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Sample records for crop area estimate

  1. Area estimation using multiyear designs and partial crop identification

    NASA Technical Reports Server (NTRS)

    Sielken, R. L., Jr.

    1983-01-01

    Progress is reported for the following areas: (1) estimating the stratum's crop acreage proportion using the multiyear area estimation model; (2) assessment of multiyear sampling designs; and (3) development of statistical methodology for incorporating partially identified sample segments into crop area estimation.

  2. Area estimation of crops by digital analysis of Landsat data

    NASA Technical Reports Server (NTRS)

    Bauer, M. E.; Hixson, M. M.; Davis, B. J.

    1978-01-01

    The study for which the results are presented had these objectives: (1) to use Landsat data and computer-implemented pattern recognition to classify the major crops from regions encompassing different climates, soils, and crops; (2) to estimate crop areas for counties and states by using crop identification data obtained from the Landsat identifications; and (3) to evaluate the accuracy, precision, and timeliness of crop area estimates obtained from Landsat data. The paper describes the method of developing the training statistics and evaluating the classification accuracy. Landsat MSS data were adequate to accurately identify wheat in Kansas; corn and soybean estimates for Indiana were less accurate. Systematic sampling of entire counties made possible by computer classification methods resulted in very precise area estimates at county, district, and state levels.

  3. Multi crop area estimation in Idaho using EDITOR

    NASA Technical Reports Server (NTRS)

    Sheffner, E. J.

    1984-01-01

    The use of LANDSAT multispectral scanner digital data for multi-crop acreage estimation in the central Snake River Plain of Idaho was examined. Two acquisitions of LANDSAT data covering ground sample units selected from a U.S. Department of Agriculture sampling frame in a four country study site were used to train a maximum likelihood classifier which, subsequently, classified all picture elements in the study site. Acreage estimates for six major crops, by county and for the four counties combined, were generated from the classification using the Battesse-Fuller model for estimation by regression in small areas. Results from the regression analysis were compared to those obtained by direct expansion of the ground data. Using the LANDSAT data significantly decreased the errors associated with the estimates for the three largest acreage crops. The late date of the second LANDSAT acquisition may have contributed to the poor results for three summer crops.

  4. Area estimation using multiyear designs and partial crop identification

    NASA Technical Reports Server (NTRS)

    Sielken, R. L., Jr.

    1984-01-01

    Statistical procedures were developed for large area assessments using both satellite and conventional data. Crop acreages, other ground cover indices, and measures of change were the principal characteristics of interest. These characteristics are capable of being estimated from samples collected possibly from several sources at varying times, with different levels of identification. Multiyear analysis techniques were extended to include partially identified samples; the best current year sampling design corresponding to a given sampling history was determined; weights reflecting the precision or confidence in each observation were identified and utilized, and the variation in estimates incorporating partially identified samples were quantified.

  5. Early-season crop area estimates for winter crops in NE Australia using MODIS satellite imagery

    NASA Astrophysics Data System (ADS)

    Potgieter, A. B.; Apan, A.; Hammer, G.; Dunn, P.

    To date, industry and crop forecasters have had a good idea of the potential crop yield for a specific season, but early-season information on crop area for a shire or region has been mostly unavailable. The question of "how early and with what accuracy?" area estimates can be determined using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) imagery was investigated in this paper. The study was conducted for two shires in Queensland, Australia for the 2003 and 2004 seasons, and focused on deriving total winter crop area estimates (including wheat, barley and chickpea). A simple metric ( ΔE), which measures the green-up rate of the crop canopy, was derived. Using the unsupervised k-means classification algorithm, the accumulated difference of two consecutive images (one month apart) for three EVI threshold cut-offs ( ΔEi, where i=250, 500 and 750) at monthly intervals from April to October was calculated. July showed the highest pixel accuracy with percent correctly classified for all thresholds of 94% and 98% for 2003 and 2004, respectively. The differences in accuracy between the three cut-offs were minimal and the T500 threshold was selected as the preferred cut-off to avoid measuring too small or too large fluctuations in the differential EVI values. When compared to the aggregated shire data (surveyed) on crop area across shires and seasons, average percent differences for the ΔE for July and August ranged from -19% to 9%. To capture most of the variability in green-up within a region, the average ΔE of July and August was used for the early-season prediction of total winter crop area estimates. This resulted in high accuracy (R 2=0.96; RMSE = 3157 ha) for predicting the total winter crop from 2000 to 2004 across both shires. This result indicated that this simple multi-temporal remote sensing approach could be used with confidence in early-season crop area prediction at least one to two months ahead of

  6. Crop Area Estimation Using High Spatial Resolution Satellite Imagery and Area Frame Sampling

    NASA Astrophysics Data System (ADS)

    Marshall, M. T.; Husak, G. J.; Pedreros, D.; Alcaraz v., G.

    2006-12-01

    The amount and extent of cropped area are essential parameters for determining food production and ultimately the state of food security in developing countries. Crop area estimation endeavors at the national- level are limited especially in remote areas of the world, due in part to the cost and time of making ground observations. Approximation of crop area using satellite imagery is a viable alternative though few studies have made use of this technique. In previous studies, misclassification of pure pixels and the presence of mixed pixels in relatively coarse Landsat images led to considerable errors in crop area estimates. This is particularly the case in developing countries where small subsistence farms are more prominent than larger mechanized farms. The aim of this study was to develop regression estimators from interpretation of 0.61 and 1 m resolution Quickbird and Ikonos panchromatic imagery respectively, to reduce bias in the crop area assessment from 30 m Landsat ETM+ images taken during the 2005 growing season of Niger. Eighty-five Ikonos and Quickbird scenes randomly stratified along the six primary livelihood zones of Niger and 30 Landsat ETM+ scenes were used to meet three objectives: 1) comprehensive dot-grid (2 km interval) classification of Landsat data for all potential cropped areas, 2) dot-grid (500 m interval) classification of Ikonos and Quickbird data for subsets of Landsat scenes, 3) area frame bias-estimation for each livelihood zone, and 4) validation of model design and process. Percent cropped area from Quickbird and Ikonos images showed high and significant correlations with percent cropped area from Landsat ETM+ for each livelihood zone. A split sample validation of the regression estimators and relative efficiency of the process shows potential to be used in other developing countries. Future studies should attempt to develop regression estimators involving automated textural-based classification techniques (e.g. image segmentation

  7. Crop identification and area estimation over large geographic areas using LANDSAT MSS data

    NASA Technical Reports Server (NTRS)

    Bauer, M. E. (Principal Investigator)

    1977-01-01

    The author has identified the following significant results. LANDSAT MSS data was adequate to accurately identify wheat in Kansas; corn and soybean estimates in Indiana were less accurate. Computer-aided analysis techniques were effectively used to extract crop identification information from LANDSAT data. Systematic sampling of entire counties made possible by computer classification methods resulted in very precise area estimates at county, district, and state levels. Training statistics were successfully extended from one county to other counties having similar crops and soils if the training areas sampled the total variation of the area to be classified.

  8. Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

    USGS Publications Warehouse

    Husak, G.J.; Marshall, M. T.; Michaelsen, J.; Pedreros, Diego; Funk, Christopher C.; Galu, G.

    2008-01-01

    Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

  9. Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

    NASA Astrophysics Data System (ADS)

    Husak, G. J.; Marshall, M. T.; Michaelsen, J.; Pedreros, D.; Funk, C.; Galu, G.

    2008-07-01

    Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

  10. Evaluation of small area crop estimation techniques using LANDSAT- and ground-derived data. [South Dakota

    NASA Technical Reports Server (NTRS)

    Amis, M. L.; Martin, M. V.; Mcguire, W. G.; Shen, S. S. (Principal Investigator)

    1982-01-01

    Studies completed in fiscal year 1981 in support of the clustering/classification and preprocessing activities of the Domestic Crops and Land Cover project. The theme throughout the study was the improvement of subanalysis district (usually county level) crop hectarage estimates, as reflected in the following three objectives: (1) to evaluate the current U.S. Department of Agriculture Statistical Reporting Service regression approach to crop area estimation as applied to the problem of obtaining subanalysis district estimates; (2) to develop and test alternative approaches to subanalysis district estimation; and (3) to develop and test preprocessing techniques for use in improving subanalysis district estimates.

  11. Evaluation of large area crop estimation techniques using LANDSAT and ground-derived data. [Missouri

    NASA Technical Reports Server (NTRS)

    Amis, M. L.; Lennington, R. K.; Martin, M. V.; Mcguire, W. G.; Shen, S. S. (Principal Investigator)

    1981-01-01

    The results of the Domestic Crops and Land Cover Classification and Clustering study on large area crop estimation using LANDSAT and ground truth data are reported. The current crop area estimation approach of the Economics and Statistics Service of the U.S. Department of Agriculture was evaluated in terms of the factors that are likely to influence the bias and variance of the estimator. Also, alternative procedures involving replacements for the clustering algorithm, the classifier, or the regression model used in the original U.S. Department of Agriculture procedures were investigated.

  12. Efficiency assessment of using satellite data for crop area estimation in Ukraine

    NASA Astrophysics Data System (ADS)

    Gallego, Francisco Javier; Kussul, Nataliia; Skakun, Sergii; Kravchenko, Oleksii; Shelestov, Andrii; Kussul, Olga

    2014-06-01

    The knowledge of the crop area is a key element for the estimation of the total crop production of a country and, therefore, the management of agricultural commodities markets. Satellite data and derived products can be effectively used for stratification purposes and a-posteriori correction of area estimates from ground observations. This paper presents the main results and conclusions of the study conducted in 2010 to explore feasibility and efficiency of crop area estimation in Ukraine assisted by optical satellite remote sensing images. The study was carried out on three oblasts in Ukraine with a total area of 78,500 km2. The efficiency of using images acquired by several satellite sensors (MODIS, Landsat-5/TM, AWiFS, LISS-III, and RapidEye) combined with a field survey on a stratified sample of square segments for crop area estimation in Ukraine is assessed. The main criteria used for efficiency analysis are as follows: (i) relative efficiency that shows how much time the error of area estimates can be reduced with satellite images, and (ii) cost-efficiency that shows how much time the costs of ground surveys for crop area estimation can be reduced with satellite images. These criteria are applied to each satellite image type separately, i.e., no integration of images acquired by different sensors is made, to select the optimal dataset. The study found that only MODIS and Landsat-5/TM reached cost-efficiency thresholds while AWiFS, LISS-III, and RapidEye images, due to its high price, were not cost-efficient for crop area estimation in Ukraine at oblast level.

  13. Research on estimation crop planting area by integrating the optical and microwave remote sensing data

    NASA Astrophysics Data System (ADS)

    Liu, Jiang; Yu, Fan; Liu, Dandan; Tian, Jing; Zhang, Weicheng; Wang, Qiang; Yang, Jinling; Zhang, Lei

    2015-12-01

    Considering the problem in monitoring agricultural condition in the semi-arid areas of Northwest of China, we propose a new method for estimation of crop planting area, using the single phase optical and microwave remote sensing data collaboratively, which have demonstrated their respective advantages in the extraction of surface features. In the model, the ASAR backscatter coefficient is normalized by the incident angle at first, then the classifier based on Bayesian network is developed, and the VV, VH polarization of ASAR and all the 7 TM bands are taken as the input of the classifier to get the class labels of each pixel of the images. Moreover the crop planting areas can be extracted by the classification results. At last, the model is validated for the necessities of normalization by the incident angle and integration of TM and ASAR respectively. It results that the estimation accuracy of crop planting area of corn and other crops garden are 98.47% and 78.25% respectively using the proposed method, with an improvement of estimation accuracy of about 3.28% and 4.18% relative to single TM classification. These illustrate that synthesis of optical and microwave remote sensing data is efficient and potential in estimation crop planting area.

  14. [Estimating Leaf Area Index of Crops Based on Hyperspectral Compact Airborne Spectrographic Imager (CASI) Data].

    PubMed

    Tang, Jian-min; Liao, Qin-hong; Liu, Yi-qing; Yang, Gui-jun; Feng, Hai-kuanr; Wang, Ji-hua

    2015-05-01

    The fast estimation of leaf area index (LAI) is significant for learning the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study used the hyperspectral compact airborne spectrographic imager (CASI) data of Zhangye city, in Heihe River basin, on July 7, 2012, and extracted the spectral reflectance accurately. The potential of broadband and red-edge vegetation index for estimating the LAI of crops was comparatively investigated by combined with the field measured data. On this basis, the sensitive wavebands for estimating the LAI of crops were selected and two new spectral indexes (NDSI and RSI) were constructed, subsequently, the spatial distribution of LAI in study area was analyzed. The result showed that broadband vegetation index NDVI had good effect for estimating the LAI when the vegetation coverage is relatively lower, the R2 and RMSE of estimation model were 0. 52, 0. 45 (p<0. 01) , respectively. For red-edge vegetation index, CIred edge took the different crop types into account fully, thus it gained the same estimation accuracy with NDVI. NDSI(569.00, 654.80) and RSI(597.60, 654.80) were constructed by using waveband combination algorithm, which has superior estimation results than NDVI and CIred edge. The R2 of estimation model used NDSI(569.00, 654.80) was 0. 77(p<0. 000 1), it mainly used the wavebands near the green peak and red valley of vegetation spectrum. The spatial distribution map of LAI was made according to the functional relationship between the NDSI(569.00, 654.80) and LAI. After analyzing this map, the LAI values were lower in the northwest of study area, this indicated that more fertilizer should be increased in this area. This study can provide technical support for the agricultural administrative department to learn the growth of crops quickly and make a suitable fertilization strategy. PMID:26415459

  15. Crop area estimation based on remotely-sensed data with an accurate but costly subsample

    NASA Technical Reports Server (NTRS)

    Gunst, R. F.

    1985-01-01

    Research activities conducted under the auspices of National Aeronautics and Space Administration Cooperative Agreement NCC 9-9 are discussed. During this contract period research efforts are concentrated in two primary areas. The first are is an investigation of the use of measurement error models as alternatives to least squares regression estimators of crop production or timber biomass. The secondary primary area of investigation is on the estimation of the mixing proportion of two-component mixture models. This report lists publications, technical reports, submitted manuscripts, and oral presentation generated by these research efforts. Possible areas of future research are mentioned.

  16. Identification and area estimation of agricultural crops by computer classification of Landsat MSS data

    NASA Technical Reports Server (NTRS)

    Bauer, M. E.; Cipra, J. E.; Anuta, P. E.; Etheridge, J. B.

    1979-01-01

    Landsat Multispectral Scanner (MSS) data covering a three-county area in northern Illinois were classified using computer-aided techniques as corn, soybeans, or 'other.' Recognition of test fields was 80% accurate. County estimates of the area of corn and soybeans agreed closely with those made by the USDA. Results of the use of a priori information in classification, techniques to produce unbiased area estimates, and the use of temporal and spatial features for classification are discussed. The extendability, variability, and size of training sets, wavelength band selection, and spectral characteristics of crops were also investigated.

  17. Satellite Estimates of Crop Area and Maize Yield in Zambia's Agricultural Districts

    NASA Astrophysics Data System (ADS)

    Azzari, G.; Lobell, D. B.

    2015-12-01

    Predicting crop yield and area from satellite is a valuable tool to monitor different aspects of productivity dynamics and food security. In Sub-Saharan Africa, where the agricultural landscape is complex and dominated by smallholder systems, such dynamics need to be investigated at the field scale. We leveraged the large data pool and computational power of Google Earth Engine to 1) generate 30 m resolution cover maps of selected provinces of Zambia, 2) estimate crop area, and 3) produce yearly maize yield maps using the recently developed SCYM (Scalable satellite-based Crop Yield Mapper) algorithm. We will present our results and their validation against a ground survey dataset collected yearly by the Zambia Ministry of Agriculture from about 12,500 households.

  18. Crop identification and area estimation by computer-aided analysis of Landsat data

    NASA Technical Reports Server (NTRS)

    Bauer, M. E.; Hixson, M. M.; Davis, B. J.; Etheridge, J. B.

    1977-01-01

    This report describes the results of a study involving the use of computer-aided analysis techniques applied to Landsat MSS data for identification and area estimation of winter wheat in Kansas and corn and soybeans in Indiana. Key elements of the approach included use of aerial photography for classifier training, stratification of Landsat data and extension of training statistics to areas without training data, and classification of a systematic sample of pixels from each county. Major results and conclusions are: (1) Landsat data was adequate for accurate identification and area estimation of winter wheat in Kansas, but corn and soybean estimates for Indiana were less accurate; (2) computer-aided analysis techniques can be effectively used to extract crop identification information from Landsat MSS data, and (3) systematic sampling of entire counties made possible by computer classification methods resulted in very precise area estimates at county as well as district and state levels.

  19. Leaf area index estimation in different crops: Case study for wheat, maize, soybean, and potato

    NASA Astrophysics Data System (ADS)

    Gitelson, A. A.; Nguy-Robertson, A. L.; Peng, Y.; Arkebauer, T. J.; Pimstein, A.; Herrmann, I.; Karnieli, A.; Rundquist, D. C.; Bonfil, D.

    2012-12-01

    Vegetation indices (VIs) have been shown to be a proxy of green leaf area index (gLAI); however, it has not been verified whether the relationships VI vs. gLAI are the same, as well as VIs retaining their accuracy, for various crop types for estimating gLAI. The goal of this study was to (1) determine if the best VIs used in previous studies for gLAI estimation in maize and soybean may be applicable for potato and wheat and vice versa, and (2) determining the cause of a hysteresis between green up and reproductive stages for the VI vs. gLAI relationship. Spectral measurements of wheat and potato were obtained in Israel and of maize and soybean in the USA. In Israel, remote estimates of gLAI were compared with in-situ canopy transmittance measurements of irrigated potato and wheat under various nitrogen treatments from 2004-2007 for a total of 15 field-years. In eastern Nebraska, USA, remote estimates of maize and soybean gLAI data were compared with destructive gLAI determination in two irrigated/rainfed maize/soybean rotation sites and in one irrigated site under continuous maize. These data were collected during eight years (2001-2008) for a total of 24 field-years. For all four crops, the ten VIs examined showed similarities in relationships between VIs and gLAI with the exception of Red-edge Inflection Point (REIP) and the MERIS Terrestrial Chlorophyll Index (MTCI). REIP and MTCI have very different relationships with maize and soybean gLAI in green up and reproductive stages, thus, they require re-parameterization during the season. This study outlines the two major factors that influence the VI vs. gLAI relationship in the green up and reproductive stages. While the results suggest that relationships VI vs. gLAI are quite close for all four crops, different methodologies in determining the ground-truth measurements of gLAI prevent us to confirm whether algorithms calibrated for one crop can be used with no re-parameterization for other crops. These concerns

  20. Estimating Hydrologic Fluxes, Crop Water Use, and Agricultural Land Area in China using Data Assimilation

    NASA Astrophysics Data System (ADS)

    Smith, Tiziana; McLaughlin, Dennis B.; Hoisungwan, Piyatida

    2016-04-01

    Crop production has significantly altered the terrestrial environment by changing land use and by altering the water cycle through both co-opted rainfall and surface water withdrawals. As the world's population continues to grow and individual diets become more resource-intensive, the demand for food - and the land and water necessary to produce it - will continue to increase. High-resolution quantitative data about water availability, water use, and agricultural land use are needed to develop sustainable water and agricultural planning and policies. However, existing data covering large areas with high resolution are susceptible to errors and can be physically inconsistent. China is an example of a large area where food demand is expected to increase and a lack of data clouds the resource management dialogue. Some assert that China will have insufficient land and water resources to feed itself, posing a threat to global food security if they seek to increase food imports. Others believe resources are plentiful. Without quantitative data, it is difficult to discern if these concerns are realistic or overly dramatized. This research presents a quantitative approach using data assimilation techniques to characterize hydrologic fluxes, crop water use (defined as crop evapotranspiration), and agricultural land use at 0.5 by 0.5 degree resolution and applies the methodology in China using data from around the year 2000. The approach uses the principles of water balance and of crop water requirements to assimilate existing data with a least-squares estimation technique, producing new estimates of water and land use variables that are physically consistent while minimizing differences from measured data. We argue that this technique for estimating water fluxes and agricultural land use can provide a useful basis for resource management modeling and policy, both in China and around the world.

  1. Crop area estimation based on remotely-sensed data with an accurate but costly subsample

    NASA Technical Reports Server (NTRS)

    Gunst, R. F.

    1983-01-01

    Alternatives to sampling-theory stratified and regression estimators of crop production and timber biomass were examined. An alternative estimator which is viewed as especially promising is the errors-in-variable regression estimator. Investigations established the need for caution with this estimator when the ratio of two error variances is not precisely known.

  2. Peatlands under cultivation for arable crops; a new area estimate for Ireland

    NASA Astrophysics Data System (ADS)

    Donlan, Jennifer; Byrne, Ken

    2015-04-01

    Peatlands cover 20% of the Irish landscape and store between 53% and 61% of total soil carbon stocks. Eighty percent of these have been drained for peat cutting, afforestation and conversion to agricultural use. As a signatory to the United Nations framework Convention on Climate Change, Ireland is required to make an annual inventory of greenhouse gas emissions and sinks in the agricultural sector. While guidelines on the compilation of such inventories are provided by the IPCC 2006 Guidelines, reporting at higher Tiers requires the collection of national specific information including the accuracy of inventories. Total land area (including accuracy estimates) and national emission factors are lacking for agricultural activity on drained organic soils i.e. converted peatlands. Locations of organic (peat) soils under cultivation were identified using a map overlay analysis and existing geographic data on peat habitats and agricultural activities. The result was 3688 ha of land cultivated for arable crops overlaid areas classified as peatland. A design-based accuracy assessment and probability sampling method were chosen to assess the accuracy of the overlay. The focus of the analysis was on the accuracy of the peat data. The agricultural data was considered quite robust, so it was used to limit the area included in the assessment. Ground truthing was carried out at randomly chosen locations within areas mapped as 1) areas cultivated for arable crops and 2) peat habitats or a 100m buffer surrounding those areas. Sixty-nine sites were sampled and an error matrix was constructed comparing the map classification at the sample location to the samples taken there. The overall accuracy was 77%. There was a high producer's accuracy (84%) and a low user's accuracy (28%) for the peat category. Area estimate of peatlands under cultivation for arable crops was 1235 ± 784 ha. Future policies will require the identification of strategies to reduce greenhouse gas emissions and

  3. Georeferenced scanning system to estimate the leaf wall area in tree crops.

    PubMed

    del-Moral-Martínez, Ignacio; Arnó, Jaume; Escolà, Alexandre; Sanz, Ricardo; Masip-Vilalta, Joan; Company-Messa, Joaquim; Rosell-Polo, Joan R

    2015-01-01

    This paper presents the use of a terrestrial light detection and ranging (LiDAR) system to scan the vegetation of tree crops to estimate the so-called pixelated leaf wall area (PLWA). Scanning rows laterally and considering only the half-canopy vegetation to the line of the trunks, PLWA refers to the vertical projected area without gaps detected by LiDAR. As defined, PLWA may be different depending on the side from which the LiDAR is applied. The system is completed by a real-time kinematic global positioning system (RTK-GPS) sensor and an inertial measurement unit (IMU) sensor for positioning. At the end, a total leaf wall area (LWA) is computed and assigned to the X, Y position of each vertical scan. The final value of the area depends on the distance between two consecutive scans (or horizontal resolution), as well as the number of intercepted points within each scan, since PLWA is only computed when the laser beam detects vegetation. To verify system performance, tests were conducted related to the georeferencing task and synchronization problems between GPS time and central processing unit (CPU) time. Despite this, the overall accuracy of the system is generally acceptable. The Leaf Area Index (LAI) can then be estimated using PLWA as an explanatory variable in appropriate linear regression models. PMID:25868079

  4. Georeferenced Scanning System to Estimate the Leaf Wall Area in Tree Crops

    PubMed Central

    del-Moral-Martínez, Ignacio; Arnó, Jaume; Escolà, Alexandre; Sanz, Ricardo; Masip-Vilalta, Joan; Company-Messa, Joaquim; Rosell-Polo, Joan R.

    2015-01-01

    This paper presents the use of a terrestrial light detection and ranging (LiDAR) system to scan the vegetation of tree crops to estimate the so-called pixelated leaf wall area (PLWA). Scanning rows laterally and considering only the half-canopy vegetation to the line of the trunks, PLWA refers to the vertical projected area without gaps detected by LiDAR. As defined, PLWA may be different depending on the side from which the LiDAR is applied. The system is completed by a real-time kinematic global positioning system (RTK-GPS) sensor and an inertial measurement unit (IMU) sensor for positioning. At the end, a total leaf wall area (LWA) is computed and assigned to the X, Y position of each vertical scan. The final value of the area depends on the distance between two consecutive scans (or horizontal resolution), as well as the number of intercepted points within each scan, since PLWA is only computed when the laser beam detects vegetation. To verify system performance, tests were conducted related to the georeferencing task and synchronization problems between GPS time and central processing unit (CPU) time. Despite this, the overall accuracy of the system is generally acceptable. The Leaf Area Index (LAI) can then be estimated using PLWA as an explanatory variable in appropriate linear regression models. PMID:25868079

  5. Crop identification and acreage estimation over large geographic areas using LANDSAT MSS data. [south central Kansas

    NASA Technical Reports Server (NTRS)

    Bauer, M. E. (Principal Investigator)

    1976-01-01

    The author has identified the following significant results. The comparison between the acreage estimates for the April LANDSAT data and the USDA Statistical Reporting Service estimates show no significant difference for the south central crop reporting district in Kansas. A paired-t test with an alpha = .05 was run comparing the percentages of wheat in each county. The results of their test showed no significant difference between the two estimates for wheat.

  6. Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images

    NASA Astrophysics Data System (ADS)

    Shang, Jiali; Liu, Jiangui; Huffman, Ted; Qian, Budong; Pattey, Elizabeth; Wang, Jinfei; Zhao, Ting; Geng, Xiaoyuan; Kroetsch, David; Dong, Taifeng; Lantz, Nicholas

    2014-01-01

    This study investigates the use of two different optical sensors, the multispectral imager (MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8 for mapping within-field variability of crop growth conditions and tracking the seasonal growth dynamics. The study was carried out in southern Ontario, Canada, during the 2013 growing season for three annual crops, corn, soybeans, and winter wheat. Plant area index (PAI) was measured at different growth stages using digital hemispherical photography at two corn fields, two winter wheat fields, and two soybean fields. Comparison between several conventional vegetation indices derived from concurrently acquired image data by the two sensors showed a good agreement. The two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI) were derived from the surface reflectance of the two sensors. The study showed that EVI2 was more resistant to saturation at high biomass range than NDVI. A linear relationship could be used for crop green effective PAI estimation from EVI2, with a coefficient of determination (R2) of 0.85 and root-mean-square error of 0.53. The estimated multitemporal product of green PAI was found to be able to capture the seasonal dynamics of the three crops.

  7. Assimilating Leaf Area Index Estimates from Remote Sensing into the Simulations of a Cropping Systems Model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Spatial extrapolation of cropping systems models for regional crop growth and water use assessment and farm-level precision management has been limited by the vast model input requirements and the model sensitivity to parameter uncertainty. Remote sensing has been proposed as a viable source of spat...

  8. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops

    NASA Astrophysics Data System (ADS)

    Kross, Angela; McNairn, Heather; Lapen, David; Sunohara, Mark; Champagne, Catherine

    2015-02-01

    Leaf area index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer decision making processes. This study demonstrates the applicability of RapidEye multi-spectral data for estimation of LAI and biomass of two crop types (corn and soybean) with different canopy structure, leaf structure and photosynthetic pathways. The advantages of Rapid Eye in terms of increased temporal resolution (∼daily), high spatial resolution (∼5 m) and enhanced spectral information (includes red-edge band) are explored as an individual sensor and as part of a multi-sensor constellation. Seven vegetation indices based on combinations of reflectance in green, red, red-edge and near infrared bands were derived from RapidEye imagery between 2011 and 2013. LAI and biomass data were collected during the same period for calibration and validation of the relationships between vegetation indices and LAI and dry above-ground biomass. Most indices showed sensitivity to LAI from emergence to 8 m2/m2. The normalized difference vegetation index (NDVI), the red-edge NDVI and the green NDVI were insensitive to crop type and had coefficients of variations (CV) ranging between 19 and 27%; and coefficients of determination ranging between 86 and 88%. The NDVI performed best for the estimation of dry leaf biomass (CV = 27% and r2 = 090) and was also insensitive to crop type. The red-edge indices did not show any significant improvement in LAI and biomass estimation over traditional multispectral indices. Cumulative vegetation indices showed strong performance for estimation of total dry above-ground biomass, especially for corn (CV ≤ 20%). This study demonstrated that continuous crop LAI monitoring over time and space at the field level can be achieved using a combination of RapidEye, Landsat and SPOT data and sensor-dependant best-fit functions. This approach eliminates/reduces the need for reflectance

  9. Multi-crop area estimation and mapping on a microprocessor/mainframe network

    NASA Technical Reports Server (NTRS)

    Sheffner, E.

    1985-01-01

    The data processing system is outlined for a 1985 test aimed at determining the performance characteristics of area estimation and mapping procedures connected with the California Cooperative Remote Sensing Project. The project is a joint effort of the USDA Statistical Reporting Service-Remote Sensing Branch, the California Department of Water Resources, NASA-Ames Research Center, and the University of California Remote Sensing Research Program. One objective of the program was to study performance when data processing is done on a microprocessor/mainframe network under operational conditions. The 1985 test covered the hardware, software, and network specifications and the integration of these three components. Plans for the year - including planned completion of PEDITOR software, testing of software on MIDAS, and accomplishment of data processing on the MIDAS-VAX-CRAY network - are discussed briefly.

  10. Two phase sampling for wheat acreage estimation. [large area crop inventory experiment

    NASA Technical Reports Server (NTRS)

    Thomas, R. W.; Hay, C. M.

    1977-01-01

    A two phase LANDSAT-based sample allocation and wheat proportion estimation method was developed. This technique employs manual, LANDSAT full frame-based wheat or cultivated land proportion estimates from a large number of segments comprising a first sample phase to optimally allocate a smaller phase two sample of computer or manually processed segments. Application to the Kansas Southwest CRD for 1974 produced a wheat acreage estimate for that CRD within 2.42 percent of the USDA SRS-based estimate using a lower CRD inventory budget than for a simulated reference LACIE system. Factor of 2 or greater cost or precision improvements relative to the reference system were obtained.

  11. Spectral procedures for estimating crop biomass

    SciTech Connect

    Wanjura, D.F.; Hatfield, J.L.

    1985-05-01

    Spectral reflectance was measured semi-weekly and used to estimate leaf area and plant dry weight accumulation in cotton, soybeans, and sunflower. Integration of spectral crop growth cycle curves explained up to 95 and 91%, respectively, of the variation in cotton lint yield and dry weight. A theoretical relationship for dry weight accumulation, in which only intercepted radiation or intercepted radiation and solar energy to biomass conversion efficiency were spectrally estimated, explained 99 and 96%, respectively, of the observed plant dry weight variation of the three crops. These results demonstrate the feasibility of predicting crop biomass from spectral measurements collected frequently during the growing season. 15 references.

  12. Vegetation monitoring and estimation of evapotranspiration using remote sensing-based models in heterogeneous areas with patchy natural vegetation and crops

    NASA Astrophysics Data System (ADS)

    Carpintero, Elisabet; Andreu, Ana; Gonzalez-Dugo, Maria P.

    2015-04-01

    The integration of remotely sensed data into models for estimating evapotranspiration (ET) has increased significantly in recent years, allowing the extension of these models application from point to regional scale. Remote sensors provide distributed information about the status of vegetation and allow for a regular monitoring of water consumption. Currently, there are two types of approaches for estimating ET based either on the soil water balance, or surface energy balance. The first one uses the reflectance of vegetated surfaces in the visible and near infrared regions of the electromagnetic spectrum (VIS / NIR) to characterize the vegetation and its role in the water balance (Gonzalez-Dugo and Mateos, 2008). On the other hand, thermal-based energy balance models use the radiometric surface temperature registered by the sensor on thermal infrared (TIR) bands as the primary boundary condition for estimating ET (Kustas and Norman, 1996). The aim of this work is to carry out, using Landsat-8 satellite images, a continuous monitoring of growth and evapotranspiration of the different vegetation types, both natural and cultivated, in a region located in Southern Spain during the season August 2013 / September 2014. The region, with about 13800 ha, is marked by strong contrasts in the physical environment, with significant altitudinal gradient combined with a great variety of soil types and vegetation. It is characterized by a variation of grassland, scrubs, conifers, oaks and irrigated crops. In this work, a daily soil water balance has been applied using the vegetation index-basal crop coefficient approach (RSWB). This model is based on FAO-56 methodology (Allen et al., 1998), which determines the evapotranspiration of vegetation with the concepts of crop coefficient and reference ET. The crop coefficient accounts for the influence of the plants on the evapotranspiration, considering the effect of changes in canopy biophysical properties throughout the growth cycle

  13. Estimating crop proportions from remotely sensed data

    NASA Technical Reports Server (NTRS)

    Feiveson, A. H. (Principal Investigator)

    1979-01-01

    The classification/pixel-count method for estimating the proportion of wheat in each segment is theoretically biased even if all distributional assumptions are met. Alternative ways to estimate crop proportions are examined and their performance testing is considered. Topics covered include general linear functional estimates, the method of moments, and maximum likelihood estimators.

  14. Estimating Canopy Dark Respiration for Crop Models

    NASA Technical Reports Server (NTRS)

    Monje Mejia, Oscar Alberto

    2014-01-01

    Crop production is obtained from accurate estimates of daily carbon gain.Canopy gross photosynthesis (Pgross) can be estimated from biochemical models of photosynthesis using sun and shaded leaf portions and the amount of intercepted photosyntheticallyactive radiation (PAR).In turn, canopy daily net carbon gain can be estimated from canopy daily gross photosynthesis when canopy dark respiration (Rd) is known.

  15. Large Area Crop Inventory Experiment (LACIE). Phase 1: Evaluation report

    NASA Technical Reports Server (NTRS)

    1976-01-01

    It appears that the Large Area Crop Inventory Experiment over the Great Plains, can with a reasonable expectation, be a satisfactory component of a 90/90 production estimator. The area estimator produced more accurate area estimates for the total winter wheat region than for the mixed spring and winter wheat region of the northern Great Plains. The accuracy does appear to degrade somewhat in regions of marginal agriculture where there are small fields and abundant confusion crops. However, it would appear that these regions tend also to be marginal with respect to wheat production and thus increased area estimation errors do not greatly influence the overall production estimation accuracy in the United States. The loss of segments resulting from cloud cover appears to be a random phenomenon that introduces no significant bias into the estimates. This loss does increase the variance of the estimates.

  16. Estimation of crop water requirements: extending the one-step approach to dual crop coefficients

    NASA Astrophysics Data System (ADS)

    Lhomme, J. P.; Boudhina, N.; Masmoudi, M. M.; Chehbouni, A.

    2015-07-01

    Crop water requirements are commonly estimated with the FAO-56 methodology based upon a two-step approach: first a reference evapotranspiration (ET0) is calculated from weather variables with the Penman-Monteith equation, then ET0 is multiplied by a tabulated crop-specific coefficient (Kc) to determine the water requirement (ETc) of a given crop under standard conditions. This method has been challenged to the benefit of a one-step approach, where crop evapotranspiration is directly calculated from a Penman-Monteith equation, its surface resistance replacing the crop coefficient. Whereas the transformation of the two-step approach into a one-step approach has been well documented when a single crop coefficient (Kc) is used, the case of dual crop coefficients (Kcb for the crop and Ke for the soil) has not been treated yet. The present paper examines this specific case. Using a full two-layer model as a reference, it is shown that the FAO-56 dual crop coefficient approach can be translated into a one-step approach based upon a modified combination equation. This equation has the basic form of the Penman-Monteith equation but its surface resistance is calculated as the parallel sum of a foliage resistance (replacing Kcb) and a soil surface resistance (replacing Ke). We also show that the foliage resistance, which depends on leaf stomatal resistance and leaf area, can be inferred from the basal crop coefficient (Kcb) in a way similar to the Matt-Shuttleworth method.

  17. Accuracy assessment in the Large Area Crop Inventory Experiment

    NASA Technical Reports Server (NTRS)

    Houston, A. G.; Pitts, D. E.; Feiveson, A. H.; Badhwar, G.; Ferguson, M.; Hsu, E.; Potter, J.; Chhikara, R.; Rader, M.; Ahlers, C.

    1979-01-01

    The Accuracy Assessment System (AAS) of the Large Area Crop Inventory Experiment (LACIE) was responsible for determining the accuracy and reliability of LACIE estimates of wheat production, area, and yield, made at regular intervals throughout the crop season, and for investigating the various LACIE error sources, quantifying these errors, and relating them to their causes. Some results of using the AAS during the three years of LACIE are reviewed. As the program culminated, AAS was able not only to meet the goal of obtaining accurate statistical estimates of sampling and classification accuracy, but also the goal of evaluating component labeling errors. Furthermore, the ground-truth data processing matured from collecting data for one crop (small grains) to collecting, quality-checking, and archiving data for all crops in a LACIE small segment.

  18. Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery

    NASA Astrophysics Data System (ADS)

    Potgieter, A. B.; Lawson, K.; Huete, A. R.

    2013-08-01

    There are increasing societal and plant industry demands for more accurate, objective and near real-time crop production information to meet both economic and food security concerns. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to monitor large agricultural areas at acceptable pixel scale, cost and accuracy. Fitting parametric profiles to growing season vegetation index time series reduces the volume of data and provides simple quantitative parameters that relates to crop phenology (sowing date, flowering). In this study, we modelled various Gaussian profiles to time sequential MODIS enhanced vegetation index (EVI) images over winter crops in Queensland, Australia. Three simple Gaussian models were evaluated in their effectiveness to identify and classify various winter crop types and coverage at both pixel and regional scales across Queensland's main agricultural areas. Equal to or greater than 93% classification accuracies were obtained in determining crop acreage estimates at pixel scale for each of the Gaussian modelled approaches. Significant high to moderate correlations (log-linear transformation) were also obtained for determining total winter crop (R2 = 0.93) areas as well as specific crop acreage for wheat (R2 = 0.86) and barley (R2 = 0.83). Conversely, it was much more difficult to predict chickpea acreage (R2 ≤ 0.26), mainly due to very large uncertainties in survey data. The quantitative approach utilised here further had additional benefits of characterising crop phenology in terms of length of growing season and providing regression diagnostics of how well the fitted profiles matched the EVI time series. The Gaussian curve models utilised here are novel in application and therefore will enhance the use and adoption of remote sensing technologies in targeted agricultural application. With innate simplicity and accuracies comparable to other

  19. Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil

    PubMed Central

    Ricardo Ducati, Jorge; da Silveira, Luiz Gonzaga

    2014-01-01

    Estimations of crop area were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from moderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm (MCDA) to estimate soybean crop areas was performed for fields in the Mato Grosso state, Brazil. Using the MCDA approach, soybean crop area estimations can be provided for December (first forecast) using images from the sowing period and for February (second forecast) using images from the sowing period and the maximum crop development period. The area estimates were compared to official agricultural statistics from the Brazilian Institute of Geography and Statistics (IBGE) and from the National Company of Food Supply (CONAB) at different crop levels from 2000/2001 to 2010/2011. At the municipality level, the estimates were highly correlated, with R2 = 0.97 and RMSD = 13,142 ha. The MCDA was validated using field campaign data from the 2006/2007 crop year. The overall map accuracy was 88.25%, and the Kappa Index of Agreement was 0.765. By using pre-defined parameters, MCDA is able to provide the evolution of annual soybean maps, forecast of soybean cropping areas, and the crop area expansion in the Mato Grosso state. PMID:24983007

  20. Assessing the MODIS crop detection algorithm for soybean crop area mapping and expansion in the Mato Grosso state, Brazil.

    PubMed

    Gusso, Anibal; Arvor, Damien; Ducati, Jorge Ricardo; Veronez, Mauricio Roberto; da Silveira, Luiz Gonzaga

    2014-01-01

    Estimations of crop area were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from moderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm (MCDA) to estimate soybean crop areas was performed for fields in the Mato Grosso state, Brazil. Using the MCDA approach, soybean crop area estimations can be provided for December (first forecast) using images from the sowing period and for February (second forecast) using images from the sowing period and the maximum crop development period. The area estimates were compared to official agricultural statistics from the Brazilian Institute of Geography and Statistics (IBGE) and from the National Company of Food Supply (CONAB) at different crop levels from 2000/2001 to 2010/2011. At the municipality level, the estimates were highly correlated, with R (2) = 0.97 and RMSD = 13,142 ha. The MCDA was validated using field campaign data from the 2006/2007 crop year. The overall map accuracy was 88.25%, and the Kappa Index of Agreement was 0.765. By using pre-defined parameters, MCDA is able to provide the evolution of annual soybean maps, forecast of soybean cropping areas, and the crop area expansion in the Mato Grosso state. PMID:24983007

  1. Estimating Crop Water use From Remotely Sensed NDVI, Crop Models and Reference ET

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Crop water use can be estimated from reference evapotranspiration, ETo, calculated from weather station data, and estimated crop coefficients, Kc. However, because Kc varies with crop growth rate, planting density, and management practices, generic Kc curves often don’t match actual crop water use....

  2. Imputing historical statistics, soils information, and other land-use data to crop area

    NASA Technical Reports Server (NTRS)

    Perry, C. R., Jr.; Willis, R. W.; Lautenschlager, L.

    1982-01-01

    In foreign crop condition monitoring, satellite acquired imagery is routinely used. To facilitate interpretation of this imagery, it is advantageous to have estimates of the crop types and their extent for small area units, i.e., grid cells on a map represent, at 60 deg latitude, an area nominally 25 by 25 nautical miles in size. The feasibility of imputing historical crop statistics, soils information, and other ancillary data to crop area for a province in Argentina is studied.

  3. Large Area Crop Inventory Experiment (LACIE). Feasibility of assessing crop condition and yield from LANDSAT data

    NASA Technical Reports Server (NTRS)

    1978-01-01

    The author has identified the following significant results. Yield modelling for crop production estimation derived a means of predicting the within-a-year yield and the year-to-year variability of yield over some fixed or randomly located unit of area. Preliminary studies indicated that the requirements for interpreting LANDSAT data for yield may be sufficiently similar to those of signature extension that it is feasible to investigate the automated estimation of production. The concept of an advanced yield model consisting of both spectral and meteorological components was endorsed. Rationale for using meteorological parameters originated from known between season and near harvest dynamics in crop environmental-condition-yield relationships.

  4. The Large Area Crop Inventory Experiment (LACIE)

    NASA Technical Reports Server (NTRS)

    Macdonald, R. B.

    1976-01-01

    A Large Area Crop Inventory Experiment (LACIE) was undertaken to prove out an economically important application of remote sensing from space. The experiment focused upon determination of wheat acreages in the U.S. Great Plains and upon the development and testing of yield models. The results and conclusions are presented.

  5. Large Area Crop Inventory Experiment (LACIE). Phase 2 evaluation report

    NASA Technical Reports Server (NTRS)

    1977-01-01

    Documentation of the activities of the Large Area Crop Inventory Experiment during the 1976 Northern Hemisphere crop year is presented. A brief overview of the experiment is included as well as phase two area, yield, and production estimates for the United States Great Plains, Canada, and the Union of Soviet Socialist Republics spring winter wheat regions. The accuracies of these estimates are compared with independent government estimates. Accuracy assessment of the United States Great Plains yardstick region based on a through blind sight analysis is given, and reasons for variations in estimating performance are discussed. Other phase two technical activities including operations, exploratory analysis, reporting, methods of assessment, phase three and advanced system design, technical issues, and developmental activities are also included.

  6. Use Of Crop Canopy Size To Estimate Water Requirements Of Vegetable Crops

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Planting time, plant density, variety, and cultural practices vary widely for horticultural crops. It is difficult to estimate crop water requirements for crops with these variations. Canopy size, or factional ground cover, as an indicator of intercepted sunlight, is related to crop water use. We...

  7. Preliminary evaluation of spectral, normal and meteorological crop stage estimation approaches

    NASA Technical Reports Server (NTRS)

    Cate, R. B.; Artley, J. A.; Doraiswamy, P. C.; Hodges, T.; Kinsler, M. C.; Phinney, D. E.; Sestak, M. L. (Principal Investigator)

    1980-01-01

    Several of the projects in the AgRISTARS program require crop phenology information, including classification, acreage and yield estimation, and detection of episodal events. This study evaluates several crop calendar estimation techniques for their potential use in the program. The techniques, although generic in approach, were developed and tested on spring wheat data collected in 1978. There are three basic approaches to crop stage estimation: historical averages for an area (normal crop calendars), agrometeorological modeling of known crop-weather relationships agrometeorological (agromet) crop calendars, and interpretation of spectral signatures (spectral crop calendars). In all, 10 combinations of planting and biostage estimation models were evaluated. Dates of stage occurrence are estimated with biases between -4 and +4 days while root mean square errors range from 10 to 15 days. Results are inconclusive as to the superiority of any of the models and further evaluation of the models with the 1979 data set is recommended.

  8. Crop Residue Coverage Estimation Using ASTER Imagery

    NASA Astrophysics Data System (ADS)

    Lewis, D.; Yao, H.; Kincaid, R.

    2006-12-01

    Soil erosion and its related runoff is a serious problem in U.S. agriculture. USDA has classified 33 percent of U.S. agricultural land as being highly erodible. It is well recognized that residue coverage on the soil surface can reduce soil erosion. The National Food Security Act of 1985 requires that agricultural producers protect all highly erodible cropland from excessive erosion. The 2002 Farm Bill gave U.S. Department of Agriculture's (USDA) Natural Resource Conservation Service (NRCS) the authority to make a determination of compliance. NRCS is currently running several programs to implement conservation practices and to monitor compliance. To be in compliance, growers must keep crop residue cover more than 30 percent of the field. This requires field-level assessment. The NRCS does not have the resources to regularly survey every field. One potential approach for compliance decision making is using data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor onboard NASA's Terra satellite. ASTER data provides 15 bands of 15 meter visible/NIR (VNIR) and 30 meter SWIR resolution data. Both the spatial resolution and spectral wavelength range and resolution are suitable for field level residue cover estimation. The objective of this study was to explore the potential of using ASTER data for crop residue cover estimation. The results indicate that ASTER imagery has good capability to identify residue within the corn fields and moderate capability in soybean residue estimation. SWIR bands have the most promise in separating crop residue when compared to the VNIR bands. Satellite based remote sensing imagery could be a potential rapid decision making tool for NRCS's compliance programs.

  9. Large Area Crop Inventory Experiment (LACIE). Executive summary

    NASA Technical Reports Server (NTRS)

    1978-01-01

    The author has identified the following significant results. The Large Area Crop Inventory Experiment (LACIE), completed June 30, 1978, has met the USDA at-harvest goals (90% accuracy with a 90% confidence level) in the US Great Plains and U.S.S.R. for two consecutive years. In addition, in the U.S.S.R., LACIE indicated a shortfall in the '76-'77 wheat crop about two months prior to harvest, thus demonstrating the capability of LACIE to make accurate preharvest estimates.

  10. Independent Peer Evaluation of the Large Area Crop Inventory Experiment (LACIE): The LACIE Symposium

    NASA Technical Reports Server (NTRS)

    1978-01-01

    Yield models and crop estimate accuracy are discussed within the Large Area Crop Inventory Experiment. The wheat yield estimates in the United States, Canada, and U.S.S.R. are emphasized. Experimental results design, system implementation, data processing systems, and applications were considered.

  11. Large area crop inventory experiment crop assessment subsystem software requirements document

    NASA Technical Reports Server (NTRS)

    1975-01-01

    The functional data processing requirements are described for the Crop Assessment Subsystem of the Large Area Crop Inventory Experiment. These requirements are used as a guide for software development and implementation.

  12. Unsupervised linear unmixing of hyperspectral image for crop yield estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Multispectral and hyperspectral imagery are often used for estimating crop yield. This paper describes an unsupervised unmixing scheme of hyperspectral images to estimate crop yield. From the hyperspectral images, the endmembers and their abundance maps are computed by unsupervised unmixing. The abu...

  13. The California Biomass Crop Adoption Model estimates biofuel feedstock crop production across diverse agro-ecological zones within the state, under different future climates

    NASA Astrophysics Data System (ADS)

    Kaffka, S.; Jenner, M.; Bucaram, S.; George, N.

    2012-12-01

    Both regulators and businesses need realistic estimates for the potential production of biomass feedstocks for biofuels and bioproducts. This includes the need to understand how climate change will affect mid-tem and longer-term crop performance and relative advantage. The California Biomass Crop Adoption Model is a partial mathematical programming optimization model that estimates the profit level needed for new crop adoption, and the crop(s) displaced when a biomass feedstock crop is added to the state's diverse set of cropping systems, in diverse regions of the state. Both yield and crop price, as elements of profit, can be varied. Crop adoption is tested against current farmer preferences derived from analysis of 10 years crop production data for all crops produced in California, collected by the California Department of Pesticide Regulation. Analysis of this extensive data set resulted in 45 distinctive, representative farming systems distributed across the state's diverse agro-ecological regions. Estimated yields and water use are derived from field trials combined with crop simulation, reported elsewhere. Crop simulation is carried out under different weather and climate assumptions. Besides crop adoption and displacement, crop resource use is also accounted, derived from partial budgets used for each crop's cost of production. Systematically increasing biofuel crop price identified areas of the state where different types of crops were most likely to be adopted. Oilseed crops like canola that can be used for biodiesel production had the greatest potential to be grown in the Sacramento Valley and other northern regions, while sugar beets (for ethanol) had the greatest potential in the northern San Joaquin Valley region, and sweet sorghum in the southern San Joaquin Valley. Up to approximately 10% of existing annual cropland in California was available for new crop adoption. New crops are adopted if the entire cropping system becomes more profitable. In

  14. Effects of climate change on suitable rice cropping areas, cropping systems and crop water requirements in southern China

    DOE PAGESBeta

    Ye, Qing; Yang, Xiaoguang; Dai, Shuwei; Chen, Guangsheng; Li, Yong; Zhang, Caixia

    2015-06-05

    Here, we discuss that rice is one of the main crops grown in southern China. Global climate change has significantly altered the local water availability and temperature regime for rice production. In this study, we explored the influence of climate change on suitable rice cropping areas, rice cropping systems and crop water requirements (CWRs) during the growing season for historical (from 1951 to 2010) and future (from 2011 to 2100) time periods. The results indicated that the land areas suitable for rice cropping systems shifted northward and westward from 1951 to 2100 but with different amplitudes.

  15. Advancing Methods for Estimating Cropland Area

    NASA Astrophysics Data System (ADS)

    King, L.; Hansen, M.; Stehman, S. V.; Adusei, B.; Potapov, P.; Krylov, A.

    2014-12-01

    Measurement and monitoring of complex and dynamic agricultural land systems is essential with increasing demands on food, feed, fuel and fiber production from growing human populations, rising consumption per capita, the expansion of crops oils in industrial products, and the encouraged emphasis on crop biofuels as an alternative energy source. Soybean is an important global commodity crop, and the area of land cultivated for soybean has risen dramatically over the past 60 years, occupying more than 5% of all global croplands (Monfreda et al 2008). Escalating demands for soy over the next twenty years are anticipated to be met by an increase of 1.5 times the current global production, resulting in expansion of soybean cultivated land area by nearly the same amount (Masuda and Goldsmith 2009). Soybean cropland area is estimated with the use of a sampling strategy and supervised non-linear hierarchical decision tree classification for the United States, Argentina and Brazil as the prototype in development of a new methodology for crop specific agricultural area estimation. Comparison of our 30 m2 Landsat soy classification with the National Agricultural Statistical Services Cropland Data Layer (CDL) soy map shows a strong agreement in the United States for 2011, 2012, and 2013. RapidEye 5m2 imagery was also classified for soy presence and absence and used at the field scale for validation and accuracy assessment of the Landsat soy maps, describing a nearly 1 to 1 relationship in the United States, Argentina and Brazil. The strong correlation found between all products suggests high accuracy and precision of the prototype and has proven to be a successful and efficient way to assess soybean cultivated area at the sub-national and national scale for the United States with great potential for application elsewhere.

  16. A Simple Method to Estimate Harvest Index in Grain Crops

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Several methods have been proposed to simulate yield in crop simulation models. In this work we present a simple method to estimate harvest index (HI) of grain crops based on fractional post-anthesis growth (fG = fraction of growth that occurred post-anthesis). We propose that there is a linear or c...

  17. Cropping frequency and area response to climate variability can exceed yield response

    NASA Astrophysics Data System (ADS)

    Cohn, Avery S.; Vanwey, Leah K.; Spera, Stephanie A.; Mustard, John F.

    2016-06-01

    The sensitivity of agricultural output to climate change has often been estimated by modelling crop yields under climate change scenarios or with statistical analysis of the impacts of year-to-year climatic variability on crop yields. However, the area of cropland and the number of crops harvested per growing season (cropping frequency) both also affect agricultural output and both also show sensitivity to climate variability and change. We model the change in agricultural output associated with the response of crop yield, crop frequency and crop area to year-to-year climate variability in Mato Grosso (MT), Brazil, a key agricultural region. Roughly 70% of the change in agricultural output caused by climate was determined by changes in frequency and/or changes in area. Hot and wet conditions were associated with the largest losses and cool and dry conditions with the largest gains. All frequency and area effects had the same sign as total effects, but this was not always the case for yield effects. A focus on yields alone may therefore bias assessments of the vulnerability of agriculture to climate change. Efforts to reduce climate impacts to agriculture should seek to limit production losses not only from crop yield, but also from changes in cropland area and cropping frequency.

  18. Rice Yield Estimation Through Assimilating Satellite Data Into a Crop Simumlation Model

    NASA Astrophysics Data System (ADS)

    Son, N. T.; Chen, C. F.; Chen, C. R.; Chang, L. Y.; Chiang, S. H.

    2016-06-01

    Rice is globally the most important food crop, feeding approximately half of the world's population, especially in Asia where around half of the world's poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government's yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.

  19. SSG-4 - An automated spring small grains proportion estimator. [for Landsat crop classification

    NASA Technical Reports Server (NTRS)

    Dennis, T. B.; Cate, R. B.; Smyrski, M. M.; Baker, T. C.; Nazare, C. V.

    1982-01-01

    In connection with an implementation of the classification procedures employed in the Large Area Crop Inventory Experiment (LACIE), a human analyst had to provide labeled samples. The present investigation is concerned with an automated proportion estimation procedure which has been derived from the early field-labeling procedures used in LACIE. This procedure was developed for the U.S./Canada Spring Small Grains Pilot Experiment. It is demonstrated that the considered spatial/color-based proportion estimation procedure provides the agricultural remote-sensing community with the basic tools to develop unbiased and highly efficient procedures for obtaining crop area estimates at the end of the season.

  20. Evaluation of crop acreage estimation methods using Landsat data as auxiliary input

    NASA Technical Reports Server (NTRS)

    Chhikara, R. S.; Houston, A. G.; Lundgren, J. C.

    1985-01-01

    The regression and ratio estimators are studied in the context of improving upon the ground survey estimates of crop acreages by utilizing Landsat data. The approach is to formulate analytically the estimation problem that utilizes ground survey data, as collected by the U.S. Department of Agriculture, and Landsat data, which provide a complete coverage for an area of interest, and then to conduct simulation studies. It is shown over a wide range of conditions that the regression estimator is the most efficient unless there is a low correlation between the actual and estimated crop acreages in the sampled area segments, in which case a ratio type estimator is superior. Estimation of the variance of the regression estimator is also investigated.

  1. The limits of crop productivity: validating theoretical estimates and determining the factors that limit crop yields in optimal environments

    NASA Technical Reports Server (NTRS)

    Bugbee, B.; Monje, O.

    1992-01-01

    Plant scientists have sought to maximize the yield of food crops since the beginning of agriculture. There are numerous reports of record food and biomass yields (per unit area) in all major crop plants, but many of the record yield reports are in error because they exceed the maximal theoretical rates of the component processes. In this article, we review the component processes that govern yield limits and describe how each process can be individually measured. This procedure has helped us validate theoretical estimates and determine what factors limit yields in optimal environments.

  2. Incorporating remote sensing data in crop model to monitor crop growth and predict yield in regional area

    NASA Astrophysics Data System (ADS)

    Guo, Jianmao; Lu, Weisong; Zhang, Guoping; Qian, Yonglan; Yu, Qiang; Zhang, Jiahua

    2006-12-01

    Accurate crop growth monitoring and yield predicting is very important to food security and agricultural sustainable development. Crop models can be forceful tools for monitoring crop growth status and predicting yield over homogeneous areas, however, their application to a larger spatial domains is hampered by lack of sufficient spatial information about model inputs, such as the value of some of their parameters and initial conditions, which may have great difference between regions even fields. The use of remote sensing data helps to overcome this problem. By incorporating remote sensing data into the WOFOST crop model (through LAI), it is possible to incorporate remote sensing variables (vegetation index) for each point of the spatial domain, and it is possible for this point to re-estimate new values of the parameters or initial conditions, to which the model is particularly sensitive. This paper describes the use of such a method on a local scale, for winter wheat, focusing on the parameters describing emergence and early crop growth. These processes vary greatly depending on the soil, climate and seedbed preparation, and affect yield significantly. The WOFOST crop model is calibrated under standard conditions and then evaluated under test conditions to which the emergence and early growth parameters of the WOFOST model are adjusted by incorporating remote sensing data. The inversion of the combined model allows us to accurately monitoring crop growth status and predicting yield on a regional scale.

  3. Estimation of crop coefficients by means of optimized vegetation indices for corn

    NASA Astrophysics Data System (ADS)

    Gonzalez-Piqueras, Jose; Calera, Alfonso; Gilabert, Maria A.; Cuesta, Andres; De la Cruz Tercero, Fernando

    2004-02-01

    A linear relationship between NDVI and basal crop coefficient (Kcb) allows to compute the spectral crop coefficient (Krcb). Due to the influence of soil variations varying surface humidity on NDVI, five soil optimized indices have been used to obtain a linear relationship normalized for soil background effect (SAVI, OSAVI, TSAVI, MSAVI and GESAVI). Data used on this work have been obtained from a field campaign for corn in the area of Barrax, Spain), describing crop growth stages with green fraction cover (GFC), and leaf area index (LAI). SAVI with optimized factor L set to 0.5 is a good estimator of Krcb from sparse to dense vegetation, nevertheless the soil line based index ( GESAVI) due to a wider range of variation are more sensitive to leaf variations at high levels of vegetation amount. Spectral crop coefficients obtained from SAVI and soil line based GESAVI are sensitive to crop hazards by weather anomalies and estimates in real time the basal crop coefficients to estimate the amount of water removed by the crop from the active root zone.

  4. Time Series Analysis of Remote Sensing Observations for Citrus Crop Growth Stage and Evapotranspiration Estimation

    NASA Astrophysics Data System (ADS)

    Sawant, S. A.; Chakraborty, M.; Suradhaniwar, S.; Adinarayana, J.; Durbha, S. S.

    2016-06-01

    Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (http://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system

  5. Improving Spectral Crop Coefficient Approach with Raw Image Digital Count Data to Estimate Crop Water Use

    NASA Astrophysics Data System (ADS)

    Shafian, S.; Maas, S. J.; Rajan, N.

    2014-12-01

    Water resources and agricultural applications require knowledge of crop water use (CWU) over a range of spatial and temporal scales. Due to the spatial density of meteorological stations, the resolution of CWU estimates based on these data is fairly coarse and not particularly suitable or reliable for water resources planning, irrigation scheduling and decision making. Various methods have been developed for quantifying CWU of agricultural crops. In this study, an improved version of the spectral crop coefficient which includes the effects of stomatal closure is applied. Raw digital count (DC) data in the red, near-infrared, and thermal infrared (TIR) spectral bands of Landsat-7 and Landsat-8 imaging sensors are used to construct the TIR-ground cover (GC) pixel data distribution and estimate the effects of stomatal closure. CWU is then estimated by combining results of the spectral crop coefficient approach and the stomatal closer effect. To test this approach, evapotranspiration was measured in 5 agricultural fields in the semi-arid Texas High Plains during the 2013 and 2014 growing seasons and compared to corresponding estimated values of CWU determined using this approach. The results showed that the estimated CWU from this approach was strongly correlated (R2 = 0.79) with observed evapotranspiration. In addition, the results showed that considering the stomatal closer effect in the proposed approach can improve the accuracy of the spectral crop coefficient method. These results suggest that the proposed approach is suitable for operational estimation of evapotranspiration and irrigation scheduling where irrigation is used to replace the daily CWU of a crop.

  6. Estimating cropland NPP using national crop inventory and MODIS derived crop specific parameters

    NASA Astrophysics Data System (ADS)

    Bandaru, V.; West, T. O.; Ricciuto, D. M.

    2011-12-01

    Estimates of cropland net primary production (NPP) are needed as input for estimates of carbon flux and carbon stock changes. Cropland NPP is currently estimated using terrestrial ecosystem models, satellite remote sensing, or inventory data. All three of these methods have benefits and problems. Terrestrial ecosystem models are often better suited for prognostic estimates rather than diagnostic estimates. Satellite-based NPP estimates often underestimate productivity on intensely managed croplands and are also limited to a few broad crop categories. Inventory-based estimates are consistent with nationally collected data on crop yields, but they lack sub-county spatial resolution. Integrating these methods will allow for spatial resolution consistent with current land cover and land use, while also maintaining total biomass quantities recorded in national inventory data. The main objective of this study was to improve cropland NPP estimates by using a modification of the CASA NPP model with individual crop biophysical parameters partly derived from inventory data and MODIS 8day 250m EVI product. The study was conducted for corn and soybean crops in Iowa and Illinois for years 2006 and 2007. We used EVI as a linear function for fPAR, and used crop land cover data (56m spatial resolution) to extract individual crop EVI pixels. First, we separated mixed pixels of both corn and soybean that occur when MODIS 250m pixel contains more than one crop. Second, we substituted mixed EVI pixels with nearest pure pixel values of the same crop within 1km radius. To get more accurate photosynthetic active radiation (PAR), we applied the Mountain Climate Simulator (MTCLIM) algorithm with the use of temperature and precipitation data from the North American Land Data Assimilation System (NLDAS-2) to generate shortwave radiation data. Finally, county specific light use efficiency (LUE) values of each crop for years 2006 to 2007 were determined by application of mean county inventory

  7. Flexible Strategies for Coping with Rainfall Variability: Seasonal Adjustments in Cropped Area in the Ganges Basin.

    PubMed

    Siderius, Christian; Biemans, Hester; van Walsum, Paul E V; van Ierland, Ekko C; Kabat, Pavel; Hellegers, Petra J G J

    2016-01-01

    One of the main manifestations of climate change will be increased rainfall variability. How to deal with this in agriculture will be a major societal challenge. In this paper we explore flexibility in land use, through deliberate seasonal adjustments in cropped area, as a specific strategy for coping with rainfall variability. Such adjustments are not incorporated in hydro-meteorological crop models commonly used for food security analyses. Our paper contributes to the literature by making a comprehensive model assessment of inter-annual variability in crop production, including both variations in crop yield and cropped area. The Ganges basin is used as a case study. First, we assessed the contribution of cropped area variability to overall variability in rice and wheat production by applying hierarchical partitioning on time-series of agricultural statistics. We then introduced cropped area as an endogenous decision variable in a hydro-economic optimization model (WaterWise), coupled to a hydrology-vegetation model (LPJmL), and analyzed to what extent its performance in the estimation of inter-annual variability in crop production improved. From the statistics, we found that in the period 1999-2009 seasonal adjustment in cropped area can explain almost 50% of variability in wheat production and 40% of variability in rice production in the Indian part of the Ganges basin. Our improved model was well capable of mimicking existing variability at different spatial aggregation levels, especially for wheat. The value of flexibility, i.e. the foregone costs of choosing not to crop in years when water is scarce, was quantified at 4% of gross margin of wheat in the Indian part of the Ganges basin and as high as 34% of gross margin of wheat in the drought-prone state of Rajasthan. We argue that flexibility in land use is an important coping strategy to rainfall variability in water stressed regions. PMID:26934389

  8. Flexible Strategies for Coping with Rainfall Variability: Seasonal Adjustments in Cropped Area in the Ganges Basin

    PubMed Central

    Siderius, Christian; Biemans, Hester; van Walsum, Paul E. V.; van Ierland, Ekko C.; Kabat, Pavel; Hellegers, Petra J. G. J.

    2016-01-01

    One of the main manifestations of climate change will be increased rainfall variability. How to deal with this in agriculture will be a major societal challenge. In this paper we explore flexibility in land use, through deliberate seasonal adjustments in cropped area, as a specific strategy for coping with rainfall variability. Such adjustments are not incorporated in hydro-meteorological crop models commonly used for food security analyses. Our paper contributes to the literature by making a comprehensive model assessment of inter-annual variability in crop production, including both variations in crop yield and cropped area. The Ganges basin is used as a case study. First, we assessed the contribution of cropped area variability to overall variability in rice and wheat production by applying hierarchical partitioning on time-series of agricultural statistics. We then introduced cropped area as an endogenous decision variable in a hydro-economic optimization model (WaterWise), coupled to a hydrology-vegetation model (LPJmL), and analyzed to what extent its performance in the estimation of inter-annual variability in crop production improved. From the statistics, we found that in the period 1999–2009 seasonal adjustment in cropped area can explain almost 50% of variability in wheat production and 40% of variability in rice production in the Indian part of the Ganges basin. Our improved model was well capable of mimicking existing variability at different spatial aggregation levels, especially for wheat. The value of flexibility, i.e. the foregone costs of choosing not to crop in years when water is scarce, was quantified at 4% of gross margin of wheat in the Indian part of the Ganges basin and as high as 34% of gross margin of wheat in the drought-prone state of Rajasthan. We argue that flexibility in land use is an important coping strategy to rainfall variability in water stressed regions. PMID:26934389

  9. Linear unmixing of multidate hyperspectral imagery for crop yield estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this paper, we have evaluated an unsupervised unmixing approach, vertex component analysis (VCA), for the application of crop yield estimation. The results show that abundance maps of the vegetation extracted by the approach are strongly correlated to the yield data (the correlation coefficients ...

  10. Evaluating SPOT 5 Multispectral Imagery for Crop Yield Estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    High resolution satellite imagery has the potential for mapping within-field variability in crop growth and yield. This study examined SPOT 5 multispectral imagery for estimating grain sorghum yield. A SPOT 5 image with 10-m spatial resolution and four spectral bands (green, red, near-infrared and m...

  11. Effect of mixed (boundary) pixels on crop proportion estimation

    NASA Technical Reports Server (NTRS)

    Chhikara, R. S.

    1984-01-01

    In estimating acreage proportions of crop types in a segment using Landsat data, considerable problem is caused by the presence of mixed pixels. Due to lack of understanding of their spectral characteristics, mixed pixels have been treated in the past as pure while clustering and classifying the segment data. This paper examines this approach of treating mixed pixels as pure pixels and the effect of mixed pixels on the bias and variance of a crop type proportion estimate. First, the spectral response of a boundary pixel is modeled and an analytical expression for the bias and variance of a proportion estimate is obtained. This is followed by a numerical illustration of the effect of mixed pixels on bias and variance. It is shown that as the size of the mixed pixel class increases in a segment, the variance increases, however, such increase does not always affect the bias of the proportion estimate.

