SMAP based SWDI (SMAP_SWDI) was computed to improve agriculture drought monitoring by using the latest soil moisture retrieval satellite technology. The formulation of SWDI does not depend on longer data and it will overcome the limited (short) length of SMAP data for agricultural drought studies. The SMAP_SWDI is further compared with in situ Atmospheric Water Deficit (AWD) Index. The comparison shows close agreement between SMAP_SWDI and AWD in drought monitoring over Contiguous United States (CONUS), especially in terms of drought characteristics. The SMAP_SWDI was used to construct drought maps for CONUS and compared with well-known drought indices, such as, AWD, Palmer Z-Index, sc-PDSI and SPEI. Overall the SMAP_SWDI is an effective agricultural drought indicator and it provides continuity and introduces new spatial mapping capability for drought monitoring. As an agricultural drought index, SMAP_SWDI has potential to capture short term moisture information similar to AWD and related drought indices.

Canadian Experiment for Soil Moisture in 2010 (CanEX-SM10): Overview and Preliminary Results

NASA Technical Reports Server (NTRS)

Magagi, Ramata; Berg, Aaron; Goita, Kalifa; Belair, Stephane; Jackson, Tom; Toth, B.; Walker, A.; McNairn, H.; O'Neill, P.; Moghdam. M;

Sensitivity of convective precipitation to soil moisture and vegetation during break spell of Indian summer monsoon

NASA Astrophysics Data System (ADS)

Kutty, Govindan; Sandeep, S.; Vinodkumar; Nhaloor, Sreejith

2017-07-01

Indian summer monsoon rainfall is characterized by large intra-seasonal fluctuations in the form of active and break spells in rainfall. This study investigates the role of soil moisture and vegetation on 30-h precipitation forecasts during the break monsoon period using Weather Research and Forecast (WRF) model. The working hypothesis is that reduced rainfall, clear skies, and wet soil condition during the break monsoon period enhance land-atmosphere coupling over central India. Sensitivity experiments are conducted with modified initial soil moisture and vegetation. The results suggest that an increase in antecedent soil moisture would lead to an increase in precipitation, in general. The precipitation over the core monsoon region has increased by enhancing forest cover in the model simulations. Parameters such as Lifting Condensation Level, Level of Free Convection, and Convective Available Potential Energy indicate favorable atmospheric conditions for convection over forests, when wet soil conditions prevail. On spatial scales, the precipitation is more sensitive to soil moisture conditions over northeastern parts of India. Strong horizontal gradient in soil moisture and orographic uplift along the upslopes of Himalaya enhanced rainfall over the east of Indian subcontinent.

Evaluating ESA CCI Soil Moisture in East Africa

NASA Technical Reports Server (NTRS)

McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R.; Wang, Shugong; Peters-Lidard, Christa D.; Verdin, James P.

2016-01-01

To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASAs Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R greater than 0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.

Assessment of Evapotranspiration and Soil Moisture Content Across Different Scales of Observation

PubMed Central

Verstraeten, Willem W.; Veroustraete, Frank; Feyen, Jan

2008-01-01

The proper assessment of evapotranspiration and soil moisture content are fundamental in food security research, land management, pollution detection, nutrient flows, (wild-) fire detection, (desert) locust, carbon balance as well as hydrological modelling; etc. This paper takes an extensive, though not exhaustive sample of international scientific literature to discuss different approaches to estimate land surface and ecosystem related evapotranspiration and soil moisture content. This review presents: (i)a summary of the generally accepted cohesion theory of plant water uptake and transport including a shortlist of meteorological and plant factors influencing plant transpiration;(ii)a summary on evapotranspiration assessment at different scales of observation (sap-flow, porometer, lysimeter, field and catchment water balance, Bowen ratio, scintillometer, eddy correlation, Penman-Monteith and related approaches);(iii)a summary on data assimilation schemes conceived to estimate evapotranspiration using optical and thermal remote sensing; and(iv)for soil moisture content, a summary on soil moisture retrieval techniques at different spatial and temporal scales is presented. Concluding remarks on the best available approaches to assess evapotranspiration and soil moisture content with and emphasis on remote sensing data assimilation, are provided. PMID:27879697

Using SMOS brightness temperature and derived surface-soil moisture to characterize surface conditions and validate land surface models.

NASA Astrophysics Data System (ADS)

Polcher, Jan; Barella-Ortiz, Anaïs; Piles, Maria; Gelati, Emiliano; de Rosnay, Patricia

2017-04-01

The SMOS satellite, operated by ESA, observes the surface in the L-band. On continental surface these observations are sensitive to moisture and in particular surface-soil moisture (SSM). In this presentation we will explore how the observations of this satellite can be exploited over the Iberian Peninsula by comparing its results with two land surface models : ORCHIDEE and HTESSEL. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies. When comparing the surface-soil moisture of the models with the product derived operationally by ESA from SMOS observations similar results are found. The spatial correlation over the IP between SMOS and ORCHIDEE SSM estimates is poor (ρ 0.3). A single value decomposition (SVD) analysis of rainfall and SSM shows that the co-varying patterns of these variables are in reasonable agreement between both products. Moreover the first three SVD soil moisture patterns explain over 80% of the SSM variance simulated by the model while the explained fraction is only 52% of the remotely sensed values. These results suggest that the rainfall-driven soil moisture variability may not account for the poor spatial correlation between SMOS and ORCHIDEE products. Other reasons have to be sought to explain the poor agreement in spatial patterns between satellite derived and modelled SSM. This presentation will hopefully contribute to the discussion of how SMOS and other observations can be used to prepare, carry-out and exploit a field campaign over the Iberian Peninsula which aims at improving our understanding of semi-arid land surface processes.

Monitoring and Characterizing Seasonal Drought, Water Supply Pattern and Their Impact on Vegetation Growth Using Satellite Soil Moisture Data, GRACE Water Storage and In-situ Observations.

NASA Astrophysics Data System (ADS)

A, G.; Velicogna, I.; Kimball, J. S.; Kim, Y.; Colliander, A.; Njoku, E. G.

2015-12-01

We combine soil moisture (SM) data from AMSR-E, AMSR-2 and SMAP, terrestrial water storage (TWS) changes from GRACE, in-situ groundwater measurements and atmospheric moisture data to delineate and characterize the evolution of drought and its impact on vegetation growth. GRACE TWS provides spatially continuous observations of total terrestrial water storage changes and regional drought extent, persistence and severity, while satellite derived soil moisture estimates provide enhanced delineation of plant-available soil moisture. Together these data provide complementary metrics quantifying available plant water supply. We use these data to investigate the supply changes from water components at different depth in relation to satellite based vegetation metrics, including vegetation greenness (NDVI) measures from MODIS and related higher order productivity (GPP) before, during and following the major drought events observed in the continental US for the past 14 years. We observe consistent trends and significant correlations between monthly time series of TWS, SM, NDVI and GPP. We study how changes in atmosphere moisture stress and coupling of water storage components at different depth impact on the spatial and temporal correlation between TWS, SM and vegetation metrics. In Texas, we find that surface SM and GRACE TWS agree with each other in general, and both capture the underlying water supply constraints to vegetation growth. Triggered by a transit increase in precipitation following the 2011 hydrological drought, vegetation productivity in Texas shows more sensitivity to surface SM than TWS. In the Great Plains, the correspondence between TWS and vegetation productivity is modulated by temperature-induced atmosphere moisture stress and by the coupling between surface soil moisture and groundwater through irrigation.

Spatiotemporal Variability of Soil Hydraulic Properties from Field Data and Remote Sensing in the Walnut Gulch Experimental Watershed

NASA Astrophysics Data System (ADS)

Becker, R.; Gebremichael, M.; Marker, M.

2015-12-01

Soil moisture is one of the main input variables for hydrological models. However due to the high spatial and temporal variability of soil properties it is often difficult to obtain accurate soil information at the required resolution. The new satellite SMAP promises to deliver soil moisture information at higher resolutions and could therefore improve the results of hydrological models. Nevertheless it still has to be investigated how precisely the SMAP soil moisture data can be used to delineate rainfall-runoff generation processes and if SMAP imagery can significantly improve the results of surface runoff models. Important parameters to understand the spatiotemporal distribution of soil humidity are infiltration and hydraulic conductivities apart from soil texture and macrostructure. During the SMAPVEX15-field campaign data on hydraulic conductivity and infiltration rates is collected in the Walnut Gulch Experimental Watershed (WGEW) in Southeastern Arizona in order to analyze the spatiotemporal variability of soil hydraulic properties. A Compact Constant Head Permeameter is used for in situ measurements of saturated hydraulic conductivity within the soil layers and a Hood Infiltrometer is used to determine infiltration rates at the undisturbed soil surface. Sampling sites were adjacent to the USDA-ARS meteorological and soil moisture measuring sites in the WGEW to take advantage of the long-term database of soil and climate data. Furthermore a sample plot of 3x3km was selected, where the spatial variability of soil hydraulic properties within a SMAP footprint was investigated. The results of the ground measurement based analysis are then compared with the remote sensing data derived from SMAP and aircraft-based microwave data to determine how well these spatiotemporal variations are captured by the remotely sensed data with the final goal of evaluating the use of future satellite soil moisture products for the improvement of rainfall runoff models. The results reveal several interesting features on the spatiotemporal variability of soil moisture at multiple scales, and the capabilities and limitations of remote sensing derived products in reproducing them.

4.4 Development of a 30-Year Soil Moisture Climatology for Situational Awareness and Public Health Applications

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Zavodsky, Bradley T.; White, Kristopher D.; Bell, Jesse E.

2015-01-01

This paper provided a brief background on the work being done at NASA SPoRT and the CDC to create a soil moisture climatology over the CONUS at high spatial resolution, and to provide a valuable source of soil moisture information to the CDC for monitoring conditions that could favor the development of Valley Fever. The soil moisture climatology has multi-faceted applications for both the NOAA/NWS situational awareness in the areas of drought and flooding, and for the Public Health community. SPoRT plans to increase its interaction with the drought monitoring and Public Health communities by enhancing this testbed soil moisture anomaly product. This soil moisture climatology run will also serve as a foundation for upgrading the real-time (currently southeastern CONUS) SPoRT-LIS to a full CONUS domain based on LIS version 7 and incorporating real-time GVF data from the Suomi-NPP Visible Infrared Imaging Radiometer Suite (Vargas et al. 2013) into LIS-Noah. The upgraded SPoRT-LIS run will serve as a testbed proof-of-concept of a higher-resolution NLDAS-2 modeling member. The climatology run will be extended to near real-time using the NLDAS-2 meteorological forcing from 2011 to present. The fixed 1981-2010 climatology shall provide the soil moisture "normals" for the production of real-time soil moisture anomalies. SPoRT also envisions a web-mapping type of service in which an end-user could put in a request for either an historical or real-time soil moisture anomaly graph for a specified county (as exemplified by Figure 2) and/or for local and regional maps of soil moisture proxy percentiles. Finally, SPoRT seeks to assimilate satellite soil moisture data from the current Soil Moisture Ocean Salinity (SMOS; Blankenship et al. 2014) and the recently-launched NASA Soil Moisture Active Passive (SMAP; Entekhabi et al. 2010) missions, using the EnKF capability within LIS. The 9-km combined active radar and passive microwave retrieval product from SMAP (Das et al. 2011) has the potential to provide valuable information about the near-surface soil moisture state for improving land surface modeling output.

Monitoring hillslope moisture dynamics with surface ERT for enhancing spatial significance of hydrometric point measurements

NASA Astrophysics Data System (ADS)

Hübner, R.; Heller, K.; Günther, T.; Kleber, A.

2015-01-01

Besides floodplains, hillslopes are basic units that mainly control water movement and flow pathways within catchments of subdued mountain ranges. The structure of their shallow subsurface affects water balance, e.g. infiltration, retention, and runoff. Nevertheless, there is still a gap in the knowledge of the hydrological dynamics on hillslopes, notably due to the lack of generalization and transferability. This study presents a robust multi-method framework of electrical resistivity tomography (ERT) in addition to hydrometric point measurements, transferring hydrometric data into higher spatial scales to obtain additional patterns of distribution and dynamics of soil moisture on a hillslope. A geoelectrical monitoring in a small catchment in the eastern Ore Mountains was carried out at weekly intervals from May to December 2008 to image seasonal moisture dynamics on the hillslope scale. To link water content and electrical resistivity, the parameters of Archie's law were determined using different core samples. To optimize inversion parameters and methods, the derived spatial and temporal water content distribution was compared to tensiometer data. The results from ERT measurements show a strong correlation with the hydrometric data. The response is congruent to the soil tension data. Water content calculated from the ERT profile shows similar variations as that of water content from soil moisture sensors. Consequently, soil moisture dynamics on the hillslope scale may be determined not only by expensive invasive punctual hydrometric measurements, but also by minimally invasive time-lapse ERT, provided that pedo-/petrophysical relationships are known. Since ERT integrates larger spatial scales, a combination with hydrometric point measurements improves the understanding of the ongoing hydrological processes and better suits identification of heterogeneities.

Estimation of Soil Moisture Under Vegetation Cover at Multiple Frequencies

NASA Astrophysics Data System (ADS)

Jadghuber, Thomas; Hajnsek, Irena; Weiß, Thomas; Papathanassiou, Konstantinos P.

2015-04-01

Soil moisture under vegetation cover was estimated by a polarimetric, iterative, generalized, hybrid decomposition and inversion approach at multiple frequencies (X-, C- and L-band). Therefore the algorithm, originally designed for longer wavelength (L-band), was adapted to deal with the short wavelength scattering scenarios of X- and C-band. The Integral Equation Method (IEM) was incorporated together with a pedo-transfer function of Dobson et al. to account for the peculiarities of short wavelength scattering at X- and C-band. DLR's F-SAR system acquired fully polarimetric SAR data in X-, C- and L-band over the Wallerfing test site in Lower Bavaria, Germany in 2014. Simultaneously, soil and vegetation measurements were conducted on different agricultural test fields. The results indicate a spatially continuous inversion of soil moisture in all three frequencies (inversion rates >92%), mainly due to the careful adaption of the vegetation volume removal including a physical constraining of the decomposition algorithm. However, for X- and C-band the inversion results reveal moisture pattern inconsistencies and in some cases an incorrectly high inversion of soil moisture at X-band. The validation with in situ measurements states a stable performance of 2.1- 7.6vol.% at L-band for the entire growing period. At C- and X-band a reliable performance of 3.7-13.4vol.% in RMSE can only be achieved after distinct filtering (X- band) leading to a loss of almost 60% in spatial inversion rate. Hence, a robust inversion for soil moisture estimation under vegetation cover can only be conducted at L-band due to a constant availability of the soil signal in contrast to higher frequencies (X- and C-band).

Study Variability of Seasonal Soil Moisture in Ensemble of CMIP5 Models Over South Asia During 1950-2005

NASA Astrophysics Data System (ADS)

Fahim, A. M.; Shen, R.; Yue, Z.; Di, W.; Mushtaq Shah, S.

2015-12-01

Moisture in the upper most layer of soil column from 14 different models under Coupled Model Intercomparison Project Phase-5 (CMIP5) project were analyzed for four seasons of the year. Aim of this study was to explore variability in soil moisture over south Asia using multi model ensemble and relationship between summer rainfall and soil moisture for spring and summer season. GLDAS (Global Land Data Assimilation System) dataset set was used for comparing CMIP5 ensemble mean soil moisture in different season. Ensemble mean represents soil moisture well in accordance with the geographical features; prominent arid regions are indicated profoundly. Empirical Orthogonal Function (EOF) analysis was applied to study the variability. First component of EOF explains 17%, 16%, 11% and 11% variability for spring, summer, autumn and winter season respectively. Analysis reveal increasing trend in soil moisture over most parts of Afghanistan, Central and north western parts of Pakistan, northern India and eastern to south eastern parts of China, in spring season. During summer, south western part of India exhibits highest negative trend while rest of the study area show minute trend (increasing or decreasing). In autumn, south west of India is under highest negative loadings. During winter season, north western parts of study area show decreasing trend. Summer rainfall has very week (negative or positive) spatial correlation, with spring soil moisture, while possess higher correlation with summer soil moisture. Our studies have significant contribution to understand complex nature of land - atmosphere interactions, as soil moisture prediction plays an important role in the cycle of sink and source of many air pollutants. Next level of research should be on filling the gaps between accurately measuring the soil moisture using satellite remote sensing and land surface modelling. Impact of soil moisture in tracking down different types of pollutant will also be studied.

Spatial and temporal structure within moisture measurements of a stormwater control system

EPA Science Inventory

Moisture sensing is a mature soil research technology commonly applied to agriculture. Such sensors may be appropriated for use in novel stormwater research applications. Knowledge of moisture (with respect to space and time) in infiltration based stormwater control measures (SCM...

Improvment of the Trapezoid Method Using Raw Landsat Image Digital Count Data for Soil Moisture Estimation in the Texas (usa) High Plains

NASA Astrophysics Data System (ADS)

Shafian, S.; Maas, S. J.

2015-12-01

Variations in soil moisture strongly affect surface energy balances, regional runoff, land erosion and vegetation productivity (i.e., potential crop yield). Hence, the estimation of soil moisture is very valuable in the social, economic, humanitarian (food security) and environmental segments of society. Extensive efforts to exploit the potential of remotely sensed observations to help quantify this complex variable are ongoing. This study aims at developing a new index, the Thermal Ground cover Moisture Index (TGMI), for estimating soil moisture content. This index is based on empirical parameterization of the relationship between raw image digital count (DC) data in the thermal infrared spectral band and ground cover (determined from raw image digital count data in the red and near-infrared spectral bands).The index uses satellite-derived information only, and the potential for its operational application is therefore great. This study was conducted in 18 commercial agricultural fields near Lubbock, TX (USA). Soil moisture was measured in these fields over two years and statistically compared to corresponding values of TGMI determined from Landsat image data. Results indicate statistically significant correlations between TGMI and field measurements of soil moisture (R2 = 0.73, RMSE = 0.05, MBE = 0.17 and AAE = 0.049), suggesting that soil moisture can be estimated using this index. It was further demonstrated that maps of TGMI developed from Landsat imagery could be constructed to show the relative spatial distribution of soil moisture across a region.

Spatial and temporal variations of soil moisture under Rosmarinus officinalis and Quercus coccifera in a burned soil

NASA Astrophysics Data System (ADS)

Gimeno-García, E.; Pascual-Aguilar, J. A.; Llovet, J.

2009-04-01

When studying surface runoff processes, measurement of the soil moisture content (SMC) at the surface could be used to identify sinks and sources areas of runoff. Surface soil moisture patterns variability have been studied in a burned Mediterranean semi-arid area. Since surface SMC and soil water repellency (SWR) are influenced by fire and vegetation (see previous abstract), and soil water dynamics and vegetation dynamics are functionally related, it could be expected to find some changes during the following months after fire when vegetation starts to recover. The identification of these changes is the main goal of this research. The study area is located at the municipality of Les Useres, 40 km from Castellón city (E Spain), where a wildfire occured in August 2007. We selected a burned SSE facing hillslope, located at 570 m a.s.l., with 12° slope angle, in which it was possible to identify the presence of two unique shrub species: Quercus coccifera L. and Rosmarinus officinalis L., which were distributed in a patchy mosaic. Twenty microsites with burned R. officinalis and eight microsites with burned Q. coccifera were selected in an area of 7 m wide by 14 m long. At the burned microsites, it was possible to distinguish three concentric zones (I, II and III) around the stumps showing differences on their soil surface appearance, which indicate a gradient of fire severity. Those differences were considered for field soil moisture measurements. Five measurements of SMC separated approximately 10 cm per zone at each microsite (n= 420) were carried out after different rainfall events. Volumetric soil moisture was measured by means of the moisture meter HH2 with ThetaProbe sensor type ML2x, 6 cm long. SMC was monitored on three occasions, always one day after the following rainfall events: (1) the first rainfall event after fire, when 11 mm were registered (Oct-07); (2) four months later than fire (Dec-07), after six consecutive raining days with a total rain volume of 172 mm; and (3) ten months after fire (Jun-08), when 50 mm were registered in the previous ten days. The spatial pattern of SMC was determined trough geostatistical analysis using GS+ software, calculating the semivariograms, to analyse the spatial correlation scale, interpolating data to estimate values of SMC at unsampled locations by means of kriging and finally, the results of kriging were displayed as different contour maps. Results showed that spatial pattern of SMC was highly variable, with important differences recorded within short distances. In fact, the range of spatial correlation (a0), which is the distance at that spatial correlation exists, varied between 0.5 to 1.4 m. A0 also varied according to the time from fire, with values of 0.5 m in the first rainfall after fire, 0.9 m four months later and 1.4 m ten months after fire occurs. This result suggests that the extent of the wettest areas increase as the vegetation recover. After the first rainfall, the SMC spatial pattern seems to be related to the soil microsite characteristics, mainly organic matter content, presence of hydrophobicity and soil clay content. Generally, the highest SMC (26-31%) appears at the burned bare soil areas. Four months later, as the same time as Q. coccifera resprouts, and in the R. officinalis microsites an important regrowth of Brachypodium resutum is observed, the spatial pattern of SMC changed according this plant cover distribution. This pattern is more clearly observed ten months after fire, when the highest SMC values were located at Q. coccifera and B. resutum areas (28-33%). At this time, no evidence of germination of R. officinalis (obligate seeder specie) was found. The lowest SMC (19-22%) appeared at the half lower part of the plot, where there was a central strip dominated by bare soil, with scarce presence of resprouter species. These results showed that at detailed working scale, the soil moisture pattern in this burned area was highly heterogeneous and the microsite characteristics (mainly soil properties and vegetation regrowth) seem to control the SMC spatial pattern. The interaction of soil-plant-water is more complex that the few environmental factors analysed here, and future research is needed to consider other site factors, such as microtopography, surface stoniness and outcrops, root density, between others. However, the obtained results reflect the capacity of vegetated patches to act as moisture holding areas ten months after fire occurs.

An initial assessment of a SMAP soil moisture disaggregation scheme using TIR surface evaporation data over the continental United States

NASA Astrophysics Data System (ADS)

Mishra, Vikalp; Ellenburg, W. Lee; Griffin, Robert E.; Mecikalski, John R.; Cruise, James F.; Hain, Christopher R.; Anderson, Martha C.

2018-06-01

The Soil Moisture Active Passive (SMAP) mission is dedicated toward global soil moisture mapping. Typically, an L-band microwave radiometer has spatial resolution on the order of 36-40 km, which is too coarse for many specific hydro-meteorological and agricultural applications. With the failure of the SMAP active radar within three months of becoming operational, an intermediate (9-km) and finer (3-km) scale soil moisture product solely from the SMAP mission is no longer possible. Therefore, the focus of this study is a disaggregation of the 36-km resolution SMAP passive-only surface soil moisture (SSM) using the Soil Evaporative Efficiency (SEE) approach to spatial scales of 3-km and 9-km. The SEE was computed using thermal-infrared (TIR) estimation of surface evaporation over Continental U.S. (CONUS). The disaggregation results were compared with the 3 months of SMAP-Active (SMAP-A) and Active/Passive (AP) products, while comparisons with SMAP-Enhanced (SMAP-E), SMAP-Passive (SMAP-P), as well as with more than 180 Soil Climate Analysis Network (SCAN) stations across CONUS were performed for a 19 month period. At the 9-km spatial scale, the TIR-Downscaled data correlated strongly with the SMAP-E SSM both spatially (r = 0.90) and temporally (r = 0.87). In comparison with SCAN observations, overall correlations of 0.49 and 0.47; bias of -0.022 and -0.019 and unbiased RMSD of 0.105 and 0.100 were found for SMAP-E and TIR-Downscaled SSM across the Continental U.S., respectively. At 3-km scale, TIR-Downscaled and SMAP-A had a mean temporal correlation of only 0.27. In terms of gain statistics, the highest percentage of SCAN sites with positive gains (>55%) was observed with the TIR-Downscaled SSM at 9-km. Overall, the TIR-based downscaled SSM showed strong correspondence with SMAP-E; compared to SCAN, and overall both SMAP-E and TIR-Downscaled performed similarly, however, gain statistics show that TIR-Downscaled SSM slightly outperformed SMAP-E.

Crop moisture estimation over the southern Great Plains with dual polarization 1.66 centimeter passive microwave data from Nimbus 7

NASA Technical Reports Server (NTRS)

Mcfarland, M. J.; Harder, P. H., II; Wilke, G. D.; Huebner, G. L., Jr.

1984-01-01

Moisture content of snow-free, unfrozen soil is inferred using passive microwave brightness temperatures from the scanning multichannel microwave radiometer (SMMR) on Nimbus-7. Investigation is restricted to the two polarizations of the 1.66 cm wavelength sensor. Passive microwave estimates of soil moisture are of two basic categories; those based upon soil emissivity and those based upon the polarization of soil emission. The two methods are compared and contrasted through the investigation of 54 potential functions of polarized brightness temperatures and, in some cases, ground-based temperature measurements. Of these indices, three are selected for the estimated emissivity, the difference between polarized brightness temperatures, and the normalized polarization difference. Each of these indices is about equally effective for monitoring soil moisture. Using an antecedent precipitation index (API) as ground control data, temporal and spatial analyses show that emissivity data consistently give slightly better soil moisture estimates than depolarization data. The difference, however, is not statistically significant. It is concluded that polarization data alone can provide estimates of soil moisture in areas where the emissivity cannot be inferred due to nonavailability of surface temperature data.

Multi-scale soil moisture model calibration and validation: An ARS Watershed on the South Fork of the Iowa River

USDA-ARS?s Scientific Manuscript database

Soil moisture monitoring with in situ technology is a time consuming and costly endeavor for which a method of increasing the resolution of spatial estimates across in situ networks is necessary. Using a simple hydrologic model, the resolution of an in situ watershed network can be increased beyond...

Integrating real-time and manual monitored data to predict hillslope soil moisture dynamics with high spatio-temporal resolution using linear and non-linear models

USDA-ARS?s Scientific Manuscript database

Spatio-temporal variability of soil moisture (') is a challenge that remains to be better understood. A trade-off exists between spatial coverage and temporal resolution when using the manual and real-time ' monitoring methods. This restricted the comprehensive and intensive examination of ' dynamic...

Spatial patterns of soil moisture connected to monthly-seasonal precipitation variability in a monsoon region

Treesearch

Yongqiang Liu

2003-01-01

The relations between monthly-seasonal soil moisture and precipitation variability are investigated by identifying the coupled patterns of the two hydrological fields using singular value decomposition (SVD). SVD is a technique of principal component analysis similar to empirical orthogonal knctions (EOF). However, it is applied to two variables simultaneously and is...

FINAL REPORT: Temporal and Spatial Distribution of Soil Moisture in Heterogeneous Vadose Zone with Moisture Barriers as Affected by Atmospheric Boundary Conditions

DTIC Science & Technology

2015-12-07

Wallen, B., K.M. Smits and S.E. Howington. Thermal conductivity of binary sand mixtures evaluated through the full range of saturation. Hydrology Days...and T.H. Illangasekare. 2011. Thermal conductivity of soils as affected by temperature, Proceedings from Hydrology Days. Colorado State University...is mixed with very fine soil). Although it is well known that the apparent thermal conductivity (λ) of partially wet soil is a function of water (θ

Spatial Distribution of the Relationship Between Soil Moisture and Soil Particle Size in Typical Plots on Loess Plateau

NASA Astrophysics Data System (ADS)

Zhang, X.; Zhao, W.; Liu, Y.; Fang, X.

