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Sample records for crop area estimate

  1. Advancing methods for global crop area estimation

    NASA Astrophysics Data System (ADS)

    King, M. L.; Hansen, M.; Adusei, B.; Stehman, S. V.; Becker-Reshef, I.; Ernst, C.; Noel, J.

    2012-12-01

    Cropland area estimation is a challenge, made difficult by the variety of cropping systems, including crop types, management practices, and field sizes. A MODIS derived indicator mapping product (1) developed from 16-day MODIS composites has been used to target crop type at national scales for the stratified sampling (2) of higher spatial resolution data for a standardized approach to estimate cultivated area. A global prototype is being developed using soybean, a global commodity crop with recent LCLUC dynamic and a relatively unambiguous spectral signature, for the United States, Argentina, Brazil, and China representing nearly ninety percent of soybean production. Supervised classification of soy cultivated area is performed for 40 km2 sample blocks using time-series, Landsat imagery. This method, given appropriate data for representative sampling with higher spatial resolution, represents an efficient and accurate approach for large area crop type estimation. Results for the United States sample blocks have exhibited strong agreement with the National Agricultural Statistics Service's (NASS's) Cropland Data Layer (CDL). A confusion matrix showed a 91.56% agreement and a kappa of .67 between the two products. Field measurements and RapidEye imagery have been collected for the USA, Brazil and Argentina in further assessing product accuracies. The results of this research will demonstrate the value of MODIS crop type indicator products and Landsat sample data in estimating soybean cultivated area at national scales, enabling an internally consistent global assessment of annual soybean production.

  2. 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.

  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 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.

  7. 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.

  8. 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.

  9. Crop Yield and Area can be Reliably Estimated Using Farmer Supplied Yield Data, Remote Sensing and Crop Models in Australia.

    NASA Astrophysics Data System (ADS)

    Lawes, R.

    2016-12-01

    The Australian grain growing region is vast and occupies where some 25 million tonnes of wheat is produced from latitudes -27 to -42, where soils, crops and climates vary considerably. Predicting the area of individual crops is time consuming and currently conducted by survey, while yield estimates are derived from these areas and from information about grain receivables with little pre-harvest information available to industry. The existing approach fails to provide reliable, timely, small scale information about production. Similarly, previous attempts to predict yield using satellite derived information rely on information collected using the existing systems to calibrate models. We have developed a crop productivity and yield model - called C-Store Crop - that uses remotely sensed vegetation indices, along with site based rainfall, radiation and temperature information. Model calibration using 3000 points derived from farmer supplied yield maps for wheat, barley, canola and chickpea showed strong relationships (>70%) between modelled plant mass and observed crop yield at the paddock scale. C-Store Crop is being applied at 250m and 25m grid resolution. Farmer supplied yield data was also used to train a combination of Radar and Landsat images collected whilst the crop is growing to discriminate between crop types. Landsat information alone was unable to discriminate legume and cereal crops. Problems such as cloud prevented accessing appropriate scenes. Inclusion of Radar information reduced errors of commission and omission. By combining the C-Store Crop model with remote estimates of crop type, we anticipate predicting crop type and crop yield with uncertainty estimates across the Australian continent.

  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. Estimating crop water requirements of a command area using multispectral video imagery and geographic information systems

    NASA Astrophysics Data System (ADS)

    Ahmed, Rashid Hassan

    This research focused on the potential use of multispectral video remote sensing for irrigation water management. Two methods for estimating crop evapotranspiration were investigated, the energy balance estimation from multispectral video imagery and use of reflectance-based crop coefficients from multitemporal multispectral video imagery. The energy balance method was based on estimating net radiation, and soil and sensible heat fluxes, using input from the multispectral video imagery. The latent heat flux was estimated as a residual. The results were compared to surface heat fluxes measured on the ground. The net radiation was estimated within 5% of the measured values. However, the estimates of sensible and soil heat fluxes were not consistent with the measured values. This discrepancy was attributed to the methods for estimating the two fluxes. The degree of uncertainty in the parameters used in the methods made their application too limited for extrapolation to large agricultural areas. The second method used reflectance-based crop coefficients developed from the multispectral video imagery using alfalfa as a reference crop. The daily evapotranspiration from alfalfa was estimated using a nearby weather station. With the crop coefficients known for a canal command area, irrigation scheduling was simulated using the soil moisture balance method. The estimated soil moisture matched the actual soil moisture measured using the neutron probe method. Also, the overall water requirement estimated by this method was found to be in close agreement with the canal water deliveries. The crop coefficient method has great potential for irrigation management of large agricultural areas.

  13. 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.

  14. Allometric method to estimate leaf area index for row crops

    USDA-ARS?s Scientific Manuscript database

    Leaf area index (LAI) is critical for predicting plant metabolism, biomass production, evapotranspiration, and greenhouse gas sequestration, but direct LAI measurements are difficult and labor intensive. Several methods are available to measure LAI indirectly or calculate LAI using allometric method...

  15. Research on Estimation Crop Planting Area by Integrating the Optical and Microwave Remote Sensing Data

    NASA Astrophysics Data System (ADS)

    Jia, Y.; Yu, F.

    2013-07-01

    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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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

  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

    2015-04-10

    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.

  4. Estimation of green leaf area index of crops: Sensitivity of vegetation indices

    NASA Astrophysics Data System (ADS)

    Nguy-Robertson, A. L.; Gitelson, A. A.; Peng, Y.; Vina, A.; Arkebauer, T. J.; Rundquist, D.