  12. Crop parameters estimation by fuzzy inference system using X-band scatterometer data

    NASA Astrophysics Data System (ADS)

    Pandey, Abhishek; Prasad, R.; Singh, V. P.; Jha, S. K.; Shukla, K. K.

    2013-03-01

    Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level.

  13. Drought impacts and resilience on crops via evapotranspiration estimations

    NASA Astrophysics Data System (ADS)

    Timmermans, Joris; Asadollahi Dolatabad, Saeid

    2015-04-01

    Currently, the global needs for food and water is at a critical level. It has been estimated that 12.5 % of the global population suffers from malnutrition and 768 million people still do not have access to clean drinking water. This need is increasing because of population growth but also by climate change. Changes in precipitation patterns will result either in flooding or droughts. Consequently availability, usability and affordability of water is becoming challenge and efficient use of water and water management is becoming more important, particularly during severe drought events. Drought monitoring for agricultural purposes is very hard. While meteorological drought can accurately be monitored using precipitation only, estimating agricultural drought is more difficult. This is because agricultural drought is dependent on the meteorological drought, the impacts on the vegetation, and the resilience of the crops. As such not only precipitation estimates are required but also evapotranspiration at plant/plot scale. Evapotranspiration (ET) describes the amount of water evaporated from soil and vegetation. As 65% of precipitation is lost by ET, drought severity is highly linked with this variable. In drought research, the precise quantification of ET and its spatio-temporal variability is therefore essential. In this view, remote sensing based models to estimate ET, such as SEBAL and SEBS, are of high value. However the resolution of current evapotranspiration products are not good enough for monitoring the impact of the droughts on the specific crops. This limitation originates because plot scales are in general smaller than the resolution of the available satellite ET products. As such remote sensing estimates of evapotranspiration are always a combination of different land surface types and cannot be used for plant health and drought resilience studies. The goal of this research is therefore to enable adequate resolutions of daily evapotranspiration estimates

  14. Global sensitivity of high-resolution estimates of crop water footprint

    NASA Astrophysics Data System (ADS)

    Tuninetti, Marta; Tamea, Stefania; D'Odorico, Paolo; Laio, Francesco; Ridolfi, Luca

    2015-10-01

    Most of the human appropriation of freshwater resources is for agriculture. Water availability is a major constraint to mankind's ability to produce food. The notion of virtual water content (VWC), also known as crop water footprint, provides an effective tool to investigate the linkage between food and water resources as a function of climate, soil, and agricultural practices. The spatial variability in the virtual water content of crops is here explored, disentangling its dependency on climate and crop yields and assessing the sensitivity of VWC estimates to parameter variability and uncertainty. Here we calculate the virtual water content of four staple crops (i.e., wheat, rice, maize, and soybean) for the entire world developing a high-resolution (5 × 5 arc min) model, and we evaluate the VWC sensitivity to input parameters. We find that food production almost entirely depends on green water (>90%), but, when applied, irrigation makes crop production more water efficient, thus requiring less water. The spatial variability of the VWC is mostly controlled by the spatial patterns of crop yields with an average correlation coefficient of 0.83. The results of the sensitivity analysis show that wheat is most sensitive to the length of the growing period, rice to reference evapotranspiration, maize and soybean to the crop planting date. The VWC sensitivity varies not only among crops, but also across the harvested areas of the world, even at the subnational scale.

  15. A new remote sensing procedure for the estimation of crop water requirements

    NASA Astrophysics Data System (ADS)

    Spiliotopoulos, M.; Loukas, A.; Mylopoulos, N.

    2015-06-01

    The objective of this work is the development of a new approach for the estimation of water requirements for the most important crops located at Karla Watershed, central Greece. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) was used as a basis for the derivation of actual evapotranspiration (ET) and crop coefficient (ETrF) values from Landsat ETM+ imagery. MODIS imagery has been also used, and a spatial downscaling procedure is followed between the two sensors for the derivation of a new NDVI product with a spatial resolution of 30 m x 30 m. GER 1500 spectro-radiometric measurements are additionally conducted during 2012 growing season. Cotton, alfalfa, corn and sugar beets fields are utilized, based on land use maps derived from previous Landsat 7 ETM+ images. A filtering process is then applied to derive NDVI values after acquiring Landsat ETM+ based reflectance values from the GER 1500 device. ETrF vs NDVI relationships are produced and then applied to the previous satellite based downscaled product in order to finally derive a 30 m x 30 m daily ETrF map for the study area. CropWat model (FAO) is then applied, taking as an input the new crop coefficient values with a spatial resolution of 30 m x 30 m available for every crop. CropWat finally returns daily crop water requirements (mm) for every crop and the results are analyzed and discussed.

  16. Higher U.S. Crop Prices Trigger Little Area Expansion so Marginal Land for Biofuel Crops Is Limited

    SciTech Connect

    Swinton, S.; Babcock, Bruce; James, Laura; Bandaru, Varaprasad

    2011-06-12

    By expanding energy biomass production on marginal lands that are not currently used for crops, food price increases and indirect climate change effects can be mitigated. Studies of the availability of marginal lands for dedicated bioenergy crops have focused on biophysical land traits, ignoring the human role in decisions to convert marginal land to bioenergy crops. Recent history offers insights about farmer willingness to put non-crop land into crop production. The 2006-09 leap in field crop prices and the attendant 64% gain in typical profitability led to only a 2% increase in crop planted area, mostly in the prairie states

  17. Soil Moisture as an Estimator for Crop Yield in Germany

    NASA Astrophysics Data System (ADS)

    Peichl, Michael; Meyer, Volker; Samaniego, Luis; Thober, Stephan

    2015-04-01

    Annual crop yield depends on various factors such as soil properties, management decisions, and meteorological conditions. Unfavorable weather conditions, e.g. droughts, have the potential to drastically diminish crop yield in rain-fed agriculture. For example, the drought in 2003 caused direct losses of 1.5 billion EUR only in Germany. Predicting crop yields allows to mitigate negative effects of weather extremes which are assumed to occur more often in the future due to climate change. A standard approach in economics is to predict the impact of climate change on agriculture as a function of temperature and precipitation. This approach has been developed further using concepts like growing degree days. Other econometric models use nonlinear functions of heat or vapor pressure deficit. However, none of these approaches uses soil moisture to predict crop yield. We hypothesize that soil moisture is a better indicator to explain stress on plant growth than estimations based on precipitation and temperature. This is the case because the latter variables do not explicitly account for the available water content in the root zone, which is the primary source of water supply for plant growth. In this study, a reduced form panel approach is applied to estimate a multivariate econometric production function for the years 1999 to 2010. Annual crop yield data of various crops on the administrative district level serve as depending variables. The explanatory variable of major interest is the Soil Moisture Index (SMI), which quantifies anomalies in root zone soil moisture. The SMI is computed by the mesoscale Hydrological Model (mHM, www.ufz.de/mhm). The index represents the monthly soil water quantile at a 4 km2 grid resolution covering entire Germany. A reduced model approach is suitable because the SMI is the result of a stochastic weather process and therefore can be considered exogenous. For the ease of interpretation a linear functionality is preferred. Meteorological

  18. Development of Crop Yield Estimation Method by Applying Seasonal Climate Prediction in Asia-Pacific Region

    NASA Astrophysics Data System (ADS)

    Shin, Y.; Lee, E.

    2015-12-01

    Under the influence of recent climate change, abnormal weather condition such as floods and droughts has issued frequently all over the world. The occurrence of abnormal weather in major crop production areas leads to soaring world grain prices because it influence the reduction of crop yield. Development of crop yield estimation method is important means to accommodate the global food crisis caused by abnormal weather. However, due to problems with the reliability of the seasonal climate prediction, application research on agricultural productivity has not been much progress yet. In this study, it is an object to develop long-term crop yield estimation method in major crop production countries worldwide using multi seasonal climate prediction data collected by APEC Climate Center. There are 6-month lead seasonal predictions produced by six state-of-the-art global coupled ocean-atmosphere models(MSC_CANCM3, MSC_CANCM4, NASA, NCEP, PNU, POAMA). First of all, we produce a customized climate data through temporal and spatial downscaling methods for use as a climatic input data to the global scale crop model. Next, we evaluate the uncertainty of climate prediction by applying multi seasonal climate prediction in the crop model. Because rice is the most important staple food crop in the Asia-Pacific region, we assess the reliability of the rice yields using seasonal climate prediction for main rice production countries. RMSE(Root Mean Squire Error) and TCC(Temporal Correlation Coefficient) analysis is performed in Asia-Pacific countries, major 14 rice production countries, to evaluate the reliability of the rice yield according to the climate prediction models. We compare the rice yield data obtained from FAOSTAT and estimated using the seasonal climate prediction data in Asia-Pacific countries. In addition, we show that the reliability of seasonal climate prediction according to the climate models in Asia-Pacific countries where rice cultivation is being carried out.

  19. Bistatic measurements for the estimation of rice crop variables using artificial neural network

    NASA Astrophysics Data System (ADS)

    Gupta, D. K.; Kumar, P.; Mishra, V. N.; Prasad, R.; Dikshit, P. K. S.; Dwivedi, S. B.; Ohri, A.; Singh, R. S.; Srivastava, V.

    2015-03-01

    An outdoor rice crop bed (4 × 4 m2) was specially prepared for a bistatic ground based scatterometer measurements at various growth stages of rice crop from transplanting to ripening stage at like polarizations (HH- and VV-) in the angular range of 20-70° at the steps of 5°. The computed scattering coefficients showed increasing behavior from transplanting to reproductive stage and started decreasing at ripening stage. The angular dependency of scattering coefficient was found to decrease initially with age and became negligible near the ripening stage of rice crop. The polynomial regression analysis showed higher values of coefficient of determination (R2) at 30° incidence angle for both like polarizations. Two types of feed forward back propagation neural network (FFBPNN) models were developed for the estimation of rice crop growth variables namely FFBPANN-I and FFBPANN-II model. The FFBPANN-I model was developed with one input neuron (HH- or VV-polarized scattering coefficient) and one output neuron (biomass or leaf area index or plant height or chlorophyll content) while the FFBPANN-II model was developed with two input neurons (HH- and VV-polarized scattering coefficient) and four output neurons (biomass, leaf area index, plant height and chlorophyll content). Performances of both the types of FFBPANN models were found good for the estimation of rice crop variables. However, the performance of FFBPANN-II model was found better in comparison to the FFBPANN-I model at suitable incidence angle 30°.

  20. Estimating maize grain yield from crop biophysical parameters using remote sensing

    NASA Astrophysics Data System (ADS)

    Guindin-Garcia, Noemi

    The overall objective of this investigation was to develop a robust technique to predict maize (Zea mays L.) grain yield that could be applied at a regional level using remote sensing with or without a simple crop growth simulation model. This study evaluated capabilities and limitations of the Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Index 250-m and MODIS surface reflectance 500-m products to track and retrieve information over maize fields. Results demonstrated the feasibility of using MODIS data to estimate maize green leaf area index (LAIg). Estimates of maize LAIg obtained from Wide Dynamic Range Vegetation Index using data retrieved from MODIS 250-m products (e.g. MOD13Q1) can be incorporated in crop simulation models to improve LAIg simulations by the Muchow-Sinclair-Bennet (MSB) model reducing the RMSE of LAIg simulations for all years of study under irrigation. However, more accurate estimates of LAIg did not necessarily imply better final yield (FY) predictions in the MSB maize model. The approach of incorporating better LAIg estimates into crop simulation models may not offer a panacea for problem solving; this approach is limited in its ability to simulate other factors influencing crop yields. On the other hand, the approach of relating key crop biophysical parameters at the optimum stage with maize grain final yields is a robust technique to early FY estimation over large areas. Results suggest that estimates of LAI g obtained during the mid-grain filling period can used to detect variability of maize grain yield and this technique offers a rapid and accurate (RMSE < 900 kg ha-1) method to detect FY at county level using MODIS 250-m products.

  1. Estimation of crop parameters using multi-temporal optical and radar polarimetric satellite data

    NASA Astrophysics Data System (ADS)

    Betbeder, Julie; Fieuzal, Remy; Philippets, Yannick; Ferro-Famil, Laurent; Baup, Frederic

    2015-10-01

    This paper is concerned with the estimation of wheat and rapeseed crops parameters (height, leaf area index and dry biomass), during their whole vegetation cycle, using satellite time series both acquired in optical and microwave domains. Crop monitoring at a fine scale represents an important stake from an environmental point of view as it provides essential information to combine increase of production and sustainable management of agricultural landscapes. The aim of this paper is to compare the potential of optical and SAR parameters (backscattering coefficients and polarimetric parameters) for crop parameters estimation. Satellite (Formosat-2, Spot-4/5 and Radarsat-2) and ground data were acquired during the MCM'10 experiment conducted by the CESBIO laboratory in 2010. A vegetation index was derived from the optical images: the NDVI and backscattering coefficients and polarimetric parameters were computed from Radarsat-2 images. Results of this study show the high interest of using SAR parameters (backscattering coefficients and polarimetric parameters) for crop parameters estimation during the whole vegetation cycle instead of using optical vegetation index. Polarimetric parameters do not improve wheat parameters estimation (e.g. backscattering coefficient σ° VV corresponds to the best parameter for wheat height estimation (r2 = 0.60)) but show their high potential for rapeseed height and dry biomass monitoring (i.e. Shannon Entropy polarimetry (SEp ; r2 = 0.70) and Radar Vegetation Index (RVI ; r2 = 0.80) respectively).

  2. Large Area Crop Inventory Experiment (LACIE). Review of LACIE methodology, a project evaluation of technical acceptability

    NASA Technical Reports Server (NTRS)

    1976-01-01

    The author has identified the following significant results. Results indicated that the LANDSAT data and the classification technology can estimate the small grains area within a sample segment accurately and reliably enough to meet the LACIE goals. Overall, the LACIE estimates in a 9 x 11 kilometer segment agree well with ground and aircraft determined area within these segments. The estimated c.v. of the random classification error was acceptably small. These analyses confirmed that bias introduced by various factors, such as LANDSAT spatial resolution, lack of spectral resolution, classifier bias, and repeatability, was not excessive in terms of the required performance criterion. Results of these tests did indicate a difficulty in differentiating wheat from other closely related small grains. However, satisfactory wheat area estimates were obtained through the reduction of the small grain area estimates in accordance with relative amounts of these crops as determined from historic data; these procedures are being further refined.

  3. Evaluation of the use of remote-sensing data to identify crop types and estimate irrigated acreage, Uvalde and Medina counties, Texas, 1989

    USGS Publications Warehouse

    Raymond, L.H.; Nalley, G.M.; Rettman, P.L.

    1992-01-01

    Results were verified using crop acreages reported by the U.S. Department of Agriculture, Agricultural Stabilization and Conservation Service (ASCS). The total areas for all irrigated crops estimated using remote-sensing data were about 8 percent higher for Uvalde County and about 4 percent higher for Medina County than the areas reported by the ASCS. Irrigated-crop areas subsequently were multiplied by the respective duties of water to calculate the total quantity of water pumped from the aquifer for irrigation. Pumpage did not differ for the two estimates of crop areas for Uvalde County and differed by about 3 percent for Medina County.

  4. Asia Rice Crop Estimation and Monitoring (Asia-RiCE) for GEOGLAM

    NASA Astrophysics Data System (ADS)

    Oyoshi, K.; Tomiyama, N.; Okumura, T.; Sobue, S.

    2013-12-01

    Food security is a critical issue for the international community because of rapid population and economic growth, and climate change. In June 2011, the meeting of G20 agriculture ministers was held to discuss food security and food price volatility, and they agreed on an 'Action Plan on Food Price Volatility and Agriculture'. This plan includes a GEO Global Agricultural Monitoring (GEOGLAM) initiative. The aim of GEOGLAM is to reinforce the international community's ability to produce and disseminate relevant, timely, and accurate forecasts of agricultural production on regional, national, and global scales by utilizing remote sensing technology. GEOGLAM focused on four major grain crops, wheat, maize, soybeans and rice. In particular, Asian countries are responsible for approximately 90% of the world rice production and consumption, rice is the most significant cereal crop in Asian region. Hence, Asian space and agricultural agencies with an interest in the development of rice crop monitoring technology launched an Asia-Rice Crop Estimation & Monitoring (Asia-RiCE) component for the GEOGLAM initiative. In Asian region, rice is mainly cultivated in rainy season, and a large amount of cloud limits rice crop monitoring with optical sensors. But, Synthetic Aperture RADAR (SAR) is all-weather sensor and can observe land surface even if the area is covered by cloud. Therefore, SAR technology would be powerful tool to monitor rice crop in Asian region. Asia-RiCE team required mainly SAR observation data including ALOS-2, RISAT-1, Sentinel-1 and RADARSAT, TerraSAR-X, COSMO-SkyMed for Asia-RiCE GEOGLAM Phase 1 implementation (2013-2015) to the Committee on Earth Observations (CEOS) in the GEOGLAM-CEOS Global Agricultural Monitoring Co-community Meeting held in June 2013. And also, rice crop has complicated cropping systems such as rein-fed or irrigated cultivation, single, double or sometimes triple cropping. In addition, each agricultural field is smaller than that of

  5. Estimation of crop water requirements using remote sensing for operational water resources management

    NASA Astrophysics Data System (ADS)

    Vasiliades, Lampros; Spiliotopoulos, Marios; Tzabiras, John; Loukas, Athanasios; Mylopoulos, Nikitas

    2015-06-01

    An integrated modeling system, developed in the framework of "Hydromentor" research project, is applied to evaluate crop water requirements for operational water resources management at Lake Karla watershed, Greece. The framework includes coupled components for operation of hydrotechnical projects (reservoir operation and irrigation works) and estimation of agricultural water demands at several spatial scales using remote sensing. The study area was sub-divided into irrigation zones based on land use maps derived from Landsat 5 TM images for the year 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) was used to derive actual evapotranspiration (ET) and crop coefficient (ETrF) values from Landsat TM imagery. Agricultural water needs were estimated using the FAO method for each zone and each control node of the system for a number of water resources management strategies. Two operational strategies of hydro-technical project development (present situation without operation of the reservoir and future situation with the operation of the reservoir) are coupled with three water demand strategies. In total, eight (8) water management strategies are evaluated and compared. The results show that, under the existing operational water resources management strategies, the crop water requirements are quite large. However, the operation of the proposed hydro-technical projects in Lake Karla watershed coupled with water demand management measures, like improvement of existing water distribution systems, change of irrigation methods, and changes of crop cultivation could alleviate the problem and lead to sustainable and ecological use of water resources in the study area.

  6. Crop-specific seasonal estimates of irrigation-water demand in South Asia

    NASA Astrophysics Data System (ADS)

    Biemans, Hester; Siderius, Christian; Mishra, Ashok; Ahmad, Bashir

    2016-05-01

    Especially in the Himalayan headwaters of the main rivers in South Asia, shifts in runoff are expected as a result of a rapidly changing climate. In recent years, our insight into these shifts and their impact on water availability has increased. However, a similar detailed understanding of the seasonal pattern in water demand is surprisingly absent. This hampers a proper assessment of water stress and ways to cope and adapt. In this study, the seasonal pattern of irrigation-water demand resulting from the typical practice of multiple cropping in South Asia was accounted for by introducing double cropping with monsoon-dependent planting dates in a hydrology and vegetation model. Crop yields were calibrated to the latest state-level statistics of India, Pakistan, Bangladesh and Nepal. The improvements in seasonal land use and cropping periods lead to lower estimates of irrigation-water demand compared to previous model-based studies, despite the net irrigated area being higher. Crop irrigation-water demand differs sharply between seasons and regions; in Pakistan, winter (rabi) and monsoon summer (kharif) irrigation demands are almost equal, whereas in Bangladesh the rabi demand is ~ 100 times higher. Moreover, the relative importance of irrigation supply versus rain decreases sharply from west to east. Given the size and importance of South Asia improved regional estimates of food production and its irrigation-water demand will also affect global estimates. In models used for global water resources and food-security assessments, processes like multiple cropping and monsoon-dependent planting dates should not be ignored.

  7. Crop-specific seasonal estimates of irrigation water demand in South Asia

    NASA Astrophysics Data System (ADS)

    Biemans, H.; Siderius, C.; Mishra, A.; Ahmad, B.

    2015-08-01

    Especially in the Himalayan headwaters of the main rivers in South Asia, shifts in runoff are expected as a result of a rapidly changing climate. In recent years, our insight in these shifts and their impact on water availability has increased. However, a similar detailed understanding of the seasonal pattern in water demand is surprisingly absent. This hampers a proper assessment of water stress and ways to cope and adapt. In this study, the seasonal pattern of irrigation water demand resulting from the typical practice of multiple-cropping in South Asia was accounted for by introducing double-cropping with monsoon-dependent planting dates in a hydrology and vegetation model. Crop yields were calibrated to the latest subnational statistics of India, Pakistan, Bangladesh and Nepal. The representation of seasonal land use and more accurate cropping periods lead to lower estimates of irrigation water demand compared to previous model-based studies, despite the net irrigated area being higher. Crop irrigation water demand differs sharply between seasons and regions; in Pakistan, winter (Rabi) and summer (Kharif) irrigation demands are almost equal, whereas in Bangladesh the Rabi demand is ~ 100 times higher. Moreover, the relative importance of irrigation supply vs. rain decreases sharply from west to east. Given the size and importance of South Asia, improved regional estimates of food production and its irrigation water demand will also affect global estimates. In models used for global water resources and food-security assessments, processes like multiple-cropping and monsoon-dependent planting dates should not be ignored.

  8. Crop biomass and evapotranspiration estimation using SPOT and Formosat-2 Data

    NASA Astrophysics Data System (ADS)

    Veloso, Amanda; Demarez, Valérie; Ceschia, Eric; Claverie, Martin

    2013-04-01

    The use of crop models allows simulating plant development, growth and yield under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. We propose here an approach to estimate time courses of dry aboveground biomass, yield and evapotranspiration (ETR) for summer (maize, sunflower) and winter crops (wheat) by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. Only high spatial resolution and gap-free satellite time series can provide enough information for efficient crop monitoring applications. The potential of remote sensing data is often limited by cloud cover and/or gaps in observation. Data from different sensor systems need then to be combined. For this work, we employed a unique set of Formosat-2 and SPOT images (164 images) and in-situ measurements, acquired from 2006 to 2010 in southwest France. Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, dry aboveground biomass, grain yield and ETR. Crop and soil model parameters were determined using both in-situ measurements and values found in the literature. Phenological parameters were calibrated by the

  9. Assimilation of remote sensing data into crop growth model to improve the estimation of regional winter wheat yield

    NASA Astrophysics Data System (ADS)

    Liu, Chaoshun; Gao, Wei; Liu, Pudong; Sun, Zhibin

    2014-10-01

    Accurate regional crop growth monitoring and yield prediction is very critical for the national food security assessment and sustainable development of agriculture, especially for China, which has the largest population in the world. Remote sensing data and crop growth model have been successfully used in the crop production prediction. However, both of them have inherent limitation and uncertainty. The data assimilation method which combines crop growth model and remotely sensed data has been proven to be the most effective method in regional yield estimation. The aim of this paper is to improve the estimation of regional winter wheat yield of crop growth model by using data assimilation schemes with Ensemble Kalman Filter (EnKF) algorithm. WOrld FOod STudies (WOFOST) crop growth model was chosen as the crop growth model which was calibrated and validated by the field measured data. MODIS Leaf Area Index (LAI) values were used as remote sensing observations to adjust the LAI simulated by the WOFOST model based on EnKF. The results illustrate that the EnKF algorithm has significantly improved the regional winter wheat yield estimates over the WOFOST simulation without assimilation in both potential and water-limited modes. Although this study clearly implies that the assimilation of the remotely sensed data into crop growth model with EnKF algorithm has the potential to improve the prediction of regional crop yield and has great potential in agricultural applications, high resolution meteorological data and detailed crop field management are necessary to reach a high accuracy of regional crop yield estimation.

  10. General multiyear aggregation technology: Methodology and software documentation. [estimating seasonal crop acreage proportions

    NASA Technical Reports Server (NTRS)

    Baker, T. C. (Principal Investigator)

    1982-01-01

    A general methodology is presented for estimating a stratum's at-harvest crop acreage proportion for a given crop year (target year) from the crop's estimated acreage proportion for sample segments from within the stratum. Sample segments from crop years other than the target year are (usually) required for use in conjunction with those from the target year. In addition, the stratum's (identifiable) crop acreage proportion may be estimated for times other than at-harvest in some situations. A by-product of the procedure is a methodology for estimating the change in the stratum's at-harvest crop acreage proportion from crop year to crop year. An implementation of the proposed procedure as a statistical analysis system routine using the system's matrix language module, PROC MATRIX, is described and documented. Three examples illustrating use of the methodology and algorithm are provided.

  11. Are satellite based rainfall estimates accurate enough for crop modelling under Sahelian climate?

    NASA Astrophysics Data System (ADS)

    Ramarohetra, J.; Sultan, B.

    2012-04-01

    Agriculture is considered as the most climate dependant human activity. In West Africa and especially in the sudano-sahelian zone, rain-fed agriculture - that represents 93% of cultivated areas and is the means of support of 70% of the active population - is highly vulnerable to precipitation variability. To better understand and anticipate climate impacts on agriculture, crop models - that estimate crop yield from climate information (e.g rainfall, temperature, insolation, humidity) - have been developed. These crop models are useful (i) in ex ante analysis to quantify the impact of different strategies implementation - crop management (e.g. choice of varieties, sowing date), crop insurance or medium-range weather forecast - on yields, (ii) for early warning systems and to (iii) assess future food security. Yet, the successful application of these models depends on the accuracy of their climatic drivers. In the sudano-sahelian zone , the quality of precipitation estimations is then a key factor to understand and anticipate climate impacts on agriculture via crop modelling and yield estimations. Different kinds of precipitation estimations can be used. Ground measurements have long-time series but an insufficient network density, a large proportion of missing values, delay in reporting time, and they have limited availability. An answer to these shortcomings may lie in the field of remote sensing that provides satellite-based precipitation estimations. However, satellite-based rainfall estimates (SRFE) are not a direct measurement but rather an estimation of precipitation. Used as an input for crop models, it determines the performance of the simulated yield, hence SRFE require validation. The SARRAH crop model is used to model three different varieties of pearl millet (HKP, MTDO, Souna3) in a square degree centred on 13.5°N and 2.5°E, in Niger. Eight satellite-based rainfall daily products (PERSIANN, CMORPH, TRMM 3b42-RT, GSMAP MKV+, GPCP, TRMM 3b42v6, RFEv2 and

  12. GPP estimates in a biodiesel crop using MERIS products

    NASA Astrophysics Data System (ADS)

    Sánchez, M. L.; Pardo, N.; Pérez, I.; García, M. A.; Paredes, V.

    2012-04-01

    Greenhouse gas emissions in Spain in 2008-2009 were 34.3 % higher than the base-year level, significantly above the burden-sharing target of 15 % for the period 2008-2012. Based on this result, our country will need to make a major effort to meet the committed target on time using domestic measures as well as others foreseen in the Kyoto Protocol, such as LULUFC activities. In this framework, agrofuels, in other words biofuels produced by crops that contain high amounts of vegetable oil such as sorghum, sunflower, rape seed and jatropha, appear to be an interesting mitigation alternative. Bearing in mind the meteorological conditions in Spain, sunflower and rape seed in particular are considered the most viable crops. Sunflower cultivated surface in Spain has remained fairly constant in recent years, in contrast to rapeseed crop surface which, although still scarce, has followed an increasing trend. In order to assess rape seed ability as a CO2 sink as well as to describe GPP dynamic evolution, we installed an eddy correlation station in an agricultural plot of the Spanish plateau. Measurements at the plot consisted of 30-min NEE flux measurements (using a LI-7500 and a METEK USA-1 sonic anemometer) as well as other common meteorological variables. Measurements were performed from March to October. This paper presents the results of the GPP 8-d estimated values using a Light Use Efficiency Model, LUE. Input data for the LUE model were the FPAR 8-d products supplied by MERIS, the PAR in situ measurements, and a scalar f varying, between 0 and 1, to take into account the reduction of the maximum PAR conversion efficiency, ɛ0, under limiting environmental conditions. The f values were assumed to be dependent on air temperature and the evaporative fraction, EF, which was considered as a proxy of soil moisture. ɛ0, a key parameter, which depends on biome types, was derived through the results of a linear regression fit between the GPP 8-d eddy covariance composites

  13. Spring Small Grains Area Estimation

    NASA Technical Reports Server (NTRS)

    Palmer, W. F.; Mohler, R. J.

    1986-01-01

    SSG3 automatically estimates acreage of spring small grains from Landsat data. Report describes development and testing of a computerized technique for using Landsat multispectral scanner (MSS) data to estimate acreage of spring small grains (wheat, barley, and oats). Application of technique to analysis of four years of data from United States and Canada yielded estimates of accuracy comparable to those obtained through procedures that rely on trained analysis.

  14. Estimation of corn and soybeans yield using remote sensing and crop yield data in the United States

    NASA Astrophysics Data System (ADS)

    Kim, Nari; Lee, Yang-Won

    2014-10-01

    The crop yield estimation is essential for the food security and the economic development of any nation. Particularly, the United States is the world largest grain exporter, and the total amount of corn exported from the U.S. accounted for 49.2% of the world corn trade in 2010 and 2011. Thus, accurate estimation of crop yield in U.S. is very significant for not only the U.S. crop producers but also decision makers of food importing countries. Estimating the crop yield using remote sensing data plays an important role in the Agricultural Sector, and it is actively discussed and studied in many countries. This is because remote sensing can observe the large areas repetitively. Consequently, the use of various techniques based on remote sensing data is steadily increasing to accurately estimate for crop yield. Therefore, the objective of this study is to estimate the accurate yield of corn and soybeans using climate dataset of PRISM climate group and Terra/MODIS products in the United States. We construct the crop yield estimation model for the decade (2001-2010) and perform predictions and validation for 2011 and 2012.

  15. Crop Acreage Estimation: Landsat TM and Resourcesat-1 AWiFS Sensor Assessment of the Mississippi River Delta, 2005

    NASA Technical Reports Server (NTRS)

    Boryan, Claire; Johnson, Dave; Craig, Mike; Seffrin, Bob; Mueller, RIck

    2007-01-01

    AWiFs data are appropriate for crop acreage estimation over large, spectrally homogenous, crop areas such as the Mid-West, the Delta and the Northern Great Plains. Regression and Kappa statistics for soybean, corn, cotton, rice and sorghum produced using both the Landsat TM and AWiFS data are very similar. AWiFS data appear to be a suitable alternative or supplement to Landsat TM data for production of NASS'Cropland Data Layer product.

  16. SACRA - a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI

    NASA Astrophysics Data System (ADS)

    Kotsuki, S.; Tanaka, K.

    2015-11-01

    To date, many studies have performed numerical estimations of biomass production and agricultural water demand to understand the present and future supply-demand relationship. A crop calendar (CC), which defines the date or month when farmers sow and harvest crops, is an essential input for the numerical estimations. This study aims to present a new global data set, the SAtellite-derived CRop calendar for Agricultural simulations (SACRA), and to discuss advantages and disadvantages compared to existing census-based and model-derived products. We estimate global CC at a spatial resolution of 5 arcmin using satellite-sensed normalized difference vegetation index (NDVI) data, which corresponds to vegetation vitality and senescence on the land surface. Using the time series of the NDVI averaged from three consecutive years (2004-2006), sowing/harvesting dates are estimated for six crops (temperate-wheat, snow-wheat, maize, rice, soybean and cotton). We assume time series of the NDVI represent the phenology of one dominant crop and estimate CCs of the dominant crop in each grid. The dominant crops are determined using harvested areas based on census-based data. The cultivation period of SACRA is identified from the time series of the NDVI; therefore, SACRA considers current effects of human decisions and natural disasters. The difference between the estimated sowing dates and other existing products is less than 2 months (< 62 days) in most of the areas. A major disadvantage of our method is that the mixture of several crops in a grid is not considered in SACRA. The assumption of one dominant crop in each grid is a major source of discrepancy in crop calendars between SACRA and other products. The disadvantages of our approach may be reduced with future improvements based on finer satellite sensors and crop-type classification studies to consider several dominant crops in each grid. The comparison of the CC also demonstrates that identification of wheat type (sowing in

  17. The estimation of soil parameters using observations on crop biophysical variables and the crop model STICS improve the predictions of agro environmental variables.

    NASA Astrophysics Data System (ADS)

    Varella, H.-V.

    2009-04-01

    Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters, especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless, observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In this work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters is made, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, a Bayesian data assimilation method is chosen (because of available prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prior information and the result with the posterior information provided by the Bayesian data assimilation method. The result of the

  18. Large Area Crop Inventory Experiment (LACIE). An overview of the Large Area Crop Inventory Experiment and the outlook for a satellite crop inventory. [Great Plains Corridor (North America), Canada, U.S.S.R., Brazil, China, India, and Australia

    NASA Technical Reports Server (NTRS)

    Erb, R. B. (Principal Investigator)

    1979-01-01

    The author has identified the following significant results. The most important LACIE finding was that the technology worked very well in estimating wheat production in important geographic locations. Based on working through the many successes and shortcomings of LACIE, it can be stated with confidence that: (1) the current technology can successfully monitor what production in regions having similar characteristics to those of the U.S.S.R. wheat areas and the U.S. hard red winter wheat areas; (2) with additional applied research, significant improvements in capabilities to monitor wheat in these and other important production regions can be expected in the near future; (3) the remote sensing and weather effects modeling technology approached used by LACIE is generally applicable to other major crops and crop-producing regions of the world; and (4) with suitable effort, this technology can now advance rapidly and could be widespread use in the late 1980's.

  19. Regression model estimation of early season crop proportions: North Dakota, some preliminary results

    NASA Technical Reports Server (NTRS)

    Lin, K. K. (Principal Investigator)

    1982-01-01

    To estimate crop proportions early in the season, an approach is proposed based on: use of a regression-based prediction equation to obtain an a priori estimate for specific major crop groups; modification of this estimate using current-year LANDSAT and weather data; and a breakdown of the major crop groups into specific crops by regression models. Results from the development and evaluation of appropriate regression models for the first portion of the proposed approach are presented. The results show that the model predicts 1980 crop proportions very well at both county and crop reporting district levels. In terms of planted acreage, the model underpredicted 9.1 percent of the 1980 published data on planted acreage at the county level. It predicted almost exactly the 1980 published data on planted acreage at the crop reporting district level and overpredicted the planted acreage by just 0.92 percent.

  20. Estimation rice yield based on integration remote sensing information and crop model

    NASA Astrophysics Data System (ADS)

    Guo, Jianmao; Wang, Qi; Zheng, Tengfei; Li, Xujie; Shi, Junyi; Zhu, Jinhui

    2012-10-01

    Crop model is a powerful tool in crop growth monitoring and yield forecasting, however crop model is developed based on single point scale, due to regional differentiation、field variation and other reasons lead to input parameters and initial conditions which required by crop model simulation are hard to obtain, the application of crop model has been greatly limited in the regional scale, the introduction of remote sensing will solve this problem, remote sensing is combined with the crop model WOFOST, using the state variable retrieved by remote sensing to optimize crop model simulation, revaluing the sensitive parameters and initial conditions which needed in crop model on the region scale, in order to take the advantage of crop model in the area.This study is on the basis of adaptive adjustment and amendment of crop model WOFOST, build a winter wheat growth simulation model which is suitable for Yucheng, Shandong; Using the field experiment data calibration and validation the WOFOST model, discussed the method which combined crop simulation model and remote sensing under water stress level, using remote sensing calibrated some key processes of crop simulation or reinitialize、parameterize the crop simulation model in order to achieve the optimization model; Explored some reasonable and practical method of remote sensing information application in crop simulation at regional scale, with more research, make it possible to monitor regional crop growth and forecast the output.

  1. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine.

    PubMed

    Möller, M; Alchanatis, V; Cohen, Y; Meron, M; Tsipris, J; Naor, A; Ostrovsky, V; Sprintsin, M; Cohen, S

    2007-01-01

    Achieving high quality wine grapes depends on the ability to maintain mild to moderate levels of water stress in the crop during the growing season. This study investigates the use of thermal imaging for monitoring water stress. Experiments were conducted on a wine-grape (Vitis vinifera cv. Merlot) vineyard in northern Israel. Irrigation treatments included mild, moderate, and severe stress. Thermal and visible (RGB) images of the crop were taken on four days at midday with a FLIR thermal imaging system and a digital camera, respectively, both mounted on a truck-crane 15 m above the canopy. Aluminium crosses were used to match visible and thermal images in post-processing and an artificial wet surface was used to estimate the reference wet temperature (T(wet)). Monitored crop parameters included stem water potential (Psi(stem)), leaf conductance (g(L)), and leaf area index (LAI). Meteorological parameters were measured at 2 m height. CWSI was highly correlated with g(L) and moderately correlated with Psi(stem). The CWSI-g(L) relationship was very stable throughout the season, but for that of CWSI-Psi(stem) both intercept and slope varied considerably. The latter presumably reflects the non-direct nature of the physiological relationship between CWSI and Psi(stem). The highest R(2) for the CWSI to g(L) relationship, 0.91 (n=12), was obtained when CWSI was computed using temperatures from the centre of the canopy, T(wet) from the artificial wet surface, and reference dry temperature from air temperature plus 5 degrees C. Using T(wet) calculated from the inverted Penman-Monteith equation and estimated from an artificially wetted part of the canopy also yielded crop water-stress estimates highly correlated with g(L) (R(2)=0.89 and 0.82, respectively), while a crop water-stress index using 'theoretical' reference temperatures computed from climate data showed significant deviations in the late season. Parameter variability and robustness of the different CWSI estimates

  2. Could an abandoned mercury mine area be cropped?

    PubMed

    Rocio, Millán; Elvira, Esteban; Pilar, Zornoza; María-José, Sierra

    2013-08-01

    The Almadén area (Spain) is known for its high natural mercury background as well as for the anthropogenic impact due to mining activities. After the end of these activities, appropriate alternative use of the soil has to be found, and agricultural activities stand out as an environmentally-friendly and potentially profitable alternative, giving to the soil a sustainable use without risks for human or animal health according to current legislation. Experiments performed at different scales (involving hydroponics, growth in pots and lysimeters) allow recommendations to be made regarding the adequacy of cultivation of different crops for animal or human consumption before they are sown in the field. Regarding crops for animal feeding, mercury accumulation in vegetative organs represents a higher potential risk for animals. Nevertheless, seeds and fruits can be used, both for human and animal consumption. Finally, this work will lead the way to obtain a scientific basis for elaborating a list of recommendations on sustainable and safe alternative land use, according to current international legislation. PMID:23489985

  3. Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis

    NASA Astrophysics Data System (ADS)

    McMahon, T. A.; Peel, M. C.; Lowe, L.; Srikanthan, R.; McVicar, T. R.

    2013-04-01

    This guide to estimating daily and monthly actual, potential, reference crop and pan evaporation covers topics that are of interest to researchers, consulting hydrologists and practicing engineers. Topics include estimating actual evaporation from deep lakes and from farm dams and for catchment water balance studies, estimating potential evaporation as input to rainfall-runoff models, and reference crop evapotranspiration for small irrigation areas, and for irrigation within large irrigation districts. Inspiration for this guide arose in response to the authors' experiences in reviewing research papers and consulting reports where estimation of the actual evaporation component in catchment and water balance studies was often inadequately handled. Practical guides using consistent terminology that cover both theory and practice are not readily available. Here we provide such a guide, which is divided into three parts. The first part provides background theory and an outline of the conceptual models of potential evaporation of Penman, Penman-Monteith and Priestley-Taylor, as well as discussions of reference crop evapotranspiration and Class-A pan evaporation. The last two sub-sections in this first part include techniques to estimate actual evaporation from (i) open-surface water and (ii) landscapes and catchments (Morton and the advection-aridity models). The second part addresses topics confronting a practicing hydrologist, e.g. estimating actual evaporation for deep lakes, shallow lakes and farm dams, lakes covered with vegetation, catchments, irrigation areas and bare soil. The third part addresses six related issues: (i) automatic (hard wired) calculation of evaporation estimates in commercial weather stations, (ii) evaporation estimates without wind data, (iii) at-site meteorological data, (iv) dealing with evaporation in a climate change environment, (v) 24 h versus day-light hour estimation of meteorological variables, and (vi) uncertainty in evaporation

  4. Utility of multi temporal satellite images for crop water requirements estimation and irrigation management in the Jordan Valley

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Identifying the spatial and temporal distribution of crop water requirements is a key for successful management of water resources in the dry areas. Climatic data were obtained from three automated weather stations to estimate reference evapotranspiration (ETO) in the Jordan Valley according to the...

  5. Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models

    PubMed Central

    Trnka, Miroslav; Eitzinger, Josef; Kapler, Pavel; Dubrovský, Martin; Semerádová, Daniela; Žalud, Zden ěk; Formayer, Herbert

    2007-01-01

    The results of previous studies have suggested that estimated daily global radiation (RG) values contain an error that could compromise the precision of subsequent crop model applications. The following study presents a detailed site and spatial analysis of the RG error propagation in CERES and WOFOST crop growth models in Central European climate conditions. The research was conducted i) at the eight individual sites in Austria and the Czech Republic where measured daily RG values were available as a reference, with seven methods for RG estimation being tested, and ii) for the agricultural areas of the Czech Republic using daily data from 52 weather stations, with five RG estimation methods. In the latter case the RG values estimated from the hours of sunshine using the Ångström-Prescott formula were used as the standard method because of the lack of measured RG data. At the site level we found that even the use of methods based on hours of sunshine, which showed the lowest bias in RG estimates, led to a significant distortion of the key crop model outputs. When the Ångström-Prescott method was used to estimate RG, for example, deviations greater than ±10 per cent in winter wheat and spring barley yields were noted in 5 to 6 per cent of cases. The precision of the yield estimates and other crop model outputs was lower when RG estimates based on the diurnal temperature range and cloud cover were used (mean bias error 2.0 to 4.1 per cent). The methods for estimating RG from the diurnal temperature range produced a wheat yield bias of more than 25 per cent in 12 to 16 per cent of the seasons. Such uncertainty in the crop model outputs makes the reliability of any seasonal yield forecasts or climate change impact assessments questionable if they are based on this type of data. The spatial assessment of the RG data uncertainty propagation over the winter wheat yields also revealed significant differences within the study area. We found that RG estimates based on

  6. Satellite-based crop coefficient and regional water use estimates for Hawaiian sugarcane

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Water availability is a major limiting factor for sustainable production of potential biofuel crops in Maui, Hawaii. It is essential to improve regional, near-real time estimates of crop water use to facilitate optimal water management. Satellite remote-sensing offers multiple methods to estimate w...

  7. Estimated winter wheat yield from crop growth predicted by LANDSAT

    NASA Technical Reports Server (NTRS)

    Kanemasu, E. T.

    1977-01-01

    An evapotranspiration and growth model for winter wheat is reported. The inputs are daily solar radiation, maximum temperature, minimum temperature, precipitation/irrigation and leaf area index. The meteorological data were obtained from National Weather Service while LAI was obtained from LANDSAT multispectral scanner. The output provides daily estimates of potential evapotranspiration, transpiration, evaporation, soil moisture (50 cm depth), percentage depletion, net photosynthesis and dry matter production. Winter wheat yields are correlated with transpiration and dry matter accumulation.

  8. Crop residue inventory estimates for Texas High Plains cotton

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Interest in the use of cotton crop by-products for the production of bio-fuels and value-added products is increasing. Research documenting the availability of cotton crop by-products after machine harvest is needed. The objectives of this work were to document the total biomass production for moder...

  9. Remote estimation of crop gross primary production with Landsat data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An accurate and synoptic quantification of gross primary productivity (GPP) in crops is essential for studies of carbon budgets at regional and global scales. In this study, we developed a model relating crop GPP to a product of total canopy chlorophyll (Chl) content and potential incident photosynt...

  10. Combining Remote Sensing imagery of both fine and coarse spatial resolution to Estimate Crop Evapotranspiration and quantifying its Influence on Crop Growth Monitoring.

    NASA Astrophysics Data System (ADS)

    Sepulcre-Cantó, Guadalupe; Gellens-Meulenberghs, Françoise; Arboleda, Alirio; Duveiller, Gregory; Piccard, Isabelle; de Wit, Allard; Tychon, Bernard; Bakary, Djaby; Defourny, Pierre

    2010-05-01

    This study has been carried out in the framework of the GLOBAM -Global Agricultural Monitoring system by integration of earth observation and modeling techniques- project whose objective is to fill the methodological gap between the state of the art of local crop monitoring and the operational requirements of the global monitoring system programs. To achieve this goal, the research aims to develop an integrated approach using remote sensing and crop growth modeling. Evapotranspiration (ET) is a valuable parameter in the crop monitoring context since it provides information on the plant water stress status, which strongly influences crop development and, by extension, crop yield. To assess crop evapotranspiration over the GLOBAM study areas (300x300 km sites in Northern Europe and Central Ethiopia), a Soil-Vegetation-Atmosphere Transfer (SVAT) model forced with remote sensing and numerical weather prediction data has been used. This model runs at pre-operational level in the framework of the EUMETSAT LSA-SAF (Land Surface Analysis Satellite Application Facility) using SEVIRI and ECMWF data, as well as the ECOCLIMAP database to characterize the vegetation. The model generates ET images at the Meteosat Second Generation (MSG) spatial resolution (3 km at subsatellite point),with a temporal resolution of 30 min and monitors the entire MSG disk which covers Europe, Africa and part of Sud America . The SVAT model was run for 2007 using two approaches. The first approach is at the standard pre-operational mode. The second incorporates remote sensing information at various spatial resolutions going from LANDSAT (30m) to SEVIRI (3-5 km) passing by AWIFS (56m) and MODIS (250m). Fine spatial resolution data consists of crop type classification which enable to identify areas where pure crop specific MODIS time series can be compiled and used to derive Leaf Area Index estimations for the most important crops (wheat and maize). The use of this information allowed to characterize

  11. SEBAL Model Using to Estimate Irrigation Water Efficiency & Water Requirement of Alfalfa Crop

    NASA Astrophysics Data System (ADS)

    Zeyliger, Anatoly; Ermolaeva, Olga

    2013-04-01

    The sustainability of irrigation is a complex and comprehensive undertaking, requiring an attention to much more than hydraulics, chemistry, and agronomy. A special combination of human, environmental, and economic factors exists in each irrigated region and must be recognized and evaluated. A way to evaluate the efficiency of irrigation water use for crop production is to consider the so-called crop-water production functions, which express the relation between the yield of a crop and the quantity of water applied to it or consumed by it. The term has been used in a somewhat ambiguous way. Some authors have defined the Crop-Water Production Functions between yield and the total amount of water applied, whereas others have defined it as a relation between yield and seasonal evapotranspiration (ET). In case of high efficiency of irrigation water use the volume of water applied is less than the potential evapotranspiration (PET), then - assuming no significant change of soil moisture storage from beginning of the growing season to its end-the volume of water may be roughly equal to ET. In other case of low efficiency of irrigation water use the volume of water applied exceeds PET, then the excess of volume of water applied over PET must go to either augmenting soil moisture storage (end-of-season moisture being greater than start-of-season soil moisture) or to runoff or/and deep percolation beyond the root zone. In presented contribution some results of a case study of estimation of biomass and leaf area index (LAI) for irrigated alfalfa by SEBAL algorithm will be discussed. The field study was conducted with aim to compare ground biomass of alfalfa at some irrigated fields (provided by agricultural farm) at Saratov and Volgograd Regions of Russia. The study was conducted during vegetation period of 2012 from April till September. All the operations from importing the data to calculation of the output data were carried by eLEAF company and uploaded in Fieldlook web

  12. Estimating riparian and agricultural evapotranspiration by reference crop evapotranspiration and MODIS Enhanced Vegetation Index

    USGS Publications Warehouse

    Nagler, Pamela L.; Glenn, Edward P.; Nguyen, Uyen; Scott, Russell; Doody, Tania

    2013-01-01

    Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ETa) based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ETo). The algorithm was calibrated with five years of ETa data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ETa data for alfalfa and cotton from the literature. The algorithm was based on an equation of the form ETa = ETo [a(1 − e−bEVI) − c], where the term (1 − e−bEVI) is derived from the Beer-Lambert Law to express light absorption by a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ETa across riparian plants and crops (r2 = 0.73). It was then tested against water balance data for five irrigation districts and flux tower data for two riparian zones for which season-long or multi-year ETa data were available. Predictions were within 10% of measured results in each case, with a non-significant (P = 0.89) difference between mean measured and modeled ETa of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes.

  13. Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products.

    PubMed

    Li, Jing; Bo, Yu; Xie, Shaodong

    2016-06-01

    With the objective of reducing the large uncertainties in the estimations of emissions from crop residue open burning, an improved method for establishing emission inventories of crop residue open burning at a high spatial resolution of 0.25°×0.25° and a temporal resolution of 1month was established based on the moderate resolution imaging spectroradiometer (MODIS) Thermal Anomalies/Fire Daily Level3 Global Product (MOD/MYD14A1). Agriculture mechanization ratios and regional crop-specific grain-to-straw ratios were introduced to improve the accuracy of related activity data. Locally observed emission factors were used to calculate the primary pollutant emissions. MODIS satellite data were modified by combining them with county-level agricultural statistical data, which reduced the influence of missing fire counts caused by their small size and cloud cover. The annual emissions of CO2, CO, CH4, nonmethane volatile organic compounds (NMVOCs), N2O, NOx, NH3, SO2, fine particles (PM2.5), organic carbon (OC), and black carbon (BC) were 150.40, 6.70, 0.51, 0.88, 0.01, 0.13, 0.07, 0.43, 1.09, 0.34, and 0.06Tg, respectively, in 2012. Crop residue open burning emissions displayed typical seasonal and spatial variation. The highest emission regions were the Yellow-Huai River and Yangtse-Huai River areas, and the monthly emissions were highest in June (37%). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of within ±126% for N2O to a high of within ±169% for NH3. PMID:27266312

  14. Estimation of crops biomass and evapotranspiration from high spatial and temporal resolutions remote sensing data

    NASA Astrophysics Data System (ADS)

    Claverie, Martin; Demarez, Valérie; Duchemin, Benoît.; Ceschia, Eric; Hagolle, Olivier; Ducrot, Danielle; Keravec, Pascal; Beziat, Pierre; Dedieu, Pierre

    2010-05-01

    Carbon and water cycles are closely related to agricultural activities. Agriculture has been indeed identified by IPCC 2007 report as one of the options to sequester carbon in soil. Concerning the water resources, their consumptions by irrigated crops are called into question in view of demographic pressure. In the prospect of an assessment of carbon production and water consumption, the use of crop models at a regional scale is a challenging issue. The recent availability of high spatial resolution (10 m) optical sensors associated to high temporal resolution (1 day) such as FORMOSAT-2 and, in the future, Venµs and SENTINEL-2 will offer new perspectives for agricultural monitoring. In this context, the objective of this work is to show how multi-temporal satellite observations acquired at high spatial resolution are useful for a regional monitoring of following crops biophysical variables: leaf area index (LAI), aboveground biomass (AGB) and evapotranspiration (ET). This study focuses on three summer crops dominant in South-West of France: maize, sunflower and soybean. A unique images data set (82 FORMOSAT-2 images over four consecutive years, 2006-2009) was acquired for this project. The experimental data set includes LAI and AGB measurements over eight agricultural fields. Two fields were intensively monitored where ET flux were measured with a 30 minutes time step using eddy correlation methods. The modelisation approach is based on FAO-56 method coupled with a vegetation functioning model based on Monteith theory: the SAFY model [5]. The model operates at a daily time step model to provide estimates of plant characteristics (LAI, AGB), soil conditions (soil water content) and water use (ET). As a key linking variable, LAI is deduced from FORMOSAT-2 reflectances images, and then introduced into the SAFY model to provide spatial and temporal estimates of these biophysical variables. Most of the SAFY parameters are crop related and have been fixed according to

  15. Assessment of Spectral Indices for Crop Residue Cover Estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The quantification of surficial crop residue (non-photosynthetic vegetation) cover is important for assessing agricultural tillage practices, rangeland health, and brush fire hazards. The Cellulose Absorption Index (CAI) and the Shortwave Infrared Normalized Difference Residue Index (SINDRI) are two...

  16. Estimation Of Cultivated Area In Small Plot Agriculture In Africa For Food Security Purposes

    NASA Astrophysics Data System (ADS)

    Holecz, Francesco; Collivignarelli, Francesco; Barbieri, Massimo

    2013-12-01

    Cultivated area in small plot agriculture in Africa is estimated using a synergetic approach based on multi-sensor, multi-temporal Synthetic Aperture Radar (SAR) data. The method - which is based on the utilization of ALOS PALSAR-1, Cosmo-SkyMed, ENVISAT ASAR data involving different processing techniques - consists in the generation of three independent and complementary products, which in turn they are fused, enabling the generation of the cultivated area. Each intermediate product has a clear meaning within agriculture and food security: i) the potential crop extent prior to the crop season; ii) the potential area at start of the crop season; iii) the crop growth extent during the rainfed crop season. The proposed methodology has been implemented and demonstrated in Malawi. The obtained results show an overall accuracy exceeding 90%.

  17. Estimated flows of gases and carbon within CEEF ecosystem composed of human, crops and goats

    NASA Astrophysics Data System (ADS)

    Tako, Y.; Komatsubara, O.; Honda, G.; Arai, R.; Nitta, K.

    The Closed Ecology Experiment Facilities (CEEF) can be used as a test bed for Controlled Ecological Life Support Systems (CELSS), because technologies developed for the CEEF system facilitate self-sufficient material circulation necessary for long term missions such as Lunar and Mars exploration. In the experiment conducted under closed condition in FY2003, rice and soybeans were cultivated sequentially in two chambers and a chamber, each having a cultivation area of 30 m2 and floor area of 43 m2, inside the Plantation Module with artificial lighting of the CEEF. In the chamber having a cultivation area of 60 m2 and floor area of 65 m2, inside the Plantation Module with natural and artificial lighting, peanuts and safflowers were also cultivated. Stable transplant (or seeding) and harvest of each crop were maintained during a month. Flows of CO2, O2 and carbon to and from the crops were analyzed during the stable cultivation period. Simulated works and stay in the CEEF lasting five days were conducted two times under ventilating condition in FY2003. Gas exchange of human was estimated using heart rate data collected during the experiments and correlation between gas exchange rate and heart rate. Gas exchange rate and carbon balance of female goats were determined using an open-flow measurement system with a gastight chamber. From these results, flows of gases and carbon in the CEEF were discussed.

  18. Evaluation of the kriging method to predict 7-h seasonal mean ozone concentrations for estimating crop losses

    SciTech Connect

    Lefohn, A.S.; Knudsen, H.P.; Logan, J.A.; Simpson, J.; Bhumralkar, C.

    1987-05-01

    Using kriging, a statistical technique, the National Crop Loss Assessment Network (NCLAN) program estimated growing season 5-month (May-September) ambient 7-h mean O3 concentrations for each of the major crop growing areas of the US for 1978-1982. The O3 estimates were used to predict economic benefits anticipated by lowering O3 levels in the US. This paper reviews NCLAN's use of kriging to estimate 7-h seasonal mean O3 concentrations for crop growing regions. Although the original kriging program used by NCLAN incorrectly calculated the diagonal elements of the kriging equations, this omission did not result in significant errors in the predicted estimates. Most of the data used in estimating the 7-h seasonal values were obtained from urban areas; the use of these data tended to underestimate the 7-h seasonal O3 concentrations in rural areas. It is recommended that only O3 data that are representative of agricultural areas and have been collected under accepted quality assurance programs be used in future kriging efforts.

  19. Evaluation of the kriging method to predict 7-h seasonal mean ozone concentrations for estimating crop losses (journal version)

    SciTech Connect

    Lefohn, A.S.; Knudsen, H.P.; Logan, J.A.; Simpson, J.; Bhumralkar, C.

    1987-01-01

    Using kriging, a statistical technique, the National Crop Loss Assessment Network (NCLAN) program estimated growing season 5-month (May-September) ambient 7-h mean O/sub 3/ concentrations for each of the major crop growing areas of the United States for 1978-1982. The O/sub 3/ estimates were used to predict economic benefits anticipated by lowering O/sub 3/ levels in the United States. This paper reviews NCLAN's use of kriging to estimate 7-h seasonal mean O/sub 3/ concentrations for crop growing regions. Although the original kriging program used by NCLAN incorrectly calculated the diagonal elements of the kriging equations, this omission did not result in significant errors in the predicted estimates. Most of the data used in estimating the 7-h seasonal values were obtained from urban areas; the use of these data tended to underestimate the 7-h seasonal O/sub 3/ concentrations in rural areas. It is recommended that only O/sub 3/ data that are representative of agricultural areas and have been collected under accepted quality-assurance programs be used in future kriging efforts.

  20. Using Biome-BGC to estimate production in annual crops - A study in Nebraska

    NASA Astrophysics Data System (ADS)

    Heinsch, F. A.; Jolly, W. M.; Kimball, J. S.; Oechel, W. C.; Verma, S. B.

    2004-12-01

    The Biome-BGC ecosystem process model (Version 4.1.2) has been used successfully in many ecosystems, but was not developed for use with agricultural crops. Therefore, program modifications are needed for use with crops, including the addition of carbon allocation to fruiting and the inclusion of springtime planting. The program has been modified and tested using both C3 (soybean) and C4 (maize) vegetation. Results from the Biome-BGC model runs were validated using AmeriFlux tower eddy CO2 flux-based estimates as well as two years of biomass and yield estimates at the University of Nebraska Agricultural Research and Development Center (ARDL) near Mead, NE. The model was also used to obtain tower site and regional estimates of NEE, GPP and NPP. Preliminary results indicate that the model works well in estimating both productivity and yield of both maize and soybean. These results are combined and scaled to a 7 x 7-km area equivalent to that of the MODIS subset (resolution = 1 km2) centered on the research farm and available from Fluxnet and the Oak Ridge National Laboratory (http://www.fluxnet.ornl.gov/fluxnet/modis.cfm). The comparisons provide a means to test the ability of the MODIS algorithms to capture seasonal variations and agricultural carbon dynamics. The results of this study will be used in the future for spatial extrapolation to scales from 1 - 20,000 km2 to evaluate relative accuracies of MODIS GPP/NPP regional data and provide estimates of the regional carbon balance for the larger 20,000 km2 area within the National Institute for Global Environmental Change (NIGEC) Great Plains and Midwestern study regions.

  1. Airborne and ground-based remote sensing for the estimation of evapotranspiration and yield of bean, potato, and sugar beet crops

    NASA Astrophysics Data System (ADS)

    Jayanthi, Harikishan

    The focus of this research was two-fold: (1) extend the reflectance-based crop coefficient approach to non-grain (potato and sugar beet), and vegetable crops (bean), and (2) develop vegetation index (VI)-yield statistical models for potato and sugar beet crops using high-resolution aerial multispectral imagery. Extensive crop biophysical sampling (leaf area index and aboveground dry biomass sampling) and canopy reflectance measurements formed the backbone of developing of canopy reflectance-based crop coefficients for bean, potato, and sugar beet crops in this study. Reflectance-based crop coefficient equations were developed for the study crops cultivated in Kimberly, Idaho, and subsequently used in water availability simulations in the plant root zone during 1998 and 1999 seasons. The simulated soil water profiles were compared with independent measurements of actual soil water profiles in the crop root zone in selected fields. It is concluded that the canopy reflectance-based crop coefficient technique can be successfully extended to non-grain crops as well. While the traditional basal crop coefficients generally expect uniform growth in a region the reflectance-based crop coefficients represent the actual crop growth pattern (in less than ideal water availability conditions) in individual fields. Literature on crop canopy interactions with sunlight states that there is a definite correspondence between leaf area index progression in the season and the final yield. In case of crops like potato and sugar beet, the yield is influenced not only on how early and how quickly the crop establishes its canopy but also on how long the plant stands on the ground in a healthy state. The integrated area under the crop growth curve has shown excellent correlations with hand-dug samples of potato and sugar beet crops in this research. Soil adjusted vegetation index-yield models were developed, and validated using multispectral aerial imagery. Estimated yield images were

  2. Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing technology can rapidly provide spatial information on crop growth status, which ideally could be used to invert radiative transfer models or ecophysiological models for estimating a variety of crop biophysical properties. However, the outcome of the model inversion procedure will be ...

  3. Automatic corn-soybean classification using Landsat MSS data. I - Near-harvest crop proportion estimation. II - Early season crop proportion estimation

    NASA Technical Reports Server (NTRS)

    Badhwar, G. D.

    1984-01-01

    The techniques used initially for the identification of cultivated crops from Landsat imagery depended greatly on the iterpretation of film products by a human analyst. This approach was not very effective and objective. Since 1978, new methods for crop identification are being developed. Badhwar et al. (1982) showed that multitemporal-multispectral data could be reduced to a simple feature space of alpha and beta and that these features would separate corn and soybean very well. However, there are disadvantages related to the use of alpha and beta parameters. The present investigation is concerned with a suitable method for extracting the required features. Attention is given to a profile model for crop discrimination, corn-soybean separation using profile parameters, and an automatic labeling (target recognition) method. The developed technique is extended to obtain a procedure which makes it possible to estimate the crop proportion of corn and soybean from Landsat data early in the growing season.

  4. SPAD-based leaf nitrogen estimation is impacted by environmental factors and crop leaf characteristics

    PubMed Central

    Xiong, Dongliang; Chen, Jia; Yu, Tingting; Gao, Wanlin; Ling, Xiaoxia; Li, Yong; Peng, Shaobing; Huang, Jianliang

    2015-01-01

    Chlorophyll meters are widely used to guide nitrogen (N) management by monitoring leaf N status in agricultural systems, but the effects of environmental factors and leaf characteristics on leaf N estimations are still unclear. In the present study, we estimated the relationships among SPAD readings, chlorophyll content and leaf N content per leaf area for seven species grown in multiple environments. There were similar relationships between SPAD readings and chlorophyll content per leaf area for the species groups, but the relationship between chlorophyll content and leaf N content per leaf area, and the relationship between SPAD readings and leaf N content per leaf area varied widely among the species groups. A significant impact of light-dependent chloroplast movement on SPAD readings was observed under low leaf N supplementation in both rice and soybean but not under high N supplementation. Furthermore, the allocation of leaf N to chlorophyll was strongly influenced by short-term changes in growth light. We demonstrate that the relationship between SPAD readings and leaf N content per leaf area is profoundly affected by environmental factors and leaf features of crop species, which should be accounted for when using a chlorophyll meter to guide N management in agricultural systems. PMID:26303807

  5. Crop Evapotranspiration Estimates using Canopy Reflectance and Canopy Temperature

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Sustainability of irrigated agriculture with declining water supplies is a critical agricultural issue in the US Great Plains. Imposing water deficits on crops during non-critical growth periods must be implemented to maximize net economic output per unit of water consumed by the plant. An irrigat...

  6. ESTIMATING CROP WATER USE FOR CAMELINA WITH REMOTE SENSING

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Camelina (Camelina sativa [L.] Crtz.) is an oilseed crop with apparently low water requirements and therefore could be very attractive for growers in arid lands. Verifying this potential for environments such as the U.S. Southwest, however, requires field experiments that test yield response to diff...

  7. Assessment of spectral indicies for crop residue cover estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The quantification of surficial crop residue cover is important for assessing agricultural tillage practices, rangeland health, and brush fire hazards. The Cellulose Absorption Index (CAI) and the Shortwave Infrared Normalized Difference Residue Index (SINDRI) are two spectral indices that have show...

  8. Estimating Crop Residue Distribution Using Airborne and Satellite Remote Sensing

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Crop residue management and reduced tillage are commonly accepted best management practices that improve soil quality through the sequestration of soil organic carbon. A major goal of this study was to evaluate remote sensing data for rapid quantification of conservation tillage at the field and wa...

  9. [Estimation of risk areas for hepatitis A].

    PubMed

    Braga, Ricardo Cerqueira Campos; Valencia, Luís Iván Ortiz; Medronho, Roberto de Andrade; Escosteguy, Claudia Caminha

    2008-08-01

    This study estimated hepatitis A risk areas in a region of Duque de Caxias, Rio de Janeiro State, Brazil. A cross-sectional study consisting of a hepatitis A serological survey and a household survey were conducted in 19 census tracts. Of these, 11 tracts were selected and 1,298 children from one to ten years of age were included in the study. Geostatistical techniques allowed modeling the spatial continuity of hepatitis A, non-use of filtered drinking water, time since installation of running water, and number of water taps per household and their spatial estimation through ordinary and indicator kriging. Adjusted models for the outcome and socioeconomic variables were isotropic; risk maps were constructed; cross-validation of the four models was satisfactory. Spatial estimation using the kriging method detected areas with increased risk of hepatitis A, independently of the urban administrative area in which the census tracts were located. PMID:18709215

  10. Paddy crop yield estimation in Kashmir Himalayan rice bowl using remote sensing and simulation model.

    PubMed

    Muslim, Mohammad; Romshoo, Shakil Ahmad; Rather, A Q

    2015-06-01

    The Kashmir Himalayan region of India is expected to be highly prone to the change in agricultural land use because of its geo-ecological fragility, strategic location vis-à-vis the Himalayan landscape, its trans-boundary river basins, and inherent socio-economic instabilities. Food security and sustainability of the region are thus greatly challenged by these impacts. The effect of future climate change, increased competition for land and water, labor from non-agricultural sectors, and increasing population adds to this complex problem. In current study, paddy rice yield at regional level was estimated using GIS-based environment policy integrated climate (GEPIC) model. The general approach of current study involved combining regional level crop database, regional soil data base, farm management data, and climatic data outputs with GEPIC model. The simulated yield showed that estimated production to be 4305.55 kg/ha (43.05 q h(-1)). The crop varieties like Jhelum, K-39, Chenab, China 1039, China-1007, and Shalimar rice-1 grown in plains recorded average yield of 4783.3 kg/ha (47.83 q ha(-1)). Meanwhile, high altitude areas with varieties like Kohsaar, K-78 (Barkat), and K-332 recorded yield of 4102.2 kg/ha (41.02 q ha(-1)). The observed and simulated yield showed a good match with R (2) = 0.95, RMSE = 132.24 kg/ha, respectively. PMID:25937498

  11. Use of landsat thematic mapper data to identify crop types and estimate irrigated acreage, Uvalde and Medina counties, Texas, 1991

    USGS Publications Warehouse

    Raymond, L.H.; McFarlane, S.I.

    1994-01-01

    Landsat Thematic Mapper (TM) data were used to estimate that about 51,000 acres of crops were irrigated with water pumped from the Edwards aquifer in Uvalde and Medina Counties, Texas in 1991. Areas calculated for irrigated crops were 31,000 acres for Uvalde County and 20,000 acres for Medina County. Quantities of water pumped from the Edwards aquifer to irrigate crops in 1991 were estimated as 65,000 acre-feet for Uvalde County and 18,000 acre-feet for Medina County. Differences in estimates of ground water used for irrigation in the two counties were attributed primarily to greater pre- cipitation in Medina County than in Uvalde County. The total number of acres of irrigated crops estimated using Landsat TM data was about 9 percent lower in Uvalde County and about 13 percent lower in Medina County than the number of acres calculated from data reported by the U.S. Department of Agriculture, Agricultural Stabilization and Conservation Service (ASCS). The total quantity of water pumped from the Edwards aquifer in 1991 for irrigation in the two counties, about 83,000 acre- feet, was about 5 percent greater developed than the quantity calculated from data reported by the ASCS.