2017-12-01

Soil water overconsumption is threatening the sustainability of regional vegetation rehabilitation in the Loess Plateau of China. The use of fractal geometry theory in describing soil quality improves the accuracy of the relevant research. Typical grasslands, shrublands, forests, cropland and orchards under different precipitation regimes were selected, and in this study, the spatial distribution of the relationship between soil moisture and soil particle size in typical slopes on Loess Plateau were investigated to provide support for the predict of soil moisture by using soil physical characteristics in the Loess Plateau. During the sampling year, the mean annual precipitation gradients were divided at an interval of 70 mm from 370mm to 650mm. Grasslands with Medicago sativa L. or Stipa bungeana Trin., shrublands with Caragana Korshinskii Kom. or Hippophae rhamnoides L., forests with Robinia pseudoacacia Linn., orchards with apple trees and croplands with corn or potatoes were chosen to represent the natural grassland. A soil auger with a diameter of 5 cm was used to obtain soil samples at depths of 0-5 m at intervals of 20 cm.The Van Genuchten model, fractal theory and redundancy analysis (RDA) were used to estimate and analyze the soil water characteristic curve, soil particle size distribution, and fractal dimension and the correlations between the relevant parameters. The results showed that (1) the change of the singular fractal dimension is positively correlated with soil water content, while D0 (capacity dimension) is negatively correlated with soil water content as the depth increases; (2) the relationship between soil moisture and soil particle size shows differences under different plants and precipitation gradient.

From Sub-basin to Grid Scale Soil Moisture Disaggregation in SMART, A Semi-distributed Hydrologic Modeling Framework

NASA Astrophysics Data System (ADS)

Ajami, H.; Sharma, A.

2016-12-01

A computationally efficient, semi-distributed hydrologic modeling framework is developed to simulate water balance at a catchment scale. The Soil Moisture and Runoff simulation Toolkit (SMART) is based upon the delineation of contiguous and topologically connected Hydrologic Response Units (HRUs). In SMART, HRUs are delineated using thresholds obtained from topographic and geomorphic analysis of a catchment, and simulation elements are distributed cross sections or equivalent cross sections (ECS) delineated in first order sub-basins. ECSs are formulated by aggregating topographic and physiographic properties of the part or entire first order sub-basins to further reduce computational time in SMART. Previous investigations using SMART have shown that temporal dynamics of soil moisture are well captured at a HRU level using the ECS delineation approach. However, spatial variability of soil moisture within a given HRU is ignored. Here, we examined a number of disaggregation schemes for soil moisture distribution in each HRU. The disaggregation schemes are either based on topographic based indices or a covariance matrix obtained from distributed soil moisture simulations. To assess the performance of the disaggregation schemes, soil moisture simulations from an integrated land surface-groundwater model, ParFlow.CLM in Baldry sub-catchment, Australia are used. ParFlow is a variably saturated sub-surface flow model that is coupled to the Common Land Model (CLM). Our results illustrate that the statistical disaggregation scheme performs better than the methods based on topographic data in approximating soil moisture distribution at a 60m scale. Moreover, the statistical disaggregation scheme maintains temporal correlation of simulated daily soil moisture while preserves the mean sub-basin soil moisture. Future work is focused on assessing the performance of this scheme in catchments with various topographic and climate settings.

MODIS-based spatiotemporal patterns of soil moisture and evapotranspiration interactions in Tampa Bay urban watershed

NASA Astrophysics Data System (ADS)

Chang, Ni-Bin; Xuan, Zhemin; Wimberly, Brent

2011-09-01

Soil moisture and evapotranspiration (ET) is affected by both water and energy balances in the soilvegetation- atmosphere system, it involves many complex processes in the nexus of water and thermal cycles at the surface of the Earth. These impacts may affect the recharge of the upper Floridian aquifer. The advent of urban hydrology and remote sensing technologies opens new and innovative means to undertake eventbased assessment of ecohydrological effects in urban regions. For assessing these landfalls, the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing images can be used for the estimation of such soil moisture change in connection with two other MODIS products - Enhanced Vegetation Index (EVI), Land Surface Temperature (LST). Supervised classification for soil moisture retrieval was performed for Tampa Bay area on the 2 kmx2km grid with MODIS images. Machine learning with genetic programming model for soil moisture estimation shows advances in image processing, feature extraction, and change detection of soil moisture. ET data that were derived by Geostationary Operational Environmental Satellite (GOES) data and hydrologic models can be retrieved from the USGS web site directly. Overall, the derived soil moisture in comparison with ET time series changes on a seasonal basis shows that spatial and temporal variations of soil moisture and ET that are confined within a defined region for each type of surfaces, showing clustered patterns and featuring space scatter plot in association with the land use and cover map. These concomitant soil moisture patterns and ET fluctuations vary among patches, plant species, and, especially, location on the urban gradient. Time series plots of LST in association with ET, soil moisture and EVI reveals unique ecohydrological trends. Such ecohydrological assessment can be applied for supporting the urban landscape management in hurricane-stricken regions.

A practical approach for deriving all-weather soil moisture content using combined satellite and meteorological data

NASA Astrophysics Data System (ADS)

Leng, Pei; Li, Zhao-Liang; Duan, Si-Bo; Gao, Mao-Fang; Huo, Hong-Yuan

2017-09-01

Soil moisture has long been recognized as one of the essential variables in the water cycle and energy budget between Earth's surface and atmosphere. The present study develops a practical approach for deriving all-weather soil moisture using combined satellite images and gridded meteorological products. In this approach, soil moisture over the Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky pixels are estimated from the Vegetation Index/Temperature (VIT) trapezoid scheme in which theoretical dry and wet edges were determined pixel to pixel by China Meteorological Administration Land Data Assimilation System (CLDAS) meteorological products, including air temperature, solar radiation, wind speed and specific humidity. For cloudy pixels, soil moisture values are derived by the calculation of surface and aerodynamic resistances from wind speed. The approach is capable of filling the soil moisture gaps over remaining cloudy pixels by traditional optical/thermal infrared methods, allowing for a spatially complete soil moisture map over large areas. Evaluation over agricultural fields indicates that the proposed approach can produce an overall generally reasonable distribution of all-weather soil moisture. An acceptable accuracy between the estimated all-weather soil moisture and in-situ measurements at different depths could be found with an Root Mean Square Error (RMSE) varying from 0.067 m3/m3 to 0.079 m3/m3 and a slight bias ranging from 0.004 m3/m3 to -0.011 m3/m3. The proposed approach reveals significant potential to derive all-weather soil moisture using currently available satellite images and meteorological products at a regional or global scale in future developments.

Implications of complete watershed soil moisture measurements to hydrologic modeling

NASA Technical Reports Server (NTRS)

Engman, E. T.; Jackson, T. J.; Schmugge, T. J.

1983-01-01

A series of six microwave data collection flights for measuring soil moisture were made over a small 7.8 square kilometer watershed in southwestern Minnesota. These flights were made to provide 100 percent coverage of the basin at a 400 m resolution. In addition, three flight lines were flown at preselected areas to provide a sample of data at a higher resolution of 60 m. The low level flights provide considerably more information on soil moisture variability. The results are discussed in terms of reproducibility, spatial variability and temporal variability, and their implications for hydrologic modeling.

Using Electromagnetic Induction Technique to Detect Hydropedological Dynamics: Principles and Applications

NASA Astrophysics Data System (ADS)

Zhu, Qing; Liao, Kaihua; Doolittle, James; Lin, Henry

2014-05-01

Hydropedological dynamics including soil moisture variation, subsurface flow, and spatial distributions of different soil properties are important parameters in ecological, environmental, hydrological, and agricultural modeling and applications. However, technical gap exists in mapping these dynamics at intermediate spatial scale (e.g., farm and catchment scales). At intermediate scales, in-situ monitoring provides detailed data, but is restricted in number and spatial coverage; while remote sensing provides more acceptable spatial coverage, but has comparatively low spatial resolution, limited observation depths, and is greatly influenced by the surface condition and climate. As a non-invasive, fast, and convenient geophysical tool, electromagnetic induction (EMI) measures soil apparent electrical conductivity (ECa) and has great potential to bridge this technical gap. In this presentation, principles of different EMI meters are briefly introduced. Then, case studies of using repeated EMI to detect spatial distributions of subsurface convergent flow, soil moisture dynamics, soil types and their transition zones, and different soil properties are presented. The suitability, effectiveness, and accuracy of EMI are evaluated for mapping different hydropedological dynamics. Lastly, contributions of different hydropedological and terrain properties on soil ECa are quantified under different wetness conditions, seasons, and land use types using Classification and Regression Tree model. Trend removal and residual analysis are then used for further mining of EMI survey data. Based on these analyses, proper EMI survey designs and data processing are proposed.

Coupling rainfall observations and satellite soil moisture for predicting event soil loss in Central Italy

NASA Astrophysics Data System (ADS)

Todisco, Francesca; Brocca, Luca; Termite, Loris Francesco; Wagner, Wolfgang

2015-04-01

The accuracy of water soil loss prediction depends on the ability of the model to account for effects of the physical phenomena causing the output and the accuracy by which the parameters have been determined. The process based models require considerable effort to obtain appropriate parameter values and their failure to produce better results than achieved using the USLE/RUSLE model, encourages the use of the USLE/RUSLE model in roles of which it was not designed. In particular it is widely used in watershed models even at the event temporal scale. At hillslope scale, spatial variability in soil and vegetation result in spatial variations in soil moisture and consequently in runoff within the area for which soil loss estimation is required, so the modeling approach required to produce those estimates needs to be sensitive to those spatial variations in runoff. Some models include explicit consideration of runoff in determining the erosive stresses but this increases the uncertainty of the prediction due to the difficulty in parameterising the models also because the direct measures of surface runoff are rare. The same remarks are effective also for the USLE/RUSLE models including direct consideration of runoff in the erosivity factor (i.e. USLE-M by Kinnell and Risse, 1998, and USLE-MM by Bagarello et al., 2008). Moreover actually most of the rainfall-runoff models are based on the knowledge of the pre-event soil moisture that is a fundamental variable in the rainfall-runoff transformation. In addiction soil moisture is a readily available datum being possible to have easily direct pre-event measures of soil moisture using in situ sensors or satellite observations at larger spatial scale; it is also possible to derive the antecedent water content with soil moisture simulation models. The attempt made in the study is to use the pre-event soil moisture to account for the spatial variation in runoff within the area for which the soil loss estimates are required. More specifically the analysis was focused on the evaluation of the effectiveness of coupling modeled or satellite-derived soil moisture with USLE-derived models in predicting event unit soil loss at the plot scale in a silty-clay soil in Central Italy. To this end was used the database of the Masse experimental station developed considering for a given erosive event (an event yielding a measurable soil loss) the simultaneous measures of the total runoff amount, Qe (mm), and soil loss per unit area, Ae (Mg-ha-1) at plot scale and of the rainfall data required to derive the erosivity factor Re according to Wischmeiser and Smith (1978), with a MIT=6 h (Bagarello et al., 2013; Todisco et al., 2012). To the purpose of this investigation only data collected on the λ = 22 m long plots were considered: 63 erosive events in the period 2008-2013, 18 occurred during the dry period (from June to September) and the other 45 in the complementary period (wet period). The models tested are the USLE/RUSLE and some USLE-derived formulations in which the event erosivity factor, Re, is corrected by the antecedent soil moisture, θ, and powered to an exponent α > 0 (α =1: linear model; α ≠ 1: power model). Both soil moisture data the satellite retrieved (θ = θsat) and the estimates (θ = θest) of Soil Water Balance model (Brocca et al., 2011) were tested. The results have been compared with those obtained by the USLE/RUSLE, USLE-M and USLE-MM models coupled with a parsimonious rainfall-runoff model, MILc, (Brocca et al. 2011) for the prediction of runoff volume (that in these models is the term used to correct the erosivity factor Re). The results showed that: including direct consideration of antecedent soil moisture and runoff in the event rainfall-runoff factor of the RUSLE/USLE enhanced the capacity of the model to account for variations in event soil loss when soil moisture and runoff volume are measured or predicted reasonably well; the accuracy of the original USLE/RUSLE model was always the lowest; the accuracy in estimating the event soil loss of a models with erosivity factor that includes the estimated runoff is always overcome by at least one model that uses the antecedent soil moisture θ in the erosivity index; the power models generally, at Masse, work better than the linear. The more accurate models are that with the estimated antecedent soil moisture, θest, when all the database is used and with the satellite retrieved soil moisture, θsat, when only the wet periods' events are considered. In fact it was also verified that much of the inaccuracy of the tested models is due to summer rainfall events, probably because of the particular characteristics that the soil assumes in the dry period (superficial crusts causing higher runoff): in this cases, high soil losses are observed in association to low values of soil moisture, while the simulated runoff assume low values too, since they are based on the antecedent wetness conditions. Thus, the analyses were repeated excluding the summer events. As expected, the performance of all the models increases, but still the use of θ provides the best results. The results of the analysis open interesting scenarios in the use of USLE-derived models for the unit event soil loss estimation at large scale. In particular the use of the soil moisture to correct the rainfall erosivity factor acquires a great practical importance, since it is a relatively simple measurable data and moreover because remote sensing soil moisture data are widely available and useful in large-scale erosion assessment. Bagarello, V., Di Piazza, G. V., Ferro, V., Giordano, G., 2008. Predicting unit soil loss in Sicily, south Italy. Hydrol. Process. 22, 586-595. Bagarello, V., Ferro, V., Giordano, G., Mannocchi, F., Todisco, F., Vergni, L., 2013. Predicting event soil loss form bare plots at two Italian sites. Catena 109, 96-102. Brocca, L., Melone, F., Moramarco, T., 2011. Distributed rainfall-runoff modeling for flood frequency estimation and flood forecasting. Hydrol. Process. 25, 2801-2813. Kinnell, P. I. A., Risse, L. M., 1998. USLE-M: empirical modeling rainfall erosion through runoff and sediment concentration. Soil Sci. Soc. Am. J. 62, 1667-1672. Todisco, F., Vergni, L., Mannocchi, F., Bomba, C., 2012. Calibration of the soil loss measurement at the Masse experimental station. Catena 91, 4-9. Wischmeier, W. H., Smith, D. D., 1978. Predicting rainfall-erosion losses - A guide to conservation planning. Agriculture Handbook 537, United Stated Department of Agriculture.

Feasibility analysis of using inverse modeling for estimating natural groundwater recharge from a large-scale soil moisture monitoring network

NASA Astrophysics Data System (ADS)

Wang, Tiejun; Franz, Trenton E.; Yue, Weifeng; Szilagyi, Jozsef; Zlotnik, Vitaly A.; You, Jinsheng; Chen, Xunhong; Shulski, Martha D.; Young, Aaron

2016-02-01

Despite the importance of groundwater recharge (GR), its accurate estimation still remains one of the most challenging tasks in the field of hydrology. In this study, with the help of inverse modeling, long-term (6 years) soil moisture data at 34 sites from the Automated Weather Data Network (AWDN) were used to estimate the spatial distribution of GR across Nebraska, USA, where significant spatial variability exists in soil properties and precipitation (P). To ensure the generality of this study and its potential broad applications, data from public domains and literature were used to parameterize the standard Hydrus-1D model. Although observed soil moisture differed significantly across the AWDN sites mainly due to the variations in P and soil properties, the simulations were able to capture the dynamics of observed soil moisture under different climatic and soil conditions. The inferred mean annual GR from the calibrated models varied over three orders of magnitude across the study area. To assess the uncertainties of the approach, estimates of GR and actual evapotranspiration (ETa) from the calibrated models were compared to the GR and ETa obtained from other techniques in the study area (e.g., remote sensing, tracers, and regional water balance). Comparison clearly demonstrated the feasibility of inverse modeling and large-scale (>104 km2) soil moisture monitoring networks for estimating GR. In addition, the model results were used to further examine the impacts of climate and soil on GR. The data showed that both P and soil properties had significant impacts on GR in the study area with coarser soils generating higher GR; however, different relationships between GR and P emerged at the AWDN sites, defined by local climatic and soil conditions. In general, positive correlations existed between annual GR and P for the sites with coarser-textured soils or under wetter climatic conditions. With the rapidly expanding soil moisture monitoring networks around the globe, this study may have important applications in aiding water resources management in different regions.

Synergistic Use of SMOS Measurements with SMAP Derived and In-situ Data over Valencia Anchor Station by Using Downscaling Technique

NASA Astrophysics Data System (ADS)

Ansari Amoli, Abdolreza; Lopez-Baeza, Ernesto; Mahmoudi, Ali; Mahmoodi, Ali

2016-07-01

Synergistic Use of SMOS Measurements with SMAP Derived and In-situ Data over the Valencia Anchor Station by Using a Downscaling Technique Ansari Amoli, A.(1),Mahmoodi, A.(2) and Lopez-Baeza, E.(3) (1) Department of Earth Physics and Thermodynamics, University of Valencia, Spain (2) Centre d'Etudes Spatiales de la BIOsphère (CESBIO), France (3) Department of Earth Physics and Thermodynamics, University of Valencia, Spain Soil moisture products from active sensors are not operationally available. Passive remote sensors return more accurate estimates, but their resolution is much coarser. One solution to overcome this problem is the synergy between radar and radiometric data by using disaggregation (downscaling) techniques. Few studies have been conducted to merge high resolution radar and coarse resolution radiometer measurements in order to obtain an intermediate resolution product. In this paper we present an algorithm using combined available SMAP (Soil Moisture Active and Passive) radar and SMOS (Soil Moisture and Ocean Salinity) radiometer measurements to estimate surface soil moisture over the Valencia Anchor Station (VAS), Valencia, Spain. The goal is to combine the respective attributes of the radar and radiometer observations to estimate soil moisture at a resolution of 3 km. The algorithm disaggregates the coarse resolution SMOS (15 km) radiometer brightness temperature product based on the spatial variation of the high resolution SMAP (3 km) radar backscatter. The disaggregation of the radiometer brightness temperature uses the radar backscatter spatial patterns within the radiometer footprint that are inferred from the radar measurements. For this reason the radar measurements within the radiometer footprint are scaled by parameters that are derived from the temporal fluctuations in the radar and radiometer measurements.

Characterizing Seasonal Drought, Water Supply Pattern and Their Impact on Vegetation Growth Using Satellite Soil Moisture Data, GRACE Water Storage and Precipitation Observations

NASA Astrophysics Data System (ADS)

A, G.; Velicogna, I.; Kimball, J. S.; Du, J.; Kim, Y.; Njoku, E. G.; Colliander, A.

2016-12-01

We combine soil moisture (SM) data from AMSR-E, AMSR-2 and SMAP, terrestrial water storage (TWS) changes from GRACE and precipitation measurements from GPCP to delineate and characterize drought and water supply pattern and its impact on vegetation growth. GRACE TWS provides spatially continuous observations of total terrestrial water storage changes and regional drought extent, persistence and severity, while satellite derived soil moisture estimates provide enhanced delineation of plant-available soil moisture. Together these data provide complementary metrics quantifying available plant water supply and have important implications for water resource management. We use these data to investigate the supply changes from different water components in relation to satellite based vegetation productivity metrics from MODIS, before, during and following the major drought events observed in the continental US during the past 13 years. We observe consistent trends and significant correlations between monthly time series of TWS, SM, and vegetation productivity. In Texas and surrounding semi-arid areas, we find that the spatial pattern of the vegetation-moisture relation follows the gradient in mean annual precipitation. In Texas, GRACE TWS and surface SM show strong coupling and similar characteristic time scale in relatively normal years, while during the 2011 onward hydrological drought, GRACE TWS manifests a longer time scale than that of surface SM, implying stronger drought persistence in deeper water storage. In the Missouri watershed, we find a spatially varying vegetation-moisture relationship where in the drier northwestern portion of the basin, the inter-annual variability in summer vegetation productivity is closely associated with changes in carry-on GRACE TWS from spring, whereas in the moist southeastern portion of the basin, summer precipitation is the dominant controlling factor on vegetation growth.

Field-Scale Soil Moisture Observations in Irrigated Agriculture Fields Using the Cosmic-ray Neutron Rover

NASA Astrophysics Data System (ADS)

Franz, T. E.; Avery, W. A.; Finkenbiner, C. E.; Wang, T.; Brocca, L.

2014-12-01

Approximately 40% of global food production comes from irrigated agriculture. With the increasing demand for food even greater pressures will be placed on water resources within these systems. In this work we aimed to characterize the spatial and temporal patterns of soil moisture at the field-scale (~500 m) using the newly developed cosmic-ray neutron rover near Waco, NE. Here we mapped soil moisture of 144 quarter section fields (a mix of maize, soybean, and natural areas) each week during the 2014 growing season (May to September). The 11 x11 km study domain also contained 3 stationary cosmic-ray neutron probes for independent validation of the rover surveys. Basic statistical analysis of the domain indicated a strong inverted parabolic relationship between the mean and variance of soil moisture. The relationship between the mean and higher order moments were not as strong. Geostatistical analysis indicated the range of the soil moisture semi-variogram was significantly shorter during periods of heavy irrigation as compared to non-irrigated periods. Scaling analysis indicated strong power law behavior between the variance of soil moisture and averaging area with minimal dependence of mean soil moisture on the slope of the power law function. Statistical relationships derived from the rover dataset offer a novel set of observations that will be useful in: 1) calibrating and validating land surface models, 2) calibrating and validating crop models, 3) soil moisture covariance estimates for statistical downscaling of remote sensing products such as SMOS and SMAP, and 4) provide center-pivot scale mean soil moisture data for optimal irrigation timing and volume amounts.

Rapid prototyping of soil moisture estimates using the NASA Land Information System

NASA Astrophysics Data System (ADS)

Anantharaj, V.; Mostovoy, G.; Li, B.; Peters-Lidard, C.; Houser, P.; Moorhead, R.; Kumar, S.

2007-12-01

The Land Information System (LIS), developed at the NASA Goddard Space Flight Center, is a functional Land Data Assimilation System (LDAS) that incorporates a suite of land models in an interoperable computational framework. LIS has been integrated into a computational Rapid Prototyping Capabilities (RPC) infrastructure. LIS consists of a core, a number of community land models, data servers, and visualization systems - integrated in a high-performance computing environment. The land surface models (LSM) in LIS incorporate surface and atmospheric parameters of temperature, snow/water, vegetation, albedo, soil conditions, topography, and radiation. Many of these parameters are available from in-situ observations, numerical model analysis, and from NASA, NOAA, and other remote sensing satellite platforms at various spatial and temporal resolutions. The computational resources, available to LIS via the RPC infrastructure, support e- Science experiments involving the global modeling of land-atmosphere studies at 1km spatial resolutions as well as regional studies at finer resolutions. The Noah Land Surface Model, available with-in the LIS is being used to rapidly prototype soil moisture estimates in order to evaluate the viability of other science applications for decision making purposes. For example, LIS has been used to further extend the utility of the USDA Soil Climate Analysis Network of in-situ soil moisture observations. In addition, LIS also supports data assimilation capabilities that are used to assimilate remotely sensed soil moisture retrievals from the AMSR-E instrument onboard the Aqua satellite. The rapid prototyping of soil moisture estimates using LIS and their applications will be illustrated during the presentation.

Soil moisture variation patterns observed in Hand County, South Dakota

NASA Technical Reports Server (NTRS)

Jones, E. B.; Owe, M.; Schmugge, T. J. (Principal Investigator)

1981-01-01

Soil moisture data were taken during 1976 (April, June, October), 1977 (April, May, June), and 1978 (May, June, July) Hand County, South Dakota as part of the ground truth used in NASA's aircraft experiments to study the use of microwave radiometers for the remote sensing of soil moisture. The spatial variability observed on the ground during each of the sampling events was studied. The data reported are the mean gravimetric soil moisture contained in three surface horizon depths: 0 to 2.5, 0 to 5 and 0 to 10 cm. The overall moisture levels ranged from extremely dry conditions in June 1976 to very wet in May 1978, with a relatively even distribution of values within that range. It is indicated that well drained sites have to be partitioned from imperfectly drained areas when attempting to characterize the general moisture profile throughout an area of varying soil and cover type conditions. It is also found that the variability in moisture content is greatest in the 0 to 2.5 cm measurements and decreases as the measurements are integrated over a greater depth. It is also determined that the sampling intensity of 10 measurements per km is adequate to estimate the mean moisture with an uncertainty of + or - 3 percent under average moisture conditions in areas of moderate to good drainage.

From ASCAT to Sentinel-1: Soil Moisture Monitoring using European C-Band Radars

NASA Astrophysics Data System (ADS)

Wagner, Wolfgang; Bauer-Marschallinger, Bernhard; Hochstöger, Simon

2016-04-01

The Advanced Scatterometer (ASCAT) is a C-Band radar instrument flown on board of the series of three METOP satellites. Albeit not operating in one of the more favorable longer wavelength ranges (S, L or P-band) as the dedicated Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, it is one of main microwave sensors used for monitoring of soil moisture on a global scale. Its attractiveness for soil moisture monitoring applications stems from its operational status, high radiometric accuracy and stability, short revisit time, multiple viewing directions and long heritage (Wagner et al. 2013). From an application perspective, its main limitation is its spatial resolution of about 25 km, which does not allow resolving soil moisture patterns driven by smaller-scale hydrometeorological processes (e.g. convective precipitation, runoff patterns, etc.) that are themselves related to highly variable land surface characteristics (soil characteristics, topography, vegetation cover, etc.). Fortunately, the technique of aperture synthesis allows to significantly improve the spatial resolution of spaceborne radar instruments up to the meter scale. Yet, past Synthetic Aperture Radar (SAR) missions had not yet been designed to achieve a short revisit time required for soil moisture monitoring. This has only changed recently with the development and launch of SMAP (Entekhabi et al. 2010) and Sentinel-1 (Hornacek et al. 2012). Unfortunately, the SMAP radar failed only after a few months of operations, which leaves Sentinel-1 as the only currently operational SAR mission capable of delivering high-resolution radar observations with a revisit time of about three days for Europe, about weekly for most crop growing regions worldwide, and about bi-weekly to monthly over the rest of the land surface area. Like ASCAT, Sentinel-1 acquires C-band backscatter data in VV polarization over land. Therefore, for the interpretation of both ASCAT and Sentinel-1 backscatter observation, the same physical processes and geophysical variables (e.g. vegetation optical depth, surface roughness, soil volume scattering, etc.) need to be considered. The difference lies mainly in the scaling, i.e. how prominently the different variables influence the C-band data at the different spatial (25 km versus 20 m) and temporal (daily versus 3-30 days repeat coverage) scales. Therefore, while the general properties of soil moisture retrievals schemes used for ASCAT and Sentinel-1 can be the same, the details of the algorithm and parameterization will be different. This presentation will review similarities and differences of soil moisture retrieval approaches used for ASCAT and Sentinel-1, with a focus on the change detection method developed by TU Wien. Some first comparisons of ASCAT and Sentinel-1 soil moisture data over Europe will also be shown. REFERENCES Entekhabi, D., Njoku, E.G., O'Neill, P.E., Kellog, K.H., Crow, W.T., Edelstein, W.N., Entin, J.K., Goodman, S.D., Jackson, T.J., Johnson, J., Kimball, J., Piepmeier, J.R., Koster, R., Martin, N., McDonald, K.C., Moghaddam, M., Moran, S., Reichle, R., Shi, J.C., Spencer, M.W., Thurman, S.W., Tsang, L., & Van Zyl, J. (2010). The Soil Moisture Active Passive (SMAP) mission. Proceedings of the IEEE, 98, 704-716 Hornacek, M., Wagner, W., Sabel, D., Truong, H.L., Snoeij, P., Hahmann, T., Diedrich, E., & Doubkova, M. (2012). Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval via Change Detection Using Sentinel-1. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 1303-1311 Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S., Figa-Saldana, J., De Rosnay, P., Jann, A., Schneider, S., Komma, J., Kubu, G., Brugger, K., Aubrecht, C., Züger, C., Gangkofer, U., Kienberger, S., Brocca, L., Wang, Y., Blöschl, G., Eitzinger, J., Steinnocher, K., Zeil, P., & Rubel, F. (2013). The ASCAT soil moisture product: A review of its specifications, validation results, and emerging applications. Meteorologische Zeitschrift, 22, 5-33

Spatial distribution of tree species governs the spatio-temporal interaction of leaf area index and soil moisture across a forested landscape.