    2011-12-01

    Green leaf area index (gLAI) is an important biophysical characteristic used in climate, ecological, and crop yield models. There is a need for a rapid and accurate estimation of gLAI on a global scale. Traditionally used vegetation indices (VIs) have shown to saturate at moderate-to-high gLAI (e.g. NDVI) or are less sensitive to gLAI at low-to-moderate values of gLAI. The goal of this study was to determine the best suitable VIs for use in a combined vegetation index for estimating gLAI in crops in the entire wide dynamic range of gLAI. The study area consisted of three fields in eastern Nebraska, USA under different management conditions for the years 2001-2008 for a total of 24 field-years. The dynamic range of maize was 0-6.5 m2/m2 and soybean was 0-5.5 m2/m2. NDVI-like indices were the most sensitive to gLAI below 3 m2/m2 while Simple Ratio (SR) and the Chlorophyll Indices (CI) were more sensitive to gLAI above 3 m2/m2. MTCI was the only VI that was equally sensitive to gLAI in the entire dynamic range; however, it was species-specific. Only Red Edge NDVI and CIred edge were not species-specific. In order to benefit from different sensitivities of the indices to low-to-moderate and moderate-to-high gLAI, this study suggests building relationships using VIs in specific dynamic ranges of maximal sensitivity to gLAI. We suggest using NDVI and Simple Ratio (maize: RMSE = 0.71 m2/m2; soybean: RMSE = 0.53 m2/m2) for MODIS data. We suggest the using non-species-specific VIs, Red Edge NDVI and CIred edge (RMSE = 0.63 m2/m2) for MERIS data. For users which prefer to use a single index, we suggest a scaled combined vegetation index using Red Edge NDVI and CIred edge (RMSE = 0.56 m2/m2); however, this approach reduces the sensitivity of the specific indices in the dynamic range of which they are most sensitive.

  5. Estimating Crop Yields From Multispectral Reflectance

    NASA Technical Reports Server (NTRS)

    Daughtry, C.

    1986-01-01

    Three reports describe research on proposed method for estimating crop yields by combining meteorological data with satellite measurements of reflected radiation to estimate crop-absorbed radiation. Concept, when tested over large areas, forms basis for evaluating crop conditions and estimating yields over regions where ground observations too costly or too difficult.

  6. National-scale crop type mapping and area estimation using multi-resolution remote sensing and field survey

    NASA Astrophysics Data System (ADS)

    Song, X. P.; Potapov, P.; Adusei, B.; King, L.; Khan, A.; Krylov, A.; Di Bella, C. M.; Pickens, A. H.; Stehman, S. V.; Hansen, M.

    2016-12-01

    Reliable and timely information on agricultural production is essential for ensuring world food security. Freely available medium-resolution satellite data (e.g. Landsat, Sentinel) offer the possibility of improved global agriculture monitoring. Here we develop and test a method for estimating in-season crop acreage using a probability sample of field visits and producing wall-to-wall crop type maps at national scales. The method is first illustrated for soybean cultivated area in the US for 2015. A stratified, two-stage cluster sampling design was used to collect field data to estimate national soybean area. The field-based estimate employed historical soybean extent maps from the U.S. Department of Agriculture (USDA) Cropland Data Layer to delineate and stratify U.S. soybean growing regions. The estimated 2015 U.S. soybean cultivated area based on the field sample was 341,000 km2 with a standard error of 23,000 km2. This result is 1.0% lower than USDA's 2015 June survey estimate and 1.9% higher than USDA's 2016 January estimate. Our area estimate was derived in early September, about 2 months ahead of harvest. To map soybean cover, the Landsat image archive for the year 2015 growing season was processed using an active learning approach. Overall accuracy of the soybean map was 84%. The field-based sample estimated area was then used to calibrate the map such that the soybean acreage of the map derived through pixel counting matched the sample-based area estimate. The strength of the sample-based area estimation lies in the stratified design that takes advantage of the spatially explicit cropland layers to construct the strata. The success of the mapping was built upon an automated system which transforms Landsat images into standardized time-series metrics. The developed method produces reliable and timely information on soybean area in a cost-effective way and could be implemented in an operational mode. The approach has also been applied for other crops in

  7. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  9. 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

  10. 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.

  11. The Large Area Crop Inventory Experiment (LACIE). Part 2: Yield estimates from meteorological information

    NASA Technical Reports Server (NTRS)

    Waite, P. J.

    1975-01-01

    Crop-weather models developed for wheat from an assembled base of approximately 45 years of climatic records and historic wheat yield and production data at the stratum level are described. Utilization of meteorological data acquired by the NOAA satellites is discussed.

  12. 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.

  13. 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.

  14. Growth stage estimation. [crop calendars

    NASA Technical Reports Server (NTRS)

    Whitehead, V. S.; Phinney, D. E.; Crea, W. E. (Principal Investigator)

    1979-01-01

    Of the three candidate approaches to adjustment of the crop calendar to account for year-to-year weather differences, the Robertson triquadratic unit, a function of a nonlinear function of maximum and minimum temperature and day length, best described the rate of phenological development of wheat. The adjustable crop calendar (ACC) as implemented for LACIE is used to calculate the daily increment of development through six physiological stages of growth. Topics covered include dormancy modeling, the spring restart model, spring wheat starter model, winter starter model, winter wheat starter model, inclusion of the moisture variable, and display of crop stage estimation results. Assessment of the ACC accuracy over the period of LACIE operation indicates that the adjustable crop calendars used provided more accurate information than would have been available using historical norms. The models performed best under the conditions from which they were derived (Canadian spring wheat) and most poorly for the dwarf varieties and Southern Hemisphere applications.