  12. Small area estimation for semicontinuous data.

    PubMed

    Chandra, Hukum; Chambers, Ray

    2016-03-01

    Survey data often contain measurements for variables that are semicontinuous in nature, i.e. they either take a single fixed value (we assume this is zero) or they have a continuous, often skewed, distribution on the positive real line. Standard methods for small area estimation (SAE) based on the use of linear mixed models can be inefficient for such variables. We discuss SAE techniques for semicontinuous variables under a two part random effects model that allows for the presence of excess zeros as well as the skewed nature of the nonzero values of the response variable. In particular, we first model the excess zeros via a generalized linear mixed model fitted to the probability of a nonzero, i.e. strictly positive, value being observed, and then model the response, given that it is strictly positive, using a linear mixed model fitted on the logarithmic scale. Empirical results suggest that the proposed method leads to efficient small area estimates for semicontinuous data of this type. We also propose a parametric bootstrap method to estimate the MSE of the proposed small area estimator. These bootstrap estimates of the MSE are compared to the true MSE in a simulation study. PMID:24962713

  13. Crop proportion estimation problems in AgRISTARS

    NASA Technical Reports Server (NTRS)

    Heydorn, R. P.

    1982-01-01

    Some mathematical/statistical problems within the AgRISTARS program amendable to investigations involving the use of surface fitting techniques are overviewed. The Bayes and maximum likelihood rules, bias determination, regression estimators, parameter estimation, and classifier design are addressed.

  14. Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index

    NASA Astrophysics Data System (ADS)

    Holzman, M. E.; Rivas, R.; Piccolo, M. C.

    2014-05-01

    Soil moisture availability affects rainfed crop yield. Therefore, the development of methods for pre-harvest yield prediction is essential for the food security. A study was carried out to estimate regional crop yield using the Temperature Vegetation Dryness Index (TVDI). Triangular scatters from land surface temperature (LST) and enhanced vegetation index (EVI) space from MODIS (Moderate Resolution Imaging Spectroradiometer) were utilized to obtain TVDI and to estimate soil moisture availability. Then soybean and wheat crops yield was estimated on four agro-climatic zones of Argentine Pampas. TVDI showed a strong correlation with soil moisture measurements, with R2 values ranged from 0.61 to 0.83 and also it was in agreement with spatial pattern of soil moisture. Moreover, results showed that TVDI data can be used effectively to predict crop yield on the Argentine Pampas. Depending on the agro-climatic zone, R2 values ranged from 0.68 to 0.79 for soybean crop and 0.76 to 0.81 for wheat. The RMSE values were 366 and 380 kg ha-1 for soybean and they varied between 300 and 550 kg ha-1 in the case of wheat crop. When expressed as percentages of actual yield, the RMSE values ranged from 12% to 13% for soybean and 14% to 22% for wheat. The bias values indicated that the obtained models underestimated soybean and wheat yield. Accurate crop grain yield forecast using the developed regression models was achieved one to three months before harvest. In many cases the results were better than others obtained using only a vegetation index, showing the aptitude of surface temperature and vegetation index combination to reflect the crop water condition. Finally, the analysis of a wide range of soil moisture availability allowed us to develop a generalized model of crop yield and dryness index relationship which could be applicable in other regions and crops at regional scale.

  15. Estimating crop net primary production using inventory data and MODIS-derived parameters

    SciTech Connect

    Bandaru, Varaprasad; West, Tristram O.; Ricciuto, Daniel M.; Izaurralde, Roberto C.

    2013-06-03

    National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale and over national and continental extents. Existing satellite-based NPP products tend to underestimate NPP on croplands. A new Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP. The method is documented here and evaluated for corn and soybean crops in Iowa and Illinois in years 2006 and 2007. The method includes a crop-specific enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), shortwave radiation data estimated using Mountain Climate Simulator (MTCLIM) algorithm and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that correspond to the Cropland Data Layer (CDL) land cover product. The modeling framework represented well the gradient of NPP across Iowa and Illinois, and also well represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 980 g C m-2 yr-1 and 420 g C m-2 yr-1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Estimated gross primary productivity (GPP) derived from AgI-LUE were in close agreement with eddy flux tower estimates. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.

  16. Estimating Effective Stream Shade in Riparian Areas

    NASA Astrophysics Data System (ADS)

    Sydow, L.; Link, T. E.; Gravelle, J. A.

    2009-12-01

    Concern about the effects of land cover change on stream temperature dynamics necessitates the quantification of effective stream shade for riparian management and water quality modeling. Accurate quantification of stream shade with radiometers is both challenging and expensive over large areas characterized by complex and spatially variable canopies. To address these challenges, a number of shade estimation methods have been developed for rapid stream cover assessments. The main objective of this study was to determine which of four canopy cover estimation methods best characterized effective shade in harvested and unharvested stream reaches. An associated objective was to understand how canopy cover and type affected the accuracy of the methods. The four methods tested were a manual canopy densiometer, analysis of standard imagery from a digital camera, the Solar Pathfinder, and analysis of hemispherical imagery using Hemiview software. These were compared to measurements of percent incoming shortwave radiation quantified with Hukseflux NR-01 radiometers at each location. Four stream reaches in the Mica Creek Experimental Watershed were used to assess the estimation methods under different amounts and types of canopy cover: an unharvested area, a partial cut, an open clear cut, and a clear cut with dense understory. All estimation methods were most accurate in the most shaded location (dense understory clear cut) and least accurate in the open clear cut, the least shaded location. The values estimated by Hemiview proved to be the most accurate in all four areas, differing from the true value by ~5% on average; the Solar Pathfinder was the second most accurate with an error of ~8%. The results from the digital camera and canopy densiometer were comparable, at ~15% difference from the true value. While Hemiview is the most expensive and time consuming of the four methods, it was the most accurate for estimating effective stream shade in this study.

  17. Remote Sensing Crop Leaf Area Index Using Unmanned Airborne Vehicles

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing with unmanned airborne vehicles (UAVs) has more potential for within-season crop management than conventional satellite imagery because: (1) pixels have very high resolution, (2) cloud cover would not prevent acquisition during critical periods of growth, and (3) quick delivery of inf...

  18. Modeling of groundwater draft based on satellite-derived crop acreage estimation over an arid region of northwest India

    NASA Astrophysics Data System (ADS)

    Bhadra, Bidyut Kumar; Kumar, Sanjay; Paliwal, Rakesh; Jeyaseelan, A. T.

    2016-06-01

    Over-exploitation of groundwater for agricultural crops puts stress on the sustainability of natural resources in the arid region of Rajasthan state, India. Hydrogeological study of groundwater levels of the study area during the pre-monsoon (May to June), post-monsoon (October to November) and post-irrigation (February to March) seasons of 2004-2005 to 2011-2012 shows a steady decline of groundwater levels at the rate of 1.28-1.68 m/year, mainly due to excessive groundwater draft for irrigation. Due to the low density of the groundwater observation-well network in the study area, assessment of groundwater draft, and thus groundwater resource management, becomes a difficult task. To overcome the situation, a linear groundwater draft model (LGDM) has been developed based on the empirical relationship between satellite-derived crop acreage and the observed groundwater draft for the year 2003-2004. The model has been validated for a decade, during three year-long intervals (2005-2006, 2008-2009 and 2011-2012) using groundwater draft, estimated through a discharge factor method. Further, the estimated draft was validated through observed pumping data from random sampled villages (2011-2012). The results suggest that the developed LGDM model provides a good alternative to the estimation of groundwater draft based on satellite-based crop area in the absence of groundwater observation wells in arid regions of northwest India.

  19. Estimated Impacts of Emissions Reductions on Wheat and Maize Crops

    NASA Astrophysics Data System (ADS)

    Tebaldi, C.; Lobell, D. B.

    2015-12-01

    An ability to quantify the impacts associated with different emissions scenarios acrossa broad range of economic and environmental outcomes would be helpful for guidingpolicy on energy and greenhouse gas emissions. One outcome of particular interest,especially for food insecure populations, are effects on agricultural productivity. Inthis study we use empirical models of the relation between climate and CO2concentration on the one hand, and changes in crop yields on the other, tocharacterize the differential impacts on the future productivity of two major crops oftwo level of forcings: those associated with RCP4.5 and those associated withRCP8.5. This study is part of a larger project on the Benefits of ReducingAnthropogenic Climate changE (BRACE). We consider differential effects on maize andwheat yields at the global scale from expected changes in mean temperature andprecipitation under the two scenarios. We also characterize differential levels ofexposure to damaging heat extremes. Several time horizons are considered,characterizing expected impacts over the short, middle and long terms over the 21stcentury.

  20. Estimating Biophysical Crop Properties by a Machine Learning Model Inversion using Hyperspectral Imagery of Different Resolution

    NASA Astrophysics Data System (ADS)

    Preidl, S.; Doktor, D.

    2013-12-01

    This study investigates how image resolution and phenology affects the quality of biophysical variable estimation of different crop types. Hence, several hyperspectral at-sensor radiance images (400-2500 nm) of 1, 2 and 3 meter resolution were acquired by an AISA dual airborne system to estimate leaf chlorophyll content and leaf area index (LAI) of different crop types. The study area describes a climatic gradient that ranges from the Magdeburg Börde (130 meter a.s.l.) to the northeast of the Harz Mountain (450 meter a.s.l.), Germany. The 35 kilometer long flight strip is recorded on the same day at all three resolutions. Ground measurements were conducted simultaneously to the flight campaigns on selected crop fields. The SLC model was coupled with the atmospheric model MODTRAN4 to build up a look-up table (LUT) of simulated at-sensor radiances. To support a fast and more accurate inversion process, LUT-spectra were selected for model inversion which location in the PCA space (spanned by the first three principal components) is similar to the one of the measured spectra. A support vector regression (SVR) was trained on the reduced LUT to perform a pixel-based inversion of the hyperspectral images, subsequently. A multi-parameter sensitivity analysis was recently developed to define the most influential parameters for a reasonable model setup in the first place. This completes the development of an automated inversion process chain to estimate leaf and canopy biophysical properties. To achieve reasonable inversion results each pixel should be radiatively independent from its surrounding pixels. Image texture is used to calculate the second-order statistical variance between pixel pairs quantifying spatial heterogeneity throughout the spectral domain. The texture measurement can be employed as an uncertainty assessment of the biophysical variable estimation map. Results show that vegetated areas within the field are representing spectrally homogeneous systems. In

  1. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The scale mismatch between remotely sensed observations and crop growth models simulated state variables decreases the reliability of crop yield estimates. To overcome this problem, we used a two-step data assimilation phases: first we generated a complete leaf area index (LAI) time series by combin...

  2. Irrigated rice area estimation using remote sensing techniques: Project's proposal and preliminary results. [Rio Grande do Sul, Brazil

    NASA Technical Reports Server (NTRS)

    Parada, N. D. J. (Principal Investigator); Deassuncao, G. V.; Moreira, M. A.; Novaes, R. A.

    1984-01-01

    The development of a methodology for annual estimates of irrigated rice crop in the State of Rio Grande do Sul, Brazil, using remote sensing techniques is proposed. The project involves interpretation, digital analysis, and sampling techniques of LANDSAT imagery. Results are discussed from a preliminary phase for identifying and evaluating irrigated rice crop areas in four counties of the State, for the crop year 1982/1983. This first phase involved just visual interpretation techniques of MSS/LANDSAT images.

  3. Remote sensing-based estimates of annual and seasonal emissions from crop residue burning in the contiguous United States.

    PubMed

    McCarty, Jessica L

    2011-01-01

    Crop residue burning is an extensive agricultural practice in the contiguous United States (CONUS). This analysis presents the results of a remote sensing-based study of crop residue burning emissions in the CONUS for the time period 2003-2007 for the atmospheric species of carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), nitrogen dioxide (NO2, sulfur dioxide (SO2), PM2.5 (particulate matter [PM] < or = 2.5 microm in aerodynamic diameter), and PM10 (PM < or = 10 microm in aerodynamic diameter). Cropland burned area and associated crop types were derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products. Emission factors, fuel load, and combustion completeness estimates were derived from the scientific literature, governmental reports, and expert knowledge. Emissions were calculated using the bottom-up approach in which emissions are the product of burned area, fuel load, and combustion completeness for each specific crop type. On average, annual crop residue burning in the CONUS emitted 6.1 Tg of CO2, 8.9 Gg of CH4, 232.4 Gg of CO, 10.6 Gg of NO2, 4.4 Gg of SO2, 20.9 Gg of PM2.5, and 28.5 Gg of PM10. These emissions remained fairly consistent, with an average interannual variability of crop residue burning emissions of +/- 10%. The states with the highest emissions were Arkansas, California, Florida, Idaho, Texas, and Washington. Most emissions were clustered in the southeastern United States, the Great Plains, and the Pacific Northwest. Air quality and carbon emissions were concentrated in the spring, summer, and fall, with an exception because of winter harvesting of sugarcane in Florida, Louisiana, and Texas. Sugarcane, wheat, and rice residues accounted for approximately 70% of all crop residue burning and associated emissions. Estimates of CO and CH4 from agricultural waste burning by the U.S. Environmental Protection Agency were 73 and 78% higher than the CO and CH4 emission estimates from this analysis, respectively. This analysis

  4. Projecting crop yield in northern high latitude area.

    PubMed

    Matsumura, Kanichiro

    2014-01-01

    validation periods is used. To show the reproducing projection between observed and calculated values, the root mean squared error for skill score (RMSE SS) with the persistence model serving as the reference model is used. The persistence model is used as a benchmark. The results show that SADs near USA border show better RMSE SS values and mode 3's time coefficients can be a useful predictor especially for inland province such as Manitoba. Among 27 Canadian Prairie's SADs with perfect yield data, 67% of Alberta's SADs, 86% of Manitoba's SADs, and 77% of Saskatchewan's SADs can get positive skill scores. In each SAD, future yield projection is calculated applying predictors in 2013 for the obtained eight sets of models and eight sets of forecasted values in 2013 are averaged and a near future projection result is obtained. Series of outputs including calculated forecasted yield value in each SAD is provided by smart phone application. A system for providing climatic condition for a point with a permission of Climatic Research Unit - University of East Anglia and for obtaining patent is proposed. There are several patented systems similar to the system proposed in this paper. However, these patents are different in essence. The system proposed in this paper consists of two parts. First part is to estimate equations using time series data. The second part is to acquire and apply latest climatic conditions for obtained equations and calculate future projection. If the procedure is refined and devices are originally developed, series of idea can be patented. For future work, crop index, Hokkaido is also introduced. PMID:25733071

  5. Establishing a method for estimating crop water requirements using the SEBAL method in Cyprus

    NASA Astrophysics Data System (ADS)

    Papadavid, G.; Toulios, L.; Hadjimitsis, D.; Kountios, G.

    2014-08-01

    Water allocation to crops has always been of great importance in agricultural process. In this context, and under the current conditions, where Cyprus is facing a severe drought the last five years, purpose of this study is basically to estimate the needed crop water requirements for supporting irrigation management and monitoring irrigation on a systematic basis for Cyprus using remote sensing techniques. The use of satellite images supported by ground measurements has provided quite accurate results. Intended purpose of this paper is to estimate the Evapotranspiration (ET) of specific crops which is the basis for irrigation scheduling and establish a procedure for monitoring and managing irrigation water over Cyprus, using remotely sensed data from Landsat TM/ ETM+ and a sound methodology used worldwide, the Surface Energy Balance Algorithm for Land (SEBAL). The methodology set in this paper refers to COST action ES1106 (Agri-Wat) for determining crop water requirements as part of the water footprint and virtual water-trade.

  6. Role of fish distribution on estimates of standing crop in a cooling reservoir

    USGS Publications Warehouse

    Barwick, D. Hugh

    1984-01-01

    Estimates of fish standing crop from coves in Keowee Reservoir, South Carolina, were obtained in May and August for 3 consecutive years. Estimates were significantly higher in May than in August for most of the major species of fish collected, suggesting that considerable numbers of fish had migrated from the coves by August. This change in fish distribution may have resulted from the operation of a 2,580-megawatt nuclear power plant which altered reservoir stratification. Because fish distribution is sensitive to conditions of reservoir stratification, and because power plants often alter reservoir stratification, annual cove sampling in August may not be sufficient to produce comparable estimates of fish standing crop on which to assess the impact of power plant operations on fish populations. Comparable estimates of fish standing crop can probably be obtained from cooling reservoirs by collecting annual samples at similar water temperatures and concentrations of dissolved oxygen.

  7. An Integrated Lysimeter and Satellite Imagery Approach for Estimating Crop Evapotranspiration

    NASA Astrophysics Data System (ADS)

    Goorahoo, D.; Cassel-Sharma, F.; Johnson, L.; Melton, F. S.

    2014-12-01

    Accurate estimation of crop water requirement (CWR) is essential for the implementation efficient irrigation schedules in an effort to optimize water use efficiency. This is particularly important in the central San Joaquin Valley (SJV), California, USA, where severe droughts have accentuated the need to conserve water and improve on-farm water management. In the current study, we adopt an integrated approach for estimation of crop evapotranspiration (ETc) involving the use of weighing lysimeters and satellite imagery. In the first phase of the study with the crop lysimeter, conducted on a clay loam soil with processing tomatoes grown under sub-surface drip irrigation, observations of crop ground cover were conducted weekly and evapotranspiration (ET) data were collected daily to derive relationships between crop coefficients and fractional cover. Data collected during the first year of the study, indicted that the crop coefficients (Kc) obtained at peak season were relatively higher than those generally reported for tomatoes commonly grown in the central SJV. Overall, there was a good correlation between fractional cover and crop coefficients (r2 = 0.91), with the average peak ET and Kc values ranging from 6 to 7 mm per day and from 0.8 to 0.9, respectively. Data obtained from satellite imagery, representing relatively larger spatial measurements than the lysimeters, are being compared with the surface observations from the lysimeters and will also be discussed in our presentation.

  8. Reconstructing rice phenology curves with frequency-based analysis and multi-temporal NDVI in double-cropping area in Jiangsu, China

    NASA Astrophysics Data System (ADS)

    Wang, Hongshuo; Lin, Hui; Munroe, Darla K.; Zhang, Xiaodong; Liu, Pengfei

    2016-06-01

    Crop phenology retrieval in the double-cropping area of China is of great significance in crop yield estimation and water management under the influences of global change. In this study, rice phenology in Jiangsu Province, China was extracted from multi-temporal MODIS NDVI using frequency-based analysis. Pure MODIS pixels of rice were selected with the help of TM images. Discrete Fourier Transformation (DFT), Discrete Wavelet Transformation (DWT), and Empirical Mode Decomposition (EMD) were performed to decompose time series into components of different frequencies. Rice phenology in the double-cropping area is mainly located on the last 2 IMFs of EMD and the first 2‒3 frequencies of DFT and DWT. Compared with DFT and DWT, EMD is limited to fewer frequencies. Multi-temporal MODIS NDVI data combined with frequency-based analysis can retrieve rice phenology dates with on average 79% valid estimates. The sorting result for effective estimations from different methods is DWT (85%)>EMD (80%)>DFT (74%). Planting date (88%) is easier to estimate than harvesting date (70%). Rice planting date is easily affected by the former cropping mode within the same year in a double-cropping region. This study sheds light on understanding crop phenology dynamics in the frequency domain of multi-temporal MODIS data.

  9. Development of estimation method for crop yield using MODIS satellite imagery data and process-based model for corn and soybean in US Corn-Belt region

    NASA Astrophysics Data System (ADS)

    Lee, J.; Kang, S.; Jang, K.; Ko, J.; Hong, S.

    2012-12-01

    Crop productivity is associated with the food security and hence, several models have been developed to estimate crop yield by combining remote sensing data with carbon cycle processes. In present study, we attempted to estimate crop GPP and NPP using algorithm based on the LUE model and a simplified respiration model. The state of Iowa and Illinois was chosen as the study site for estimating the crop yield for a period covering the 5 years (2006-2010), as it is the main Corn-Belt area in US. Present study focuses on developing crop-specific parameters for corn and soybean to estimate crop productivity and yield mapping using satellite remote sensing data. We utilized a 10 km spatial resolution daily meteorological data from WRF to provide cloudy-day meteorological variables but in clear-say days, MODIS-based meteorological data were utilized to estimate daily GPP, NPP, and biomass. County-level statistics on yield, area harvested, and productions were used to test model predicted crop yield. The estimated input meteorological variables from MODIS and WRF showed with good agreements with the ground observations from 6 Ameriflux tower sites in 2006. For examples, correlation coefficients ranged from 0.93 to 0.98 for Tmin and Tavg ; from 0.68 to 0.85 for daytime mean VPD; from 0.85 to 0.96 for daily shortwave radiation, respectively. We developed county-specific crop conversion coefficient, i.e. ratio of yield to biomass on 260 DOY and then, validated the estimated county-level crop yield with the statistical yield data. The estimated corn and soybean yields at the county level ranged from 671 gm-2 y-1 to 1393 gm-2 y-1 and from 213 gm-2 y-1 to 421 gm-2 y-1, respectively. The county-specific yield estimation mostly showed errors less than 10%. Furthermore, we estimated crop yields at the state level which were validated against the statistics data and showed errors less than 1%. Further analysis for crop conversion coefficient was conducted for 200 DOY and 280 DOY

  10. Crop Evapotranspiration in San Joaquin Valley by Landsat Reflectance-based and Energy-balance Estimation Methods

    NASA Astrophysics Data System (ADS)

    Johnson, L.

    2011-12-01

    Evapotranspiration (ET) estimates are needed to support agricultural and natural resource management. Satellite based measurements offer the potential to efficiently monitor ET over large areas. In this study, two analysis methods were applied to Landsat-5 Thematic Mapper imagery to estimate crop evapotranspiration (ETc) in California's San Joaquin Valley. The Landsat L1T images (path 42, row 35) were collected monthly during the main growing season (Apr-Nov) in 2009. In the first method, the images were transformed to surface reflectance, and subsequently to NDVI. The NDVI was used to estimate mean fractional cover of several major crop types including almond, orange, grape, cotton, corn, alfalfa, and tomato across a total of 115 fields. Prior relationships developed by weighing lysimeter were used to convert fractional cover to a crop coefficient expressing ETc relative to grass reference evapotranspiration (ETo). Measurements of ETo by the California Irrigation Management Information System (CIMIS) were then used to calculate ETc on each overpass date. These reflectance-based estimates were compared with values retrieved by the Surface Energy Balance Algorithm for Land (SEBAL). SEBAL combined spectral radiances in Landsat optical and thermal bands with CIMIS meteorological data to derive ET as a surface energy budget residual by applying radiative, aerodynamic and energy balance physics in 25 computational steps. Reasonably strong agreement resulted, with mean absolute error (MAE) between the two approaches <1 mm/d, and coefficients of determination ranging from 0.78-0.90, for most of the crop types examined. Stronger agreement was found for fields deemed by SEBAL to contain unstressed crop (observed ET at-or-near potential) during satellite overpass, with MAE reductions averaging about 30 percent and coefficients of determination largely of range 0.90-0.94.

  11. [Quantification of crop residue burned areas based on burning indices using Landsat 8 image].

    PubMed

    Ma, Jian-hang; Song, Kai-shar; Wen, Zhi-dan; Shao, Tian-tian; Li, Bo-nan; Qi, Cai

    2015-11-01

    Crop residue burning leads to atmospheric pollution and is an enormous waste of crop residue resource. Crop residue burning can be monitored timely in large regions as the fire points can be recognized through remotely sensed image via thermal infrared bands. However, the area, the detailed distribution pattern and especially the severity of the burning areas cannot be derived only by the thermal remote sensing approach. The burning index, which was calculated with two or more spectral bands at where the burned and unburned areas have distinct spectral characteristics, is widely used in the forest fire investigation. However its potential application for crop residue burning evaluation has not been explored. With two Landsat 8 images that cover a part of the Songnen Plain, three burning indices, i.e., the normalized burned ratio (NBR), the normalized burned ratio incorporating the thermal band (NBRT), and the burned area index (BAI), were used to classify the crop residue burned and unburned areas. The overall classification accuracies were 91.9%, 92.3%, and 87.8%, respectively. The correlation analysis between the indices and the crop residue coverage indicated that the NBR and NBRT were positively correlated with the crop residue coverage (R2 = 0.73 and 0.64, respectively) with linear regression models, while the BAI was exponentially correlated with the crop residue coverage (R2 = 0.68). The results indicated that the use of burning indices in crop residue burning monitoring could quantify crop residue burning severity and provide valuable data for evaluating atmospheric pollution. PMID:26915202

  12. Comparative Evaluation of Inversion Approaches of the Radiative Transfer Model for Estimation of Crop Biophysical Parameters

    NASA Astrophysics Data System (ADS)

    Mridha, Nilimesh; Sahoo, Rabi N.; Sehgal, Vinay K.; Krishna, Gopal; Pargal, Sourabh; Pradhan, Sanatan; Gupta, Vinod K.; Kumar, Dasika Nagesh

    2015-04-01

    The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for parameters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.

  13. Remote Estimation of Gross Primary Production in Crops at Field and Regional Levels

    NASA Astrophysics Data System (ADS)

    Gitelson, A. A.; Vina, A.; Verma, S. B.; Rundquist, D. C.

    2007-12-01

    Accurate estimation of spatially distributed CO2 fluxes is of great importance for regional and global studies of carbon balance. We have found that in irrigated and rainfed crops (maize and soybean), GPP is closely related to total crop chlorophyll content. The finding allowed development of a new technique for remote estimation of crop chlorophyll specifically for assessing gross primary production. The technique is based on reflectance in two spectral channels: the near-infrared and either the green or the red-edge. The technique provided accurate estimations of daily GPP in both crops. Validation using independent datasets for irrigated and rainfed maize and soybean documented the robustness of the technique. We report also about applying the developed technique for GPP retrieval from data acquired by both an airborne imaging spectrometer (AISA-Eagle) and Landsat ETM+. The Chlorophyll Index, retrieved from Landsat ETM+ data, was found to be an accurate surrogate measure for daily crop GPP with a root mean square error of GPP prediction of less than 1.58 g C m-2d-1 in a GPP range of 1.88 g C m-2d-1 to 23.1 g C m-2d-1. These results suggest new possibilities for analyzing the spatio-temporal variation of the GPP of crops using not only the extensive archive of Landsat Thematic Mapper imagery acquired since the early 1980s but also the 500-m/pixel data currently being acquired by MODIS.

  14. Large Area Crop Inventory Experiment (LACIE). Evaluation of the LACIE transition year crop calendar model. [Wheat growth in the Great Plains Corridor, North America

    NASA Technical Reports Server (NTRS)

    Cheffin, R. E.; Woolley, S. K. (Principal Investigator)

    1979-01-01

    The author has identified the following significant results. The estimates of developmental stage dates from the LACIE adjustable crop calendar (ACC) winter wheat model was somewhat more accurate than the historical crop calendar after jointing. The ACC winter wheat model was not so accurate for the Texas Panhandle as it was for the other areas of the USPG-7 because dry soil conditions delayed fall planting in the Panhandle. Since the LACIE ACC winter wheat model does not contain a moisture term and it was started with historical planting dates, lengthy delays in planting mean that the ACC model will probably be started early and will estimate the developmental growth stages to occur too early in the season. The LACIE ACC spring wheat model was also started early in most areas because of late planting due to fields wet from melting snow and rain. The starter model used to estimate spring planting dates was not accurate under these wet soil conditions and tended to predict the developmental stages to occur earlier than the dates observed in the fields.

  15. Stratum variance estimation for sample allocation in crop surveys. [Great Plains Corridor

    NASA Technical Reports Server (NTRS)

    Perry, C. R., Jr.; Chhikara, R. S. (Principal Investigator)

    1980-01-01

    The problem of determining stratum variances needed in achieving an optimum sample allocation for crop surveys by remote sensing is investigated by considering an approach based on the concept of stratum variance as a function of the sampling unit size. A methodology using the existing and easily available information of historical crop statistics is developed for obtaining initial estimates of tratum variances. The procedure is applied to estimate stratum variances for wheat in the U.S. Great Plains and is evaluated based on the numerical results thus obtained. It is shown that the proposed technique is viable and performs satisfactorily, with the use of a conservative value for the field size and the crop statistics from the small political subdivision level, when the estimated stratum variances were compared to those obtained using the LANDSAT data.

  16. Assessment of Spectral Indices for Crop Residue Cover Estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Agricultural soils are an important terrestrial carbon (C) stock, accounting for a significant portion of global C estimates. Soil tillage method is important in agricultural C sequestration models. Traditional intensive tillage systems greatly disturb the soil and have been shown to deplete the so...

  17. Estimating crop net primary production using national inventory data and MODIS-derived parameters

    NASA Astrophysics Data System (ADS)

    Bandaru, Varaprasad; West, Tristram O.; Ricciuto, Daniel M.; César Izaurralde, R.

    2013-06-01

    National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux between land and atmosphere. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale as well as national and continental scales. Existing satellite-based NPP products tend to underestimate NPP on croplands. An Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP over large multi-state regions. The method is documented here and evaluated for corn (Zea mays L.) and soybean (Glycine max L. Merr.) in Iowa and Illinois in 2006 and 2007. The method includes a crop-specific Enhanced Vegetation Index (EVI), shortwave radiation data estimated using the Mountain Climate Simulator (MTCLIM) algorithm, and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that corresponds to the Cropland Data Layer (CDL) land cover product. Results from the modeling framework captured the spatial NPP gradient across croplands of Iowa and Illinois, and also represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 917 g C m-2 yr-1 and 409 g C m-2 yr-1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Site comparisons with flux tower data show AgI-LUE NPP in close agreement with tower-derived NPP, lower than inventory-based NPP, and higher than MOD17A3 NPP. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.

  18. Large area aggregation and mean-squared prediction error estimation for LACIE yield and production forecasts. [wheat

    NASA Technical Reports Server (NTRS)

    Chhikara, R. S.; Feiveson, A. H. (Principal Investigator)

    1979-01-01

    Aggregation formulas are given for production estimation of a crop type for a zone, a region, and a country, and methods for estimating yield prediction errors for the three areas are described. A procedure is included for obtaining a combined yield prediction and its mean-squared error estimate for a mixed wheat pseudozone.

  19. Ecoclimatic indicators to study climate suitability of areas for the cultivation of specific crops

    NASA Astrophysics Data System (ADS)

    Caubel, J.; Garcia de Cortazar Atauri, I.; Cufi, J.; Huard, F.; Launay, M.; Ripoche, D.; Graux, A.; deNoblet, N.

    2013-12-01

    Climatic conditions play a fundamental role in the suitability of geographical areas for cropping. In the context of climate change, we could expect changes in overall climatic conditions and so, on the suitability for cropping. Therefore, assessing the future climate suitability of areas for cropping is decisive for anticipating agriculture in a given area. Moreover, it is crucial to have access to the split up information concerning the effect of climate on the achievement of the main ecophysiological processes and cultural practices taking place during the crop cycle. In this way, stakeholders can envisage land use adaptations under climate change conditions, such as changes in cultural practices or development of new varieties for example. We proposed an aggregation tool of ecoclimatic indicators to design evaluation trees of climate suitability of areas for cropping, GETARI (Generic Evaluation Tool of Ecoclimatic Indicators). It calculates an overall climate suitability index at the annual scale, from a designed evaluation tree. This aggregation tool allows to characterize climate suitability according to crop ecophysiology, grain/fruit quality or crop management. GETARI proposes the major ecophysiological processes and cultural practices taking place during phenological periods, together with the climatic effects that are known to affect their achievement. The climatic effects on the ecophysiological processes (or cultural practices) during phenological periods are captured by the ecoclimatic indicators, which are agroclimatic indicators calculated over phenological periods. They give information about crop response to climate through ecophysiological or agronomic thresholds. Those indices of suitability are normalized and aggregated according to aggregation rules in order to compute an overall climate index. In order to illustrate how GETARI can be used, we designed evaluation trees in order to study the climate suitability for maize cropping regarding

  20. An assessment of Landsat data acquisition history on identification and area estimation of corn and soybeans

    NASA Technical Reports Server (NTRS)

    Hixson, M. M.; Bauer, M. E.; Scholz, D. K.

    1980-01-01

    During the past decade, numerous studies have demonstrated the potential of satellite remote sensing for providing accurate and timely crop area information. This study assessed the impact of Landsat data acquisition history on classification and area estimation accuracy of corn and soybeans. Multitemporally registered Landsat MSS data from four acquisitions during the 1978 growing season were used in classification of eight sample segments in the U.S. Corn Belt. The results illustrate the importance of selecting Landsat acquisitions based on spectral differences in crops at certain growth stages.

  1. The auxiliary use of LANDSAT data in estimating crop acreages: Results of the 1975 Illinois crop-acreage experiment

    NASA Technical Reports Server (NTRS)

    Gleason, C. (Principal Investigator); Starbuck, R. R.; Sigman, R. S.; Hanuschak, G. A.; Craig, M. E.; Cook, P. W.; Allen, R. D.

    1977-01-01

    The author has identified the following significant results. It was found that classifier performance was influenced by a number of temporal, methodological, and geographical factors. Best results were obtained when corn was tasselled and near the dough stage of development. Dates earlier or later in the growing season produced poor results. Atmospheric effects on results cannot be independently measured or completely separated from the effects due to the maturity stage of the crops. Poor classifier performance was observed in areas where considerable spectral confusion was present.

  2. A National Assessment of Promising Areas for Switchgrass, Hybrid Poplar, or Willow Energy Crop Production

    SciTech Connect

    Graham, R.L.; Walsh, M.E.

    1999-02-01

    The objective of this paper is to systematically assess the cropland acreage that could support energy crops and the expected farm gate and delivered prices of energy crops. The assessment is based on output from two modeling approaches: (1) the Oak Ridge County-Level Energy Crop (ORECCL) database (1996 version) and (2) the Oak Ridge Integrated Bioenergy Analysis System (ORIBAS). The former provides county-level estimates of suitable acres, yields, and farmgate prices of energy crops (switchgrass, hybrid poplar, willow) for all fifty states. The latter estimates delivered feedstock prices and quantities within a state at a fine resolution (1 km2) and considers the interplay between transportation costs, farmgate prices, cropland density, and facility demand. It can be used to look at any type of feedstock given the appropriate input parameters. For the purposes of this assessment, ORIBAS has been used to estimate farmgate and delivered switchgrass prices in 11 states (AL, FL, GA, IA, M N, MO, ND, NE, SC, SD, and TN). Because the potential for energy crop production can be considered from several perspectives, and is evolving as policies, economics and our basic understanding of energy crop yields and production costs change, this assessment should be viewed as a snapshot in time.

  3. Determination of the optimal level for combining area and yield estimates

    NASA Technical Reports Server (NTRS)

    Bauer, M. E. (Principal Investigator); Hixson, M. M.; Jobusch, C. D.

    1981-01-01

    Several levels of obtaining both area and yield estimates of corn and soybeans in Iowa were considered: county, refined strata, refined/split strata, crop reporting district, and state. Using the CCEA model form and smoothed weather data, regression coefficients at each level were derived to compute yield and its variance. Variances were also computed with stratum level. The variance of the yield estimates was largest at the state and smallest at the county level for both crops. The refined strata had somewhat larger variances than those associated with the refined/split strata and CRD. For production estimates, the difference in standard deviations among levels was not large for corn, but for soybeans the standard deviation at the state level was more than 50% greater than for the other levels. The refined strata had the smallest standard deviations. The county level was not considered in evaluation of production estimates due to lack of county area variances.

  4. Development of High Resolution Data for Irrigated Area and Cropping Patterns in India

    NASA Astrophysics Data System (ADS)

    K a, A.; Mishra, V.

    2015-12-01

    Information of crop phenology and its individual effect on irrigation is essential to improve the simulation of land surface states and fluxes. We use moderate resolution imaging spectroradiometer (MODIS) - Normalized difference vegetation index (NDVI) at 250 m resolution for monitoring temporal changes in irrigation and cropping patterns in India. We used the obtained dataset of cropping pattern for quantifying the effect of irrigation on land surface states and fluxes by using an uncoupled land surface model. The cropping patterns are derived by using the planting, heading, harvesting, and growing dates for each agro-ecological zone separately. Moreover, we developed a high resolution irrigated area maps for the period of 1999-2014 for India. The high resolution irrigated area was compared with relatively coarse resolution (~ 10km) irrigated area from the Food and Agricultural Organization. To identify the seasonal effects we analyzed the spatial and temporal change of irrigation and cropping pattern for different temporal seasons. The new irrigation area information along with cropping pattern was used to study the water budget in India using the Noah Land surface Model (Noah LSM) for the period of 1999-2014.

  5. Estimation of winter wheat yield by using remote sensing data and crop model

    NASA Astrophysics Data System (ADS)

    Guo, Jianmao; Zheng, Tengfei; Wang, Qi; Yang, Jia; Shi, Junyi; Zhu, Jinhui

    2012-10-01

    Remote sensing data combined with crop model is an important application and development trend of current agricultural information technology, it can solve the problem that remote sensing or crop model cannot solve alone. In order to simulate crop growth and yield prediction in large scale, this paper using field test data to calibrate and validation the model parameters before apply to the winter wheat WOFOST model, than according to the actual environment of Xinxiang, simulate the growth in 3 different condition in the 2002-2003 growing season. Contrast the simulation value WOFOST model, using the Landsat-7 ETM retrieving leaf area index, define winter wheat's growth condition in each pixel, the remote sensing information combined with crop model is accomplished at pixel scale. Based on the actual production of Xinxiang winter wheat in 2003,compare the simulate results with the corresponding parameter, results shows that the method of this study method is feasible.

  6. Comparison of Crop Evapotranspiration Estimates from Reference Evapotranspiration Equations and a Variational Data Assimilation Approach

    NASA Astrophysics Data System (ADS)

    Bateni, S. M.; Michalik, T.; Multsch, S.; Breuer, L.

    2015-12-01

    Crop evapotranspiration (ETc) is a key component of water resources management in irrigation of farmlands as it determines the crop water consumption. Numerous methods have been used to estimate ETc for scheduling irrigation and evaluating the soil water balance. However, there is a significant difference in ETc estimates from various models, which leads to a large uncertainty in the soil water balance, crop water consumption, and irrigation scheduling. In this study, several commonly-used ETc equations (Turc, Priestley-Taylor, Hargreaves-Samani, Penman-Monteith) are compared with the variational data assimilation approach (VDA) of Bateni et al. (2013). The ETc equations initially estimate the reference evapotranspiration (ETo), which is the evapotranspiration from a healthy and actively-transpiring grass field with ample water in the soil. Thereafter, ETc is calculated by multiplying ETo by the crop coefficient (Kc), which accounts for the crop type and soil water stress. To properly apply the Kc to non-standard conditions, a daily water balance estimation for the root zone is required, which is done by two soil water budget models (Cropwat, Hydrus-1D) that compute incoming and outgoing water flows in the soil profile. In contrast to these methods that estimate ETc in two steps, the VDA approach directly predicts ETc by assimilating sequences of land surface temperature into the heat diffusion equation and thus it is expected to provide more accurate ETc estimates. All approaches are applied over three cropland sites namely, Bondville, Fermi, and Mead in the summer of 2006 and 2007. These sites are part of the AmeriFlux network and provide a wide variety of hydrological conditions. The results show that the variational data assimilation approach performs better compared to other equations.

  7. How Universal Is the Relationship Between Remotely Sensed Vegetation Indices (VI) and Crop Leaf Area Index (LAI)?

    NASA Technical Reports Server (NTRS)

    Kang, Yanghui; Ozdogan, Mutlu; Zipper, Samuel C.; Roman, Miguel

    2016-01-01

    Global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. This research enables the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.

  8. Minimizing instrumentation requirement for estimating crop water stress index and transpiration of maize

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Research was conducted in northern Colorado in 2011 to estimate the Crop Water Stress Index (CWSI) and actual water transpiration (Ta) of maize under a range of irrigation regimes. The main goal was to obtain these parameters with minimum instrumentation and measurements. The results confirmed that ...

  9. Assimilation of active and passive microwave observations for improved estimates of soil moisture and crop growth

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An Ensemble Kalman Filter-based data assimilation framework that links a crop growth model with active and passive (AP) microwave models was developed to improve estimates of soil moisture (SM) and vegetation biomass over a growing season of soybean. Complementarities in AP observations were incorpo...

  10. Evaluating high resolution SPOT 5 satellite imagery for crop yield estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    High resolution satellite imagery has the potential for mapping within-field variability in crop growth and yield. This study examined SPOT 5 multispectral imagery for estimating grain sorghum yield. A SPOT 5 image with 10-m spatial resolution and four spectral bands (green, red, near-infrared, and ...

  11. Crop yield estimation based on unsupervised linear unmixing of multidate hyperspectral imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral imagery, which contains hundreds of spectral bands, has the potential to better describe the biological and chemical attributes on the plants than multispectral imagery and has been evaluated in this paper for the purpose of crop yield estimation. The spectrum of each pixel in a hypers...

  12. Performance assessment of the cellulose absorption index (CAI) method for estimating crop residue cover

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Accurate and quick field estimation of crop residues are important for carbon sequestration and biofuel production programs. Landscape-scale assessment of this vital information has promoted the use of remote sensing technology. The cellulose absorption index (CAI) technique has outperformed other ...

  13. Improved Remotely-Sensed Estimates of Crop Residue Cover by Incorporating Soils Information

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing allows for the rapid determination of crop residue cover. The Cellulose Absorption Index (CAI) has been shown to more accurately estimate residue cover and non-photosynthetic vegetation than other indices. CAI is useful as values are linear areal mixtures of soil and residue spectra...

  14. Productivity and carbon dioxide exchange of the leguminous crops: Estimates from flux tower measurements

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Net CO2 exchange data on legume crops at 17 flux tower sites in North America and 3 sites in Europe representing 29 site-years of measurements were partitioned into gross photosynthesis and ecosystem respiration using a light-response function method, resulting in new estimates of ecosystem-scale ec...

  15. Integrating remote sensing, geographic information system and modeling for estimating crop yield

    NASA Astrophysics Data System (ADS)

    Salazar, Luis Alonso

    This thesis explores various aspects of the use of remote sensing, geographic information system and digital signal processing technologies for broad-scale estimation of crop yield in Kansas. Recent dry and drought years in the Great Plains have emphasized the need for new sources of timely, objective and quantitative information on crop conditions. Crop growth monitoring and yield estimation can provide important information for government agencies, commodity traders and producers in planning harvest, storage, transportation and marketing activities. The sooner this information is available the lower the economic risk translating into greater efficiency and increased return on investments. Weather data is normally used when crop yield is forecasted. Such information, to provide adequate detail for effective predictions, is typically feasible only on small research sites due to expensive and time-consuming collections. In order for crop assessment systems to be economical, more efficient methods for data collection and analysis are necessary. The purpose of this research is to use satellite data which provides 50 times more spatial information about the environment than the weather station network in a short amount of time at a relatively low cost. Specifically, we are going to use Advanced Very High Resolution Radiometer (AVHRR) based vegetation health (VH) indices as proxies for characterization of weather conditions.

  16. Taxonomic classification of world map units in crop producing areas of Argentina and Brazil with representative US soil series and major land resource areas in which they occur

    NASA Technical Reports Server (NTRS)

    Huckle, H. F. (Principal Investigator)

    1980-01-01

    The most probable current U.S. taxonomic classification of the soils estimated to dominate world soil map units (WSM)) in selected crop producing states of Argentina and Brazil are presented. Representative U.S. soil series the units are given. The map units occurring in each state are listed with areal extent and major U.S. land resource areas in which similar soils most probably occur. Soil series sampled in LARS Technical Report 111579 and major land resource areas in which they occur with corresponding similar WSM units at the taxonomic subgroup levels are given.

  17. Irrigation Trials for ET Estimation and Water Management in California Specialty Crops

    NASA Astrophysics Data System (ADS)

    Johnson, L.; Cahn, M.; Martin, F.; Lund, C.; Melton, F. S.

    2012-12-01

    Accurate estimation of crop evapotranspiration (ETc) can support efficient irrigation water management, which in turn brings benefits including surface water conservation, mitigation of groundwater depletion/degradation, energy savings, and crop quality assurance. Past research in California has revealed strong relationships between canopy fractional cover (Fc) and ETc of certain specialty crops, while additional research has shown the potential of monitoring Fc by satellite remote sensing. California's Central Coast is the leading region of cool season vegetable production in the U.S. Monterey County alone produces more than 80,000 ha of lettuce and broccoli (about half of U.S. production), valued at $1.5 billion in 2009. Under this study, we are conducting ongoing irrigation trials on these crops at the USDA Agricultural Research Station (Salinas) to compare irrigation scheduling via plant-based ETc approaches, by way of Fc, with current industry standard-practice. The following two monitoring approaches are being evaluated - 1) a remote sensing model employed by NASA's prototype Satellite Irrigation Management System, and 2) an online irrigation scheduling tool, CropManage, recently developed by U.C. Cooperative Extension. Both approaches utilize daily grass-reference ETo data as provided by the California Irrigation Management Irrigation System (CIMIS). A sensor network is deployed to monitor applied irrigation, volumetric soil water content, soil water potential, deep drainage, and standard meteorologic variables in order to derive ETc by a water balance approach. Evaluations of crop yield and crop quality are performed by the research team and by commercial growers. Initial results to-date indicate that applied water reductions based on Fc measurements are possible with little-to-no impact on yield of crisphead lettuce (Lactuca sativa). Additional results for both lettuce and broccoli trials, conducted during summer-fall 2012, are presented with respect to

  18. Large Area Crop Inventory Experiment (LACIE). Development of procedure M for multicrop inventory, with tests of a spring-wheat configuration

    NASA Technical Reports Server (NTRS)

    Horvath, R. (Principal Investigator); Cicone, R.; Crist, E.; Kauth, R. J.; Lambeck, P.; Malila, W. A.; Richardson, W.

    1979-01-01

    The author has identified the following significant results. An outgrowth of research and development activities in support of LACIE was a multicrop area estimation procedure, Procedure M. This procedure was a flexible, modular system that could be operated within the LACIE framework. Its distinctive features were refined preprocessing (including spatially varying correction for atmospheric haze), definition of field like spatial features for labeling, spectral stratification, and unbiased selection of samples to label and crop area estimation without conventional maximum likelihood classification.

  19. Catchment Area Treatment (CAT) Plan and Crop Area Optimization for Integrated Management in a Water Resource Project

    NASA Astrophysics Data System (ADS)

    Jaiswal, R. K.; Thomas, T.; Galkate, R. V.; Ghosh, N. C.; Singh, S.

    2013-09-01

    A scientifically developed catchment area treatment (CAT) plan and optimized pattern of crop areas may be the key for sustainable development of water resource, profitability in agriculture and improvement of overall economy in drought affected Bundelkhand region of Madhya Pradesh (India). In this study, an attempt has been made to develop a CAT plan using spatial variation of geology, geomorphology, soil, drainage, land use in geographical information system for selection of soil and water conservation measures and crop area optimization using linear programming for maximization of return considering water availability, area affinity, fertilizers, social and market constraints in Benisagar reservoir project of Chhatarpur district (M.P.). The scientifically developed CAT plan based on overlaying of spatial information consists of 58 mechanical measure (49 boulder bunds, 1 check dam, 7 cully plug and 1 percolation tank), 2.60 km2 land for agro forestry, 2.08 km2 land for afforestation in Benisagar dam and 67 mechanical measures (45 boulder bunds and 22 gully plugs), 7.79 km2 land for agro forestry, 5.24 km2 land for afforestation in Beniganj weir catchment with various agronomic measures for agriculture areas. The linear programming has been used for optimization of crop areas in Benisagar command for sustainable development considering various scenarios of water availability, efficiencies, affinity and fertilizers availability in the command. Considering present supply condition of water, fertilizers, area affinity and making command self sufficient in most of crops, the net benefit can be increase to Rs. 1.93 crores from 41.70 km2 irrigable area in Benisagar command by optimizing cropping pattern and reducing losses during conveyance and application of water.

  20. Thermography for estimating near-surface soil moisture under developing crop canopies

    NASA Technical Reports Server (NTRS)

    Heilman, J. L.; Moore, D. G.

    1980-01-01

    Previous investigations of thermal infrared techniques using remote sensors (thermography) for estimating soil water content have been limited primarily to bare soil. Ground-based and aircraft investigations were conducted to evaluate the potential for extending the thermography approach to developing crop canopies. A significant exponential relationship was found between the volumetric soil water content in the 0-4 cm soil layer and the diurnal difference between surface soil temperature measured at 0230 and 1330 LST (satellite overpass times of NASA's Heat Capacity Mapping Mission - HCMM). Surface soil temperatures were estimated using minimum air temperature, percent cover of the canopy and remote measurements of canopy temperature. Results of the investigation demonstrated that thermography can potentially be used to estimate soil temperature and soil moisture throughout a complete growing season for a number of different crops and soils.

  1. Estimating water and nitrate leaching in tree crops using inverse modelled plant and soil hydraulic properties

    NASA Astrophysics Data System (ADS)

    Couvreur, Valentin; Kandelous, Maziar; Mairesse, Harmony; Baram, Shahar; Moradi, Ahmad; Pope, Katrin; Hopmans, Jan

    2015-04-01

    Groundwater quality is specifically vulnerable in irrigated agricultural lands in California and many other (semi-)arid regions of the world. The routine application of nitrogen fertilizers with irrigation water in California is likely responsible for the high nitrate concentrations in groundwater, underlying much of its main agricultural areas. To optimize irrigation/fertigation practices, it is essential that irrigation and fertilizers are applied at the optimal concentration, place, and time to ensure maximum root uptake and minimize leaching losses to the groundwater. The applied irrigation water and dissolved fertilizer, root nitrate and water uptake interact with soil and root properties in a complex manner that cannot easily be resolved. It is therefore that coupled experimental-modelling studies are required to allow for unravelling of the relevant complexities that result from typical variations of crop properties, soil texture and layering across farmer-managed fields. A combined field monitoring and modelling approach was developed to quantify from simple measurements the leaching of water and nitrate below the root zone. The monitored state variables are soil water content within the root zone, soil matric potential below the root zone, and nitrate concentration in the soil solution. Plant and soil properties of incremented complexity are optimized with the software HYDRUS in an inverse modelling scheme, which allows estimating leaching under constraint of hydraulic principles. Questions of optimal irrigation and fertilization timing can then be addressed using predictive results and global optimization algorithms.

  2. An assessment of Landsat data acquisition history on identification and area estimation of corn and soybeans

    USGS Publications Warehouse

    Hixson, M. M.; Bauer, M. E.; Scholz, Donna K.

    1982-01-01

    In the past decade, numerous studies have demonstrated the potential of satellite remote sensing for providing accurate timely crop area information. This study assessed the impact of Landsat data acquisition history on classification and area estimation accuracy of corn and soybeans in the U.S. Corn Belt. The results illustrate the importance of selecting Landsat acquisitions based on spectral differences in crops at certain development stages. Although early season information can provide estimates of total corn and soybean areas, acquisitions from about emergence and after tasseling of the corn seem to provide a minimal set for accurate identification of corn and soybeans in the U.S. Corn Belt. Additional acquisitions provide only marginally greater separability for corn and soybeans.

  3. Estimating the Sensitivity of CLM-Crop to Plant Date and Growing Season Length

    NASA Astrophysics Data System (ADS)

    Drewniak, B. A.; Kotamarthi, V. R.

    2012-12-01

    The Community Land Model (CLM), the land component of the Community Earth System Model (CESM), is designed to estimate the land surface response to climate through simulated vegetation phenology and soil carbon and nitrogen dynamics. Since human influences play a significant role shaping the land surface, the vegetation has been expanded to include agriculture (CLM-Crop) for three crop types: corn, soybean, and spring wheat. CLM-Crop parameters, which define crop phenology, are optimized against AmeriFlux observations of gross primary productivity, net ecosystem exchange, and stored biomass and carbon, for two sites in the U.S. growing corn and soybean. However, there is uncertainty in the measurements and using a small subset of data to determine model parameters makes validation difficult. In order to account for the differences in plant behavior across climate zones, an input dataset is used to define the planting dates and the length of the growing season. In order to improve model performance, and to understand the impacts of uncertainty from the input data, we evaluate the sensitivity of crop productivity and production against planting date and the length of the growing season. First, CLM-Crop is modified to establish plant date based on temperature trends for the previous 10-day period, constrained against the range of observed planting dates. This new climate-based model is compared with the standard fixed plant dates to determine how sensitive the model is to when seeding occurs, and how comparable the climate calculated plant dates are to the fixed dates. Next, the length of the growing season will be revised to account for an alternative climate. Finally, both the climate-based planting and new growth season will be simulated together. Results of the different model runs will be compared to the standard model and to observations to determine the importance of planting date and growing season length on crop productivity and yield.

  4. Estimating seasonal crop ET using calendar and heat unit based crop coefficients in the Texas High Plains Evapotranspiration Network

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Texas High Plains Evapotranspiration (TXHPET) network utilizes a heat unit-based approach (growing degree day concept) in the timing of various crop growth stages along with crop coefficients for computation of crop water use with the newly standardized ASCE/EWRI reference evapotranspiration (E...

  5. Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters.

    PubMed

    Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong

    2016-05-30

    Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data. PMID:27410085

  6. Light- and water-use efficiency model synergy: a revised look at crop yield estimation for agricultural decision-making

    NASA Astrophysics Data System (ADS)

    Marshall, M.; Tu, K. P.

    2015-12-01

    Large-area crop yield models (LACMs) are commonly employed to address climate-driven changes in crop yield and inform policy makers concerned with climate change adaptation. Production efficiency models (PEMs), a class of LACMs that rely on the conservative response of carbon assimilation to incoming solar radiation absorbed by a crop contingent on environmental conditions, have increasingly been used over large areas with remote sensing spectral information to improve the spatial resolution of crop yield estimates and address important data gaps. Here, we present a new PEM that combines model principles from the remote sensing-based crop yield and evapotranspiration (ET) model literature. One of the major limitations of PEMs is that they are evaluated using data restricted in both space and time. To overcome this obstacle, we first validated the model using 2009-2014 eddy covariance flux tower Gross Primary Production data in a rice field in the Central Valley of California- a critical agro-ecosystem of the United States. This evaluation yielded a Willmot's D and mean absolute error of 0.81 and 5.24 g CO2/d, respectively, using CO2, leaf area, temperature, and moisture constraints from the MOD16 ET model, Priestley-Taylor ET model, and the Global Production Efficiency Model (GLOPEM). A Monte Carlo simulation revealed that the model was most sensitive to the Enhanced Vegetation Index (EVI) input, followed by Photosynthetically Active Radiation, vapor pressure deficit, and air temperature. The model will now be evaluated using 30 x 30m (Landsat resolution) biomass transects developed in 2011 and 2012 from spectroradiometric and other non-destructive in situ metrics for several cotton, maize, and rice fields across the Central Valley. Finally, the model will be driven by Daymet and MODIS data over the entire State of California and compared with county-level crop yield statistics. It is anticipated that the new model will facilitate agro-climatic decision-making in

  7. A Study of Estimating Winter Wheat Yields by Using Satellite Data Assimilation with Crop Growth Model

    NASA Astrophysics Data System (ADS)

    Kuwata, K.

    2013-12-01

    Accurate information of crop yield is important for production planning in agriculture. Crop growth model is a effective tool to comprehend crop growth situation. Accordingly, we use the MOSIS data for two types of utilization to provide necessary information for DSSAT. The objective of this study is developing a method of estimating winter wheat yield without adequate information of the field. The first use is estimation of solar radiation, which is required as input data into DSSAT. Since MODIS is observing the earth everyday, solar radiation can be estimated in a region where a climate observation system is not developed. The second use is data assimilation that provides appropriate parameter of cultivation management to DSSAT. MODIS LAI and Dry Matter Production (DMP) estimated from MODIS GPP are assimilated into DSSAT. Before developing data assimilation, we have accomplished sensitivity analysis of DSSAT. As the result of the analysis, we found that planting date and amount of applied fertilizer have correlated strongly with LAI and Dry Matter (DM) for specific growth period. Based on the result, we estimated winter wheat yield by assimilating MODIS LAI and DMP observed during the specific period. In contrast, previous study estimated crop yield by assimilating satellite data observed for the whole growth period. Three different assimilation schemes were tested to verify the accuracy of our method. Our results showed that the estimated winter wheat yield agreed very well with the Japanese agricultural experiment station data. Among different assimilating scenarios, the best result was obtained when MODIS LAI and DMP observed for specific growth period; the Root Square Mean Error (RMSE) was 406.52 kg ha2. The distribution map of full year incident PAR in Asia. Estimated Winter Wheat Yield in Japan In the case 1, detail information gathered by experiment reports.In the case 2, all management parameters are determined by reference to cultivation manuals.In the

  8. Remote estimation of gross primary productivity in crops: from close range to satellite observations

    NASA Astrophysics Data System (ADS)

    Peng, Y.; Gitelson, A. A.; Sakamoto, T.; Masek, J. G.; Rundquist, D.; Nguy-Robertson, A. L.; Verma, S.; Suyker, A.

    2013-12-01

    An accurate estimation of crop gross primary productivity (GPP) is essential for monitoring regional and global carbon exchanges. In this study, with ten-year observations throughout 2001 to 2010 at three irrigated and rainfed AmerFlux sites in Mead, Nebraska, a simple model was tested to estimate crop GPP using a product of chlorophyll-related vegetation index and photosynthetically active radiation (PAR). Vegetation indices (VI), a proxy of canopy chlorophyll, were calculated from canopy reflectance at various spatial and temporal resolution, including daily observations of four-band radiance 6 m above the ground, weekly in-situ measurements of hyperspectral reflectance, and satellite data (Landsat and MODIS). This model was able to estimate GPP accurately in croplands with different crop species, field managements and climatic conditions. It showed that the used VI was quite sensitive to detect daily GPP variation in crops even under stressed conditions when total Chl content is closely tied to seasonal dynamic of GPP. To minimize the uncertainty of GPP variations, which do not follow fluctuations of incoming PAR, potential PAR was introduced into the model as a better representative of radiation absorbed by canopy for photosynthesis. The model using satellite data and potential PAR is entirely based on remotely sensed data not requiring any ground-based observation. The indices using green and NIR Landsat bands were found to be the most accurate in GPP estimation with coefficients of variation below 13% for maize and 15% for soybean. Using MODIS 250 m data, EVI2 and WDRVI were accurate estimating GPP with coefficient of variation below 20% in maize and 25% in soybean.

  9. The estimation of rice paddy yield with GRAMI crop model and Geostationary Ocean Color Imager (GOCI) image over South Korea

    NASA Astrophysics Data System (ADS)

    Yeom, J. M.; Kim, H. O.

    2014-12-01

    In this study, we estimated the rice paddy yield with moderate geostationary satellite based vegetation products and GRAMI model over South Korea. Rice is the most popular staple food for Asian people. In addition, the effects of climate change are getting stronger especially in Asian region, where the most of rice are cultivated. Therefore, accurate and timely prediction of rice yield is one of the most important to accomplish food security and to prepare natural disasters such as crop defoliation, drought, and pest infestation. In the present study, GOCI, which is world first Geostationary Ocean Color Image, was used for estimating temporal vegetation indices of the rice paddy by adopting atmospheric correction BRDF modeling. For the atmospheric correction with LUT method based on Second Simulation of the Satellite Signal in the Solar Spectrum (6S), MODIS atmospheric products such as MOD04, MOD05, MOD07 from NASA's Earth Observing System Data and Information System (EOSDIS) were used. In order to correct the surface anisotropy effect, Ross-Thick Li-Sparse Reciprocal (RTLSR) BRDF model was performed at daily basis with 16day composite period. The estimated multi-temporal vegetation images was used for crop classification by using high resolution satellite images such as Rapideye, KOMPSAT-2 and KOMPSAT-3 to extract the proportional rice paddy area in corresponding a pixel of GOCI. In the case of GRAMI crop model, initial conditions are determined by performing every 2 weeks field works at Chonnam National University, Gwangju, Korea. The corrected GOCI vegetation products were incorporated with GRAMI model to predict rice yield estimation. The predicted rice yield was compared with field measurement of rice yield.

  10. Use of Sharpened Land Surface Temperature for Daily Evapotranspiration Estimation over Irrigated Crops in Arid Lands

    NASA Astrophysics Data System (ADS)

    Rosas Aguilar, J.; McCabe, M. F.; Houborg, R.; Gao, F.

    2014-12-01

    Satellite remote sensing provides data on land surface characteristics, useful for mapping land surface energy fluxes and evapotranspiration (ET). Land-surface temperature (LST) derived from thermal infrared (TIR) satellite data has been reliably used as a remote indicator of ET and surface moisture status. However, TIR imagery usually operates at a coarser resolution than that of shortwave sensors on the same satellite platform, making it sometimes unsuitable for monitoring of field-scale crop conditions. This study applies the data mining sharpener (DMS; Gao et al., 2012) technique to data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which sharpens the 1 km thermal data down to the resolution of the optical data (250-500 m) based on functional LST and reflectance relationships established using a flexible regression tree approach. The DMS approach adopted here has been enhanced/refined for application over irrigated farming areas located in harsh desert environments in Saudi Arabia. The sharpened LST data is input to an integrated modeling system that uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI) in conjunction with model reanalysis data and remotely sensed data from polar orbiting (MODIS) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate daily estimates of evapotranspiration. Results are evaluated against available flux tower observations over irrigated maize near Riyadh in Saudi Arabia. Successful monitoring of field-scale changes in surface fluxes are of importance towards an efficient water use in areas where fresh water resources are scarce and poorly monitored. Gao, F.; Kustas, W.P.; Anderson, M.C. A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land. Remote Sens. 2012, 4, 3287-3319.

  11. Improving the S-Shape Solar Radiation Estimation Method for Supporting Crop Models

    PubMed Central

    Fodor, Nándor

    2012-01-01

    In line with the critical comments formulated in relation to the S-shape global solar radiation estimation method, the original formula was improved via a 5-step procedure. The improved method was compared to four-reference methods on a large North-American database. According to the investigated error indicators, the final 7-parameter S-shape method has the same or even better estimation efficiency than the original formula. The improved formula is able to provide radiation estimates with a particularly low error pattern index (PIdoy) which is especially important concerning the usability of the estimated radiation values in crop models. Using site-specific calibration, the radiation estimates of the improved S-shape method caused an average of 2.72 ± 1.02 (α = 0.05) relative error in the calculated biomass. Using only readily available site specific metadata the radiation estimates caused less than 5% relative error in the crop model calculations when they were used for locations in the middle, plain territories of the USA. PMID:22645451

  12. a Method to Estimate Temporal Interaction in a Conditional Random Field Based Approach for Crop Recognition

    NASA Astrophysics Data System (ADS)

    Diaz, P. M. A.; Feitosa, R. Q.; Sanches, I. D.; Costa, G. A. O. P.

    2016-06-01

    This paper presents a method to estimate the temporal interaction in a Conditional Random Field (CRF) based approach for crop recognition from multitemporal remote sensing image sequences. This approach models the phenology of different crop types as a CRF. Interaction potentials are assumed to depend only on the class labels of an image site at two consecutive epochs. In the proposed method, the estimation of temporal interaction parameters is considered as an optimization problem, whose goal is to find the transition matrix that maximizes the CRF performance, upon a set of labelled data. The objective functions underlying the optimization procedure can be formulated in terms of different accuracy metrics, such as overall and average class accuracy per crop or phenological stages. To validate the proposed approach, experiments were carried out upon a dataset consisting of 12 co-registered LANDSAT images of a region in southeast of Brazil. Pattern Search was used as the optimization algorithm. The experimental results demonstrated that the proposed method was able to substantially outperform estimates related to joint or conditional class transition probabilities, which rely on training samples.

  13. Assimilation of MODIS-derived LAI by radiative transfer modelling to crop growth simulation model for rice crop monitoring and yield estimation in the Mekong delta, Vietnam

    NASA Astrophysics Data System (ADS)

    Nguyen, H.; de Bie, K.; Verhoef, W.

    2014-12-01

    Successful monitoring of rice crops and estimation of its yields in Mekong delta provide vital information to government agencies, rice production stakeholders and insurance companies in making their decisions and plans to establish solutions to protect rice smallholders from the risks involved. Remote sensing-based information promises a cost-effective way to observe rice crop growth in the largest rice producing region of Vietnam. For an extensive rice cultivation region as the Mekong delta, the use of divergence statistic to extract information from long-term or hypertemporal optical remote sensing NDVI profile to map rice cropping patterns has shown a high degree of success. The result map provides accurate information on where rice grew, when it was seeded and harvested, how many time it was cultivated every year. In addition, by using 8-day MODIS TERRA surface reflectance in Soil-Leaf-Canopy (SLC) radiative transfer model, 70 percent variation of seasonal rice LAI values was able to capture, making it useful to be assimilated into a rice crop growth simulation model (ORYZA 2000) to estimate the regional rice production in the season of 2008-2009. Tested results from 56 rice fields located in different rice cropping patterns showed that yields estimated using ORYZA2000 can explain 83 percent variation of field measured yields. However, simulated yields by ORYZA 2000 were used to overestimate by the model since some of model parameters could not be recalibrated due to the lack of field experiment data. This suggest that in the future, in order to gain a better results of rice crop monitoring and yield estimation, apart from improving the estimation of MODIS -derived LAIs by using SLC, calibrating crop growth simulation's parameter have to be taken into account.

  14. Copper and lead levels in crops and soils of the Holland Marsh Area-Ontario

    SciTech Connect

    Czuba, M.; Hutchinson, T.C.

    1980-01-01

    A study was made of the occurrence, distribution, and concentrations of the heavy metals copper (Cu) and lead (Pb) in the soils and crops of the important horticultural area north of Toronto known as the Holland Marsh. The soils are deep organic mucks (> 85% organic matter), derived by the drainage of black marshland soils, which has been carried out over the past 40 years. A comparison is made between the Pb and Cu concentrations in undrained, uncultivated areas of the marsh and in the intensively used horticultural area. Analyses show a marked accumulation of Cu in surface layers of cultivated soils, with a mean surface concentration of 130 ppM, declining to 20 ppM at a 32-cm depth. Undrained (virgin) soils of the same marshes had < 20 ppM at all depths. Lead concentrations also declined through the profile, from concentrations of 22 to 10 ppM. In comparison, undrained areas had elevated Pb levels. Cultivation appeared to have increased Cu, but lowered Pb in the marsh. Copper and lead levels found in the crops were generally higher in the young spring vegetables than in the mature fall ones. Leafy crops, especially lettuce (Lactuca L.) and celery (Apium graveolens), accumulated higher Pb levels in their foliage compared with levels in root crops. Cultivation procedures, including past pesticide applications and fertilizer additions, appeared to be principal sources of Cu. Mobility from the soil and into the plant for these elements in the marsh muck soils is discussed.

  15. FARM WORKERS IN A SPECIALIZED SEASONAL CROP AREA, STANISLAUS COUNTY, CALIFORNIA.

    ERIC Educational Resources Information Center

    METZLER, WILLIAM H.