PubMed

Naithani, Kusum J; Baldwin, Doug C; Gaines, Katie P; Lin, Henry; Eissenstat, David M

2013-01-01

Quantifying coupled spatio-temporal dynamics of phenology and hydrology and understanding underlying processes is a fundamental challenge in ecohydrology. While variation in phenology and factors influencing it have attracted the attention of ecologists for a long time, the influence of biodiversity on coupled dynamics of phenology and hydrology across a landscape is largely untested. We measured leaf area index (L) and volumetric soil water content (θ) on a co-located spatial grid to characterize forest phenology and hydrology across a forested catchment in central Pennsylvania during 2010. We used hierarchical Bayesian modeling to quantify spatio-temporal patterns of L and θ. Our results suggest that the spatial distribution of tree species across the landscape created unique spatio-temporal patterns of L, which created patterns of water demand reflected in variable soil moisture across space and time. We found a lag of about 11 days between increase in L and decline in θ. Vegetation and soil moisture become increasingly homogenized and coupled from leaf-onset to maturity but heterogeneous and uncoupled from leaf maturity to senescence. Our results provide insight into spatio-temporal coupling between biodiversity and soil hydrology that is useful to enhance ecohydrological modeling in humid temperate forests.

Characterizing Drought Impacted Soils in the San Joaquin Valley of California Using Remote Sensing

NASA Astrophysics Data System (ADS)

Wahab, L. M.; Miller, D.; Roberts, D. A.

2017-12-01

California's San Joaquin Valley is an extremely agriculturally productive region of the country, and understanding the state of soils in this region is an important factor in maintaining this high productivity. In this study, we quantified changing soil cover during the drought and analyzed spatial changes in salinity, organic matter, and moisture using unique soil spectral characteristics. We used data from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) from Hyperspectral Infrared Imager (HyspIRI) campaign flights in 2013 and 2014 over the San Joaquin Valley. A mixture model was applied to both images that identified non- photosynthetic vegetation, green vegetation, and soil cover fractions through image endmembers of each of these three classes. We optimized the spectral library used to identify these classes with Iterative Endmember Selection (IES), and the images were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA). Maps of soil electrical conductivity, organic matter, soil saturated moisture, and field moisture were generated for the San Joaquin Valley based on indices developed by Ben-Dor et al. [2002]. Representative polygons were chosen to quantify changes between years. Maps of spectrally distinct soils were also generated for 2013 and 2014, in order to determine the spatial distribution of these soil types as well as their temporal dynamics between years. We estimated that soil cover increased by 16% from 2013-2014. Six spectrally distinct soil types were identified for the region, and it was determined that the distribution of these soil types was not constant for most areas between 2013 and 2014. Changes in soil pH, electrical conductivity, and soil moisture were strongly tied in the region between 2013 and 2014.

Modeling uncertainty and correlation in soil properties using Restricted Pairing and implications for ensemble-based hillslope-scale soil moisture and temperature estimation

NASA Astrophysics Data System (ADS)

Flores, A. N.; Entekhabi, D.; Bras, R. L.

2007-12-01

Soil hydraulic and thermal properties (SHTPs) affect both the rate of moisture redistribution in the soil column and the volumetric soil water capacity. Adequately constraining these properties through field and lab analysis to parameterize spatially-distributed hydrology models is often prohibitively expensive. Because SHTPs vary significantly at small spatial scales individual soil samples are also only reliably indicative of local conditions, and these properties remain a significant source of uncertainty in soil moisture and temperature estimation. In ensemble-based soil moisture data assimilation, uncertainty in the model-produced prior estimate due to associated uncertainty in SHTPs must be taken into account to avoid under-dispersive ensembles. To treat SHTP uncertainty for purposes of supplying inputs to a distributed watershed model we use the restricted pairing (RP) algorithm, an extension of Latin Hypercube (LH) sampling. The RP algorithm generates an arbitrary number of SHTP combinations by sampling the appropriate marginal distributions of the individual soil properties using the LH approach, while imposing a target rank correlation among the properties. A previously-published meta- database of 1309 soils representing 12 textural classes is used to fit appropriate marginal distributions to the properties and compute the target rank correlation structure, conditioned on soil texture. Given categorical soil textures, our implementation of the RP algorithm generates an arbitrarily-sized ensemble of realizations of the SHTPs required as input to the TIN-based Realtime Integrated Basin Simulator with vegetation dynamics (tRIBS+VEGGIE) distributed parameter ecohydrology model. Soil moisture ensembles simulated with RP- generated SHTPs exhibit less variance than ensembles simulated with SHTPs generated by a scheme that neglects correlation among properties. Neglecting correlation among SHTPs can lead to physically unrealistic combinations of parameters that exhibit implausible hydrologic behavior when input to the tRIBS+VEGGIE model.

Improving UK Chalk hydrometeorology across spatial scales using a small hydrometeorological network

NASA Astrophysics Data System (ADS)

Rosolem, Rafael; Iwema, Joost; Rahman, Mostaquimur; Desilets, Darin; Koltermann da Silva, Juliana

2016-04-01

Chalk in the UK acts as a primary aquifer providing up to 80% of the public water supply locally. Chalk outcrops are located over most of southern and eastern England. Despite its importance, the characterization of Chalk in hydrometeorological models is still very limited. There is a need for a comprehensive and coherent integration of observations and modeling efforts across spatial scales for better understanding Chalk hydrometeorology. Here we introduce the "A MUlti-scale Soil moisture-Evapotranspiration Dynamics" (AMUSED) project. AMUSED goal is to better identify the key dominant processes controlling changes in soil moisture and surface fluxes (e.g., evapotranspiration) across spatial scales by combining ground-based observations with hydrometeorological models and satellite remote sensing products. The AMUSED observational platform consists of three sites located in Upper Chalk region of the Lambourn Catchment located in southern England covering approximately 2 square-km characterized by distinct combinations of soil and vegetation types. The network includes standard meteorological measurements, an eddy covariance system for turbulent fluxes and cosmic-ray neutron sensors for integrated soil moisture estimates at intermediate scales. Here we present our initial results from our three sites.

Simultaneous field measurements of biogenic emissions of nitric oxide and nitrous oxide

NASA Technical Reports Server (NTRS)

Anderson, Iris Cofman; Levine, Joel S.

1987-01-01

Seasonal and diurnal emissions of NO and N2O from agricultural sites in Jamestown, Virginia and Boulder, Colorado are estimated in terms of soil temperature; percent moisture; and exchangeable nitrate, nitrite, and ammonium concentrations. The techniques and procedures used to analyze the soil parameters are described. The spatial and temporal variability of the NO and N2O emissions is studied. A correlation between NO fluxes in the Virginia sample and nitrate concentration, temperature, and percent moisture is detected, and NO fluxes for the Colorado site correspond with temperature and moisture. It is observed that the N2O emissions are only present when percent moisture approaches or exceeds the field capacity of the soil. The data suggest that NO is produced primarily by nitrification in aerobic soils, and N2O is formed by denitrification in anaerobic soils.

Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

NASA Technical Reports Server (NTRS)

Li, Bailing; Toll, David; Zhan, Xiwu; Cosgrove, Brian

2011-01-01

Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.

Using a spatially-distributed hydrologic biogeochemistry model to study the spatial variation of carbon processes in a Critical Zone Observatory

NASA Astrophysics Data System (ADS)

Shi, Y.; Eissenstat, D. M.; Davis, K. J.; He, Y.

2016-12-01

Forest carbon processes are affected by, among other factors, soil moisture, soil temperature, soil nutrients and solar radiation. Most of the current biogeochemical models are 1-D and represent one point in space. Therefore, they cannot resolve the topographically driven hill-slope land surface heterogeneity or the spatial pattern of nutrient availability. A spatially distributed forest ecosystem model, Flux-PIHM-BGC, has been developed by coupling a 1-D mechanistic biogeochemical model Biome-BGC (BBGC) with a spatially distributed land surface hydrologic model, Flux-PIHM. Flux-PIHM is a coupled physically based model, which incorporates a land-surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model. Flux-PIHM is able to represent the link between groundwater and the surface energy balance, as well as the land surface heterogeneities caused by topography. In the coupled Flux-PIHM-BGC model, each Flux-PIHM model grid couples a 1-D BBGC model, while soil nitrogen is transported among model grids via subsurface water flow. In each grid, Flux-PIHM provides BBGC with soil moisture, soil temperature, and solar radiation information, while BBGC provides Flux-PIHM with leaf area index. The coupled Flux-PIHM-BGC model has been implemented at the Susquehanna/Shale Hills critical zone observatory (SSHCZO). Model results suggest that the vegetation and soil carbon distribution is primarily constrained by nitorgen availability (affected by nitorgen transport via topographically driven subsurface flow), and also constrained by solar radiation and root zone soil moisture. The predicted vegetation and soil carbon distribution generally agrees with the macro pattern observed within the watershed. The coupled ecosystem-hydrologic model provides an important tool to study the impact of topography on watershed carbon processes, as well as the impact of climate change on water resources.

Footprint Characteristics of Cosmic-Ray Neutron Sensors for Soil Moisture Monitoring

NASA Astrophysics Data System (ADS)

Schrön, Martin; Köhli, Markus; Zreda, Marek; Dietrich, Peter; Zacharias, Steffen

2015-04-01

Cosmic-ray neutron sensing is a unique and an increasingly accepted method to monitor the effective soil water content at the field scale. The technology is famous for its low maintenance, non-invasiveness, continuous measurement, and most importantly, for its large footprint. Being more representative than point data and finer resolved than remote-sensing products, cosmic-ray neutron derived soil moisture products provide unrivaled advantage for mesoscale hydrologic and land surface models. The method takes advantage of neutrons induced by cosmic radiation which are extraordinarily sensitive to hydrogen and behave like a hot gas. Information about nearby water sources are quickly mixed in a domain of tens of hectares in air. Since experimental determination of the actual spatial extent is hardly possible, scientists have applied numerical models to address the footprint characteristics. We have revisited previous neutron transport simulations and present a modified conceptual design and refined physical assumptions. Our revised study reveals new insights into probing distance and water sensitivity of detected neutrons under various environmental conditions. These results sharpen the range of interpretation concerning the spatial extent of integral soil moisture products derived from cosmic-ray neutron counts. Our findings will have important impact on calibration strategies, on scales for data assimilation and on the interpolation of soil moisture data derived from mobile cosmic-ray neutron surveys.

Microwave soil moisture estimation in humid and semiarid watersheds

NASA Technical Reports Server (NTRS)

O'Neill, P. E.; Jackson, T. J.; Chauhan, N. S.; Seyfried, M. S.

1993-01-01

Land surface hydrologic-atmospheric interactions in humid and semi-arid watersheds were investigated. Active and passive microwave sensors were used to estimate the spatial and temporal distribution of soil moisture at the catchment scale in four areas. Results are presented and discussed. The eventual use of this information in the analysis and prediction of associated hydrologic processes is examined.

Downscaling Coarse Scale Microwave Soil Moisture Product using Machine Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.; Yan, H.

2016-12-01

Soil moisture (SM) is a key variable in partitioning and examining the global water-energy cycle, agricultural planning, and water resource management. It is also strongly coupled with climate change, playing an important role in weather forecasting and drought monitoring and prediction, flood modeling and irrigation management. Although satellite retrievals can provide an unprecedented information of soil moisture at a global-scale, the products might be inadequate for basin scale study or regional assessment. To improve the spatial resolution of SM, this work presents a novel approach based on Machine Learning (ML) technique that allows for downscaling of the satellite soil moisture to fine resolution. For this purpose, the SMAP L-band radiometer SM products were used and conditioned on the Variable Infiltration Capacity (VIC) model prediction to describe the relationship between the coarse and fine scale soil moisture data. The proposed downscaling approach was applied to a western US basin and the products were compared against the available SM data from in-situ gauge stations. The obtained results indicated a great potential of the machine learning technique to derive the fine resolution soil moisture information that is currently used for land data assimilation applications.

SMOS+RAINFALL: Evaluating the ability of different methodologies to improve rainfall estimations using soil moisture data from SMOS

NASA Astrophysics Data System (ADS)

Pellarin, Thierry; Brocca, Luca; Crow, Wade; Kerr, Yann; Massari, Christian; Román-Cascón, Carlos; Fernández, Diego

2017-04-01

Recent studies have demonstrated the usefulness of soil moisture retrieved from satellite for improving rainfall estimations of satellite based precipitation products (SBPP). The real-time version of these products are known to be biased from the real precipitation observed at the ground. Therefore, the information contained in soil moisture can be used to correct the inaccuracy and uncertainty of these products, since the value and behavior of this soil variable preserve the information of a rain event even for several days. In this work, we take advantage of the soil moisture data from the Soil Moisture and Ocean Salinity (SMOS) satellite, which provides information with a quite appropriate temporal and spatial resolution for correcting rainfall events. Specifically, we test and compare the ability of three different methodologies for this aim: 1) SM2RAIN, which directly relate changes in soil moisture to rainfall quantities; 2) The LMAA methodology, which is based on the assimilation of soil moisture in two models of different complexity (see EGU2017-5324 in this same session); 3) The SMART method, based on the assimilation of soil moisture in a simple hydrological model with a different assimilation/modelling technique. The results are tested for 6 years over 10 sites around the world with different features (land surface, rainfall climatology, orography complexity, etc.). These preliminary and promising results are shown here for the first time to the scientific community, as also the observed limitations of the different methodologies. Specific remarks on the technical configurations, filtering/smoothing of SMOS soil moisture or re-scaling techniques are also provided from the results of different sensitivity experiments.

Individual contributions of climate and vegetation change to soil moisture trends across multiple spatial scales.

PubMed

Feng, Huihui

2016-09-07

Climate and vegetation change are two dominating factors for soil moisture trend. However, their individual contributions remain unknown due to their complex interaction. Here, I separated their contributions through a trajectory-based method across the global, regional and local scales. Our results demonstrated that climate change accounted for 98.78% and 114.64% of the global drying and wetting trend. Vegetation change exhibited a relatively weak influence (contributing 1.22% and -14.64% of the global drying and wetting) because it occurred in a limited area on land. Regionally, the impact of vegetation change cannot be neglected, which contributed -40.21% of the soil moisture change in the wetting zone. Locally, the contributions strongly correlated to the local environmental characteristics. Vegetation negatively affected soil moisture trends in the dry and sparsely vegetated regions and positively in the wet and densely vegetated regions. I conclude that individual contributions of climate and vegetation change vary at the global, regional and local scales. Climate change dominates the soil moisture trends, while vegetation change acts as a regulator to drying or wetting the soil under the changing climate.

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.

Temporal changes in soil water repellency linked to the soil respiration and CH4 and CO2 fluxes

NASA Astrophysics Data System (ADS)

Qassem, Khalid; Urbanek, Emilia; van Keulen, Geertje

2014-05-01

Soil water repellency (SWR) is known to be a spatially and temporally variable phenomenon. The seasonal changes in soil moisture lead to development of soil water repellency, which in consequence may affect the microbial activity and in consequence alter the CO2 and CH4 fluxes from soils. Soil microbial activity is strongly linked to the temperature and moisture status of the soil. In terms of CO2 flux intermediate moisture contents are most favourable for the optimal microbial activity and highest CO2 fluxes. Methanogenesis occurs primarily in anaerobic water-logged habitats while methanotrophy is a strictly aerobic process. In the study we hypothesise that the changes in CO2 and CH4 fluxes are closely linked to critical moisture thresholds for soil water repellency. This research project aims to adopt a multi-disciplinary approach to comprehensively determine the effect of SWR on CO2 and CH4 fluxes. Research is conducted in situ at four sites exhibiting SWR in the southern UK. Flux measurements are carried out concomitant with meteorological and SWR observations Field observations are supported by laboratory measurements carried out on intact soil samples collected at the above identified field sites. The laboratory analyses are conducted under constant temperatures with controlled changes of soil moisture content. Methanogenic and Methanotrophic microbial populations are being analysed at different SWR and moisture contents using the latest metagenomic and metatranscriptomic approaches. Currently available data show that greenhouse gas flux are closely linked with soil moisture thresholds for SWR development.

Simulation of semi-arid hydrological processes at different spatial resolutions using the AgroEcoSystem-Watershed (AgES-W) model

NASA Astrophysics Data System (ADS)

Green, T. R.; Erksine, R. H.; David, O.; Ascough, J. C., II; Kipka, H.; Lloyd, W. J.; McMaster, G. S.

2015-12-01

Water movement and storage within a watershed may be simulated at different spatial resolutions of land areas or hydrological response units (HRUs). Here, effects of HRU size on simulated soil water and surface runoff are tested using the AgroEcoSystem-Watershed (AgES-W) model with three different resolutions of HRUs. We studied a 56-ha agricultural watershed in northern Colorado, USA farmed primarily under a wheat-fallow rotation. The delineation algorithm was based upon topography (surface flow paths), land use (crop management strips and native grass), and mapped soil units (three types), which produced HRUs that follow the land use and soil boundaries. AgES-W model parameters that control surface and subsurface hydrology were calibrated using simulated daily soil moisture at different landscape positions and depths where soil moisture was measured hourly and averaged up to daily values. Parameter sets were both uniform and spatially variable with depth and across the watershed (5 different calibration approaches). Although forward simulations were computationally efficient (less than 1 minute each), each calibration required thousands of model runs. Execution of such large jobs was facilitated by using the Object Modeling System with the Cloud Services Innovation Platform to manage four virtual machines on a commercial web service configured with a total of 64 computational cores and 120 GB of memory. Results show how spatially distributed and averaged soil moisture and runoff at the outlet vary with different HRU delineations. The results will help guide HRU delineation, spatial resolution and parameter estimation methods for improved hydrological simulations in this and other semi-arid agricultural watersheds.

The sensitivity of US wildfire occurrence to pre-season soil moisture conditions across ecosystems.

PubMed

Jensen, Daniel; Reager, John T; Zajic, Brittany; Rousseau, Nick; Rodell, Matthew; Hinkley, Everett

2018-01-01

It is generally accepted that year-to-year variability in moisture conditions and drought are linked with increased wildfire occurrence. However, quantifying the sensitivity of wildfire to surface moisture state at seasonal lead-times has been challenging due to the absence of a long soil moisture record with the appropriate coverage and spatial resolution for continental-scale analysis. Here we apply model simulations of surface soil moisture that numerically assimilate observations from NASA's Gravity Recovery and Climate Experiment (GRACE) mission with the US Forest Service's historical Fire-Occurrence Database over the contiguous United States. We quantify the relationships between pre-fire-season soil moisture and subsequent-year wildfire occurrence by land-cover type and produce annual probable wildfire occurrence and burned area maps at 0.25-degree resolution. Cross-validated results generally indicate a higher occurrence of smaller fires when months preceding fire season are wet, while larger fires are more frequent when soils are dry. This result is consistent with the concept of increased fuel accumulation under wet conditions in the pre-season. These results demonstrate the fundamental strength of the relationship between soil moisture and fire activity at long lead-times and are indicative of that relationship's utility for the future development of national-scale predictive capability.

The sensitivity of US wildfire occurrence to pre-season soil moisture conditions across ecosystems

NASA Astrophysics Data System (ADS)

Jensen, Daniel; Reager, John T.; Zajic, Brittany; Rousseau, Nick; Rodell, Matthew; Hinkley, Everett

2018-01-01

It is generally accepted that year-to-year variability in moisture conditions and drought are linked with increased wildfire occurrence. However, quantifying the sensitivity of wildfire to surface moisture state at seasonal lead-times has been challenging due to the absence of a long soil moisture record with the appropriate coverage and spatial resolution for continental-scale analysis. Here we apply model simulations of surface soil moisture that numerically assimilate observations from NASA’s Gravity Recovery and Climate Experiment (GRACE) mission with the USDA Forest Service’s historical Fire-Occurrence Database over the contiguous United States. We quantify the relationships between pre-fire-season soil moisture and subsequent-year wildfire occurrence by land-cover type and produce annual probable wildfire occurrence and burned area maps at 0.25 degree resolution. Cross-validated results generally indicate a higher occurrence of smaller fires when months preceding fire season are wet, while larger fires are more frequent when soils are dry. This is consistent with the concept of increased fuel accumulation under wet conditions in the pre-season. These results demonstrate the fundamental strength of the relationship between soil moisture and fire activity at long lead-times and are indicative of that relationship’s utility for the future development of national-scale predictive capability.

Irrigation Signals Detected From SMAP Soil Moisture Retrievals

NASA Astrophysics Data System (ADS)

Lawston, Patricia M.; Santanello, Joseph A.; Kumar, Sujay V.

2017-12-01

Irrigation can influence weather and climate, but the magnitude, timing, and spatial extent of irrigation are poorly represented in models, as are the resulting impacts of irrigation on the coupled land-atmosphere system. One way to improve irrigation representation in models is to assimilate soil moisture observations that reflect an irrigation signal to improve model states. Satellite remote sensing is a promising avenue for obtaining these needed observations on a routine basis, but to date, irrigation detection in passive microwave satellites has proven difficult. In this study, results show that the new enhanced soil moisture product from the Soil Moisture Active Passive satellite is able to capture irrigation signals over three semiarid regions in the western United States. This marks an advancement in Earth-observing satellite skill and the ability to monitor human impacts on the water cycle.

Soil Moisture fusion across scales using a multiscale nonstationary Spatial Hierarchical Model

NASA Astrophysics Data System (ADS)

Kathuria, D.; Mohanty, B.; Katzfuss, M.

2017-12-01

Soil moisture (SM) datasets from remote sensing (RS) platforms (such as SMOS and SMAP) and reanalysis products from land surface models are typically available on a coarse spatial granularity of several square km. Ground based sensors, on the other hand, provide observations on a finer spatial scale (meter scale or less) but are sparsely available. SM is affected by high variability due to complex interactions between geologic, topographic, vegetation and atmospheric variables and these interactions change dynamically with footprint scales. Past literature has largely focused on the scale specific effect of these covariates on soil moisture. The present study proposes a robust Multiscale-Nonstationary Spatial Hierarchical Model (MN-SHM) which can assimilate SM from point to RS footprints. The spatial structure of SM across footprints is modeled by a class of scalable covariance functions whose nonstationary depends on atmospheric forcings (such as precipitation) and surface physical controls (such as topography, soil-texture and vegetation). The proposed model is applied to fuse point and airborne ( 1.5 km) SM data obtained during the SMAPVEX12 campaign in the Red River watershed in Southern Manitoba, Canada with SMOS ( 30km) data. It is observed that precipitation, soil-texture and vegetation are the dominant factors which affect the SM distribution across various footprint scales (750 m, 1.5 km, 3 km, 9 km,15 km and 30 km). We conclude that MN-SHM handles the change of support problems easily while retaining reasonable predictive accuracy across multiple spatial resolutions in the presence of surface heterogeneity. The MN-SHM can be considered as a complex non-stationary extension of traditional geostatistical prediction methods (such as Kriging) for fusing multi-platform multi-scale datasets.

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

NASA Astrophysics Data System (ADS)

Yi, Yonghong; Kimball, John S.; Chen, Richard H.; Moghaddam, Mahta; Reichle, Rolf H.; Mishra, Umakant; Zona, Donatella; Oechel, Walter C.

2018-01-01

An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modeling and analysis framework combining field observations, local-scale ( ˜ 50 m resolution) active layer thickness (ALT) and soil moisture maps derived from low-frequency (L + P-band) airborne radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modeled ALT results show good correspondence with in situ measurements in higher-permafrost-probability (PP ≥ 70 %) areas (n = 33; R = 0.60; mean bias = 1.58 cm; RMSE = 20.32 cm), but with larger uncertainty in sporadic and discontinuous permafrost areas. The model results also reveal widespread ALT deepening since 2001, with smaller ALT increases in northern Alaska (mean trend = 0.32±1.18 cm yr-1) and much larger increases (> 3 cm yr-1) across interior and southern Alaska. The positive ALT trend coincides with regional warming and a longer snow-free season (R = 0.60 ± 0.32). A spatially integrated analysis of the radar retrievals and model sensitivity simulations demonstrated that uncertainty in the spatial and vertical distribution of soil organic carbon (SOC) was the largest factor affecting modeled ALT accuracy, while soil moisture played a secondary role. Potential improvements in characterizing SOC heterogeneity, including better spatial sampling of soil conditions and advances in remote sensing of SOC and soil moisture, will enable more accurate predictions of active layer conditions and refinement of the modeling framework across a larger domain.

Empirical Modeling of Planetary Boundary Layer Dynamics Under Multiple Precipitation Scenarios Using a Two-Layer Soil Moisture Approach: An Example From a Semiarid Shrubland

NASA Astrophysics Data System (ADS)

Sanchez-Mejia, Zulia Mayari; Papuga, Shirley A.

2017-11-01

In semiarid regions, where water resources are limited and precipitation dynamics are changing, understanding land surface-atmosphere interactions that regulate the coupled soil moisture-precipitation system is key for resource management and planning. We present a modeling approach to study soil moisture and albedo controls on planetary boundary layer height (PBLh). We used Santa Rita Creosote Ameriflux and Tucson Airport atmospheric sounding data to generate empirical relationships between soil moisture, albedo, and PBLh. Empirical relationships showed that ˜50% of the variation in PBLh can be explained by soil moisture and albedo with additional knowledge gained by dividing the soil profile into two layers. Therefore, we coupled these empirical relationships with soil moisture estimated using a two-layer bucket approach to model PBLh under six precipitation scenarios. Overall we observed that decreases in precipitation tend to limit the recovery of the PBL at the end of the wet season. However, increases in winter precipitation despite decreases in summer precipitation may provide opportunities for positive feedbacks that may further generate more winter precipitation. Our results highlight that the response of soil moisture, albedo, and the PBLh will depend not only on changes in annual precipitation, but also on the frequency and intensity of this change. We argue that because albedo and soil moisture data are readily available at multiple temporal and spatial scales, developing empirical relationships that can be used in land surface-atmosphere applications have great potential for exploring the consequences of climate change.