  15. Winter wheat yield estimation of remote sensing research based on WOFOST crop model and leaf area index assimilation

    NASA Astrophysics Data System (ADS)

    Chen, Yanling; Gong, Adu; Li, Jing; Wang, Jingmei

    2017-04-01

    Accurate crop growth monitoring and yield predictive information are significant to improve the sustainable development of agriculture and ensure the security of national food. Remote sensing observation and crop growth simulation models are two new technologies, which have highly potential applications in crop growth monitoring and yield forecasting in recent years. However, both of them have limitations in mechanism or regional application respectively. Remote sensing information can not reveal crop growth and development, inner mechanism of yield formation and the affection of environmental meteorological conditions. Crop growth simulation models have difficulties in obtaining data and parameterization from single-point to regional application. In order to make good use of the advantages of these two technologies, the coupling technique of remote sensing information and crop growth simulation models has been studied. Filtering and optimizing model parameters are key to yield estimation by remote sensing and crop model based on regional crop assimilation. Winter wheat of GaoCheng was selected as the experiment object in this paper. And then the essential data was collected, such as biochemical data and farmland environmental data and meteorological data about several critical growing periods. Meanwhile, the image of environmental mitigation small satellite HJ-CCD was obtained. In this paper, research work and major conclusions are as follows. (1) Seven vegetation indexes were selected to retrieve LAI, and then linear regression model was built up between each of these indexes and the measured LAI. The result shows that the accuracy of EVI model was the highest (R2=0.964 at anthesis stage and R2=0.920 at filling stage). Thus, EVI as the most optimal vegetation index to predict LAI in this paper. (2) EFAST method was adopted in this paper to conduct the sensitive analysis to the 26 initial parameters of the WOFOST model and then a sensitivity index was constructed

  16. Estimation of inter-annual winter crop area variation and spatial distribution with low resolution NDVI data by using neural networks trained on high resolution images

    NASA Astrophysics Data System (ADS)

    Atzberger, C.; Rembold, F.

    2009-09-01

    The current work aimed at testing a methodology which can be applied to low spatial resolution satellite data to assess interannual crop area variations on a regional scale. The methodology is based on the assumption that within mixed pixels such variations are reflected by changes in the related multitemporal Normalised Difference Vegetation Index (NDVI) profiles. This implies that low resolution NDVI images with high temporal frequency can be used to update land cover estimates derived from higher resolution cartography. More particularly, changes in the shape of annual NDVI profiles can be detected by a Neural Network trained by using high resolution images for a subset of the study years. By taking into account the respective proportions of the remaining land covers within a given low resolution pixel, the accuracy of the net can be further increased. The proposed methodology was applied in a study region in central Italy to estimate area changes of winter crops from low resolution NDVI profiles. The accuracy of such estimates was assessed by comparison to official agricultural statistics using a bootstrap approach. The method showed promise for estimating crop area variation on a regional scale and proved to have a significantly higher forecast capability than other methods used previously for the same study area.

  17. Multi-Crop Specific Area Frame Stratification Based on Geospatial Crop Planting Frequency Data Layers

    NASA Astrophysics Data System (ADS)

    Boryan, C. G.; Yang, Z.; Willis, P.; Di, L.

    2016-12-01

    Area sampling frames (ASFs) are the basis of many statistical programs around the world. When an ASF's stratification is based on generalized percent cultivation, the ASF usually cannot identify the planting location of specific crops targeted for agricultural surveys. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency data is proposed. The Crop Planting Frequency Data Layers are crop specific geospatial data sets derived from multi-year Cropland Data Layers. Therefore, the ASF stratification based on the crop planting frequency data is crop specific. This paper investigates using 2008-2013 geospatial Crop Frequency Data Layers to create a novel multi-crop specific stratification for South Dakota, U.S. The crop specific ASF stratification is developed based on crop frequency statistics calculated at the primary sampling unit (PSU) level based on the corn, soybean and wheat planting frequency data layers, three major crops in South Dakota. Strata are formed using a k means clustering algorithm. It is observed that the crop frequency based ASF stratification predicts corn, soybean and wheat planting patterns well as verified by 2014 Farm Service Agency (FSA) Common Land Unit (CLU) and 578 administrative data. This finding demonstrates that the novel multi-crop specific stratification based on crop planting frequency data is crop type independent and applicable to all major crops. Further, these results indicate that the new multi-crop specific ASF stratification has great potential to improve ASF accuracy, efficiency and crop estimates.

  18. Crop monitoring using remote sensing orientated for government decision making and agricultural management: a case study of China's soybean planting area estimation

    NASA Astrophysics Data System (ADS)

    Yang, Bangjie; Qian, Yonglan; Pei, Zhiyuan; Jiao, Xianfeng

    2006-12-01

    China is one of the main soybean production countries in the world and soybean is of great importance in agricultural industry, domestic consumption and international trade. In recent years, however, China has become the largest soybean importer in the world. Therefore timely credible information about soybean planting area and production is essential for government decision making and agricultural management on domestic consumption and international trade. Moreover, information on soybean planting and continuous planting location is critical for distributing farmer subsidies and production management. In this paper, an operational system based on multi-resolution remotely sensed data was developed for the soybean area inventory and continuous cropping area monitoring. A stratified sampling method is employed to extract and locate major soybean-planting regions, which are later surveyed using remote sensing data. At the same time, sub regions are constructed based on cropping systems in which remotely sensed data of different resolutions are applied for the soybean area estimation and replanting area location assessment.