    SPECIALIZATION IN THE CROPS BEST ADAPTED TO THE LOCAL AREA IS SEEN AS A HIGHLY PRODUCTIVE SYSTEM OF AGRICULTURE, BUT BY CREATING THE NEED FOR LARGE NUMBERS OF WORKERS FOR SHORT PERIODS OF TIME, IT CAUSES UNEMPLOYMENT AND MIGRATION. A SURVEY OF FRUIT AND VEGETABLE WORKERS IN STANISLAUS COUNTY, CALIFORNIA IN 1962-63 REVEALS--(1) THEIR EARNINGS ARE…

  16. Advances in regional crop yield estimation over the United States using satellite remote sensing data

    NASA Astrophysics Data System (ADS)

    Johnson, D. M.; Dorn, M. F.; Crawford, C.

    2015-12-01

    Since the dawn of earth observation imagery, particularly from systems like Landsat and the Advanced Very High Resolution Radiometer, there has been an overarching desire to regionally estimate crop production remotely. Research efforts integrating space-based imagery into yield models to achieve this need have indeed paralleled these systems through the years, yet development of a truly useful crop production monitoring system has been arguably mediocre in coming. As a result, relatively few organizations have yet to operationalize the concept, and this is most acute in regions of the globe where there are not even alternative sources of crop production data being collected. However, the National Agricultural Statistics Service (NASS) has continued to push for this type of data source as a means to complement its long-standing, traditional crop production survey efforts which are financially costly to the government and create undue respondent burden on farmers. Corn and soybeans, the two largest field crops in the United States, have been the focus of satellite-based production monitoring by NASS for the past decade. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been seen as the most pragmatic input source for modeling yields primarily based on its daily revisit capabilities and reasonable ground sample resolution. The research methods presented here will be broad but provides a summary of what is useful and adoptable with satellite imagery in terms of crop yield estimation. Corn and soybeans will be of particular focus but other major staple crops like wheat and rice will also be presented. NASS will demonstrate that while MODIS provides a slew of vegetation related products, the traditional normalized difference vegetation index (NDVI) is still ideal. Results using land surface temperature products, also generated from MODIS, will also be shown. Beyond the MODIS data itself, NASS research has also focused efforts on understanding a

  17. Soybean Area and Yield Estimation Using MODIS and Landsat Data in the Conterminous United States

    NASA Astrophysics Data System (ADS)

    Song, X. P.; Hansen, M.; Potapov, P.; Stehman, S. V.; Krylov, A.; King, L.; Adusei, B.

    2015-12-01

    The world's population is projected to grow to 9 billion by 2050. The increasing population, amplified by people's increasing consumption of animal products will create a massive demand for food and feed from grain production. As such, global food security will remain a worldwide concern for the next half century. Addressing the food security issue requires data and information support, including research and operational programs for crop monitoring, modeling and yield forecasting. Satellite observations, owing to their synoptic and repetitive nature, have the unique advantage of providing timely information on crop growth at regional to global scales. However, it remains a challenge to accurately identify crop type, estimate areal extent and forecast crop yield with satellite data. Here we employ a stratified random sampling framework for estimating soybean area and yield in the conterminous United States using satellite data collected by the MODIS and Landsat sensors. Complementing each other, the temporally-rich MODIS data are used to capture rapid phenological transitions of soybean crops, whereas the moderate-resolution Landsat data are used to delineate more spatial details for accurate area estimation. For every sample, we derive generic phenological metrics from MODIS and Landsat data and employ machine learning algorithms to identify soybean pixels with reference data generated from RapidEye images and verified by extensive field visits. We also characterize empirical relationships between satellite metrics and soybean yield compiled by the USDA National Agricultural Statistics Service (NASS). Preliminary results suggest that MODIS data alone underestimate soybean area considerably, whereas Landsat data can provide accurate estimate on soybean area. However, soybean yield can be predicted using MODIS-based reflectance metrics. Our sample depict well the spatial variation of soybean yield over the conterminous United States. In addition, the area

  18. Estimation of runoff mitigation by morphologically different cover crop root systems

    NASA Astrophysics Data System (ADS)

    Yu, Yang; Loiskandl, Willibald; Kaul, Hans-Peter; Himmelbauer, Margarita; Wei, Wei; Chen, Liding; Bodner, Gernot

    2016-07-01

    Hydrology is a major driver of biogeochemical processes underlying the distinct productivity of different biomes, including agricultural plantations. Understanding factors governing water fluxes in soil is therefore a key target for hydrological management. Our aim was to investigate changes in soil hydraulic conductivity driven by morphologically different root systems of cover crops and their impact on surface runoff. Root systems of twelve cover crop species were characterized and the corresponding hydraulic conductivity was measured by tension infiltrometry. Relations of root traits to Gardner's hydraulic conductivity function were determined and the impact on surface runoff was estimated using HYDRUS 2D. The species differed in both rooting density and root axes thickness, with legumes distinguished by coarser axes. Soil hydraulic conductivity was changed particularly in the plant row where roots are concentrated. Specific root length and median root radius were the best predictors for hydraulic conductivity changes. For an intensive rainfall simulation scenario up to 17% less rainfall was lost by surface runoff in case of the coarsely rooted legumes Melilotus officinalis and Lathyrus sativus, and the densely rooted Linum usitatissimum. Cover crops with coarse root axes and high rooting density enhance soil hydraulic conductivity and effectively reduce surface runoff. An appropriate functional root description can contribute to targeted cover crop selection for efficient runoff mitigation.

  19. Crop suitability monitoring for improved yield estimations with 100m PROBA-V data

    NASA Astrophysics Data System (ADS)

    Özüm Durgun, Yetkin; Gilliams, Sven; Gobin, Anne; Duveiller, Grégory; Djaby, Bakary; Tychon, Bernard

    2015-04-01

    This study has been realised within the framework of a PhD targeting to advance agricultural monitoring with improved yield estimations using SPOT VEGETATION remotely sensed data. For the first research question, the aim was to improve dry matter productivity (DMP) for C3 and C4 plants by adding a water stress factor. Additionally, the relation between the actual crop yield and DMP was studied. One of the limitations was the lack of crop specific maps which leads to the second research question on 'crop suitability monitoring'. The objective of this work is to create a methodological approach based on the spectral and temporal characteristics of PROBA-V images and ancillary data such as meteorology, soil and topographic data to improve the estimation of annual crop yields. The PROBA-V satellite was launched on 6th May 2013, and was designed to bridge the gap in space-borne vegetation measurements between SPOT-VGT (March 1998 - May 2014) and the upcoming Sentinel-3 satellites scheduled for launch in 2015/2016. PROBA -V has products in four spectral bands: BLUE (centred at 0.463 µm), RED (0.655 µm), NIR (0.845 µm), and SWIR (1.600 µm) with a spatial resolution ranging from 1km to 300m. Due to the construction of the sensor, the central camera can provide a 100m data product with a 5 to 8 days revisiting time. Although the 100m data product is still in test phase a methodology for crop suitability monitoring was developed. The multi-spectral composites, NDVI (Normalised Difference Vegetation Index) (NIR_RED/NIR+RED) and NDII (Normalised Difference Infrared Index) (NIR-SWIR/NIR+SWIR) profiles are used in addition to secondary data such as digital elevation data, precipitation, temperature, soil types and administrative boundaries to improve the accuracy of crop yield estimations. The methodology is evaluated on several FP7 SIGMA test sites for the 2014 - 2015 period. Reference data in the form of vector GIS with boundaries and cover type of agricultural fields are

  20. Impacts of Reprojection and Sampling of MODIS Satellite Images on Estimating Crop Evapotranspiration Using METRIC model

    NASA Astrophysics Data System (ADS)

    Pun, M.; Kilic, A.; Allen, R.

    2014-12-01

    Landsat satellite images have been used frequently to map evapotranspiration (ET) andbiophysical variables at the field scale with surface energy balance algorithms. Although Landsat images have high spatial resolution with 30m cell size, it has limitations for real time monitoring of crop ET by providing only two to four images per month for an area, which, when encountered with cloudy days, further deteriorates the availability of images and snapshots of ET behavior. Therefore real time monitoring essentially has to include near-daily thermal satellites such as MODIS/VIIRS into the time series. However, the challenge with field scale monitoring with these systems is the large size of the thermal band which is 375 m with VIIRS and 1000 meter with MODIS. To maximize the accuracy of ET estimates during infusion of MODIS products into land surface models for monitoring field scale ET, it is important to assess the geometric accuracy of the various MODIS products, for example, spatial correspondence among the 250 m red and near-infrared bands, the 500 m reflectance bands; and the 1000 m thermal bands and associated products. METRIC model was used with MODIS images to estimate ET from irrigated and rainfed fields in Nebraska. Our objective was to assess geometric accuracy of MODIS image layers and how to correctly handle these data for highest accuracy of estimated ET at the individual field scale during the extensive drought of 2012. For example, the particular tool used to subset and reproject MODIS swath images from level-1 and level-2 products (e.g., using the MRTSwath and other tools), the initial starting location (upper left hand corner), and the projection system all effect how pixel corners of the various resolution bands align. Depending on the approach used, origin of pixel corners can vary from image to image date and therefore impacts the pairing of ET information from multiple dates the consistency and accuracy of sampling ET from within field interiors

  1. Kohonen self-organizing map estimator for the reference crop evapotranspiration

    NASA Astrophysics Data System (ADS)

    Adeloye, Adebayo J.; Rustum, Rabee; Kariyama, Ibrahim D.

    2011-08-01

    Reference crop evapotranspiration (ETo) estimation is of importance in irrigation water management for the calculation of crop water requirements and its scheduling, in rainfall-runoff modeling and in numerous other water resources studies. Due to its importance, several direct and indirect methods have been employed to determine the reference crop evapotranspiration but success has been limited because the direct measurement methods lack in precision and accuracy due to scale issues and other problems, while some of the more accurate indirect methods, e.g., the Penman-Monteith benchmark model, are time-consuming and require weather input data that are not routinely monitored. This paper has used the Kohonen self-organizing map (KSOM), unsupervised artificial neural networks, to predict the ETo. based on observed daily weather data at two climatically diverse basins: a small experimental catchment in temperate Edinburgh, UK and a semiarid lake basin in Udaipur, India. This was achieved by using the powerful clustering capability of the KSOM to analyze the multidimensional data array comprising the estimated ETo (based on the Food and Agricultural Organization (FAO) Penman-Monteith model) and different subsets of climatic variables known to affect it. The findings indicate that the KSOM-based ETo estimates even with fewer input variables were in good agreement with those obtained using the conventional FAO Penman-Monteith formulation employing the full complement of weather data at the two locations. More crucially, the KSOM-based estimates were also found to be significantly superior to those estimated using currently recommended empirical ETo methods for data scarce situations such as those in developing countries.

  2. Physically-based Methods for the Estimation of Crop Water Requirements from E.O. Optical Data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The estimation of evapotranspiration (ET) represent the basic information for the evaluation of crop water requirements. A widely used method to compute ET is based on the so-called "crop coefficient" (Kc), defined as the ratio of total evapotranspiration by reference evapotranspiration ET0. The val...

  3. The Importance of Rotational Crops for Biodiversity Conservation in Mediterranean Areas.

    PubMed

    Chiatante, Gianpasquale; Meriggi, Alberto

    2016-01-01

    Nowadays we are seeing the largest biodiversity loss since the extinction of the dinosaurs. To conserve biodiversity it is essential to plan protected areas using a prioritization approach, which takes into account the current biodiversity value of the sites. Considering that in the Mediterranean Basin the agro-ecosystems are one of the most important parts of the landscape, the conservation of crops is essential to biodiversity conservation. In the framework of agro-ecosystem conservation, farmland birds play an important role because of their representativeness, and because of their steady decline in the last Century in Western Europe. The main aim of this research was to define if crop dominated landscapes could be useful for biodiversity conservation in a Mediterranean area in which the landscape was modified by humans in the last thousand years and was affected by the important biogeographical phenomenon of peninsula effect. To assess this, we identify the hotspots and the coldspots of bird diversity in southern Italy both during the winter and in the breeding season. In particular we used a scoring method, defining a biodiversity value for each cell of a 1-km grid superimposed on the study area, using data collected by fieldwork following a stratified random sampling design. This value was analysed by a multiple linear regression analysis and was predicted in the whole study area. Then we defined the hotspots and the coldspots of the study area as 15% of the cells with higher and lower value of biodiversity, respectively. Finally, we used GAP analysis to compare hotspot distribution with the current network of protected areas. This study showed that the winter hotspots of bird diversity were associated with marshes and water bodies, shrublands, and irrigated crops, whilst the breeding hotspots were associated with more natural areas (e.g. transitional wood/shrubs), such as open areas (natural grasslands, pastures and not irrigated crops). Moreover, the

  4. The Importance of Rotational Crops for Biodiversity Conservation in Mediterranean Areas

    PubMed Central

    Chiatante, Gianpasquale; Meriggi, Alberto

    2016-01-01

    Nowadays we are seeing the largest biodiversity loss since the extinction of the dinosaurs. To conserve biodiversity it is essential to plan protected areas using a prioritization approach, which takes into account the current biodiversity value of the sites. Considering that in the Mediterranean Basin the agro-ecosystems are one of the most important parts of the landscape, the conservation of crops is essential to biodiversity conservation. In the framework of agro-ecosystem conservation, farmland birds play an important role because of their representativeness, and because of their steady decline in the last Century in Western Europe. The main aim of this research was to define if crop dominated landscapes could be useful for biodiversity conservation in a Mediterranean area in which the landscape was modified by humans in the last thousand years and was affected by the important biogeographical phenomenon of peninsula effect. To assess this, we identify the hotspots and the coldspots of bird diversity in southern Italy both during the winter and in the breeding season. In particular we used a scoring method, defining a biodiversity value for each cell of a 1-km grid superimposed on the study area, using data collected by fieldwork following a stratified random sampling design. This value was analysed by a multiple linear regression analysis and was predicted in the whole study area. Then we defined the hotspots and the coldspots of the study area as 15% of the cells with higher and lower value of biodiversity, respectively. Finally, we used GAP analysis to compare hotspot distribution with the current network of protected areas. This study showed that the winter hotspots of bird diversity were associated with marshes and water bodies, shrublands, and irrigated crops, whilst the breeding hotspots were associated with more natural areas (e.g. transitional wood/shrubs), such as open areas (natural grasslands, pastures and not irrigated crops). Moreover, the

  5. The Tasseled Cap Transformation for RapidEye data and the estimation of vital and senescent crop parameters

    NASA Astrophysics Data System (ADS)

    Schonert, M.; Zillmann, E.; Weichelt, H.; Eitel, J. U. H.; Magney, T. S.; Lilienthal, H.; Siegmann, B.; Jarmer, T.

    2015-04-01

    The retrieval of crop biophysical parameters using spectral indices obtained from high temporal and spatial resolution satellite data, is a valuable tool to monitor crop growth and status. Tasseled Cap Features (TCFs) for RapidEye data were derived from spectral variances typically present in agricultural scenes. The TCF Greenness (GRE) was aligned to the spectral variance of vital vegetation, and therefore, it represents the typical reflectance characteristics of green vegetation, with relatively higher reflectance at the nearinfrared (NIR) range. The TCF Yellowness (YEL) was aligned to correspond to the reflectance characteristics of senescent crops, with relatively higher reflectance in the visible portion of the spectrum due to chlorophyll breakdown, and lower reflectance in the NIR range due to cell structure decomposition compared to vital green vegetation. The goal of this work was to assess the potential of RapidEye's TCFs for the prediction of green leaf area index (LAI), plant chlorophyll (Chl), and nitrogen (N) concentration, as well as the identification of senescence patterns. The linear relationships between the biophysical parameters and the TCFs were compared to the performance of the widely used indices NDVI and PSRI. Preliminary results indicate that GRE is strongly related to LAI in vital crops and suggests a higher predictive power than NDVI. YEL demonstrated a strong linear relation and a higher potential to estimate Chl and N concentration in senescent soft white winter wheat (Triticum aestivum L.) in comparison to PSRI. PSRI showed a stronger correlation to Chl in senescent soft white spring wheat (Triticum aestivum L.), compared to YEL. Results indicate that YEL may be used to characterize the variability in senescence status within fields. This information, in conjunction with soil fertility and yield maps, can potentially be used to designate precision management zones.

  6. Coupling a land-surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

    NASA Astrophysics Data System (ADS)

    Tsarouchi, G. M.; Buytaert, W.; Mijic, A.

    2014-10-01

    Land-Surface Models (LSMs) are tools that represent energy and water flux exchanges between land and the atmosphere. Although much progress has been made in adding detailed physical processes into these models, there is much room left for improved estimates of evapotranspiration fluxes, by including a more reasonable and accurate representation of crop dynamics. Recent studies suggest a strong land-surface-atmosphere coupling over India and since this is one of the most intensively cultivated areas in the world, the strong impact of crops on the evaporative flux cannot be neglected. In this study we dynamically couple the LSM JULES with the crop growth model InfoCrop. JULES in its current version (v3.4) does not simulate crop growth. Instead, it treats crops as natural grass, while using prescribed vegetation parameters. Such simplification might lead to modelling errors. Therefore we developed a coupled modelling scheme that simulates dynamically crop development and parametrized it for the two main crops of the study area, wheat and rice. This setup is used to examine the impact of inter-seasonal land cover changes in evapotranspiration fluxes of the Upper Ganges River basin (India). The sensitivity of JULES with regard to the dynamics of the vegetation cover is evaluated. Our results show that the model is sensitive to the changes introduced after coupling it with the crop model. Evapotranspiration fluxes, which are significantly different between the original and the coupled model, are giving an approximation of the magnitude of error to be expected in LSMs that do not include dynamic crop growth. For the wet season, in the original model, the monthly Mean Error ranges from 7.5 to 24.4 mm month-1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 5.4-11.6 mm month-1. For the dry season, in the original model, the monthly Mean Error ranges from 10 to 17 mm month-1, depending on

  7. Coupling a land surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

    NASA Astrophysics Data System (ADS)

    Tsarouchi, G. M.; Buytaert, W.; Mijic, A.

    2014-06-01

    Land surface models are tools that represent energy and water flux exchanges between land and the atmosphere. Although much progress has been made in adding detailed physical processes into these models, there is much room left for improved estimates of evapotranspiration fluxes, by including a more reasonable and accurate representation of crop dynamics. Recent studies suggest a strong land surface-atmosphere coupling over India and since this is one of the most intensively cultivated areas in the world, the strong impact of crops on the evaporative flux cannot be neglected. In this study we dynamically couple the land surface model JULES with the crop growth model InfoCrop. JULES in its current version does not simulate crop growth. Instead, it treats crops as natural grass, while using prescribed vegetation parameters. Such simplification might lead to modelling errors. Therefore we developed a coupled modelling scheme that simulates dynamically crop development and parameterised it for the two main crops of the study area, wheat and rice. This setup is used to examine the impact of inter-seasonal land cover changes in evapotranspiration fluxes of the Upper Ganges river basin (India). The sensitivity of JULES with regard to the dynamics of the vegetation cover is evaluated. Our results show that the model is sensitive to the changes introduced after coupling it with the crop model. Evapotranspiration fluxes, which are significantly different between the original and the coupled model, are giving an approximation of the magnitude of error to be expected in LSMs that do not include dynamic crop growth. For the wet season, in the original model, the monthly Mean Error ranges from 7.5 to 24.4 mm m-1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 7-14 mm m-1. For the dry season, in the original model, the monthly Mean Error ranges from 10 to 17 mm m-1, depending on different

  8. Productivity and carbon dioxide exchange of leguminous crops: estimates from flux tower measurements

    USGS Publications Warehouse

    Gilmanov, Tagir G.; Baker, John M.; Bernacchi, Carl J.; Billesbach, David P.; Burba, George G.; Castro, Saulo; Chen, Jiquan; Eugster, Werner; Fischer, Marc L.; Gamon, John A.; Gebremedhin, Maheteme T.; Glenn, Aaron J.; Griffis, Timothy J.; Hatfield, Jerry L.; Heuer, Mark W.; Howard, Daniel M.; Leclerc, Monique Y.; Loescher, Henry W.; Marloie, Oliver; Meyers, Tilden P.; Olioso, Albert; Phillips, Rebecca L.; Prueger, John H.; Skinner, R. Howard; Suyker, Andrew E.; Tenuta, Mario; Wylie, Bruce K.

    2014-01-01

    Net CO2 exchange data of legume crops at 17 flux tower sites in North America and three sites in Europe representing 29 site-years of measurements were partitioned into gross photosynthesis and ecosystem respiration by using the nonrectangular hyperbolic light-response function method. The analyses produced net CO2 exchange data and new ecosystem-scale ecophysiological parameter estimates for legume crops determined at diurnal and weekly time steps. Dynamics and annual totals of gross photosynthesis, ecosystem respiration, and net ecosystem production were calculated by gap filling with multivariate nonlinear regression. Comparison with the data from grain crops obtained with the same method demonstrated that CO2 exchange rates and ecophysiological parameters of legumes were lower than those of maize (Zea mays L.) but higher than for wheat (Triticum aestivum L.) crops. Year-round annual legume crops demonstrated a broad range of net ecosystem production, from sinks of 760 g CO2 m–2 yr–1 to sources of –2100 g CO2 m–2 yr–1, with an average of –330 g CO2 m–2 yr–1, indicating overall moderate CO2–source activity related to a shorter period of photosynthetic uptake and metabolic costs of N2 fixation. Perennial legumes (alfalfa, Medicago sativa L.) were strong sinks for atmospheric CO2, with an average net ecosystem production of 980 (range 550–1200) g CO2 m–2 yr–1.

  9. Towards a Quantitative Use of Satellite Remote Sensing in Crop Growth Models for Large Scale Agricultural Production Estimate (Invited)

    NASA Astrophysics Data System (ADS)

    Defourny, P.

    2013-12-01

    The development of better agricultural monitoring capabilities is clearly considered as a critical step for strengthening food production information and market transparency thanks to timely information about crop status, crop area and yield forecasts. The documentation of global production will contribute to tackle price volatility by allowing local, national and international operators to make decisions and anticipate market trends with reduced uncertainty. Several operational agricultural monitoring systems are currently operating at national and international scales. Most are based on the methods derived from the pioneering experiences completed some decades ago, and use remote sensing to qualitatively compare one year to the others to estimate the risks of deviation from a normal year. The GEO Agricultural Monitoring Community of Practice described the current monitoring capabilities at the national and global levels. An overall diagram summarized the diverse relationships between satellite EO and agriculture information. There is now a large gap between the current operational large scale systems and the scientific state of the art in crop remote sensing, probably because the latter mainly focused on local studies. The poor availability of suitable in-situ and satellite data over extended areas hampers large scale demonstrations preventing the much needed up scaling research effort. For the cropland extent, this paper reports a recent research achievement using the full ENVISAT MERIS 300 m archive in the context of the ESA Climate Change Initiative. A flexible combination of classification methods depending to the region of the world allows mapping the land cover as well as the global croplands at 300 m for the period 2008 2012. This wall to wall product is then compared with regards to the FP 7-Geoland 2 results obtained using as Landsat-based sampling strategy over the IGADD countries. On the other hand, the vegetation indices and the biophysical variables

  10. Development of rotation sample designs for the estimation of crop acreages

    NASA Technical Reports Server (NTRS)

    Lycthuan-Lee, T. G. (Principal Investigator)

    1981-01-01

    The idea behind the use of rotation sample designs is that the variation of the crop acreage of a particular sample unit from year to year is usually less than the variation of crop acreage between units within a particular year. The estimation theory is based on an additive mixed analysis of variance model with years as fixed effects, (a sub t), and sample units as a variable factor. The rotation patterns are decided upon according to: (1) the number of sample units in the design each year; (2) the number of units retained in the following years; and (3) the number of years to complete the rotation pattern. Different analytic formulae for the variance of (a sub t) and the variance comparisons in using a complete survey of the rotation patterns.

  11. Crop Coefficients of Some Selected Crops of Andhra Pradesh

    NASA Astrophysics Data System (ADS)

    Reddy, K. Chandrasekhar; Arunajyothy, S.; Mallikarjuna, P.

    2015-06-01

    Precise information on crop coefficients for estimating crop evapotranspiration (ETc) for regional scale irrigation planning is a major impediment in many regions. Crop coefficients suggested based on lysimeter data by earlier investigators have to be locally calibrated to account for the differences in the crop canopy under given climatic conditions. In the present study crop coefficients were derived based on reference crop evapotranspiration (ET0) estimated from Penman-Monteith equation and lysimeter measured ETc for groundnut, paddy, tobacco, sugarcane and castor crops at Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar centers of Andhra Pradesh respectively. Crop coefficients derived were compared with those recommended by FAO-56. The mean crop coefficients at different stages of growth were significantly different from those of FAO-56 curve though a similar trend was observed. A third order polynomial crop coefficient model has therefore been developed as a function of time (days after sowing the crop) for deriving suitable crop coefficients. The crop coefficient models suggested may be adopted to estimate crop evapotranspiration in the study area with reasonable degree of accuracy.

  12. Estimating biomass, yield, evaprotranspiration and carbon fluxes for winter wheat by using high resolution remote sensing data combined with a crop model

    NASA Astrophysics Data System (ADS)

    Veloso, A.; Ceschia, E.; Demarez, V.

    2013-12-01

    The use of crop models allows simulating plant development, growth, yield, CO2 and water fluxes under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. Besides, monitoring spatial and temporal variation in water budget and amount of carbon fixed by these crops is an ultimate goal of earth climate change studies. We propose here an approach to estimate time courses of dry aboveground biomass (DAM), yield and evapotranspiration (ETR) for winter wheat by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. This model is then coupled with a ';carbon flux module' for estimating the components of the carbon budget (gross primary production (GPP), ecosystem respiration (Reco), ...). Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. For this work, we employed a unique set of Formosat-2 and SPOT images acquired from 2006 to 2011 in southwest France. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, biomass (NPP), grain yield and ETR. The carbon flux module simulates GPP, the autotrophic respiration (Ra) that is defined as the sum of plant growth and maintenance respiration and the heterotrophic respiration (Rh

  13. Agricultural land application of pulp and paper mill sludges in the Donnacona area, Quebec: Chemical evaluation and crop response

    SciTech Connect

    Veillette, A.X.; Tanguay, M.G.

    1997-12-31

    Primary paper mill sludges from a thermomechanical pulp (TMP) mill were land applied at the rate of 20 metric ton per hectare (t/ha) for agricultural purposes in the Donnacona area, Quebec, in May 1994 and May 1995. Eleven agricultural sites featuring various crops were tested over two seasons to measure the impact of TMP primary paper mill sludges on soil, plant tissue and crop yield. Cereal and potato crops showed a significant increase in yield. TMP Primary sludges were also applied at the rate of 225 t/ha for land reclamation purposes of one site at the end of 1994. Soils were tested every second month. Chemical crop analyses were also performed. The first year crop response was satisfactory. Combined (primary and secondary) TMP sludges were added at the rate of 200 t/ha in the beginning of 1996. Soil, vadose zone water and crop analysis are being investigated. Impressive crop responses were obtained in the 1996 season.

  14. Analyzing C-band SAR polarimetric information for LAI and crop yield estimations

    NASA Astrophysics Data System (ADS)

    Molijn, Ramses A.; Iannini, Lorenzo; Mousivand, Ali; Hanssen, Ramon F.

    2014-10-01

    In this study, space remote sensing data and crop specific information from the ESA-led AgriSAR 2009 campaign are used for studying the profiles of C-band SAR backscatter signals and multispectral-based leaf area index (LAI) over the growth period of canola, pea and wheat. In addition, the correlations between radar backscatter parameters and the crop yields were analyzed, based on extracted statistics of temporal profiles. The results show that the HV backscatter and LAI are correlated differently before and after LAI peak. In addition, the coefficient of determination between peakrelated statistics from polarimetric indicator profiles and yield for pea fields can reach up to 0.68, and for canola and wheat up to 0.47 and 0.5, respectively. HV backscatter and coherence between HH and VV are most.

  15. Bare soil erosion modelling with rainfall simulations: experiments on crop and recently burned areas

    NASA Astrophysics Data System (ADS)

    Catani, F.; Menci, S.; Moretti, S.; Keizer, J.

    2006-12-01

    The use of numerical models is of fundamental importance in the comprehension and prediction of soil erosion. At the very basis of the calibration process of the numerical models are the direct measurements of the governing parameters, carried out during field or laboratory tests. To measure and model soil erosion rainfall simulations can be used, that allow the reproduction of project rainfall having chosen characteristics of intensity and duration. The main parameters that rainfall simulators can measure are hydraulic conductivity, parameters of soil erodibility, rate and features of splash erosion, discharge coefficient and sediment yield. Other important parameters can be estimated during the rainfall simulations through the use of photogrammetric instruments able to memorize high definition stereographic models of the soil plot under analysis at different time steps. In this research rainfall simulator experiments (rse) were conducted to measure and quantify runoff and erosion processes on selected bare soil plots. The selected plots are located in some vineyards, olive groves and crops in central Italy and in some recently burned areas in north-central Portugal, affected by a wildfire during early July 2005 and, at the time, largely covered by commercial eucalypt plantations. On the Italian crops the choice of the rainfall intensities and durations were performed on the basis of the previous knowledge of the selected test areas. The procedure was based on an initial phase of soil wetting and a following phase of 3 erosion cycles. The first should reproduce the effects of a normal rainfall with a return time of 2 years (23 mm/h). The second should represent a serious episode with a return time of 10 years (34 mm/h). The third has the objective to reproduce and understand the effects of an intense precipitation event, with a return time of 50 years (41 mm/h). During vineyards experiments some photogrammetric surveys were carried out as well. In the Portugal

  16. LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Optical remote sensing of crop nitrogen (N) status is developing into a powerful diagnostic tool that can improve N management decisions. Crop N status is a function of dry mass per unit area (W) and N concentration (%Na), which can be used to calculate N nutrition index (NNI),where NNI is %Na/%Nc (...

  17. Worldwide Historical Estimates of Leaf Area Index, 1932-2000

    SciTech Connect

    Scurlock, JMO

    2002-02-06

    Approximately 1000 published estimates of leaf area index (LAI) from nearly 400 unique field sites, covering the period 1932-2000, have been compiled into a single data set. LA1 is a key parameter for global and regional models of biosphere/atmosphere exchange of carbon dioxide, water vapor, and other materials. It also plays an integral role in determining the energy balance of the land surface. This data set provides a benchmark of typical values and ranges of LA1 for a variety of biomes and land cover types, in support of model development and validation of satellite-derived remote sensing estimates of LA1 and other vegetation parameters. The LA1 data are linked to a bibliography of over 300 original source references. These historic LA1 data are mostly from natural and seminatural (managed) ecosystems, although some agricultural estimates are also included. Although methodologies for determining LA1 have changed over the decades, it is useful to represent the inconsistencies (e.g., in maximum value reported for a particular biome) that are actually found in the scientific literature. Needleleaf (coniferous) forests are by far the most commonly measured biome/land cover types in this compilation, with 22% of the measurements from temperate evergreen needleleaf forests, and boreal evergreen needleleaf forests and crops the next most common (about 9% each). About 40% of the records in the data set were published in the past 10 years (1991-2000), with a further 20% collected between 1981 and 1990. Mean LAI ({+-} standard deviation), distributed between 15 biome/land cover classes, ranged from 1.31 {+-} 0.85 for deserts to 8.72 {+-} 4.32 for tree plantations, with evergreen forests (needleleaf and broadleaf) displaying the highest LA1 among the natural terrestrial vegetation classes. We have identified statistical outliers in this data set, both globally and according to the different biome/land cover classes, but despite some decreases in mean LA1 values reported

  18. A critical analysis of three remote sensing-based actual evapotranspiration assessment methods over sparse crops agricultural areas

    NASA Astrophysics Data System (ADS)

    Cammalleri, Carmelo; Ciraolo, Giuseppe; La Loggia, Goffredo; Minacapilli, Mario

    2010-10-01

    During last two decades the increasing availability of remotely sensed acquisitions in the thermal infrared part of the spectrum has encouraged hydrologist community to develop models and methodologies based on these kind of data. The aim of this paper is to compare three methods developed to assess the actual evapotranspiration spatial distribution by means of remote sensing data. The comparison was focused on the differences between the "single" (SEBAL) and "two" source (TSEB) surface energy balance approaches and the S-SEBI semi-empirical method. The first assumes a semiempirical internal calibration for the sensible heat flux assessment; the second uses a physically based approach in order to assess separately the soil and vegetation fluxes. Finally, the last one is based on the correlation between albedo and surface temperature for evaporative fraction estimations. The models were applied using 7 high resolution images, collected by an airborne platform between June and October 2008, approximately every 3 weeks. The acquired data include multi-spectral images (red, green and near infrared) and thermal infrared images for surface temperature estimation. The study area, located in the south-west cost of Sicily, Italy), is characterised by the presence of typical Mediterranean cultivations: olive, vineyard and citrus. Due to irrigation supplies and rainfall events, the water availability for the crops varies in time and this allowed to perform the comparison in a wide range of the modelled variables. Additionally, the availability of high spatial resolution images allowed the testing of the models performances at field scale despite the high vegetation fragmentation of the study area. The comparison of models performance highlights a good agreements of model estimations, analyzed by means of MAD (Mean Absolute Differences) and MAPD (Mean Absolute Percent Differences) indices, especially in terms of study area averaged fluxes. The analysis in correspondence of

  19. Bayes estimation on parameters of the single-class classifier. [for remotely sensed crop data

    NASA Technical Reports Server (NTRS)

    Lin, G. C.; Minter, T. C.

    1976-01-01

    Normal procedures used for designing a Bayes classifier to classify wheat as the major crop of interest require not only training samples of wheat but also those of nonwheat. Therefore, ground truth must be available for the class of interest plus all confusion classes. The single-class Bayes classifier classifies data into the class of interest or the class 'other' but requires training samples only from the class of interest. This paper will present a procedure for Bayes estimation on the mean vector, covariance matrix, and a priori probability of the single-class classifier using labeled samples from the class of interest and unlabeled samples drawn from the mixture density function.

  20. Development of an Assimilation Scheme for the Estimation of Drought-Induced Yield Losses Based on Multi-Source Remote Sensing and the AcquaCrop Model

    NASA Astrophysics Data System (ADS)

    Silvestro, Paolo Cosmo; Casa, Raffaele; Pignatti, Stefano; Castaldi, Fabio; Yang, Hao; Yang, Guijun

    2014-11-01

    In the context of the Dragon-3 Farmland Drought project, our research deals with the development of methods for the assimilation of biophysical variables, estimated from multi-source remote sensing, into the AquaCrop model, in order to estimate the yield losses due to drought both at the farm and at the regional scale. The first part of this project was employed to refine a methodology to obtain maps of leaf area index (LAI), canopy cover (CC), fraction of adsorbed photosynthetically active radiation (FAPAR) and chlorophyll (Cab) from satellite optical data, using algorithms based on the training of artificial neural networks (ANN) on PROSAIL model simulations. In the second part, retrieved values of CC were assimilated into the AquaCrop model using the assimilation method of the Ensemble Kalman Filter to estimate grain wheat yield at the field scale.

  1. A double-hurdle model estimation of cocoa farmers' willingness to pay for crop insurance in Ghana.

    PubMed

    Okoffo, Elvis Dartey; Denkyirah, Elisha Kwaku; Adu, Derick Taylor; Fosu-Mensah, Benedicta Yayra

    2016-01-01

    Agriculture is an important sector in Ghana's economy, however, with high risk due to natural factors like climate change, pests and diseases and bush fires among others. Farmers in the Brong-Ahafo region of Ghana which is known as one of the major cocoa producing regions, face these risks which sometimes results in crop failure. The need for farmers to therefore insure their farms against crop loss is crucial. Insurance has been a measure to guard against risk. The aim of this study was to assess cocoa farmers' willingness to access crop insurance, the factors affecting willingness to pay (WTP) for crop insurance scheme and insurance companies' willingness to provide crop insurance to cocoa farmers. Multi-stage sampling technique was used to sample 240 farmers from four communities in the Dormaa West District in Brong-Ahafo Region. The double-hurdle model shows that age, marital status and education significantly and positively influenced cocoa farmer's willingness to insure their farms whiles household size and cropped area negatively influenced farmers' willingness to insure their farms. Similarly, age, household size and cropped area significantly and positively influenced the premium cocoa farmers were willing to pay whiles marital status and cocoa income negatively influenced the premium farmers were willing to pay. The contingent valuation method shows that the maximum, minimum and average amounts cocoa farmers are willing to pay for crop insurance per production cost per acre was GH¢128.40, GH¢32.10 and GH¢49.32 respectively. Insurance companies do not have crop insurance policy but willing to provide crop insurance policy to cocoa farmers on a condition that farmers adopt modern cultivation practices to reduce the level of risk. The study recommends that cocoa farmers should be well educated on crop insurance and should be involved in planning the crop insurance scheme in order to conclude on the premium to be paid by them. PMID:27386322

  2. Global Climate Niche Estimates for Bioenergy Crops and Invasive Species of Agronomic Origin: Potential Problems and Opportunities

    PubMed Central

    Barney, Jacob N.; DiTomaso, Joseph M.

    2011-01-01

    The global push towards a more biomass-based energy sector is ramping up efforts to adopt regionally appropriate high-yielding crops. As potential bioenergy crops are being moved around the world an assessment of the climatic suitability would be a prudent first step in identifying suitable areas of productivity and risk. Additionally, this assessment also provides a necessary step in evaluating the invasive potential of bioenergy crops, which present a possible negative externality to the bioeconomy. Therefore, we provide the first global climate niche assessment for the major graminaceous (9), herbaceous (3), and woody (4) bioenergy crops. Additionally, we contrast these with climate niche assessments for North American invasive species that were originally introduced for agronomic purposes as examples of well-intentioned introductions gone awry. With few exceptions (e.g., Saccharum officinarum, Pennisetum purpureum), the bioenergy crops exhibit broad climatic tolerance, which allows tremendous flexibility in choosing crops, especially in areas with high summer rainfall and long growing seasons (e.g., southeastern US, Amazon Basin, eastern Australia). Unsurprisingly, the invasive species of agronomic origin have very similar global climate niche profiles as the proposed bioenergy crops, also demonstrating broad climatic tolerance. The ecoregional evaluation of bioenergy crops and known invasive species demonstrates tremendous overlap at both high (EI≥30) and moderate (EI≥20) climate suitability. The southern and western US ecoregions support the greatest number of invasive species of agronomic origin, especially the Southeastern USA Plains, Mixed Woods Plains, and Mediterranean California. Many regions of the world have a suitable climate for several bioenergy crops allowing selection of agro-ecoregionally appropriate crops. This model knowingly ignores the complex biotic interactions and edaphic conditions, but provides a robust assessment of the climate

  3. Global climate niche estimates for bioenergy crops and invasive species of agronomic origin: potential problems and opportunities.

    PubMed

    Barney, Jacob N; DiTomaso, Joseph M

    2011-01-01

    The global push towards a more biomass-based energy sector is ramping up efforts to adopt regionally appropriate high-yielding crops. As potential bioenergy crops are being moved around the world an assessment of the climatic suitability would be a prudent first step in identifying suitable areas of productivity and risk. Additionally, this assessment also provides a necessary step in evaluating the invasive potential of bioenergy crops, which present a possible negative externality to the bioeconomy. Therefore, we provide the first global climate niche assessment for the major graminaceous (9), herbaceous (3), and woody (4) bioenergy crops. Additionally, we contrast these with climate niche assessments for North American invasive species that were originally introduced for agronomic purposes as examples of well-intentioned introductions gone awry. With few exceptions (e.g., Saccharum officinarum, Pennisetum purpureum), the bioenergy crops exhibit broad climatic tolerance, which allows tremendous flexibility in choosing crops, especially in areas with high summer rainfall and long growing seasons (e.g., southeastern US, Amazon Basin, eastern Australia). Unsurprisingly, the invasive species of agronomic origin have very similar global climate niche profiles as the proposed bioenergy crops, also demonstrating broad climatic tolerance. The ecoregional evaluation of bioenergy crops and known invasive species demonstrates tremendous overlap at both high (EI≥30) and moderate (EI≥20) climate suitability. The southern and western US ecoregions support the greatest number of invasive species of agronomic origin, especially the Southeastern USA Plains, Mixed Woods Plains, and Mediterranean California. Many regions of the world have a suitable climate for several bioenergy crops allowing selection of agro-ecoregionally appropriate crops. This model knowingly ignores the complex biotic interactions and edaphic conditions, but provides a robust assessment of the climate

  4. Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment

    Technology Transfer Automated Retrieval System (TEKTRAN)

    We develop a robust understanding of the effects of assimilating remote sensing observations of leaf area index and soil moisture (in the top 5 cm) on DSSAT-CSM CropSim-Ceres wheat yield estimates. Synthetic observing system simulation experiments compare the abilities of the Ensemble Kalman Filter...

  5. Estimation of Crop Gross Primary Production (GPP). 2; Do Scaled (MODIS) Vegetation Indices Improve Performance?

    NASA Technical Reports Server (NTRS)

    Zhang, Qingyuan; Cheng, Yen-Ben; Lyapustin, Alexei I.; Wang, Yujie; Zhang, Xiaoyang; Suyker, Andrew; Verma, Shashi; Shuai, Yanmin; Middleton, Elizabeth M.

    2015-01-01

    Satellite remote sensing estimates of Gross Primary Production (GPP) have routinely been made using spectral Vegetation Indices (VIs) over the past two decades. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the green band Wide Dynamic Range Vegetation Index (WDRVIgreen), and the green band Chlorophyll Index (CIgreen) have been employed to estimate GPP under the assumption that GPP is proportional to the product of VI and photosynthetically active radiation (PAR) (where VI is one of four VIs: NDVI, EVI, WDRVIgreen, or CIgreen). However, the empirical regressions between VI*PAR and GPP measured locally at flux towers do not pass through the origin (i.e., the zero X-Y value for regressions). Therefore they are somewhat difficult to interpret and apply. This study investigates (1) what are the scaling factors and offsets (i.e., regression slopes and intercepts) between the fraction of PAR absorbed by chlorophyll of a canopy (fAPARchl) and the VIs, and (2) whether the scaled VIs developed in (1) can eliminate the deficiency and improve the accuracy of GPP estimates. Three AmeriFlux maize and soybean fields were selected for this study, two of which are irrigated and one is rainfed. The four VIs and fAPARchl of the fields were computed with the MODerate resolution Imaging Spectroradiometer (MODIS) satellite images. The GPP estimation performance for the scaled VIs was compared to results obtained with the original VIs and evaluated with standard statistics: the coefficient of determination (R2), the root mean square error (RMSE), and the coefficient of variation (CV). Overall, the scaled EVI obtained the best performance. The performance of the scaled NDVI, EVI and WDRVIgreen was improved across sites, crop types and soil/background wetness conditions. The scaled CIgreen did not improve results, compared to the original CIgreen. The scaled green band indices (WDRVIgreen, CIgreen) did not exhibit superior performance to either the

  6. Pesticide occurrence in groundwater in areas of high-density row crop production in Alabama, 2009

    USGS Publications Warehouse

    Moreland, Richard S.

    2011-01-01

    High-density row crop production occurs in three areas of Alabama that are underlain by productive aquifers, northern Alabama, southeastern Alabama, and Baldwin County in southwestern Alabama. The U.S. Geological Survey collected five groundwater samples from each of these three areas during 2009 for analysis of selected pesticides. Results of these analyses showed detections for 37 of 152 analytes. The three most frequently detected compounds were atrazine, 2-Chloro-4-isopropylamino-6-amino-triazine (CIAT), and metolachlor. The highest concentration for any analyte was 4.08 micrograms per liter for metolachlor.

  7. Evaluation of spatial filtering on the accuracy of wheat area estimate

    NASA Technical Reports Server (NTRS)

    Dejesusparada, N. (Principal Investigator); Moreira, M. A.; Chen, S. C.; Delima, A. M.

    1982-01-01

    A 3 x 3 pixel spatial filter for postclassification was used for wheat classification to evaluate the effects of this procedure on the accuracy of area estimation using LANDSAT digital data obtained from a single pass. Quantitative analyses were carried out in five test sites (approx 40 sq km each) and t tests showed that filtering with threshold values significantly decreased errors of commission and omission. In area estimation filtering improved the overestimate of 4.5% to 2.7% and the root-mean-square error decreased from 126.18 ha to 107.02 ha. Extrapolating the same procedure of automatic classification using spatial filtering for postclassification to the whole study area, the accuracy in area estimate was improved from the overestimate of 10.9% to 9.7%. It is concluded that when single pass LANDSAT data is used for crop identification and area estimation the postclassification procedure using a spatial filter provides a more accurate area estimate by reducing classification errors.

  8. Assessment of area favourable for crop sowing using AMSR-E derived Soil Moisture Index (AMSR-E SMI)

    NASA Astrophysics Data System (ADS)

    Chakraborty, Abhishek; Sesha Sai, M. V. R.; Murthy, C. S.; Roy, P. S.; Behera, G.

    2012-08-01

    Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture product was used to assess the progression of Area Favourable for Crop Sowing (AFCS) over Andhra Pradesh State of India during summer monsoon. The AMSR-E soil moisture data were normalized with respect to soil texture to calculate AMSR-E Soil Moisture Index (AMSR-E SMI). The index had significant correlation (r value 0.7-0.8) with the amount of rainfall during early monsoon period. Progression of soil wetness condition was mapped week-wise by thresholding the AMSR-E SMI. Logical criteria were developed based on the surface soil moisture content, its persistence and the type of crop to classify AFCS. The estimated AFCS was found to have significant correlation (r = 0.92 and root mean square error = 0.66) with the reported official sown area by Directorate of Economics & Statistics, Govt. of Andhra Pradesh. The study demonstrated the potential use of AMSR-E SMI for assessment of agricultural drought during early monsoon season at regional level.

  9. Surface renewal performance to independently estimate sensible and latent heat fluxes in heterogeneous crop surfaces

    NASA Astrophysics Data System (ADS)

    Suvočarev, K.; Shapland, T. M.; Snyder, R. L.; Martínez-Cob, A.

    2014-02-01

    Surface renewal (SR) analysis is an interesting alternative to eddy covariance (EC) flux measurements. We have applied two recent SR approaches, with different theoretical background, that from Castellví (2004), SRCas, and that from Shapland et al. (2012a,b), SRShap. We have applied both models for sensible (H) and latent (LE) heat flux estimation over heterogeneous crop surfaces. For this, EC equipments, including a sonic anemometer CSAT3 and a krypton hygrometer KH20, were located in two zones of drip irrigated orchards of late and early maturing peaches. The measurement period was June-September 2009. The SRCas is based on similarity concepts for independent estimation of the calibration factor (α), which varies with respect to the atmospheric stability. The SRShap is based on analysis of different ramp dimensions, separating the ones that are flux-bearing from the others that are isotropic. According to the results obtained here, there was a high agreement between the 30-min turbulent fluxes independently derived by EC and SRCas. The SRShap agreement with EC was slightly lower. Estimation of fluxes determined by SRCas resulted in higher values (around 11% for LE) with respect to EC, similarly to previously published works over homogeneous canopies. In terms of evapotranspiration, the root mean square error (RMSE) between EC and SR was only 0.07 mm h-1 (for SRCas) and 0.11 mm h-1 (for SRShap) for both measuring spots. According to the energy balance closure, the SRCas method was as reliable as the EC in estimating the turbulent fluxes related to irrigated agriculture and watershed distribution management, even when applied in heterogeneous cropping systems.

  10. Estimating ground cover of field crops using medium-resolution multispectral satellite imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing is useful for estimating plant canopy characteristics, such as leaf area index (LAI) and ground cover (GC). When the source of remote sensing data is medium-resolution satellite imagery, plant canopy characteristics can be estimated for numerous fields within an agricultural region. I...

  11. Estimating ground cover of field crops using medium resolution multispectral satellite imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing is useful for estimating plant canopy characteristics, such as leaf area index (LAI) and ground cover (GC). When the source of remote sensing data is medium-resolution satellite imagery, plant canopy characteristics can be estimated for numerous fields within an agricultural region. I...

  12. Estimation of sampling uncertainty for pesticide residues in root vegetable crops.

    PubMed

    Farkas, Zsuzsa; Horváth, Zsuzsanna; Kerekes, Kata; Ambrus, Árpád; Hámos, András; Szabó, Mária Szeitzné

    2014-01-01

    The sampling uncertainty for pesticide residues in carrots, parsley leaves and selected medium size crops was estimated with simple random sampling by applying range statistics. The primary samples taken from treated fields consisted of individual carrots or a handful of parsley leaves. The samples were analysed with QUEChERs extraction method and LCMS/MS detection with practical LOQ of 0.001 mg/kg. The results indicate that the average sampling uncertainties estimated with simple random sampling and range statistics were practically the same. The confidence interval for the estimated sampling uncertainty decreased with the number of replicate samples taken from one lot and the number of lots sampled. The estimated relative ranges of sampling uncertainty are independent from the relative standard deviation of the primary samples. Consequently the conclusions drawn from these experiments are generally applicable. There is no optimum for sample size and number of lots to be tested for estimation of sampling uncertainty. Taking a minimum of 6 replicate samples from at least 8-12 lots is recommended to obtain a relative 95% range of sampling uncertainty within 50%. The cost of sampling/analyses, the consequences of wrong decision should also be taken into account when a sampling plan is prepared. PMID:24138463

  13. Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing

    USGS Publications Warehouse

    Marshall, Michael T.; Thenkabail, Prasad S.

    2015-01-01

    Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), and fraction of vegetation cover (FVC). The spectral predictors included 196 hyperspectral narrowbands (HNBs) from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR); H, FAPAR, and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an R2 of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.

  14. Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images

    NASA Astrophysics Data System (ADS)

    Wang, Jie; Xiao, Xiangming; Qin, Yuanwei; Dong, Jinwei; Zhang, Geli; Kou, Weili; Jin, Cui; Zhou, Yuting; Zhang, Yao

    2015-05-01

    As farmland systems vary over space and time (season and year), accurate and updated maps of paddy rice are needed for studies of food security and environmental problems. We selected a wheat-rice double-cropped area from fragmented landscapes along the rural-urban complex (Jiangsu Province, China) and explored the potential utility of integrating time series optical images (Landsat-8, MODIS) and radar images (PALSAR) in mapping paddy rice planting areas. We first identified several main types of non-cropland land cover and then identified paddy rice fields by selecting pixels that were inundated only during paddy rice flooding periods. These key temporal windows were determined based on MODIS Land Surface Temperature and vegetation indices. The resultant paddy rice map was evaluated using regions of interest (ROIs) drawn from multiple high-resolution images, Google Earth, and in-situ cropland photos. The estimated overall accuracy and Kappa coefficient were 89.8% and 0.79, respectively. In comparison with the National Land Cover Data (China) from 2010, the resultant map better detected changes in the paddy rice fields and revealed more details about their distribution. These results demonstrate the efficacy of using images from multiple sources to generate paddy rice maps for two-crop rotation systems.

  15. Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images.

    PubMed

    Wang, Jie; Xiao, Xiangming; Qin, Yuanwei; Dong, Jinwei; Zhang, Geli; Kou, Weili; Jin, Cui; Zhou, Yuting; Zhang, Yao

    2015-01-01

    As farmland systems vary over space and time (season and year), accurate and updated maps of paddy rice are needed for studies of food security and environmental problems. We selected a wheat-rice double-cropped area from fragmented landscapes along the rural-urban complex (Jiangsu Province, China) and explored the potential utility of integrating time series optical images (Landsat-8, MODIS) and radar images (PALSAR) in mapping paddy rice planting areas. We first identified several main types of non-cropland land cover and then identified paddy rice fields by selecting pixels that were inundated only during paddy rice flooding periods. These key temporal windows were determined based on MODIS Land Surface Temperature and vegetation indices. The resultant paddy rice map was evaluated using regions of interest (ROIs) drawn from multiple high-resolution images, Google Earth, and in-situ cropland photos. The estimated overall accuracy and Kappa coefficient were 89.8% and 0.79, respectively. In comparison with the National Land Cover Data (China) from 2010, the resultant map better detected changes in the paddy rice fields and revealed more details about their distribution. These results demonstrate the efficacy of using images from multiple sources to generate paddy rice maps for two-crop rotation systems. PMID:25965027

  16. Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images

    PubMed Central

    Wang, Jie; Xiao, Xiangming; Qin, Yuanwei; Dong, Jinwei; Zhang, Geli; Kou, Weili; Jin, Cui; Zhou, Yuting; Zhang, Yao

    2015-01-01

    As farmland systems vary over space and time (season and year), accurate and updated maps of paddy rice are needed for studies of food security and environmental problems. We selected a wheat-rice double-cropped area from fragmented landscapes along the rural–urban complex (Jiangsu Province, China) and explored the potential utility of integrating time series optical images (Landsat-8, MODIS) and radar images (PALSAR) in mapping paddy rice planting areas. We first identified several main types of non-cropland land cover and then identified paddy rice fields by selecting pixels that were inundated only during paddy rice flooding periods. These key temporal windows were determined based on MODIS Land Surface Temperature and vegetation indices. The resultant paddy rice map was evaluated using regions of interest (ROIs) drawn from multiple high-resolution images, Google Earth, and in-situ cropland photos. The estimated overall accuracy and Kappa coefficient were 89.8% and 0.79, respectively. In comparison with the National Land Cover Data (China) from 2010, the resultant map better detected changes in the paddy rice fields and revealed more details about their distribution. These results demonstrate the efficacy of using images from multiple sources to generate paddy rice maps for two-crop rotation systems. PMID:25965027

  17. COMBINING REMOTELY SENSED DATA AND GROUND-BASED RADIOMETERS TO ESTIMATE CROP COVER AND SURFACE TEMPERATURES AT DAILY TIME STEPS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Estimation of evapotranspiration (ET) is important for monitoring crop water stress and for developing decision support systems for irrigation scheduling. Techniques to estimate ET have been available for many years, while more recently remote sensing data have extended ET into a spatially distribut...

  18. Responses of Crop Water Use Efficiency to Climate Change and Agronomic Measures in the Semiarid Area of Northern China.

    PubMed

    Zhang, Jingting; Ren, Wei; An, Pingli; Pan, Zhihua; Wang, Liwei; Dong, Zhiqiang; He, Di; Yang, Jia; Pan, Shufen; Tian, Hanqin

    2015-01-01

    It has long been concerned how crop water use efficiency (WUE) responds to climate change. Most of existing researches have emphasized the impact of single climate factor but have paid less attention to the effect of developed agronomic measures on crop WUE. Based on the long-term field observations/experiments data, we investigated the changing responses of crop WUE to climate variables (temperature and precipitation) and agronomic practices (fertilization and cropping patterns) in the semi-arid area of northern China (SAC) during two periods, 1983-1999 and 2000-2010 (drier and warmer). Our results suggest that crop WUE was an intrinsical system sensitive to climate change and agronomic measures. Crops tend to reach the maximum WUE (WUEmax) in warm-dry environment while reach the stable minimum WUE (WUEmin) in warm-wet environment, with a difference between WUEmax and WUEmin ranging from 29.0%-55.5%. Changes in temperature and precipitation in the past three decades jointly enhanced crop WUE by 8.1%-30.6%. Elevated fertilizer and rotation cropping would increase crop WUE by 5.6-11.0% and 19.5-92.9%, respectively. These results indicate crop has the resilience by adjusting WUE, which is not only able to respond to subsequent periods of favorable water balance but also to tolerate the drought stress, and reasonable agronomic practices could enhance this resilience. However, this capacity would break down under impact of climate changes and unconscionable agronomic practices (e.g. excessive N/P/K fertilizer or traditional continuous cropping). Based on the findings in this study, a conceptual crop WUE model is constructed to indicate the threshold of crop resilience, which could help the farmer develop appropriate strategies in adapting the adverse impacts of climate warming. PMID:26336098

  19. Responses of Crop Water Use Efficiency to Climate Change and Agronomic Measures in the Semiarid Area of Northern China

    PubMed Central

    Zhang, Jingting; Ren, Wei; An, Pingli; Pan, Zhihua; Wang, Liwei; Dong, Zhiqiang; He, Di; Yang, Jia; Pan, Shufen; Tian, Hanqin

    2015-01-01

    It has long been concerned how crop water use efficiency (WUE) responds to climate change. Most of existing researches have emphasized the impact of single climate factor but have paid less attention to the effect of developed agronomic measures on crop WUE. Based on the long-term field observations/experiments data, we investigated the changing responses of crop WUE to climate variables (temperature and precipitation) and agronomic practices (fertilization and cropping patterns) in the semi-arid area of northern China (SAC) during two periods, 1983–1999 and 2000–2010 (drier and warmer). Our results suggest that crop WUE was an intrinsical system sensitive to climate change and agronomic measures. Crops tend to reach the maximum WUE (WUEmax) in warm-dry environment while reach the stable minimum WUE (WUEmin) in warm-wet environment, with a difference between WUEmax and WUEmin ranging from 29.0%-55.5%. Changes in temperature and precipitation in the past three decades jointly enhanced crop WUE by 8.1%-30.6%. Elevated fertilizer and rotation cropping would increase crop WUE by 5.6–11.0% and 19.5–92.9%, respectively. These results indicate crop has the resilience by adjusting WUE, which is not only able to respond to subsequent periods of favorable water balance but also to tolerate the drought stress, and reasonable agronomic practices could enhance this resilience. However, this capacity would break down under impact of climate changes and unconscionable agronomic practices (e.g. excessive N/P/K fertilizer or traditional continuous cropping). Based on the findings in this study, a conceptual crop WUE model is constructed to indicate the threshold of crop resilience, which could help the farmer develop appropriate strategies in adapting the adverse impacts of climate warming. PMID:26336098

  20. Estimating irrigation demand using satellite remote sensing: a case study of Paphos District area in Cyprus

    NASA Astrophysics Data System (ADS)

    Hadjimitsis, Diofantos G.; Papadavid, Giorgos; Themistokleous, Kyriacos; Kounoudes, Anastasis; Toulios, Leonidas

    2008-10-01

    The monitoring of agricultural areas in Cyprus provides important data for efficient water supply plans and for avoiding unnecessary water lost due to inefficient irrigation. In this context, satellite remote sensing techniques may be useful as an efficient tool for monitoring agricultural areas. The objective of this study is to present the overall methodology for monitoring agricultural areas and estimating the irrigation demand in Cyprus using satellite remote sensing, irrigation models and other auxiliary data. Field spectro-radiometric measurements using SVC-HR 1024 and GER 1500 were undertaken to determine the spectral signature of different types of crops so as to assist our classification techniques. Final crop maps using Landsat TM and ETM+ can be produced and the optimal amount of irrigation demand required for certain types of crops can be determined in order to avoid any non-effective water management. This paper presents the overall methodology of the proposed research study designed to enable the implementation of an integrated approach by combining satellite remote sensing, irrigation models, micro-sensor technology and in-situ spectroradiometric measurements to determine the irrigation demand and finally to validate our results.

  1. A simple method to estimate vegetation indices and crop canopy factors using field spectroscopy for solanum tuberosum during the whole phenological cycle

    NASA Astrophysics Data System (ADS)

    Perdikou, S.; Papadavid, G.; Hadjimitsis, M.; Hadjimitsis, D.; Neofytou, N.

    2013-08-01

    Field spectroscopy is a part of the remote sensing techniques and very important for studies in agriculture. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data of the spring potatoes for estimating spectral vegetation indices (SVI's). A field campaign was undertaken from September to the end of November 2012 for the collection of spectro-radiometric measurements. The study area was in the Mandria Village in Paphos district in Cyprus. This paper demonstrates how crop canopy factors can be statistically related to remotely sensed data, namely vegetation indices. The paper is a part of an EU cofounded project regarding estimating crop water requirements using remote sensing techniques and informing the farmers through 3G smart telephony.

  2. Rice Crop Monitoring and Yield Estimation Through Cosmo Skymed and TerraSAR-X: A SAR-Based Experience in India

    NASA Astrophysics Data System (ADS)

    Pazhanivelan, S.; Kannan, P.; Nirmala Mary, P. Christy; Subramanian, E.; Jeyaraman, S.; Nelson, A.; Setiyono, T.; Holecz, F.; Barbieri, M.; Yadav, M.

    2015-04-01

    Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87- 92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85- 96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.

  3. Effect of impactor area on collision rate estimates

    SciTech Connect

    Canavan, G.H.

    1996-08-01

    Analytic and numercial estimates provide an assessment of the effect of impactor area on space debris collision rates, which is sufficiently small and insensitive to parameters of inerest that it could be neglected or corrected.

  4. The Large Area Crop Inventory Experiment /LACIE/ - A summary of three years' experience

    NASA Technical Reports Server (NTRS)

    Erb, R. B.; Moore, B. H.

    1979-01-01

    Aims, history and schedule of the Large Area Crop Inventory Experiment (LACIE) conducted by NASA, USDA and NOAA from 1974-1977 are described. The LACIE experiment designed to research, develop, apply and evaluate a technology to monitor wheat production in important regions throughout the world (U.S., Canada, USSR, Brasil) utilized quantitative multispectral data collected by Landsat in concert with current weather data and historical information. The experiment successfully exploited computer data and mathematical models to extract timely corp information. A follow-on activities for the early 1980's is planned focusing especially on the early warning of changes affecting production and quality of renewable resources and commodity production forecast.

  5. ESTIMATING PROPORTION OF AREA OCCUPIED UNDER COMPLEX SURVEY DESIGNS

    EPA Science Inventory

    Estimating proportion of sites occupied, or proportion of area occupied (PAO) is a common problem in environmental studies. Typically, field surveys do not ensure that occupancy of a site is made with perfect detection. Maximum likelihood estimation of site occupancy rates when...

  6. Remote sensing of canopy dynamics and biochemical variables estimation of fodder crops

    NASA Astrophysics Data System (ADS)

    Rai, Suchit K.; Das, S. K.; Rai, A. K.

    2010-04-01

    Non-destructive monitoring and diagnosis of plant nitrogen (N) concentration status is necessary for precision in N management. Leaf -N and chlorophyll (Chl) concentration of fodder crops are important indicators of plant N status. Studies were conducted to determine the relationship between canopy hyperspectral reflectance (325 to 1075 nm) and Chl or N concentration in field grown fodder crops [bajra (Pennisetum typhoides, sorghum (Sorghum bicolor L.) in Kharif season and oat (Avena sativa) in Rabi season] without and with recommended dose of nitrogen of different crops. Nitrogen fertilizer application mainly affected leaf reflectance at 575 and 623 nm in sorghum, 565 and 657 nm in bajra and 563 and 716 nm in oat. The reflectance ratio at R581/R397 (R2=0.46**) and R619/R462 nm (R2=0.79***) had the highest correlation with sorghum and bajra leaf N concentration respectively with greatest R2 values. However in oat single reflectance at R542 (R2=0.53**) had the highest correlation with leaf N concentration. Similarly, sorghum, bajra and oat leaf Chl concentration were highly correlated with R677/R527 (R2=0.63**), R688/R409 (R2=0.71***) and R695 (R2=0.56** ), respectively. A linear relationship was found between sorghum leaf N and a simple ratio at R581/R397 (Intercept=8.85, slope=-2.64, R2=0.44). Bajra leaf N concentration was associated closely with ratio of R619/ R462, (R2= 0.78***). Oat leaf N concentration could be best estimate through single reflectance at R695 (Slope=-0.48, Intercept=0.15; R2=0.56). Similarly sorghum, bajra and oat leaf Chl could be best-estimated using reflectance ratio of R677/R527, R615/R411 and R695, respectively. Thus our results suggest that spectral reflectance measurements hold promise for the assessment of some physiological parameter at the leaf level real time monitoring of sorghum and bajra N status and N fertilizer management.

  7. Airborne hyperspectral imagery for mapping crop yield variability

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Information concerning the spatial variation in crop yield has become necessary for site-specific crop management. Traditional satellite imagery has long been used to monitor crop growing conditions and to estimate crop yields over large geographic areas. However, this type of imagery has limited us...

  8. Development of daily temperature scenarios and their impact on paddy crop evapotranspiration in Kangsabati command area

    NASA Astrophysics Data System (ADS)

    Dhage, P. M.; Raghuwanshi, N. S.; Singh, R.; Mishra, A.

    2016-02-01

    Production of the principal paddy crop in West Bengal state of India is vulnerable to climate change due to limited water resources and strong dependence on surface irrigation. Therefore, assessment of impact of temperature scenarios on crop evapotranspiration (ETc) is essential for irrigation management in Kangsabati command (West Bengal). In the present study, impact of the projected temperatures on ETc was studied under climate change scenarios. Further, the performance of the bias correction and spatial downscaling (BCSD) technique was compared with the two well-known downscaling techniques, namely, multiple linear regression (MLR) and Kernel regression (KR), for the projections of daily maximum and minimum air temperatures for four stations, namely, Purulia, Bankura, Jhargram, and Kharagpur. In National Centers for Environmental Prediction (NCEP) and General Circulation Model (GCM), 14 predictors were used in MLR and KR techniques, whereas maximum and minimum surface air temperature predictor of CanESM2 GCM was used in BCSD technique. The comparison results indicated that the performance of the BCSD technique was better than the MLR and KR techniques. Therefore, the BCSD technique was used to project the future temperatures of study locations with three Representative Concentration Pathway (RCP) scenarios for the period of 2006-2100. The warming tendencies of maximum and minimum temperatures over the Kangsabati command area were projected as 0.013 and 0.014 °C/year under RCP 2.6, 0.015 and 0.023 °C/year under RCP 4.5, and 0.056 and 0.061 °C/year under RCP 8.5 for 2011-2100 period, respectively. As a result, kharif (monsoon) crop evapotranspiration demand of Kangsabati reservoir command (project area) will increase by approximately 10, 8, and 18 % over historical demand under RCP 2.6, 4.5, and 8.5 scenarios, respectively.

  9. Chemical control of ambrosia Artemisiifolia on non-crop areas: are there alternatives to glyphosate?

    PubMed

    Lombard, A; Gauvrit, C; Chauvel, B

    2005-01-01

    We compared glyphosate, glufosinate and metsulfuron-methyl to control Ambrosia artemisiifolia under non-crop conditions. A laboratory study showed that A. artemisiifolia is an easy-to-wet species and that glufosinate and glyphosate are quickly absorbed by its leaves (nearly 100% in 24 h). Metsulfuron-methyl absorption was slower (about 50% in 24 h) but was strongly promoted by terpenic alcohol and esterified rapeseed oil. In the greenhouse, all three herbicides were efficacious against A. artemisiifolia, with ED50s of <23, 23 and 0.8 g ha(-1) for glufosinate, glyphosate and metsulfuron-methyl, respectively. These results were confirmed on a non-crop area for glufosinate and glyphosate, which at half the registered dose reached high efficacies at both the 4 to 6-node and flowering stages of A. artemisiifolia. By contrast, metsulfuron-methyl showed no efficacy. However, after treatment at the 4- to 6-node stage, new emergence of A. artemisiifolia led to the presence of vigorous plants that bore numerous flowers and produced high levels of pollen. After treatment at the flowering stage, flower production by A. artemisiifolia was not significantly affected, but achene weight was decreased by 60 to 70% and seed viability was only 8 to 13% for the treated plants, as compared to 85% for the control. No significant difference was observed between the two herbicides and between the doses. It is concluded that glufosinate can be an alternative to glyphosate for the chemical control of A. artemisiifolia on non-crop areas. However, with both herbicides, it is difficult to attain the two objectives of reducing seed production and pollen production by means of only one treatment. PMID:16637214

  10. USING MULTISPECTRAL IMAGERY AND LINEAR SPECTRAL UNMIXING TECHNIQUES FOR ESTIMATING CROP YIELD VARIABILITY

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Vegetation indices derived from multispectral imagery are commonly used to extract crop growth and yield information. Spectral unmixing techniques provide an alternative approach to quantifying crop canopy abundance within each image pixel and have the potential for mapping crop yield variability. T...