Characterization of soil spatial variability for site-specific management using soil electrical conductivity and other remotely sensed data

NASA Astrophysics Data System (ADS)

Bang, Jisu

Field-scale characterization of soil spatial variability using remote sensing technology has potential for achieving the successful implementation of site-specific management (SSM). The objectives of this study were to: (i) examine the spatial relationships between apparent soil electrical conductivity (EC a) and soil chemical and physical properties to determine if EC a could be useful to characterize soil properties related to crop productivity in the Coastal Plain and Piedmont of North Carolina; (ii) evaluate the effects of in-situ soil moisture variation on ECa mapping as a basis for characterization of soil spatial variability and as a data layer in cluster analysis as a means of delineating sampling zones; (iii) evaluate clustering approaches using different variable sets for management zone delineation to characterize spatial variability in soil nutrient levels and crop yields. Studies were conducted in two fields in the Piedmont and three fields in the Coastal Plain of North Carolina. Spatial measurements of ECa via electromagnetic induction (EMI) were compared with soil chemical parameters (extractable P, K, and micronutrients; pH, cation exchange capacity [CEC], humic matter or soil organic matter; and physical parameters (percentage sand, silt, and clay; and plant-available water [PAW] content; bulk density; cone index; saturated hydraulic conductivity [Ksat] in one of the coastal plain fields) using correlation analysis across fields. We also collected ECa measurements in one coastal plain field on four days with significantly different naturally occurring soil moisture conditions measured in five increments to 0.75 m using profiling time-domain reflectometry probes to evaluate the temporal variability of ECa associated with changes in in-situ soil moisture content. Nonhierarchical k-means cluster analysis using sensor-based field attributes including vertical ECa, near-infrared (NIR) radiance of bare-soil from an aerial color infrared (CIR) image, elevation, slope, and their combinations was performed to delineate management zones. The strengths and signs of the correlations between ECa and measured soil properties varied among fields. Few strong direct correlations were found between ECa and the soil chemical and physical properties studied (r2 < 0.50), but correlations improved considerably when zone mean ECa and zone means of selected soil properties among ECa zones were compared. The results suggested that field-scale ECa survey is not able to directly predict soil nutrient levels at any specific location, but could delimit distinct zones of soil condition among which soil nutrient levels differ, providing an effective basis for soil sampling on a zone basis. (Abstract shortened by UMI.)

[Effect of irregular bedrock topography on the soil profile pattern of water content in a Karst hillslope.

PubMed

Jia, Jin Tian; Fu, Zhi Yong; Chen, Hong Song; Wang, Ke Lin; Zhou, Wei Jun

2016-06-01

Based on three manually excavated trenches (projection length of 21 m, width of 1 m) along a typical Karst hillslope, the changing trends for soil-bedrock structure, average water content of soil profile and soil-bedrock interface water content along each individual trench were studied. The effect of irregular bedrock topography on soil moisture distribution was discussed. The results showed that the surface topography was inconsistent with the bedrock topography in the Karst hill-slopes. The bedrock topography was highly irregular with a maximum variation coefficient of 82%. The distribution pattern of soil profile of moisture was significantly affected by the underlying undulant bedrock. The soil water content was related to slope position when the fluctuation was gentle, and displayed a linear increase from upslope to downslope. When the bedrock fluctuation increased, the downslope linear increasing trend for soil water content became unapparent, and the spatial continuity of soil moisture was weakened. The soil moisture was converged in rock dents and cracks. The average water content of soil profile was significantly positively correlated with the soil-bedrock interface water content, while the latter responded more sensitively to the bedrock fluctuation.

Soil moisture at local scale: Measurements and simulations

NASA Astrophysics Data System (ADS)

Romano, Nunzio

2014-08-01

Soil moisture refers to the water present in the uppermost part of a field soil and is a state variable controlling a wide array of ecological, hydrological, geotechnical, and meteorological processes. The literature on soil moisture is very extensive and is developing so rapidly that it might be considered ambitious to seek to present the state of the art concerning research into this key variable. Even when covering investigations about only one aspect of the problem, there is a risk of some inevitable omission. A specific feature of the present essay, which may make this overview if not comprehensive at least of particular interest, is that the reader is guided through the various traditional and more up-to-date methods by the central thread of techniques developed to measure soil moisture interwoven with applications of modeling tools that exploit the observed datasets. This paper restricts its analysis to the evolution of soil moisture at the local (spatial) scale. Though a somewhat loosely defined term, it is linked here to a characteristic length of the soil volume investigated by the soil moisture sensing probe. After presenting the most common concepts and definitions about the amount of water stored in a certain volume of soil close to the land surface, this paper proceeds to review ground-based methods for monitoring soil moisture and evaluates modeling tools for the analysis of the gathered information in various applications. Concluding remarks address questions of monitoring and modeling of soil moisture at scales larger than the local scale with the related issue of data aggregation. An extensive, but not exhaustive, list of references is provided, enabling the reader to gain further insights into this subject.

Quantifying agricultural drought impacts using soil moisture model and drought indices in South Korea

NASA Astrophysics Data System (ADS)

Nam, W. H.; Bang, N.; Hong, E. M.; Pachepsky, Y. A.; Han, K. H.; Cho, H.; Ok, J.; Hong, S. Y.

2017-12-01

Agricultural drought is defined as a combination of abnormal deficiency of precipitation, increased crop evapotranspiration demands from high-temperature anomalies, and soil moisture deficits during the crop growth period. Soil moisture variability and their spatio-temporal trends is a key component of the hydrological balance, which determines the crop production and drought stresses in the context of agriculture. In 2017, South Korea has identified the extreme drought event, the worst in one hundred years according to the South Korean government. The objective of this study is to quantify agricultural drought impacts using observed and simulated soil moisture, and various drought indices. A soil water balance model is used to simulate the soil water content in the crop root zone under rain-fed (no irrigation) conditions. The model used includes physical process using estimated effective rainfall, infiltration, redistribution in soil water zone, and plant water uptake in the form of actual crop evapotranspiration. Three widely used drought indices, including the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), and the Self-Calibrated Palmer Drought Severity Index (SC-PDSI) are compared with the observed and simulated soil moisture in the context of agricultural drought impacts. These results demonstrated that the soil moisture model could be an effective tool to provide improved spatial and temporal drought monitoring for drought policy.

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.

Divergent hydrological response to large-scale afforestation and vegetation greening in China

PubMed Central

Ciais, Philippe; Huang, Ling; Wang, Kai; Zhou, Liming

2018-01-01

China has experienced substantial changes in vegetation cover, with a 10% increase in the leaf area index and an ~41.5 million-hectare increase in forest area since the 1980s. Earlier studies have suggested that increases in leaf area and tree cover have led to a decline in soil moisture and runoff due to increased evapotranspiration (ET), especially in dry regions of China. However, those studies often ignored precipitation responses to vegetation increases, which could offset some of the negative impact on soil moisture by increased ET. We investigated 30-year vegetation impacts on regional hydrology by allowing for vegetation-induced changes in precipitation using a coupled land-atmosphere global climate model, with a higher spatial resolution zoomed grid over China. We found high spatial heterogeneity in the vegetation impacts on key hydrological variables across China. In North and Southeast China, the increased precipitation from vegetation greening and the increased forest area, although statistically insignificant, supplied enough water to cancel out enhanced ET, resulting in weak impact on soil moisture. In Southwest China, however, the increase in vegetation cover significantly reduced soil moisture while precipitation was suppressed by the weakened summer monsoon. In Northeast China, the only area where forest cover declined, soil moisture was significantly reduced, by −8.1 mm decade−1, likely because of an intensified anticyclonic circulation anomaly during summer. These results suggest that offline model simulations can overestimate the increase of soil dryness in response to afforestation in North China, if vegetation feedbacks lead to increased precipitation like in our study. PMID:29750196

Watershed-Scale Heterogeneity of the Biophysical Controls on Soil Respiration

NASA Astrophysics Data System (ADS)

Riveros, D. A.; Pacific, V. J.; McGlynn, B. L.; Welsch, D. L.; Epstein, H. E.; Muth, D. J.; Marshall, L.; Wraith, J.

2006-12-01

Large gaps exist in our understanding of the variability of soil respiration response to changing hydrologic conditions across spatial and temporal scales. Determining the linkages between the hydrologic cycle and the biophysical controls of soil respiration from the local point, to the plot, to the watershed scale is critical to understanding the dynamics of net ecosystem CO2 exchange (NEE). To study the biophysical controls of soil respiration, we measured soil CO2 concentration, soil CO2 flux, dissolved CO2 in stream water, soil moisture, soil temperature, groundwater dynamics, and precipitation at 20-minute intervals throughout the growing season at 4 sites and at weekly intervals at 62 sites covering the range of topographic position, slope, aspect, land cover, and upslope accumulated area conditions in a 555-ha subalpine watershed in central Montana. Our goal was to quantify watershed-scale heterogeneity in soil CO2 concentrations and surface efflux and gain understanding of the biophysical controls on soil respiration. We seek to improve our ability to evaluate and predict soil respiration responses to a dynamic hydrologic cycle across multiple temporal and spatial scales. We found that time lags between biophysical controls and soil respiration can occur from hourly to daily scales. The sensitivity of soil respiration to changes in environmental conditions is controlled by the antecedent soil moisture and by topographic position. At the watershed scale, significant differences in soil respiration exist between upland (dry) and lowland (wet) sites. However, differences in the magnitude and timing of soil respiration also exist within upland settings due to heterogeneity in soil temperature, soil moisture, and soil organic matter. Finally, we used a process-based model to simulate respiration at different times of the year across spatial locations. Our simulations highlight the importance of autotrophic and heterotrophic respiration (production) over diffusivity and soil physical properties (transport). Our work begins to address the disconnect between point, footprint, watershed scale estimates of ecosystem respiration and the role of a dynamic hydrologic cycle.

Integration of Hydrogeophysical Datasets for Improved Water Resource Management in Irrigated Systems

NASA Astrophysics Data System (ADS)

Finkenbiner, C. E.; Franz, T. E.; Heeren, D.; Gibson, J. P.; Russell, M. V.

2016-12-01

With an average irrigation water use efficiency of approximately 45% in the United States, improvements in water management can be made within agricultural systems. Advancements in precision irrigation technologies allow application rates and times to vary within a field. Current limitations in applying these technologies are often attributed to the quantification of soil spatial variability. This work aims to increase our understanding of soil hydrologic fluxes at intermediate spatial scales. Field capacity and wilting point values for a field near Sutherland, NE were downloaded from the USDA SSURGO database. Stationary and roving cosmic-ray neutron probes (CRNP) (sensor measurement volume of 300 m radius sphere and 30 cm vertical soil depth) were combined in order to characterize the spatial and temporal patterns of soil moisture at the site. We used a data merging technique to produce a statistical daily soil moisture product at a range of key spatial scales in support of current irrigation technologies: the individual sprinkler ( 102 m2) for variable rate irrigation, the individual wedge ( 103 m2) for variable speed irrigation, and the quarter section (0.82 km2) for uniform rate irrigation. The results show our CRNP "observed" field capacity was higher compared to the SSURGO products. The measured hydraulic properties from sixty-two soil cores collected from the field correlate well with our "observed" CRNP values. We hypothesize that our results, when provided to irrigators, will decrease water losses due to runoff and deep percolation as sprinkler managers can better estimate irrigation application depths and times in relation to soil moisture depletion below field capacity and above maximum allowable depletion. The incorporation of the CRNP into current irrigation practices has the potential to greatly increase agricultural water use efficiency. Moreover, the defined soil hydraulic properties at various spatial scales offers additional valuable datasets for the land surface modeling community.

Impact of climate change on soil thermal and moisture regimes in Serbia: An analysis with data from regional climate simulations under SRES-A1B.

PubMed

Mihailović, D T; Drešković, N; Arsenić, I; Ćirić, V; Djurdjević, V; Mimić, G; Pap, I; Balaž, I

2016-11-15

We considered temporal and spatial variations to the thermal and moisture regimes of the most common RSGs (Reference Soil Groups) in Serbia under the A1B scenario for the 2021-2050 and 2071-2100 periods, with respect to the 1961-1990 period. We utilized dynamically downscaled global climate simulations from the ECHAM5 model using the coupled regional climate model EBU-POM (Eta Belgrade University-Princeton Ocean Model). We analysed the soil temperature and moisture time series using simple statistics and a Kolmogorov complexity (KC) analysis. The corresponding metrics were calculated for 150 sites. In the future, warmer and drier regimes can be expected for all RSGs in Serbia. The calculated soil temperature and moisture variations include increases in the mean annual soil temperature (up to 3.8°C) and decreases in the mean annual soil moisture (up to 11.3%). Based on the KC values, the soils in Serbia are classified with respect to climate change impacts as (1) less sensitive (Vertisols, Umbrisols and Dystric Cambisols) or (2) more sensitive (Chernozems, Eutric Cambisols and Planosols). Copyright © 2016 Elsevier B.V. All rights reserved.

Integration of Satellite, Global Reanalysis Data and Macroscale Hydrological Model for Drought Assessment in Sub-Tropical Region of India

NASA Astrophysics Data System (ADS)

Pandey, V.; Srivastava, P. K.

2018-04-01

Change in soil moisture regime is highly relevant for agricultural drought, which can be best analyzed in terms of Soil Moisture Deficit Index (SMDI). A macroscale hydrological model Variable Infiltration Capacity (VIC) was used to simulate the hydro-climatological fluxes including evapotranspiration, runoff, and soil moisture storage to reconstruct the severity and duration of agricultural drought over semi-arid region of India. The simulations in VIC were performed at 0.25° spatial resolution by using a set of meteorological forcing data, soil parameters and Land Use Land Cover (LULC) and vegetation parameters. For calibration and validation, soil parameters obtained from National Bureau of Soil Survey and Land Use Planning (NBSSLUP) and ESA's Climate Change Initiative soil moisture (CCI-SM) data respectively. The analysis of results demonstrates that most of the study regions (> 80 %) especially for central northern part are affected by drought condition. The year 2001, 2002, 2007, 2008 and 2009 was highly affected by agricultural drought. Due to high average and maximum temperature, we observed higher soil evaporation that reduces the surface soil moisture significantly as well as the high topographic variations; coarse soil texture and moderate to high wind speed enhanced the drying upper soil moisture layer that incorporate higher negative SMDI over the study area. These findings can also facilitate the archetype in terms of daily time step data, lengths of the simulation period, various hydro-climatological outputs and use of reasonable hydrological model.

Geophysical surveys combined with laboratory soil column experiments to identify and explore risk areas for soil and water pollution in feedlots

NASA Astrophysics Data System (ADS)

Espejo-Pérez, Antonio Jesus; Sainato, Claudia Mabel; Jairo Márquez-Molina, John; Giráldez, Juan Vicente; Vanderlinden, Karl

2014-05-01

Changes of land use without a correct planning may produce its deterioration with their social, economical and environmental irreversible consequences over short to medium time range. In Argentina, the expansion of soybean fields induced a reduction of the area of pastures dedicated to stockbreeding. As cattle activity is being progressively concentrated on small pens, at feedlots farms, problems of soil and water pollution, mainly by nitrate, have been detected. The characterization of the spatial and temporal variability of soil water content is very important because the mostly advective transport of solutes. To avoid intensive soil samplings, very expensive, one has to recur to geophysical exploration methods. The objective of this work was to identify risk areas within a feedlot of the NW zone of Buenos Aires Province, in Argentina through geophysical methods. The surveys were carried out with an electromagnetic induction profiler EMI-400 (GSSI) and a Time domain Reflectometry (TDR) survey of depth 0-0.10 m with soil sampling and measurement of moisture content with gravimetric method (0-1.0 m). Several trenches were dug inside the pens and also at a test site, where texture, apparent density, saturated hydraulic conductivity (Ks), electrical conductivity of the saturation paste extract and organic matter content (OM) were measured. The water retention curves for these soils were also determined. At one of the pens undisturbed soil columns were extracted at 3 locations. Laboratory analysis for 0-1.0 m indicated that soil texture was classified as sandy loam, average organic matter content (OM) was greater than 2.3% with low values of apparent density in the first 10 cm. The range of spatial dependence of data suggested that the number of soil samples could be reduced. Soil apparent electrical conductivity (ECa) and soil moisture were well correlated and indicated a clear spatial pattern in the corrals. TDR performance was acceptable to identify the spatial pattern of moisture, although the absolute values were far from the real values obtained by gravimetric method due to the effect of the high OM. The lower zone in one of the pens showed greater values of ECa and soil moisture, in agreement with a major water retention and a lower Ks. The water retention was higher in the other corral with higher variability in Ks. A general decrease of soil moisture was found near 0.2 m soil depth. Leaching experiments detected greater volumes with higher electrical conductivity in low lying areas of the pen. Although differences were not observed as clearly as before, the low and intermediate low areas of the pen showed a faster rate of leaching. In summary geophysical surveys allowed identifying risk areas of high ECa and moisture which in fact had higher volumes of leachate with elevated electrical conductivities. This may be a good approach to control and reduce soil and groundwater contamination and to model in future works the process in order to establish management decisions.

High-resolution Mapping of Permafrost and Soil Freeze/thaw Dynamics in the Tibetan Plateau Based on Multi-sensor Satellite Observations

NASA Astrophysics Data System (ADS)

Zhang, W.; Yi, Y.; Yang, K.; Kimball, J. S.

2016-12-01

The Tibetan Plateau (TP) is underlain by the world's largest extent of alpine permafrost ( 2.5×106 km2), dominated by sporadic and discontinuous permafrost with strong sensitivity to climate warming. Detailed permafrost distributions and patterns in most of the TP region are still unknown due to extremely sparse in-situ observations in this region characterized by heterogeneous land cover and large temporal dynamics in surface soil moisture conditions. Therefore, satellite-based temperature and moisture observations are essential for high-resolution mapping of permafrost distribution and soil active layer changes in the TP region. In this study, we quantify the TP regional permafrost distribution at 1-km resolution using a detailed satellite data-driven soil thermal process model (GIPL2). The soil thermal model is calibrated and validated using in-situ soil temperature/moisture observations from the CAMP/Tibet field campaign (9 sites: 0-300 cm soil depth sampling from 1997-2007), a multi-scale soil moisture and temperature monitoring network in the central TP (CTP-SMTMN, 57 sites: 5-40 cm, 2010-2014) and across the whole plateau (China Meteorology Administration, 98 sites: 0-320 cm, 2000-2015). Our preliminary results using the CAMP/Tibet and CTP-SMTMN network observations indicate strong controls of surface thermal and soil moisture conditions on soil freeze/thaw dynamics, which vary greatly with underlying topography, soil texture and vegetation cover. For regional mapping of soil freeze/thaw and permafrost dynamics, we use the most recent soil moisture retrievals from the NASA SMAP (Soil Moisture Active Passive) sensor to account for the effects of temporal soil moisture dynamics on soil thermal heat transfer, with surface thermal conditions defined by MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature records. Our study provides the first 1-km map of spatial patterns and recent changes of permafrost conditions in the TP.

Research on Applicability Analysis of Drought Index in Liaoning Area

NASA Astrophysics Data System (ADS)

Wang, Xin; Ding, Hua; Shuang Sun, Li; Li, Ru Ren; Liu, Yu Mei

2018-05-01

Based on brightness temperature data of AMSR-E (advanced microwave scanning radiometer — earth observing system) in 2009 and 2011, the inversion on 8 brightness temperature ratios is performed as alternative drought indexes in this paper. The correlation analysis is made through the soil moisture extracted from inversion drought index and data itself, and 3 kinds of alternative drought that relatively coincide with soil moisture of AMSR-E data itself are selected. And then on this basis, the analysis on the change situation of 3 kinds of microwave moisture indexes in 10 pixel × 10 pixel rectangular region of Shenyang and Chaoyang is made, and the evaluation on the monitoring advantages and disadvantages of 3 kinds of indexes on soil moisture is performed, so as to obtain the optimal index PIv6.9 for drought monitoring. In the end, in order to further study PIv6.9 on soil moisture monitoring situation within the range of Liaoning province, four days with relatively large precipitation are selected according to meteorological station data in 2009, the precipitation data of 51 meteorological stations in Liaoning province are interpolated within the range of the whole province by utilizing Kriging method, and the contrastive analysis on the spatial distribution of precipitation and PIv6.9 index is made. The results show that PIv6.9 can best reflect the spatial distribution characteristics of drought status in Liaoning province.

Evaluation of the cosmic-ray neutron method for measuring integral soil moisture dynamics of a forested head water catchment

NASA Astrophysics Data System (ADS)

Bogena, H. R.; Metzen, D.; Baatz, R.; Hendricks Franssen, H.; Huisman, J. A.; Montzka, C.; Vereecken, H.

2011-12-01

Measurements of low-energy secondary neutron intensity above the soil surface by cosmic-ray soil moisture probes (CRP) can be used to estimate soil moisture content. CRPs utilise the fact that high-energy neutrons initiated by cosmic rays are moderated (slowed to lower energies) most effectively by collisions with hydrogen atoms contained in water molecules in the soil. The conversion of neutron intensity to soil moisture content can potentially be complicated because neutrons are also moderated by aboveground water storage (e.g. vegetation water content, canopy storage of interception). Recently, it was demonstrated experimentally that soil moisture content derived from CRP measurements agrees well with average moisture content from gravimetric soil samples taken within the footprint of the cosmic ray probe, which is proposed to be up to several hundred meters in size [1]. However, the exact extension and shape of the CRP integration footprint is still an open question and it is also unclear how CRP measurements are affected by the soil moisture distribution within the footprint both in horizontal and vertical directions. In this paper, we will take advantage of an extensive wireless soil moisture sensor network covering most of the estimated footprint of the CRP. The network consists of 150 nodes and 900 soil moisture sensors which were installed in the small forested Wüstebach catchment (~27 ha) in the framework of the Transregio32 and the Helmholtz initiative TERENO (Terrestrial Environmental Observatories) [2]. This unique soil moisture data set provides a consistent picture of the hydrological status of the catchment in a high spatial and temporal resolution and thus the opportunity to evaluate the CRP measurements in a rigorous way. We will present first results of the comparison with a specific focus on the sensitivity of the CRP measurements to soil moisture variation in both the horizontal and vertical direction. Furthermore, the influence of forest biomass and shallow groundwater table fluctuations on the attenuation of cosmic-ray neutrons will be considered.

The PRESSCA operational early warning system for landslide forecasting: the 11-12 November 2013 rainfall event in Central Italy.

NASA Astrophysics Data System (ADS)

Ciabatta, Luca; Brocca, Luca; Ponziani, Francesco; Berni, Nicola; Stelluti, Marco; Moramarco, Tommaso

2014-05-01

The Umbria Region, located in Central Italy, is one of the most landslide risk prone area in Italy, almost yearly affected by landslides events at different spatial scales. For early warning procedures aimed at the assessment of the hydrogeological risk, the rainfall thresholds represent the main tool for the Italian Civil Protection System. As shown in previous studies, soil moisture plays a key-role in landslides triggering. In fact, acting on the pore water pressure, soil moisture influences the rainfall amount needed for activating a landslide. In this work, an operational physically-based early warning system, named PRESSCA, that takes into account soil moisture for the definition of rainfall thresholds is presented. Specifically, the soil moisture conditions are evaluated in PRESSCA by using a distributed soil water balance model that is recently coupled with near real-time satellite soil moisture product obtained from ASCAT (Advanced SCATterometer) and from in-situ monitoring data. The integration of three different sources of soil moisture information allows to estimate the most accurate possible soil moisture condition. Then, both observed and forecasted rainfall data are compared with the soil moisture-based thresholds in order to obtain risk indicators over a grid of ~ 5 km. These indicators are then used for the daily hydrogeological risk evaluation and management by the Civil Protection regional service, through the sharing/delivering of near real-time landslide risk scenarios (also through an open source web platform: www.cfumbria.it). On the 11th-12th November, 2013, Umbria Region was hit by an exceptional rainfall event with up to 430mm/72hours that resulted in significant economic damages, but fortunately no casualties among the population. In this study, the results during the rainfall event of PRESSCA system are described, by underlining the model capability to reproduce, two days in advance, landslide risk scenarios in good spatial and temporal agreement with the occurred actual conditions. High-resolution risk scenarios (100mx100m), obtained by coupling PRESSCA forecasts with susceptibility and vulnerability layers, are also produced. The results show good relationship between the PRESSCA forecast and the reported landslides to the Civil Protection Service during the rainfall event, confirming the system robustness. The good forecasts of PRESSCA system have surely contributed to start well in advance the Civil Protection operations (alerting local authorities and population).

Topographical controls on soil moisture distribution and runoff response in a first order alpine catchment

NASA Astrophysics Data System (ADS)

Penna, Daniele; Gobbi, Alberto; Mantese, Nicola; Borga, Marco

2010-05-01

Hydrological processes driving runoff generation in mountain basins depend on a wide number of factors which are often strictly interconnected. Among them, topography is widely recognized as one of the dominant controls influencing soil moisture distribution in the root zone, depth to water table and location and extent of saturated areas possibly prone to runoff production. Morphological properties of catchments are responsible for the alternation between steep slopes and relatively flat areas which have the potentials to control the storage/release of water and hence the hydrological response of the whole watershed. This work aims to: i) identify the role of topography as the main factor controlling the spatial distribution of near-surface soil moisture; ii) evaluate the possible switch in soil moisture spatial organization between wet and relatively dry periods and the stability of patterns during triggering of surface/subsurface runoff; iii) assess the possible connection between the develop of an ephemeral river network and the groundwater variations, examining the influence of the catchment topographical properties on the hydrological response. Hydro-meteorological data were collected in a small subcatchment (Larch Creek Catchment, 0.033 km²) of Rio Vauz basin (1.9 km²), in the eastern Italian Alps. Precipitation, discharge, water table level over a net of 14 piezometric wells and volumetric soil moisture at 0-30 cm depth were monitored continuously during the late spring-early autumn months in 2007 and 2008. Soil water content at 0-6 and 0-20 cm depth was measured manually during 22 field surveys in summer 2007 over a 44-sampling point experimental plot (approximately 3000 m²). In summer 2008 the sampling grid was extended to 64 points (approximately 4500 m²) and 28 field surveys were carried out. The length of the ephemeral stream network developed during rainfall events was assessed by a net of 24 Overland Flow Detectors (OFDs), which are able to detect the presence/absence of surface runoff. Results show a significant correlation between plot-averaged soil moisture at 0-20 cm depth, local slope and local curvature, while poor correlations were found with aspect and solar radiation: this suggests a sharp control of the catchment topological architecture (likely coupled with soil properties) on soil moisture distribution. This was also confirmed by the visual inspection of interpolated maps which reveal the persistence of high values of soil moisture in hollow areas and, conversely, of low values over the hillslopes. Moreover, a strong correlation between plot-averaged soil moisture patterns over time, with no decline after rainfall events, indicates a good temporal stability of water content distribution and its independence from the triggering of surface flow and transient lateral subsurface flow during wet conditions. The analysis of the time lag between storm centroid and piezometric peak shows an increasing delay of water table reaction with increasing distance from the stream, revealing different groundwater dynamics between the near-stream and the hillslope zone. Furthermore, the significant correlation between groundwater time lag monitored for the net of piezometers and the local slope suggests a topographical influence on the temporal and spatial variability of subsurface runoff. Finally, the extent of the ephemeral stream network was clearly dependent on the amount of precipitation but a different percentage of active OFDs and piezometers for the same rainfall event suggests a decoupling between patterns of surface and subsurface flows in the study area. Key words: topographical controls, soil moisture patterns, groundwater level, overland flow.

Multifractal and Singularity Maps of soil surface moisture distribution derived from 2D image analysis.