  19. Estimation of flood losses to agricultural crops using remote sensing

    NASA Astrophysics Data System (ADS)

    Tapia-Silva, Felipe-Omar; Itzerott, Sibylle; Foerster, Saskia; Kuhlmann, Bernd; Kreibich, Heidi

    2011-01-01

    The estimation of flood damage is an important component of risk-oriented flood design, risk mapping, financial appraisals and comparative risk analyses. However, research on flood loss modelling, especially in the agricultural sector, has not yet gained much attention. Agricultural losses strongly depend on the crops affected, which need to be predicted accurately. Therefore, three different methods to predict flood-affected crops using remote sensing and ancillary data were developed, applied and validated. These methods are: (a) a hierarchical classification based on standard curves of spectral response using satellite images, (b) disaggregation of crop statistics using a Monte Carlo simulation and probabilities of crops to be cultivated on specific soils and (c) analysis of crop rotation with data mining Net Bayesian Classifiers (NBC) using soil data and crop data derived from a multi-year satellite image analysis. A flood loss estimation model for crops was applied and validated in flood detention areas (polders) at the Havel River (Untere Havelniederung) in Germany. The polders were used for temporary storage of flood water during the extreme flood event in August 2002. The flood loss to crops during the extreme flood event in August 2002 was estimated based on the results of the three crop prediction methods. The loss estimates were then compared with official loss data for validation purposes. The analysis of crop rotation with NBC obtained the best result, with 66% of crops correctly classified. The accuracy of the other methods reached 34% with identification using Normalized Difference Vegetation Index (NDVI) standard curves and 19% using disaggregation of crop statistics. The results were confirmed by evaluating the loss estimation procedure, in which the damage model using affected crops estimated by NBC showed the smallest overall deviation (1%) when compared to the official losses. Remote sensing offers various possibilities for the improvement of

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. Chloride profile technique to estimate water movement through unsatured zone in a cropped area in subhumid climate (Po Valley—NW Italy)

    NASA Astrophysics Data System (ADS)

    Lo Russo, Stefano; Zavattaro, Laura; Acutis, Marco; Zuppi, Gian Maria

    2003-01-01

    Two methods based on a chloride concentration profile were applied to evaluate the annual groundwater recharge in a subhumid area cropped with maize where chloride anthropogenic inputs were greater than the natural ones. The site is located in the alluvial Po plain (NW Italy). The two methods were a steady-state model and an approximate diffusive movement equation. They were applied to the Cl - content of retention water extracted from porous cups on 24 sampling dates through one year. The sampling depth ranged from 0.2 to 2.6 m, and the concentration was steady in time below 1.6 m. Considering all the approximations introduced (homogeneous and non-dispersive medium, constant diffusion coefficient with depth, all the liquid phase in movement, no macroporosity, steady state conditions), the results were consistent with those obtained with a long-term mass-balance method. The mean annual recharge assessed using the steady-state chloride profile method was 205 mm yr -1, using the approximate diffusive movement equation it was 216 mm yr -1, while using the mass-balance method the calculated mean recharge was 174 mm yr -1. The estimate showed a moderate dependence with the sampling date. Chloride inputs were wet and dry atmospheric deposition, irrigation water and fertilizers. The incorporation of chloride fertilizers to the soil is one of the more unusual aspects of the chloride approach in this study.

  5. Mapping crop coefficients in irrigated areas from Landsat TM images

    NASA Astrophysics Data System (ADS)

    D'Urso, Guido; Menenti, Massimo

    1995-11-01

    It is well known that reflectance of Earth surface largely depends upon amount of biomass, crop type, development stage, ground coverage. The knowledge of these parameters -- together with groundbased meteorological data -- allows for the estimate of crop water requirements and their spatial distribution. Recent research has shown the possibility of using multispectral satellite images in combination with other information for mapping crop coefficients in irrigated areas. This approach is based on the assumption that crop coefficients (Kc) are greatly influenced by canopy development and vegetation fractional ground cover; since these parameters directly affect the reflectance of cropped areas, it is possible to establish a correlation between multispectral measurements of canopies reflectance and the corresponding Kc values. Within this frame, two different approaches may be applied: (1) definition of spectral classes corresponding to different crop coefficient values and successive supervised classification for the derivation of crop coefficients maps; (2) use of analytical relationships between the surface reflectance and the corresponding values of vegetation parameters, i.e., the leaf area index, the albedo and the surface roughness, needed for the calculation of the potential evapotranspiration according to the combination type equation. The two different techniques are discussed with reference to the results of their application to specific case-studies. The aim of this report is to illustrate the suitability of remote sensing techniques as an operational tool for assessing crop water demand at regional scale.

  6. 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

  7. 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 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

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

    USDA-ARS?s Scientific Manuscript database

    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....

  9. 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.

  10. 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.

  11. 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.

  12. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  14. 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.

  15. 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.

  16. 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.

  17. SAFIS Area Estimation Techniques

    Treesearch

    Gregory A. Reams

    2000-01-01

    The Southern Annual Forest inventory System (SAFIS) is in various stages of implementation in 8 of the 13 southern states served by the Southern Research Station of the USDA Forest Service. Compared to periodic inventories, SAFIS requires more rapid generation of land use and land cover maps. The current photo system for phase one area estimation has changed little...