  11. Using satellite remote sensing to estimate winter cover crop nutrient uptake efficiency

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The practice of planting winter cover crops following summer row crops is recognized as an important agricultural conservation measure with potential to reduce nitrogen losses to groundwater. Sequestration of residual soil nitrogen in growing cover crop biomass can significantly reduce wintertime nu...

  12. Using metabolomics to estimate unintended effects in transgenic crop plants: problems, promises and opportunities

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Transgenic crops are widespread in some countries and sectors of the agro-economy, but are also highly contentious. Proponents of transgenic crop improvement often cite the “substantial equivalence” of transgenic crops to their non-transgenic parents and sibling varieties. Opponents of transgenic cr...

  13. Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

    NASA Astrophysics Data System (ADS)

    Dong, Taifeng; Liu, Jiangui; Qian, Budong; Zhao, Ting; Jing, Qi; Geng, Xiaoyuan; Wang, Jinfei; Huffman, Ted; Shang, Jiali

    2016-07-01

    A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAIL) alone (R2 = 0.77 and RMSE = 231 g m-2). Assimilation of GLAI derived from the fusion dataset (GLAIF) resulted in a R2 of 0.71 and RMSE of 193 g m-2 while assimilating the combination of GLAIL and GLAIF led to further improvements (R2 = 0.76 and RMSE = 176 g m-2). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution

  14. Calibration and algorithm development for estimation of nitrogen in wheat crop using tractor mounted N-sensor.

    PubMed

    Singh, Manjeet; Kumar, Rajneesh; Sharma, Ankit; Singh, Bhupinder; Thind, S K

    2015-01-01

    The experiment was planned to investigate the tractor mounted N-sensor (Make Yara International) to predict nitrogen (N) for wheat crop under different nitrogen levels. It was observed that, for tractor mounted N-sensor, spectrometers can scan about 32% of total area of crop under consideration. An algorithm was developed using a linear relationship between sensor sufficiency index (SIsensor) and SISPAD to calculate the N app as a function of SISPAD. There was a strong correlation among sensor attributes (sensor value, sensor biomass, and sensor NDVI) and different N-levels. It was concluded that tillering stage is most prominent stage to predict crop yield as compared to the other stages by using sensor attributes. The algorithms developed for tillering and booting stages are useful for the prediction of N-application rates for wheat crop. N-application rates predicted by algorithm developed and sensor value were almost the same for plots with different levels of N applied. PMID:25811039

  15. Calibration and Algorithm Development for Estimation of Nitrogen in Wheat Crop Using Tractor Mounted N-Sensor

    PubMed Central

    Singh, Manjeet; Kumar, Rajneesh; Sharma, Ankit; Singh, Bhupinder; Thind, S. K.

    2015-01-01

    The experiment was planned to investigate the tractor mounted N-sensor (Make Yara International) to predict nitrogen (N) for wheat crop under different nitrogen levels. It was observed that, for tractor mounted N-sensor, spectrometers can scan about 32% of total area of crop under consideration. An algorithm was developed using a linear relationship between sensor sufficiency index (SIsensor) and SISPAD to calculate the Napp as a function of SISPAD. There was a strong correlation among sensor attributes (sensor value, sensor biomass, and sensor NDVI) and different N-levels. It was concluded that tillering stage is most prominent stage to predict crop yield as compared to the other stages by using sensor attributes. The algorithms developed for tillering and booting stages are useful for the prediction of N-application rates for wheat crop. N-application rates predicted by algorithm developed and sensor value were almost the same for plots with different levels of N applied. PMID:25811039

  16. The large area crop inventory experiment: An experiment to demonstrate how space-age technology can contribute to solving critical problems here on earth

    NASA Technical Reports Server (NTRS)

    1977-01-01

    The large area crop inventory experiment is being developed to predict crop production through satellite photographs. This experiment demonstrates how space age technology can contribute to solving practical problems of agriculture management.

  17. LARGE AREA MONITORING FOR PESTICIDAL TRANSGENIC CROPS: HOW SPECTRAL IMAGING MAY PLAY A ROLE

    EPA Science Inventory

    Crops genetically engineered to contain a bacterial gene that expresses an insecticidal protein from Bacillus thuringiensis are regulated by EPA under the Federal Insecticide Fungicide and Rodenticide Act (FIFRA). EPA has declared crops containing transgenic pesticidal traits to...

  18. Large Area Crop Inventory Experiment (LACIE). Results of LACIE integrated drought analysis (Southern U.S. Great Plains drought 1975-76)

    NASA Technical Reports Server (NTRS)

    Thompson, D. R.

    1976-01-01

    The development and intensification of the drought in the United States southern Great Plains was monitored during the initial growing period of the 1975-76 winter wheat crop. Because of the severity of the drought conditions, a drought analysis plan was developed and implemented beginning on March 8, 1976. Sample segments and full-frame imagery were used at 9-day intervals to identify the drought area and quantify the effects on the wheat acreage. Yield model simulations were run to extrapolate the effects of the drought on yield estimates at harvest, assuming 10 and 90 percent of normal rainfall for subsequent months and 30-day forecast. A survey of LANDSAT data for improvement of distribution of rainfall patterns in the drought area was done for April and yield models run for drought affected crop reporting districts. Special aggregations were performed by the Crop Assessment Subsystem on the drought area to evaluate the utility of remote sensing to monitor the effect of the drought on wheat area, yield, and production.

  19. Comment on "Technical Note: On the Matt-Shuttleworth approach to estimate crop water requirements" by Lhomme et al. (2014)

    NASA Astrophysics Data System (ADS)

    Shuttleworth, W. J.

    2014-05-01

    It is clear from Lhomme et al. (2014) that aspects of the explanation of the Matt-Shuttleworth approach can generate confusion. Presumably this is because the description in Shuttleworth (2006) was not sufficiently explicit and simple. This paper explains the logic behind the Matt-Shuttleworth approach clearly, simply and concisely. It shows how the Matt-Shuttleworth can be implemented using a few simple equations and provides access to ancillary calculation resources that can be used for such implementation. If the crop water requirement community decided that it is preferable to use the Penman-Monteith equation to estimate crop water requirements directly for all crops, the United Nations Food and Agriculture Organization could now update Irrigation and Drainage Paper 56 using the Matt-Shuttleworth approach by deriving tabulated values of surface resistance from Table 12 of Allen et al. (1998), with the estimation of crop evaporation then being directly made in a one-step calculation using an equation similar to that already recommended by the United Nations Food and Agriculture Organization for calculating reference crop evaporation.

  20. Comment on "Technical Note: On the Matt-Shuttleworth approach to estimate crop water requirements" by Lhomme et al. (2014)

    NASA Astrophysics Data System (ADS)

    Shuttleworth, W. J.

    2014-11-01

    It is clear from Lhomme et al. (2014) that aspects of the explanation of the Matt-Shuttleworth approach can generate confusion. Presumably this is because the description in Shuttleworth (2006) was not sufficiently explicit and simple. This paper explains the logic behind the Matt-Shuttleworth approach clearly, simply and concisely. It shows how the Matt-Shuttleworth can be implemented using a few simple equations and provides access to ancillary calculation resources that can be used for such implementation. If the crop water requirement community decided that it is preferable to use the Penman-Monteith equation to estimate crop water requirements directly for all crops, the United Nations Food and Agriculture Organization could now update Irrigation and Drainage Paper 56 using the Matt-Shuttleworth approach by deriving tabulated values of surface resistance from Table 12 of Allen et al. (1998), with the estimation of crop evaporation then being directly made in a one-step calculation using an equation similar to that already recommended by the United Nations Food and Agriculture Organization for calculating reference crop evaporation.

  1. Scaling Up Stomatal Conductance from Leaf to Canopy Using a Dual-Leaf Model for Estimating Crop Evapotranspiration

    PubMed Central

    Ding, Risheng; Kang, Shaozhong; Du, Taisheng; Hao, Xinmei; Zhang, Yanqun

    2014-01-01

    The dual-source Shuttleworth-Wallace model has been widely used to estimate and partition crop evapotranspiration (λET). Canopy stomatal conductance (Gsc), an essential parameter of the model, is often calculated by scaling up leaf stomatal conductance, considering the canopy as one single leaf in a so-called “big-leaf” model. However, Gsc can be overestimated or underestimated depending on leaf area index level in the big-leaf model, due to a non-linear stomatal response to light. A dual-leaf model, scaling up Gsc from leaf to canopy, was developed in this study. The non-linear stomata-light relationship was incorporated by dividing the canopy into sunlit and shaded fractions and calculating each fraction separately according to absorbed irradiances. The model includes: (1) the absorbed irradiance, determined by separately integrating the sunlit and shaded leaves with consideration of both beam and diffuse radiation; (2) leaf area for the sunlit and shaded fractions; and (3) a leaf conductance model that accounts for the response of stomata to PAR, vapor pressure deficit and available soil water. In contrast to the significant errors of Gsc in the big-leaf model, the predicted Gsc using the dual-leaf model had a high degree of data-model agreement; the slope of the linear regression between daytime predictions and measurements was 1.01 (R2 = 0.98), with RMSE of 0.6120 mm s−1 for four clear-sky days in different growth stages. The estimates of half-hourly λET using the dual-source dual-leaf model (DSDL) agreed well with measurements and the error was within 5% during two growing seasons of maize with differing hydrometeorological and management strategies. Moreover, the estimates of soil evaporation using the DSDL model closely matched actual measurements. Our results indicate that the DSDL model can produce more accurate estimation of Gsc and λET, compared to the big-leaf model, and thus is an effective alternative approach for estimating and

  2. Scaling up stomatal conductance from leaf to canopy using a dual-leaf model for estimating crop evapotranspiration.

    PubMed

    Ding, Risheng; Kang, Shaozhong; Du, Taisheng; Hao, Xinmei; Zhang, Yanqun

    2014-01-01

    The dual-source Shuttleworth-Wallace model has been widely used to estimate and partition crop evapotranspiration (λET). Canopy stomatal conductance (Gsc), an essential parameter of the model, is often calculated by scaling up leaf stomatal conductance, considering the canopy as one single leaf in a so-called "big-leaf" model. However, Gsc can be overestimated or underestimated depending on leaf area index level in the big-leaf model, due to a non-linear stomatal response to light. A dual-leaf model, scaling up Gsc from leaf to canopy, was developed in this study. The non-linear stomata-light relationship was incorporated by dividing the canopy into sunlit and shaded fractions and calculating each fraction separately according to absorbed irradiances. The model includes: (1) the absorbed irradiance, determined by separately integrating the sunlit and shaded leaves with consideration of both beam and diffuse radiation; (2) leaf area for the sunlit and shaded fractions; and (3) a leaf conductance model that accounts for the response of stomata to PAR, vapor pressure deficit and available soil water. In contrast to the significant errors of Gsc in the big-leaf model, the predicted Gsc using the dual-leaf model had a high degree of data-model agreement; the slope of the linear regression between daytime predictions and measurements was 1.01 (R2 = 0.98), with RMSE of 0.6120 mm s-1 for four clear-sky days in different growth stages. The estimates of half-hourly λET using the dual-source dual-leaf model (DSDL) agreed well with measurements and the error was within 5% during two growing seasons of maize with differing hydrometeorological and management strategies. Moreover, the estimates of soil evaporation using the DSDL model closely matched actual measurements. Our results indicate that the DSDL model can produce more accurate estimation of Gsc and λET, compared to the big-leaf model, and thus is an effective alternative approach for estimating and partitioning

  3. LACIE large area acreage estimation. [United States of America

    NASA Technical Reports Server (NTRS)

    Chhikara, R. S.; Feiveson, A. H. (Principal Investigator)

    1979-01-01

    A sample wheat acreage for a large area is obtained by multiplying its small grains acreage estimate as computed by the classification and mensuration subsystem by the best available ratio of wheat to small grains acreages obtained from historical data. In the United States, as in other countries with detailed historical data, an additional level of aggregation was required because sample allocation was made at the substratum level. The essential features of the estimation procedure for LACIE countries are included along with procedures for estimating wheat acreage in the United States.

  4. Sampling for area estimation: A comparison of full-frame sampling with the sample segment approach. [Kansas

    NASA Technical Reports Server (NTRS)

    Hixson, M. M.; Bauer, M. E.; Davis, B. J.

    1979-01-01

    The effect of sampling on the accuracy (precision and bias) of crop area estimates made from classifications of LANDSAT MSS data was investigated. Full-frame classifications of wheat and non-wheat for eighty counties in Kansas were repetitively sampled to simulate alternative sampling plants. Four sampling schemes involving different numbers of samples and different size sampling units were evaluated. The precision of the wheat area estimates increased as the segment size decreased and the number of segments was increased. Although the average bias associated with the various sampling schemes was not significantly different, the maximum absolute bias was directly related to sampling unit size.

  5. Generalized flood-frequency estimates for urban areas in Missouri

    USGS Publications Warehouse

    Gann, Ector Eugene

    1971-01-01

    A method is presented for estimating flood-frequency information for urban areas in Missouri. Flood-frequency relations are presented which provide an estimate of the flood-peak discharge for floods with recurrence intervals from 2.33 to 100 years for basins with various degrees of existing or projected urban development. Drainage area sizes for which the relations are applicable range from 0.1 to 50 square miles. These generalized relations will be useful to the urban planner and designer until more comprehensive studies are completed for the individual urban areas within the State. The relations will also be of use in the definition of flood-hazard areas in Missouri.

  6. Would species richness estimators change the observed species area relationship?

    NASA Astrophysics Data System (ADS)

    Borges, Paulo A. V.; Hortal, Joaquín; Gabriel, Rosalina; Homem, Nídia

    2009-01-01

    We evaluate whether the description of the species area relationship (SAR) can be improved by using richness estimates instead of observed richness values. To do this, we use three independent datasets gathered with standardized survey methods from the native laurisilva forest of the Azorean archipelago, encompassing different distributional extent and biological groups: soil epigean arthropods at eight forest fragments in Terceira Island, canopy arthropods inhabiting Juniperus brevifolia at 16 forest fragments of six different islands, and bryophytes of seven forest fragments from Terceira and Pico islands. Species richness values were estimated for each forest fragment using seven non-parametric estimators (ACE, ICE, Chao1, Chao2, Jackknife1, Jackknife2 and Bootstrap; five in the case of bryophytes). These estimates were fitted to classical log-log species-area curves and the intercept, slope and goodness of fit of these curves were compared with those obtained from the observed species richness values to determine if significant differences appear in these parameters. We hypothesized that the intercepts would be higher in the estimated data sets compared with the observed data, as estimated richness values are typically higher than observed values. We found partial support for the hypothesis - intercepts of the SAR obtained from estimated richness values were significantly higher in the case of epigean arthropods and bryophyte datasets. In contrast, the slope and goodness of fit obtained with estimated values were not significantly different from those obtained from observed species richness in all groups, although a few small differences appeared. We conclude that, although little is gained using these estimators if data come from standardized surveys, their estimations could be used to analyze macroecological relationships with non-standardized observed data, provided that survey incompleteness and/or unevenness are also taken into account.

  7. LACIE - A look to the future. [Large Area Crop Inventory Experiment

    NASA Technical Reports Server (NTRS)

    Macdonald, R. B.; Hall, F. G.

    1977-01-01

    The Large Area Crop Inventory Experiment (LACIE) is a 'proof of concept' project designed to demonstrate the applicability of remote sensing technology to the global monitoring of wheat. This paper discusses the need for more timely and reliable monitoring of food and fiber supplies, reviews the monitoring systems currently utilized by the USDA and United Nations Food and Agriculture Organization in the United States and in foreign countries, and elucidates the fundamentals involved in assessing the impact of variable weather and economic conditions on wheat acreage, yield, and production. The experiment's approach to production monitoring is described briefly, and its status is reviewed as of the conclusion of 2 years of successful operation. Examples of acreage and yield monitoring in the Soviet Union are used to illustrate the experiment's approach.

  8. Retrieving Crops Green Area Index from High Temporal and Spatial Resolution Remote Sensing Data

    NASA Astrophysics Data System (ADS)

    Veloso, A.; Demarez, V.; Ceschia, E.

    2012-04-01

    This paper aims at firstly evaluating the correspondence between Normalized Difference Vegetation Index (NDVI) products from Formosat-2 (F2) and SPOT sensors and then to perform a comparative analysis of two methods for retrieving Green Area Index from high spatial and temporal resolution satellite data (F2 and SPOT). For this purpose, an empirical approach using NDVI plus field data and a Neural Network approach using the PROSAIL model are compared over four different crops: maize, soybean, sunflower and wheat. The performance of both methods were evaluated and compared with in-situ direct (destructive) and indirect (hemispherical photos) measurements. Results suggest better performances for the empirical approach (R², RMSE). Still the physically-based method leads to good results (R², RMSE). The latter seems to be more promising due to its portability and independence from field measurements. Therefore new perspectives to improve this approach are being envisaged.

  9. Results of Large Area Crop Inventory Experiment (LACIE) drought analysis (South Dakota drought 1976)

    NASA Technical Reports Server (NTRS)

    Thompson, D. R.

    1976-01-01

    LACIE using techniques developed from the southern Great Plains drought analysis indicated the potential for drought damage in South Dakota. This potential was monitored and as it became apparent that a drought was developing, LACIE implemented some of the procedures used in the southern Great Plains drought. The technical approach used in South Dakota involved the normal use of LACIE sample segments (5 x 6 nm) every 18 days. Full frame color transparencies (100 x 100 nm) were used on 9 day intervals to identify the drought area and to track overtime. The green index number (GIN) developed using the Kauth transformation was computed for all South Dakota segments and selected North Dakota segments. A scheme for classifying segments as drought affected or not affected was devised and tested on all available 1976 South Dakota data. Yield model simulations were run for all CRD's Crop Reporting District) in South Dakota.

  10. Classifying cropping area of middle Heihe River Basin in China using multitemporal Normalized Difference Vegetation Index data

    NASA Astrophysics Data System (ADS)

    Han, Huibang; Ma, Mingguo; Wang, Xufeng; Ma, Shoucun

    2014-01-01

    Accurate information regarding the structure of crops is critical for the improvement and optimization of land surface models. Multitemporal remote sensing imagery is more effective to determine the crop structure than the single-temporal images because they contain phenological information. Crop structure was extracted based on time series of moderate-resolution imaging spectroradiometer (MODIS) data in the middle Heihe River Basin. A time series of Normalized Difference Vegetation Index (NDVI) data with a 3-day temporal resolution was composed based on daily MODIS reflectance products (MOD 09) from January to December 2011. A total of 120 scenes of composited imagery were integrated into an image data cube of NDVI time series, which was used to extract crop structure for the study area. The spectral curves of corn, wheat, rape, vegetables, and other crops are based on both in situ measurements and visual interpretation. The major crop types were classified by using the adaptive boosting (Adaboost) and support vector machine (SVM) algorithms. The results show that the classification accuracy of Adaboost and SVM was 86.01% and 70.28%, respectively, with Kappa coefficients of 0.8351 and 0.6438, respectively. Summarizing the classification methods used in this study effectively characterize the spatial distribution of the main crops.

  11. Impacts of Different Assimilation Methodologies on Crop Yield Estimates Using Active and Passive Microwave Dataset at L-Band

    NASA Astrophysics Data System (ADS)

    Liu, P.; Bongiovanni, T. E.; Monsivais-Huertero, A.; Bindlish, R.; Judge, J.

    2013-12-01

    Accurate estimates of crop yield are important for managing agricultural production and food security. Although the crop growth models, such as the Decision Support System Agrotechnology Transfer (DSSAT), have been used to simulate crop growth and development, the crop yield estimates still diverge from the reality due to different sources of errors in the models and computation. Auxiliary observations may be incorporated into such dynamic models to improve predictions using data assimilation. Active and passive (AP) microwave observations at L-band (1-2 GHz) are sensitive to dielectric and geometric properties of soil and vegetation, including soil moisture (SM), vegetation water content (VWC), surface roughness, and vegetation structure. Because SM and VWC are one of the governing factors in estimating crop yield, microwave observations may be used to improve crop yield estimates. Current studies have shown that active observations are more sensitive to the surface roughness of soil and vegetation structure during the growing season, while the passive observations are more sensitive to the SM. Backscatter and emission models linked with the DSSAT model (DSSAT-A-P) allow assimilation of microwave observations of backscattering coefficient (σ0) and brightness temperature (TB) may provide biophysically realistic estimates of model states and parameters. The present ESA Soil Moisture Ocean Salinity (SMOS) mission provides passive observations at 1.41 GHz at 25 km every 2-3 days, and the NASA/CNDAE Aquarius mission provides L-band AP observations at spatial resolution of 150 km with a repeat coverage of 7 days for global SM products. In 2014, the planned NASA Soil Moisture Active Passive mission will provide AP observations at 1.26 and 1.41 GHz at the spatial resolutions of 3 and 30 km, respectively, with a repeat coverage of 2-3 days. The goal of this study is to understand the impacts of assimilation of asynchronous and synchronous AP observations on crop yield

  12. Methods for estimating the uncertainty of climate change effects in a crop model

    SciTech Connect

    Liebetrau, A.M.; Scott, M.J.

    1990-11-01

    One of the most difficult problems facing policy makers in deciding what to do about the possible effects of future climate change on the earth's natural and human resources (the so-called Greenhouse Effect) is how to proceed in the face of serious uncertainty. Nowhere has this been made more obvious than in the uncertainty associated with the effects of climate on agriculture. The U.S. Department of Energy has funded a program to develop or modify the analytical tools required to estimate the effects of changed climate on a number of natural resource sectors. In the agricultural sector, the primary analytical tool used in this activity has been the Erosion-Production Impact Calculator (EPIC). EPIC is a mathematical cropping systems model developed originally to examine the relationship between soil erosion and soil productivity, but which has now been modified to account for the effects of climate and CO{sub 2} fertilization. The EPIC computer code is a highly detailed tool that incorporates extensive information on weather, plant physiology, and farming practices. In this paper, we describe a process of uncertainty analysis that will evaluate simultaneously the effects of temporal and geographic variability of weather, uncertainty of farm prices, and availability of different farming practices to estimate the effect of climate change on farm income. The second section of this paper discusses the methods and data that are used in making this evaluation; the third section provides information on the sampling schemes used in the exercise; and the fourth section discusses the empirical application of the techniques, together with conclusions. 24 refs., 2 figs., 1 tab.

  13. Estimating 3-dimensional colony surface area of field corals

    EPA Science Inventory

    Colony surface area is a critical descriptor for biological and physical attributes of reef-building (scleractinian, stony) corals. The three-dimensional (3D) size and structure of corals are directly related to many ecosystem values and functions. Most methods to estimate colony...

  14. Using satellite remote sensing to estimate winter cover crop nutrient uptake efficiency

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The practice of planting winter cover crops (rye, wheat, barley) following summer row crops (corn, soybean) is recognized as an effective agricultural conservation practice that can significantly reduce nitrogen losses to groundwater. Accordingly, state cost-share programs have been established to p...

  15. Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remotely sensed data such as spectral reflectance and infrared canopy temperature can be used to quantify crop canopy cover and/or crop water stress, often through the use of vegetation indices calculated from the near-infrared and red bands, and stress indices calculated from the thermal wavelength...

  16. Estimated Yield of Some Alternative Crops Under Varying Irrigation in Northeast Colorado

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Much of the irrigated acres in northeastern Colorado are devoted to corn grain production. Diversifying irrigated agricultural production in this region could result in water savings if alternative crops were grown that have lower water requirements than corn. Making such crop choice decisions initi...

  17. Improved Remotely-Sensed Estimates of Crop Residue Cover by Incorporating Soils Information.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Conservation tillage (CT) methods, which include reduced- and no-till methods, leave substantial quantities of crop residues on the soil surface. These crop residues act as a barrier to wind and water to reduce soil erosion and evaporation. Long-term CT also increases soil organic carbon (SOC) cont...

  18. Radiometer footprint model to estimate sunlit and shaded components for row crops

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper describes a geometric model for computing the relative proportion of sunlit vegetation, shaded vegetation, sunlit soil, and shaded soil appearing in a circular or elliptical radiometer footprint for row crops, where the crop rows were modeled as continuous ellipses. The model was validate...

  19. Mitigating the effects of water on remotely sensed estimates of crop residue cover

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Crop residues on the soil surface decrease soil erosion and increase soil organic carbon and the management of crop residues is an integral part of many conservation tillage systems. Current methods of measuring residue cover are inadequate for characterizing the spatial variability of residue cove...

  20. Gridded rainfall estimation for distributed modeling in western mountainous areas

    NASA Astrophysics Data System (ADS)

    Moreda, F.; Cong, S.; Schaake, J.; Smith, M.

    2006-05-01

    Estimation of precipitation in mountainous areas continues to be problematic. It is well known that radar-based methods are limited due to beam blockage. In these areas, in order to run a distributed model that accounts for spatially variable precipitation, we have generated hourly gridded rainfall estimates from gauge observations. These estimates will be used as basic data sets to support the second phase of the NWS-sponsored Distributed Hydrologic Model Intercomparison Project (DMIP 2). One of the major foci of DMIP 2 is to better understand the modeling and data issues in western mountainous areas in order to provide better water resources products and services to the Nation. We derive precipitation estimates using three data sources for the period of 1987-2002: 1) hourly cooperative observer (coop) gauges, 2) daily total coop gauges and 3) SNOw pack TELemetry (SNOTEL) daily gauges. The daily values are disaggregated using the hourly gauge values and then interpolated to approximately 4km grids using an inverse-distance method. Following this, the estimates are adjusted to match monthly mean values from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). Several analyses are performed to evaluate the gridded estimates for DMIP 2 experiments. These gridded inputs are used to generate mean areal precipitation (MAPX) time series for comparison to the traditional mean areal precipitation (MAP) time series derived by the NWS' California-Nevada River Forecast Center for model calibration. We use two of the DMIP 2 basins in California and Nevada: the North Fork of the American River (catchment area 885 sq. km) and the East Fork of the Carson River (catchment area 922 sq. km) as test areas. The basins are sub-divided into elevation zones. The North Fork American basin is divided into two zones above and below an elevation threshold. Likewise, the Carson River basin is subdivided in to four zones. For each zone, the analyses include: a) overall

  1. Estimating global specific leaf area from MODIS leaf area index and model-simulated foliage mass

    NASA Astrophysics Data System (ADS)

    Baruah, P. J.; Yasuoka, Y.; Ito, A.; Dye, D.

    2006-12-01

    Specific leaf area (SLA) is an important leaf trait that is universally correlated positively to leaf nitrogen, leaf turnover rates, relative growth rate and most importantly, photosynthetic capacity. Though SLA is genetically encoded, it is often spatially variable within a species and within a single biome due to variable environmental conditions. However, without a global SLA map, global ecosystem models that use SLA, generally fix a single value for a particular biome. In this study, we develop a methodology to estimate global SLA from a remote sensing-derived key ecosystem variable, leaf area index and foliage mass estimated by a terrestrial ecosystem model SimCYCLE. SimCYCLE uses climatic inputs, land-cover data and biomass-allocation to estimate leaf biomass in a process-based scheme. Model-estimated foliage mass and MODIS leaf area index are assumed to represent the most-accurate ground condition to estimate SLA for the entire globe at 0.5 degree resolution. Validation of estimated specific leaf area is done with a published field-sampled global dataset, and additional field-sampled SLA data collected from published literatures. The validation data is also used for rectification of unrealistic values of estimated SLA to produce a global SLA map, which we strongly believe, would be valuable to improve estimates of carbon dynamic across individual biomes upon assimilation with the ecosystem models.

  2. Estimating crop canopy coverage of cotton plants within the FOV of an infrared thermometer using a two band photodiode sensor

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The majority of irrigated area in the Southern High Plains region of Texas is by center pivot systems; all are drawing water from the Ogallala Aquifer. Automating the center pivot system to schedule irrigations automatically based on crop canopy temperature measured with infrared thermometers (IRT)...

  3. Space Data for Crop Management

    NASA Technical Reports Server (NTRS)

    1990-01-01

    CROPIX, Inc., formed in 1984 by Frank Lamb, president of the Eastern Oregon Farming Company, monitors primarily potato crops in a 20,000 square mile area of northern Oregon and central Washington. Potatoes are a high value specialty crop that can be more profitable to the farmer if he has advance knowledge of market conditions, knows when to harvest, and when to take it to market. By processing and collecting data collected by the NASA-developed Landsat Earth Resources survey satellites, Lamb is able to provide accurate information on crop acreage and conditions on a more timely basis than the routine estimates by the USDA. CROPIX uses Landsat data to make acreage estimates of crops, and to calculate a field-by-field vegetative index number. CROPIX then distributes to its customers a booklet containing color-coded maps, an inventory of crops, plus data and graphs on crop conditions and other valuable information.

  4. Are clear-cut areas estimated from LANDSAT imagery reliable?

    NASA Technical Reports Server (NTRS)

    Lee, Y. J.

    1975-01-01

    The reliability of LANDSAT imagery for estimation of clear-cut areas was evaluated by comparison with data obtained from high-altitude photos and logging historical map and from field inspections. A mature forest was selected as a test site because of its continuous clear-cut operation. The forest is about 50 km northwest of Victoria, British Columbia, Canada, and consists of 9092 ha. Areas clear-cut within the past year were overestimated by 12.9%, those clear-cut 1-year or more by 2.2%, whereas uncut mature timber was underestimated by 3.6%. Three clear-cut areas were missed in the logging map and two in the LANDSAT enhancement. The difference between area estimates was significant when all 26 areas were included but not when 2 overestimated areas were excluded from the analysis. The study indicates that LANDSAT imagery color enhancement is a useful tool in up-dating clear-cut areas for long-term planning in forest management.

  5. A Unified Experimental Approach for Estimation of Irrigationwater and Nitrate Leaching in Tree Crops

    NASA Astrophysics Data System (ADS)

    Hopmans, J. W.; Kandelous, M. M.; Moradi, A. B.

    2014-12-01

    Groundwater quality is specifically vulnerable in irrigated agricultural lands in California and many other(semi-)arid regions of the world. The routine application of nitrogen fertilizers with irrigation water in California is likely responsible for the high nitrate concentrations in groundwater, underlying much of its main agricultural areas. To optimize irrigation/fertigation practices, it is essential that irrigation and fertilizers are applied at the optimal concentration, place, and time to ensure maximum root uptake and minimize leaching losses to the groundwater. The applied irrigation water and dissolved fertilizer, as well as root growth and associated nitrate and water uptake, interact with soil properties and fertilizer source(s) in a complex manner that cannot easily be resolved. It is therefore that coupled experimental-modeling studies are required to allow for unraveling of the relevant complexities that result from typical field-wide spatial variations of soil texture and layering across farmer-managed fields. We present experimental approaches across a network of tree crop orchards in the San Joaquin Valley, that provide the necessary soil data of soil moisture, water potential and nitrate concentration to evaluate and optimize irrigation water management practices. Specifically, deep tensiometers were used to monitor in-situ continuous soil water potential gradients, for the purpose to compute leaching fluxes of water and nitrate at both the individual tree and field scale.

  6. Seasonal Phenology and Species Composition of the Aphid Fauna in a Northern Crop Production Area

    PubMed Central

    Kirchner, Sascha M.; Hiltunen, Lea; Döring, Thomas F.; Virtanen, Elina; Palohuhta, Jukka P.; Valkonen, Jari P. T.

    2013-01-01

    Background The species diversity of aphids and seasonal timing of their flight activity can have significant impacts on crop production, as aphid species differ in their ability to transmit plant viruses and flight timing affects virus epidemiology. The aim of the study was to characterise the species composition and phenology of aphid fauna in Finland in one of the northernmost intensive crop production areas of the world (latitude 64°). Methodology/Principal Findings Flight activity was monitored in four growing seasons (2007–010) using yellow pan traps (YPTs) placed in 4–8 seed potato fields and a Rothamsted suction trap. A total of 58,528 winged aphids were obtained, identified to 83 taxa based on morphology, and 34 species were additionally characterised by DNA barcoding. Seasonal flight activity patterns analysed based on YPT catch fell into three main phenology clusters. Monoecious taxa showed early or middle-season flight activity and belonged to species living on shrubs/trees or herbaceous plants, respectively. Heteroecious taxa occurred over the entire potato growing season (ca. 90 days). Abundance of aphids followed a clear 3-year cycle based on suction trap data covering a decade. Rhopalosiphum padi occurring at the end of the potato growing season was the most abundant species. The flight activity of Aphis fabae, the main vector of Potato virus Y in the region, and Aphis gossypii peaked in the beginning of potato growing season. Conclusions/Significance Detailed information was obtained on phenology of a large number aphid species, of which many are agriculturally important pests acting as vectors of plant viruses. Aphis gossypii is known as a pest in greenhouses, but our study shows that it occurs also in the field, even far in the north. The novel information on aphid phenology and ecology has wide implications for prospective pest management, particularly in light of climate change. PMID:23967149

  7. Using Metabolomics To Estimate Unintended Effects in Transgenic Crop Plants: Problems, Promises, and Opportunities

    PubMed Central

    Hoekenga, Owen A.

    2008-01-01

    Transgenic crops are widespread in some countries and sectors of the agro-economy, but are also highly contentious. Proponents of transgenic crop improvement often cite the “substantial equivalence” of transgenic crops to the their nontransgenic parents and sibling varieties. Opponents of transgenic crop improvement dismiss the substantial equivalence standard as being without statistical basis and emphasize the possible unintended effects to food quality and composition due to genetic transformation. Systems biology approaches should help consumers, regulators, and other stakeholders make better decisions regarding transgenic crop improvement by characterizing the composition of conventional and transgenically improved crop species and products. In particular, metabolomic profiling via mass spectrometry and nuclear magnetic resonance can make broad and deep assessments of food quality and content. The metabolome observed in a transgenic variety can then be assessed relative to the consumer and regulator accepted phenotypic range observed among conventional varieties. I briefly discuss both targeted (closed architecture) and nontargeted (open architecture) metabolomics with respect to the transgenic crop debate and highlight several challenges to the field. While most experimental examples come from tomato (Solanum lycoperiscum), analytical methods from all of systems biology are discussed. PMID:19137102

  8. F-Area Type IV Tank Liner Life Estimation

    SciTech Connect

    Wiersman, B. J.

    2005-10-01

    The Savannah River Site (SRS) is proceeding with closure of the Type IV waste tanks that are located in F-area. These tanks are underground concrete vaults and have been in service since the early 1960's. The interior of the concrete vault is lined with carbon steel plate. The time necessary for the carbon steel plate to disintegrate was estimated. These calculations assumed that the concrete structure was degraded and therefore the exterior of the liner is exposed to the soil conditions. Two corrosion mechanisms were examined: pitting and general corrosion. Data from soil corrosion studies performed by the National Bureau of Standards (NBS) was utilized to estimate the corrosion rate of the carbon steel liner. The following conclusions were made: (1) Cecil Clay Loam in Atlanta, Georgia, a soil tested by the NBS, is representative of the SRS soil conditions near the F-Area Type IV tanks. (2) The time to tank wall disintegration due to general corrosion only was estimated to be 770 years after concrete vault failure. (3) The time to tank wall disintegration due to pitting corrosion was estimated to be 675 years after concrete vault failure. (4) The lower bound estimate for the time to tank wall disintegration is 675 years.

  9. Estimating the Effects of the Terminal Area Productivity Program

    NASA Technical Reports Server (NTRS)

    Lee, David A.; Kostiuk, Peter F.; Hemm, Robert V., Jr.; Wingrove, Earl R., III; Shapiro, Gerald

    1997-01-01

    The report describes methods and results of an analysis of the technical and economic benefits of the systems to be developed in the NASA Terminal Area Productivity (TAP) program. A runway capacity model using parameters that reflect the potential impact of the TAP technologies is described. The runway capacity model feeds airport specific models which are also described. The capacity estimates are used with a queuing model to calculate aircraft delays, and TAP benefits are determined by calculating the savings due to reduced delays. The report includes benefit estimates for Boston Logan and Detroit Wayne County airports. An appendix includes a description and listing of the runway capacity model.

  10. Using high resolution CIR imagery in the classification of non-cropped areas in agricultural landscapes in the UK

    NASA Astrophysics Data System (ADS)

    O'Connell, Jerome; Bradter, Ute; Benton, Tim G.

    2013-10-01

    With global food demand on course to double in the next 50 years the pressures of agricultural intensification on ecosystem services in highly managed landscapes are increasing. Within an agricultural landscape non-cropped areas are a key component of ecological heterogeneity and the sustainability of ecosystem services. Management of the landscape for both production of food and ecosystem services requires configuring the non-cropped areas in an optimal way, which, in turn requires large scale information on the distribution of non-cropped areas. In this study the Canny edge detection algorithm was used to delineate 93% of all boundaries within 422 ha of agricultural land in south east England. The resulting image was used in conjunction with vegetation indices derived from Color Infra Red (CIR) aerial photography and auxiliary landuse data in an Object Orientated (OO) Knowledge Based Classifier (KBC) to identify non-cropped areas. An overall accuracy of 94.27% (Kappa 0.91) for the KBC compared favorably with 63.04% (Kappa 0.55) for a pixel based hybrid classifier of the same area.

  11. Estimation of yield and water requirements of maize crops combining high spatial and temporal resolution images with a simple crop model, in the perspective of the Sentinel-2 mission

    NASA Astrophysics Data System (ADS)

    Battude, Marjorie; Bitar, Ahmad Al; Brut, Aurore; Cros, Jérôme; Dejoux, Jean-François; Huc, Mireille; Marais Sicre, Claire; Tallec, Tiphaine; Demarez, Valérie

    2016-04-01

    Water resources are under increasing pressure as a result of global change and of a raising competition among the different users (agriculture, industry, urban). It is therefore important to develop tools able to estimate accurately crop water requirements in order to optimize irrigation while maintaining acceptable production. In this context, remote sensing is a valuable tool to monitor vegetation development and water demand. This work aims at developing a robust and generic methodology mainly based on high resolution remote sensing data to provide accurate estimates of maize yield and water needs at the watershed scale. Evapotranspiration (ETR) and dry aboveground biomass (DAM) of maize crops were modeled using time series of GAI images used to drive a simple agro-meteorological crop model (SAFYE, Duchemin et al., 2005). This model is based on a leaf partitioning function (Maas, 1993) for the simulation of crop biomass and on the FAO-56 methodology for the ETR simulation. The model also contains a module to simulate irrigation. This study takes advantage of the SPOT4 and SPOT5 Take5 experiments initiated by CNES (http://www.cesbio.ups-tlse.fr/multitemp/). They provide optical images over the watershed from February to May 2013 and from April to August 2015 respectively, with a temporal and spatial resolution similar to future images from the Sentinel-2 and VENμS missions. This dataset was completed with LandSat8 and Deimos1 images in order to cover the whole growing season while reducing the gaps in remote sensing time series. Radiometric, geometric and atmospheric corrections were achieved by the THEIA land data center, and the KALIDEOS processing chain. The temporal dynamics of the green area index (GAI) plays a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Consistent seasonal dynamics of the remotely sensed GAI was estimated by applying a radiative transfer model based on artificial neural networks (BVNET, Baret

  12. LIFE CLIMATREE project: A novel approach for accounting and monitoring carbon sequestration of tree crops and their potential as carbon sink areas

    NASA Astrophysics Data System (ADS)

    Stergiou, John; Tagaris, Efthimios; -Eleni Sotiropoulou, Rafaella

    2016-04-01

    Climate Change Mitigation is one of the most important objectives of the Kyoto Convention, and is mostly oriented towards reducing GHG emissions. However, carbon sink is retained only in the calculation of the forests capacity since agricultural land and farmers practices for securing carbon stored in soils have not been recognized in GHG accounting, possibly resulting in incorrect estimations of the carbon dioxide balance in the atmosphere. The agricultural sector, which is a key sector in the EU, presents a consistent strategic framework since 1954, in the form of Common Agricultural Policy (CAP). In its latest reform of 2013 (reg. (EU) 1305/13) CAP recognized the significance of Agriculture as a key player in Climate Change policy. In order to fill this gap the "LIFE ClimaTree" project has recently founded by the European Commission aiming to provide a novel method for including tree crop cultivations in the LULUCF's accounting rules for GHG emissions and removal. In the framework of "LIFE ClimaTree" project estimation of carbon sink within EU through the inclusion of the calculated tree crop capacity will be assessed for both current and future climatic conditions by 2050s using the GISS-WRF modeling system in a very fine scale (i.e., 9km x 9km) using RCP8.5 and RCP4.5 climate scenarios. Acknowledgement: LIFE CLIMATREE project "A novel approach for accounting and monitoring carbon sequestration of tree crops and their potential as carbon sink areas" (LIFE14 CCM/GR/000635).

  13. Variation in the estimations of ETo and crop water use due to the sensor accuracy of the meteorological variables

    NASA Astrophysics Data System (ADS)

    Moratiel, R.; Martínez-Cob, A.; Latorre, B.

    2013-06-01

    In agricultural ecosystems the use of evapotranspiration (ET) to improve irrigation water management is generally widespread. Commonly, the crop ET (ETc) is estimated by multiplying the reference crop evapotranspiration (ETo) by a crop coefficient (Kc). Accurate estimation of ETo is critical because it is the main factor affecting the calculation of crop water use and water management. The ETo is generally estimated from recorded meteorological variables at reference weather stations. The main objective of this paper was assessing the effect of the uncertainty due to random noise in the sensors used for measurement of meteorological variables on the estimation of ETo, crop ET and net irrigation requirements of grain corn and alfalfa in three irrigation districts of the middle Ebro River basin. Five scenarios were simulated, four of them individually considering each recorded meteorological variable (temperature, relative humidity, solar radiation and wind speed) and a fifth scenario combining together the uncertainty of all sensors. The uncertainty in relative humidity for irrigation districts Riegos del Alto Aragón (RAA) and Bardenas (BAR), and temperature for irrigation district Canal de Aragón y Cataluña (CAC), were the two most important factors affecting the estimation of ETo, corn ET (ETc_corn), alfalfa ET (ETc_alf), net corn irrigation water requirements (IRncorn) and net alfalfa irrigation water requirements (IRnalf). Nevertheless, this effect was never greater than ±0.5% over annual scale time. The wind speed variable (Scenario 3) was the third variable more influential in the fluctuations (±) of evapotranspiration, followed by solar radiation. Considering the accuracy for all sensors over annual scale time, the variation was about ±1% of ETo, ETc_corn, ETc_alf, IRncorn, and IRnalf. The fluctuations of evapotranspiration were higher at shorter time scale. ETo daily fluctuation remained lower than 5 % during the growing season of corn and alfalfa

  14. Rain Volume Estimation over Areas Using Satellite and Radar Data

    NASA Technical Reports Server (NTRS)

    Doneaud, A. A.; Miller, J. R., Jr.; Johnson, L. R.; Vonderhaar, T. H.; Laybe, P.

    1984-01-01

    The application of satellite data to a recently developed radar technique used to estimate convective rain volumes over areas on a dry environment (the northern Great Plains) is discussed. The area time integral technique (ATI) provides a means of estimating total rain volumes over fixed and floating target areas of the order of 1,000 to 100,000 km(2) for clusters lasting 40 min. The basis of the method is the existence of a strong correlation between the area coverage integrated over the lifetime of the storm (ATI) and the rain volume. One key element in this technique is that it does not require the consideration of the structure of the radar intensities inside the area coverage to generate rain volumes, but only considers the rain event per se. This fact might reduce or eliminate some sources of error in applying the technique to satellite data. The second key element is that the ATI once determined can be converted to total rain volume by using a constant factor (average rain rate) for a given locale.

  15. Estimation of vegetation fraction in arid areas using ALOS imagery

    NASA Astrophysics Data System (ADS)

    Matkan, A. A.; Darvishzadeh, R.; Hosseiniasl, A.; Ebrahimi, M.

    2010-10-01

    Fraction of vegetation (Fv) plays an important role in ecosystems. Estimation of Fv is essential for drought monitoring, natural resources studies, estimation of soil erosion volume etc. The aim of this study is to estimate Fv in an arid area in Iran using ALOS Imagery (June 2008). In order to find the best index for estimation of Fv, Seventeen vegetation indices (ARVI, DVI, EVI, GEMI, IPVI, MSAVI1, MSAVI2, NDVI, PVI, SAVI, SARVI, SARVI2, SR, TSAVI, WDVI) were used. The canopy cover percentage of 52 sample plots (50m by 50m) was measured in the field in June 2009. Regression models were used to assess the relationships between the field data and the calculated Fv. The 52 sample plots were randomly divided two times to 30 calibrations and 22 validations, and to 35 and 17 samples. Results revealed that selecting the calibration and validation data randomly leads to different results. Therefore, cross-validation method was used to reduce random division effect. Results indicated that, among all indices, vegetation indices such as MSAVI1, PVI, WDVI and TSAVI which are based on soil line have higher R2 and lower RMSE (R2 > 0.63, RMSE ~ 3%). The results confirm the dominant effect of soil reflectance in arid areas.

  16. Parameterization of ALMANAC crop simulation model for non-irrigated dry bean in semi-arid temperate areas in Mexico

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Simulation models can be used to make management decisions when properly parameterized. This study aimed to parameterize the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) crop simulation model for dry bean in the semi-arid temperate areas of Mexico. The par...

  17. How universal is the relationship between remotely sensed vegetation indices and crop leaf area index? A global assessment

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This study aims to assess the relationship between Leaf Area Index (LAI) and remotely sensed Vegetation Indices (VIs) for major crops, based on a globally explicit dataset of in situ LAI measurements over a significant set of locations. We used a total of 1394 LAI measurements from 29 sites spannin...

  18. Hand surface area estimation formula using 3D anthropometry.

    PubMed

    Hsu, Yao-Wen; Yu, Chi-Yuang

    2010-11-01

    Hand surface area is an important reference in occupational hygiene and many other applications. This study derives a formula for the palm surface area (PSA) and hand surface area (HSA) based on three-dimensional (3D) scan data. Two-hundred and seventy subjects, 135 males and 135 females, were recruited for this study. The hand was measured using a high-resolution 3D hand scanner. Precision and accuracy of the scanner is within 0.67%. Both the PSA and HSA were computed using the triangular mesh summation method. A comparison between this study and previous textbook values (such as in the U.K. teaching text and Lund and Browder chart discussed in the article) was performed first to show that previous textbooks overestimated the PSA by 12.0% and HSA by 8.7% (for the male, PSA 8.5% and HSA 4.7%, and for the female, PSA 16.2% and HSA 13.4%). Six 1D measurements were then extracted semiautomatically for use as candidate estimators for the PSA and HSA estimation formula. Stepwise regressions on these six 1D measurements and variable dependency test were performed. Results show that a pair of measurements (hand length and hand breadth) were able to account for 96% of the HSA variance and up to 98% of the PSA variance. A test of the gender-specific formula indicated that gender is not a significant factor in either the PSA or HSA estimation. PMID:20865628

  19. Increased area of a highly suitable host crop increases herbivore pressure in intensified agricultural landscapes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    tLandscape simplification associated with agricultural intensification has important effects on economi-cally important arthropods. The declining cover of natural and semi-natural habitats, in particular, hasbeen shown to reduce natural-enemy attack of crop pests, but also in some cases reduced crop...

  20. Estimation of energy and moisture fluxes for dynamic vegetation using coupled SVAT and crop-growth models

    NASA Astrophysics Data System (ADS)

    Casanova, Joaquin J.; Judge, Jasmeet

    2008-07-01

    A Soil Vegetation Atmosphere Transfer (SVAT) model, viz. Land Surface Process (LSP) model, is coupled with a widely used crop-growth model, DSSAT, to estimate energy and moisture fluxes at the land surface and in the vadose zone for growing vegetation. In this study, we present detailed observations of soil and crop characteristics, and various components of energy and water balance during a season-long field experiment for sweet corn. The data set is used to calibrate the LSP with Latin Hypercube Sampling and Pareto ranking. We compare the observations with model estimates of crop growth and development, land surface fluxes, soil moisture and temperature profiles from both the stand-alone LSP and coupled LSP-DSSAT models. We find that the model estimates of radiation fluxes, soil moisture, and soil temperature, by both the LSP and LSP-DSSAT are very similar, indicating that the LSP-DSSAT model can be used to simulate fluxes for dynamic vegetation without the need of in situ vegetation observations. Moreover, because coupling was achieved without structurally changing either of the models, the methodology in this study can be extended to coupling other SVAT and vegetation growth models.

  1. Parameter Estimation for a crop model: separate and joint calibration of soil and plant parameters

    NASA Astrophysics Data System (ADS)

    Hildebrandt, A.; Jackisch, C.; Luis, S.

    2008-12-01

    Vegetation plays a major role both in the atmospheric and terrestrial water cycle. A great deal of vegetation cover in the developed world consists of agricultural used land (i.e. 44 % of the territory of the EU). Therefore, crop models have become increasingly prominent for studying the impact of Global Change both on economic welfare as well as on influence of vegetation on climate, and feedbacks with hydrological processes. By doing so, it is implied that crop models properly reflect the soil water balance and vertical exchange with the atmosphere. Although crop models can be incorporated in Surface Vegetation Atmosphere Transfer Schemes for that purpose, their main focus has traditionally not been on predicting water and energy fluxes, but yield. In this research we use data from two lysimeters in Brandis (Saxony, Germany), which have been planted with the crops of the surrounding farm, to test the capability of the crop model in SWAP. The lysimeters contain different natural soil cores, leading to substantially different yield. This experiment gives the opportunity to test, if the crop model is portable - that is if a calibrated crop can be moved between different locations. When using the default parameters for the respective environment, the model does neither quantitatively nor qualitatively reproduce the difference in yield and LAI for the different lysimeters. The separate calibration of soil and plant parameter was poor compared to the joint calibration of plant and soil parameters. This suggests that the model is not portable, but needs to be calibrated for individual locations, based on measurements or expert knowledge.

  2. Estimation of underground structure at Prambanan area, Yogyakarta, Indonesia

    NASA Astrophysics Data System (ADS)

    Thein, Pyi Soe; Pramumijoyo, Subagyo; Brotopuspito, Kirbani Sri; Kiyono, Junji; Wilopo, Wahyu; Husein, Salahuddin; Setianto, Agung

    2015-04-01

    In this study, we investigated the underground structure at Prambanan area, Yogyakarta. We performed single observations of microtremor at 124 sites in Prambanan area. The results enabled us to estimate the site-dependent shaking characteristics of earthquake ground motion. We also conducted 2-site bore holes investigation to gain a representative determination of the soil condition of subsurface structures in Prambanan. From the SPT of borehole observations, the prambanan area corresponds to relatively soil condition with Vs ≤ 298 m/s, the predominant periods due to horizontal vertical ratios (HVSRs) are in the range of 0.48 to 1.19 s and the frequency are in the range of 0.95 to 1.92 Hz. By making these observations, we can obtain a relationship between the predominant periods, frequency and distribution of the first layer thickness of the sediment.

  3. Estimation of underground structure at Prambanan area, Yogyakarta, Indonesia

    SciTech Connect

    Thein, Pyi Soe; Pramumijoyo, Subagyo; Wilopo, Wahyu; Husein, Salahuddin; Setianto, Agung; Brotopuspito, Kirbani Sri; Kiyono, Junji

    2015-04-24

    In this study, we investigated the underground structure at Prambanan area, Yogyakarta. We performed single observations of microtremor at 124 sites in Prambanan area. The results enabled us to estimate the site-dependent shaking characteristics of earthquake ground motion. We also conducted 2-site bore holes investigation to gain a representative determination of the soil condition of subsurface structures in Prambanan. From the SPT of borehole observations, the prambanan area corresponds to relatively soil condition with Vs ≤ 298 m/s, the predominant periods due to horizontal vertical ratios (HVSRs) are in the range of 0.48 to 1.19 s and the frequency are in the range of 0.95 to 1.92 Hz. By making these observations, we can obtain a relationship between the predominant periods, frequency and distribution of the first layer thickness of the sediment.

  4. Estimation of a water budget for 1972-2000 for the Grasslands Area, central part of the Western San Joaquin Valley, California

    USGS Publications Warehouse

    Brush, Charles F.; Belitz, Kenneth; Phillips, Steven P.

    2004-01-01

    Equitable implementation of regulations restricting discharges from agricultural drains into the San Joaquin River requires a greater understanding of the influence of extreme precipitation events on the ground-water flow system. As part of a larger investigation, this study estimated ground-water recharge and ground-water pumpage, two important components of the water budget in the Grasslands drainage area in the central part of the western San Joaquin Valley, California, for the water years 1972 through 2000. These estimates will be used as inputs to a numerical simulation model of the regional ground-water flow system in the continuing investigation. Crop-acreage and surface-water delivery data were compiled for 14 water districts and 6 other areas comprising approximately 97 percent of the 600-square-mile study area. Little ground-water pumpage data exists for the study area. A climate-based approach was employed to estimate annual water-table recharge flux and ground-water pumpage for 11 water-budget areas. Ground-water pumpage was estimated from the residual irrigation demand after crop consumption of surface water. Estimated recharge flux to the water table for the entire study area averaged 0.8 ft/yr, and estimated ground-water pumpage per unit area for the entire study area averaged 0.5 ft/yr. Increased discharges from agricultural drains in the late 1990s may have been due partly to 4 years of high recharge from precipitation over the 6-year period from 1993 to 1998. Knowledge of the ratio of annual crop water demand to annual potential evapotranspiration, expressed as an aggregate crop coefficient, Kd, will facilitate estimation of annual water-budget components in future studies. Annual aggregate crop coefficients, calculated each year for the entire study area, were nearly constant at 0.59 from 1983 to 2000, and reasonably constant at 0.53 prior to 1983. The overall trend suggests continuous reductions in recharge from irrigation over time. This

  5. CO2 uptake and ecophysiological parameters of the grain crops of midcontinent North America: estimates from flux tower measurements

    USGS Publications Warehouse

    Gilmanov, Tagir; Wylie, Bruce; Tieszen, Larry; Meyers, Tilden R.; Baron, Vern S.; Bernacchi, Carl J.; Billesbach, David P.; Burba, George G.; Fischer, Marc L.; Glenn, Aaron J.; Hanan, Niall P.; Hatfield, Jerry L.; Heuer, Mark W.; Hollinger, Steven E.; Howard, Daniel M.; Matamala, Roser; Prueger, John H.; Tenuta, Mario; Young, David G.

    2013-01-01

    We analyzed net CO2 exchange data from 13 flux tower sites with 27 site-years of measurements over maize and wheat fields across midcontinent North America. A numerically robust “light-soil temperature-VPD”-based method was used to partition the data into photosynthetic assimilation and ecosystem respiration components. Year-round ecosystem-scale ecophysiological parameters of apparent quantum yield, photosynthetic capacity, convexity of the light response, respiration rate parameters, ecological light-use efficiency, and the curvature of the VPD-response of photosynthesis for maize and wheat crops were numerically identified and interpolated/extrapolated. This allowed us to gap-fill CO2 exchange components and calculate annual totals and budgets. VPD-limitation of photosynthesis was systematically observed in grain crops of the region (occurring from 20 to 120 days during the growing season, depending on site and year), determined by the VPD regime and the numerical value of the curvature parameter of the photosynthesis-VPD-response, σVPD. In 78% of the 27 site-years of observations, annual gross photosynthesis in these crops significantly exceeded ecosystem respiration, resulting in a net ecosystem production of up to 2100 g CO2 m−2 year−1. The measurement-based photosynthesis, respiration, and net ecosystem production data, as well as the estimates of the ecophysiological parameters, provide an empirical basis for parameterization and validation of mechanistic models of grain crop production in this economically and ecologically important region of North America.

  6. Small area estimation for estimating the number of infant mortality in West Java, Indonesia

    NASA Astrophysics Data System (ADS)

    Anggreyani, Arie; Indahwati, Kurnia, Anang

    2016-02-01

    Demographic and Health Survey Indonesia (DHSI) is a national designed survey to provide information regarding birth rate, mortality rate, family planning and health. DHSI was conducted by BPS in cooperation with National Population and Family Planning Institution (BKKBN), Indonesia Ministry of Health (KEMENKES) and USAID. Based on the publication of DHSI 2012, the infant mortality rate for a period of five years before survey conducted is 32 for 1000 birth lives. In this paper, Small Area Estimation (SAE) is used to estimate the number of infant mortality in districts of West Java. SAE is a special model of Generalized Linear Mixed Models (GLMM). In this case, the incidence of infant mortality is a Poisson distribution which has equdispersion assumption. The methods to handle overdispersion are binomial negative and quasi-likelihood model. Based on the results of analysis, quasi-likelihood model is the best model to overcome overdispersion problem. The basic model of the small area estimation used basic area level model. Mean square error (MSE) which based on resampling method is used to measure the accuracy of small area estimates.

  7. A STELLA Model to Estimate Water and Nitrogen Dynamics in a Short-Rotation Woody Crop Plantation.

    PubMed

    Ouyang, Ying; Zhang, Jiaen; Leininger, Theodor D; Frey, Brent R

    2015-01-01

    Although short-rotation woody crop biomass production technology has demonstrated a promising potential to supply feedstocks for bioenergy production, the water and nutrient processes in the woody crop planation ecosystem are poorly understood. In this study, a computer model was developed to estimate the dynamics of water and nitrogen (N) species (e.g., NH-N, NO-N, particulate organic N, and soluble organic N [SON]) in a woody crop plantation using STELLA (tructural hinking and xperiential earning aboratory with nimation) software. A scenario was performed to estimate diurnal and monthly water and N variations of a 1-ha mature cottonwood plantation over a 1-yr simulation period. A typical monthly variation pattern was found for soil water evaporation, leaf water transpiration, and root water uptake, with an increase from winter to summer and a decrease from summer to the following winter. Simulations further revealed that the rate of soil water evaporation was one order of magnitude lower than that of leaf water transpiration. In most cases, the relative monthly water loss rates could be expressed as evapotranspiration > root uptake > percolation > runoff. Leaching of NO-N and SON depended not only on soil N content but also on rainfall rate and duration. Leaching of NO-N from the cottonwood plantation was about two times higher than that of SON. The relative monthly rate of N leaching was NO-N > SON > NH-N. This study suggests that the STELLA model developed is a useful tool for estimating water and N dynamics from a woody crop plantation. PMID:25602335

  8. The large area crop inventory experiment: A major demonstration of space remote sensing

    NASA Technical Reports Server (NTRS)

    Macdonald, R. B.; Hall, F. G.

    1977-01-01

    Strategies are presented in agricultural technology to increase the resistance of crops to a wider range of meteorological conditions in order to reduce year-to-year variations in crop production. Uncertainties in agricultral production, together with the consumer demands of an increasing world population, have greatly intensified the need for early and accurate annual global crop production forecasts. These forecasts must predict fluctuation with an accuracy, timeliness and known reliability sufficient to permit necessary social and economic adjustments, with as much advance warning as possible.

  9. Tracking Crop Leaf Area Index and Chlorophyll Content Using RapidEye Data in Northern Ontario, Canada

    NASA Astrophysics Data System (ADS)

    Shang, J.; Liu, J.; Ma, B.; Zhao, T.; Kovacs, J. M.; Jiao, X.; Dong, T.; Huffman, T.; Geng, X.; Walters, D.

    2014-12-01

    Information on crop phenological state such as flowering, maturing, drying, senescence, and harvesting is essential for crop production surveillance and yield prediction. Earth Observation data provide an important information source for monitoring crop development at various temporal and spatial scales. In particular, the availability of many high-spatial-resolution space sensors offers a powerful tool for precision farming. This study reports the results of a two-year (2012, 2013) study over spring wheat and canola fields using six different vegetation indices derived from the high-resolution (6.5m) RapidEye optical satellite data in northern Ontario, Canada. The study revealed that for both wheat and canola, significant relationships were observed between the ground-derived leaf area index (LAI) and all 6 vegetation indices tested. For spring wheat, the strongest relationship was found between LAI and the Modified Triangular Vegetation Index 2 (MTVI2), with a coefficient of determination (R2) of 0.95. For canola, a R2 of 0.92 was achieved. Strong relationships were also found between all six vegetation indices and the chlorophyll concentration index (CCI) measured in the fields using a CCM-200 device. The strongest correlation exists between CCI and the ratio of Modified the Chlorophyll Absorption Reflected Index (MCARI) and the Optimized Soil Adjusted Vegetation Index (OSAVI), with an R2 of 0.86. It suggests that RapidEye data can be used to track field-scale crop LAI and monitor crop chlorophyll content.

  10. Regional estimation of soil C stocks and CO2 emissions as influenced by cropping systems and soil type

    NASA Astrophysics Data System (ADS)

    Farina, Roberta; Marchetti, Alessandro; Di Bene, Claudia

    2015-04-01

    Soil organic matter (SOM) is of crucial importance for agricultural soil quality and fertility. At global level soil contains about three times the carbon stored in the vegetation and about twice that present in the atmosphere. Soil could act as source and sink of carbon, influencing the balance of CO2 concentration and consequently the global climate. The sink/source ratio depends on many factors that encompass climate, soil characteristics and different land management practices. Thus, the relatively large gross exchange of GHGs between atmosphere and soils and the significant stocks of carbon in soils, may have significant impact on climate and on soil quality. To quantify the dynamics of C induced by land cover change and the spatial and temporal dynamics of C sources and sinks at regional and, potentially, at national and global scales, we propose a methodology, based on a bio-physical model combined with a spatial explicit database to estimate C stock changes and emissions/removals. The study has been conducted in a pilot region in Italy (Apulia, Foggia province), considering the typical cropping systems of the area, namely rainfed cereals, tomato, vineyard and olives. For this purpose, the model RothC10N (Farina et al., 2013), that simulates soil C dynamics, has been modified to work directly in batch using data of climate, soil (over 290 georeferenced soil profiles), annual agriculture land use (1200 observations) The C inputs from crops have been estimated using statistics and data from literature. The model was run to equilibrium for each point of soil, in order to make all the data homogeneous in terms of time. The obtained data were interpolate with geostatisical procedures, obtaining a set of 30x30 km grid with the initial soil C. The new layer produced, together with soil and land use layers, were used for a long-term run (12 years). Results showed that olive groves and vineyards were able to stock a considerable amount of C (from 0.4 to 1.5 t ha-1 y

  11. Exploring the Usefulness of MISR-HR Products to Estimate Maize Crop Extent and Using Field Evidence to Evaluate the Results in South Africa's Free State Province

    NASA Astrophysics Data System (ADS)

    Verstraete, M. M.; Knox, N. M.; Hunt, L. A.; Kleyn, L.

    2014-12-01

    The MISR instrument on NASA's Terra platform has been operating for almost 15 years. Standard products are generated at a spatial resolution of 1.1 km or coarser, but a recently developed method to re-analyze the Level-1B2 data allows the retrieval of biogeophysical products at the native spatial resolution of the instrument (275 m). This development opens new opportunities to better address issues such as the management of agricultural production and food security. South African maize production is of great economic and social importance, not only nationally, but on the global market too, being one of the top ten maize producing countries. Seasonal maize production statistics are currently based on a combination of field measurements and estimates derived from manually digitizing high resolution imagery from the SPOT satellite. The field measurements are collected using the Producer Independent Crop Estimate System (PICES) developed by Crop Estimates Committee of the Department of Agriculture, Forestry and Fisheries. There is a strong desire to improve the quality of these statistics, to generate those earlier, and to automate the process to encompass larger areas. This paper will explore the feasibility of using the MISR-HR spectral and directional products, combined with the finer spatial resolution and the relatively frequent coverage afforded by that instrument, to address these needs. The study area is based in the Free State, South Africa, one of the primary maize growing areas in the country, and took place during the 2012-2013 summer growing season. The significance of the outcomes will be evaluated in the context of the 14+ years of available MISR data.

  12. Estimation on Peak Ground Acceleration in Taiwan Area

    NASA Astrophysics Data System (ADS)

    Chen, K.

    2013-12-01

    Since the Taiwan area is situated on the subduction zone, where the Philippine Sea Plate is subducting underneath the Eurasian Plate, much attention is focused on seismic risk mitigation. The purpose of this paper is to study the potential of earthquakes hazard in the Taiwan area, using the seismic acceleration data collected by the Central Weather Bureau from 1993 to 2008. The theoretic Peak Ground Acceleration (PGA) is calculated basing on well developed velocity and attenuation models. The theoretic maximum amplitudes of the ground acceleration in Taiwan area for 30 events have been calculated by using the attenuation equation and the Q-structures. The deviations between the observed and theoretic PGA of 163 seismic stations in Taiwan area can be obtained. The results show that most of the deviation is small than 30%. It indicates that we can predict the maximum amplitude of ground acceleration for any events occurred in Taiwan area under the accuracy of 70 % (probability). The estimation of PGA for any earthquake will be obtained using the theoretic and corrected with these deviations.