NASA Astrophysics Data System (ADS)

Cumbrera, Ramiro; Millán, Humberto; Martín-Sotoca, Juan Jose; Pérez Soto, Luis; Sanchez, Maria Elena; Tarquis, Ana Maria

2016-04-01

Soil moisture distribution usually presents extreme variation at multiple spatial scales. Image analysis could be a useful tool for investigating these spatial patterns of apparent soil moisture at multiple resolutions. The objectives of the present work were (i) to describe the local scaling of apparent soil moisture distribution and (ii) to define apparent soil moisture patterns from vertical planes of Vertisol pit images. Two soil pits (0.70 m long × 0.60 m width × 0.30 m depth) were excavated on a bare Mazic Pellic Vertisol. One was excavated in April/2011 and the other pit was established in May/2011 after 3 days of a moderate rainfall event. Digital photographs were taken from each Vertisol pit using a Kodak™ digital camera. The mean image size was 1600 × 945 pixels with one physical pixel ≈373 μm of the photographed soil pit. For more details see Cumbrera et al. (2012). Geochemical exploration have found with increasingly interests and benefits of using fractal (power-law) models to characterize geochemical distribution, using the concentration-area (C-A) model (Cheng et al., 1994; Cheng, 2012). This method is based on the singularity maps of a measure that at each point define areas with self-similar properties that are shown in power-law relationships in Concentration-Area plots (C-A method). The C-A method together with the singularity map ("Singularity-CA" method) define thresholds that can be applied to segment the map. We have applied it to each soil image. The results show that, in spite of some computational and practical limitations, image analysis of apparent soil moisture patterns could be used to study the dynamical change of soil moisture sampling in agreement with previous results (Millán et al., 2016). REFERENCES Cheng, Q., Agterberg, F. P. and Ballantyne, S. B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51, 109-130. Cheng, Q. (2012). Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas. Journal of Geochemical Exploration, 122, 55-70. Cumbrera, R., Ana M. Tarquis, Gabriel Gascó, Humberto Millán (2012) Fractal scaling of apparent soil moisture estimated from vertical planes of Vertisol pit images. Journal of Hydrology (452-453), 205-212. Martin Sotoca; J.J. Antonio Saa-Requejo, Juan Grau and Ana M. Tarquis (2016). Segmentation of singularity maps in the context of soil porosity. Geophysical Research Abstracts, 18, EGU2016-11402. Millán, H., Cumbrera, R. and Ana M. Tarquis (2016) Multifractal and Levy-stable statistics of soil surface moisture distribution derived from 2D image analysis. Applied Mathematical Modelling, 40(3), 2384-2395.

Examining the Suitability of a Sparse In Situ Soil Moisture Monitoring Network for Assimilation into a Spatially Distributed Hydrologic Model

NASA Astrophysics Data System (ADS)

De Vleeschouwer, N.; Verhoest, N.; Pauwels, V. R. N.

2015-12-01

The continuous monitoring of soil moisture in a permanent network can yield an interesting data product for use in hydrological data assimilation. Major advantages of in situ observations compared to remote sensing products are the potential vertical extent of the measurements, the finer temporal resolution of the observation time series, the smaller impact of land cover variability on the observation bias, etc. However, two major disadvantages are the typical small integration volume of in situ measurements and the often large spacing between monitoring locations. This causes only a small part of the modelling domain to be directly observed. Furthermore, the spatial configuration of the monitoring network is typically temporally non-dynamic. Therefore two questions can be raised. Do spatially sparse in situ soil moisture observations contain a sufficient data representativeness to successfully assimilate them into the largely unobserved spatial extent of a distributed hydrological model? And if so, how is this assimilation best performed? Consequently two important factors that can influence the success of assimilating in situ monitored soil moisture are the spatial configuration of the monitoring network and the applied assimilation algorithm. In this research the influence of those factors is examined by means of synthetic data-assimilation experiments. The study area is the ± 100 km² catchment of the Bellebeek in Flanders, Belgium. The influence of the spatial configuration is examined by varying the amount of locations and their position in the landscape. The latter is performed using several techniques including temporal stability analysis and clustering. Furthermore the observation depth is considered by comparing assimilation of surface layer (5 cm) and deeper layer (50 cm) observations. The impact of the assimilation algorithm is assessed by comparing the performance obtained with two well-known algorithms: Newtonian nudging and the Ensemble Kalman Filter.

Determination of erosion thresholds and aeolian dune stabilization mechanisms via robotic shear strength measurements

NASA Astrophysics Data System (ADS)

Qian, F.; Lee, D. B.; Bodek, S.; Roberts, S.; Topping, T. T.; Robele, Y.; Koditschek, D. E.; Jerolmack, D. J.

2017-12-01

Understanding the parameters that control the spatial variation in aeolian soil erodibility is crucial to the development of sediment transport models. Currently, in-situ measurements of erodibility are time consuming and lack robustness. In an attempt to remedy this issue, we perform field and laboratory tests to determine the suitability of a novel mechanical shear strength method to assess soil erodibility. These tests can be performed quickly ( 1 minute) by a semi-autonomous robot using its direct-drive leg, while environmental controls such as soil moisture and grain size are simultaneously characterized. The robot was deployed at White Sands National Monument to delineate and understand erodibility gradients at two different scales: (1) from dry dune crest to moist interdune (distance 10s m), where we determined that shear strength increases by a factor of three with increasing soil moisture; and (2) from barren barchan dunes to vegetated and crusted parabolics downwind (distance 5 km), where we found that shear strength was enhanced by a factor of two relative to loose sand. Interestingly, shear strength varied little from carbonate-crusted dune surfaces to bio-crust covered interdunes in the downwind parabolic region, indicating that varied surface crusts contribute similarly to erosion resistance. To isolate the control of soil moisture on erodibility, we performed laboratory experiments in a sandbox. These results verify that the observed increase in soil erodibility from barchan crest to interdune at White Sands is dominated by soil moisture, and the variation in parabolic dune and barchan interdune areas results from a combination of soil moisture, bio-activity, and crust development. This study highlights that spatial variation of soil erodibility in arid environments is large enough to significantly affect sediment transport, and that probing soil erodibility with a robot has the potential to improve our understanding of this multifaceted problem.

Impact of Soil Moisture Dynamics on ASAR Observed Backscatters and its Spatial Variability over the Upstream of the Heihe River Basin, China

NASA Astrophysics Data System (ADS)

Wang, Shuguo

2013-01-01

The so called change detection method is a promising way to acquire soil moisture (SM) dynamics dependent on time series of radar backscatter (σ0) observations. The current study is a preceded step for using this method to carry out SM inversion at basin scale, in order to investigate the applicability of the change detection method in the Heihe River Basin, and to inspect the sensitivity of SAR signals to soil moisture variations. At the meantime, a prior knowledge of SM dynamics and land heterogeneities that may contribute to backscatter observations can be obtained. The impact of land surface states on spatial and temporal σ0 variability measured by ASAR has been evaluated in the upstream of the Heihe River Basin, which was one of the foci experimental areas (FEAs) in Watershed Allied Telemetry Experimental Research (WATER). Based on the in situ measurements provided by an automatic meteorological station (AMS) established at the A’rou site and time series of ASAR observations focused on a 1 km2 area, the relationships between the temporal dynamics of σ0 with in situ SM variations, and land heterogeneities of the study area according to the characteristics of spatial variability of σ0, were identified. The in situ measurements of soil moisture and temperature show a very clear seasonal freeze/thaw cycle in the study site. The temporal σ0 evolvement is basically coherent with ground measurements.

Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data

NASA Astrophysics Data System (ADS)

Lu, Yang; Steele-Dunne, Susan C.; Farhadi, Leila; van de Giesen, Nick

2017-12-01

Surface heat fluxes play a crucial role in the surface energy and water balance. In situ measurements are costly and difficult, and large-scale flux mapping is hindered by surface heterogeneity. Previous studies have demonstrated that surface heat fluxes can be estimated by assimilating land surface temperature (LST) and soil moisture to determine two key parameters: a neutral bulk heat transfer coefficient (CHN) and an evaporative fraction (EF). Here a methodology is proposed to estimate surface heat fluxes by assimilating Soil Moisture Active Passive (SMAP) soil moisture data and Geostationary Operational Environmental Satellite (GOES) LST data into a dual-source (DS) model using a hybrid particle assimilation strategy. SMAP soil moisture data are assimilated using a particle filter (PF), and GOES LST data are assimilated using an adaptive particle batch smoother (APBS) to account for the large gap in the spatial and temporal resolution. The methodology is implemented in an area in the U.S. Southern Great Plains. Assessment against in situ observations suggests that soil moisture and LST estimates are in better agreement with observations after assimilation. The RMSD for 30 min (daytime) flux estimates is reduced by 6.3% (8.7%) and 31.6% (37%) for H and LE on average. Comparison against a LST-only and a soil moisture-only assimilation case suggests that despite the coarse resolution, assimilating SMAP soil moisture data is not only beneficial but also crucial for successful and robust flux estimation, particularly when the uncertainties in the model estimates are large.

A Combined Soil Moisture Product of the Tibetan Plateau using Different Sensors Simultaneously

NASA Astrophysics Data System (ADS)

Zeng, Y.; Dente, L.; Su, B.; Wang, L.

2012-12-01

It is always challenging to find a single satellite-derived soil moisture product that has complete coverage of the Tibetan Plateau for a long time period and is suitable for climate change studies at sub-continental scale. Meanwhile, having a number of independent satellite-derived soil moisture data sets does not mean that it is straightforward to create long-term consistent time series, due to the differences among the data sets related to the different retrieval approaches. Therefore, this study is focused on the development and validation of a simple Bayesian based method to merge/blend different satellite-derived soil moisture data. The merging method was firstly tested over the Maqu region (north-eastern fringe of the Tibetan Plateau), where in situ soil moisture data were collected, for the period from May 2008 to December 2010. The in situ data provided by the 20 monitoring stations in the Maqu region were compared to the AMSR-E soil moisture products by VUA-NASA and the ASCAT soil moisture products by TU Wien, in order to determine bias and standard deviation. It was found that the bias between the satellite and the in situ data varies with seasons. The satellite-derived products were first corrected for the bias and then merged. This is generally caused by notable differences in the represented depth, spatial extent and so on. The systematic bias is affected by the spatial variability and the temporal stability (Dente et al. 2012). The dependence of the bias on season was investigated and identified as the monsoon season only (May-September), in winter only (December - February), and in the period between the monsoon season and winter (March-April, October-November, called the transition season) (Dente et al. 2012, Su et al. 2011). After the date merging procedure, the standard deviations between the satellite and the in situ data reduced from 0.0839 to 0.0622 for ASCAT data, and from 0.0682 to 0.0593 for AMSR-E data. The developed merging method is therefore suitable to provide a more accurate soil moisture product than the AMSR-E and ASCAT products. As the merging method was shown to be promising over the Maqu region, it will be extended to the entire Tibetan Plateau. Then, the combined soil moisture product will be validated over the monitored sites located in Ngari and Naqu regions. References: Dente, L.; Vekerdy, Z.; Wen, J.; Su, Z., (2012) Maqu network for validation of satellite-derived soil moisture products. International Journal of Applied Earth Observation and Geoinformation, 17, 55-65. Su, Z., Wen, J., Dente, L., van der Velde, R. and ... [et al.] (2011) The Tibetan plateau observatory of plateau scale soil moisture and soil temperature, Tibet - Obs, for quantifying uncertainties in coarse resolution satellite and model products. In: Hydrology and earth system sciences (HESS): open access, 15 (2011)7 pp. 2303-2016.

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.

Assessment of Evapotranspiration and Soil Moisture Content Across Different Scales of Observation.

PubMed

Verstraeten, Willem W; Veroustraete, Frank; Feyen, Jan

2008-01-09

The proper assessment of evapotranspiration and soil moisture content arefundamental in food security research, land management, pollution detection, nutrient flows,(wild-) fire detection, (desert) locust, carbon balance as well as hydrological modelling; etc.This paper takes an extensive, though not exhaustive sample of international scientificliterature to discuss different approaches to estimate land surface and ecosystem relatedevapotranspiration and soil moisture content. This review presents:(i) a summary of the generally accepted cohesion theory of plant water uptake andtransport including a shortlist of meteorological and plant factors influencing planttranspiration;(ii) a summary on evapotranspiration assessment at different scales of observation (sapflow,porometer, lysimeter, field and catchment water balance, Bowen ratio,scintillometer, eddy correlation, Penman-Monteith and related approaches);(iii) a summary on data assimilation schemes conceived to estimate evapotranspirationusing optical and thermal remote sensing; and(iv) for soil moisture content, a summary on soil moisture retrieval techniques atdifferent spatial and temporal scales is presented.Concluding remarks on the best available approaches to assess evapotranspiration and soilmoisture content with and emphasis on remote sensing data assimilation, are provided.

The South Fork Experimental Watershed: Soil moisture and precipitation network for satellite validation

NASA Astrophysics Data System (ADS)

Cosh, M. H.; Prueger, J. H.; McKee, L.; Bindlish, R.

2013-12-01

A recently deployed long term network for the study of soil moisture and precipitation was deployed in north central iowa, in cooperation between USDA and NASA. This site will be a joint calibration/validation network for the Soil Moisture Active Passive (SMAP) and Global Precipitation Measurement (GPM) missions. At total of 20 dual gauge precipitation gages were established across a watershed landscape with an area of approximately 600 km2. In addition, four soil moisture probes were installed in profile at 5, 10, 20, and 50 cm. The network was installed in April of 2013, at the initiation of the Iowa Flood Study (IFloodS) which was a six week intensive ground based radar observation period, conducted in coordination with NASA and the University of Iowa. This site is a member watershed of the Group on Earth Observations Joint Experiments on Crop Assessment and Monitoring (GEO-JECAM) program. A variety of quality control procedures are examined and spatial and temporal stability aspects of the network are examined. Initial comparisons of the watershed to soil moisture estimates from satellites are also conducted.

SoilNet - A Zigbee based soil moisture sensor network

NASA Astrophysics Data System (ADS)

Bogena, H. R.; Weuthen, A.; Rosenbaum, U.; Huisman, J. A.; Vereecken, H.

2007-12-01

Soil moisture plays a key role in partitioning water and energy fluxes, in providing moisture to the atmosphere for precipitation, and controlling the pattern of groundwater recharge. Large-scale soil moisture variability is driven by variation of precipitation and radiation in space and time. At local scales, land cover, soil conditions, and topography act to redistribute soil moisture. Despite the importance of soil moisture, it is not yet measured in an operational way, e.g. for a better prediction of hydrological and surface energy fluxes (e.g. runoff, latent heat) at larger scales and in the framework of the development of early warning systems (e.g. flood forecasting) and the management of irrigation systems. The SoilNet project aims to develop a sensor network for the near real-time monitoring of soil moisture changes at high spatial and temporal resolution on the basis of the new low-cost ZigBee radio network that operates on top of the IEEE 802.15.4 standard. The sensor network consists of soil moisture sensors attached to end devices by cables, router devices and a coordinator device. The end devices are buried in the soil and linked wirelessly with nearby aboveground router devices. This ZigBee wireless sensor network design considers channel errors, delays, packet losses, and power and topology constraints. In order to conserve battery power, a reactive routing protocol is used that determines a new route only when it is required. The sensor network is also able to react to external influences, e.g. such as rainfall occurrences. The SoilNet communicator, routing and end devices have been developed by the Forschungszentrum Juelich and will be marketed through external companies. We will present first results of experiments to verify network stability and the accuracy of the soil moisture sensors. Simultaneously, we have developed a data management and visualisation system. We tested the wireless network on a 100 by 100 meter forest plot equipped with 25 end devices each consisting of 6 vertically arranged soil moisture sensors. The next step will be the instrumentation of two small catchments (~30 ha) with a 30 m spacing of the end devices. juelich.de/icg/icg-4/index.php?index=739

Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network

NASA Astrophysics Data System (ADS)

Fang, K.; Shen, C.; Kifer, D.; Yang, X.

2017-12-01

The Soil Moisture Active Passive (SMAP) mission has delivered high-quality and valuable sensing of surface soil moisture since 2015. However, its short time span, coarse resolution, and irregular revisit schedule have limited its use. Utilizing a state-of-the-art deep-in-time neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 soil moisture data using climate forcing, model-simulated moisture, and static physical attributes as inputs. The system removes most of the bias with model simulations and also improves predicted moisture climatology, achieving a testing accuracy of 0.025 to 0.03 in most parts of Continental United States (CONUS). As the first application of LSTM in hydrology, we show that it is more robust than simpler methods in either temporal or spatial extrapolation tests. We also discuss roles of different predictors, the effectiveness of regularization algorithms and impacts of training strategies. With high fidelity to SMAP products, our data can aid various applications including data assimilation, weather forecasting, and soil moisture hindcasting.

Developing Soil Moisture Profiles Utilizing Remotely Sensed MW and TIR Based SM Estimates Through Principle of Maximum Entropy

NASA Astrophysics Data System (ADS)

Mishra, V.; Cruise, J. F.; Mecikalski, J. R.

2015-12-01

Developing accurate vertical soil moisture profiles with minimum input requirements is important to agricultural as well as land surface modeling. Earlier studies show that the principle of maximum entropy (POME) can be utilized to develop vertical soil moisture profiles with accuracy (MAE of about 1% for a monotonically dry profile; nearly 2% for monotonically wet profiles and 3.8% for mixed profiles) with minimum constraints (surface, mean and bottom soil moisture contents). In this study, the constraints for the vertical soil moisture profiles were obtained from remotely sensed data. Low resolution (25 km) MW soil moisture estimates (AMSR-E) were downscaled to 4 km using a soil evaporation efficiency index based disaggregation approach. The downscaled MW soil moisture estimates served as a surface boundary condition, while 4 km resolution TIR based Atmospheric Land Exchange Inverse (ALEXI) estimates provided the required mean root-zone soil moisture content. Bottom soil moisture content is assumed to be a soil dependent constant. Mulit-year (2002-2011) gridded profiles were developed for the southeastern United States using the POME method. The soil moisture profiles were compared to those generated in land surface models (Land Information System (LIS) and an agricultural model DSSAT) along with available NRCS SCAN sites in the study region. The end product, spatial soil moisture profiles, can be assimilated into agricultural and hydrologic models in lieu of precipitation for data scarce regions.Developing accurate vertical soil moisture profiles with minimum input requirements is important to agricultural as well as land surface modeling. Previous studies have shown that the principle of maximum entropy (POME) can be utilized with minimal constraints to develop vertical soil moisture profiles with accuracy (MAE = 1% for monotonically dry profiles; MAE = 2% for monotonically wet profiles and MAE = 3.8% for mixed profiles) when compared to laboratory and field data. In this study, vertical soil moisture profiles were developed using the POME model to evaluate an irrigation schedule over a maze field in north central Alabama (USA). The model was validated using both field data and a physically based mathematical model. The results demonstrate that a simple two-constraint entropy model under the assumption of a uniform initial soil moisture distribution can simulate most soil moisture profiles within the field area for 6 different soil types. The results of the irrigation simulation demonstrated that the POME model produced a very efficient irrigation strategy with loss of about 1.9% of the total applied irrigation water. However, areas of fine-textured soil (i.e. silty clay) resulted in plant stress of nearly 30% of the available moisture content due to insufficient water supply on the last day of the drying phase of the irrigation cycle. Overall, the POME approach showed promise as a general strategy to guide irrigation in humid environments, with minimum input requirements.

The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale.

PubMed

Hursh, Andrew; Ballantyne, Ashley; Cooper, Leila; Maneta, Marco; Kimball, John; Watts, Jennifer

2017-05-01

Soil respiration (Rs) is a major pathway by which fixed carbon in the biosphere is returned to the atmosphere, yet there are limits to our ability to predict respiration rates using environmental drivers at the global scale. While temperature, moisture, carbon supply, and other site characteristics are known to regulate soil respiration rates at plot scales within certain biomes, quantitative frameworks for evaluating the relative importance of these factors across different biomes and at the global scale require tests of the relationships between field estimates and global climatic data. This study evaluates the factors driving Rs at the global scale by linking global datasets of soil moisture, soil temperature, primary productivity, and soil carbon estimates with observations of annual Rs from the Global Soil Respiration Database (SRDB). We find that calibrating models with parabolic soil moisture functions can improve predictive power over similar models with asymptotic functions of mean annual precipitation. Soil temperature is comparable with previously reported air temperature observations used in predicting Rs and is the dominant driver of Rs in global models; however, within certain biomes soil moisture and soil carbon emerge as dominant predictors of Rs. We identify regions where typical temperature-driven responses are further mediated by soil moisture, precipitation, and carbon supply and regions in which environmental controls on high Rs values are difficult to ascertain due to limited field data. Because soil moisture integrates temperature and precipitation dynamics, it can more directly constrain the heterotrophic component of Rs, but global-scale models tend to smooth its spatial heterogeneity by aggregating factors that increase moisture variability within and across biomes. We compare statistical and mechanistic models that provide independent estimates of global Rs ranging from 83 to 108 Pg yr -1 , but also highlight regions of uncertainty where more observations are required or environmental controls are hard to constrain. © 2016 John Wiley & Sons Ltd.

Drought characteristics' role in widespread aspen forest mortality across Colorado, USA.

PubMed

Anderegg, Leander D L; Anderegg, William R L; Abatzoglou, John; Hausladen, Alexandra M; Berry, Joseph A

2013-05-01

Globally documented widespread drought-induced forest mortality has important ramifications for plant community structure, ecosystem function, and the ecosystem services provided by forests. Yet the characteristics of drought seasonality, severity, and duration that trigger mortality events have received little attention despite evidence of changing precipitation regimes, shifting snow melt timing, and increasing temperature stress. This study draws upon stand level ecohydrology and statewide climate and spatial analysis to examine the drought characteristics implicated in the recent widespread mortality of trembling aspen (Populus tremuloides Michx.). We used isotopic observations of aspen xylem sap to determine water source use during natural and experimental drought in a region that experienced high tree mortality. We then drew upon multiple sources of climate data to characterize the drought that triggered aspen mortality. Finally, regression analysis was used to examine the drought characteristics most associated with the spatial patterns of aspen mortality across Colorado. Isotopic analysis indicated that aspens generally utilize shallow soil moisture with little plasticity during drought stress. Climate analysis showed that the mortality-inciting drought was unprecedented in the observational record, especially in 2002 growing season temperature and evaporative deficit, resulting in record low shallow soil moisture reserves. High 2002 summer temperature and low shallow soil moisture were most associated with the spatial patterns of aspen mortality. These results suggest that the 2002 drought subjected Colorado aspens to the most extreme growing season water stress of the past century by creating high atmospheric moisture demand and depleting the shallow soil moisture upon which aspens rely. Our findings highlight the important role of drought characteristics in mediating widespread aspen forest mortality, link this aspen die-off to regional climate change trends, and provide insight into future climate vulnerability of these forests. © 2013 Blackwell Publishing Ltd.

Effects of spatial variability of soil hydraulic properties on water dynamics

NASA Astrophysics Data System (ADS)

Gumiere, Silvio Jose; Caron, Jean; Périard, Yann; Lafond, Jonathan

2013-04-01

Soil hydraulic properties may present spatial variability and dependence at the scale of watersheds or fields even in man-made single soil structures, such as cranberry fields. The saturated hydraulic conductivity (Ksat) and soil moisture curves were measured at two depths for three cranberry fields (about 2 ha) at three different sites near Québec city, Canada. Two of the three studied fields indicate strong spatial dependence for Ksat values and soil moisture curves both in horizontal and vertical directions. In the summer of 2012, the three fields were equipped with 55 tensiometers installed at a depth of 0.10 m in a regular grid. About 20 mm of irrigation water were applied uniformly by aspersion to the fields, raising soil water content to near saturation condition. Soil water tension was measured once every hour during seven days. Geostatistical techniques such as co-kriging and cross-correlograms estimations were used to investigate the spatial dependence between variables. The results show that soil tension varied faster in high Ksat zones than in low Ksatones in the cranberry fields. These results indicate that soil water dynamic is strongly affected by the variability of saturated soil hydraulic conductivity, even in a supposed homogenous anthropogenic soil. This information may have a strong impact in irrigation management and subsurface drainage efficiency as well as other water conservation issues. Future work will involve 3D numerical modeling of the field water dynamics with HYDRUS software. The anticipated outcome will provide valuable information for the understanding of the effect of spatial variability of soil hydraulic properties on soil water dynamics and its relationship with crop production and water conservation.

Field-scale soil moisture space-time geostatistical modeling for complex Palouse landscapes in the inland Pacific Northwest

NASA Astrophysics Data System (ADS)

Chahal, M. K.; Brown, D. J.; Brooks, E. S.; Campbell, C.; Cobos, D. R.; Vierling, L. A.

2012-12-01

Estimating soil moisture content continuously over space and time using geo-statistical techniques supports the refinement of process-based watershed hydrology models and the application of soil process models (e.g. biogeochemical models predicting greenhouse gas fluxes) to complex landscapes. In this study, we model soil profile volumetric moisture content for five agricultural fields with loess soils in the Palouse region of Eastern Washington and Northern Idaho. Using a combination of stratification and space-filling techniques, we selected 42 representative and distributed measurement locations in the Cook Agronomy Farm (Pullman, WA) and 12 locations each in four additional grower fields that span the precipitation gradient across the Palouse. At each measurement location, soil moisture was measured on an hourly basis at five different depths (30, 60, 90, 120, and 150 cm) using Decagon 5-TE/5-TM soil moisture sensors (Decagon Devices, Pullman, WA, USA). This data was collected over three years for the Cook Agronomy Farm and one year for each of the grower fields. In addition to ordinary kriging, we explored the correlation of volumetric water content with external, spatially exhaustive indices derived from terrain models, optical remote sensing imagery, and proximal soil sensing data (electromagnetic induction and VisNIR penetrometer)

Short-term precipitation exclusion alters microbial responses to soil moisture in a wet tropical forest.

PubMed

Waring, Bonnie G; Hawkes, Christine V

2015-05-01

Many wet tropical forests, which contain a quarter of global terrestrial biomass carbon stocks, will experience changes in precipitation regime over the next century. Soil microbial responses to altered rainfall are likely to be an important feedback on ecosystem carbon cycling, but the ecological mechanisms underpinning these responses are poorly understood. We examined how reduced rainfall affected soil microbial abundance, activity, and community composition using a 6-month precipitation exclusion experiment at La Selva Biological Station, Costa Rica. Thereafter, we addressed the persistent effects of field moisture treatments by exposing soils to a controlled soil moisture gradient in the lab for 4 weeks. In the field, compositional and functional responses to reduced rainfall were dependent on initial conditions, consistent with a large degree of spatial heterogeneity in tropical forests. However, the precipitation manipulation significantly altered microbial functional responses to soil moisture. Communities with prior drought exposure exhibited higher respiration rates per unit microbial biomass under all conditions and respired significantly more CO2 than control soils at low soil moisture. These functional patterns suggest that changes in microbial physiology may drive positive feedbacks to rising atmospheric CO2 concentrations if wet tropical forests experience longer or more intense dry seasons in the future.

Evaluation of the soil moisture prediction accuracy of a space radar using simulation techniques. [Kansas

NASA Technical Reports Server (NTRS)

Ulaby, F. T. (Principal Investigator); Dobson, M. C.; Stiles, J. A.; Moore, R. K.; Holtzman, J. C.

1981-01-01

Image simulation techniques were employed to generate synthetic aperture radar images of a 17.7 km x 19.3 km test site located east of Lawrence, Kansas. The simulations were performed for a space SAR at an orbital altitude of 600 km, with the following sensor parameters: frequency = 4.75 GHz, polarization = HH, and angle of incidence range = 7 deg to 22 deg from nadir. Three sets of images were produced corresponding to three different spatial resolutions; 20 m x 20 m with 12 looks, 100 m x 100 m with 23 looks, and 1 km x 1 km with 1000 looks. Each set consisted of images for four different soil moisture distributions across the test site. Results indicate that, for the agricultural portion of the test site, the soil moisture in about 90% of the pixels can be predicted with an accuracy of = + or - 20% of field capacity. Among the three spatial resolutions, the 1 km x 1 km resolution gave the best results for most cases, however, for very dry soil conditions, the 100 m x 100 m resolution was slightly superior.