  18. SAFIS area estimation techniques

    Treesearch

    Gregory A. Reams

    2000-01-01

    The Southern Annual Forest Inventory System (SAFIS) is in various stages of implementation in 8 of the 13 southern states served by the Southern Research Station of the USDA Forest Service. Compared to periodic inventories, SAFIS requires more rapid generation of land use and land cover maps. The current photo system for phase one area estimation has changed little...

  19. Remote Estimation of Crop Biophysical Characteristics: Problems and Solutions

    NASA Astrophysics Data System (ADS)

    Gitelson, A. A.; Rundquist, D. C.; Keydan, G. P.

    2007-12-01

    Characterization of crop physiological and phenological status, or crop condition, requires robust retrievals of important crop biophysical properties, preferably using non-destructive methods. The sensitivity of the widely used Normalized Difference Vegetation Index (NDVI) saturates at moderate levels of aboveground biomass; i.e., when leaf area index (LAI) increases above about 2. We report the results of our investigation of the performance of an advanced suite of four vegetation indices that expand the dynamic ranges of canopy biophysical properties over high biomass surfaces. The indices are: 1) the Visible Atmospherically Resistant Vegetation Index (VARI), for retrieving the fractional cover of green vegetation; 2) the Wide Dynamic Range Vegetation Index (WDRVI), which allows retrieval of LAI: 3) the Green NDVI and Red Edge NDVI, designed to yield the photosynthetically active component of total absorbed photosynthetically active radiation (fAPAR); and 4) Green and Red Edge Chlorophyll Indices for measuring the total chlorophyll content in a vegetation canopy. We discuss the results of estimating the biophysical characteristics noted above using close range sensing (reflectance taken 6 meters above the canopy), an airborne imaging spectrometer and satellite observations. The techniques were tested for maize and soybean in agricultural fields under irrigated and rainfed conditions. It is possible to accurately estimate the fractional cover of green vegetation, the photosynthetically active component of absorbed PAR, green LAI and chlorophyll content in crops with different canopy architectures and leaf structures with green leaf area indices ranging from 0 to more than 6.

  20. Crop acreage estimation using a Landsat-based estimator as an auxiliary variable

    NASA Technical Reports Server (NTRS)

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

    1986-01-01

    The problem of improving upon the ground survey estimates of crop acreages by utilizing Landsat data is addressed. Three estimators, called regression, ratio, and stratified ratio, are studied for bias and variance, and their relative efficiencies are compared. 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 complete coverage for an area of interest, and then to conduct simulation studies. It is shown over a wide range of parametric 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 the ratio and stratified ratio estimators are better. Furthermore, it is seen that the regression estimator is potentially biased due to estimating the regression coefficient from the training sample segments. Estimation of the variance of the regression estimator is also investigated. Two variance estimators are considered, the large sample variance estimator and an alternative estimator suggested by Cochran. The large sample estimate of variance is found to be biased and inferior to the Cochran estimate for small sample sizes.

  1. Estimating yield gaps at the cropping system level.

    PubMed

    Guilpart, Nicolas; Grassini, Patricio; Sadras, Victor O; Timsina, Jagadish; Cassman, Kenneth G

    2017-05-01

    Yield gap analyses of individual crops have been used to estimate opportunities for increasing crop production at local to global scales, thus providing information crucial to food security. However, increases in crop production can also be achieved by improving cropping system yield through modification of spatial and temporal arrangement of individual crops. In this paper we define the cropping system yield potential as the output from the combination of crops that gives the highest energy yield per unit of land and time, and the cropping system yield gap as the difference between actual energy yield of an existing cropping system and the cropping system yield potential. Then, we provide a framework to identify alternative cropping systems which can be evaluated against the current ones. A proof-of-concept is provided with irrigated rice-maize systems at four locations in Bangladesh that represent a range of climatic conditions in that country. The proposed framework identified (i) realistic alternative cropping systems at each location, and (ii) two locations where expected improvements in crop production from changes in cropping intensity (number of crops per year) were 43% to 64% higher than from improving the management of individual crops within the current cropping systems. The proposed framework provides a tool to help assess food production capacity of new systems (e.g. with increased cropping intensity) arising from climate change, and assess resource requirements (water and N) and associated environmental footprint per unit of land and production of these new systems. By expanding yield gap analysis from individual crops to the cropping system level and applying it to new systems, this framework could also be helpful to bridge the gap between yield gap analysis and cropping/farming system design.

  2. 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.

  3. 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.

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

    DOE PAGES

    Ye, Qing; Yang, Xiaoguang; Dai, Shuwei; ...

    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.

  5. 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

  6. Statistical estimates to emulate yields from global gridded crop models

    NASA Astrophysics Data System (ADS)

    Blanc, Elodie

    2016-04-01

    This study provides a statistical emulator of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly weather variables for over a century at the grid cell level. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields and temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather, especially for extreme temperature and precipitation events. In- and out-of-sample validations show that the statistical models are able to closely replicate crop yields projected by the crop models and perform well out-of-sample. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools will be useful for climate change impact assessments and to account for uncertainty in crop modeling.