  13. Using FAO-56 model to estimate soil and crop water status: Application to a citrus orchard under regulated deficit irrigation

    NASA Astrophysics Data System (ADS)

    Provenzano, Giuseppe; Gonzàles-Altozano, Pablo; Manzano-Juàrez, Juan; Rallo, Giovanni

    2015-04-01

    Agro-hydrological models allow schematizing exchange processes in the soil-plant-atmosphere continuum (SPAC) on a wide range of spatial and temporal scales. Each section of the SPAC system is characterized by complex behaviours arising, for instance, the adaptive plant strategies in response to soil water deficit conditions. Regulated deficit irrigation (RDI) has been considered as one of the potential strategies for sustainable crop production in regions characterized by water scarcity. Moreover, reducing water supply at certain growth stages can improve water use efficiency (WUE) and quality of productions, without affecting significantly crop yield. Environmental policy requires to improve WUE in crops with high water requirements, so that it is necessary to identify easy-to-use tools aimed at irrigation water saving strategies, without the need of tedious and time consuming experiments. Accurate evaluation of crop water status and actual transpiration plays a key role in irrigation scheduling under RDI, in order to avoid that water stress becomes too severe and detrimental to yield and fruit quality. Objective of the research was to assess the suitability of FAO56 agro-hydrological model (Allen et al., 1998) on citrus orchards under different water deficit conditions, to estimate soil and crop water status. The ability of the model to predict actual crop water stress was evaluated based on the temporal dynamic of simulated relative transpirations and on the similarities with the corresponding dynamic of measured midday stem water potentials, MSWP. During dry periods, simulated relative crop transpiration was correlated to MSWP with the aim to assess the model ability to predict crop water stress and to identify "plant-based" irrigation scheduling parameters. Experiments were carried out during three years from 2009 and 2011 in Senyera (39° 3' 35.4" N, 0° 30' 28.2" W), Spain, in a commercial orchard planted with Navelina/Cleopatra citrus trees. Three RDI

  14. Tracer Testing for Estimating Heat Transfer Area in Fractured Reservoirs

    SciTech Connect

    Pruess, Karsten; van Heel, Ton; Shan, Chao

    2004-05-12

    A key parameter governing the performance and life-time of a Hot Fractured Rock (HFR) reservoir is the effective heat transfer area between the fracture network and the matrix rock. We report on numerical modeling studies into the feasibility of using tracer tests for estimating heat transfer area. More specifically, we discuss simulation results of a new HFR characterization method which uses surface-sorbing tracers for which the adsorbed tracer mass is proportional to the fracture surface area per unit volume. Sorption in the rock matrix is treated with the conventional formulation in which tracer adsorption is volume-based. A slug of solute tracer migrating along a fracture is subject to diffusion across the fracture walls into the adjacent rock matrix. Such diffusion removes some of the tracer from the fluid in the fractures, reducing and retarding the peak in the breakthrough curve (BTC) of the tracer. After the slug has passed the concentration gradient reverses, causing back-diffusion from the rock matrix into the fracture, and giving rise to a long tail in the BTC of the solute. These effects become stronger for larger fracture-matrix interface area, potentially providing a means for estimating this area. Previous field tests and modeling studies have demonstrated characteristic tailing in BTCs for volatile tracers in vapor-dominated reservoirs. Simulated BTCs for solute tracers in single-phase liquid systems show much weaker tails, as would be expected because diffusivities are much smaller in the aqueous than in the gas phase, by a factor of order 1000. A much stronger signal of fracture-matrix interaction can be obtained when sorbing tracers are used. We have performed simulation studies of surface-sorbing tracers by implementing a model in which the adsorbed tracer mass is assumed proportional to the fracture-matrix surface area per unit volume. The results show that sorbing tracers generate stronger tails in BTCs, corresponding to an effective

  15. A One-Layer Satellite Surface Energy Balance for Estimating Evapotranspiration Rates and Crop Water Stress Indexes

    PubMed Central

    Barbagallo, Salvatore; Consoli, Simona; Russo, Alfonso

    2009-01-01

    Daily evapotranspiration fluxes over the semi-arid Catania Plain area (Eastern Sicily, Italy) were evaluated using remotely sensed data from Landsat Thematic Mapper TM5 images. A one-source parameterization of the surface sensible heat flux exchange using satellite surface temperature has been used. The transfer of sensible and latent heat is described by aerodynamic resistance and surface resistance. Required model inputs are brightness, temperature, fractional vegetation cover or leaf area index, albedo, crop height, roughness lengths, net radiation, air temperature, air humidity and wind speed. The aerodynamic resistance (rah) is formulated on the basis of the Monin-Obukhov surface layer similarity theory and the surface resistance (rs) is evaluated from the energy balance equation. The instantaneous surface flux values were converted into evaporative fraction (EF) over the heterogeneous land surface to derive daily evapotranspiration values. Remote sensing-based assessments of crop water stress (CWSI) were also made in order to identify local irrigation requirements. Evapotranspiration data and crop coefficient values obtained from the approach were compared with: (i) data from the semi-empirical approach “Kc reflectance-based”, which integrates satellite data in the visible and NIR regions of the electromagnetic spectrum with ground-based measurements and (ii) surface energy flux measurements collected from a micrometeorological tower located in the experiment area. The expected variability associated with ET flux measurements suggests that the approach-derived surface fluxes were in acceptable agreement with the observations. PMID:22389585

  16. A one-layer satellite surface energy balance for estimating evapotranspiration rates and crop water stress indexes.

    PubMed

    Barbagallo, Salvatore; Consoli, Simona; Russo, Alfonso

    2009-01-01

    Daily evapotranspiration fluxes over the semi-arid Catania Plain area (Eastern Sicily, Italy) were evaluated using remotely sensed data from Landsat Thematic Mapper TM5 images. A one-source parameterization of the surface sensible heat flux exchange using satellite surface temperature has been used. The transfer of sensible and latent heat is described by aerodynamic resistance and surface resistance. Required model inputs are brightness, temperature, fractional vegetation cover or leaf area index, albedo, crop height, roughness lengths, net radiation, air temperature, air humidity and wind speed. The aerodynamic resistance (r(ah)) is formulated on the basis of the Monin-Obukhov surface layer similarity theory and the surface resistance (r(s)) is evaluated from the energy balance equation. The instantaneous surface flux values were converted into evaporative fraction (EF) over the heterogeneous land surface to derive daily evapotranspiration values. Remote sensing-based assessments of crop water stress (CWSI) were also made in order to identify local irrigation requirements. Evapotranspiration data and crop coefficient values obtained from the approach were compared with: (i) data from the semi-empirical approach "K(c) reflectance-based", which integrates satellite data in the visible and NIR regions of the electromagnetic spectrum with ground-based measurements and (ii) surface energy flux measurements collected from a micrometeorological tower located in the experiment area. The expected variability associated with ET flux measurements suggests that the approach-derived surface fluxes were in acceptable agreement with the observations. PMID:22389585

  17. Penman-Monteith approaches for estimating crop evapotranspiration in screenhouses—a case study with table-grape

    NASA Astrophysics Data System (ADS)

    Pirkner, Moran; Dicken, Uri; Tanny, Josef

    2014-07-01

    In arid and semi-arid regions many crops are grown under screens or in screenhouses to protect them from excessive radiation, strong winds, hailstorms and insects, and to reduce crop water requirements. Screens modify the crop microclimate, which means that it is necessary to accurately estimate crop water use under screens in order to improve the irrigation management and thereby increase water-use efficiency. The goal of the present study was to develop a set of calibrated relationships between inside and outside climatic variables, which would enable growers to predict crop water use under screens, based on standard external meteorological measurements and evapotranspiration (ET) models. Experiments were carried out in the Jordan Valley region of eastern Israel in a table-grape vineyard that was covered with a transparent screen providing 10 % shading. An eddy covariance system was deployed in the middle of the vineyard and meteorological variables were measured inside and outside the screenhouse. Two ET models were evaluated: a classical Penman-Monteith model (PM) and a Penman-Monteith model modified for screenhouse conditions by the inclusion of an additional boundary-layer resistance (PMsc). Energy-balance closure analysis, presented as a linear relation between half-hourly values of available and consumed energy (1,344 data points), yielded the regression Y = 1.05X-9.93 (W m-2), in which Y = sum of latent and sensible heat fluxes, and X = net radiation minus soil heat flux, with R 2 = 0.81. To compensate for overestimation of the eddy fluxes, ET was corrected by forcing the energy balance closure. Average daily ET under the screen was 5.4 ± 0.54 mm day-1, in general agreement with the model estimates and the applied irrigation. The results showed that measured ET under the screen was, on average, 34 % lower than that estimated outside, indicating significant potential water saving through screening irrigated vineyards. The PM model was somewhat more

  18. Spectral estimates of crop residue cover and density for standing and flat wheat stubble

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Crop residue is important for erosion control, soil water storage, filling gaps in various agroecosystem-based modeling, and sink for atmospheric carbon. The use of remote sensing technology provides a fast, objective, and efficient tool for measuring and managing this resource. The challenge is t...

  19. Hyperspectral remote sensing estimation of crop residue cover: Soil mineralogy, surface conditions, and their effects

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Conservation tillage practices can enhance soil organic carbon content (SOC), improve soil structure, and reduce erosion. However, direct assessment of tillage practice for monitoring SOC change over large regions is difficult. Remote sensing of crop residue cover (CRC) can help assess tillage pra...

  20. Estimating plant available water for general crop simulations in ALMANAC/APEX/EPIC/SWAT

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Process-based simulation models ALMANAC/APEX/EPIC/SWAT contain generalized plant growth subroutines to predict biomass and crop yield. Environmental constraints typically restrict plant growth and yield. Water stress is often an important limiting factor; it is calculated as the sum of water use f...

  1. Improved Remote Crop Residue Cover Estimation by Incorporation of Soil and Residue Information

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Modern agricultural practices are increasingly making use of conservation (reduced- and no-till) methods, in order to minimize soil erosion and increase soil organic carbon (SOC) content. These methods result in increased crop residue cover after planting when compared to conventional tillage metho...

  2. Soil organic carbon and water content effects on remote crop residue cover estimation

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Conservation tillage (CT) systems help protect the soil and environment, and improve net farm profitability. CT methods leave increased amounts of crop residue cover (CRC) on the soil surface, minimizing soil erosion and evaporation. CT uses less fuel, disturbs soil less, and requires less fertili...

  3. Quantitative estimation of the fluorescent parameters for crop leaves with the Bayesian inversion

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In this study, the fluorescent parameters of crop leaves were retrieved from the leaf hyperspectral measurements by inverting the FluorMODleaf model, which is a leaf-level fluorescence model that is based on the widely used and validated PROSPECT (leaf optical properties) model and can simulate the ...

  4. A remote-sensing driven tool for estimating crop stress and yields

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Biophysical crop simulation models are normally forced with precipitation data recorded with either gages or ground-based radar. However, ground based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would...

  5. Image based remote sensing method for modeling black-eyed beans ( Vigna unguiculata) Leaf Area Index (LAI) and Crop Height (CH) over Cyprus

    NASA Astrophysics Data System (ADS)

    Papadavid, Giorgos; Fasoula, Dionysia; Hadjimitsis, Michael; Skevi Perdikou, P.; Hadjimitsis, Diofantos G.

    2013-03-01

    In this paper, Leaf Area Index (LAI) and Crop Height (CH) are modeled to the most known spectral vegetation index — NDVI — using remotely sensed data. This approach has advantages compared to the classic approaches based on a theoretical background. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data for estimating a spectral vegetation index (NDVI), for establishing a semiempirical relationship between black-eyed beans' canopy factors and remotely sensed data. Such semi-empirical models can be used then for agricultural and environmental studies. A field campaign was undertaken with measurements of LAI and CH using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric (GER1500) measurements between May and June of 2010. Field spectroscopy and remotely sensed imagery have been combined and used in order to retrieve and validate the results of this study. The results showed that there are strong statistical relationships between LAI or CH and NDVI which can be used for modeling crop canopy factors (LAI, CH) to remotely sensed data. The model for each case was verified by the factor of determination. Specifically, these models assist to avoid direct measurements of the LAI and CH for all the dates for which satellite images are available and support future users or future studies regarding crop canopy parameters.

  6. Image based remote sensing method for modeling black-eyed beans (Vigna unguiculata) Leaf Area Index (LAI) and Crop Height (CH) over Cyprus

    NASA Astrophysics Data System (ADS)

    Papadavid, Giorgos; Fasoula, Dionysia; Hadjimitsis, Michael; Skevi Perdikou, P.; Hadjimitsis, Diofantos

    2013-03-01

    In this paper, Leaf Area Index (LAI) and Crop Height (CH) are modeled to the most known spectral vegetation index — NDVI — using remotely sensed data. This approach has advantages compared to the classic approaches based on a theoretical background. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data for estimating a spectral vegetation index (NDVI), for establishing a semiempirical relationship between black-eyed beans' canopy factors and remotely sensed data. Such semi-empirical models can be used then for agricultural and environmental studies. A field campaign was undertaken with measurements of LAI and CH using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric (GER1500) measurements between May and June of 2010. Field spectroscopy and remotely sensed imagery have been combined and used in order to retrieve and validate the results of this study. The results showed that there are strong statistical relationships between LAI or CH and NDVI which can be used for modeling crop canopy factors (LAI, CH) to remotely sensed data. The model for each case was verified by the factor of determination. Specifically, these models assist to avoid direct measurements of the LAI and CH for all the dates for which satellite images are available and support future users or future studies regarding crop canopy parameters.

  7. Rain volume estimation over areas using satellite and radar data

    NASA Technical Reports Server (NTRS)

    Doneaud, A. A.; Vonderhaar, T. H.

    1985-01-01

    An investigation of the feasibility of rain volume estimation using satellite data following a technique recently developed with radar data called the Arera Time Integral was undertaken. Case studies were selected on the basis of existing radar and satellite data sets which match in space and time. Four multicell clusters were analyzed. Routines for navigation remapping amd smoothing of satellite images were performed. Visible counts were normalized for solar zenith angle. A radar sector of interest was defined to delineate specific radar echo clusters for each radar time throughout the radar echo cluster lifetime. A satellite sector of interest was defined by applying small adjustments to the radar sector using a manual processing technique. The radar echo area, the IR maximum counts and the IR counts matching radar echo areas were found to evolve similarly, except for the decaying phase of the cluster where the cirrus debris keeps the IR counts high.

  8. Estimation of human carrying capacity in rainforest areas.

    PubMed

    Fearnside, P M

    1990-06-01

    Tropical rainiorest areas are rapidly being settled as a result of continued growth of local populations, spontaneous migration from non-rainiorest areas and planned settlement projects undertaken by governments. National decision makers frequently view rainiorest settlement as a solution to the problems of other regions undergoing population growth, land tenure concentration, environmental degradation, agricultural mechanization and population displacement by development projects. Natural habitats are replaced by settlements that often cannot support the density of population expected of them. Inappropriate assumptions can lead to estimates that are orders of magnitude too high, such as an FAO calculation that Brazil could support over seven billion people if Amazonia were converted to intensive agriculture. Inadequate information on human carrying capacity allows planners to foster unrealistic expectations. PMID:21232352

  9. Rain volume estimation over areas using satellite and radar data

    NASA Technical Reports Server (NTRS)

    Doneaud, A. A.; Vonderhaar, T. H.

    1985-01-01

    The feasibility of rain volume estimation over fixed and floating areas was investigated using rapid scan satellite data following a technique recently developed with radar data, called the Area Time Integral (ATI) technique. The radar and rapid scan GOES satellite data were collected during the Cooperative Convective Precipitation Experiment (CCOPE) and North Dakota Cloud Modification Project (NDCMP). Six multicell clusters and cells were analyzed to the present time. A two-cycle oscillation emphasizing the multicell character of the clusters is demonstrated. Three clusters were selected on each day, 12 June and 2 July. The 12 June clusters occurred during the daytime, while the 2 July clusters during the nighttime. A total of 86 time steps of radar and 79 time steps of satellite images were analyzed. There were approximately 12-min time intervals between radar scans on the average.

  10. Wheat cultivation: Identification and estimation of areas using LANDSAT data

    NASA Technical Reports Server (NTRS)

    Dejesusparada, N. (Principal Investigator); Mendonca, F. J.; Cottrell, D. A.; Tardin, A. T.; Lee, D. C. L.; Shimabukuro, Y. E.; Moreira, M. A.; Delimaefernandocelsosoaresmaia, A. M.

    1981-01-01

    The feasibility of using automatically processed multispectral data obtained from LANDSAT to identify wheat and estimate the areas planted with this grain was investigated. Three 20 km by 40 km segments in a wheat growing region of Rio Grande do Sul were aerially photographed using type 2443 Aerochrome film. Three maps corresponding to each segment were obtained from the analysis of the photographs which identified wheat, barley, fallow land, prepared soil, forests, and reforested land. Using basic information about the fields and maps made from the photographed areas, an automatic classification of wheat was made using MSS data from two different periods: July to September and July to October 1979. Results show that orbital data is not only useful in characterizing the growth of wheat, but also provides information of the intensity and extent of adverse climate which affects cultivation. The temporal and spatial characteristics of LANDSAR data are also demonstrated.

  11. Research in satellite-aided crop inventory and monitoring

    NASA Technical Reports Server (NTRS)

    Erickson, J. D.; Dragg, J. L.; Bizzell, R. M.; Trichel, M. C. (Principal Investigator)

    1982-01-01

    Automated information extraction procedures for analysis of multitemporal LANDSAT data in non-U.S. crop inventory and monitoring are reviewed. Experiments to develope and evaluate crop area estimation technologies for spring small grains, summer crops, corn, and soybeans are discussed.

  12. Rainfall Estimation Over Tropical Oceans. 1; Area Average Rain Rate

    NASA Technical Reports Server (NTRS)

    Cuddapah, Prabhakara; Cadeddu, Maria; Meneghini, R.; Short, David A.; Yoo, Jung-Moon; Dalu, G.; Schols, J. L.; Weinman, J. A.

    1997-01-01

    Multichannel dual polarization microwave radiometer SSM/I observations over oceans do not contain sufficient information to differentiate quantitatively the rain from other hydrometeors on a scale comparable to the radiometer field of view (approx. 30 km). For this reason we have developed a method to retrieve average rain rate over a mesoscale grid box of approx. 300 x 300 sq km area over the TOGA COARE region where simultaneous radiometer and radar observations are available for four months (Nov. 92 to Feb. 93). The rain area in the grid box, inferred from the scattering depression due to hydrometeors in the 85 Ghz brightness temperature, constitutes a key parameter in this method. Then the spectral and polarization information contained in all the channels of the SSM/I is utilized to deduce a second parameter. This is the ratio S/E of scattering index S, and emission index E calculated from the SSM/I data. The rain rate retrieved from this method over the mesoscale area can reproduce the radar observed rain rate with a correlation coefficient of about 0.85. Furthermore monthly total rainfall estimated from this method over that area has an average error of about 15%.

  13. Development of linear models for estimation of leaflet area in common bean (Phaseolus vulgaris L.)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Plant leaf area is an important physiological trait used in plant physiology, soil and crop science, and agroclimatology studies. Direct, non-destructive methods for predicting leaf area have been shown to be correlated with actual leaf area and also allow for repeated plant sampling. The objective ...

  14. Digital Cover Photography for Estimating Leaf Area Index (LAI) in Apple Trees Using a Variable Light Extinction Coefficient

    PubMed Central

    Poblete-Echeverría, Carlos; Fuentes, Sigfredo; Ortega-Farias, Samuel; Gonzalez-Talice, Jaime; Yuri, Jose Antonio

    2015-01-01

    Leaf area index (LAI) is one of the key biophysical variables required for crop modeling. Direct LAI measurements are time consuming and difficult to obtain for experimental and commercial fruit orchards. Devices used to estimate LAI have shown considerable errors when compared to ground-truth or destructive measurements, requiring tedious site-specific calibrations. The objective of this study was to test the performance of a modified digital cover photography method to estimate LAI in apple trees using conventional digital photography and instantaneous measurements of incident radiation (Io) and transmitted radiation (I) through the canopy. Leaf area of 40 single apple trees were measured destructively to obtain real leaf area index (LAID), which was compared with LAI estimated by the proposed digital photography method (LAIM). Results showed that the LAIM was able to estimate LAID with an error of 25% using a constant light extinction coefficient (k = 0.68). However, when k was estimated using an exponential function based on the fraction of foliage cover (ff) derived from images, the error was reduced to 18%. Furthermore, when measurements of light intercepted by the canopy (Ic) were used as a proxy value for k, the method presented an error of only 9%. These results have shown that by using a proxy k value, estimated by Ic, helped to increase accuracy of LAI estimates using digital cover images for apple trees with different canopy sizes and under field conditions. PMID:25635411

  15. Application of a crop model forced with remote sensing data at high spatio-temporal resolution to estimate evaporation and yields of irrigated grasslands in the South Eastern France

    NASA Astrophysics Data System (ADS)

    Couralt, D.; Hadria, R.; Ruget, F.; Duchemin, B.; Hagolle, O.

    2009-09-01

    This study focused on the feasibility of using remote sensing data acquired at high spatial and temporal resolution (FORMOSAT-2 images(http://www.spotimage.fr/web/en/977--formosat-2-images.php) for crop monitoring at regional scale. The monitoring of agricultural practices such as grassland mowing and irrigation is essential to simulate accurately all processes related to crop system. This information is needed for example in crop simulation models to estimate production, water and fertilizer consumption and can thus serve to better understand the interactions between agriculture and climate. The analysis of these interactions is especially important in Mediterranean region where the effects of climate changes and crop management modifications are increasingly marked. In this context, an experiment was conducted in 2006 in Crau region in the South-Eastern France. In this area, permanent grassland represents 67 % of the usable agricultural area, and it is often used with irrigation (47 % of the permanent grassland). A time series of 36 FORMOSAT-2 images was acquired with a three days frequency from March to October 2006. Information concerning grassland mowing and irrigation was collected through a survey over 120 fields. The high FORMOSAT-2 revisit frequency allowed replicating the dynamics of Leaf Area index (LAI), and detecting to some extents cultural practices like vegetation cut. Simple automatic algorithms were developed to obtain daily values of LAI for each grasslands field linked with the main agricultural practices performed (cut and irrigation dates). This information was then used in a crop model called STICS (http://147.100.66.194/stics/) to estimate the spatial variability of evapotranspiration and drainage associated with the aerial biomass productions. Comparisons between simulated and observed yields gave satisfactory results. The great spatial variations of evapotranspiration were strongly related to the crop and water management. Such

  16. Cropping practices, soil properties, pedotransfer functions and organic carbon storage at Kuanria canal command area in India.

    PubMed

    Mandal, Krishna Gopal; Kundu, Dilip Kumar; Singh, Ravender; Kumar, Ashwani; Rout, Rajalaxmi; Padhi, Jyotiprakash; Majhi, Pradipta; Sahoo, Dillip Kumar

    2013-01-01

    Effects of cropping practices on soil properties viz. particle size distribution, pH, bulk density (BD), field capacity (FC, -33 kPa), permanent wilting point (PWP, -1500 kPa), available water capacity (AWC) and soil organic carbon (SOC) were assessed. The pedotransfer functions (PTFs) were developed for saturated hydraulic conductivity (Ks), water retention at FC and PWP of soils for different sites under major cropping system in a canal irrigated area. The results revealed that the soils are mainly composed of sand and clay with the clay contents ranging from 29.6 to 48.8%, BD of 1.44-1.72 Mg m(-3), and 0.34 to 0.95% SOC. The Ks decreased, and water retention at FC, PWP and AWC increased significantly with soil depth due to greater clay contents in lower soil depths. The PTFs were best represented as the power functions for prediction of Ks with clay content as predictor variable; whereas the PTFs for water retention at FC and PWP were better represented as the exponential functions. SOC content was higher under rice-sugarcane crop rotation compared to other systems. SOC storage in the surface layer was higher in rice-sugarcane rotation (18.90-20.53 Mg ha(-1)) than other sites. The developed PTFs would be very useful in soil and water management strategies for the study area or elsewhere having similar soil and cropping practices. The information on SOC storage in the Kuanria region would help for better soil and crop planning in future. PMID:24303340

  17. Estimation of Some Bio-Physical Indicators for Sustainable Crop Production in the Eastern Nile Basin of Sudan Using Landsat-8 Imagery and SEBAL Model

    NASA Astrophysics Data System (ADS)

    Guma Biro Turk, Khalid

    2016-07-01

    Crop production under modern irrigation systems require unique management at field level and hence better utilization of agricultural inputs and water resources. This study aims to make use of remote sensing (RS) data and the surface energy balance algorithm for land (SEBAL) to improve the on-farm management. The study area is located in the Eastern part of the Blue Nile River about 60 km south of Khartoum, Sudan. Landsat-8 data were used to estimate a number of bio-physical indicators during the growing season of the year 2014/2015. Accordingly, in-situ weather data and SEBAL model were applied to calculate: the reference (ET0), actual (ETa) and potential (ETp) evapotranspiration, soil moisture (SM), crop factor (kc), nitrogen (N), biomass production (BP) and crop water productivity (CWP). Results revealed that ET0 showed steady variation throughout the year, varying from 5 to 7 mm/day. However, ETa and ETp showed clear temporal variation attributed to frequent cutting of the alfalfa, almost monthly. The BP of the alfalfa was observed to be high when there is no cutting activates were made before the image acquisition date. Nevertheless the CWP trends are following the biomass production ones, low when there is no biomass and high when the biomass is high. The application of SEBAL model within the study area using the Landsat-8 imagery indicates that it's possible to produce field-based bio-physical indicators, which can be useful in monitoring and managing the field during the growing season. However, a cross-calibration with the in-situ data should be considered in order to maintain the spatial variability within the field. Keywords: Bio-physical Indicators; Remote Sensing; SEBAL; Landsat-8; Eastern Nile Basin

  18. Crop residues

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Crop residues [e.g., corn (Zea mays) stover and small grain straw] are sometimes excluded when discussing cellulosic energy crops per se, but because of the vast area upon which they are grown and their current role in the development of cellulosic energy systems. This chapter focuses on current cor...

  19. Assessing changes to South African maize production areas in 2055 using empirical and process-based crop models

    NASA Astrophysics Data System (ADS)

    Estes, L.; Bradley, B.; Oppenheimer, M.; Beukes, H.; Schulze, R. E.; Tadross, M.

    2010-12-01

    Rising temperatures and altered precipitation patterns associated with climate change pose a significant threat to crop production, particularly in developing countries. In South Africa, a semi-arid country with a diverse agricultural sector, anthropogenic climate change is likely to affect staple crops and decrease food security. Here, we focus on maize production, South Africa’s most widely grown crop and one with high socio-economic value. We build on previous coarser-scaled studies by working at a finer spatial resolution and by employing two different modeling approaches: the process-based DSSAT Cropping System Model (CSM, version 4.5), and an empirical distribution model (Maxent). For climate projections, we use an ensemble of 10 general circulation models (GCMs) run under both high and low CO2 emissions scenarios (SRES A2 and B1). The models were down-scaled to historical climate records for 5838 quinary-scale catchments covering South Africa (mean area = 164.8 km2), using a technique based on self-organizing maps (SOMs) that generates precipitation patterns more consistent with observed gradients than those produced by the parent GCMs. Soil hydrological and mechanical properties were derived from textural and compositional data linked to a map of 26422 land forms (mean area = 46 km2), while organic carbon from 3377 soil profiles was mapped using regression kriging with 8 spatial predictors. CSM was run using typical management parameters for the several major dryland maize production regions, and with projected CO2 values. The Maxent distribution model was trained using maize locations identified using annual phenology derived from satellite images coupled with airborne crop sampling observations. Temperature and precipitation projections were based on GCM output, with an additional 10% increase in precipitation to simulate higher water-use efficiency under future CO2 concentrations. The two modeling approaches provide spatially explicit projections of

  20. Empirically-Based Crop Insurance for China: A Pilot Study in the Down-middle Yangtze River Area of China

    NASA Astrophysics Data System (ADS)

    Wang, Erda; Yu, Yang; Little, Bertis B.; Chen, Zhongxin; Ren, Jianqiang

    Factors that caused slow growth in crop insurance participation and its ultimate failure in China were multi-faceted including high agricultural production risk, low participation rate, inadequate public awareness, high loss ratio, insufficient and interrupted government financial support. Thus, a clear and present need for data driven analyses and empirically-based risk management exists in China. In the present investigation, agricultural production data for two crops (corn, rice) in five counties in Jiangxi Province and Hunan province for design of a pilot crop insurance program in China. A crop insurance program was designed which (1) provides 75% coverage, (2) a 55% premium rate reduction for the farmer compared to catastrophic coverage most recently offered, and uses the currently approved governmental premium subsidy level. Thus a safety net for Chinese farmers that help maintain agricultural production at a level of self-sufficiency that costs less than half the current plans requires one change to the program: ≥80% of producers must participate in an area.

  1. Large Area Crop Inventory Experiment (LACIE). Accuracy assessment report phase 1A, November - December 1974. [Kansas

    NASA Technical Reports Server (NTRS)

    1976-01-01

    The author has identified the following significant results. Results of the accuracy assessment activity for Phase IA of LACIE indicated that (1) The 90/90 criteria could be reached if the degree of accuracy of the LACIE performance in Kansas could be equaled in other areas. (2) The classification of both wheat and nonwheat fields was significantly accurate for the three ITS segments analyzed. The wheat field classification accuracy varied for the segments. However, this was not so with respect to nonwheat fields. (3) Biophase as well as its interaction with segment location turned out to be an important factor for the classification performance. Analyst interpretation of segments for training the classifier was a significant error-contributing factor in the estimation of wheat acreage at both the field and the segment levels.

  2. Flood Damage Estimation of Coastal Area Considering Climate Change

    NASA Astrophysics Data System (ADS)

    Lee, J.; Kim, K.; Choi, C.; Han, D.; Kim, H. S.

    2015-12-01

    There are various researches to respond to the natural disasters efficiently such as floods caused by climate change. Most of the studies have assessed the impact of climate change on floods by the increase of future rainfall and the sea level rise separately. However, we have to consider the effects of the combined floods by future heavy rainfall and sea level rise. This means the urban floods in coastal area can be occurred due to the combined inundation by overflow of urban drainage system and by sea level rise. Then we can estimate the flood damage from the combined floods due to the climate change. Hence, this study selected Taehwa River basin, Korea which flows from the west to the east and extends to Ulsan bay. This study analysed on the influence of hydrologic alteration in the coastal area by considering the sea level rise as well as the future precipitation according to climate change. It is prepared the flood inundation map which is related to the increase of precipitation and sea level for assuring how the sea level rise effects on the coastal area caused by the climate change, and the flood damage assessment is estimated to compare the hydrologic alteration quantitatively. The study result showed that flood level in the channel flows to the mouth rose as the water surface elevation rose due to the rise in sea level. In addition, the extent of increase in flood level caused by sea level rise was greater at a location nearer the outlet and it was smaller at a place farther from the outlet. It could be verified that the change of the inundation depth and damage area caused by the rise in sea level can have an effect on the flood damage assessment. It is important factor to analyse not only the increase of precipitation cuased of climate change in coastal rivers but also the change of rise in sea level, the flood water level, the flood inundation and the flood damage assessment. The result of this study could be used as basic data for creating technology

  3. Estimation of surface area and length with the orientator.

    PubMed

    Mattfeldt, T; Mall, G; Gharehbaghi, H; Möller, P

    1990-09-01

    The orientator is a new technique for the estimation of length and surface density and other stereological parameters using isotropic sections. It is an unbiased, design-based approach to the quantitative study of anisotropic structures such as muscle, myocardium, bone and cartilage. A simple method for the practical generation of such isotropic planes in biological specimens is described. No special technical equipment is necessary. Knowledge of an axis of anisotropy can be exploited to optimize the efficiency. To randomize directions in space, points are selected with uniform probability in a square using various combinations of simple random, stratified random, and systematic random sampling. The point patterns thus produced are mapped onto the surface of a hemisphere. The mapped points define directions of sectional planes in space. The mapping algorithm ensures that these planes are isotropic, hence unbiased estimates of surface and length density can be obtained via the classical stereological formulae. Various implementations of the orientator are outlined: the prototype version, the orientator-generated ortrip, two systematic versions, and the smooth version. Orientator sections can be generated without difficulty in large specimens; we investigated human skeletal muscle, myocardium, placenta, and gut tissue. Slight practical modifications extend the applicability of the method to smaller organs like rat hearts. At the ultrastructural level, a correction procedure for the loss of anisotropic mitochondrial membranes due to oblique orientation relative to the electron beam is suggested. Other potential applications of the orientator in anisotropic structures include the estimation of individual particle surface area with isotropic nucleators, the determination of the connectivity of branching networks with isotropic disectors, and generation of isotropic sections for second-order stereology (three-dimensional pattern analysis). PMID:2243364

  4. Benefit Estimates of Terminal Area Productivity Program Technologies

    NASA Technical Reports Server (NTRS)

    Hemm, Robert; Shapiro, Gerald; Lee, David; Gribko, Joana; Glaser, Bonnie

    1999-01-01

    This report documents benefit analyses for the NASA Terminal Area Technology (TAP) technology programs. Benefits are based on reductions in arrival delays at ten major airports over the 10 years from 2006 through 2015. Detailed analytic airport capacity and delay models were constructed to produce the estimates. The goal of TAP is enable good weather operations tempos in all weather conditions. The TAP program includes technologies to measure and predict runway occupancy times, reduce runway occupancy times in bad weather, accurately predict wake vortex hazards, and couple controller automation with aircraft flight management systems. The report presents and discusses the estimate results and describes the models. Three appendixes document the model algorithms and discuss the input parameters selected for the TAP technologies. The fourth appendix is the user's guide for the models. The results indicate that the combined benefits for all TAP technologies at all 10 airports range from $550 to $650 million per year (in constant 1997 dollars). Additional benefits will accrue from reductions in departure delays. Departure delay benefits are calculated by the current models.

  5. Worldwide Historical Estimates of Leaf Area Index, 1932-2000

    NASA Technical Reports Server (NTRS)

    Scurlock, J. M. O.; Asner, G. P.; Gower, S. T.

    2001-01-01

    Approximately 1000 published estimates of leaf area index (LAI) from nearly 400 unique field sites, covering the period 1932-2000, have been compiled into a single data set. LA1 is a key parameter for global and regional models of biosphere/atmosphere exchange of carbon dioxide, water vapor, and other materials. It also plays an integral role in determining the energy balance of the land surface. This data set provides a benchmark of typical values and ranges of LA1 for a variety of biomes and land cover types, in support of model development and validation of satellite-derived remote sensing estimates of LA1 and other vegetation parameters. The LA1 data are linked to a bibliography of over 300 originalsource references.This report documents the development of this data set, its contents, and its availability on the Internet from the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics. Caution is advised in using these data, which were collected using a wide range of methodologies and assumptions that may not allow comparisons among sites.

  6. Evaporative loss from the interrow of irrigated crops in a semi-arid agricultural area

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Soil evaporation plays an important role in the water balance of irrigated crops, especially in arid and semi-arid regions. Irrigation scheduling may affect the fraction of evaporative loss (E) from the total evapotranspiration (ET), and thus affect the water use efficiency. During the second intens...

  7. Perennial Crop Nursery Sector Progress - Pacific Area-Wide Program for Methyl Bromide Alternatives

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Nursery producers of perennial crops including fruit and nut trees, grapevines, and ornamentals in California rely on preplant soil fumigation to meet CDFA requirements for nematode free planting stock. Certified clean stock is essential for successful establishment and future productivity of new o...

  8. Remote Sensing Crop Leaf Area Index Using Unmanned Airborne Vehicles (UAV's)

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing with unmanned airborne vehicles (UAVs) has more potential for within-season crop management than conventional satellite imagery because: (1) pixels have very high resolution, (2) cloud cover would not prevent acquisition during critical periods of growth, and (3) quick delivery of inf...

  9. Preliminary evaluation of the Environmental Research Institute of Michigan crop calendar shift algorithm for estimation of spring wheat development stage. [North Dakota, South Dakota, Montana, and Minnesota

    NASA Technical Reports Server (NTRS)

    Phinney, D. E. (Principal Investigator)

    1980-01-01

    An algorithm for estimating spectral crop calendar shifts of spring small grains was applied to 1978 spring wheat fields. The algorithm provides estimates of the date of peak spectral response by maximizing the cross correlation between a reference profile and the observed multitemporal pattern of Kauth-Thomas greenness for a field. A methodology was developed for estimation of crop development stage from the date of peak spectral response. Evaluation studies showed that the algorithm provided stable estimates with no geographical bias. Crop development stage estimates had a root mean square error near 10 days. The algorithm was recommended for comparative testing against other models which are candidates for use in AgRISTARS experiments.

  10. Improving risk estimates of runoff producing areas: formulating variable source areas as a bivariate process.

    PubMed

    Cheng, Xiaoya; Shaw, Stephen B; Marjerison, Rebecca D; Yearick, Christopher D; DeGloria, Stephen D; Walter, M Todd

    2014-05-01

    Predicting runoff producing areas and their corresponding risks of generating storm runoff is important for developing watershed management strategies to mitigate non-point source pollution. However, few methods for making these predictions have been proposed, especially operational approaches that would be useful in areas where variable source area (VSA) hydrology dominates storm runoff. The objective of this study is to develop a simple approach to estimate spatially-distributed risks of runoff production. By considering the development of overland flow as a bivariate process, we incorporated both rainfall and antecedent soil moisture conditions into a method for predicting VSAs based on the Natural Resource Conservation Service-Curve Number equation. We used base-flow immediately preceding storm events as an index of antecedent soil wetness status. Using nine sub-basins of the Upper Susquehanna River Basin, we demonstrated that our estimated runoff volumes and extent of VSAs agreed with observations. We further demonstrated a method for mapping these areas in a Geographic Information System using a Soil Topographic Index. The proposed methodology provides a new tool for watershed planners for quantifying runoff risks across watersheds, which can be used to target water quality protection strategies. PMID:24632403

  11. Economic evaluation of crop acreage estimation by multispectral remote sensing. [Michigan

    NASA Technical Reports Server (NTRS)

    Manderscheid, L. V.; Nalepka, R. F. (Principal Investigator); Myers, W.; Safir, G.; Ilhardt, D.; Morgenstern, J. P.; Sarno, J.

    1976-01-01

    The author has identified the following significant results. Photointerpretation of S190A and S190B imagery showed significantly better resolution with the S190B system. A small tendancy to underestimate acreage was observed. This averaged 6 percent and varied with field size. The S190B system had adequate resolution for acreage measurement but the color film did not provide adequate contrast to allow detailed classification of ground cover from imagery of a single date. In total 78 percent of the fields were correctly classified but with 56 percent correct for the major crop, corn.

  12. Specific Leaf Area and Dry Matter Content Estimate Thickness in Laminar Leaves

    PubMed Central

    VILE, DENIS; GARNIER, ÉRIC; SHIPLEY, BILL; LAURENT, GÉRARD; NAVAS, MARIE-LAURE; ROUMET, CATHERINE; LAVOREL, SANDRA; DÍAZ, SANDRA; HODGSON, JOHN G.; LLORET, FRANCISCO; MIDGLEY, GUY F.; POORTER, HENDRIK; RUTHERFORD, MIKE C.; WILSON, PETER J.; WRIGHT, IAN J.

    2005-01-01

    • Background and Aims Leaf thickness plays an important role in leaf and plant functioning, and relates to a species' strategy of resource acquisition and use. As such, it has been widely used for screening purposes in crop science and community ecology. However, since its measurement is not straightforward, a number of estimates have been proposed. Here, the validity of the (SLA × LDMC)−1 product is tested to estimate leaf thickness, where SLA is the specific leaf area (leaf area/dry mass) and LDMC is the leaf dry matter content (leaf dry mass/fresh mass). SLA and LDMC are two leaf traits that are both more easily measurable and often reported in the literature. • Methods The relationship between leaf thickness (LT) and (SLA × LDMC)−1 was tested in two analyses of covariance using 11 datasets (three original and eight published) for a total number of 1039 data points, corresponding to a wide range of growth forms growing in contrasted environments in four continents. • Key Results and Conclusions The overall slope and intercept of the relationship were not significantly different from one and zero, respectively, and the residual standard error was 0·11. Only two of the eight datasets displayed a significant difference in the intercepts, and the only significant difference among the most represented growth forms was for trees. LT can therefore be estimated by (SLA × LDMC)−1, allowing leaf thickness to be derived from easily and widely measured leaf traits. PMID:16159941

  13. Levels of organochlorine pesticides in crops and related products from Vojvodina, Serbia: estimated dietary intake.

    PubMed

    Skrbić, B; Predojević, Z

    2008-05-01

    Levels of 16 organochlorine pesticides (OCPs) were investigated in 39 composite samples of agricultural crops, related by-products, and foodstuffs collected in Vojvodina, Serbia, in 2002 through 2004. After extraction and cleanup, OCPs were determined by capillary gas chromatography using electron-capture detection. The highest mean level of 0.971 ng/g whole weight (ww) was found for alpha-HCH in wheat flour samples. OCPs levels were well lower than the respective maximum residue limits set by current European and Serbian regulations. Mean OCP levels were low (<1 ng/g ww) for all sample types. The most frequently determined residue was 4,4'-DDT (identified in 76.9% of all samples analyzed), followed by gamma-HCH (66.7%), beta-HCH (48.7%), and endosulfan II (41.0%). OCP levels were compared with data from other international surveys. Calculated daily intakes of OCPs by way of consumption of the crop products included in this study according to data of the Serbian National Institute for Statistics were compared with the acceptable daily intakes established by the Food and Agriculture Organization/World Health Organization. The average level of contamination of the Vojvodina diet was believed to be harmless regarding the studied food commodities. PMID:18197356

  14. Biosolids, Crop, and Ground-Water Data for a Biosolids-Application Area Near Deer Trail, Colorado, 2004 Through 2006

    USGS Publications Warehouse

    Yager, Tracy J.B.; Smith, David B.; Crock, James G.

    2009-01-01

    From 2004 through 2006, the U.S. Geological Survey monitored the chemical composition of biosolids, crops, dust, and ground water related to biosolids applications near Deer Trail, Colorado, in cooperation with the Metro Wastewater Reclamation District. This monitoring effort was a continuation of the monitoring program begun in 1999 in cooperation with the Metro Wastewater Reclamation District and the North Kiowa Bijou Groundwater Management District. The monitoring program addresses concerns from the public about the chemical effects from applications of biosolids to farmland in the Deer Trail, Colorado, area. This report presents chemical data from 2004 through 2006 for biosolids, crops, and alluvial and bedrock ground water. The chemical data include the constituents of highest concern to the public (arsenic, cadmium, copper, lead, mercury, molybdenum, nickel, selenium, zinc, and plutonium) in addition to many other constituents. The ground-water section also includes climate and water-level data.

  15. Biosolids, crop, and groundwater data for a biosolids-application area near Deer Trail, Colorado, 2009 and 2010

    USGS Publications Warehouse

    Yager, Tracy J.B.; Smith, David B.; Crock, James G.

    2012-01-01

    During 2009 and 2010, the U.S. Geological Survey monitored the chemical composition of biosolids, crops, and groundwater related to biosolids applications near Deer Trail, Colorado, in cooperation with the Metro Wastewater Reclamation District. This monitoring effort was a continuation of the monitoring program begun in 1999 in cooperation with the Metro Wastewater Reclamation District and the North Kiowa Bijou Groundwater Management District. The monitoring program addressed concerns from the public about potential chemical effects from applications of biosolids to farmland in the area near Deer Trail, Colo. This report presents chemical data from 2009 and 2010 for biosolids, crops, and alluvial and bedrock groundwater. The chemical data include the constituents of highest concern to the public (arsenic, cadmium, copper, lead, mercury, molybdenum, nickel, selenium, zinc, and plutonium) in addition to many other constituents. The groundwater section also includes data for precipitation, air temperature, and depth to groundwater at various groundwater-monitoring sites.

  16. Biosolids, crop, and groundwater data for a biosolids-application area near Deer Trail, Colorado, 2007 and 2008

    USGS Publications Warehouse

    Yager, Tracy J.B.; Smith, David B.; Crock, James G.

    2011-01-01

    During 2007 and 2008, the U.S. Geological Survey monitored the chemical composition of biosolids, crops, and groundwater related to biosolids applications near Deer Trail, Colorado, in cooperation with the Metro Wastewater Reclamation District. This monitoring effort was a continuation of the monitoring program begun in 1999 in cooperation with the Metro Wastewater Reclamation District and the North Kiowa Bijou Groundwater Management District. The monitoring program addressed concerns from the public about potential chemical effects from applications of biosolids to farmland in the area near Deer Trail, Colo. This report presents chemical data from 2007 and 2008 for biosolids, crops, and alluvial and bedrock groundwater. The chemical data include the constituents of highest concern to the public (arsenic, cadmium, copper, lead, mercury, molybdenum, nickel, selenium, zinc, and plutonium) in addition to many other constituents. The groundwater section also includes data for precipitation, air temperature, and depth to groundwater at various groundwater-monitoring sites.

  17. Vegetation-index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Crop coefficients were developed to determine crop water needs based on the evapotranspiration (ET) of a reference crop under a given set of meteorological conditions. Starting in the 1980s, crop coefficients developed through lysimeter studies or set by expert opinion began to be supplemented by r...

  18. Large Area Crop Inventory Experiment (LACIE). Evaluation of three-category classification

    NASA Technical Reports Server (NTRS)

    Havens, K. A.; Abotteen, K. M. (Principal Investigator)

    1979-01-01

    The author has identified the following signficant results. Examination of both machine estimates and stratified areal estimates produced by clustering and classification reveal no significant differences between the proportion estimates and ground truth estimates. When testing the variances of the machine estimates, a significant reduction in the variances was found when the number of starting dots was increased from 30 to 45. The variances were again reduced, although not significantly, when the number of starting dots was increased from 45 to 60. From these results, 60 starting dots are recommended for a three-category classifier. When examining the variances of the estimates for the four estimation procedures (using 60 dots), no significant differences were found between procedures. Thus, only the machine clustering may be used to produce an estimate, and the stratified areal estimate computations and maximum likelihood classification can be deleted.

  19. Optimal Estimation of Phenological Crop Model Parameters for Rice (Oryza sativa)

    NASA Astrophysics Data System (ADS)

    Sharifi, H.; Hijmans, R. J.; Espe, M.; Hill, J. E.; Linquist, B.

    2015-12-01

    Crop phenology models are important components of crop growth models. In the case of phenology models, generally only a few parameters are calibrated and default cardinal temperatures are used which can lead to a temperature-dependent systematic phenology prediction error. Our objective was to evaluate different optimization approaches in the Oryza2000 and CERES-Rice phenology sub-models to assess the importance of optimizing cardinal temperatures on model performance and systematic error. We used two optimization approaches: the typical single-stage (planting to heading) and three-stage model optimization (for planting to panicle initiation (PI), PI to heading (HD), and HD to physiological maturity (MT)) to simultaneously optimize all model parameters. Data for this study was collected over three years and six locations on seven California rice cultivars. A temperature-dependent systematic error was found for all cultivars and stages, however it was generally small (systematic error < 2.2). Both optimization approaches in both models resulted in only small changes in cardinal temperature relative to the default values and thus optimization of cardinal temperatures did not affect systematic error or model performance. Compared to single stage optimization, three-stage optimization had little effect on determining time to PI or HD but significantly improved the precision in determining the time from HD to MT: the RMSE reduced from an average of 6 to 3.3 in Oryza2000 and from 6.6 to 3.8 in CERES-Rice. With regards to systematic error, we found a trade-off between RMSE and systematic error when optimization objective set to minimize RMSE or systematic error. Therefore, it is important to find the limits within which the trade-offs between RMSE and systematic error are acceptable, especially in climate change studies where this can prevent erroneous conclusions.

  20. A TRMM Rainfall Estimation Method Applicable to Land Areas

    NASA Technical Reports Server (NTRS)

    Prabhakara, C.; Iacovazzi, R.; Weinman, J.; Dalu, G.

    1999-01-01

    Methods developed to estimate rain rate on a footprint scale over land with the satellite-borne multispectral dual-polarization Special Sensor Microwave Imager (SSM/1) radiometer have met with limited success. Variability of surface emissivity on land and beam filling are commonly cited as the weaknesses of these methods. On the contrary, we contend a more significant reason for this lack of success is that the information content of spectral and polarization measurements of the SSM/I is limited. because of significant redundancy. As a result, the complex nature and vertical distribution C, of frozen and melting ice particles of different densities, sizes, and shapes cannot resolved satisfactorily. Extinction in the microwave region due to these complex particles can mask the extinction due to rain drops. Because of these reasons, theoretical models that attempt to retrieve rain rate do not succeed on a footprint scale. To illustrate the weakness of these models, as an example we can consider the brightness temperature measurement made by the radiometer in the 85 GHz channel (T85). Models indicate that T85 should be inversely related to the rain rate, because of scattering. However, rain rate derived from 15-minute rain gauges on land indicate that this is not true in a majority of footprints. This is also supported by the ship-borne radar observations of rain in the Tropical Oceans and Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE) region over the ocean. Based on these observations. we infer that theoretical models that attempt to retrieve rain rate do not succeed on a footprint scale. We do not follow the above path of rain retrieval on a footprint scale. Instead, we depend on the limited ability of the microwave radiometer to detect the presence of rain. This capability is useful to determine the rain area in a mesoscale region. We find in a given rain event that this rain area is closely related to the mesoscale-average rain rate

  1. Estimation of Winter Wheat Yield with the Assimilation of FY-3 MERSI Data into the WOFOST Crop Growth Model

    NASA Astrophysics Data System (ADS)

    Xu, Wenbo; Fan, Jinlong

    2014-11-01

    Taking the winter wheat planting area Taihang piedmont as object of the study, and based on WOFST model and FY Satellite 250m-resolution MERSI data, this article launched the assimilation yield estimation study on winter wheat. Firstly, MERSI-LAI data is inversed from the measured biophysics data and MERSI data of winter wheat in growing season within the study area; next, WOFOST model sensitivity analysis was developed and conduct assimilation yield estimation through MERSI-LAI built by SCE algorithm and the minimum cost function of model simulation LAI; finally, conduct comparison validation between the estimation results and MODIS-LAI assimilation yield estimation results as well as the statistical data. The conclusion has been drawn that the estimation accuracy based on FY-3 MERSI data assimilation is higher than that based on MODIS data assimilation, with the RMES being reduced by 750.20kg/ha, and the yield being closer to the statistics.

  2. Estimation of plant protection product application dates for environmental fate modeling based on phenological stages of crops.

    PubMed

    Gericke, Dirk; Nekovar, Jiri; Horold, Claudia

    2010-10-01

    According to the EU directive 91/414/EEC potential environmental concentrations of pesticides have to be assessed with environmental fate models. For the calculation of pesticide concentrations it is necessary to provide an application date which has to match the specific Biologische Bundesanstalt, Bundesamt, Chemische Industrie (BBCH) stage at which the pesticide shall be applied. If these application dates are not available for a specific stage, crop and country they must be estimated, which adds an additional uncertainty to the predicted concentrations. In the present study, we therefore evaluate to which extent application dates can be derived from phenological data. For this analysis phenological data, converted to BBCH stages, of two field crops provided by the German Weather Service (DWD) were analyzed. We found a linear correlation between BBCH stages and the respective appearance dates, which can be used for interpolation of appearance dates of specific BBCH stages. Remarkably, when comparing BBCH stages from Germany and the Czech Republic almost identical correlations of appearance dates and BBCH stages were found. In the next step, soil and climate data from Joint Research Centre (JRC) were analyzed together with phenological data in order to evaluate if BBCH stages can be estimated for countries with other climate or soil conditions. This analysis revealed that temperature, global radiation and evaporation were the parameters with the strongest impact. These parameters were used for estimating appearance dates of BBCH stages for other countries. Exemplarily, appearance dates for maize BBCH were calculated for Italy. Estimated and observed appearance dates showed a high concordance (on average six days difference). Finally, the political of impact a variation of a few days on calculated pesticide concentration was analyzed. Exemplarily, the pesticide fate model FOCUS PEARL was used to estimate pesticide groundwater concentrations. When calculating

  3. Weighing Lysimeters for Developing Crop Coefficients and Efficient Irrigation Practices for Vegetable Crops

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Large weighing lysimeters are expensive but invaluable tools for measuring crop evapotranspiration and developing crop coefficients for horticultural crops. Crop coefficients are used by both growers and researchers to estimate crop water use and accurately schedule irrigations. Two lysimeters of ...

  4. Estimation of Crop Gross Primary Production (GPP): I. Impact of MODIS Observation Footprint and Impact of Vegetation BRDF Characteristics

    NASA Technical Reports Server (NTRS)

    Zhang, Qingyuan; Cheng, Yen-Ben; Lyapustin, Alexei I.; Wang, Yujie; Xiao, Xiangming; Suyker, Andrew; Verma, Shashi; Tan, Bin; Middleton, Elizabeth M.

    2014-01-01

    Accurate estimation of gross primary production (GPP) is essential for carbon cycle and climate change studies. Three AmeriFlux crop sites of maize and soybean were selected for this study. Two of the sites were irrigated and the other one was rainfed. The normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the green band chlorophyll index (CIgreen), and the green band wide dynamic range vegetation index (WDRVIgreen) were computed from the moderate resolution imaging spectroradiometer (MODIS) surface reflectance data. We examined the impacts of the MODIS observation footprint and the vegetation bidirectional reflectance distribution function (BRDF) on crop daily GPP estimation with the four spectral vegetation indices (VIs - NDVI, EVI, WDRVIgreen and CIgreen) where GPP was predicted with two linear models, with and without offset: GPP = a × VI × PAR and GPP = a × VI × PAR + b. Model performance was evaluated with coefficient of determination (R2), root mean square error (RMSE), and coefficient of variation (CV). The MODIS data were filtered into four categories and four experiments were conducted to assess the impacts. The first experiment included all observations. The second experiment only included observations with view zenith angle (VZA) = 35? to constrain growth of the footprint size,which achieved a better grid cell match with the agricultural fields. The third experiment included only forward scatter observations with VZA = 35?. The fourth experiment included only backscatter observations with VZA = 35?. Overall, the EVI yielded the most consistently strong relationships to daily GPP under all examined conditions. The model GPP = a × VI × PAR + b had better performance than the model GPP = a × VI × PAR, and the offset was significant for most cases. Better performance was obtained for the irrigated field than its counterpart rainfed field. Comparison of experiment 2 vs. experiment 1 was used to examine the observation

  5. Crop changes from the XVI century to the present in a hill/mountain area of eastern Liguria (Italy)

    PubMed Central

    Gentili, Rodolfo; Gentili, Elio; Sgorbati, Sergio

    2009-01-01

    Background Chronological information on the composition and structure of agrocenoses and detailed features of land cover referring to specific areas are uncommon in ethnobotanical studies, especially for periods before the XIX century. The aim of this study was to analyse the type of crop or the characteristics of soil cover from the XVI century to the present. Methods This diachronic analysis was accomplished through archival research on the inventories of the Parish of St. Mary and those of the Municipality of Pignone and from recent surveys conducted in an area of eastern Liguria (Italy). Results Archival data revealed that in study area the primary means of subsistence during the last five centuries, until the first half of the XX century, was chestnuts. In the XVIII and XIX centuries, crop diversification strongly increased in comparison with previous and subsequent periods. In more recent times, the abandonment of agricultural practices has favoured the re-colonisation of mixed woodland or cluster-pine woodland. Conclusion Ancient documents in the ecclesiastic or municipal inventories can be a very useful tool for enhancing the knowledge of agricultural practice, as well as of subsistence methods favoured by local populations during a particular time and for reconstructing land use change over time. PMID:19361339

  6. Estimating time and spatial distribution of snow water equivalent in the Hakusan area

    NASA Astrophysics Data System (ADS)

    Tanaka, K.; Matsui, Y.; Touge, Y.

    2015-12-01

    In the Sousei program, on-going Japanese research program for risk information on climate change, assessing the impact of climate change on water resources is attempted using the integrated water resources model which consists of land surface model, irrigation model, river routing model, reservoir operation model, and crop growth model. Due to climate change, reduction of snowfall amount, reduction of snow cover and change in snowmelt timing, change in river discharge are of increasing concern. So, the evaluation of snow water amount is crucial for assessing the impact of climate change on water resources in Japan. To validate the snow simulation of the land surface model, time and spatial distribution of the snow water equivalent was estimated using the observed surface meteorological data and RAP (Radar Analysis Precipitation) data. Target area is Hakusan. Hakusan means 'white mountain' in Japanese. Water balance of the Tedori River Dam catchment was checked with daily inflow data. Analyzed runoff was generally well for the period from 2010 to 2012. From the result for 2010-2011 winter, maximum snow water equivalent in the headwater area of the Tedori River dam reached more than 2000mm in early April. On the other hand, due to the underestimation of RAP data, analyzed runoff was under estimated from 2006 to 2009. This underestimation is probably not from the lack of land surface model, but from the quality of input precipitation data. In the original RAP, only the rain gauge data of JMA (Japan Meteorological Agency) were used in the analysis. Recently, other rain gauge data of MLIT (Ministry of Land, Infrastructure, Transport and Tourism) and local government have been added in the analysis. So, the quality of the RAP data especially in the mountain region has been greatly improved. "Reanalysis" of the RAP precipitation is strongly recommended using all the available off-line rain gauges information. High quality precipitation data will contribute to validate

  7. Global Phenological Response to Climate Change in Crop Areas using Satellite Remote Sensing of Vegetation, Humidity and Temperature over 26 years

    NASA Astrophysics Data System (ADS)

    Brown, M. E.; de Beurs, K. M.

    2012-12-01

    The recent increase in food prices has revealed that climate, combined with an expanding population and a widespread change in diet, may result in an end to an era of predictable abundance of global cereal crops. The objective of this paper is to estimate changes of agriculturally-relevant growing season parameters, including the start of the season, length of the growing period and the position of the height or peak of the season, in the primary regions with rainfed agriculture during the past 26 years. Our analysis found that globally, 27% of cereal crop areas have experienced changes in the length of the growing season since 1981, the majority of which had seasons that were at least 2.3 days per year longer on average. We also found both negative and positive trends in the start of season globally, with different effects of changing temperature and humidity being isolated depending on the country and region. We investigated the correlation between the peak timing of the growing season and agricultural production statistics for rain fed agriculture. We found that two thirds of the countries investigated had at least 25% of pixels with crop production that behaved differently than expected from the null hypothesis of no correlation. The results show that variations in the peak of the growing season have a strong effect on global food production in these countries. We show that northern hemisphere countries and states appear to have improved model fit when using phenological models based on humidity while southern hemisphere countries and states have improved model fit by phenological models based on accumulated growing degree days, showing the impact of climate variability during the past two and a half decades.

  8. A comparison of operational remote sensing-based models for estimating crop evapotranspiration

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The integration of remotely sensed data into models of actual evapotranspiration has allowed for the estimation of water consumption across agricultural regions. Two modeling approaches have been successfully applied. The first approach computes a surface energy balance using the radiometric surface...

  9. Estimating hourly crop ET using a two-source energy balance model and multispectral airborne imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Efficient water use through improved irrigation scheduling is expected to moderate fast declining groundwater levels and improve sustainability of the Ogallala Aquifer. Thus, an accurate estimation of spatial actual evapotranspiration (ET) is needed for this purpose. Therefore, during 2007, the Bush...

  10. Use of UAS remote sensing data to estimate crop ET at high spatial resolution

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Estimation of the spatial distribution of evapotranspiration (ET) based on remotely sensed imagery has become useful for managing water in irrigated agricultural at various spatial scales. However, data acquired by conventional satellites (Landsat, ASTER, etc.) lack the spatial resolution to capture...

  11. Estimating soybean genetic gain for yield in the northern United States – Influence of cropping history

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Mean on-farm USA soybean yield increased at a rate of 21.3 kg per ha per year between 1924 and 2010, due to adoption of yield-enhancing genetic and agronomic technologies. To estimate annual rates of genetic yield gain in three northern USA soybean maturity groups (MG) and determine if these estimat...

  12. Sampling for area estimation - A comparison of full-frame sampling with the sample segment approach. [from classifications of Landsat data

    NASA Technical Reports Server (NTRS)

    Hixson, M. M.; Bauer, M. E.; Davis, B. J.

    1979-01-01

    The objective of this investigation was to evaluate the effect of sampling on the accuracy (precision and bias) of crop area estimates made from classifications of Landsat MSS data. Full-frame classifications of wheat and non-wheat for eighty counties in Kansas were repetitively sampled to simulate alternative sampling plans. Four sampling schemes involving different numbers of samples and different size sampling units were evaluated. The precision of the wheat area estimates increased as the segment size decreased and the number of segments was increased. Although the average bias associated with the various sampling schemes was not significantly different, the maximum absolute bias was directly related to sampling unit size.

  13. Estimating and Validating Soil Evaporation and Crop Transpiration during the HiWATER-MUSOEXE

    NASA Astrophysics Data System (ADS)

    Song, L.

    2014-12-01

    The two-source energy balance model (TSEB) was successfully applied to estimate evaporation (E), transpiration (T), and evapotranspiration (ET) for land covered with vegetation, which has significantly important applications for the terrestrial water cycle and water resource management. However, the current composite temperature separation approaches are limited in their effectiveness in arid regions. Moreover, E and T are difficult to measure on the ground. In this study, the ground-measured soil and canopy component temperatures were used to estimate E, T, and ET, which were better validated with observed ratios of E (E/ET%) and T (T/ET%) using the stable oxygen and hydrogen isotopes, and the ET measurements using an eddy covariance (EC) system. Our results indicated that even under the strongly advective conditions, the TSEB model produced reliable estimates of the E/ET% and T/ET% ratios and of ET. The mean bias and root mean square error (RMSE) of E/ET% were 1% and 2%, respectively, and the mean bias and RMSE of T/ET% were -1% and 2%, respectively. In addition, the model exhibited relatively reliable estimates in the latent heat flux, with mean bias and RMSE values of 31 W m-2 and 6 1 W m-2, respectively, compared with the measurements from the EC system. These results demonstrated that a robust soil and vegetation component temperature calculation were crucial for estimating E, T, and ET. Moreover, the separate validation of E/ET% and T/ET% provides a good prospect for TSEB model improvements.

  14. Evapotranspiration Measurement and Crop Coefficient Estimation over a Spring Wheat Farmland Ecosystem in the Loess Plateau

    PubMed Central

    Yang, Fulin; Zhang, Qiang; Wang, Runyuan; Zhou, Jing

    2014-01-01

    Evapotranspiration (ET) is an important component of the surface energy balance and hydrological cycle. In this study, the eddy covariance technique was used to measure ET of the semi-arid farmland ecosystem in the Loess Plateau during 2010 growing season (April to September). The characteristics and environmental regulations of ET and crop coefficient (Kc) were investigated. The results showed that the diurnal variation of latent heat flux (LE) was similar to single-peak shape for each month, with the largest peak value of LE occurring in August (151.4 W m−2). The daily ET rate of the semi-arid farmland in the Loess Plateau also showed clear seasonal variation, with the maximum daily ET rate of 4.69 mm day−1. Cumulative ET during 2010 growing season was 252.4 mm, and lower than precipitation. Radiation was the main driver of farmland ET in the Loess Plateau, which explained 88% of the variances in daily ET (p<0.001). The farmland Kc values showed the obvious seasonal fluctuation, with the average of 0.46. The correlation analysis between daily Kc and its major environmental factors indicated that wind speed (Ws), relative humidity (RH), soil water content (SWC), and atmospheric vapor pressure deficit (VPD) were the major environmental regulations of daily Kc. The regression analysis results showed that Kc exponentially decreased with Ws increase, an exponentially increased with RH, SWC increase, and a linearly decreased with VPD increase. An experiential Kc model for the semi-arid farmland in the Loess Plateau, driven by Ws, RH, SWC and VPD, was developed, showing a good consistency between the simulated and the measured Kc values. PMID:24941017

  15. How healthy is urban horticulture in high traffic areas? Trace metal concentrations in vegetable crops from plantings within inner city neighbourhoods in Berlin, Germany.

    PubMed

    Säumel, Ina; Kotsyuk, Iryna; Hölscher, Marie; Lenkereit, Claudia; Weber, Frauke; Kowarik, Ingo

    2012-06-01

    Food production by urban dwellers is of growing importance in developing and developed countries. Urban horticulture is associated with health risks as crops in urban settings are generally exposed to higher levels of pollutants than those in rural areas. We determined the concentration of trace metals in the biomass of different horticultural crops grown in the inner city of Berlin, Germany, and analysed how the local setting shaped the concentration patterns. We revealed significant differences in trace metal concentrations depending on local traffic, crop species, planting style and building structures, but not on vegetable type. Higher overall traffic burden increased trace metal content in the biomass. The presence of buildings and large masses of vegetation as barriers between crops and roads reduced trace metal content in the biomass. Based on this we discuss consequences for urban horticulture, risk assessment, and planting and monitoring guidelines for cultivation and consumption of crops. PMID:22445920

  16. Research advances in satellite-aided crop forecasting

    NASA Technical Reports Server (NTRS)

    Erickson, J.; Dragg, J.; Bizzell, R.; Trichel, M.

    1982-01-01

    The underlying concept of the technology is to estimate crop area and yield for specified regions and to multiply the two to obtain production at the regional level. The treatment here is mainly of area estimation. The approach to non-U.S. crop forecasting does not require ground observations. Among the problems hampering development are the need to estimate crop areas much earlier in the season and the need for information extraction methods that reduce the quantity and quality of the data required.

  17. Large Area Crop Inventory Experiment (LACIE). An early estimate of small grains acreage

    NASA Technical Reports Server (NTRS)

    Lea, R. N.; Kern, D. M. (Principal Investigator)

    1979-01-01

    The author has identified the following significant results. A major advantage of this scheme is that it needs minimal human intervention. The entire scheme, with the exception of the choice of dates, can be computerized and the results obtained in minutes. The decision to limit the number of acquisitions processed to four was made to facilitate operation on the particular computer being used. Some earlier runs on another computer system were based on as many as seven biophase-1 acquisitions.

  18. Assimilating leaf area index estimates from remote sensing into the simulations of a cropping systems model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Practical application of agricultural systems models for farm management decision support has been limited by the vast model input requirements and the model sensitivity to parameter uncertainty. Remote sensing has been proposed as a viable source of information for guiding model simulations, but t...

  19. Estimates of spatial and temporal variation of energy crops biomass yields in the US

    NASA Astrophysics Data System (ADS)

    Song, Y.; Jain, A. K.; Landuyt, W.; Kheshgi, H. S.

    2013-12-01

    Perennial grasses, such as switchgrass (Panicum viragatum) and Miscanthus (Miscanthus x giganteus) have been identified for potential use as biomass feedstocks in the US. Current research on perennial grass biomass production has been evaluated on small-scale plots. However, the extent to which this potential can be realized at a landscape-scale will depend on the biophysical potential to grow these grasses with minimum possible amount of land that needs to be diverted from food to fuel production. To assess this potential three questions about the biomass yield for these grasses need to be answered: (1) how the yields for different grasses are varied spatially and temporally across the US; (2) whether the yields are temporally stable or not; and (3) how the spatial and temporal trends in yields of these perennial grasses are controlled by limiting factors, including soil type, water availability, climate, and crop varieties. To answer these questions, the growth processes of the perennial grasses are implemented into a coupled biophysical, physiological and biogeochemical model (ISAM). The model has been applied to quantitatively investigate the spatial and temporal trends in biomass yields for over the period 1980 -2010 in the US. The bioenergy grasses considered in this study include Miscanthus, Cave-in-Rock switchgrass and Alamo switchgrass. The effects of climate, soil and topography on the spatial and temporal trends of biomass yields are quantitatively analyzed using principal component analysis and GIS based geographically weighted regression. The spatial temporal trend results are evaluated further to classify each part of the US into four homogeneous potential yield zones: high and stable yield zone (HS), high but unstable yield zone (HU), low and stable yield zone (LS) and low but unstable yield zone (LU). Our preliminary results indicate that the yields for perennial grasses among different zones are strongly related to the different controlling factors

  20. Estimation and mitigation of N2O emission and nitrate leaching from intensive crop cultivation in the Haean catchment, South Korea.