Relating coccidioidomycosis (valley fever) incidence to soil moisture conditions.

PubMed

Coopersmith, E J; Bell, J E; Benedict, K; Shriber, J; McCotter, O; Cosh, M H

2017-04-17

Coccidioidomycosis (also called Valley fever) is caused by a soilborne fungus, Coccidioides spp. , in arid regions of the southwestern United States. Though some who develop infections from this fungus remain asymptomatic, others develop respiratory disease as a consequence. Less commonly, severe illness and death can occur when the infection spreads to other regions of the body. Previous analyses have attempted to connect the incidence of coccidioidomycosis to broadly available climatic measurements, such as precipitation or temperature. However, with the limited availability of long-term, in situ soil moisture data sets, it has not been feasible to perform a direct analysis of the relationships between soil moisture levels and coccidioidomycosis incidence on a larger temporal and spatial scale. Utilizing in situ soil moisture gauges throughout the southwest from the U.S. Climate Reference Network and a model with which to extend those estimates, this work connects periods of higher and lower soil moisture in Arizona and California between 2002 and 2014 to the reported incidence of coccidioidomycosis. The results indicate that in both states, coccidioidomycosis incidence is related to soil moisture levels from previous summers and falls. Stated differently, a higher number of coccidioidomycosis cases are likely to be reported if previous bands of months have been atypically wet or dry, depending on the location.

Relating coccidioidomycosis (valley fever) incidence to soil moisture conditions

PubMed Central

Coopersmith, E. J.; Bell, J. E.; Benedict, K.; Shriber, J.; McCotter, O.; Cosh, M. H.

2017-01-01

Coccidioidomycosis (also called Valley fever) is caused by a soilborne fungus, Coccidioides spp., in arid regions of the southwestern United States. Though some who develop infections from this fungus remain asymptomatic, others develop respiratory disease as a consequence. Less commonly, severe illness and death can occur when the infection spreads to other regions of the body. Previous analyses have attempted to connect the incidence of coccidioidomycosis to broadly available climatic measurements, such as precipitation or temperature. However, with the limited availability of long-term, in situ soil moisture data sets, it has not been feasible to perform a direct analysis of the relationships between soil moisture levels and coccidioidomycosis incidence on a larger temporal and spatial scale. Utilizing in situ soil moisture gauges throughout the southwest from the U.S. Climate Reference Network and a model with which to extend those estimates, this work connects periods of higher and lower soil moisture in Arizona and California between 2002 and 2014 to the reported incidence of coccidioidomycosis. The results indicate that in both states, coccidioidomycosis incidence is related to soil moisture levels from previous summers and falls. Stated differently, a higher number of coccidioidomycosis cases are likely to be reported if previous bands of months have been atypically wet or dry, depending on the location. PMID:29124249

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.

Assimilation of the ESA CCI Soil Moisture ACTIVE and PASSIVE Product into the SURFEX Land Surface Model using the Ensemble Transform Kalman Filter

NASA Astrophysics Data System (ADS)

Blyverket, J.; Hamer, P.; Bertino, L.; Lahoz, W. A.

2017-12-01

The European Space Agency Climate Change Initiative for soil moisture (ESA CCI SM) was initiated in 2012 for a period of six years, the objective for this period was to produce the most complete and consistent global soil moisture data record based on both active and passive sensors. The ESA CCI SM products consist of three surface soil moisture datasets: The ACTIVE product and the PASSIVE product were created by fusing scatterometer and radiometer soil moisture data, respectively. The COMBINED product is a blended product based on the former two datasets. In this study we assimilate globally both the ACTIVE and PASSIVE product at a 25 km spatial resolution. The different satellite platforms have different overpass times, an observation is mapped to the hours 00.00, 06.00, 12.00 or 18.00 if it falls within a 3 hour window centred at these times. We use the SURFEX land surface model with the ISBA diffusion scheme for the soil hydrology. For the assimilation routine we apply the Ensemble Transform Kalman Filter (ETKF). The land surface model is driven by perturbed MERRA-2 atmospheric forcing data, which has a temporal resolution of one hour and is mapped to the SURFEX model grid. Bias between the land surface model and the ESA CCI product is removed by cumulative distribution function (CDF) matching. This work is a step towards creating a global root zone soil moisture product from the most comprehensive satellite surface soil moisture product available. As a first step we consider the period from 2010 - 2016. This allows for comparison against other global root zone soil moisture products (SMAP Level 4, which is independent of the ESA CCI SM product).

Comparisons of Satellite Soil Moisture, an Energy Balance Model Driven by LST Data and Point Measurements

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Rudari, Roberto; Boni, Giorgio; Puca, Silvia

2013-04-01

Soil moisture plays a fundamental role in the partitioning of mass and energy fluxes between land surface and atmosphere, thereby influencing climate and weather, and it is important in determining the rainfall-runoff response of catchments; moreover, in hydrological modelling and flood forecasting, a correct definition of moisture conditions is a key factor for accurate predictions. Different sources of information for the estimation of the soil moisture state are currently available: satellite data, point measurements and model predictions. All are affected by intrinsic uncertainty. Among different satellite sensors that can be used for soil moisture estimation three major groups can be distinguished: passive microwave sensors (e.g., SSMI), active sensors (e.g. SAR, Scatterometers), and optical sensors (e.g. Spectroradiometers). The last two families, mainly because of their temporal and spatial resolution seem the most suitable for hydrological applications In this work soil moisture point measurements from 10 sensors in the Italian territory are compared of with the satellite products both from the HSAF project SM-OBS-2, derived from the ASCAT scatterometer, and from ACHAB, an operative energy balance model that assimilate LST data derived from MSG and furnishes daily an evaporative fraction index related to soil moisture content for all the Italian region. Distributed comparison of the ACHAB and SM-OBS-2 on the whole Italian territory are performed too.

Dust emission parameterization scheme over the MENA region: Sensitivity analysis to soil moisture and soil texture

NASA Astrophysics Data System (ADS)

Gherboudj, Imen; Beegum, S. Naseema; Marticorena, Beatrice; Ghedira, Hosni

2015-10-01

The mineral dust emissions from arid/semiarid soils were simulated over the MENA (Middle East and North Africa) region using the dust parameterization scheme proposed by Alfaro and Gomes (2001), to quantify the effect of the soil moisture and clay fraction in the emissions. For this purpose, an extensive data set of Soil Moisture and Ocean Salinity soil moisture, European Centre for Medium-Range Weather Forecasting wind speed at 10 m height, Food Agricultural Organization soil texture maps, MODIS (Moderate Resolution Imaging Spectroradiometer) Normalized Difference Vegetation Index, and erodibility of the soil surface were collected for the a period of 3 years, from 2010 to 2013. Though the considered data sets have different temporal and spatial resolution, efforts have been made to make them consistent in time and space. At first, the simulated sandblasting flux over the region were validated qualitatively using MODIS Deep Blue aerosol optical depth and EUMETSAT MSG (Meteosat Seciond Generation) dust product from SEVIRI (Meteosat Spinning Enhanced Visible and Infrared Imager) and quantitatively based on the available ground-based measurements of near-surface particulate mass concentrations (PM10) collected over four stations in the MENA region. Sensitivity analyses were performed to investigate the effect of soil moisture and clay fraction on the emissions flux. The results showed that soil moisture and soil texture have significant roles in the dust emissions over the MENA region, particularly over the Arabian Peninsula. An inversely proportional dependency is observed between the soil moisture and the sandblasting flux, where a steep reduction in flux is observed at low friction velocity and a gradual reduction is observed at high friction velocity. Conversely, a directly proportional dependency is observed between the soil clay fraction and the sandblasting flux where a steep increase in flux is observed at low friction velocity and a gradual increase is observed at high friction velocity. The magnitude of the percentage reduction/increase in the sandblasting flux decreases with the increase of the friction velocity for both soil moisture and soil clay fraction. Furthermore, these variables are interdependent leading to a gradual decrease in the percentage increase in the sandblasting flux for higher soil moisture values.

Five Things about NASA's SMAP

NASA Image and Video Library

2017-12-08

The Soil Moisture Active Passive (SMAP) mission, scheduled for launch on Jan. 29, will measure the moisture in Earth's soil with greater accuracy and higher resolution than any preceding mission, producing a global map of soil moisture every three days. Here are five quick facts about the spacecraft and what it studies. 1. Soil moisture is a tiny fraction of water with a big punch. Only 0.001 percent of Earth's total water is lodged in the top few feet of soil. That tiny percentage, however, affects all living things on land and plays an important role in moving water, carbon and heat between land and atmosphere. 2. Soil moisture can compound water risks. A flood follows a heavy rainfall -- but only if the ground cannot soak up the rain. Waterlogged soil makes a region more flood-prone. Going to the opposite extreme, a drought can parch soil to such an extent that plants are unable to grow even after a few rains have fallen. Knowing soil moisture allows hydrologists to make better decisions related to the risk of flooding and drought, such as how much water to retain in reservoirs. 3. Soil moisture controls the on-off switch for carbon dioxide cleanup. The world's vast northern forests remove carbon dioxide from the air as they grow, helping to clean up our emissions from burning fossil fuels. But when the ground freezes, that process switches off. Carbon dioxide builds up in the atmosphere until the ground thaws in the spring and plants begin growing again. Knowing where and for how long the ground is frozen or thawed is an important part of understanding the role of the northern forests in reducing greenhouse warming. SMAP will map frozen and thawed soils north of 45 degrees north latitude (about the latitude of Minneapolis), around the globe. 4. SMAP is a twofer. The spacecraft's radiometer produces an accurate reading of how much moisture is in the top two inches (five centimeters) of soil, but it has low spatial resolution, that is, one measurement covers a large area. A radar instrument produces an image with higher spatial resolution, but it can't measure soil moisture as accurately as a radiometer. Through sophisticated data processing, SMAP combines observations from the two instruments into a very accurate measurement with high spatial resolution. 5. SMAP has a huge, folding, spinning antenna. At 19 feet 8 inches (6 meters) in diameter, SMAP's rotating mesh antenna dwarfs the size of the instruments and spacecraft and is the largest rotating antenna of its kind that NASA has yet deployed. But the entire dish furls into a cylinder one foot (diameter) by four feet (30 by 120 centimeters) to fit inside the rocket’s fairing for launch, and it weighs only 128 pounds (about 58 kilograms). For more information about SMAP, see: smap.jpl.nasa.gov/ www.nasa.gov/smap/ NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Analysis of ASAR Wide Swath Mode time series for the retrieval of soil moisture in mountainous areas

NASA Astrophysics Data System (ADS)

Greifeneder, Felix; Notarnicola, Claudia; Cuozzo, Giovanni; Spindler, Nadine; Bertoldi, Giacomo; Della Chiesa, Stefano; Niedrist, Georg; Stamenkovic, Jelena; Wagner, Wolgang

2014-05-01

Soil moisture is a key element in the global cycles of water, energy, and carbon. Knowledge on the spatial and temporal distribution of the soil moisture content (SMC) is therefore essential for a number of hydrological applications as well as earth sciences like meteorology or climatology (Heathman et al., 2003). In the last few years there has been an increasing interest towards the estimation of SMC at local scales using active microwave sensors (Barret et al., 2009). Compared to passive microwave sensors, SAR offers the potential to provide data at high spatial resolution (modern sensors can acquire images with up to approximately 1 m), which is particularly important in mountainous areas. So far, these areas have been considered only marginally in research and only pioneer studies can be found in the literature (Brocca et al., 2012; Bertoldi et al. 2013). In this work we analyzed the temporal and spatial dynamics of the surface SMC (0 - 5 cm depth) on the basis of ground data collected by fixed meteorological stations located in the emerging Long-Term Ecological Research (LTER) site Mazia Valley (Province of Bolzano, South Tyrol, Italy), SAR data from ENVISATs ASAR sensor, wide swath (WS) mode (acquired between 2005 and 2012), and SMC estimates from the hydrological model GEOtop (Endrizzi et al., 2013). The SMC retrieval process was based on the support vector regression (SVR) method introduced by Pasolli et al. (2011). The training of the algorithm was based on data acquired in 2010. Furthermore, the SAR backscatter and derived SMC have been compared with time-series derived from the distributed hydrological model GEOtop. The differences in terms of temporal and spatial dynamic have been analyzed. The main goal of this work is to evaluate the spatial and temporal patterns of SAR derived SMC at field scale and to correlate them with ground information. This is a preparatory study to establish a methodology for the retrieval of SMC with high spatial and temporal sampling and to improve retrieval accuracies by integrating temporal information from different sources of ancillary data and from SAR time-series. It was found that the dynamics of both, temporal and spatial SMC patterns obtained from various data sources (ASAR, GEOtop and meteorological stations), show a similar general temporal behaviour that indicates the robustness of the retrieval algorithm with ASAR WS. However, depending on land cover, soil type and local topographic conditions different spatial patters can be found between SMC estimations coming from ASAR and from the GEOtop model. Introducing information on the temporal behaviour of the SAR signal proves to be a promising method for increasing the confidence and accuracy in estimating SMC, complementing hydrological model predictions. Following steps were identified as critical for the retrieval process: the topographic correction and geocoding of SAR data and the calibration of the meteorological stations. Both factors can have significant influence on the quality of SMC estimation. The accuracy of meteorological input and soil parameterization were identified as the most crucial challenges for SMC derived from hydrological modeling. References Barrett, B. W., E. Dwyer, and P. Whelan. "Soil moisture retrieval from active spaceborne microwave observations: An evaluation of current techniques." Remote Sensing 1, no. 3 (2009): 210-242. Bertoldi, G., S. Della Chiesa, C. Notarnicola, L. Pasolli, G. Niedrist, and U. Tappeiner. "Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT 2 images and hydrological modeling." Journal of Hydrology (2014). under revision. Brocca, L., A. Tarpanelli, T. Moramarco, F. Melone, S. M. Ratto, M. Cauduro, S. Ferraris et al. "Soil Moisture Estimation in Alpine Catchments through Modeling and Satellite Observations." Vadose Zone Journal (2013). Endrizzi, S., S. Gruber, M. Dall'Amico, and R. Rigon. "GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects." Geoscientific Model Development Discussions 6, no. 4 (2013): 6279-6341. Heathman, G. C., P. J. Starks, L. R. Ahuja, and T. J. Jackson. "Assimilation of surface soil moisture to estimate profile soil water content." Journal of Hydrology 279, no. 1 (2003): 1-17. Pasolli, L., C. Notarnicola, L. Bruzzone, G. Bertoldi, S. Della Chiesa, V. Hell, G. Niedrist. "Estimation of Soil Moisture in an Alpine catchment with RADARSAT2 Images." Applied and Environmental Soil Science 2011 (2011)

Validation and reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Cui, Yaokui; Long, Di; Hong, Yang; Zeng, Chao; Zhou, Jie; Han, Zhongying; Liu, Ronghua; Wan, Wei

2016-12-01

Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the Earth's 'third pole'. Large-scale spatially consistent and temporally continuous soil moisture datasets are of great importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is a relatively new passive microwave product, with the satellite being launched on November 5, 2010. This study validates and reconstructs FY-3B/MWRI soil moisture across the TP. First, the validation is performed using in situ measurements within two in situ soil moisture measurement networks (1° × 1° and 0.25° × 0.25°), and also compared with the Essential Climate Variable (ECV) soil moisture product from multiple active and passive satellite soil moisture products using new merging procedures. Results show that the ascending FY-3B/MWRI product outperforms the descending product. The ascending FY-3B/MWRI product has almost the same correlation as the ECV product with the in situ measurements. The ascending FY-3B/MWRI product has better performance than the ECV product in the frozen season and under the lower NDVI condition. When the NDVI is higher in the unfrozen season, uncertainty in the ascending FY-3B/MWRI product increases with increasing NDVI, but it could still capture the variability in soil moisture. Second, the FY-3B/MWRI soil moisture product is subsequently reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and NDVI, LST, and albedo, but also the relationship between the soil moisture and four-dimensional variations using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R2 higher than 0.56, RMSE less than 0.1 cm3 cm-3, and Bias less than 0.07 cm3 cm-3 for both frozen and unfrozen seasons, compared with the in situ measurements at the two networks. Third, the reconstruction method is applied to generate surface soil moisture over the TP. Both original and reconstructed FY-3B/MWRI soil moisture products could be valuable in studying meteorology, hydrology, and ecosystems over the TP.

InSAR constraints on soil moisture evolution after the March 2015 extreme precipitation event in Chile.

PubMed

Scott, C P; Lohman, R B; Jordan, T E

2017-07-07

Constraints on soil moisture can guide agricultural practices, act as input into weather, flooding and climate models and inform water resource policies. Space-based interferometric synthetic aperture radar (InSAR) observations provide near-global coverage, even in the presence of clouds, of proxies for soil moisture derived from the amplitude and phase content of radar imagery. We describe results from a 1.5 year-long InSAR time series spanning the March, 2015 extreme precipitation event in the hyperarid Atacama desert of Chile, constraining the immediate increase in soil moisture and drying out over the following months, as well as the response to a later, smaller precipitation event. The inferred temporal evolution of soil moisture is remarkably consistent between independent, overlapping SAR tracks covering a region ~100 km in extent. The unusually large rain event, combined with the extensive spatial and temporal coverage of the SAR dataset, present an unprecedented opportunity to image the time-evolution of soil characteristics over different surface types. Constraints on the timescale of shallow water storage after precipitation events are increasingly valuable as global water resources continue to be stretched to their limits and communities continue to develop in flood-prone areas.

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.

Inflatable Antenna Microwave Radiometer for Soil Moisture Measurement

NASA Technical Reports Server (NTRS)

Bailey, M. C.; Kendall, Bruce M.; Schroeder, Lyle C.; Harrington, Richard F.

1993-01-01

Microwave measurements of soil moisture are not being obtained at the required spatial Earth resolution with current technology. Recently, new novel designs for lightweight reflector systems have been developed using deployable inflatable antenna structures which could enable lightweight real-aperture radiometers. In consideration of this, a study was conducted at the NASA Langley Research Center (LaRC) to determine the feasibility of developing a microwave radiometer system using inflatable reflector antenna technology to obtain high spatial resolution radiometric measurements of soil moisture from low Earth orbit and which could be used with a small and cost effective launch vehicle. The required high resolution with reasonable swath width coupled with the L-band measurement frequency for soil moisture dictated the use of a large (30 meter class) real aperture antenna in conjunction with a pushbroom antenna beam configuration and noise-injection type radiometer designs at 1.4 and 4.3 GHz to produce a 370 kilometer cross-track swath with a 10 kilometer resolution that could be packaged for launch with a Titan 2 class vehicle. This study includes design of the inflatable structure, control analysis, structural and thermal analysis, antenna and feed design, radiometer design, payload packaging, orbital analysis, and electromagnetic losses in the thin membrane inflatable materials.

Correlations between soil respiration and soil properties in sugarcane areas under green and slash-and-burn management systems

NASA Astrophysics Data System (ADS)

Rodrigo Panosso, Alan; Milori, Débora M. B. P.; Marques Júnior, José; Martin-Neto, Ladislau; La Scala, Newton, Jr.

2010-05-01

Soil management causes changes in soil physical, chemical, and biological properties that consequently affect its CO2 emission. In this work we studied soil respiration (FCO2) in areas with sugarcane production in southern Brazil under two different sugarcane management systems: green (G), consisting of mechanized harvesting that produces a large amount of crop residues left on the soil surface, and slash-and-burn (SB), in which the residues are burned before manual harvest, leaving no residues on the soil surface. The study was conducted after the harvest period in two side-by-side grids installed in adjacent areas, having 20 measurement points each. The objective of this work was to determinate whether soil physical and chemical properties within each plot were useful in order to explain the spatial variability of FCO2, supposedly influence by each management system. Most of the soil physical properties studied showed no significant differences between management systems, but on the other hand most of the chemical properties differed significantly when SB and G areas were compared. Total FCO2 was 31% higher in the SB plot (729 g CO2 m-2) when compared to the G plot (557 g CO2 m-2) throughout the 70-day period after harvest studied. This seems to be related to the sensitivity of FCO2 to precipitation events, as respiration in this plot increased significantly with increases in soil moisture. Despite temporal variability showed to be positively related to soil moisture, inside each management system there was a negative correlation (p<0.01) between the spatial changes of FCO2 and soil moisture (MS), R= -0.56 and -0.59 for G and SB respectively. There was no spatial correlation between FCO2 and soil organic matter in each management system, however, the humification index (Hum) of organic matter was negatively linear correlated with FCO2 in SB (R= -0.53, p<0.05) while positively linear correlated in G area (R=0.42, p<0.10). The multiple regression model analysis applied in each management system indicates that 63% of the FCO2 spatial variability in G managed could be explained by the model: FCO2(G)= 4.11978 -0.07672MS + 0.0045Hum +1.5352K -0.04474FWP, where K and FWP are potassium content and free water porosity in G area, respectively. On the other hand, 75% of FCO2 spatial variability in SB managed plot was accounted by the model: FCO2(SB) = 10.66774 -0.08624MS -0.02904Hum -2.42548K. Therefore, soil moisture, humification index of organic matter and potassium level were the main properties able to explain the spatial variability of FCO2 in both sugarcane management systems. This result indicates that changes in sugarcane management systems could result in changes on the soil chemical properties, mostly, especially humification index of organic matter. It seems that in conversion from slash-and-burn to green harvest system, free water porosity turns to be an important aspect in order to explain part of FCO2 spatial variability in green managed system.

Modelling field scale spatial variation in water run-off, soil moisture, N2O emissions and herbage biomass of a grazed pasture using the SPACSYS model.

PubMed

Liu, Yi; Li, Yuefen; Harris, Paul; Cardenas, Laura M; Dunn, Robert M; Sint, Hadewij; Murray, Phil J; Lee, Michael R F; Wu, Lianhai

2018-04-01

In this study, we evaluated the ability of the SPACSYS model to simulate water run-off, soil moisture, N 2 O fluxes and grass growth using data generated from a field of the North Wyke Farm Platform. The field-scale model is adapted via a linked and grid-based approach (grid-to-grid) to account for not only temporal dynamics but also the within-field spatial variation in these key ecosystem indicators. Spatial variability in nutrient and water presence at the field-scale is a key source of uncertainty when quantifying nutrient cycling and water movement in an agricultural system. Results demonstrated that the new spatially distributed version of SPACSYS provided a worthy improvement in accuracy over the standard (single-point) version for biomass productivity. No difference in model prediction performance was observed for water run-off, reflecting the closed-system nature of this variable. Similarly, no difference in model prediction performance was found for N 2 O fluxes, but here the N 2 O predictions were noticeably poor in both cases. Further developmental work, informed by this study's findings, is proposed to improve model predictions for N 2 O. Soil moisture results with the spatially distributed version appeared promising but this promise could not be objectively verified.

Improving Evapotranspiration Estimates Using Multi-Platform Remote Sensing

NASA Astrophysics Data System (ADS)

Knipper, Kyle; Hogue, Terri; Franz, Kristie; Scott, Russell

2016-04-01

Understanding the linkages between energy and water cycles through evapotranspiration (ET) is uniquely challenging given its dependence on a range of climatological parameters and surface/atmospheric heterogeneity. A number of methods have been developed to estimate ET either from primarily remote-sensing observations, in-situ measurements, or a combination of the two. However, the scale of many of these methods may be too large to provide needed information about the spatial and temporal variability of ET that can occur over regions with acute or chronic land cover change and precipitation driven fluxes. The current study aims to improve the spatial and temporal variability of ET utilizing only satellite-based observations by incorporating a potential evapotranspiration (PET) methodology with satellite-based down-scaled soil moisture estimates in southern Arizona, USA. Initially, soil moisture estimates from AMSR2 and SMOS are downscaled to 1km through a triangular relationship between MODIS land surface temperature (MYD11A1), vegetation indices (MOD13Q1/MYD13Q1), and brightness temperature. Downscaled soil moisture values are then used to scale PET to actual ET (AET) at a daily, 1km resolution. Derived AET estimates are compared to observed flux tower estimates, the North American Land Data Assimilation System (NLDAS) model output (i.e. Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model, Mosiac Model, and Noah Model simulations), the Operational Simplified Surface Energy Balance Model (SSEBop), and a calibrated empirical ET model created specifically for the region. Preliminary results indicate a strong increase in correlation when incorporating the downscaling technique to original AMSR2 and SMOS soil moisture values, with the added benefit of being able to decipher small scale heterogeneity in soil moisture (riparian versus desert grassland). AET results show strong correlations with relatively low error and bias when compared to flux tower estimates. In addition, AET results show improved bias to those reported by SSEBop, with similar correlations and errors when compared to the empirical ET model. Spatial patterns of estimated AET display patterns representative of the basin's elevation and vegetation characteristics, with improved spatial resolution and temporal heterogeneity when compared to previous models.

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.

Terrestrial precipitation and soil moisture: A case study over southern Arizona and data development

NASA Astrophysics Data System (ADS)

Stillman, Susan

Quantifying climatological precipitation and soil moisture as well as interannual variability and trends requires extensive observation. This work focuses on the analysis of available precipitation and soil moisture data and the development of new ways to estimate these quantities. Precipitation and soil moisture characteristics are highly dependent on the spatial and temporal scales. We begin at the point scale, examining hourly precipitation and soil moisture at individual gauges. First, we focus on the Walnut Gulch Experimental Watershed (WGEW), a 150 km2 area in southern Arizona. The watershed has been measuring rainfall since 1956 with a very high density network of approximately 0.6 gauges per km2. Additionally, there are 19 soil moisture probes at 5 cm depth with data starting in 2002. In order to extend the measurement period, we have developed a water balance model which estimates monsoon season (Jul-Sep) soil moisture using only precipitation for input, and calibrated so that the modeled soil moisture fits best with the soil moisture measured by each of the 19 probes from 2002-2012. This observationally constrained soil moisture is highly correlated with the collocated probes (R=0.88), and extends the measurement period from 10 to 56 years and the number of gauges from 19 to 88. Then, we focus on the spatiotemporal variability within the watershed and the ability to estimate area averaged quantities. Spatially averaged precipitation and observationally constrained soil moisture from the 88 gauges is then used to evaluate various gridded datasets. We find that gauge-based precipitation products perform best followed by reanalyses and then satellite-based products. Coupled Model Intercomparison Project Phase 5 (CMIP5) models perform the worst and overestimate cold season precipitation while offsetting the monsoon peak precipitation forward or backward by a month. Satellite-based soil moisture is the best followed by land data assimilation systems and reanalyses. We show that while WGEW is small compared to the grid size of many of the evaluated products, unlike scaling from point to area, the effect of scaling from smaller to larger area is small. Finally, we focus on global precipitation. Global monthly gauge based precipitation data has become widely available in recent years and is necessary for analyzing the climatological and anomaly precipitation fields as well as for calibrating and evaluating other gridded products such as satellite-based and modeled precipitation. However, frequency and intensity of precipitation are also important in the partitioning of water and energy fluxes. Therefore, because daily and sub-daily observed precipitation is limited to recent years, the number of raining days per month (N) is needed. We show that the only currently available long-term N product, developed by the Climate Research Unit (CRU), is deficient in certain areas, particularly where CRU gauge data is sparse. We then develop a new global 110-year N product, which shows significant improvement over CRU using three regional daily precipitation products with far more gauges than are used in CRU.