  7. Methods to estimate irrigated reference crop evapotranspiration - a review.

    PubMed

    Kumar, R; Jat, M K; Shankar, V

    2012-01-01

    Efficient water management of crops requires accurate irrigation scheduling which, in turn, requires the accurate measurement of crop water requirement. Irrigation is applied to replenish depleted moisture for optimum plant growth. Reference evapotranspiration plays an important role for the determination of water requirements for crops and irrigation scheduling. Various models/approaches varying from empirical to physically base distributed are available for the estimation of reference evapotranspiration. Mathematical models are useful tools to estimate the evapotranspiration and water requirement of crops, which is essential information required to design or choose best water management practices. In this paper the most commonly used models/approaches, which are suitable for the estimation of daily water requirement for agricultural crops grown in different agro-climatic regions, are reviewed. Further, an effort has been made to compare the accuracy of various widely used methods under different climatic conditions.

  8. 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.

  9. 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.

  10. Damage estimation on agricultural crops by a flood

    NASA Astrophysics Data System (ADS)

    del Carmen Silva-Aguila, Nalleli; López-Caloca, Alejandra; Silván-Cárdenas, José Luis

    2011-11-01

    Southeastern Mexico, particularly Tabasco's flatlands which experienced a severe flood in 2007, was used as a case study for testing a methodology for the estimation of direct damage looses on agricultural crops by flooding. We proposed an accurate delineation of agricultural lands of multispectral images (SPOT-5) which consist on ensemble classifiers trough a majority voting, that combine spatial and spectral information. Finally in order to evaluate the impact of floodwater, a radar data (RADARSAT-1), were used for both, delineating the flood extent and estimating water depth. These layers were overlaid on the agricultural crop classification layer, and crop yield damage was estimated using a depth damage function. The results of this research quantified and evaluated the overall economic loss (tangible damage) from the impact of floodwater on agricultural crops.

  11. Remote estimation of canopy chlorophyll content in crops

    NASA Astrophysics Data System (ADS)

    Gitelson, Anatoly A.; Viña, Andrés; Ciganda, Verónica; Rundquist, Donald C.; Arkebauer, Timothy J.

    2005-04-01

    Accurate estimation of spatially distributed chlorophyll content (Chl) in crops is of great importance for regional and global studies of carbon balance and responses to fertilizer (e.g., nitrogen) application. In this paper a recently developed conceptual model was applied for remotely estimating Chl in maize and soybean canopies. We tuned the spectral regions to be included in the model, according to the optical characteristics of the crops studied, and showed that the developed technique allowed accurate estimation of total Chl in both crops, explaining more than 92% of Chl variation. This new technique shows great potential for remotely tracking the physiological status of crops, with contrasting canopy architectures, and their responses to environmental changes.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. Satellite Estimation of Fractional Cover in Several California Specialty Crops

    NASA Technical Reports Server (NTRS)

    Johnson, Lee; Cahn, Michael; Rosevelt, Carolyn; Guzman, Alberto; Farrara, Barry; Melton, Forrest S.

    2016-01-01

    Past research in California and elsewhere has revealed strong relationships between satellite NDVI, photosynthetically active vegetation fraction (Fc), and crop evapotranspiration (ETc). Estimation of ETc can support efficiency of irrigation practice, which enhances water security and may mitigate nitrate leaching. The U.C. Cooperative Extension previously developed the CropManage (CM) web application for evaluation of crop water requirement and irrigation scheduling for several high-value specialty crops. CM currently uses empirical equations to predict daily Fc as a function of crop type, planting date and expected harvest date. The Fc prediction is transformed to fraction of reference ET and combined with reference data from the California Irrigation Management Information System to estimate daily ETc. In the current study, atmospherically-corrected Landsat NDVI data were compared with in-situ Fc estimates on several crops in the Salinas Valley during 2011-2014. The satellite data were observed on day of ground collection or were linearly interpolated across no more than an 8-day revisit period. Results will be presented for lettuce, spinach, celery, broccoli, cauliflower, cabbage, peppers, and strawberry. An application programming interface (API) allows CM and other clients to automatically retrieve NDVI and associated data from NASA's Satellite Irrigation Management Support (SIMS) web service. The SIMS API allows for queries both by individual points or user-defined polygons, and provides data for individual days or annual timeseries. Updates to the CM web app will convert these NDVI data to Fc on a crop-specific basis. The satellite observations are expected to play a support role in Salinas Valley, and may eventually serve as a primary data source as CM is extended to crop systems or regions where Fc is less predictable.

  19. Satellite Estimation of Fractional Cover in Several California Specialty Crops

    NASA Astrophysics Data System (ADS)

    Johnson, L.; Cahn, M.; Rosevelt, C.; Guzman, A.; Lockhart, T.; Farrara, B.; Melton, F. S.

    2016-12-01

    Past research in California and elsewhere has revealed strong relationships between satellite NDVI, photosynthetically active vegetation fraction (Fc), and crop evapotranspiration (ETc). Estimation of ETc can support efficiency of irrigation practice, which enhances water security and may mitigate nitrate leaching. The U.C. Cooperative Extension previously developed the CropManage (CM) web application for evaluation of crop water requirement and irrigation scheduling for several high-value specialty crops. CM currently uses empirical equations to predict daily Fc as a function of crop type, planting date and expected harvest date. The Fc prediction is transformed to fraction of reference ET and combined with reference data from the California Irrigation Management Information System to estimate daily ETc. In the current study, atmospherically-corrected Landsat NDVI data were compared with in-situ Fc estimates on several crops in the Salinas Valley during 2011-2014. The satellite data were observed on day of ground collection or were linearly interpolated across no more than an 8-day revisit period. Results will be presented for lettuce, spinach, celery, broccoli, cauliflower, cabbage, peppers, and strawberry. An application programming interface (API) allows CM and other clients to automatically retrieve NDVI and associated data from NASA's Satellite Irrigation Management Support (SIMS) web service. The SIMS API allows for queries both by individual points or user-defined polygons, and provides data for individual days or annual timeseries. Updates to the CM web app will convert these NDVI data to Fc on a crop-specific basis. The satellite observations are expected to play a support role in Salinas Valley, and may eventually serve as a primary data source as CM is extended to crop systems or regions where Fc is less predictable.