    PubMed

    Kim, Youngsun; Seo, Youngho; Kraus, David; Klatt, Steffen; Haas, Edwin; Tenhunen, John; Kiese, Ralf

    2015-10-01

    Considering intensive agricultural management practices and environmental conditions, the LandscapeDNDC model was applied for simulation of yields, N2O emission and nitrate leaching from major upland crops and temperate deciduous forest of the Haean catchment, South Korea. Fertilization rates were high (up to 314 kg N ha(-1) year(-1)) and resulted in simulated direct N2O emissions from potato, radish, soybean and cabbage fields of 1.9 and 2.1 kg N ha(-1) year(-1) in 2009 and 2010, respectively. Nitrate leaching was identified as the dominant pathway of N losses in the Haean catchment with mean annual rates of 112.2 and 125.4 kg N ha(-1) year(-1), causing threats to water quality and leading to substantial indirect N2O emissions of 0.84 and 0.94 kg N ha(-1) year(-1) in 2009 and 2010 as estimates by applying the IPCC EF5. Simulated N2O emissions from temperate deciduous forest were low (approx. 0.50 kg N ha(-1) year(-1)) and predicted nitrate leaching rates were even negligible (≤0.01 kg N ha(-1) year(-1)). On catchment scale more than 50% of the total N2O emissions and up to 75% of nitrate leaching originated from fertilized upland fields, only covering 24% of the catchment area. Taking into account area coverage of simulated upland crops and other land uses these numbers agree well with nitrate loads calculated from discharge and concentration measurements at the catchment outlet. The change of current agricultural management practices showed a high potential of reducing N2O emission and nitrate leaching while maintaining current crop yields. Reducing (39%) and splitting N fertilizer application into 3 times was most effective and lead to about 54% and 77% reducing of N2O emission and nitrate leaching from the Haean catchment, the latter potentially contributing to improved water quality in the Soyang River Dam, which is the major source of drinking water for metropolitan residents. PMID:26005748

  1. Large Area Crop Inventory Experiment (LACIE). The boundary pixel study in Kansas and North Dakota

    NASA Technical Reports Server (NTRS)

    Register, D. T. (Principal Investigator); Ona, A. L.

    1979-01-01

    The author has identified the following significant results. The statistical mapping approach to handling boundary pixels can be used as a standard for objectively comparing the cluster based technique, the maximum likelihood estimate based technique, and multicategory labeling.

  2. A simulation study of Large Area Crop Inventory Experiment (LACIE) technology

    NASA Technical Reports Server (NTRS)

    Ziegler, L. (Principal Investigator); Potter, J.

    1979-01-01

    The author has identified the following significant results. The LACIE performance predictor (LPP) was used to replicate LACIE phase 2 for a 15 year period, using accuracy assessment results for phase 2 error components. Results indicated that the (LPP) simulated the LACIE phase 2 procedures reasonably well. For the 15 year simulation, only 7 of the 15 production estimates were within 10 percent of the true production. The simulations indicated that the acreage estimator, based on CAMS phase 2 procedures, has a negative bias. This bias was too large to support the 90/90 criterion with the CV observed and simulated for the phase 2 production estimator. Results of this simulation study validate the theory that the acreage variance estimator in LACIE was conservative.

  3. A comparison of measured and estimated meteorological data for use in crop growth modeling

    NASA Technical Reports Server (NTRS)

    Perry, C. R., Jr.; Rogers, J. L.; Ritchie, J. T.

    1982-01-01

    Gridded spatial estimates of maximum temperature, minimum temperature, precipitation, and solar radiation, prepared for agricultural use from World Meteorological Organization surface reports and enhanced by polar orbiting satellites were compared with daily meteorological data measured at various agricultural research facilities across the United States to determine their level of accuracy. Preliminary results indicate that daily maximum temperature can be determined to within 9.1 degrees Celsius with ninety percent confidence. With similar levels of confidence, daily minimum temperature can be determined to within 6.7 degrees Celsius, daily solar radiation to within 231.2 cal/sq cm min, and daily precipitation to within 9.7 millimeters.

  4. Evaluation of SEBS for estimation of actual evapotranspiration using ASTER satellite data for irrigation areas of Australia

    NASA Astrophysics Data System (ADS)

    Ma, Weiqiang; Hafeez, Mohsin; Ishikawa, Hirohiko; Ma, Yaoming

    2013-05-01

    Spatial knowledge of land surface evapotranspiration (ET) is of prime interest for environmental applications, such as optimizing irrigation water use, irrigation system performance, crop water deficit, drought mitigation strategies, and accurate initialization of climate prediction models especially in arid and semiarid catchments where water shortage is a critical problem. The recent drought in Australia and concerns about climate change have highlighted the need to manage water resources more sustainably especially in the Murrumbidgee catchment which utilizes bulk water for food production. This study deals with the application of a Surface Energy Balance System (SEBS) algorithm based on Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) data and field observations has been proposed and tested for deriving ET over Coleambally Irrigation Area, located in the southwest of NSW, Australia. We have used 12 ASTER scenes covering the time period of 2002, 2003, 2004, 2005, 2006, and 2009 for estimating the actual ET over the study area. To validate the proposed methodology, the ground-measured ET was compared to the ASTER-derived actual ET values for the study area. The derived ET value over the study area is much closer to the field measurement. From the remote sensing results and observations, the root mean square error is 0.89 and the mean absolute percentage difference is 2.87 %, which demonstrate the reasonability of SEBS ET estimation for the study area.

  5. Density and Distribution of Xylocopa Nests (Hymenoptera: Apidae) in Caatinga Areas in the Surroundings of Passion Fruit Crops.

    PubMed

    Martins, C F; de Siqueira, K M M; Kiill, L H P; Sá, I I S; Aguiar, C M L

    2014-08-01

    Due to their importance as pollinators of many plant species, this study aimed to know the nest density, spatial distribution, and nesting substrates used by Xylocopa species in the Caatinga, a xerophilous vegetation of Northeastern Brazil. Three areas of Caatinga in the surroundings of passion fruit crops were sampled. The bee species found in these areas were Xylocopa grisescens Lepeletier and Xylocopa frontalis (Olivier). All nests were in Commiphora leptophloeos (Burseraceae) trees (n = 113). Phytosociological analysis showed that this tree species presented the highest absolute density (212.5 individuals/ha) and index of importance value (52.7). The distribution pattern of the C. leptophloeos was aggregated. The nests were located in dead and dried branches with an average diameter of 5.3 ± 2.0 cm (n = 43). The mean number of nests/tree was 3.1 ± 2.8 (n = 113). The less disturbed area showed 6.7 nests/ha and 4.2 nests/tree. In the disturbed areas, 0.9 nests/ha and 2.4 to 2.7 nests/tree were observed. The availability of substrate for nesting in the studied areas and its importance as a limiting factor for nesting are discussed. PMID:27193809

  6. Analysis of scanner data for crop inventories

    NASA Technical Reports Server (NTRS)

    Horvath, R. (Principal Investigator); Cicone, R. C.; Kauth, R. J.; Malila, W. A.; Pont, W.; Thelen, B.; Sellman, A.

    1981-01-01

    Accomplishments for a machine-oriented small grains labeler T&E, and for Argentina ground data collection are reported. Features of the small grains labeler include temporal-spectral profiles, which characterize continuous patterns of crop spectral development, and crop calendar shift estimation, which adjusts for planting date differences of fields within a crop type. Corn and soybean classification technology development for area estimation for foreign commodity production forecasting is reported. Presentations supporting quarterly project management reviews and a quarterly technical interchange meeting are also included.

  7. Transfer characteristics of cobalt from soil to crops in the suburban areas of Fujian Province, southeast China.

    PubMed

    Luo, Dan; Zheng, Haifeng; Chen, Yanhui; Wang, Guo; Fenghua, Ding

    2010-11-01

    The bioavailability of cobalt and its transfer from soil to vegetables and rice were investigated. Among 312 soils collected from vegetable and paddy fields in the suburban areas of some major cities of Fujian Province, southeast China, total soil Co ranged from 3.5 to 21.7 mg kg(-1), indicating a slight accumulation compared with the background value of the province. DTPA extracted 0.1-8.5% of soil total Co. Total and DTPA-extractable Co correlated with soil pH, CEC, free Fe, total Mn, clay and silt content more significantly in paddy soils than in the soils from vegetable fields. The average Co concentrations in the edible parts of vegetables and rice were 15.4 microg kg(-1) and 15.5 microg kg(-1), respectively. The transfer factor (the ratio of plant Co to soil DTPA-extractable Co, TF(DTPA)) ranged from 0.003 to 0.126 with a median of 0.049. The TF(DTPA) decreased in the order of leafy vegetables > fruit vegetables > root vegetables > rice. The TF(DTPA) of all crops decreased with increasing DTPA-extractable Co. Increase in pH, CEC, organic matter, clay, silt, free iron and total Mn limited the soil-to-plant transfer of Co to varying degrees. The transfer of Co from the soils to the edible parts of the crops was lower than that of Zn, Cu and Cd, but higher than that of Pb in the same areas. The concentrations of Co in rice and vegetables in the study areas were considered to be safe for the local residents because of the slight anthropogenic input and the low transfer potential to the edible parts of Co from the soils. PMID:20615604

  8. Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A radio-controlled unmanned helicopter-based LARS (Low-Altitude Remote Sensing) platform was used to acquire quality images of high spatial and temporal resolution, in order to estimate yield and total biomass of a rice crop (Oriza Sativa, L.). Fifteen rice field plots with five N-treatments (0, 33,...

  9. [Estimation of spur dike-affected fish habitat area].

    PubMed

    Ray-Shyan, Wu; Yan-Ru, Chen; Yi-Liang, Ge

    2012-04-01

    Based on the HEC-RAS and River 2D modes, and taking 5% change rate of weighted usable area (WUA) as the threshold to define the spur dike- affected area of target fish species Acrossocheilus paradoxus in Fazi River in Taiwan, this paper studied the affected area of the fish habitat by spur dike, and, in combining with the references about the installations of spur dikes in Taiwan in recent 10 years, analyzed the relative importance of related affecting factors such as dike height, dike length (water block rate), average slope gradient of river way, single or double spur dike, and flow discharge. In spite of the length of the dike, the affected area in downstream was farther, and was about 2-6 times as large as that in upstream. The ratio of the affected area in downstream / upstream decreased with increasing slope gradient, but increased with increasing dike length and flow discharge. When the discharge was approximate to 10 years return periods, the ratio of the affected area would be close to a constant of 2. Building double spur dike would produce a better WUA than building single spur dike. PMID:22803455

  10. Global crop forecasting

    NASA Technical Reports Server (NTRS)

    Macdonald, R. B.; Hall, F. G.

    1980-01-01

    The needs for and remote sensing means of global crop forecasting are discussed, and key results of the Large Area Crop Inventory Experiment (LACIE) are presented. Current crop production estimates provided by foreign countries are shown often to be inadequate, and the basic elements of crop production forecasts are reviewed. The LACIE project is introduced as a proof-of-concept experiment designed to assimilate remote sensing technology, monitor global wheat production, evaluate key technical problems, modify the technique accordingly and demonstrate the feasibility of a global agricultural monitoring system. The global meteorological data, sampling and aggregation techniques, Landsat data analysis procedures and yield forecast procedures used in the experiment are outlined. Accuracy assessment procedures employed to evaluate LACIE technology performance are presented, and improvements in system efficiency and capacity during the three years of operation are pointed out. Results of LACIE estimates of Soviet, U.S. and Canadian wheat production are presented which demonstrate the feasibility and accuracy of the remote-sensing approach for global food and fiber monitoring.

  11. Honeybee colony disorder in crop areas: the role of pesticides and viruses.

    PubMed

    Simon-Delso, Noa; San Martin, Gilles; Bruneau, Etienne; Minsart, Laure-Anne; Mouret, Coralie; Hautier, Louis

    2014-01-01

    As in many other locations in the world, honeybee colony losses and disorders have increased in Belgium. Some of the symptoms observed rest unspecific and their causes remain unknown. The present study aims to determine the role of both pesticide exposure and virus load on the appraisal of unexplained honeybee colony disorders in field conditions. From July 2011 to May 2012, 330 colonies were monitored. Honeybees, wax, beebread and honey samples were collected. Morbidity and mortality information provided by beekeepers, colony clinical visits and availability of analytical matrix were used to form 2 groups: healthy colonies and colonies with disorders (n = 29, n = 25, respectively). Disorders included: (1) dead colonies or colonies in which part of the colony appeared dead, or had disappeared; (2) weak colonies; (3) queen loss; (4) problems linked to brood and not related to any known disease. Five common viruses and 99 pesticides (41 fungicides, 39 insecticides and synergist, 14 herbicides, 5 acaricides and metabolites) were quantified in the samples.The main symptoms observed in the group with disorders are linked to brood and queens. The viruses most frequently found are Black Queen Cell Virus, Sac Brood Virus, Deformed Wing Virus. No significant difference in virus load was observed between the two groups. Three acaricides, 5 insecticides and 13 fungicides were detected in the analysed samples. A significant correlation was found between the presence of fungicide residues and honeybee colony disorders. A significant positive link could also be established between the observation of disorder and the abundance of crop surface around the beehive. According to our results, the role of fungicides as a potential stressor for honeybee colonies should be further studied, either by their direct and/or indirect impacts on bees and bee colonies. PMID:25048715

  12. Honeybee Colony Disorder in Crop Areas: The Role of Pesticides and Viruses

    PubMed Central

    Simon-Delso, Noa; San Martin, Gilles; Bruneau, Etienne; Minsart, Laure-Anne; Mouret, Coralie; Hautier, Louis

    2014-01-01

    As in many other locations in the world, honeybee colony losses and disorders have increased in Belgium. Some of the symptoms observed rest unspecific and their causes remain unknown. The present study aims to determine the role of both pesticide exposure and virus load on the appraisal of unexplained honeybee colony disorders in field conditions. From July 2011 to May 2012, 330 colonies were monitored. Honeybees, wax, beebread and honey samples were collected. Morbidity and mortality information provided by beekeepers, colony clinical visits and availability of analytical matrix were used to form 2 groups: healthy colonies and colonies with disorders (n = 29, n = 25, respectively). Disorders included: (1) dead colonies or colonies in which part of the colony appeared dead, or had disappeared; (2) weak colonies; (3) queen loss; (4) problems linked to brood and not related to any known disease. Five common viruses and 99 pesticides (41 fungicides, 39 insecticides and synergist, 14 herbicides, 5 acaricides and metabolites) were quantified in the samples.The main symptoms observed in the group with disorders are linked to brood and queens. The viruses most frequently found are Black Queen Cell Virus, Sac Brood Virus, Deformed Wing Virus. No significant difference in virus load was observed between the two groups. Three acaricides, 5 insecticides and 13 fungicides were detected in the analysed samples. A significant correlation was found between the presence of fungicide residues and honeybee colony disorders. A significant positive link could also be established between the observation of disorder and the abundance of crop surface around the beehive. According to our results, the role of fungicides as a potential stressor for honeybee colonies should be further studied, either by their direct and/or indirect impacts on bees and bee colonies. PMID:25048715

  13. Does the conversion of grasslands to row crop production in semi-arid areas threaten global food supplies?

    Technology Transfer Automated Retrieval System (TEKTRAN)

    In the world’s semi-arid regions, high crop demands have produced short term economic incentives to convert native grasslands to dryland row crop production, while genetic enhancements and equipment have reduced the risk of crop failure. The objectives of this paper were to discuss: 1) the importanc...

  14. Improving root-zone soil moisture estimations using dynamic root growth and crop phenology

    NASA Astrophysics Data System (ADS)

    Hashemian, Minoo; Ryu, Dongryeol; Crow, Wade T.; Kustas, William P.

    2015-12-01

    Water Energy Balance (WEB) Soil Vegetation Atmosphere Transfer (SVAT) modelling can be used to estimate soil moisture by forcing the model with observed data such as precipitation and solar radiation. Recently, an innovative approach that assimilates remotely sensed thermal infrared (TIR) observations into WEB-SVAT to improve the results has been proposed. However, the efficacy of the model-observation integration relies on the model's realistic representation of soil water processes. Here, we explore methods to improve the soil water processes of a simple WEB-SVAT model by adopting and incorporating an exponential root water uptake model with water stress compensation and establishing a more appropriate soil-biophysical linkage between root-zone moisture content, above-ground states and biophysical indices. The existing WEB-SVAT model is extended to a new Multi-layer WEB-SVAT with Dynamic Root distribution (MWSDR) that has five soil layers. Impacts of plant root depth variations, growth stages and phenological cycle of the vegetation on transpiration are considered in developing stages. Hydrometeorological and biogeophysical measurements collected from two experimental sites, one in Dookie, Victoria, Australia and the other in Ponca, Oklahoma, USA, are used to validate the new model. Results demonstrate that MWSDR provides improved soil moisture, transpiration and evaporation predictions which, in turn, can provide an improved physical basis for assimilating remotely sensed data into the model. Results also show the importance of having an adequate representation of vegetation-related transpiration process for an appropriate simulation of water transfer in a complicated system of soil, plants and atmosphere.

  15. Large Area Crop Inventory Experiment (LACIE). YES phase 1 yield feasibility report

    NASA Technical Reports Server (NTRS)

    1977-01-01

    The author has identified the following significant results. Each state model was separately evaluated to determine if a projected performance to the country level would satisfy a 90/90 criterion. All state models, except the North Dakota and Kansas models, satisfied that criterion both for district estimates aggregated to the state level and for state estimates directly from the models. In addition to the tests of the 90/90 criterion, the models were examined for their ability to adequately respond to fluctuations in weather. This portion of the analysis was based on a subjective interpretation of values of certain description statistics. As a result, 10 of the 12 models were judged to respond inadequately to variation in weather-related variables.

  16. Evaluation of spring wheat and barley crop calender models for the 1979 crop year

    NASA Technical Reports Server (NTRS)

    Nazare, C. V.; Carnes, J. G. (Principal Investigator)

    1981-01-01

    During the Large Area Crop Inventory Experiment, spring wheat planting date and crop development stage estimates based on historical normals were improved by the use of the Feyerherm planting date and Robertson spring wheat crop calendar models. The Supporting Research Crop Calendar Project element modified the Robertson model to reduce bias at cardinal growth stages within the growing season. These models were tested in 1980 along with a state-of-the-art barley model (Williams) against a ground-truth data set from 49 calendar year 1979 segments in the U.S. Great Plains spring wheat and barley region.

  17. Large Area Crop Inventory Experiment (LACIE). Detection of episodic phenomena on LANDSAT imagery. [Kansas

    NASA Technical Reports Server (NTRS)

    Chesnutwood, C. M. (Principal Investigator)

    1976-01-01

    The author has identified the following significant results. Episodic phenomena such as rainfall shortly before data pass, thin translucent clouds, cloud shadows, and aircraft condensation trails and their shadows are responsible for changes in the spectral reflectivities of some surfaces. These changes are readily detected on LANDSAT full-frame imagery. Histograms of selected areas in Kansas show a distinct decrease in mean radiance values, but also, an increase in scene contrast, in areas where recent rains had occurred. Histograms from a few individual fields indicate that the mean radiance values for winter wheat followed a different trend after a rainfall than alfalfa or grasses.

  18. ROOT DENSITY AND ROOT SURFACE AREA OF SELECTED PACIFIC NORTHWEST CROPS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Root length density and root surface area were evaluated for soft white winter and spring wheat (Triticum aestivum L), spring peas (Pisum sativum) and winter canola (Brassica napus L.) for at least two years at two locations (Pendleton, OR and Pullman, WA). Spring wheat was sampled at 3-leaf, anthe...

  19. Losses Assessment of Crops due to Typhoon Disaster in China Coastal Areas —— A Case Study of Zhanjiang City, Guangdong Province

    NASA Astrophysics Data System (ADS)

    Mo, W.; Fang, W.

    2015-12-01

    Vulnerability which quantifies the loss ratio under different hazard intensity is an important feature of the natural disaster system and has important significance to natural disaster risk assessment. Agriculture is an outdoor industry with high risk of meteorological disasters. The strong winds, heavy rain and storm surge are main typhoon hazard factors to crops. To provide a quantitative research method for the loss evaluation of crops due to typhoon disaster we first revised two vulnerability curves for crops under comprehensive intensity of typhoon based on the simulated hazard data and loss data related to historical typhoon events landing on China from 1949 to 2014;and then established a storm surge vulnerability matrix of crops regarding Zhanjiang City of Guangdong Province as the study area ; finally, we put forward three storm surge fragility curves for crops representing different states of loss. The results can effectively describe the typhoon vulnerability for crops in China coastal areas so as to provide the input to post-disaster loss assessments and catastrophe modeling applications.

  20. Estimated crop loss due to coconut mite and financial analysis of controlling the pest using the acaricide abamectin.

    PubMed

    Rezende, Daniela; Melo, José W S; Oliveira, José E M; Gondim, Manoel G C

    2016-07-01

    Reducing the losses caused by Aceria guerreronis Keifer has been an arduous task for farmers. However, there are no detailed studies on losses that simultaneously analyse correlated parameters, and very few studies that address the economic viability of chemical control, the main strategy for managing this pest. In this study the objectives were (1) to estimate the crop loss due to coconut mite and (2) to perform a financial analysis of acaricide application to control the pest. For this, the following parameters were evaluated: number and weight of fruits, liquid albumen volume, and market destination of plants with and without monthly abamectin spraying (three harvests). The costs involved in the chemical control of A. guerreronis were also quantified. Higher A. guerreronis incidence on plants resulted in a 60 % decrease in the mean number of fruits harvested per bunch and a 28 % decrease in liquid albumen volume. Mean fruit weight remained unaffected. The market destination of the harvested fruit was also affected by higher A. guerreronis incidence. Untreated plants, with higher A. guerreronis infestation intensity, produced a lower proportion of fruit intended for fresh market and higher proportions of non-marketable fruit and fruit intended for industrial processing. Despite the costs involved in controlling A. guerreronis, the difference between the profit from the treated site and the untreated site was 18,123.50 Brazilian Real; this value represents 69.1 % higher profit at the treated site. PMID:27059867

  1. Estimating relationships among water use, nitrogen uptake and biomass production in a short-rotation woody crop plantation

    NASA Astrophysics Data System (ADS)

    Ouyang, Y.

    2015-12-01

    Short-rotation woody crop has been identified as one of the best feedstocks for bioenergy production due to their fast-growth rates. However, the biomass production, nutrient uptake, and water use efficiency under adverse environmental condition are still poorly understood. In this study, a computer model was developed to undertake these issues using STELLA (Structural Thinking and Experiential Learning Laboratory with Animation) software. Two simulation scenarios were employed: one was to quantify the mechanisms of water use, nitrogen uptake and biomass production in a eucalypt plantation under the normal soil conditions, the other was to estimate the same mechanisms under the wet and dry soil conditions. In general, the rates of evaporation, transpiration, evapotranspiration (ET), and root water uptake were in the following order: ET > root uptake > leaf transpiration > soil evaporation. A profound discrepancy in water use was observed between the wet and dry soil conditions. Leaching of nitrate-N and soluble organic N depended not only on soil N content but also on rainfall rate and duration. The yield of biomass from the eucalypt was primarily regulated by water availability in a fertilized plantation.

  2. Estimation of the change in the harmfulness of selected pests in expected climate - European area

    NASA Astrophysics Data System (ADS)

    Svobodova, E.; Trnka, M.; Zalud, Z.; Semeradova, D.; Dubrovsky, M.; Sefrova, H.

    2010-09-01

    Climate change is likely to be a dominant factor affecting the lifecycle and overall occurrence of pest's species whose development is directly linked with climate conditions. This study is focused on the estimation of the potential occurrence and generation growth of selected pests causing the significant damages on the yield of crops over western part of Europe in changing climate. Modelled species involved the main pest of potato Colorado potato beetle (Leptinotarsa decemlineata, Say 1824), the pest of maize European corn borer (Ostrinia nubilalis, Hubner 1796), the pest which causes the damages in orchards and decreases the yield of apples, Codling moth (Cydia pomonella, Linnaeus 1758) and Cereal leaf beetle (Oulema melanopus, Linnaeus 1758) seriously affecting wheat production. The development of these pests' is driven mainly by temperature of the environment, which is in turn function of air temperature. The climate change is likely to lead to an earlier once and prolongation of the growing season and in the same time accelerate pests' developmental rate and will increase number of generations. Estimates of potential distribution of selected pest species for the present as well as expected climate conditions are based on the dynamical model CLIMEX. This approach exploits the expression of the overall climate suitability for the species longterm survival in terms of ecoclimatic index. The CLIMEX model was at first validated with observed data of pests' occurrences using CRU 10´ climate data set a source of climate data. All pest models listed were then used to study the effects of climate change on pests by estimating changes in population dynamics and/or infestation pressure during the first half of the 21st century. Outputs of the models were applied within the European scale in the 10´ resolution using digital terrain model. Simulations of the impacts of expected climate on the pests distribution were conducted under three global circulation models (Had

  3. Phosphorus Retention by Stormwater Detention Areas: Estimation, Enhancement, and Economics

    NASA Astrophysics Data System (ADS)

    Shukla, A.; Shukla, S.; Hodges, A.

    2015-12-01

    Stormwater detention areas (SDAs) are considered an important best management practice (BMP) both in agricultural and urban areas. In sub-tropical Florida where sandy soils and shallow water table make the nutrient leaching losses from agricultural areas inevitable, the SDAs are relied upon as a last point of treatment. Field-measured water and phosphorus (P) fluxes from an agricultural SDA showed that contrary to generally held view, the SDA was a source of P for the first year (retention efficiency = -12%). For the next year, the SDA served as a sink (54%). The source function of the SDA was a combined effect of high rainfall, dilution of agricultural drainage with rainfall from a tropical storm, and legacy-based soil P saturation. Volume reduction was the main reason for P retention because of no remaining P sorption capacity in the soil in most of the SDA area. Although a net sink of P for Year 2, an event-wise analysis showed the SDA to be a source of P for three out of seven outflow events in Year 2 indicating P release from soil. Because surface P treatment efficiency during both years was either less than or approximately the same as surface water retention efficiency, volume reduction and not sorption or biological assimilation controlled P retention. Hydraulic (e.g. increased storage), managerial (biomass harvesting) and chemical (alum treatment) modifications were evaluated by using a stormwater treatment model and field data. The model was successfully field-verified using well accepted performance measures (e.g. Nash-Sutcliffe efficiency). Maximum additional P retention was shown to be achieved by biomass harvesting (>100%) followed by chemical treatment (71%), and increased spillage level (29%). Economic feasibility of the aforementioned modifications and development of a payment for environmental services (PES) program was identified through a cost-benefit analysis for maintaining these SDAs as sink of P in the long-term.

  4. Small area estimation (SAE) model: Case study of poverty in West Java Province

    NASA Astrophysics Data System (ADS)

    Suhartini, Titin; Sadik, Kusman; Indahwati

    2016-02-01

    This paper showed the comparative of direct estimation and indirect/Small Area Estimation (SAE) model. Model selection included resolve multicollinearity problem in auxiliary variable, such as choosing only variable non-multicollinearity and implemented principal component (PC). Concern parameters in this paper were the proportion of agricultural venture poor households and agricultural poor households area level in West Java Province. The approach for estimating these parameters could be performed based on direct estimation and SAE. The problem of direct estimation, three area even zero and could not be conducted by directly estimation, because small sample size. The proportion of agricultural venture poor households showed 19.22% and agricultural poor households showed 46.79%. The best model from agricultural venture poor households by choosing only variable non-multicollinearity and the best model from agricultural poor households by implemented PC. The best estimator showed SAE better then direct estimation both of the proportion of agricultural venture poor households and agricultural poor households area level in West Java Province. The solution overcame small sample size and obtained estimation for small area was implemented small area estimation method for evidence higher accuracy and better precision improved direct estimator.

  5. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data

    NASA Astrophysics Data System (ADS)

    Duan, Si-Bo; Li, Zhao-Liang; Wu, Hua; Tang, Bo-Hui; Ma, Lingling; Zhao, Enyu; Li, Chuanrong

    2014-02-01

    Leaf area index (LAI) is a key variable for modeling energy and mass exchange between the land surface and the atmosphere. Inversion of physically based radiative transfer models is the most established technique for estimating LAI from remotely sensed data. This study aims to evaluate the suitability of the PROSAIL model for LAI estimation of three typical row crops (maize, potato, and sunflower) from unmanned aerial vehicle (UAV) hyperspectral data. LAI was estimated using a look-up table (LUT) based on the inversion of the PROSAIL model. The estimated LAI was evaluated against in situ LAI measurements. The results indicated that the LUT-based inversion of the PROSAIL model was suitable for LAI estimation of these three crops, with a root mean square error (RMSE) of approximately 0.62 m2 m-2, and a relative RMSE (RRMSE) of approximately 15.5%. Dual-angle observations were also used to estimate LAI and proved to be more accurate than single-angle observations, with an RMSE of approximately 0.55 m2 m-2 and an RRMSE of approximately 13.6%. The results demonstrate that additional directional information improves the performance of LAI estimation.

  6. Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation

    USGS Publications Warehouse

    Marshall, Michael T.; Thenkabail, Prasad S.

    2015-01-01

    Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. Therefore, we used standard data mining techniques to identify a set of MSBB data from the IKONOS, GeoEye-1, Landsat ETM+, MODIS, WorldView-2 sensors and compared their performance with HNB data from the EO-1 Hyperion sensor in explaining crop biomass variability of four important field crops (rice, alfalfa, cotton, maize). The analysis employed two-band (ratio) vegetation indices (TBVIs) and multiband (additive) vegetation indices (MBVIs) derived from Singular Value Decomposition (SVD) and stepwise regression. Results demonstrated that HNB-derived TBVIs and MBVIs performed better than MSBB-derived TBVIs and MBVIs on a per crop basis and for the pooled data: overall, HNB TBVIs explained 5–31% greater variability when compared with various MSBB TBVIs; and HNB MBVIs explained 3–33% greater variability when compared with various MSBB MBVIs. The performance of MSBB MBVIs and TBVIs improved mildly, by combining spectral information across multiple sensors involving IKONOS, GeoEye-1, Landsat ETM+, MODIS, and WorldView-2. A number of HNBs that advance crop biomass modeling were determined. Based on the highest factor loadings on the first component of the SVD, the “red-edge” spectral range (700–740 nm) centered at 722 nm (bandwidth = 10 nm) stood out prominently, while five additional and distinct portions of the recorded spectral range (400–2500 nm) centered at 539 nm, 758 nm, 914 nm, 1130 nm, 1320 nm (bandwidth = 10 nm) were also important. The best HNB vegetation indices for crop biomass estimation involved 549 and 752 nm for rice (R2 = 0.91); 925 and 1104 nm for

  7. Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation

    NASA Astrophysics Data System (ADS)

    Marshall, Michael; Thenkabail, Prasad

    2015-10-01

    Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. Therefore, we used standard data mining techniques to identify a set of MSBB data from the IKONOS, GeoEye-1, Landsat ETM+, MODIS, WorldView-2 sensors and compared their performance with HNB data from the EO-1 Hyperion sensor in explaining crop biomass variability of four important field crops (rice, alfalfa, cotton, maize). The analysis employed two-band (ratio) vegetation indices (TBVIs) and multiband (additive) vegetation indices (MBVIs) derived from Singular Value Decomposition (SVD) and stepwise regression. Results demonstrated that HNB-derived TBVIs and MBVIs performed better than MSBB-derived TBVIs and MBVIs on a per crop basis and for the pooled data: overall, HNB TBVIs explained 5-31% greater variability when compared with various MSBB TBVIs; and HNB MBVIs explained 3-33% greater variability when compared with various MSBB MBVIs. The performance of MSBB MBVIs and TBVIs improved mildly, by combining spectral information across multiple sensors involving IKONOS, GeoEye-1, Landsat ETM+, MODIS, and WorldView-2. A number of HNBs that advance crop biomass modeling were determined. Based on the highest factor loadings on the first component of the SVD, the "red-edge" spectral range (700-740 nm) centered at 722 nm (bandwidth = 10 nm) stood out prominently, while five additional and distinct portions of the recorded spectral range (400-2500 nm) centered at 539 nm, 758 nm, 914 nm, 1130 nm, 1320 nm (bandwidth = 10 nm) were also important. The best HNB vegetation indices for crop biomass estimation involved 549 and 752 nm for rice (R2 = 0.91); 925 and 1104 nm for alfalfa (R2 = 0

  8. Leaf Area Index Estimation in Vineyards from Uav Hyperspectral Data, 2d Image Mosaics and 3d Canopy Surface Models

    NASA Astrophysics Data System (ADS)

    Kalisperakis, I.; Stentoumis, Ch.; Grammatikopoulos, L.; Karantzalos, K.

    2015-08-01

    The indirect estimation of leaf area index (LAI) in large spatial scales is crucial for several environmental and agricultural applications. To this end, in this paper, we compare and evaluate LAI estimation in vineyards from different UAV imaging datasets. In particular, canopy levels were estimated from i.e., (i) hyperspectral data, (ii) 2D RGB orthophotomosaics and (iii) 3D crop surface models. The computed canopy levels have been used to establish relationships with the measured LAI (ground truth) from several vines in Nemea, Greece. The overall evaluation indicated that the estimated canopy levels were correlated (r2 > 73%) with the in-situ, ground truth LAI measurements. As expected the lowest correlations were derived from the calculated greenness levels from the 2D RGB orthomosaics. The highest correlation rates were established with the hyperspectral canopy greenness and the 3D canopy surface models. For the later the accurate detection of canopy, soil and other materials in between the vine rows is required. All approaches tend to overestimate LAI in cases with sparse, weak, unhealthy plants and canopy.

  9. Methodology for estimating burned area from AVHRR reflectance data

    SciTech Connect

    Razafimpanilo, H.; Frouin, R.; Iacobellis, S.F.; Somerville, R.C.J.

    1995-12-01

    It is well recognized that global fire activity needs to be monitored closely, because of its potential impact on climate and the environment. Two methods are described to determined burned area from Advanced Very High Resolution Radiometer (AVHRR) data. The first method, or the linear method, employs Channel 2 reflectance, R{sub 2}, and is based on the nearly linear relationship between the fraction of pixel burned, P, and R{sub 2}. The second method, or the nonlinear method, employs the Normalized Difference Vegetation Index (NDVI) derived from Channels 1 and 2 reflectances, and is based on the nonlinear relationship P = f(NDVI), a polynomial of order 2 in NDVI. The coefficients of the polynomial are parameterized as a function of the NDVI of the background before the fire event. Radiative transfer simulations indicate that the linear method, unlike the nonlinear method, must be applied to top-of-atmosphere reflectances that have been corrected for atmospheric influence. Sensitivity studies suggest that the methods are subject to some limitations. To avoid discontinuity problems, the original background (just before the fire) must be characterized by a Channel 2 reflectance above 0.07 and by a positive NDVI. To separate the useful signal from atmospheric effects, the fire scar must occupy at least 20% and 12% of the pixel area in the case of savanna and green vegetation (e.g., forest), respectively. When applied to uniform pixels, the mean relative error on the fraction of area burned is about 20% for the linear method and 10% for the nonlinear method. The linear method gives better results for nonuniform pixels, but neither method can be used when the pixel contains low reflectance backgrounds (e.g., water).

  10. Research in satellite-aided crop forecasting

    NASA Technical Reports Server (NTRS)

    Erickson, J. D.; Dragg, J. L.; Bizzell, R. M.; Trichel, M. C.

    1984-01-01

    Evaluations of remote sensing procedures developed specifically to estimate non-U.S. spring small grains area show accuracies of less than 10 percent relative difference to reference statistics for North Dakota in 1978 and good comparison with 9000 square miles of observations over four states and Saskatchewan, Canada during the years 1976-79. Processing a 5 x 6-nautical-mile sample site requires a few minutes manual time and a few minutes central processing unit time on an AS-3000 computer. Evaluations of summer crop, corn, and soybeans area estimates show unbiased summer crops estimates in the U.S. central corn belt but significant bias in one of two years for area estimates of corn and soybeans. Based on results to date, a highly automated corn/sorghum/soybean area estimation procedure should be achieved that is applicable to Argentina.

  11. An estimation of annual nitrous oxide emissions and soil quality following the amendment of high temperature walnut shell biochar and compost to a small scale vegetable crop rotation.

    PubMed

    Suddick, Emma C; Six, Johan

    2013-11-01

    Agricultural soils are responsible for emitting large quantities of nitrous oxide (N2O). The controlled incomplete thermal decomposition of agricultural wastes to produce biochar, once amended to soils, have been hypothesized to increase crop yield, improve soil quality and reduce N2O emissions. To estimate crop yields, soil quality parameters and N2O emissions following the incorporation of a high temperature (900 °C) walnut shell (HTWS) biochar into soil, a one year field campaign with four treatments (control (CONT), biochar (B), compost (COM), and biochar+compost (B+C)) was conducted in a small scale vegetable rotation system in Northern California. Crop yields from five crops (lettuce, winter cover crop, lettuce, bell pepper and Swiss chard) were determined; there were no significant differences in yield between treatments. Biochar amended soils had significant increases in % total carbon (C) and the retention of potassium (K) and calcium (Ca). Annual cumulative N2O fluxes were not significantly different between the four treatments with emissions ranging from 0.91 to 1.12 kg N2O-N ha(-1) yr(-1). Distinct peaks of N2O occurred upon the application of N fertilizers and the greatest mean emissions, ranging from 67.04 to 151.41 g N2O-N ha(-1) day(-1), were observed following the incorporation of the winter cover crop. In conclusion, HTWS biochar application to soils had a pronounced effect on the retention of exchangeable cations such as K and Ca compared to un-amended soils and composted soils, which in turn could reduce leaching of these plant available cations and could thus improve soils with poor nutrient retention. However, HTWS biochar additions to soil had neither a positive or negative effect on crop yield nor cumulative annual emissions of N2O. PMID:23490323

  12. Soil arsenic availability and the transfer of soil arsenic to crops in suburban areas in Fujian Province, southeast China.

    PubMed

    Huang, Rui-Qing; Gao, Shu-Fang; Wang, Wei-Ling; Staunton, S; Wang, Guo

    2006-09-15

    The bioavailability, soil-to-plant transfer and associated health risks of arsenic in soils collected from paddy rice fields and vegetable fields in suburban areas of some major cities of Fujian Province were investigated. The total soil concentrations of arsenic ranged from 1.29 to 25.28 mg kg(-)(1) with a mean of 6.09 mg kg(-)(1). Available (NaH(2)PO(4)-extractable) arsenic content accounted for 0.7-38.2% of total soil arsenic and was significantly correlated with total soil arsenic content. For the vegetable soils, the available fraction (ratio of available As to total As) of arsenic decreased with decreasing silt (particle size 0.02-0.002 mm) and free iron (DCB extractable) contents and with increasing soil pH and organic matter content. The available fraction of arsenic in the paddy rice soils increased with increasing free iron and organic matter contents and decreasing soil pH and silt content. The correlation of NaH(2)PO(4)-extractable arsenic with the arsenic concentration of the vegetables was much better than that of total As. The transfer factor based on the soil available arsenic (TF(avail)) was chosen to compare the accumulation ability of the various crops. The TF(avail) values of rice grains (air-dried weight basis) ranged between 0.068 and 0.44 and were higher than those of the vegetables, ranging from 0.001 to 0.12. The accumulation ability of the crops decreased in the order of rice>radish>water spinach>celery>onion>taro>leaf mustard>fragrant-flowered garlic>pakchoi>Chinese cabbage>lettuce>garlic>cowpea>cauliflower>bottle gourd>towel gourd>eggplant. Daily consumption of rice and other As-rich vegetables could result in an excessive intake of arsenic, based on the provisional tolerable intake for adults for arsenic recommended by WHO. PMID:16624379

  13. Changes of water demand - possible adaptation of agricultural crops and management options to improve water use efficiency in the Marchfeld area

    NASA Astrophysics Data System (ADS)

    Thaler, S.; Eitzinger, J.; Dubrovsky, M.; Trnka, M.

    2009-04-01

    The main objective of this study was to determine the vulnerability of current agricultural cropping systems in the Marchfeld region to climate change. The investigation area Marchfeld is located in the north-eastern (NE) part of Austria and is characterized by a semi-arid climate with low annual rainfall. It is one of the driest regions in the country, but also one of the main field crop production areas. The soil conditions in Marchfeld demonstrate a significant spatial variability, which include soils with low to moderate water-storage capacities. Higher temperatures in the next decades imply higher evaporation and consequently higher water demand for the crops. The phenological development rates of the cultivars will accelerate and an increase of heat stress as well as drought stress can be expected. These points influence intense the water balance and subsequently the yield of the crops in the investigation area. In order to improve water use efficiency under those changing conditions, a shift of average sowing dates and an adjustment of tillage were analyzed. The DSSAT cropping system model was applied for winter wheat and spring barley to assess potential yield under climate scenarios for NE Austria. The scenarios were carried out with ECHAM5, HadCM3 and NCAR PCM global circulation models (GCMs) for present conditions (reference period 1961-1990) and 2035's (2021-2050), based on SRES-A1B emission scenarios. Yield model simulations were done for all defined scenarios (climate, management, crop) and different soil classes. The simulations contain the CO2 fertilizing effect, rain fed farming, adapted sowing date and contemporary crops without consideration of potential profit cuts caused by pest or diseases. Simulation results indicate that climate change will force a delay of the sowing date for winter wheat of maximal 14 days in October. In case of spring barley, climate change allows an earlier sowing date in spring (up to 14 days). Both crops show a

  14. Rapid, Non-Destructive Estimation of Leaf Area on Field-Grown Vitis labruscana Grapevines

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Three potential variables, shoot basal diameter, leaf count per shoot and shoot length, were examined as potential rapid, non-destructive methods for estimating leaf area per shoot, a frequent component of estimates of leaf area per vine. The metrics were recorded in large field-grown vines over fi...

  15. Variations of carbonaceous aerosols from open crop residue burning with transport and its implication to estimate their lifetimes

    NASA Astrophysics Data System (ADS)

    Pan, X. L.; Kanaya, Y.; Wang, Z. F.; Komazaki, Y.; Taketani, F.; Akimoto, H.; Pochanart, P.

    2013-08-01

    Studying the correlations of carbonaceous aerosols (element carbon, EC, and organic carbon, OC) from open biomass burning helps to reduce uncertainties in emission inventories and provides necessary constraints for model simulations. In the present study, we measured apparent elemental carbon (ECa) and OC concentrations at the summit of Mount Tai (Mt. Tai) during intensive open crop residue burning (OCRB) episodes using a Sunset OCEC analyzer. In the fine particle mode, OC and ECa showed strong correlations (r > 0.9) with carbon monoxide (CO). Footprint analysis using the FLEXPART_WRF model indicated that OCRB in Central East China had a significant influence on ambient carbonaceous aerosol loadings at the summit of Mt. Tai. During campaign, ΔECa/ΔCO ratios of OCRB plumes were found to be 14.3 ± 1.0 ng m-3 ppbv at Mt. Tai. This ratio was twice larger than those for urban pollution in CEC, demonstrating that significant emissions of soot particles emitted from OCRB. ΔOC/ΔCO ratio of OCRB plumes was found to be 41.9 ± 2.6 ng m-3 ppbv averagely. The transport time of smoke particles was estimated using the FLEXPART_WRF tracer model by releasing particles from the ground layer inside geographical regions where large numbers of hotspots were detected by the MODIS sensor. The relationship between transport time and observed ΔECa/ΔCO and ΔOC/ΔCO ratios was fitted by an e-folding exponential function. Results showed that the loss rate of OC (normalized by CO) with transport was much quicker than that of ECa mass, and the corresponding lifetime of OC mass was estimated to be 28.0-44.2 h (1.2-1.8 days), much shorter than that 98.4-136.9 h (4.1-5.7 days) of ECa. Lifetime of ECa estimated for the OCRB events in CEC in the study was comparably lower than the values normally calculated by the transport models. Short lifetime of OC highlighted its vulnerability to cloud scavenging in the presence of water-soluble organic species from biomass combustion.

  16. Estimating vehicle fuel consumption in urban areas. Working paper

    SciTech Connect

    Ferreira, L.J.A.

    1981-01-01

    Traffic flow simulation and assignment techniques were used to model area-wide effects of traffic management measures. Relationships between fuel consumption under urban driving conditions and the inverse of average travel speed were inferred from experimental tests. From the results a relationship which gave urban fuel consumption as a function of journey distance, total delayed time, and number of stops, was suggested for the 'average urban passenger car' in the UK. A review of reported potential fuel savings from traffic management measures was also undertaken. The effects on fuel consumption of changing the common cycle time for a co-ordinated system of signalised intersections were evaluated using the SATURN traffic simulation/assignment model.

  17. Distribution of inorganic phosphorus in profiles and particle fractions of Anthrosols across an established riparian buffer and adjacent cropped area at the Dian lake (China)

    NASA Astrophysics Data System (ADS)

    Zhang, Guo Sheng; Cha Li, Jian

    2016-02-01

    Riparian buffers can trap sediment and nutrients sourced from upper cropland, minimizing the eutrophication risk of water quality. This study aimed to investigate the distributions of soil inorganic phosphorus (Pi) forms among profile and particle-size fractions in an established riparian buffer and adjacent cropped area at the Dian lake, southwestern China. The Ca-bound fraction (62 %) was the major proportion of the Pi in the riparian soils. After 3 years' restoration, buffer rehabilitation from cropped area had a limited impact on total phosphorus (TP) concentrations, but has contributed to a change in Pi forms. In the 0-20 cm soil layer, levels of the Olsen-P, non-occluded, Ca-bound, and total Pi were lower in the buffer than the cropped area; however, the Pi distribution between the cropped area and the buffer did not differ significantly as depth increased. The clay fraction corresponded to 57 % of TP and seemed to be both a sink for highly recalcitrant Pi and a source for labile Pi. The lower concentration of Pi forms in the silt and sand particle fraction in the surface soil was observed in the buffer area, which indicated that the Pi distribution in coarse particle fraction had sensitively responded to land use changes.

  18. Distribution of inorganic phosphorus in profiles and particle-size fractions across an established riparian buffer and adjacent cropped area at the Dian lake

    NASA Astrophysics Data System (ADS)

    Zhang, G. S.; Li, J. C.

    2015-11-01

    Riparian buffer can trap sediment and nutrients sourced from upper cropland and minimizing eutrophication risk of water quality. This study aimed to investigate the distributions of soil inorganic phosphorus (Pi) forms among profile and particle-size fractions in an established riparian buffer and adjacent cropped area at the Dian lake, Southwestern China. The Ca-bound fraction (62 %) was the major proportion of the Pi in the riparian soils. Buffer rehabilitation from cropped area had a limited impact on total phosphorus (TP) concentrations after 3 years, but has contributed to a change in Pi forms. At 0-20 cm soil layer, levels of the Olsen-P, nonoccluded, Ca-bound and total Pi were lower in the buffer than the cropped area; however, the Pi distribution between the cropped area and the buffer did not differ significantly as depth increased. The clay fraction corresponded to 57 % of TP and seemed to be both a sink for highly recalcitrant Pi and a source for labile Pi. The lower concentration of Pi forms in the silt and sand particle fraction in the surface soil was observed in the buffer area, which indicating that the Pi distribution in coarse particle fraction has sensitively responded to land-use changes.

  19. A TRMM Rainfall Estimation Method Applicable to Land Areas

    NASA Technical Reports Server (NTRS)

    Prabhakara, C.; Iacovazzi, R., Jr.; Oki, R.; Weinman, J. A.

    1998-01-01

    Utilizing multi-spectral, dual-polarization Special Sensor Microwave Imager (SSM/I) radiometer measurements, we have developed in this study a method to retrieve average rain rate, R(sub f(sub R)), in a mesoscale grid box of 2deg x 3deg over land. The key parameter of this method is the fractional rain area, f(sub R), in that grid box, which is determined with the help of a threshold on the 85 GHz scattering depression 0 deduced from the SSM/I data. In order to demonstrate the usefulness of this method, nine-months of R(sub f(sub R))are retrieved from SSM/I data over three grid boxes in the Northeastern United States. These retrievals are then compared with the corresponding ground-truth-average rain rate, R(sub g), deduced from 15-minute rain gauges. Based on nine months of rain rate retrievals over three grid boxes, we find that R(sub f(sub R)can explain about 64 % of the variance contained in R(sub g). A similar evaluation of the grid-box-average rain rates R(sub GSCAT) and R(sub SRL), given by the NASA/GSCAT and NOAA/SRL rain retrieval algorithms, is performed. This evaluation reveals that R(sub GSCAT) and R(sub SRL) can explain only about 42 % of the variance contained in R(sub g). In our method, a threshold on the 85 GHz scattering depression is used primarily to determine the fractional rain area in a mesoscale grid box. Quantitative information pertaining to the 85 GHz scattering depression in the grid box is disregarded. In the NASA/GSCAT and NOAA/SRL methods on the other hand, this quantitative information is included. Based on the performance of all three methods, we infer that the magnitude of the scattering depression is a poor indicator of rain rate. Furthermore, from maps based on the observations made by SSM/I on land and ocean we find that there is a significant redundancy in the information content of the SSM/I multi-spectral observations. This leads us to infer that observations of SSM/I at 19 and 37 GHz add only marginal information to that

  20. Easy and accurate variance estimation of the nonparametric estimator of the partial area under the ROC curve and its application.

    PubMed

    Yu, Jihnhee; Yang, Luge; Vexler, Albert; Hutson, Alan D

    2016-06-15

    The receiver operating characteristic (ROC) curve is a popular technique with applications, for example, investigating an accuracy of a biomarker to delineate between disease and non-disease groups. A common measure of accuracy of a given diagnostic marker is the area under the ROC curve (AUC). In contrast with the AUC, the partial area under the ROC curve (pAUC) looks into the area with certain specificities (i.e., true negative rate) only, and it can be often clinically more relevant than examining the entire ROC curve. The pAUC is commonly estimated based on a U-statistic with the plug-in sample quantile, making the estimator a non-traditional U-statistic. In this article, we propose an accurate and easy method to obtain the variance of the nonparametric pAUC estimator. The proposed method is easy to implement for both one biomarker test and the comparison of two correlated biomarkers because it simply adapts the existing variance estimator of U-statistics. In this article, we show accuracy and other advantages of the proposed variance estimation method by broadly comparing it with previously existing methods. Further, we develop an empirical likelihood inference method based on the proposed variance estimator through a simple implementation. In an application, we demonstrate that, depending on the inferences by either the AUC or pAUC, we can make a different decision on a prognostic ability of a same set of biomarkers. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26790540

  1. Advancing the climate data driven crop-modeling studies in the dry areas of Northern Syria and Lebanon: an important first step for assessing impact of future climate.

    PubMed

    Dixit, Prakash N; Telleria, Roberto

    2015-04-01

    Inter-annual and seasonal variability in climatic parameters, most importantly rainfall, have potential to cause climate-induced risk in long-term crop production. Short-term field studies do not capture the full nature of such risk and the extent to which modifications to crop, soil and water management recommendations may be made to mitigate the extent of such risk. Crop modeling studies driven by long-term daily weather data can predict the impact of climate-induced risk on crop growth and yield however, the availability of long-term daily weather data can present serious constraints to the use of crop models. To tackle this constraint, two weather generators namely, LARS-WG and MarkSim, were evaluated in order to assess their capabilities of reproducing frequency distributions, means, variances, dry spell and wet chains of observed daily precipitation, maximum and minimum temperature, and solar radiation for the eight locations across cropping areas of Northern Syria and Lebanon. Further, the application of generated long-term daily weather data, with both weather generators, in simulating barley growth and yield was also evaluated. We found that overall LARS-WG performed better than MarkSim in generating daily weather parameters and in 50 years continuous simulation of barley growth and yield. Our findings suggest that LARS-WG does not necessarily require long-term e.g., >30 years observed weather data for calibration as generated results proved to be satisfactory with >10 years of observed data except in area with higher altitude. Evaluating these weather generators and the ability of generated weather data to perform long-term simulation of crop growth and yield is an important first step to assess the impact of future climate on yields, and to identify promising technologies to make agricultural systems more resilient in the given region. PMID:25590537

  2. Evaluating Crop Area Mapping from MODIS Time-Series as an Assessment Tool for Zimbabwe's "Fast Track Land Reform Programme".

    PubMed

    Hentze, Konrad; Thonfeld, Frank; Menz, Gunter

    2016-01-01

    Moderate Resolution Imaging Spectroradiometer (MODIS) data forms the basis for numerous land use and land cover (LULC) mapping and analysis frameworks at regional scale. Compared to other satellite sensors, the spatial, temporal and spectral specifications of MODIS are considered as highly suitable for LULC classifications which support many different aspects of social, environmental and developmental research. The LULC mapping of this study was carried out in the context of the development of an evaluation approach for Zimbabwe's land reform program. Within the discourse about the success of this program, a lack of spatially explicit methods to produce objective data, such as on the extent of agricultural area, is apparent. We therefore assessed the suitability of moderate spatial and high temporal resolution imagery and phenological parameters to retrieve regional figures about the extent of cropland area in former freehold tenure in a series of 13 years from 2001-2013. Time-series data was processed with TIMESAT and was stratified according to agro-ecological potential zoning of Zimbabwe. Random Forest (RF) classifications were used to produce annual binary crop/non crop maps which were evaluated with high spatial resolution data from other satellite sensors. We assessed the cropland products in former freehold tenure in terms of classification accuracy, inter-annual comparability and heterogeneity. Although general LULC patterns were depicted in classification results and an overall accuracy of over 80% was achieved, user accuracies for rainfed agriculture were limited to below 65%. We conclude that phenological analysis has to be treated with caution when rainfed agriculture and grassland in semi-humid tropical regions have to be separated based on MODIS spectral data and phenological parameters. Because classification results significantly underestimate redistributed commercial farmland in Zimbabwe, we argue that the method cannot be used to produce spatial

  3. Identification and estimation of the area planted with irrigated rice based on the visual interpretation of LANDSAT MSS data

    NASA Technical Reports Server (NTRS)

    Parada, N. D. J. (Principal Investigator); Moreira, M. A.; Assuncao, G. V.; Novaes, R. A.; Mendoza, A. A. B.; Bauer, C. A.; Ritter, I. T.; Barros, J. A. I.; Perez, J. E.; Thedy, J. L. O.

    1983-01-01

    The objective was to test the feasibility of the application of MSS-LANDSAT data to irrigated rice crop identification and area evaluation, within four rice growing regions of the Rio Grande do Sul state, in order to extend the methodology for the whole state. The applied methodology was visual interpretation of the following LANDSAT products: channels 5 and 7 black and white imageries and color infrared composite imageries all at the scale of 1:250.000. For crop identification and evaluation, the multispectral criterion and the seasonal variation were utilized. Based on the results it was possible to conclude that: (1) the satellite data were efficient for crop area identification and evaluation; (2) the utilization of the multispectral criterion, allied to the seasonal variation of the rice crop areas from the other crops and, (3) the large cloud cover percentage found in the satellite data made it impossible to realize a rice crop spectral monitoring and, therefore, to define the best dates for such data acquisition for rice crop assessment.

  4. Estimation of lifetime of carbonaceous aerosol from open crop residue burning during Mount Tai Experiment 2006 (MTX2006)

    NASA Astrophysics Data System (ADS)

    Pan, X. L.; Kanaya, Y.; Wang, Z. F.; Komazaki, Y.; Taketani, F.; Akimoto, H.; Pochanart, P.; Liu, Y.

    2012-06-01

    Studying the emission ratios of carbonaceous aerosols (element carbon, EC, and organic carbon, OC) from open biomass burning helps to reduce uncertainties in emission inventories and provides necessary constraints for model simulations. We measured apparent elemental carbon (ECa) and OC concentrations at the summit of Mount Tai (Mt. Tai) during intensive open crop residue burning (OCRB) episodes using a Sunset OCEC analyzer. Equivalent black carbon (BCe) concentrations were determined using a Multiple Angle Absorption Photometer (MAAP). In the fine particle mode, OC and EC showed strong correlations (r > 0.9) with carbon monoxide (CO). Footprint analysis using the FLEXPART_WRF model indicated that OCRB in central east China (CEC) had a significant influence on ambient carbonaceous aerosol loadings at the summit of Mt. Tai. ΔECa/ΔCO ratios resulting from OCRB plumes were 14.3 ± 1.0 ng m-3 ppbv-1 at Mt. Tai. This ratio was more than three times those resulting from urban pollution in CEC, demonstrating that significant concentrations of soot particles were released from OCRB. ΔOC/ΔCO ratio from fresh OCRB plumes was found to be 41.9 ± 2.6 ng m-3 ppbv-1 in PM1. The transport time of smoke particles was estimated using the FLEXPART_WRF tracer model by releasing inert particles from the ground layer inside geographical regions where large numbers of hotspots were detected by a MODIS satellite sensor. Fitting regressions using the e-folding exponential function indicated that the removal efficiency of OC (normalized to CO) was much larger than that of ECa mass, with mean lifetimes of 27 h (1.1 days) for OC and 105 h (4.3 days) for ECa, respectively. The lifetime of black carbon estimated for the OCRB events in east China was comparably lower than the values normally adopted in the transport models. Short lifetime of organic carbon highlighted the vulnerability of OC to cloud scavenging in the presence of water-soluble organic species from biomass combustion.

  5. Farmers' Perception of Integrated Soil Fertility and Nutrient Management for Sustainable Crop Production: A Study of Rural Areas in Bangladesh

    ERIC Educational Resources Information Center

    Farouque, Md. Golam; Takeya, Hiroyuki

    2007-01-01

    This study aimed to determine farmers' perception of integrated soil fertility and nutrient management for sustainable crop production. Integrated soil fertility (ISF) and nutrient management (NM) is an advanced approach to maintain soil fertility and to enhance crop productivity. A total number of 120 farmers from eight villages in four districts…

  6. Impact of climate change estimated through statistical downscaling on crop productivity and soil water balance in Southern Italy

    NASA Astrophysics Data System (ADS)

    Ventrella, D.; Giglio, L.; Charfeddine, M.; Palatella, L.; Pizzigalli, C.; Vitale, D.; Paradisi, P.; Miglietta, M. M.; Rana, G.

    2010-09-01

    The climatic change induced by the global warming is expected to modify the agricultural activity and consequently the other social and economical sectors. In this context, an efficient management of the water resources is considered very important for Italy and in particular for Southern areas characterized by a typical Mediterranean climate in order to improve the economical and environmental sustainability of the agricultural activity. Climate warming could have a substantial impact on some agronomical practices as the choice of the crops to be included in the rotations, the sowing time and the irrigation scheduling. For a particular zone, the impact of climatic change on agricultural activity will depend also on the continuum "soil-plant-climate" and this continuum has to be included in the analysis for forecasting purposes. The Project CLIMESCO is structured in four workpackages (WP): (1) Identification of homogeneous areas, (2) Climatic change, (3) Optimization of water resources and (4) Scenarios analysis. In this study we applied a statistical downscaling method, Canonical Correlation Analysis after Principal Component Analysis filtering, to two sub-regions of agricultural interest in Sicily and Apulia (respectively, Delia basin and Capitanata). We adopt, as large scale predictors, the sea level pressure from the the EMULATE project dataset and the 1000 hPa temperature obtained from the NCEP reanalyses, while the predictands are monthly time series of maximum and minimum temperature and precipitation. As the crop growth models need daily datasets, a stochastic weather generator (the LARS-WG model) has been applied for this purpose. LARS-WG needs a preliminary calibration with daily time series of meteorological fields, that are available in the framework of CLIMESCO project. Then, the statistical relationships have been applied to two climate change scenarios (SRES A2 and B2), provided by three different GCM's: the Hadley Centre Coupled Model version 3 (Had

  7. Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment

    NASA Technical Reports Server (NTRS)

    Nearing, Grey S.; Crow, Wade T.; Thorp, Kelly R.; Moran, Mary S.; Reichle, Rolf H.; Gupta, Hoshin V.

    2012-01-01

    Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.

  8. Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: An observing system simulation experiment

    NASA Astrophysics Data System (ADS)

    Nearing, G. S.; Crow, W. T.; Thorp, K. R.; Moran, M. S.; Reichle, R. H.; Gupta, H. V.

    2012-05-01

    Observing system simulation experiments were used to investigate ensemble Bayesian state-updating data assimilation of observations of leaf area index (LAI) and soil moisture (θ) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI andθobservations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.

  9. Evaluation of the transfer of soil arsenic to maize crops in suburban areas of San Luis Potosi, Mexico.

    PubMed

    Rosas-Castor, J M; Guzmán-Mar, J L; Alfaro-Barbosa, J M; Hernández-Ramírez, A; Pérez-Maldonado, I N; Caballero-Quintero, A; Hinojosa-Reyes, L

    2014-11-01

    The presence of arsenic (As) in agricultural food products is a matter of concern because it can cause adverse health effects at low concentrations. Agricultural-product intake constitutes a principal source for As exposure in humans. In this study, the contribution of the chemical-soil parameters in As accumulation and translocation in the maize crop from a mining area of San Luis Potosi was evaluated. The total arsenic concentration and arsenic speciation were determined by HG-AFS and IC-HG-AFS, respectively. The data analysis was conducted by cluster analysis (CA) and principal component analysis (PCA). The soil pH presented a negative correlation with the accumulated As in each maize plant part, and parameters such as iron (Fe) and manganese (Mn) presented a higher correlation with the As translocation in maize. Thus, the metabolic stress in maize may induce organic acid exudation leading a higher As bioavailability. A high As inorganic/organic ratio in edible maize plant tissues suggests a substantial risk of poisoning by this metalloid. Careful attention to the chemical changes in the rhizosphere of the agricultural zones that can affect As transfer through the food chain could reduce the As-intoxication risk of maize consumers. PMID:25128885

  10. Post-stratification sampling in small area estimation (SAE) model for unemployment rate estimation by Bayes approach

    NASA Astrophysics Data System (ADS)

    Hanike, Yusrianti; Sadik, Kusman; Kurnia, Anang

    2016-02-01

    This research implemented unemployment rate in Indonesia that based on Poisson distribution. It would be estimated by modified the post-stratification and Small Area Estimation (SAE) model. Post-stratification was one of technique sampling that stratified after collected survey data. It's used when the survey data didn't serve for estimating the interest area. Interest area here was the education of unemployment which separated in seven category. The data was obtained by Labour Employment National survey (Sakernas) that's collected by company survey in Indonesia, BPS, Statistic Indonesia. This company served the national survey that gave too small sample for level district. Model of SAE was one of alternative to solved it. According the problem above, we combined this post-stratification sampling and SAE model. This research gave two main model of post-stratification sampling. Model I defined the category of education was the dummy variable and model II defined the category of education was the area random effect. Two model has problem wasn't complied by Poisson assumption. Using Poisson-Gamma model, model I has over dispersion problem was 1.23 solved to 0.91 chi square/df and model II has under dispersion problem was 0.35 solved to 0.94 chi square/df. Empirical Bayes was applied to estimate the proportion of every category education of unemployment. Using Bayesian Information Criteria (BIC), Model I has smaller mean square error (MSE) than model II.

  11. Global Crop Area Monitoring at High Resolution Exploiting Complementary Use of Free and Open SAR and VSNIR/SWIR Sensor Data Sets

    NASA Astrophysics Data System (ADS)

    Lemoine, G.; LEO, O.

    2015-12-01

    Earth Observation imaging sensors with spatial resolutions in the 10-30 m range allow for separation of the area and crop status contributions to the radiometric signatures, typically at parcel level for a wide range of arable crop production systems. These sensors complement current monitoring efforts that deploy low (100-1000 m) resolution VSNIR/SWIR sensors like MODIS, METOP or PROBA-V, which provide denser time series, but with aggregated and mixed radiometric information for cropped areas. "Free and Open" access to US Landsat imagery has recently been complemented by the European Union's Copernicus program with access to Sentinel-1A C-band SAR and Sentinel-2A visual, near and short-ware infrared (VSNIR/SWIR) sensor data in the 10-20 m resolution range. Sentinel-1A has already proven that consistent time series can be generated at its 12 day revisit frequency. The density of Sentinel-2 time series will greatly expand the availability of [partially cloud covered] VSNIR/SWIR imagery. The release of this large new data flow coincides with wider availability of "big data" processing capacity, the public release of ever more detailed ancillary data sets that support extraction of georeferenced and robust indicators on crop production and their spatial and temporal statistics and developments in crowd-sourced mobile data collection for data validation purposes. We will illustrate the use of hybrid SAR and VSNIR/SWIR data sets from Sentinel-1 and Landsat-8 (and initially released Sentinel-2 imagery) for a number of selected examples. These include crop area delineation and classification in the Netherlands with the support of detailed parcel delineation sets for validation, detection of winter cereal cultivation in Ukraine, impact of the Syrian civil war on irrigated summer crop cultivation and recent examples in support to crop anomaly detection in food insecure areas (North Korea, Sub-Saharan Africa). We discuss method implementation, operational issues and outline

  12. Estimation of Surface Area and Volume of a Nematode from Morphometric Data

    PubMed Central

    Brown, Simon; Pedley, Kevin C.; Simcock, David C.

    2016-01-01

    Nematode volume and surface area are usually based on the inappropriate assumption that the animal is cylindrical. While nematodes are approximately circular in cross section, the radius varies longitudinally. We use standard morphometric data to obtain improved estimates of volume and surface area based on (i) a geometrical approach and (ii) a Bézier representation of the nematode. These new estimators require only the morphometric data available from Cobb's ratios, but if fewer coordinates are available the geometric approach reduces to the standard estimates. Consequently, these new estimators are better than the standard alternatives. PMID:27110427

  13. Estimation of unemployment rates using small area estimation model by combining time series and cross-sectional data

    NASA Astrophysics Data System (ADS)

    Muchlisoh, Siti; Kurnia, Anang; Notodiputro, Khairil Anwar; Mangku, I. Wayan

    2016-02-01

    Labor force surveys conducted over time by the rotating panel design have been carried out in many countries, including Indonesia. Labor force survey in Indonesia is regularly conducted by Statistics Indonesia (Badan Pusat Statistik-BPS) and has been known as the National Labor Force Survey (Sakernas). The main purpose of Sakernas is to obtain information about unemployment rates and its changes over time. Sakernas is a quarterly survey. The quarterly survey is designed only for estimating the parameters at the provincial level. The quarterly unemployment rate published by BPS (official statistics) is calculated based on only cross-sectional methods, despite the fact that the data is collected under rotating panel design. The study purpose to estimate a quarterly unemployment rate at the district level used small area estimation (SAE) model by combining time series and cross-sectional data. The study focused on the application and comparison between the Rao-Yu model and dynamic model in context estimating the unemployment rate based on a rotating panel survey. The goodness of fit of both models was almost similar. Both models produced an almost similar estimation and better than direct estimation, but the dynamic model was more capable than the Rao-Yu model to capture a heterogeneity across area, although it was reduced over time.

  14. Geo-environmental model for the prediction of potential transmission risk of Dirofilaria in an area with dry climate and extensive irrigated crops. The case of Spain.

    PubMed

    Simón, Luis; Afonin, Alexandr; López-Díez, Lucía Isabel; González-Miguel, Javier; Morchón, Rodrigo; Carretón, Elena; Montoya-Alonso, José Alberto; Kartashev, Vladimir; Simón, Fernando

    2014-03-01

    Zoonotic filarioses caused by Dirofilaria immitis and Dirofilaria repens are transmitted by culicid mosquitoes. Therefore Dirofilaria transmission depends on climatic factors like temperature and humidity. In spite of the dry climate of most of the Spanish territory, there are extensive irrigated crops areas providing moist habitats favourable for mosquito breeding. A GIS model to predict the risk of Dirofilaria transmission in Spain, based on temperatures and rainfall data as well as in the distribution of irrigated crops areas, is constructed. The model predicts that potential risk of Dirofilaria transmission exists in all the Spanish territory. Highest transmission risk exists in several areas of Andalucía, Extremadura, Castilla-La Mancha, Murcia, Valencia, Aragón and Cataluña, where moderate/high temperatures coincide with extensive irrigated crops. High risk in Balearic Islands and in some points of Canary Islands, is also predicted. The lowest risk is predicted in Northern cold and scarcely or non-irrigated dry Southeastern areas. The existence of irrigations locally increases transmission risk in low rainfall areas of the Spanish territory. The model can contribute to implement rational preventive therapy guidelines in accordance with the transmission characteristics of each local area. Moreover, the use of humidity-related factors could be of interest in future predictions to be performed in countries with similar environmental characteristics. PMID:24456900

  15. Evaluation of a technique for satellite-derived area estimation of forest fires

    NASA Technical Reports Server (NTRS)

    Cahoon, Donald R., Jr.; Stocks, Brian J.; Levine, Joel S.; Cofer, Wesley R., III; Chung, Charles C.