Infiltration Processes and Flow Velocities Across the Landscape: When and Where is Macropore Flow Relevant?

NASA Astrophysics Data System (ADS)

Demand, D.; Blume, T.; Weiler, M.

2017-12-01

Preferential flow in macropores significantly affects the distributions of water and solutes in soil and many studies showed its relevance worldwide. Although some models include this process as a second pore domain, little is known about the spatial patterns and temporal dynamics. For example, while flow in the matrix is usually modeled and parameterized based on soil texture, an influence of texture on non-capillary flow for a given land-use class is poorly understood. To investigate the temporal and spatial dynamics on preferential flow we used a four-year soil moisture dataset from the mesoscale Attert catchment (288 km²) in Luxembourg. This dataset contains time series from 126 soil profiles in different textures and two land-use classes (forest, grassland). The soil moisture probes were installed in 10, 30 and 50 cm depth and measured in a 5-minute temporal resolution. Events were defined by a soil moisture increase higher than the instrument noise after a precipitation sum of more than 1 mm. Precipitation was measured next to the profiles so that each location could be associated to its unique precipitation characteristics. For every event and profile the soil moisture reaction was classified in sequential (ordered by depth) and non-sequential response. A non-sequential soil moisture reaction was used as an indicator of preferential flow. For sequential flow, the velocity was determined by the first reaction between two vertically adjacent sensors. The sensor reaction and wetting front velocity was analyzed in the context of precipitation characteristics and initial soil water content. Grassland sites showed a lower proportion of non-sequential flow than forest sites. For forest, non-sequential response is dependent on texture, rainfall intensity and initial water content. This is less distinct for the grassland sites. Furthermore, sequential reactions show higher flow velocities at sites, which also have high percentage of non-sequential response. In contrast, grassland sites show a more homogenous wetting front independent of soil texture. Compared against common modelling approaches of soil water flow, measured velocities show clear evidence of preferential flow, especially for forest soils. The analysis also shows that vegetation can alter the soil properties above the textural properties alone.

Complex terrain in the Critical Zone: How topography drives ecohydrological patterns of soil and plant carbon exchange in a semiarid mountainous system

NASA Astrophysics Data System (ADS)

Barron-Gafford, G.; Minor, R. L.; Heard, M. M.; Sutter, L. F.; Yang, J.; Potts, D. L.

2015-12-01

The southwestern U.S. is predicted to experience increasing temperatures and longer periods of inter-storm drought. High temperature and water deficit restrict plant productivity and ecosystem functioning, but the influence of future climate is predicted to be highly heterogeneous because of the complex terrain characteristic of much of the Critical Zone (CZ). Within our Critical Zone Observatory (CZO) in the Southwestern US, we monitor ecosystem-scale carbon and water fluxes using eddy covariance. This whole-ecosystem metric is a powerful integrating measure of ecosystem function over time, but details on spatial heterogeneity resulting from topographic features of the landscape are not captured, nor are interactions among below- and aboveground processes. We supplement eddy covariance monitoring with distributed measures of carbon flux from soil and vegetation across different aspects to quantify the causes and consequences of spatial heterogeneity through time. Given that (i) aspect influences how incoming energy drives evaporative water loss and (ii) seasonality drives temporal patterns of soil moisture recharge, we were able to examine the influence of these processes on CO2 efflux by investigating variation across aspect. We found that aspect was a significant source of spatial heterogeneity in soil CO2 efflux, but the influence varied across seasonal periods. Snow on South-facing aspects melted earlier and yielded higher efflux rates in the spring. However, during summer, North- and South-facing aspects had similar amounts of soil moisture, but soil temperatures were warmer on the North-facing aspect, yielding greater rates of CO2 efflux. Interestingly, aspect did not influence photosynthetic rates. Taken together, we found that physical features of the landscape yielded predictable patterns of levels and phenologies of soil moisture and temperature, but these drivers differentially influenced below- and aboveground sources of carbon exchange. Conducting these spatially distributed measurements are time consuming. Looking forward, we have begun using unmanned aerial vehicles outfitted with thermal and multi-spectral cameras to quantify patterns of water flux, NDVI, needle browning due to moisture stress, and overall phenology in the CZ.

Microwave brightness temperature and thermal inertia - towards synergistic method of high-resolution soil moisture retrieval

NASA Astrophysics Data System (ADS)

Lukowski, Mateusz; Usowicz, Boguslaw; Sagan, Joanna; Szlazak, Radoslaw; Gluba, Lukasz; Rojek, Edyta

2017-04-01

Soil moisture is an important parameter in many environmental studies, as it influences the exchange of water and energy at the interface between the land surface and the atmosphere. Accurate assessment of the soil moisture spatial and temporal variations is crucial for numerous studies; starting from a small scale of single field, then catchment, mesoscale basin, ocean conglomeration, finally ending at the global water cycle. Despite numerous advantages, such as fine accuracy (undisturbed by clouds or daytime conditions) and good temporal resolution, passive microwave remote sensing of soil moisture, e.g. SMOS and SMAP, are not applicable to a small scale - simply because of too coarse spatial resolution. On the contrary, thermal infrared-based methods of soil moisture retrieval have a good spatial resolution, but are often disturbed by clouds and vegetation interferences or night effects. The methods that base on point measurements, collected in situ by monitoring stations or during field campaigns, are sometimes called "ground truth" and may serve as a reference for remote sensing, of course after some up-scaling and approximation procedures that are, unfortunately, potential source of error. Presented research concern attempt to synergistic approach that join two remote sensing methods: passive microwave and thermal infrared, supported by in situ measurements. Microwave brightness temperature of soil was measured by ELBARA, the radiometer at 1.4 GHz frequency, installed at 6 meters high tower at Bubnow test site in Poland. Thermal inertia around the tower was modelled using the statistical-physical model whose inputs were: soil physical properties, its water content, albedo and surface temperatures measured by an infrared pyrometer, directed at the same footprint as ELBARA. The results coming from this method were compared to in situ data obtained during several field campaigns and by the stationary agrometeorological stations. The approach seems to be reasonable, as both variables, brightness temperature and thermal inertia, strongly depend on soil moisture. Despite the fact that the presented research focused on modelling in the small size, 4 ha test site, the method is promising for larger scales as well, due to similarities between ELBARA and SMOS and between pyrometer and satellite imaging spectrometers (Landsat, Sentinel etc.). The approach will merge advantages: high accuracy of passive microwave sensing with a good spatial resolution of thermal infrared methods. The work was partially funded under two ESA projects: 1) "ELBARA_PD (Penetration Depth)" No. 4000107897/13/NL/KML, funded by the Government of Poland through an ESA-PECS contract (Plan for European Cooperating States). 2) "Technical Support for the fabrication and deployment of the radiometer ELBARA-III in Bubnow, Poland" No. 4000113360/15/NL/FF/gp.

Multiscale soil moisture measurement for mapping surface runoff generation on torrential headwater catchments (Draix-Bléone field observatory, South Alps, France)

NASA Astrophysics Data System (ADS)

Florian, Mallet; Vincent, Marc; Johnny, Douvinet; Philippe, Rossello; Bouteiller Caroline, Le; Jean-Philippe, Malet; Julien, Gance

2015-04-01

Runoff generation in the headwater catchments in various land use conditions still remain a core issue in catchment hydrology (Uhlenbrook S. et al., 2003). Vegetation has a strong impact on flows distribution (interception, infiltration, evapotranspiration, runoff) but the relative influence of these mechanisms according to geomorphological determinants is still not totally understood. The "ORE Draix" located in the Alpes-de-Haute-Provence (France) allows to study these parameters using experimental watersheds equipped with a long term monitoring instrumentation (rainfall, streamflow, water, soil and air temperature, soil erosion, soil moisture...). These marl torrential watersheds have a peculiar hydrological behavior during flood events with large outflow differences between the wooded and the bare areas. We try to identify the runoff production factors by studying water storage/drainage processes within the first 30 cm depth of soil (Wilson et al., 2003, Western et al., 2004). Soil moisture can explain runoff during floods, that's why we try to upscale this variable at the watershed level. Unlike studies on soil moisture monitoring in agricultural context (flat areas), conventional remote sensing methods are difficult to apply to the badlands (elevation between 1500 masl and 1800 masl, approximately 1km² areas, steep slopes, various land uses) (Bagdhadi, 2005). This difficulty can be overcome by measuring soil moisture at different spatial (point, plot, slope, catchment) and time scales (event, season, year) using innovative approaches. In this context, we propose a monitoring of soil moisture based on geostatistical treatments crossed with measurements at different scales. These measures are provided from ground and airborne sensors deployment. Point measurements are ensured at a very high time frequency using capacitance probes. At an intermediate level, a slope is equipped with a DTS sensor (distributed temperature sensing) to obtain a 2D estimate of soilwater flow of from the surface to - 30 cm. Another distributed approach will be carried out from a measurement of cosmic neutrons mitigation (Cosmic ray sensor) to estimate a soil moisture averaged value over 40 ha (Zreda et al., 2012). Finally, the smallest scale (slope and catchment) will be approached using remote sensing with a drone and/or satellite imagery (IR, passive and active microwave). This concatenation of scales with different combinations of time steps should enable us to better understand the hydrological dynamics in torrential environments. It aims at mapping the stormflow generation on a catchment at the flood scale and defining the main determinants of surface runoff. These results may contribute to the improvement of runoff simulation and flood prediction. References : Uhlenbrook S., J.J. McDonnell and C. Leibundgut, 2003. Preface: Runoff generation implications for river basin modelling. Hydrological Processes, Special Issue, 17: 197-198. Andrew W. Western, Sen-Lin Zhou, Rodger B. Grayson, Thomas A. MacMahon, Günter Blöshl, David J. Wilson, 2004. Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. Journal of Hydrology 286. Zreda, M., Shuttleworth WJ., Zeng X., Zweck C., Desilets D., Franz TE. et al., 2012. COSMOS: the COsmic-ray Soil Moisture Observing System. Hydrology and Earth System Sciences, 16(11): 4079-4099.

Creating soil moisture maps based on radar satellite imagery

NASA Astrophysics Data System (ADS)

Hnatushenko, Volodymyr; Garkusha, Igor; Vasyliev, Volodymyr

2017-10-01

The presented work is related to a study of mapping soil moisture basing on radar data from Sentinel-1 and a test of adequacy of the models constructed on the basis of data obtained from alternative sources. Radar signals are reflected from the ground differently, depending on its properties. In radar images obtained, for example, in the C band of the electromagnetic spectrum, soils saturated with moisture usually appear in dark tones. Although, at first glance, the problem of constructing moisture maps basing on radar data seems intuitively clear, its implementation on the basis of the Sentinel-1 data on an industrial scale and in the public domain is not yet available. In the process of mapping, for verification of the results, measurements of soil moisture obtained from logs of the network of climate stations NOAA US Climate Reference Network (USCRN) were used. This network covers almost the entire territory of the United States. The passive microwave radiometers of Aqua and SMAP satellites data are used for comparing processing. In addition, other supplementary cartographic materials were used, such as maps of soil types and ready moisture maps. The paper presents a comparison of the effect of the use of certain methods of roughening the quality of radar data on the result of mapping moisture. Regression models were constructed showing dependence of backscatter coefficient values Sigma0 for calibrated radar data of different spatial resolution obtained at different times on soil moisture values. The obtained soil moisture maps of the territories of research, as well as the conceptual solutions about automation of operations of constructing such digital maps, are presented. The comparative assessment of the time required for processing a given set of radar scenes with the developed tools and with the ESA SNAP product was carried out.

Tree species, spatial heterogeneity, and seasonality drive soil fungal abundance, richness, and composition in Neotropical rainforests.

PubMed

Kivlin, Stephanie N; Hawkes, Christine V

2016-12-01

Tropical ecosystems remain poorly understood and this is particularly true for belowground soil fungi. Soil fungi may respond to plant identity when, for example, plants differentially allocate resources belowground. However, spatial and temporal heterogeneity in factors such as plant inputs, moisture, or nutrients can also affect fungal communities and obscure our ability to detect plant effects in single time point studies or within diverse forests. To address this, we sampled replicated monocultures of four tree species and secondary forest controls sampled in the drier and wetter seasons over 2 years. Fungal community composition was primarily related to vegetation type and spatial heterogeneity in the effects of vegetation type, with increasing divergence partly reflecting greater differences in soil pH and soil moisture. Across wetter versus drier dates, fungi were 7% less diverse, but up to four-fold more abundant. The combined effects of tree species and seasonality suggest that predicted losses of tropical tree diversity and intensification of drought have the potential to cascade belowground to affect both diversity and abundance of tropical soil fungi. © 2016 Society for Applied Microbiology and John Wiley & Sons Ltd.

Investigation on the dominant factors controlling the spatio-temporal distribution of soil moisture in experimental grasslands

NASA Astrophysics Data System (ADS)

Schwichtenberg, G.; Hildebrandt, A.; Samaniego-Eguiguren, L.; Kreutziger, Y.; Attinger, S.

2009-04-01

The spatio-temporal distribution of soil moisture in the unsaturated zone influences the vegetation growth, governs the runoff generation processes as well as the energy balance at the interface between biosphere and the atmosphere, by influencing evapotranspiration. A better understanding of the spatio-temporal variability and dependence of soil moisture on living versus abiotic environment would lead to an improved representation of the soil-vegetation-atmosphere processes in hydrological and climate models. The Jena Experiment site (Germany) was established October 2001 in order to analyse the interaction between plant diversity and ecosystem processes. The main experiment covers 92 plots of 20 x 20 m arranged into a grid, on which a mixture of up to 60 grassland species and of one to four plant functional groups have been seeded. Each of these plots is equipped with at least one measurement tube for soil moisture. Measurements have been conducted weekly for four growing seasons (SSF). Here, we use geostatistical methods, like variograms and multivariate regressions, to investigate in how far abiotic environment and ecosystem explain the spatial and temporal variation of soil moisture at the Jena Experiment site. We test the influence of the soil environment, biodiversity, leaf area index and groundwater table. The poster will present the results of this analysis.

Influence of spatial variability of hydraulic characteristics of soils on surface parameters obtained from remote sensing data in infrared and microwaves

NASA Technical Reports Server (NTRS)

Brunet, Y.; Vauclin, M.

1985-01-01

The correct interpretation of thermal and hydraulic soil parameters infrared from remotely sensed data (thermal infrared, microwaves) implies a good understanding of the causes of their temporal and spatial variability. Given this necessity, the sensitivity of the surface variables (temperature, moisture) to the spatial variability of hydraulic soil properties is tested with a numerical model of heat and mass transfer between bare soil and atmosphere. The spatial variability of hydraulic soil properties is taken into account in terms of the scaling factor. For a given soil, the knowledge of its frequency distribution allows a stochastic use of the model. The results are treated statistically, and the part of the variability of soil surface parameters due to that of soil hydraulic properties is evaluated quantitatively.

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.

SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann

2011-01-01

Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS ancillary data) were used to correct for surface temperature effects and to derive microwave emissivity. ECMWF data were also used for precipitation forecasts, presence of snow, and frozen ground. Vegetation options are described below. One year of soil moisture observations from a set of four watersheds in the U.S. were used to evaluate four different retrieval methodologies: (1) SMOS soil moisture estimates (version 400), (2) SeA soil moisture estimates using the SMOS/SMAP data with SMOS estimated vegetation optical depth, which is part of the SMOS level 2 product, (3) SeA soil moisture estimates using the SMOS/SMAP data and the MODIS-based vegetation climatology data, and (4) SeA soil moisture estimates using the SMOS/SMAP data and actual MODIS observations. The use of SMOS real-world global microwave observations and the analyses described here will help in the development and selection of different land surface parameters and ancillary observations needed for the SMAP soil moisture algorithms. These investigations will greatly improve the quality and reliability of this SMAP product at launch.

The Integration of SMOS Soil Moisture in a Consistent Soil Moisture Climate Record

NASA Astrophysics Data System (ADS)

de Jeu, Richard; Kerr, Yann; Wigneron, Jean Pierre; Rodriguez-Fernandez, Nemesio; Al-Yaari, Amen; van der Schalie, Robin; Dolman, Han; Drusch, Matthias; Mecklenburg, Susanne

2015-04-01

Recently, a study funded by the European Space Agency (ESA) was set up to provide guidelines for the development of a global soil moisture climate record with a special emphasis on the integration of SMOS. Three different data fusion approaches were designed and implemented on 10 year passive microwave data (2003-2013) from two different satellite sensors; the ESA Soil Moisture Ocean Salinity Mission (SMOS) and the NASA/JAXA Advanced Scanning Microwave Radiometer (AMSR-E). The AMSR-E data covered the period from January 2003 until Oct 2011 and SMOS data covered the period from June 2010 until the end of 2013. The fusion approaches included a neural network approach (Rodriguez-Fernandez et al., this conference session HS6.4), a regression approach (Wigneron et al., 2004), and an approach based on the baseline algorithm of ESAs current Climate Change Initiative soil moisture program, the Land Parameter Retrieval Model (Van der Schalie et al., this conference session HS6.4). With this presentation we will show the first results from this study including a description of the different approaches and the validation activities using both globally covered modeled datasets and ground observations from the international soil moisture network. The statistical validation analyses will give us information on the temporal and spatial performance of the three different approaches. Based on these results we will then discuss the next steps towards a seamless integration of SMOS in a consistent soil moisture climate record. References Wigneron J.-P., J.-C. Calvet, P. de Rosnay, Y. Kerr, P. Waldteufel, K. Saleh, M. J. Escorihuela, A. Kruszewski, 'Soil Moisture Retrievals from Bi-Angular L-band Passive Microwave Observations', IEEE Trans. Geosc. Remote Sens. Let., vol 1, no. 4, 277-281, 2004.

Why the predictions for monsoon rainfall fail?

NASA Astrophysics Data System (ADS)

Lee, J.

2016-12-01

To be in line with the Global Land/Atmosphere System Study (GLASS) of the Global Energy and Water Cycle Experiment (GEWEX) international research scheme, this study discusses classical arguments about the feedback mechanisms between land surface and precipitation to improve the predictions of African monsoon rainfall. In order to clarify the impact of antecedent soil moisture on subsequent rainfall evolution, several data sets will be presented. First, in-situ soil moisture field measurements acquired by the AMMA field campaign will be shown together with rain gauge data. This data set will validate various model and satellite data sets such as NOAH land surface model, TRMM rainfall, CMORPH rainfall and HadGEM climate models, SMOS soil moisture. To relate soil moisture with precipitation, two approaches are employed: one approach makes a direct comparison between the spatial distributions of soil moisture as an absolute value and rainfall, while the other measures a temporal evolution of the consecutive dry days (i.e. a relative change within the same soil moisture data set over time) and rainfall occurrences. Consecutive dry days shows consistent results of a negative feedback between soil moisture and rainfall across various data sets, contrary to the direct comparison of soil moisture state. This negative mechanism needs attention, as most climate models usually focus on a positive feedback only. The approach of consecutive dry days takes into account the systematic errors in satellite observations, reminding us that it may cause the misinterpretation to directly compare model with satellite data, due to their difference in data retrievals. This finding is significant, as the climate indices employed by the Intergovernmental Panel on Climate Change (IPCC) modelling archive are based on the atmospheric variable rathr than land.

Remote Sensing, GIS, and Vector-Borne Disease

NASA Technical Reports Server (NTRS)

Beck, Louisa R.

2001-01-01

The concept of global climate change encompasses more than merely an alteration in temperature; it also includes spatial and temporal covariations in precipitation and humidity, and more frequent occurrence of extreme weather events. The impact of these variations, which can occur at a variety of temporal and spatial scales, could have a direct impact on disease transmission through their environmental consequences for pathogen, vector, and host survival, as well as indirectly through human demographic and behavioral responses. New and future sensor systems will allow scientists to investigate the relationships between climate change and environmental risk factors at multiple spatial, temporal and spectral scales. Higher spatial resolution will provide better opportunities for mapping urban features previously only possible with high resolution aerial photography. These opportunities include housing quality (e.g., Chagas'disease, leishmaniasis) and urban mosquito habitats (e.g., dengue fever, filariasis, LaCrosse encephalitis). There are or will be many new sensors that have higher spectral resolution, enabling scientists to acquire more information about parameters such as soil moisture, soil type, better vegetation discrimination, and ocean color, to name a few. Although soil moisture content is now detectable using Landsat, the new thermal, shortwave infrared, and radar sensors will be able to provide this information at a variety of scales not achievable using Landsat. Soil moisture could become a key component in transmission risk models for Lyme disease (tick survival), helminthiases (worm habitat), malaria (vector-breeding habitat), and schistosomiasis (snail habitat).

Measuring Soil Moisture in Skeletal Soils Using a COSMOS Rover

NASA Astrophysics Data System (ADS)

Medina, C.; Neely, H.; Desilets, D.; Mohanty, B.; Moore, G. W.

2017-12-01

The presence of coarse fragments directly influences the volumetric water content of the soil. Current surface soil moisture sensors often do not account for the presence of coarse fragments, and little research has been done to calibrate these sensors under such conditions. The cosmic-ray soil moisture observation system (COSMOS) rover is a passive, non-invasive surface soil moisture sensor with a footprint greater than 100 m. Despite its potential, the COSMOS rover has yet to be validated in skeletal soils. The goal of this study was to validate measurements of surface soil moisture as taken by a COSMOS rover on a Texas skeletal soil. Data was collected for two soils, a Marfla clay loam and Chinati-Boracho-Berrend association, in West Texas. Three levels of data were collected: 1) COSMOS surveys at three different soil moistures, 2) electrical conductivity surveys within those COSMOS surveys, and 3) ground-truth measurements. Surveys with the COSMOS rover covered an 8000-h area and were taken both after large rain events (>2") and a long dry period. Within the COSMOS surveys, the EM38-MK2 was used to estimate the spatial distribution of coarse fragments in the soil around two COSMOS points. Ground truth measurements included coarse fragment mass and volume, bulk density, and water content at 3 locations within each EM38 survey. Ground-truth measurements were weighted using EM38 data, and COSMOS measurements were validated by their distance from the samples. There was a decrease in water content as the percent volume of coarse fragment increased. COSMOS estimations responded to both changes in coarse fragment percent volume and the ground-truth volumetric water content. Further research will focus on creating digital soil maps using landform data and water content estimations from the COSMOS rover.

Optimal spatial sampling techniques for ground truth data in microwave remote sensing of soil moisture

NASA Technical Reports Server (NTRS)

Rao, R. G. S.; Ulaby, F. T.

1977-01-01

The paper examines optimal sampling techniques for obtaining accurate spatial averages of soil moisture, at various depths and for cell sizes in the range 2.5-40 acres, with a minimum number of samples. Both simple random sampling and stratified sampling procedures are used to reach a set of recommended sample sizes for each depth and for each cell size. Major conclusions from statistical sampling test results are that (1) the number of samples required decreases with increasing depth; (2) when the total number of samples cannot be prespecified or the moisture in only one single layer is of interest, then a simple random sample procedure should be used which is based on the observed mean and SD for data from a single field; (3) when the total number of samples can be prespecified and the objective is to measure the soil moisture profile with depth, then stratified random sampling based on optimal allocation should be used; and (4) decreasing the sensor resolution cell size leads to fairly large decreases in samples sizes with stratified sampling procedures, whereas only a moderate decrease is obtained in simple random sampling procedures.

Drought Occurrence and Management in Kazakhstan

NASA Astrophysics Data System (ADS)

Iskakov, Y.; Mendigarin, A.; Sazanova, B.; Zhumabayev, Y.

2014-12-01

A direct and reliable indicator of drought can be measured by the productive moisture content (PMC) in soil, which uses the weight of the moisture in a soil profile. However the limited network of PMC measurement sites in Kazakhstan (123 for the total area of 2. 7 million km2) does not allow a spatial assessment of drought conditions across the vast majority of the country. To assess the availability of soil moisture and the likelihood of drought, we calculated spatial structure of soil moisture deficit, using the Selyaninov Hydrothermal Coefficient (HTCs). It was derived for the vegetatively active period from May to August. Figure 1 shows the average structure of soil moisture availability across Kazakhstan, and indicates that most of the country is vulnerable to drought. In response to this vulnerability the Kazakhstan also established the following policies and technologies to mitigate the impact of drought. Those measures include: 1. Introduction of resource-saving (soil-protective and moisture preserving) No-Till technologies. 2. Structural and technological diversification of plant growing. 3. Introduction of efficient irrigation systems for southern Kazakhstan. 4. Adaptation of weather and field reports to optimize the benefits of agrotechnical activities. 5. Re-equipment of agricultural vehicles and machinery. 6. Training and professional development of specialists in agriculture. 7. Improvement of insurance system for plant growing. 8. Improvement of systems and mechanisms of state support for small and medium agricultural producers. 9. Improvement of the system of scientific and technological and innovative support for grain production. These strategies, how they are being implemented, and the targeted goals will be presented. We will provide findings from experimental field stations, and model farms. The goals is to improve efficiency in water resources, effectively communication relevant information to farmers, policy makers and the insurance industry, as well as promote climate resilience in the advent of climate change.

Results from SMAP Validation Experiments 2015 and 2016

NASA Astrophysics Data System (ADS)

Colliander, A.; Jackson, T. J.; Cosh, M. H.; Misra, S.; Crow, W.; Powers, J.; Wood, E. F.; Mohanty, B.; Judge, J.; Drewry, D.; McNairn, H.; Bullock, P.; Berg, A. A.; Magagi, R.; O'Neill, P. E.; Yueh, S. H.

2017-12-01

NASA's Soil Moisture Active Passive (SMAP) mission was launched in January 2015. The objective of the mission is global mapping of soil moisture and freeze/thaw state. Well-characterized sites with calibrated in situ soil moisture measurements are used to determine the quality of the soil moisture data products; these sites are designated as core validation sites (CVS). To support the CVS-based validation, airborne field experiments are used to provide high-fidelity validation data and to improve the SMAP retrieval algorithms. The SMAP project and NASA coordinated airborne field experiments at three CVS locations in 2015 and 2016. SMAP Validation Experiment 2015 (SMAPVEX15) was conducted around the Walnut Gulch CVS in Arizona in August, 2015. SMAPVEX16 was conducted at the South Fork CVS in Iowa and Carman CVS in Manitoba, Canada from May to August 2016. The airborne PALS (Passive Active L-band Sensor) instrument mapped all experiment areas several times resulting in 30 coincidental measurements with SMAP. The experiments included intensive ground sampling regime consisting of manual sampling and augmentation of the CVS soil moisture measurements with temporary networks of soil moisture sensors. Analyses using the data from these experiments have produced various results regarding the SMAP validation and related science questions. The SMAPVEX15 data set has been used for calibration of a hyper-resolution model for soil moisture product validation; development of a multi-scale parameterization approach for surface roughness, and validation of disaggregation of SMAP soil moisture with optical thermal signal. The SMAPVEX16 data set has been already used for studying the spatial upscaling within a pixel with highly heterogeneous soil texture distribution; for understanding the process of radiative transfer at plot scale in relation to field scale and SMAP footprint scale over highly heterogeneous vegetation distribution; for testing a data fusion based soil moisture downscaling approach; and for investigating soil moisture impact on estimation of vegetation fluorescence from airborne measurements. The presentation will describe the collected data and showcase some of the most important results achieved so far.