  20. 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.

  1. 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

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

    USDA-ARS?s Scientific Manuscript database

    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 ...

  3. 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.

  4. Monitoring paddy rice crops through remote sensing: productivity estimation by light use efficiency model

    NASA Astrophysics Data System (ADS)

    Boschetti, Mirco; Mauri, Emanuela; Gadda, Chiara; Busetto, Lorenzo; Confalonieri, Roberto; Bocchi, Stefano; Brivio, Pietro A.

    2004-10-01

    Rice is one of the most important crops in the whole world, providing staple food for more than 3000 million people. For this reason FAO declared the year 2004 as The International Year of Rice promoting initiatives and researches on this valuable crop. Assessing the Net Primary Production (NPP) is fundamental to support a sustainable development and to give crop yield forecast essential to food security policy. Crop growth models can be useful tools for estimating growth, development and yield but require complex spatial distributed input parameters to produce valuable map. Light use efficiency (LUE) models, using satellite-borne data to achieve daily surface parameters, represent an alternative approach able to monitor differences in vegetation compound providing spatial distributed NPP maps. An experiment aimed at testing the capability of a LUE model using daily MODIS data to estimate rice crop production was conducted in a rice area of Northern Italy. Direct LAI measurements and indirect LAI2000 estimation were collected on different fields during the growing season to define a relationship with MODIS data. An hyperspectral MIVIS image was acquired in early July on the experimental site to provide high spatial resolution information on land cover distribution. LUE-NPP estimations on several fields were compared with CropSyst model outputs and field biomass measurements. A comparison of different methods performance is presented and relative advantages and drawbacks in spatialization are discussed.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. Impacts of spatial heterogeneity on crop area mapping in Canada using MODIS data

    NASA Astrophysics Data System (ADS)

    Chen, Yaoliang; Song, Xiaodong; Wang, Shusen; Huang, Jingfeng; Mansaray, Lamin R.

    2016-09-01

    Accurately mapping crop area using coarse spatial resolution remote sensing imageries is challenging due to the existence of various spatial heterogeneities. The objective of this study is to analyze the accuracy of crop classification and area estimation affected by spatial heterogeneities, especially for sample impurity and landscape heterogeneity. The Normalized Difference Vegetation Index (NDVI) time series calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09Q1 8-day composites and the derived phenology metrics were used to classify crop areas over Manitoba, Canada. The Classification and Regression Trees (CART) approach was applied in the classification. The Agriculture and Agri-Food Canada (AAFC) Land Cover Dataset with 30 m spatial resolution was used as the base map to determine the study regions and training and validation samples. The results allowed to conclude that: (1) the classification accuracy of MODIS imagery is sensitive to both sample impurity and landscape heterogeneity. Purity limitations in samples can have a large impact on the classification accuracy. Regions with more homogenous pixels are more likely to be accurately classified and vice versa; (2) the crop area estimation error is less sensitive to sample impurity. It is not only determined by the purity of training samples but also by the actual purity condition of the crop type. The purest training sample group does not correspond well with the lowest error; (3) the impact of configurational heterogeneity on the area estimation is more significant than that of the compositional heterogeneity. Overall, both the sample impurity and landscape heterogeneities can largely affect the classification accuracy while only configurational heterogeneity has significant influence on crop area estimation.

  11. Potential crop evapotranspiration and surface evaporation estimates via a gridded weather forcing dataset

    NASA Astrophysics Data System (ADS)

    Lewis, Clayton S.; Allen, L. Niel

    2017-03-01

    Absent local weather stations, a gridded weather dataset can provide information useful for water management in irrigated areas including potential crop evapotranspiration calculations. In estimating crop irrigation requirements and surface evaporation in Utah, United States of America, methodology and software were developed using the ASCE Standardized Penman-Monteith Reference Evapotranspiration equation with input climate drivers from the North American Land Data Assimilation System (NLDAS) gridded weather forcing dataset and a digital elevation model. A simple procedure was devised to correct bias in NLDAS relative humidity and air temperature data based on comparison to weather data from ground stations. Potential evapotranspiration was calculated for 18 crops (including turfgrass), wetlands (large and narrow), and open water evaporation (deep and shallow) by multiplying crop coefficient curves to reference evapotranspiration with annual curve dates set by summation of Hargreaves evapotranspiration, cumulative growing degree days, or number of days. Net potential evapotranspiration was calculated by subtracting effective precipitation estimates from the Daymet gridded precipitation dataset. Analysis of the results showed that daily estimated potential crop evapotranspiration from the model compared well with estimates from electronic weather stations (1980-2014) and with independently calculated potential crop evapotranspiration in adjacent states. Designed for this study but open sourced for other applications, software entitled GridET encapsulated the GIS-based model that provided data download and management, calculation of reference and potential crop evapotranspiration, and viewing and analysis tools. Flexible features in GridET allows a user to specify grid resolution, evapotranspiration equations, cropping information, and additional datasets with the output being transferable to other GIS software.