    1992-01-01

    The advanced very high resolution radiometer (AVHRR), has been found useful for the location and monitoring of both smoke and fires because of the daily observations, the large geographical coverage of the imagery, the spectral characteristics of the instrument, and the spatial resolution of the instrument. This paper will discuss the application of AVHRR data to assess the geographical extent of burning. Methods have been developed to estimate the surface area of burning by analyzing the surface area effected by fire with AVHRR imagery. Characteristics of the AVHRR instrument, its orbit, field of view, and archived data sets are discussed relative to the unique surface area of each pixel. The errors associated with this surface area estimation technique are determined using AVHRR-derived area estimates of target regions with known sizes. This technique is used to evaluate the area burned during the Yellowstone fires of 1988.

  16. An alternative basin characteristic for use in estimating impervious area in urban Missouri basins

    USGS Publications Warehouse

    Southard, R.E.

    1986-01-01

    A previous regression analysis of flood peaks on urban basins in St. Louis County, Missouri, indicated that the basin characteristics of percentage of impervious area and drainage area were statistically significant for estimating the 2-, 5-, 10-, 25-, 50-. and 100-yr peak discharges at ungaged urban basins. In this statewide regression analysis of the urban basins for Missouri, an alternative basin characteristic called the percentage of developed area was evaluated. A regression analysis of the percentage of developed area (independent variable), resulted in a simple equation for computing percentage of impervious area. The percentage of developed area also was evaluated using flood-frequency data for 23 streamflow gaging stations, and the use of this variable was determined to be valid. Using nationwide data, an urban basin characteristic known as the basin development factor was determined to be valid for inclusion in urban regression equations for estimating flood flows. The basin development factor and the percentage of developed area were compared for use in regression equations to estimate peak flows of streams in Missouri. The equations with the basin development factor produced peak flow estimates with slightly smaller average standard errors of estimate than the equation with the percentage of developed area; however, this study indicates that there was not enough statistical or numerical difference to warrant using the basin development factor instead of the percentage of developed area in Missouri. The selection of a basin characteristic to describe the physical conditions of a drainage basin will depend not only on its contribution to accuracy of regression equations, but also on the ease of determining the characteristics; the percentage of developed area has this advantage. A correlation analysis was made by correlating drainage area to percentage of impervious area, the percentage of developed area, and the basin development factor. The results of

  17. A reference-based approach for estimating leaf area and cover in the forest herbaceous layer.

    PubMed

    Walter, Christopher A; Burnham, Mark B; Gilliam, Frank S; Peterjohn, William T

    2015-10-01

    Cover data are used to assess vegetative response to a variety of ecological factors. Estimating cover in the herbaceous layer of forests presents a problem because the communities are structurally complex and rich in species. The currently employed techniques for estimating cover are less than optimal for measuring such rich understories because they are inaccurate, slow, or impracticable. A reference-based approach to estimating cover is presented that compares the area of foliar surfaces to the area of an observer's hand. While this technique has been used to estimate cover in prior studies, its accuracy has not been tested. We tested this hand-area method at the individual plant, population, and community scales in a deciduous forest herbaceous layer, and in a separate farm experiment. The precision, accuracy, observer bias, and species bias of the method were tested by comparing the hand-estimated leaf area index values with actual leaf area index, measured using a leaf area meter. The hand-area method was very precise when regressed against actual leaf area index at the plant, population, and community scales (R(2) of 0.97, 0.93, and 0.87). Among the deciduous sites, the hand-area method overestimated leaf area index consistently by 39.1% at all scales. There was no observer bias detected at any scale, but plant overestimation bias was detected in one species at the population scale. The hand-area method is a rapid and reliable technique for estimating leaf area index or cover in the forest herbaceous layer and should be useful to field ecologists interested in answering questions at the plant, population, or community level. PMID:26423635

  18. Economic Analysis of Energy Crop Production in the U.S. - Location, Quantities, Price, and Impacts on Traditional Agricultural Crops

    SciTech Connect

    Walsh, M.E.; De La Torre Ugarte, D.; Slinsky, S.; Graham, R.L.; Shapouri, H.; Ray, D.

    1998-10-04

    POLYSYS is used to estimate US locations where, for any given energy crop price, energy crop production can be economically competitive with conventional crops. POLYSYS is a multi-crop, multi-sector agricultural model developed and maintained by the University of Tennessee and used by the USDA-Economic Research Service. It includes 305 agricultural statistical districts (ASD) which can be aggregated to provide state, regional, and national information. POLYSYS is being modified to include switchgrass, hybrid poplar, and willow on all land suitable for their production. This paper summarizes the preliminary national level results of the POLYSYS analysis for selected energy crop prices for the year 2007 and presents the corresponding maps (for the same prices) of energy crop production locations by ASD. Summarized results include: (1) estimates of energy crop hectares (acres) and quantities (dry Mg, dry tons), (2) identification of traditional crops allocated to energy crop production and calculation of changes in their prices and hectares (acres) of production, and (3) changes in total net farm returns for traditional agricultural crops. The information is useful for identifying areas of the US where large quantities of lowest cost energy crops can most likely be produced.

  19. A Crop Simulation System for Integrating Remote Sensing and Climate Information to Reduce Model Uncertainty in Crop Yield Assessments

    NASA Astrophysics Data System (ADS)

    Ines, A. M.; Honda, K.; Yui, A.

    2012-12-01

    Uncertainties in crop yield assessments are caused by many factors, including an imperfect model, model parameters and modeling assumptions, as well as errors in data inputs, e.g. climate. Here, we present a crop simulation system that aims to reduce uncertainty in crop yield assessment due to model and data uncertainties. The system uses DSSAT-CSM as the core crop simulation model. The simulation strategy is two-folds: i) crop model parameter estimation and ii) simulation and prediction mode. In i) a noisy Monte Carlo genetic algorithm (NMCGA) is used to estimate crop, soil and management parameters and their uncertainties, where field and remote sensing data can be used in the process. In ii) simulations can be done in an incremental way, where climate data until the current day is used as inputs to the crop model while the climate inputs for rest of the simulation period are generated by a stochastic weather generator based on climatological or climate forecasts information. Also, in the prediction mode, an ensemble Kalman filter (EnKF) can be used to update crop model state variables, e.g., leaf area index (LAI) and soil moisture from remote sensing and field sensors, this can be used in tandem with the climate merging mechanism within the crop simulation system. A case study on wheat modeling in Hokkaido, Japan will be presented. Model uncertainty assessment and implications of the crop simulation system for crop assessment will be discussed.

  20. Bayes plus Brass: Estimating Total Fertility for Many Small Areas from Sparse Census Data

    PubMed Central

    Schmertmann, Carl P.; Cavenaghi, Suzana M.; Assunção, Renato M.; Potter, Joseph E.

    2013-01-01

    Small-area fertility estimates are valuable for analysing demographic change, and important for local planning and population projection. In countries lacking complete vital registration, however, small-area estimates are possible only from sparse survey or census data that are potentially unreliable. Such estimation requires new methods for old problems: procedures must be automated if thousands of estimates are required, they must deal with extreme sampling variability in many areas, and they should also incorporate corrections for possible data errors. We present a two-step algorithm for estimating total fertility in such circumstances, and we illustrate by applying the method to 2000 Brazilian Census data for over five thousand municipalities. Our proposed algorithm first smoothes local age-specific rates using Empirical Bayes methods, and then applies a new variant of Brass’s P/F parity correction procedure that is robust under conditions of rapid fertility decline. PMID:24143946

  1. A root zone model for estimating soil water balance and crop yield responses to deficit irrigation in the North China Plain

    NASA Astrophysics Data System (ADS)

    Ma, Y.; Song, X.; Feng, S.

    2012-12-01

    This study proposed a new soil water balance model by quantifying drainage out of the root zone with the simplification of the Darcy's law, which combined the advantages of conceptual and physically based models. This model was connected with the Jensen crop water production function to simulate soil water components and relative crop yield. Field experiments with the winter wheat-summer corn cropping system were conducted in Beijing area in the North China Plain (2007-2009) to evaluate the model. The model could give quite reasonable predictions of soil water content in the root zone with the average root mean square error (RMSE), mean relative error (RE) and model efficiency (EF) of 0.02 cm3/cm3, 6.69% and 0.78, respectively. Furthermore, the predicted soil water flux through the bottom of root zone agreed well with the measured ones supported by the values of RMSE (0.10 mm/d) and EF (0.92). The Jensen crop water production function with the calculated actual evapotranspitation from the soil water balance model could satisfactorily evaluate crop yield response to deficit irrigation with the EF values greater than 0.95 and the RE values lower than 6%. As an application, the model was used to obtain the optimal irrigation management schedules for the hydrologic years of 75%, 50% and 25% in the study area. The average amount of irrigation saving and reduction of water losses through drainage under optimal irrigation alternative were about 175 mm and 101.9 mm, respectively. This study indicates that the developed root zone model is more available for agricultural water management as it has minimal input requirement, robust physical meaning and satisfactory simulation performance.

  2. An evaluation of the signature extension approach to large area crop inventories utilizing space image data. [Kansas and North Dakota

    NASA Technical Reports Server (NTRS)

    Nalepka, R. F. (Principal Investigator); Cicone, R. C.; Stinson, J. L.; Balon, R. J.

    1977-01-01

    The author has identified the following significant results. Two examples of haze correction algorithms were tested: CROP-A and XSTAR. The CROP-A was tested in a unitemporal mode on data collected in 1973-74 over ten sample segments in Kansas. Because of the uniformly low level of haze present in these segments, no conclusion could be reached about CROP-A's ability to compensate for haze. It was noted, however, that in some cases CROP-A made serious errors which actually degraded classification performance. The haze correction algorithm XSTAR was tested in a multitemporal mode on 1975-76 LACIE sample segment data over 23 blind sites in Kansas and 18 sample segments in North Dakota, providing wide range of haze levels and other conditions for algorithm evaluation. It was found that this algorithm substantially improved signature extension classification accuracy when a sum-of-likelihoods classifier was used with an alien rejection threshold.

  3. Drought-related vulnerability and risk assessment of groundwater in Belgium: estimation of the groundwater recharge and crop yield vulnerability with the B-CGMS

    NASA Astrophysics Data System (ADS)

    Jacquemin, Ingrid; Verbeiren, Boud; Vanderhaegen, Sven; Canters, Frank; Vermeiren, Karolien; Engelen, Guy; Huysmans, Marijke; Batelaan, Okke; Tychon, Bernard

    2016-04-01

    Due to common belief that regions under temperate climate are not affected by (meteorological and groundwater) drought, these events and their impacts remain poorly studied: in the GroWaDRISK, we propose to take stock of this question. We aim at providing a better understanding of the influencing factors (land use and land cover changes, water demand and climate) and the drought-related impacts on the environment, water supply and agriculture. The study area is located in the North-East of Belgium, corresponding approximatively to the Dijle and Demer catchments. To establish an overview of the groundwater situation, we assess the system input: the recharge. To achieve this goal, two models, B-CGMS and WetSpass are used to evaluate the recharge, respectively, over agricultural land and over the remaining areas, as a function of climate and for various land uses and land covers. B-CGMS, which is an adapted version for Belgium of the European Crop Growth Monitoring System, is used for assessing water recharge at a daily timestep and under different agricultural lands: arable land (winter wheat, maize...), orchards, horticulture and floriculture and for grassland. B-CGMS is designed to foresee crop yield and obviously it studies the impact of drought on crop yield and raises issues for the potential need of irrigation. For both yields and water requirements, the model proposes a potential mode, driven by temperature and solar radiation, and a water-limited mode for which water availability can limit crop growth. By this way, we can identify where and when water consumption and yield are not optimal, in addition to the Crop Water Stress Index. This index is calculated for a given crop, as the number of days affected by water stress during the growth sensitive period. Both recharge and crop yield are assessed for the current situation (1980 - 2012), taking into account the changing land use/land cover, in terms of areas and localization of the agricultural land and where

  4. Procedure M - A framework for stratified area estimation. [in multispectral scanner data processing

    NASA Technical Reports Server (NTRS)

    Kauth, R. J.; Cicone, R. C.; Malila, W. A.

    1980-01-01

    This paper describes Procedure M, a systematic approach to processing multispectral scanner data for classification and acreage estimation. A general discussion of the rationale and development of the procedure is given in the context of large-area agricultural applications. Specific examples are given in the form of test results on acreage estimation of spring small grains.

  5. Comparing Broad-Band and Red Edge-Based Spectral Vegetation Indices to Estimate Nitrogen Concentration of Crops Using Casi Data

    NASA Astrophysics Data System (ADS)

    Wang, Yanjie; Liao, Qinhong; Yang, Guijun; Feng, Haikuan; Yang, Xiaodong; Yue, Jibo

    2016-06-01

    In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn't show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.

  6. Brazil Fire Characterization and Burn Area Estimation Using the Airborne Infrared Disaster Assessment (AIRDAS) System

    NASA Technical Reports Server (NTRS)

    Brass, J. A.; Riggan, P. J.; Ambrosia, V. G.; Lockwood, R. N.; Pereira, J. A.; Higgins, R. G.; Peterson, David L. (Technical Monitor)

    1995-01-01

    Remotely sensed estimations of regional and global emissions from biomass combustion have been used to characterize fire behavior, determine fire intensity, and estimate burn area. Highly temporal, low resolution satellite data have been used to calculate estimates of fire numbers and area burned. These estimates of fire activity and burned area have differed dramatically, resulting in a wide range of predictions on the ecological and environmental impacts of fires. As part of the Brazil/United States Fire Initiative, an aircraft campaign was initiated in 1992 and continued in 1994. This multi-aircraft campaign was designed to assist in the characterization of fire activity, document fire intensity and determine area burned over prescribed, agricultural and wildland fires in the savanna and forests of central Brazil. Using a unique, multispectral scanner (AIRDAS), designed specifically for fire characterization, a variety of fires and burned areas were flown with a high spatial and high thermal resolution scanner. The system was used to measure flame front size, rate of spread, ratio of smoldering to flaming fronts and fire intensity. In addition, long transects were flown to determine the size of burned areas within the cerrado and transitional ecosystems. The authors anticipate that the fire activity and burned area estimates reported here will lead to enhanced information for precise regional trace gas prediction.

  7. A thermal-based remote sensing modelling system for estimating crop water use and stress from field to regional scales

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Thermal-infrared remote sensing of land surface temperature provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. A thermal-based scheme, called the Two-Source Energy Balance (TSEB) model, solves for the soil/substrate and canopy temp...

  8. Effects of Estimating Soil Hydraulic Properties and Root Growth Factor on Soil Water Balance and Crop Production

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Increasing water use efficiency (WUE) is one of the oldest goals in agricultural sciences, yet it is still not fully understood and achieved due to the complexity of soil-weather-management interactions. System models that quantify these interactions are increasingly used for optimizing crop WUE, es...

  9. Estimation of Actual Crop ET of Paddy Using the Energy Balance Model SMARET and Validation with Field Water Balance Measurements and a Crop Growth Model (ORYZA)

    NASA Astrophysics Data System (ADS)

    Nallasamy, N. D.; Muraleedharan, B. V.; Kathirvel, K.; Narasimhan, B.

    2014-12-01

    Sustainable management of water resources requires reliable estimates of actual evapotranspiration (ET) at fine spatial and temporal resolution. This is significant in the case of rice based irrigation systems, one of the major consumers of surface water resources and where ET forms a major component of water consumption. However huge tradeoff in the spatial and temporal resolution of satellite images coupled with lack of adequate number of cloud free images within a growing season act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. The scale at which ET is determined is decided by the spatial and temporal scale of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which form inputs to energy balance models. In this context, the current study employed disaggregation algorithms (NL-DisTrad and DisNDVI) to generate time series of LST and NDVI images at fine resolution. The disaggregation algorithms aimed at generating LST and NDVI at finer scale by integrating temporal information from concurrent coarse resolution data and spatial information from a single fine resolution image. The temporal frequency of the disaggregated images is further improved by employing composite images of NDVI and LST in the spatio-temporal disaggregation method. The study further employed half-hourly incoming surface insolation and outgoing long wave radiation obtained from the Indian geostationary satellite (Kalpana-1) to convert the instantaneous ET into daily ET and subsequently to the seasonal ET, thereby improving the accuracy of ET estimates. The estimates of ET were validated with field based water balance measurements carried out in Gadana, a subbasin predominated by rice paddy fields, located in Tamil Nadu, India.

  10. Artificial neural network estimation of soil erosion and nutrient concentrations in runoff from land application areas

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The transport of sediment and nutrients from land application areas is an environmental concern. New methods are needed for estimating soil and nutrient concentrations of runoff from cropland areas on which manure is applied. Artificial Neural Networks (ANN) trained with a Backpropagation (BP) algor...

  11. A Methodological Approach to Small Area Estimation for the Behavioral Risk Factor Surveillance System

    PubMed Central

    Xu, Fang; Wallace, Robyn C.; Garvin, William; Greenlund, Kurt J.; Bartoli, William; Ford, Derek; Eke, Paul; Town, G. Machell

    2016-01-01

    Public health researchers have used a class of statistical methods to calculate prevalence estimates for small geographic areas with few direct observations. Many researchers have used Behavioral Risk Factor Surveillance System (BRFSS) data as a basis for their models. The aims of this study were to 1) describe a new BRFSS small area estimation (SAE) method and 2) investigate the internal and external validity of the BRFSS SAEs it produced. The BRFSS SAE method uses 4 data sets (the BRFSS, the American Community Survey Public Use Microdata Sample, Nielsen Claritas population totals, and the Missouri Census Geographic Equivalency File) to build a single weighted data set. Our findings indicate that internal and external validity tests were successful across many estimates. The BRFSS SAE method is one of several methods that can be used to produce reliable prevalence estimates in small geographic areas. PMID:27418213

  12. A Methodological Approach to Small Area Estimation for the Behavioral Risk Factor Surveillance System.

    PubMed

    Pierannunzi, Carol; Xu, Fang; Wallace, Robyn C; Garvin, William; Greenlund, Kurt J; Bartoli, William; Ford, Derek; Eke, Paul; Town, G Machell

    2016-01-01

    Public health researchers have used a class of statistical methods to calculate prevalence estimates for small geographic areas with few direct observations. Many researchers have used Behavioral Risk Factor Surveillance System (BRFSS) data as a basis for their models. The aims of this study were to 1) describe a new BRFSS small area estimation (SAE) method and 2) investigate the internal and external validity of the BRFSS SAEs it produced. The BRFSS SAE method uses 4 data sets (the BRFSS, the American Community Survey Public Use Microdata Sample, Nielsen Claritas population totals, and the Missouri Census Geographic Equivalency File) to build a single weighted data set. Our findings indicate that internal and external validity tests were successful across many estimates. The BRFSS SAE method is one of several methods that can be used to produce reliable prevalence estimates in small geographic areas. PMID:27418213

  13. Error analysis of leaf area estimates made from allometric regression models

    NASA Technical Reports Server (NTRS)

    Feiveson, A. H.; Chhikara, R. S.

    1986-01-01

    Biological net productivity, measured in terms of the change in biomass with time, affects global productivity and the quality of life through biochemical and hydrological cycles and by its effect on the overall energy balance. Estimating leaf area for large ecosystems is one of the more important means of monitoring this productivity. For a particular forest plot, the leaf area is often estimated by a two-stage process. In the first stage, known as dimension analysis, a small number of trees are felled so that their areas can be measured as accurately as possible. These leaf areas are then related to non-destructive, easily-measured features such as bole diameter and tree height, by using a regression model. In the second stage, the non-destructive features are measured for all or for a sample of trees in the plots and then used as input into the regression model to estimate the total leaf area. Because both stages of the estimation process are subject to error, it is difficult to evaluate the accuracy of the final plot leaf area estimates. This paper illustrates how a complete error analysis can be made, using an example from a study made on aspen trees in northern Minnesota. The study was a joint effort by NASA and the University of California at Santa Barbara known as COVER (Characterization of Vegetation with Remote Sensing).

  14. Alcohol co-production from tree crops

    SciTech Connect

    Seibert, M.; Folger, G.; Milne, T.

    1982-06-01

    A concept for the sustainable production of alcohol from fermentable substrates produced on an annual basis by the reproductive organs (pods, fruits, nuts, berries, etc.) of tree crops is presented. The advantages of tree-crop systems include suitability for use on marginal land, potential productivity equivalent to row crops, minimal maintenance and energy-input requirements, environmental compatibility, and the possibility of co-product production. Honeylocust, mesquite, and persimmon are examined as potential US tree-crop species. Other species not previously considered, including osage orange and breadfruit, are suggested as tree-crop candidates for North America and the tropical developing world, respectively. Fermentation of tree-crop organs and the economics of tree-crop systems are also discussed. Currently the greatest area of uncertainty lies in actual pod or fruit yields one can expect from large tree farms under real life conditions. However, ballpark ethanol yield estimates of from 880 to 3470 l hectare/sup -1/ (94 to 400 gal acre/sup -1/) justify further consideration of tree crop systems.

  15. An in-situ campaign of spectral measurements for monitoring the crop stress and planting area in Luancheng of North China Plain

    NASA Astrophysics Data System (ADS)

    Chen, Shui-Sen; Chen, Liang-Fu; Liu, Qin-Huo; Xiao, Qing

    2004-10-01

    In an attempt to support the demonstration application of "Spectral Library Of Chinese representative surface substances", an in-situ campaign of spectral measurements was carried out during the summer of 2003 for monitoring the crop stress and planting area in North China Plain. The experiment sites, based on Luancheng Agricultural ecosystem experiment station, Chinese Academy of Sciences, was a 5×5 km area located just south-east of Shijiazhuang, Hebei Province, China. The spectral instrument used is ASD FieldSpecFR with wavelength of 0.35~2.5μm. The experimental achievement includes: soil moisture, corn physiological & biochemical parameters, corn leaf water & chlorophyll-a,b contents, corn structure parameters, field farmland microclimate, aerological exploration data of atmosphere, corn spectra of component, corn crown & background spectra cross the growth period of crop. The paper details the whole experimental scheme and design and partial representation of results of data analysis.

  16. The area-time-integral technique to estimate convective rain volumes over areas applied to satellite data - A preliminary investigation

    NASA Technical Reports Server (NTRS)

    Doneaud, Andre A.; Miller, James R., Jr.; Johnson, L. Ronald; Vonder Haar, Thomas H.; Laybe, Patrick

    1987-01-01

    The use of the area-time-integral (ATI) technique, based only on satellite data, to estimate convective rain volume over a moving target is examined. The technique is based on the correlation between the radar echo area coverage integrated over the lifetime of the storm and the radar estimated rain volume. The processing of the GOES and radar data collected in 1981 is described. The radar and satellite parameters for six convective clusters from storm events occurring on June 12 and July 2, 1981 are analyzed and compared in terms of time steps and cluster lifetimes. Rain volume is calculated by first using the regression analysis to generate the regression equation used to obtain the ATI; the ATI versus rain volume relation is then employed to compute rain volume. The data reveal that the ATI technique using satellite data is applicable to the calculation of rain volume.

  17. Estimation of monetary values of air pollutant emissions in various US areas

    SciTech Connect

    Wang, M.Q.; Santini, D.J.

    1994-08-17

    Two general methods of estimating monetary values of air pollutants are presented in this paper. The damage estimate method directly estimated, air pollutant by simulating air quality, identifying health and other welfare impacts damage values and valuing the identified impacts of air pollution, and valuing the identified impacts. Although the method is theoretically sound, many assumptions are involved in each of its estimation steps, and uncertainty exists in each step. The control cost estimate method estimates the marginal emission control cost, which represents the opportunity cost offset by emission reductions from some given control measures. Studies conducted to estimate emission values in US regions used either the damage estimate method or the control cost estimate method. Taking emission values estimated for some US air basins, this paper establishes regression relationships between emission values and total population and air pollutant concentrations. On the basis of the established relationships, both damage-based and control-cost-based emission values are estimated for 17 major US urban areas.

  18. Large Area Crop Inventory Experiment (LACIE). LACIE phase 1 and phase 2 accuracy assessment. [Kansas, Texas, Minnesota, Montana, and North Dakota

    NASA Technical Reports Server (NTRS)

    1978-01-01

    The author has identified the following significant results. The initial CAS estimates, which were made for each month from April through August, were considerably higher than the USDA/SRS estimates. This was attributed to: (1) the practice of considering bare ground as potential wheat and counting it as wheat; (2) overestimation of the wheat proportions in segments having only a small amount of wheat; and (3) the classification of confusion crops as wheat. At the end of the season most of the segments were reworked using improved methods based on experience gained during the season. In particular, new procedures were developed to solve the three problems listed above. These and other improvements used in the rework experiment resulted in at-harvest estimates that were much closer to the USDA/SRS estimates than those obtained during the regular season.

  19. Evaluating airborne remote sensing ET estimates using eddy covariance systems and a heat flux source area function.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The use of water for production of crops must become more efficient as competition for water increases. Thus, saving water by managing irrigation systems better may be possible if irrigation scheduling is improved by accurately estimating spatially distributed actual evapotranspiration (ET). ET can ...

  20. Investigation of Aerosol Surface Area Estimation from Number and Mass Concentration Measurements: Particle Density Effect

    PubMed Central

    Ku, Bon Ki; Evans, Douglas E.

    2015-01-01

    For nanoparticles with nonspherical morphologies, e.g., open agglomerates or fibrous particles, it is expected that the actual density of agglomerates may be significantly different from the bulk material density. It is further expected that using the material density may upset the relationship between surface area and mass when a method for estimating aerosol surface area from number and mass concentrations (referred to as “Maynard’s estimation method”) is used. Therefore, it is necessary to quantitatively investigate how much the Maynard’s estimation method depends on particle morphology and density. In this study, aerosol surface area estimated from number and mass concentration measurements was evaluated and compared with values from two reference methods: a method proposed by Lall and Friedlander for agglomerates and a mobility based method for compact nonspherical particles using well-defined polydisperse aerosols with known particle densities. Polydisperse silver aerosol particles were generated by an aerosol generation facility. Generated aerosols had a range of morphologies, count median diameters (CMD) between 25 and 50 nm, and geometric standard deviations (GSD) between 1.5 and 1.8. The surface area estimates from number and mass concentration measurements correlated well with the two reference values when gravimetric mass was used. The aerosol surface area estimates from the Maynard’s estimation method were comparable to the reference method for all particle morphologies within the surface area ratios of 3.31 and 0.19 for assumed GSDs 1.5 and 1.8, respectively, when the bulk material density of silver was used. The difference between the Maynard’s estimation method and surface area measured by the reference method for fractal-like agglomerates decreased from 79% to 23% when the measured effective particle density was used, while the difference for nearly spherical particles decreased from 30% to 24%. The results indicate that the use of

  1. Estimating Small-area Populations by Age and Sex Using Spatial Interpolation and Statistical Inference Methods

    SciTech Connect

    Qai, Qiang; Rushton, Gerald; Bhaduri, Budhendra L; Bright, Eddie A; Coleman, Phil R

    2006-01-01

    The objective of this research is to compute population estimates by age and sex for small areas whose boundaries are different from those for which the population counts were made. In our approach, population surfaces and age-sex proportion surfaces are separately estimated. Age-sex population estimates for small areas and their confidence intervals are then computed using a binomial model with the two surfaces as inputs. The approach was implemented for Iowa using a 90 m resolution population grid (LandScan USA) and U.S. Census 2000 population. Three spatial interpolation methods, the areal weighting (AW) method, the ordinary kriging (OK) method, and a modification of the pycnophylactic method, were used on Census Tract populations to estimate the age-sex proportion surfaces. To verify the model, age-sex population estimates were computed for paired Block Groups that straddled Census Tracts and therefore were spatially misaligned with them. The pycnophylactic method and the OK method were more accurate than the AW method. The approach is general and can be used to estimate subgroup-count types of variables from information in existing administrative areas for custom-defined areas used as the spatial basis of support in other applications.

  2. Estimation of flooded area in the Bahr El-Jebel basin using remote sensing techniques

    NASA Astrophysics Data System (ADS)

    Shamseddin, M. A. H.; Hata, T.; Tada, A.; Bashir, M. A.; Tanakamaru, T.

    2006-07-01

    In spite of the importance of Sudd (swamp) area estimation for any hydrological project in the southern Sudan, yet, no abroad agreement on its size, due to the inaccessibility and civil war. In this study, remote sensing techniques are used to estimate the Bahr El-Jebel flooded area. MODIS-Terra (Moderate Resolution Imaging Spectroradiometer) level 1B satellite images are analyzed on basis of the unsupervised classification method. The annual mean of Bahr El-Jebel flooded area has been estimated at 20 400 km2, which is 96% of Sutcliffe and Park (1999) estimation on basis of water balance model prediction. And only, 53% of SEBAL (Surface Energy Balance Algorithm for Land) model estimation. The accuracy of the classification is 71%. The study also found the swelling and shrinkage pattern of Sudd area throughout the year is following the trends of Lake Victoria outflow patterns. The study has used two evaporation methods (open water evaporation and SEBAL model) to estimate the annual storage volume of Bahr El-Jebel River by using a water balance model. Also the storage changes due time is generated throughout the study years.

  3. Estimation of Vegetation Aerodynamic Roughness of Natural Regions Using Frontal Area Density Determined from Satellite Imagery

    NASA Technical Reports Server (NTRS)

    Jasinski, Michael F.; Crago, Richard

    1994-01-01

    Parameterizations of the frontal area index and canopy area index of natural or randomly distributed plants are developed, and applied to the estimation of local aerodynamic roughness using satellite imagery. The formulas are expressed in terms of the subpixel fractional vegetation cover and one non-dimensional geometric parameter that characterizes the plant's shape. Geometrically similar plants and Poisson distributed plant centers are assumed. An appropriate averaging technique to extend satellite pixel-scale estimates to larger scales is provided. ne parameterization is applied to the estimation of aerodynamic roughness using satellite imagery for a 2.3 sq km coniferous portion of the Landes Forest near Lubbon, France, during the 1986 HAPEX-Mobilhy Experiment. The canopy area index is estimated first for each pixel in the scene based on previous estimates of fractional cover obtained using Landsat Thematic Mapper imagery. Next, the results are incorporated into Raupach's (1992, 1994) analytical formulas for momentum roughness and zero-plane displacement height. The estimates compare reasonably well to reference values determined from measurements taken during the experiment and to published literature values. The approach offers the potential for estimating regionally variable, vegetation aerodynamic roughness lengths over natural regions using satellite imagery when there exists only limited knowledge of the vegetated surface.

  4. Accounting for unsearched areas in estimating wind turbine-caused fatality

    USGS Publications Warehouse

    Huso, Manuela M.P.; Dalthorp, Dan

    2014-01-01

    With wind energy production expanding rapidly, concerns about turbine-induced bird and bat fatality have grown and the demand for accurate estimation of fatality is increasing. Estimation typically involves counting carcasses observed below turbines and adjusting counts by estimated detection probabilities. Three primary sources of imperfect detection are 1) carcasses fall into unsearched areas, 2) carcasses are removed or destroyed before sampling, and 3) carcasses present in the searched area are missed by observers. Search plots large enough to comprise 100% of turbine-induced fatality are expensive to search and may nonetheless contain areas unsearchable because of dangerous terrain or impenetrable brush. We evaluated models relating carcass density to distance from the turbine to estimate the proportion of carcasses expected to fall in searched areas and evaluated the statistical cost of restricting searches to areas near turbines where carcass density is highest and search conditions optimal. We compared 5 estimators differing in assumptions about the relationship of carcass density to distance from the turbine. We tested them on 6 different carcass dispersion scenarios at each of 3 sites under 2 different search regimes. We found that even simple distance-based carcass-density models were more effective at reducing bias than was a 5-fold expansion of the search area. Estimators incorporating fitted rather than assumed models were least biased, even under restricted searches. Accurate estimates of fatality at wind-power facilities will allow critical comparisons of rates among turbines, sites, and regions and contribute to our understanding of the potential environmental impact of this technology.

  5. Remote sensing of agricultural crops and soils

    NASA Technical Reports Server (NTRS)

    Bauer, M. E. (Principal Investigator)

    1983-01-01

    Research in the correlative and noncorrelative approaches to image registration and the spectral estimation of corn canopy phytomass and water content is reported. Scene radiation research results discussed include: corn and soybean LANDSAT MSS classification performance as a function of scene characteristics; estimating crop development stages from MSS data; the interception of photosynthetically active radiation in corn and soybean canopies; costs of measuring leaf area index of corn; LANDSAT spectral inputs to crop models including the use of the greenness index to assess crop stress and the evaluation of MSS data for estimating corn and soybean development stages; field research experiment design data acquisition and preprocessing; and Sun-view angles studies of corn and soybean canopies in support of vegetation canopy reflection modeling.

  6. Revised stratigraphy of Area 123, Koobi Fora, Kenya, and new age estimates of its fossil mammals, including hominins.

    PubMed

    Gathogo, Patrick N; Brown, Francis H

    2006-11-01

    Recent geologic study shows that all hominins and nearly all other published mammalian fossils from Paleontological Collection Area 123, Koobi Fora, Kenya, derive from levels between the KBS Tuff (1.87+/-0.02 Ma) and the Lower Ileret Tuff (1.53+/-0.01 Ma). More specifically, the fossils derive from 53 m of section below the Lower Ileret Tuff, an interval in which beds vary markedly laterally, especially those units containing molluscs and algal stromatolites. The upper Burgi Member (approximately 2.00-1.87 Ma) crops out only in the southwestern part of Area 123. Adjacent Area 110 contains larger exposures of the member, and there the KBS Tuff is preserved as an airfall ash in lacustrine deposits and also as a fluvially redeposited ash. We observed no mammalian fossils in situ in this member in Area 123, but surface specimens have been documented in some monographic treatments. Fossil hominins from Area 123 were attributed to strata above the KBS Tuff in the 1970s, but later they were assigned to strata below the KBS Tuff (now called the upper Burgi Member). This study definitively places the Area 123 hominins in the KBS Member. Most of these hominins are between 1.60 and 1.65 myr in age, but the youngest may date to only 1.53 Ma, and the oldest, to 1.75 Ma. All are 0.15-0.30 myr younger than previously estimated. The new age estimates, in conjunction with published taxonomic attributions of fossils, suggest that at least two species of Homo coexisted in the region along with A. boisei until at least 1.65 Ma. Comparison of crania KNM-ER 1813 and KNM-ER 1470, which were believed to be of comparable age, is at the focus of the debate over whether Homo habilis sensu lato is in fact composed of two species: Homo habilis and Homo rudolfensis. These two crania are separated in time by approximately 0.25 myr, and therefore, arguments for their conspecificity no longer need to confront the issue of unusually high contemporaneous variation within a single species. PMID

  7. Large Area Crop Inventory Experiment (LACIE). Detecting and monitoring agricultural vegetative water stress over large areas using LANDSAT digital data. [Great Plains

    NASA Technical Reports Server (NTRS)

    Thompson, D. R.; Wehmanen, O. A. (Principal Investigator)

    1978-01-01

    The author has identified the following significant results. The Green Number Index technique which uses LANDSAT digital data from 5X6 nautical mile sampling frames was expanded to evaluate its usefulness in detecting and monitoring vegetative water stress over the Great Plains. At known growth stages for wheat, segments were classified as drought or non drought. Good agreement was found between the 18 day remotely sensed data and a weekly ground-based crop moisture index. Operational monitoring of the 1977 U.S.S.R. and Australian wheat crops indicated drought conditions. Drought isoline maps produced by the Green Number Index technique were in good agreement with conventional sources.

  8. Evaluation of a segment-based LANDSAT full-frame approach to corp area estimation

    NASA Technical Reports Server (NTRS)

    Bauer, M. E. (Principal Investigator); Hixson, M. M.; Davis, S. M.

    1981-01-01

    As the registration of LANDSAT full frames enters the realm of current technology, sampling methods should be examined which utilize other than the segment data used for LACIE. The effect of separating the functions of sampling for training and sampling for area estimation. The frame selected for analysis was acquired over north central Iowa on August 9, 1978. A stratification of he full-frame was defined. Training data came from segments within the frame. Two classification and estimation procedures were compared: statistics developed on one segment were used to classify that segment, and pooled statistics from the segments were used to classify a systematic sample of pixels. Comparisons to USDA/ESCS estimates illustrate that the full-frame sampling approach can provide accurate and precise area estimates.

  9. MODELING WORLD BIOENERGY CROP POTENTIAL

    NASA Astrophysics Data System (ADS)

    Hagiwara, Kensuke; Hanasaki, Naota; Kanae, Shinjiro

    Bioenergy is regarded as clean energy due to its characteristics and expected to be a new support of world energy de¬mand, but there are few integrated assessments of the potential of bioenergy considering sustainable land use. We esti¬mated the global bioenergy potential with an integrated global water resources model, the H08. It can simulate the crop yields on global-scale at a spatial resolution of 0.50.5. Seven major crops in the world were considered; namely, maize, sugar beet, sugar cane, soybean, rapeseed, rice, and wheat, of which the first 5 are commonly used to produce biofuel now. Three different land-cover types were chosen as potential area for cultivation of biofuel-producing crop: fallow land, grassland, and portion of forests (excluding areas sensitive for biodiversity such as frontier forest). We attempted to estimate the maximum global bioenergy potential and it was estimated to be 1120EJ. Bioenergy potential depends on land-use limitations for the protection of bio-diversity and security of food. In another condition which assumed more land-use limitations, bioenergy potential was estimated to be 70-233EJ.

  10. Estimation of Leaf Area Index and Plant Area Index of a Submerged Macrophyte Canopy Using Digital Photography

    PubMed Central

    Zhao, Dehua; Xie, Dong; Zhou, Hengjie; Jiang, Hao; An, Shuqing

    2012-01-01

    Non-destructive estimation using digital cameras is a common approach for estimating leaf area index (LAI) of terrestrial vegetation. However, no attempt has been made so far to develop non-destructive approaches to LAI estimation for aquatic vegetation. Using the submerged plant species Potamogeton malainus, the objective of this study was to determine whether the gap fraction derived from vertical photographs could be used to estimate LAI of aquatic vegetation. Our results suggested that upward-oriented photographs taken from beneath the water surface were more suitable for distinguishing vegetation from other objects than were downward-oriented photographs taken from above the water surface. Exposure settings had a substantial influence on the identification of vegetation in upward-oriented photographs. Automatic exposure performed nearly as well as the optimal trial exposure, making it a good choice for operational convenience. Similar to terrestrial vegetation, our results suggested that photographs taken for the purpose of distinguishing gap fraction in aquatic vegetation should be taken under diffuse light conditions. Significant logarithmic relationships were observed between the vertical gap fraction derived from upward-oriented photographs and plant area index (PAI) and LAI derived from destructive harvesting. The model we developed to depict the relationship between PAI and gap fraction was similar to the modified theoretical Poisson model, with coefficients of 1.82 and 1.90 for our model and the theoretical model, respectively. This suggests that vertical upward-oriented photographs taken from below the water surface are a feasible alternative to destructive harvesting for estimating PAI and LAI for the submerged aquatic plant Potamogeton malainus. PMID:23226557

  11. Estimation of the sugar cane cultivated area from LANDSAT images using the two phase sampling method

    NASA Technical Reports Server (NTRS)

    Parada, N. D. J. (Principal Investigator); Cappelletti, C. A.; Mendonca, F. J.; Lee, D. C. L.; Shimabukuro, Y. E.

    1982-01-01

    A two phase sampling method and the optimal sampling segment dimensions for the estimation of sugar cane cultivated area were developed. This technique employs visual interpretations of LANDSAT images and panchromatic aerial photographs considered as the ground truth. The estimates, as a mean value of 100 simulated samples, represent 99.3% of the true value with a CV of approximately 1%; the relative efficiency of the two phase design was 157% when compared with a one phase aerial photographs sample.

  12. Pollutant discharges to coastal areas: Improving upstream source estimates. Final report

    SciTech Connect

    Rohmann, S.O.

    1989-10-01

    The report describes a project NOAA's Strategic Environmental Assessments Division began to improve the estimates of pollutant discharges carried into coastal areas by rivers and streams. These estimates, termed discharges from upstream sources, take into account all pollution discharged by industries, sewage treatment plants, farms, cities, and other pollution-generating operations, as well as natural phenomena such as erosion and weathering which occur inland or upstream of the coastal US.

  13. Improving global fire carbon emissions estimates by combining moderate resolution burned area and active fire observations

    NASA Astrophysics Data System (ADS)

    Randerson, J. T.; Chen, Y.; Giglio, L.; Rogers, B. M.; van der Werf, G.

    2011-12-01

    In several important biomes, including croplands and tropical forests, many small fires exist that have sizes that are well below the detection limit for the current generation of burned area products derived from moderate resolution spectroradiometers. These fires likely have important effects on greenhouse gas and aerosol emissions and regional air quality. Here we developed an approach for combining 1km thermal anomalies (active fires; MOD14A2) and 500m burned area observations (MCD64A1) to estimate the prevalence of these fires and their likely contribution to burned area and carbon emissions. We first estimated active fires within and outside of 500m burn scars in 0.5 degree grid cells during 2001-2010 for which MCD64A1 burned area observations were available. For these two sets of active fires we then examined mean fire radiative power (FRP) and changes in enhanced vegetation index (EVI) derived from 16-day intervals immediately before and after each active fire observation. To estimate the burned area associated with sub-500m fires, we first applied burned area to active fire ratios derived solely from within burned area perimeters to active fires outside of burn perimeters. In a second step, we further modified our sub-500m burned area estimates using EVI changes from active fires outside and within of burned areas (after subtracting EVI changes derived from control regions). We found that in northern and southern Africa savanna regions and in Central and South America dry forest regions, the number of active fires outside of MCD64A1 burned areas increased considerably towards the end of the fire season. EVI changes for active fires outside of burn perimeters were, on average, considerably smaller than EVI changes associated with active fires inside burn scars, providing evidence for burn scars that were substantially smaller than the 25 ha area of a single 500m pixel. FRP estimates also were lower for active fires outside of burn perimeters. In our

  14. Rice crop risk map in Babahoyo canton (Ecuador)

    NASA Astrophysics Data System (ADS)

    Valverde Arias, Omar; Tarquis, Ana; Garrido, Alberto

    2016-04-01

    determinate which level of rice crop requirement is met. Finally we have established rice crop zones classified as: suitable, moderate suitable, marginal suitable and unsuitable. Several methods have been used to estimate the degree with which crop requirements are satisfied, pondering weights of limiting factors to adequate crop conditions. Better conditions for cropping in a specific area imply less risk in production. In this case, crop will be less affected by pests and disease, although this closely depends on crop management. Farmers have to invest less money to produce and could increase their benefit. Results are showed and discussed with the aim to study the efficiency and potential of this risk map.

  15. Spatial Variability of Ground-Water Recharge Estimates in the Glassboro Area, New Jersey

    NASA Astrophysics Data System (ADS)

    Nolan, B. T.; Baehr, A. L.

    2001-12-01

    The spatial variability of ground-water recharge estimates in the Glassboro area, NJ, was evaluated using geostatistical methods as a preliminarily assessment of aquifer vulnerability. Recharge was estimated using Darcy's law, based on parameters obtained from pedotransfer functions applied to measured soil texture values. The recharge estimates correspond to sediments overlying the Kirkwood-Cohansey aquifer, which comprises highly permeable unconsolidated sands and gravels. Knowing which areas receive greater recharge would indicate areas of greater vulnerability, depending on overlying land use. Recharge varied from -7.3 to 722 in/yr in the study area and the median was 12.1 in/yr. Experimental variograms of untransformed recharge data were erratic and related kriged maps were dominated by extreme values (250-722 in/yr) in the data set. An indicator transform stabilized the variograms. Indicator kriging (IK) reduced the influence of extreme values in the data set and yielded maps showing the probability of exceeding threshold values of recharge in the study area. The probability of exceeding the median recharge rate of 12.1 in/yr was 0.9 in the southern portion of the study area and might represent an area of focused recharge. As a check of model fit, probabilities predicted with IK were compared with the original recharge estimates and found to be strongly related. IK predictions corresponding to quintiles of recharge were used to estimate cumulative distribution functions (cdfs) for specific locations in the study area. The cdfs indicate the probability of exceeding any recharge rate at a particular location, and are shaped differently depending on location in the study area. The IK technique estimates cdfs with a single sampling realization (i.e., without a mean and variance at a given location). Additional variables were analyzed with regression to add a deterministic aspect to the analysis and to improve predictions. These variables included land slope

  16. Estimating the effective sound speed in the bottom in shallow water areas

    NASA Astrophysics Data System (ADS)

    Lunkov, A. A.; Petnikov, V. G.; Chernousov, A. D.

    2015-11-01

    Techniques are proposed for estimating the effective sound speed in sediments in shallow water areas with a soft bottom. The techniques are oriented toward determining this physical quantity for relatively small range intervals on the order of ten depths. The estimates are based on comparison of the experimental results and calculations of the characteristics of low-frequency sound fields propagating in these water areas. It is proposed to find this sound speed quantity by calculating its value for which the best agreement between experiment and calculation occurs. We present the results of testing the proposed techniques in experiments in the Klyaz'ma reservoir.

  17. Indoor patient position estimation using particle filtering and wireless body area networks.

    PubMed

    Ren, Hongliang; Meng, Max Q H; Xu, Lisheng

    2007-01-01

    Wireless Body Area Network (WBAN) has been recently promoted to monitor the physiological parameters of patient in an unobtrusive and natural way. This paper towards to make advantage of those ongoing wireless communication links between the body sensors to provide estimated position information of patients or particular body area networks, which make daily activity surveillance possible for further analysis. The proposed particle filtering based localization algorithm just picks up the received radio signal strength information from beacons or its neighbors to infer its own pose, which do not require additional hardware or instruments. Theoretical analysis and simulation experiments are presented to examine the performance of location estimating method. PMID:18002445

  18. Historical tank content estimate for the southeast quadrant of the Hanford 200 area

    SciTech Connect

    Brevick, C.H.; Stroup, J.L.; Funk, J.W., Fluor Daniel Hanford

    1997-03-14

    The Historical Tank Content Estimate for the Quadrant provides historical information on a tank-by-tank basis of the radioactive mixed wastes stored in the underground single-shell tanks for the Hanford 200 Areas. This report summarized historical information such as waste history, level history, temperature history, riser configuration, tank integrity, and inventory estimates on a tank- by-tank basis. Tank farm aerial photographs and interior tank montages are also provided for each tank. A description of the development of data for the document of the inventory estimates provided by Los Alamos National Laboratory are also given in this report.

  19. Historical tank content estimate for the northwest quadrant ofthe Hanford 200 west area

    SciTech Connect

    Brevick, C.H.; Stroup, J.L.; Funk, J.W., Fluor Daniel Hanford

    1997-03-06

    The Historical Tank Content Estimate for the Quadrant provides historical information on a tank-by-tank basis of the radioactive mixed wastes stored in the underground single-shell tanks for the Hanford 200 West Area. This report summarized historical information such as waste history, level history, temperature history, riser configuration, tank integrity, and inventory estimates on a tank-by-tank basis. Tank farm aerial photographs and interior tank montages are also provided for each tank. A description of the development of data for the document of the inventory estimates provided by Los Alamos National Labo1368ratory are also given in this report.

  20. Calibration of area based diameter distribution with individual tree based diameter estimates using airborne laser scanning

    NASA Astrophysics Data System (ADS)

    Xu, Qing; Hou, Zhengyang; Maltamo, Matti; Tokola, Timo

    2014-07-01

    Diameter distribution is essential for calculating stem volume and timber assortments of forest stands. A new method was proposed in this study to improve the estimation of stem volume and timber assortments, by means of combining the Area-based approach (ABA) and individual tree detection (ITD), the two main approaches to deriving forest attributes from airborne laser scanning (ALS) data. Two methods, replacement, and histogram matching were employed to calibrate ABA-derived diameter distributions with ITD-derived diameter estimates at plot level. The results showed that more accurate estimates were obtained when calibrations were applied. In view of the highest accuracy between ABA and ITD, calibrated diameter distributions decreased its relative RMSE of the estimated entire growing stock, saw log and pulpwood fractions by 2.81%, 3.05% and 7.73% points at best, respectively. Calibration improved pulpwood fraction significantly, which contributed to the negligible bias of the estimated entire growing stock.

  1. Recalibration of cumulative rainfall estimates by weather radar over a large area

    NASA Astrophysics Data System (ADS)

    Mazza, Alessandro; Antonini, Andrea; Melani, Samantha; Ortolani, Alberto

    2015-01-01

    The real-time measurement of rainfall is a primary information source for many purposes, such as weather forecasting, flood risk assessment, and landslide prediction and prevention. In this perspective, remote sensing techniques to monitor rainfall fields by means of radar measurements are very useful. In this work, a technique is proposed for the estimation of cumulative rainfall fields averaged over a large area, applied on the Tuscany region using the Italian weather radar network. In order to assess the accuracy of radar-based rainfall estimates, they are compared with coincident spatial rain gauge measurements. Observations are compared with average rainfall over areas as large as a few tens of kilometers. An ordinary block kriging method is applied for rain gauge data spatialization. The comparison between the two types of estimates is used for recalibrating the radar measurements. As a main result, this paper proposes a recalibrated relationship for retrieving precipitation from radar data. The accuracy of the estimate increases when considering larger areas: an area of 900 km2 has a standard deviation of less than few millimeters. This is of interest in particular for extending recalibrated radar relationships over areas where rain gauges are not available. Many applications could benefit from it, from nowcasting for civil protection activities, to hydrogeological risk mitigation or agriculture.

  2. Quantifying the impact of changes in crop area on evapotranspiration regimes in the US corn and soybean belts through phenological modeling and data assimilation

    NASA Astrophysics Data System (ADS)

    Kovalskyy, V.; Henebry, G. M.

    2010-12-01

    In recent years, fluctuations in food, feed, and fuel prices have led to shifts in the area of cropland dedicated to maize and soybean cultivation in the Northern Great Plains. We report here on a modeling experiment that compares three different simulated scenarios for actual evapotranspiration (ETa) from maize-soybean dominated areas in North Dakota, South Dakota, Nebraska, Iowa, and Minnesota during the 2000-2009 growing seasons. Scenario 1 relies on MODIS-derived crop maps to provide a baseline of subpixel crop proportions; Scenario 2 increases the proportion of maize by to 100 percent; Scenario 3 substitutes grassland for half the maize. We use a simple soil water balance model of ETa linked to an empirically derived crop specific phenology model also capable of producing seasonal trajectories of canopy attributes. This coupled model has been successfully deployed using flux tower records from multiple locations in the central US. Forcing the coupled model using data from NLDAS, we derive seasonal trajectories of daily NDVI and ETa as well as phenological transition points for maize, soybean, and grassland for each scenario. Seasonal differences in ETa among the three scenarios underscore the importance of how land use modulates land surface phenologies and, in turn, water and energy balances.

  3. Evaluation of Five Formulae for Estimating Body Surface Area of Nigerian Children

    PubMed Central

    Orimadegun, AE; Omisanjo, AO

    2014-01-01

    Background: Physiological functions are often assessed by standardizing for body surface area (BSA) to avoid excessive variation in calculations in pediatric practice. Aim: To explore the suitability of existing formulae for estimating the BSA of Nigerian children. Subjects and Methods: This cross-sectional study involved healthy children in a Local Government Area, Oyo State, Nigeria. The BSAs of 2745 children were calculated using the formulae by Boyd, Mosteller, Gehan and George, Haycock, and Dubois-DuBois, and the sixth arithmetic mean of these five formulae (mean-BSA) was performed. The outcome of interest was agreement between estimated BSA and mean-BSA for each method. The performance of each BSA estimation method was compared using bias, root mean square error and Bland-Altman plots of agreement. Results: The study participants comprised of 1229 males and 1246 females with mean (standard deviation) ages of 6.3 (3.0) years and 6.6 (3.1) years respectively (P = 0.01). Reference values for BSA estimates by gender were proposed each age group. Furthermore, BSA estimates from Boyd's and Mosteller's formulae were most similar to the mean-BSA with mathematically perfect correlations. The degree of deviation of BSA estimates from DuBois was largest with a remarkable increase at ages <6 years. Conclusion: Formulae by Boyd and Mosteller are the best BSA estimate for Nigerian children among the existing formulae. PMID:25506482

  4. Top-down methane emissions estimates for the San Francisco Bay Area from 1990 to 2012

    NASA Astrophysics Data System (ADS)

    Fairley, David; Fischer, Marc L.

    2015-04-01

    Methane is a potent greenhouse gas (GHG) that is now included in both California State and San Francisco Bay Area (SFBA) bottom-up emission inventories as part of California's effort to reduce anthropogenic GHG emissions. Here we provide a top-down estimate of methane (CH4) emissions from the SFBA by combining atmospheric measurements with the comparatively better estimated emission inventory for carbon monoxide (CO). Local enhancements of CH4 and CO are estimated using measurements from 14 air quality sites in the SFBA combined together with global background measurements. Mean annual CH4 emissions are estimated from the product of Bay Area Air Quality Management District (BAAQMD) emission inventory CO and the slope of ambient local CH4 to CO. The resulting top-down estimates of CH4 emissions are found to decrease slightly from 1990 to 2012, with a mean value of 240 ± 60 GgCH4 yr-1 (at 95% confidence) in the most recent (2009-2012) period, and correspond to reasonably a constant factor of 1.5-2.0 (at 95% confidence) times larger than the BAAQMD CH4 emission inventory. However, we note that uncertainty in these emission estimates is dominated by the variation in CH4:CO enhancement ratios across the observing sites and we expect the estimates could represent a lower-limit on CH4 emissions because BAAQMD monitoring sites focus on urban air quality and may be biased toward CO rather than CH4 sources.

  5. Estimation of land surface evaporation map over large areas using remote sensing data

    NASA Astrophysics Data System (ADS)

    Jiang, Le

    Accurate estimation of surface energy fluxes is essential for various hydrological, meteorological, agricultural and ecological applications. Over the years, a wide variety of instrument systems and estimation methodologies have been developed to measure and estimate surface fluxes. In this study, a simple scheme is proposed to estimate surface evaporation over large heterogeneous areas using remote sensing data. This approach is based on an extension of the Priestley-Taylor equation and a relationship between remotely sensed surface temperature and vegetation index. Further simplification by using more generalized form for remotely sensed surface parameters set leads to a simpler formulation for evaporative fraction within a trapezoid/triangle space of remotely sensed vegetation index and surface temperature parameter space. Compared to ground flux observations by the Atmospheric Radiation Measurement (ARM) program, six case studies varying from early spring to late summer over the central United States show that the proposed method provides better estimation accuracy for surface evaporation than the original Priestley-Taylor method. Detailed comparison with the widely used aerodynamic resistance energy balance residual method suggests that the proposed method can achieve similar or better estimation of latent heat flux over large areas with much less input parameters. The residual method, on the other hand, requires estimation of aerodynamic resistance to heat transfer that necessitates the measurements of several ground-based observations including land surface vegetation height and surface wind.

  6. The use of large-area spectral data in wheat yield estimation

    NASA Technical Reports Server (NTRS)

    Barnett, T. L.; Thompson, D. R.

    1982-01-01

    Large-area relations between satellite spectral data and end-of-season crop yield were investigated. Green Index Number (GIN) values from Landsat MSS data of sample segments throughout the U.S. Great Plains winter wheat belt in 1978 were correlated to county USDA-SRS reported yields. A linear relation between GIN and yield appeared to exist up to GIN values of 40 or 50, covering cases of severe to moderate stress. In a test on 1978 Texas winter wheat at the county level, GIN values for sample segments in the counties were used in conjunction with an agronomic-meteorological yield model. The combined fit explained significantly more of the observed yield variation at the county level than the agromet model alone.

  7. Estimating Radiological Doses to Predators Foraging in a Low-Level Radioactive Waste Management Area

    SciTech Connect

    L.Soholt; G.Gonzales; P.Fresquez; K.Bennett; E.Lopez

    2003-03-01

    Since 1957, Los Alamos National Laboratory has operated Area G as its low-level, solid radioactive waste management and disposal area. Although the waste management area is developed, plants, small mammals, and avian and mammalian predators still occupy the less disturbed and revegetated portions of the land. For almost a decade, we have monitored the concentrations of selected radionuclides in soils, plants, and small mammals at Area G. The radionuclides tritium, plutonium-238, and plutonium-239 are regularly found at levels above regional background in all three media. Based on radionuclide concentrations in mice collected from 1994 to 1999, we calculated doses to higher trophic levels (owl, hawk, kestrel, and coyote) that forage on the waste management area. These predators play important functions in the regional ecosystems and are an important part of local Native American traditional tales that identify the uniqueness of their culture. The estimated doses are compared to Department of Energy's interim limit of 0.1 rad/day for the protection of terrestrial wildlife. We used exposure parameters that were derived from the literature for each receptor, including Environmental Protection Agency's exposure factors handbook. Estimated doses to predators ranged from 9E-06 to 2E-04 rad/day, assuming that they forage entirely on the waste management area. These doses are greater than those calculated for predators foraging exclusively in reference areas, but are still well below the interim dose limit. We believe that these calculated doses represent upper-bound estimates of exposure for local predators because the larger predators forage over areas that are much greater than the 63-acre waste management area. Based on these results, we concluded that predators foraging on this area do not face a hazard from radiological exposure under current site conditions.

  8. Fire Emissions Estimates in Siberia: Evaluation of Uncertainties in Area Burned, Land Cover, and Fuel Consumption

    NASA Astrophysics Data System (ADS)

    Kukavskaya, E.; Soja, A. J.; Ivanova, G. A.; Petkov, A.; Ponomarev, E. I.; Conard, S. G.

    2012-12-01

    Wildfire is one of the main disturbance factors in the boreal zone of Russia. Fires in the Russian boreal forest range from low-severity surface fires to high-severity crown fires. Estimates of carbon emissions from fires in Russia vary substantially due to differences in ecosystem classification and mapping, burned area calculations, and estimates of fuel consumption. We examined uncertainties in different parameters used to estimate biomass burning emissions. Several fire datasets (Institute of Forest burned area product, MCD45, MCD64, MOD14/MYD14, official data) were compared to estimate uncertainties in area burned in Siberia. Area burned was found to differ significantly by data source, with satellite data being by an order of magnitude greater than ground-based data. Differences between mapped ecosystems were also compared and contrasted on the basis of five land cover maps (GLC-2000, Globcover-2009, MODIS Collection 4 and 5 Global Land Cover, and the Digitized Ecosystem map of the Former Soviet Union) to evaluate the potential for error resulting from disparate vegetation structure and fuel consumption estimates. The examination of land cover maps showed that estimates of relative proportion of fire by ecosystem type varied substantially for the same year from map to map. Fuel consumption remains one of the main uncertainties in estimates of biomass burning emissions in Siberia. Accurate fuel consumption estimates are obtained in the course of fire experiments with pre- and post-fire biomass measuring. Our large-scale experiments carried out in the course of the FIRE BEAR (Fire Effects in the Boreal Eurasia Region) Project provided quantitative and qualitative data on ecosystem state and carbon emissions due to fires of known behavior in major forest types of Siberia that could be used to verify large-scale carbon emissions estimates. Global climate change is expected to result in increase of fire hazard and area burned, leading to impacts on global air

  9. Estimation of real contact area during sliding friction from interface temperature

    NASA Astrophysics Data System (ADS)

    Chey, Sung Keun; Tian, Pengyi; Tian, Yu

    2016-06-01

    Frictional heat is one of the most important topics in tribological research. The real contact area of the frictional pair plays a significant role in accurately estimating the interface temperature, which is closely related to the frictional heat. However, conventional methods for measuring the contact area, such as constriction resistance measurements, are not suitable for dynamic frictional motion because of the electrical and thermal interferences at the contact region. In this study, a novel method is presented for estimating the real contact area during sliding friction. First, the average interface temperature was experimentally measured by the dynamic thermocouple method. Then assuming that the total frictional heat power is constant, the measured temperature was used as a constraint to determine the contact area in a finite element model, giving an estimation for the real contact area. The calculation results show that the real contact area increases with increasing normal load as predicted by contact theories, and decreases with increasing sliding speed, which could be attributable to the contact dynamics of asperities at the interface. Additionally, the limits of the proposed method is discussed.

  10. [Estimating Biomass Burned Areas from Multispectral Dataset Detected by Multiple-Satellite].

    PubMed

    Yu, Chao; Chen, Liang-fu; Li, Shen-shen; Tao, Jin-hua; Su, Lin

    2015-03-01

    Biomass burning makes up an important part of both trace gases and particulate matter emissions, which can efficiently degrade air quality and reduce visibility, destabilize the global climate system at regional to global scales. Burned area is one of the primary parameters necessary to estimate emissions, and considered to be the largest source of error in the emission inventory. Satellite-based fire observations can offer a reliable source of fire occurrence data on regional and global scales, a variety of sensors have been used to detect and map fires in two general approaches: burn scar mapping and active fire detection. However, both of the two approaches have limitations. In this article, we explore the relationship between hotspot data and burned area for the Southeastern United States, where a significant amount of biomass burnings from both prescribed and wild fire took place. MODIS (Moderate resolution imaging spectrometer) data, which has high temporal-resolution, can be used to monitor ground biomass. burning in time and provided hot spot data in this study. However, pixel size of MODIS hot spot can't stand for the real ground burned area. Through analysis of the variation of vegetation band reflectance between pre- and post-burn, we extracted the burned area from Landsat-5 TM (Thematic Mapper) images by using the differential normalized burn ratio (dNBR) which is based on TM band4 (0.84 μm) and TM band 7(2.22 μm) data. We combined MODIS fire hot spot data and Landsat-5 TM burned scars data to build the burned area estimation model, results showed that the linear correlation coefficient is 0.63 and the relationships vary as a function of vegetation cover. Based on the National Land Cover Database (NLCD), we built burned area estimation model over different vegetation cover, and got effective burned area per fire pixel, values for forest, grassland, shrub, cropland and wetland are 0.69, 1.27, 0.86, 0.72 and 0.94 km2 respectively. We validated the

  11. Space-Time Smoothing of Complex Survey Data: Small Area Estimation for Child Mortality

    PubMed Central

    Mercer, Laina D; Wakefield, Jon; Pantazis, Athena; Lutambi, Angelina M; Masanja, Honorati; Clark, Samuel

    2016-01-01

    Many people living in low and middle-income countries are not covered by civil registration and vital statistics systems. Consequently, a wide variety of other types of data including many household sample surveys are used to estimate health and population indicators. In this paper we combine data from sample surveys and demographic surveillance systems to produce small area estimates of child mortality through time. Small area estimates are necessary to understand geographical heterogeneity in health indicators when full-coverage vital statistics are not available. For this endeavor spatio-temporal smoothing is beneficial to alleviate problems of data sparsity. The use of conventional hierarchical models requires careful thought since the survey weights may need to be considered to alleviate bias due to non-random sampling and non-response. The application that motivated this work is estimation of child mortality rates in five-year time intervals in regions of Tanzania. Data come from Demographic and Health Surveys conducted over the period 1991–2010 and two demographic surveillance system sites. We derive a variance estimator of under five years child mortality that accounts for the complex survey weighting. For our application, the hierarchical models we consider include random effects for area, time and survey and we compare models using a variety of measures including the conditional predictive ordinate (CPO). The method we propose is implemented via the fast and accurate integrated nested Laplace approximation (INLA). PMID:27468328

  12. Bayes plus Brass: estimating total fertility for many small areas from sparse census data.

    PubMed

    Schmertmann, Carl P; Cavenaghi, Suzana M; Assunção, Renato M; Potter, Joseph E

    2013-01-01

    Estimates of fertility in small areas are valuable for analysing demographic change, and important for local planning and population projection. In countries lacking complete vital registration, however, small-area estimates are possible only from sparse survey or census data that are potentially unreliable. In these circumstances estimation requires new methods for old problems: procedures must be automated if thousands of estimates are required; they must deal with extreme sampling variability in many areas; and they should also incorporate corrections for possible data errors. We present a two-step procedure for estimating total fertility in such circumstances and illustrate it by applying the method to data from the 2000 Brazilian Census for over 5,000 municipalities. Our proposed procedure first smoothes local age-specific rates using Empirical Bayes methods and then applies a new variant of Brass's P/F parity correction procedure that is robust to conditions of rapid fertility decline. Supplementary material at the project website ( http://schmert.net/BayesBrass ) will allow readers to replicate all the authors' results in this paper using their data and programs. PMID:24143946

  13. A study of area clustering using factor analysis in small area estimation (An analysis of per capita expenditures of subdistricts level in regency and municipality of Bogor)

    NASA Astrophysics Data System (ADS)

    Wahyudi, Notodiputro, Khairil Anwar; Kurnia, Anang; Anisa, Rahma

    2016-02-01

    Empirical Best Linear Unbiased Prediction (EBLUP) is one of indirect estimating methods which used to estimate parameters of small areas. EBLUP methods works in using auxiliary variables of area while adding the area random effects. In estimating non-sampled area, the standard EBLUP can no longer be used due to no information of area random effects. To obtain more proper estimation methods for non sampled area, the standard EBLUP model has to be modified by adding cluster information. The aim of this research was to study clustering methods using factor analysis by means of simulation, provide better cluster information. The criteria used to evaluate the goodness of fit of the methods in the simulation study were the mean percentage of clustering accuracy. The results of the simulation study showed the use of factor analysis in clustering has increased the average percentage of accuracy particularly when using Ward method. The method was taken into account to estimate the per capita expenditures based on Small Area Estimation (SAE) techniques. The method was eventually used to estimate the per capita expenditures from SUSENAS and the quality of the estimates was measured by RMSE. This research has shown that the standard-modified EBLUP model provided with factor analysis better estimates when compared with standard EBLUP model and the standard-modified EBLUP without the factor analysis. Moreover, it was also shown that the clustering information is important in estimating non sampled area.

  14. Footprint (A Screening Model for Estimating the Area of a Plume Produced from Gasoline Containing Ethanol

    EPA Science Inventory

    FOOTPRINT is a simple and user-friendly screening model to estimate the length and surface area of BTEX plumes in ground water produced from a spill of gasoline that contains ethanol. Ethanol has a potential negative impact on the natural biodegradation of BTEX compounds in groun...

  15. Wheat cultivation: Identifying and estimating area by means of LANDSAT data

    NASA Technical Reports Server (NTRS)

    Dejesusparada, N. (Principal Investigator); Mendonca, F. J.; Cottrell, D. A.; Tardin, A. T.; Lee, D. C. L.; Shimabukuro, Y. E.; Moreira, M. A.; Delima, A. M.; Maia, F. C. S.

    1981-01-01

    Automatic classification of LANDSAT data supported by aerial photography for identification and estimation of wheat growing areas was evaluated. Data covering three regions in the State of Rio Grande do Sul, Brazil were analyzed. The average correct classification of IMAGE-100 data was 51.02% and 63.30%, respectively, for the periods of July and of September/October, 1979.