A simple nudging scheme to assimilate ASCAT soil moisture data in the WRF model

NASA Astrophysics Data System (ADS)

Capecchi, V.; Gozzini, B.

2012-04-01

The present work shows results obtained in a numerical experiment using the WRF (Weather and Research Forecasting, www.wrf-model.org) model. A control run where soil moisture is constrained by GFS global analysis is compared with a test run where soil moisture analysis is obtained via a simple nudging scheme using ASCAT data. The basic idea of the assimilation scheme is to "nudge" the first level (0-10 cm below ground in NOAH model) of volumetric soil moisture of the first-guess (say θ(b,1) derived from global model) towards the ASCAT derived value (say ^θ A). The soil moisture analysis θ(a,1) is given by: { θ + K (^θA - θ ) l = 1 θ(a,1) = θ(b,l) (b,l) l > 1 (b,l) (1) where l is the model soil level. K is a constant scalar value that is user specified and in this study it is equal to 0.2 (same value as in similar studies). Soil moisture is critical for estimating latent and sensible heat fluxes as well as boundary layer structure. This parameter is, however, poorly assimilated in current global and regional numerical models since no extensive soil moisture observation network exists. Remote sensing technologies offer a synoptic view of the dynamics and spatial distribution of soil moisture with a frequent temporal coverage and with a horizontal resolution similar to mesoscale NWP model. Several studies have shown that measurements of normalized backscatter (surface soil wetness) from the Advanced Scatterometer (ASCAT) operating at microwave frequencies and boarded on the meteorological operational (Metop) satellite, offer quality information about surface soil moisture. Recently several studies deal with the implementation of simple assimilation procedures (nudging, Extended Kalman Filter, etc...) to integrate ASCAT data in NWP models. They found improvements in screen temperature predictions, particularly in areas such as North-America and in the Tropics, where it is strong the land-atmosphere coupling. The ECMWF (Newsletter No. 127) is currently implementing and testing an EKF for combining conventional observations and remote sensed soil moisture data in order to produce a more accurate analysis. In the present work verification skills (RMSE, BIAS, correlation) of both control and test run are presented using observed data collected by International Soil Moisture Network. Moreover improvements in temperature predictions are evaluated.

Drought Indicators Based on Model Assimilated GRACE Terrestrial Water Storage Observations

NASA Technical Reports Server (NTRS)

Houborg, Rasmus; Rodell, Matthew; Li, Bailing; Reichle, Rolf; Zaitchik, Benjamin F.

2012-01-01

The Gravity Recovery and Climate Experiment (GRACE) twin satellites observe time variations in Earth's gravity field which yield valuable information about changes in terrestrial water storage (TWS). GRACE is characterized by low spatial (greater than 150,000 square kilometers) and temporal (greater than 10 day) resolution but has the unique ability to sense water stored at all levels (including groundwater) systematically and continuously. The GRACE Data Assimilation System (GRACE-DAS), based on the Catchment Land Surface Model (CLSM) enhances the value of the GRACE water storage data by enabling spatial and temporal downscaling and vertical decomposition into moisture 39 components (i.e. groundwater, soil moisture, snow), which individually are more useful for scientific applications. In this study, GRACE-DAS was applied to North America and GRACE-based drought indicators were developed as part of a larger effort that investigates the possibility of more comprehensive and objective identification of drought conditions by integrating spatially, temporally and vertically disaggregated GRACE data into the U.S. and North American Drought Monitors. Previously, the Drought Monitors lacked objective information on deep soil moisture and groundwater conditions, which are useful indicators of drought. Extensive datasets of groundwater storage from USGS monitoring wells and soil moisture from the Soil Climate Analysis Network (SCAN) were used to assess improvements in the hydrological modeling skill resulting from the assimilation of GRACE TWS data. The results point toward modest, but statistically significant, improvements in the hydrological modeling skill across major parts of the United States, highlighting the potential value of GRACE assimilated water storage field for improving drought detection.

SURFEX modeling of soil moisture fields over the Valencia Anchor Station and their comparison to different SMOS products and in situ measurements

NASA Astrophysics Data System (ADS)

Coll Pajaron, M. Amparo; Lopez-Baeza, Ernesto; Fernandez-Moran, Roberto; Samiro Khodayar-Pardo, D.

2016-07-01

Soil moisture is a difficult variable to obtain proper representation because of its high temporal and spatial variability. It is a significant parameter in agriculture, hydrology, meteorology and related disciplines. {it SVAT (Soil-Vegetation-Atmosphere-Transfer)} models can be used to simulate the temporal behaviour and spatial distribution of soil moisture in a given area. In this work, we use the {bf SURFEX (Surface Externalisée)} model developed at the {it Centre National de Recherches Météorologiques (CNRM)} at Météo-France (http://www.cnrm.meteo.fr/surfex/) to simulate soil moisture at the {bf Valencia Anchor Station}. SURFEX integrates the {bf ISBA (Interaction Sol-Biosphère-Atmosphère}; surfaces with vegetation) module to describe the land surfaces (http://www.cnrm.meteo.fr/isbadoc/model.html) that have been adapted to describe the land covers of our study area. The Valencia Anchor Station was chosen as a core validation site for the {it SMOS (Soil Moisture and Ocean Salinity)} mission and as one of the hydrometeorological sites for the {it HyMeX (HYdrological cycle in Mediterranean EXperiment)} programme. This site represents a reasonably homogeneous and mostly flat area of about 50x50 km2. The main cover type is vineyards (65%), followed by fruit trees, shrubs, and pine forests, and a few small scattered industrial and urban areas. Except for the vineyard growing season, the area remains mostly under bare soil conditions. In spite of its relatively flat topography, the small altitude variations of the region clearly influence climate. This oscillates between semiarid and dry sub-humid. Annual mean temperatures are between 12 ºC and 14.5 ºC, and annual precipitation is about 400-450 mm. The duration of frost free periods is from May to November, with maximum precipitation in spring and autumn. The first part of this investigation consists in simulating soil moisture fields over the Valencia Anchor Station to be compared with SMOS level-2 (resolution 15 km) and level-3 (resolution 25 km) soil moisture maps and high resolution SMOS pixel-disaggregated soil moisture products, obtained by combining SMOS level-2 with MODIS NDVI and LST data (resolution 1 km) (Piles et al., 2011). In situ measurements from the Valencia Anchor Station network of soil moisture stations are also available as reference covering a reduced number of different vegetation cover and soil types, as well as estimations from the ESA ELBARA-II L-band radiometer installed over a vineyard crop to monitor SMOS validation conditions. Different interpolation methods have been applied to all significant atmospheric forcing parameters from the two met stations available in the area (pressure, temperature, relative humidity and precipitation) in order to obtain a good representation of soil conditions. The period of investigation covers the complete year 2012 of which we will particularly focus on selected periods.

MISTRALE: Soil moisture mapping service based on a UAV-embedded GNSS-Reflectometry sensor

NASA Astrophysics Data System (ADS)

Van de Vyvere, Laura; Desenfans, Olivier

2016-04-01

Around 70 percent of worldwide freshwater is used by agriculture. To be able to feed an additional 2 billion people by 2030, water demand is expected to increase tremendously in the next decades. Farmers are challenged to produce "more crop per drop". In order to optimize water resource management, it is crucial to improve soil moisture situation awareness, which implies both a better temporal and spatial resolution. To this end, the objective of the MISTRALE project (Monitoring soIl moiSture and waTeR-flooded Areas for agricuLture and Environment) is to provide UAV-based soil moisture maps that could complement satellite-based and field measurements. In addition to helping farmers make more efficient decisions about where and when to irrigate, MISTRALE moisture maps are an invaluable tool for risk management and damage evaluation, as they provide highly relevant information for wetland and flood-prone area monitoring. In order to measure soil water content, a prototype of a new sensor, called GNSS-Reflectometry (GNSS-R), is being developed in MISTRALE. This approach consists in comparing the direct signal, i.e. the signal travelling directly from satellite to receiver (in this case, embedded in the UAV), with its ground-reflected equivalent. Since soil dielectric properties vary with moisture content, the reflected signal's peak power is affected by soil moisture, unlike the direct one. In order to mitigate the effect of soil surface roughness on measurements, both left-hand and right-hand circular polarization reflected signals have to be recorded and processed. When it comes to soil moisture, using GNSS signals instead of traditional visible/NIR imagery has many advantages: it is operational under cloud cover, during the night, and also under vegetation (bushes, grass, trees). In addition, compared to microwaves, GNSS signal (which lies in L-band, between 1.4 and 1.8 GHz) is less influenced by variation on thermal background. GNSS frequencies are then ideal candidates for soil moisture observation. In the context of the MISTRALE project, both GPS and GALILEO signals will be used. Thanks to a higher number of available satellites and to the GALILEO signals characteristics, the sensor's measurements accuracy will be improved. The GNSS-R sensor will be embedded on Boreal, a fixed-wing UAV weighing less than 20 kg and allowing about 5 kg payload. Boreal is able to fly continuously for 10 hours and has a range of 1000 km. Due to the low elevation (100-150m) of UAV flights, high spatial resolution can be achieved. Test flights have already been performed in Pech Rouge and Camargue areas, France. During these campaigns, soil moisture maps were computed using GNSS-R data. These were successfully correlated with in-situ measurements, considered as ground truth, demonstrating the feasibility of the MISTRALE concept. The MISTRALE project is co-funded by GSA (European GNSS Agency) under H2020 program framework for research and innovation.

Synergy of the SimSphere land surface process model with ASTER imagery for the retrieval of spatially distributed estimates of surface turbulent heat fluxes and soil moisture content

NASA Astrophysics Data System (ADS)

Petropoulos, George; Wooster, Martin J.; Carlson, Toby N.; Drake, Nick

2010-05-01

Accurate information on spatially explicit distributed estimates of key land-atmosphere fluxes and related land surface parameters is of key importance in a range of disciplines including hydrology, meteorology, agriculture and ecology. Estimation of those parameters from remote sensing frequently employs the integration of such data with mathematical representations of the transfers of energy, mass and radiation between soil, vegetation and atmosphere continuum, known as Soil Vegetation Atmosphere Transfer (SVAT) models. The ability of one such inversion modelling scheme to resolve for key surface energy fluxes and of soil surface moisture content is examined here using data from a multispectral high spatial resolution imaging instrument, the Advanced Spaceborne Thermal Emission and Reflection Scanning Radiometer (ASTER) and SimSphere one-dimensional SVAT model. Accuracy of the investigated methodology, so-called as the "triangle" method, is verified using validated ground observations obtained from selected days collected from nine CARBOEUROPE IP sites representing a variety of climatic, topographic and environmental conditions. Subsequently, a new framework is suggested for the retrieval of two additional parameters by the investigated method, namely the Evaporative (EF) and the Non-Evaporative (NEF) Fractions. Results indicated a close agreement between the inverted surface fluxes and surface moisture availability maps as well as of the EF and NEF parameters with the observations both spatially and temporally with accuracies comparable to those obtained in similar experiments with high spatial resolution data. Inspection of the inverted surface fluxes maps regionally, showed an explainable distribution in the range of the inverted parameters in relation with the surface heterogeneity. Overall performance of the "triangle" inversion methodology was found to be affected predominantly by the SVAT model "correct" initialisation representative of the test site environment, most importantly the atmospheric conditions required in the SVAT model initial conditions. This study represents the first comprehensive evaluation of the performance of this particular methodological implementation at a European setting using the SimSphere SVAT with the ASTER data. The present work is also very timely in that, a variation of this specific inversion methodology has been proposed for the operational retrieval of the soil surface moisture content by National Polar-orbiting Operational Environmental Satellite System (NPOESS), in a series of satellite platforms that are due to be launched in the next 12 years starting from 2012. KEYWORDS: micrometeorology, surface heat fluxes, soil moisture content, ASTER, triangle method, SimSphere, CarboEurope IP

Passive microwave remote sensing of soil moisture - Results from HAPEX, FIFE and MONSOON 90

NASA Technical Reports Server (NTRS)

Schmugge, T.; Jackson, T. J.; Kustas, W. P.; Wang, J. R.

1992-01-01

HAPEX (Hydrologic Atmospheric Pilot Experiment), FIFE (First ISLSCP Field Experiment) and MONSOON 90 which used an imaging microwave radiometer operating at a frequency of 1.42 GHz are reported. For FIFE and MONSOON 90, a wide range of moisture conditions were present and it was possible to observe the drydown of the soil following heavy rain and to map its spatial variation. The quantitative agreement of microwave observations and ground measurements was very good. In HAPEX there were no significant rains and conditions were generally rather dry, however, moisture variations due to irrigation were observed.

Passive microwave remote sensing of soil moisture: Results from HAPEX, FIFE, and MONSOON 90

NASA Technical Reports Server (NTRS)

Schmugge, Thomas; Jackson, T. J.; Wang, J. R.

1991-01-01

HAPEX (Hydrologic Atmospheric Pilot Experiment), FIFE (First ISLSCP Field Experiment) and MONSOON 90 which used an imaging microwave radiometer operating at a frequency of 1.42 GHz are reported. For FIFE and MONSOON 90, a wide range of moisture conditions were present and it was possible to observe the drydown of the soil following heavy rain and to map its spatial variation. The quantitive agreement of microwave observations and ground measurements was very good. In HAPEX there were no significant rains and conditions were generally rather dry, however, moisture variations due to irrigation were observed.

Cosmic Ray Neutron Sensing in Complex Systems

NASA Astrophysics Data System (ADS)

Piussi, L. M.; Tomelleri, E.; Tonon, G.; Bertoldi, G.; Mejia Aguilar, A.; Monsorno, R.; Zebisch, M.

2017-12-01

Soil moisture is a key variable in environmental monitoring and modelling: being located at the soil-atmosphere boundary, it is a driving force for water, energy and carbon fluxes. Nevertheless its importance, soil moisture observations lack of long time-series at high acquisition frequency in spatial meso-scale resolutions: traditional measurements deliver either long time series with high measurement frequency at spatial point scale or large scale and low frequency acquisitions. The Cosmic Ray Neutron Sensing (CRNS) technique fills this gap because it supplies information from a footprint of 240m of diameter and 15 to 83 cm of depth at a temporal resolution varying between 15 minutes and 24 hours. In addition, being a passive sensing technique, it is non-invasive. For these reasons, CRNS is gaining more and more attention from the scientific community. Nevertheless, the application of this technique in complex systems is still an open issue: where different Hydrogen pools are present and where their distributions vary appreciably with space and time, the traditional calibration method shows some limits. In order to obtain a better understanding of the data and to compare them with remote sensing products and spatially distributed traditional measurements (i.e. Wireless Sensors Network), the complexity of the surrounding environment has to be taken into account. In the current work we assessed the effects of spatial-temporal variability of soil moisture within the footprint, in a steep, heterogeneous mountain grassland area. Measurement were performed with a Cosmic Ray Neutron Probe (CRNP) and a mobile Wireless Sensors Network. We performed an in-deep sensitivity analysis of the effects of varying distributions of soil moisture on the calibration of the CRNP and our preliminary results show how the footprint shape varies depending on these dynamics. The results are then compared with remote sensing data (Sentinel 1 and 2). The current work is an assessment of different calibration procedures and their effect on the measurement outcome. We found that the response of the CRNP follows quite well the punctual measurement performed by a TDR installed on the site, but discrepancies could be explained by using the Wireless Sensors Network to perform a spatially weighted calibration and to introduce temporal dynamics.

Soil Moisture Controls the Thermal Habitat of Active Layer Soils in the McMurdo Dry Valleys, Antarctica

NASA Astrophysics Data System (ADS)

Wlostowski, A. N.; Gooseff, M. N.; Adams, B. J.

2018-01-01

Antarctic soil ecosystems are strongly controlled by abiotic habitat variables. Regional climate change in the McMurdo Dry Valleys is expected to cause warming over the next century, leading to an increase in frequency of freeze-thaw cycling in the soil habitat. Previous studies show that physiological stress associated with freeze-thaw cycling adversely affects invertebrate populations by decreasing abundance and positively selecting for larger body sizes. However, it remains unclear whether or not climate warming will indeed enhance the frequency of annual freeze-thaw cycling and associated physiological stresses. This research quantifies the frequency, rate, and spatial heterogeneity of active layer freezing to better understand how regional climate change may affect active layer soil thermodynamics, and, in turn, affect soil macroinvertebrate communities. Shallow active layer temperature, specific conductance, and soil moisture were observed along natural wetness gradients. Field observations show that the frequency and rate of freeze events are nonlinearly related to freezable soil moisture (θf). Over a 2 year period, soils at θf < 0.080 m3/m3 experienced between 15 and 35 freeze events and froze rapidly compared to soils with θf > 0.080 m3/m3, which experienced between 2 and 6 freeze events and froze more gradually. A numerical soil thermodynamic model is able to simulate observed freezing rates across a range of θf, reinforcing a well-known causal relationship between soil moisture and active layer freezing dynamics. Findings show that slight increases in soil moisture can potentially offset the effect of climate warming on exacerbating soil freeze-thaw cycling.

On the spatial distribution of the transpiration and soil moisture of a Mediterranean heterogeneous ecosystem in water-limited conditions.

NASA Astrophysics Data System (ADS)

Curreli, Matteo; Corona, Roberto; Montaldo, Nicola; Albertson, John D.; Oren, Ram

2014-05-01

Mediterranean ecosystems are characterized by a strong heterogeneity, and often by water-limited conditions. In these conditions contrasting plant functional types (PFT, e.g. grass and woody vegetation) compete for the water use. Both the vegetation cover spatial distribution and the soil properties impact the soil moisture (SM) spatial distribution. Indeed, vegetation cover density and type affects evapotranspiration (ET), which is the main lack of the soil water balance in these ecosystems. With the objective to carefully estimate SM and ET spatial distribution in a Mediterranean water-limited ecosystem and understanding SM and ET relationships, an extended field campaign is carried out. The study was performed in a heterogeneous ecosystem in Orroli, Sardinia (Italy). The experimental site is a typical Mediterranean ecosystem where the vegetation is distributed in patches of woody vegetation (wild olives mainly) and grass. Soil depth is low and spatially varies between 10 cm and 40 cm, without any correlation with the vegetation spatial distribution. ET, land-surface fluxes and CO2 fluxes are estimated by an eddy covariance technique based micrometeorological tower. But in heterogeneous ecosystems a key assumption of the eddy covariance theory, the homogeneity of the surface, is not preserved and the ET estimate may be not correct. Hence, we estimate ET of the woody vegetation using the thermal dissipation method (i.e. sap flow technique) for comparing the two methodologies. Due the high heterogeneity of the vegetation and soil properties of the field a total of 54 sap flux sensors were installed. 14 clumps of wild olives within the eddy covariance footprint were identified as the most representative source of flux and they were instrumented with the thermal dissipation probes. Measurements of diameter at the height of sensor installation (height of 0.4 m above ground) were recorded in all the clumps. Bark thickness and sapwood depth were measured on several trees to obtain a generalized estimates of sapwood depth. The known of allometric relationships between sapwood area, diameter and canopy cover area within the eddy covariance footprint helped for the application of a reliable scaling procedure of the local sap flow estimates which are in a good agreement with the estimates of ET eddy covariance based. Soil moisture were also extensively monitored through 25 probes installed in the eddy covariance footprint. Results show that comparing eddy covariance and sap flow ET estimates eddy covariance technique is still accurate in this heterogeneous field, whereas the key assumption, surface homogeneity, is not preserved. Furthermore, interestingly wild olives still transpire at higher rates for the driest soil moisture conditions, confirming the hydraulic redistribution from soil below the roots, and from roots penetrating deep cracks in the underlying basalt parent rock.

Effects of rainfall partitioning in the seasonal and spatial variability of soil water content in a Mediterranean downy oak forest

NASA Astrophysics Data System (ADS)

Garcia-Estringana, P.; Latron, J.; Molina, A. J.; Llorens, P.

2012-04-01

Rainfall partitioning fluxes (throughfall and stemflow) have a large degree of temporal and spatial variability and may consequently lead to significant changes in the volume and composition of water that reach the understory and the soil. The objective of this work is to study the effect of rainfall partitioning on the seasonal and spatial variability of the soil water content in a Mediterranean downy oak forest (Quercus pubescens), located in the Vallcebre research catchments (42° 12'N, 1° 49'E). The monitoring design, started on July 2011, consists of a set of 20 automatic rain recorders and 40 automatic soil moisture probes located below the canopy. One hundred hemispheric photographs of the canopy were used to place the instruments at representative locations (in terms of canopy cover) within the plot. Bulk rainfall, stemflow and meteorological conditions above the forest cover are also automatically recorded. Canopy cover, in leaf and leafless periods, as well as biometric characteristics of the plot, are also regularly measured. This work presents the first results describing throughfall and soil moisture spatial variability during both the leaf and leafless periods. The main drivers of throughfall variability, as canopy structure and meteorological conditions are also analysed.

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.

A soil moisture index derived from thermal infrared sensor on-board geostationary satellites over Europe, Africa and Australia

NASA Astrophysics Data System (ADS)

Ghilain, Nicolas; Trigo, Isabel; Arboleda, Alirio; Barrios, Jose-Miguel; Batelaan, Okke; Gellens-Meulenberghs, Françoise

2017-04-01

Soil moisture plays a central role in the water cycle. In particular, it is a major component which variability controls the evapotranspiration process. Over the past years, there has been a large commitment of the remote sensing research community to develop satellites and retrieval algorithm for soil moisture monitoring over continents. Most of those rely on the observation in the microwave lengths, making use either of passive, active or both methods combined. However, the available derived products are given at a relatively low spatial resolution for applications at the kilometer scale over entire continents, and with a revisit time that may not be adequate for all applications, as for example agriculture. Thermal infrared observations from a combination of geostationary satellites offer a global view of continents every hour (or even at higher frequency) at a few kilometers resolution, which makes them attractive as another, and potentially complementary, source of information of surface soil moisture. In this study, the Copernicus LST and the LSA-SAF LST are used to derive soil moisture over entire continents (Europe, Africa, Australia). The derived soil moisture is validated against in-situ observations and compared to other available products from remote sensing (SMOS, ASCAT) and from numerical weather prediction (ECMWF). We will present the result of this validation, and will show how it could be used in continental scale evapotranspiration monitoring.

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.

Global Scale Simultaneous Retrieval of Smoothened Vegetation Optical Depth and Surface Roughness Parameter using AMSR-E X-band Observations

NASA Astrophysics Data System (ADS)

Lanka, Karthikeyan; Pan, Ming; Konings, Alexandra; Piles, María; D, Nagesh Kumar; Wood, Eric

2017-04-01

Traditionally, passive microwave retrieval algorithms such as Land Parameter Retrieval Model (LPRM) estimate simultaneously soil moisture and Vegetation Optical Depth (VOD) using brightness temperature (Tb) data. The algorithm requires a surface roughness parameter which - despite implications - is generally assumed to be constant at global scale. Due to inherent noise in the satellite data and retrieval algorithm, the VOD retrievals are usually observed to be highly fluctuating at daily scale which may not occur in reality. Such noisy VOD retrievals along with spatially invariable roughness parameter may affect the quality of soil moisture retrievals. The current work aims to smoothen the VOD retrievals (with an assumption that VOD remains constant over a period of time) and simultaneously generate, for the first time, global surface roughness map using multiple descending X-band Tb observations of AMSR-E. The methodology utilizes Tb values under a moving-time-window-setup to estimate concurrently the soil moisture of each day and a constant VOD in the window. Prior to this step, surface roughness parameter is estimated using the complete time series of Tb record. Upon carrying out the necessary sensitivity analysis, the smoothened VOD along with soil moisture retrievals is generated for the 10-year duration of AMSR-E (2002-2011) with a 7-day moving window using the LPRM framework. The spatial patterns of resulted global VOD maps are in coherence with vegetation biomass and climate conditions. The VOD results also exhibit a smoothening effect in terms of lower values of standard deviation. This is also evident from time series comparison of VOD and LPRM VOD retrievals without optimization over moving windows at several grid locations across the globe. The global surface roughness map also exhibited spatial patterns that are strongly influenced by topography and land use conditions. Some of the noticeable features include high roughness over mountainous regions and heavily vegetated tropical rainforests, low roughness in desert areas and moderate roughness value over higher latitudes. The new datasets of VOD and surface roughness can help improving the quality of soil moisture retrievals. Also, the methodology proposed is generic by nature and can be implemented over currently operating AMSR2, SMOS, and SMAP soil moisture missions.

Statistical analysis of simulated global soil moisture and its memory in an ensemble of CMIP5 general circulation models

NASA Astrophysics Data System (ADS)

Wiß, Felix; Stacke, Tobias; Hagemann, Stefan

2014-05-01

Soil moisture and its memory can have a strong impact on near surface temperature and precipitation and have the potential to promote severe heat waves, dry spells and floods. To analyze how soil moisture is simulated in recent general circulation models (GCMs), soil moisture data from a 23 model ensemble of Atmospheric Model Intercomparison Project (AMIP) type simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are examined for the period 1979 to 2008 with regard to parameterization and statistical characteristics. With respect to soil moisture processes, the models vary in their maximum soil and root depth, the number of soil layers, the water-holding capacity, and the ability to simulate freezing which all together leads to very different soil moisture characteristics. Differences in the water-holding capacity are resulting in deviations in the global median soil moisture of more than one order of magnitude between the models. In contrast, the variance shows similar absolute values when comparing the models to each other. Thus, the input and output rates by precipitation and evapotranspiration, which are computed by the atmospheric component of the models, have to be in the same range. Most models simulate great variances in the monsoon areas of the tropics and north western U.S., intermediate variances in Europe and eastern U.S., and low variances in the Sahara, continental Asia, and central and western Australia. In general, the variance decreases with latitude over the high northern latitudes. As soil moisture trends in the models were found to be negligible, the soil moisture anomalies were calculated by subtracting the 30 year monthly climatology from the data. The length of the memory is determined from the soil moisture anomalies by calculating the first insignificant autocorrelation for ascending monthly lags (insignificant autocorrelation folding time). The models show a great spread of autocorrelation length from a few months in the tropics, north western Canada, eastern U.S. and northern Europe up to few years in the Sahara, the Arabian Peninsula, continental Eurasia and central U.S. Some models simulate very long memory all over the globe. This behavior is associated with differences between the models in the maximum root and soil depth. Models with shallow roots and deep soils exhibit longer memories than models with similar soil and root depths. Further analysis will be conducted to clearly divide models into groups based on their inter-model spatial correlation of simulated soil moisture characteristics.

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.

Uncertain soil moisture feedbacks in model projections of Sahel precipitation

NASA Astrophysics Data System (ADS)

Berg, Alexis; Lintner, Benjamin R.; Findell, Kirsten; Giannini, Alessandra

2017-06-01

Given the uncertainties in climate model projections of Sahel precipitation, at the northern edge of the West African Monsoon, understanding the factors governing projected precipitation changes in this semiarid region is crucial. This study investigates how long-term soil moisture changes projected under climate change may feedback on projected changes of Sahel rainfall, using simulations with and without soil moisture change from five climate models participating in the Global Land Atmosphere Coupling Experiment-Coupled Model Intercomparison Project phase 5 experiment. In four out of five models analyzed, soil moisture feedbacks significantly influence the projected West African precipitation response to warming; however, the sign of these feedbacks differs across the models. These results demonstrate that reducing uncertainties across model projections of the West African Monsoon requires, among other factors, improved mechanistic understanding and constraint of simulated land-atmosphere feedbacks, even at the large spatial scales considered here.