  12. Estimating crop acreage from space-simulated multispectral scanner data

    NASA Technical Reports Server (NTRS)

    Nalepka, R. F.; Hyde, P. D.

    1973-01-01

    The need for multispectral data processing methods to permit the estimation of proportions of objects and materials appearing within the instantaneous field of view of a scanning system is discussed. An algorithm developed for proportion estimation is described as well as other supporting processing techniques. Application of this algorithm to space-simulated multispectral scanner data is discussed and some results presented and compared. Results indicate that, for this data set, the true proportions of the various crops contained within this data set are with one exception more closely in agreement with the proportions determined by the proportion estimation algorithm than with the proportions determined by conventional classfication algorithm.

  13. 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

  14. Sub-pixel Area Calculation Methods for Estimating Irrigated Areas.

    PubMed

    Thenkabailc, Prasad S; Biradar, Chandrashekar M; Noojipady, Praveen; Cai, Xueliang; Dheeravath, Venkateswarlu; Li, Yuanjie; Velpuri, Manohar; Gumma, Muralikrishna; Pandey, Suraj

    2007-10-31

    The goal of this paper was to develop and demonstrate practical methods forcomputing sub-pixel areas (SPAs) from coarse-resolution satellite sensor data. Themethods were tested and verified using: (a) global irrigated area map (GIAM) at 10-kmresolution based, primarily, on AVHRR data, and (b) irrigated area map for India at 500-mbased, primarily, on MODIS data. The sub-pixel irrigated areas (SPIAs) from coarse-resolution satellite sensor data were estimated by multiplying the full pixel irrigated areas(FPIAs) with irrigated area fractions (IAFs). Three methods were presented for IAFcomputation: (a) Google Earth Estimate (IAF-GEE); (b) High resolution imagery (IAF-HRI); and (c) Sub-pixel de-composition technique (IAF-SPDT). The IAF-GEE involvedthe use of "zoom-in-views" of sub-meter to 4-meter very high resolution imagery (VHRI)from Google Earth and helped determine total area available for irrigation (TAAI) or netirrigated areas that does not consider intensity or seasonality of irrigation. The IAF-HRI isa well known method that uses finer-resolution data to determine SPAs of the coarser-resolution imagery. The IAF-SPDT is a unique and innovative method wherein SPAs aredetermined based on the precise location of every pixel of a class in 2-dimensionalbrightness-greenness-wetness (BGW) feature-space plot of red band versus near-infraredband spectral reflectivity. The SPIAs computed using IAF-SPDT for the GIAM was within2 % of the SPIA computed using well known IAF-HRI. Further the fractions from the 2 methods were significantly correlated. The IAF-HRI and IAF-SPDT help to determine annualized or gross irrigated areas (AIA) that does consider intensity or seasonality (e.g., sum of areas from season 1, season 2, and continuous year-round crops). The national census based irrigated areas for the top 40 irrigated nations (which covers about 90% of global irrigation) was significantly better related (and had lesser uncertainties and errors) when compared to SPIAs than

  15. 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.

  16. A synergistic approach using optical and SAR data to estimate crop's irrigation requirements

    NASA Astrophysics Data System (ADS)

    Rolim, João.; Navarro Ferreira, Ana; Saraiva, Cátia; Catalão, João.

    2016-10-01

    A study conducted in the scope of the Alcantara initiative in Angola shown that optical and SAR images allows the estimation of crop's irrigation requirements (CIR) based on a soil water balance model (IrrigRotation). The methodology was applied to east central Portugal, to evaluate its transferability in cases of different climatic conditions and crop types. SPOT-5 Take-5 and Sentinel-1A data from April to September 2015 are used to generate NDVI and backscattering maize crop time series. Both time series are then correlated and a linear regression equation is computed for some maize parcels identified in the test area. Next, basal crop coefficients (Kcb) are determined empirically from the Kcb-NDVI relationships applied within the PLEIADeS project and also from the Kcb-SAR relationships retrieved from the linear fit of both EO data for other maize parcels. These Kcb allow to overcome a major drawback related to the use of the FAO tabulated Kcb, only available for the initial, mid and late season of a certain crop type. More frequent Kcb values also allow a better identification of the crop's phenological stages lengths. CIR estimated from EO data are comparable to the ones obtained with tabulated FAO 56 Kcb values for crops produced under standard conditions, while for crops produced in suboptimal conditions, EO data allow to improve the estimation of the CIR. Although CIR results are promising, further research is required in order to improve the Kcb initial and Kcb end values to avoid the overestimation of the CIR.

  17. 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.

  18. How does spatial and temporal resolution of vegetation index impact crop yield estimation?

    USDA-ARS?s Scientific Manuscript database

    Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing data have long been used in crop yield estimation for decades. The process-based approach uses light use efficiency model to estimate crop yield. Vegetation index (VI) ...

  19. 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

    The NASA-U.S. Department of Agriculture Large Area Crop Inventory Experiment (LACIE), aimed at using multispectral remote sensing data from Landsat 1 and 2 to generate accurate annual global crop production forecasts, is discussed. The forecasts take into account meteorological conditions as well as yield and acreage, and may be used to increase the discrimination of U.S. harvest estimates down to regional levels and to provide more accurate early-season predictions. Sample problems involving the determination of wheat harvests and the monitoring of drought conditions are described. Difficulties related to misidentification of abnormally-developing plantations, the automatic classification of homogeneous spectral groups, the computerized generation of colored maps, and the estimation of yields during years when exceptional meteorological conditions prevail are also considered. Samples of Landsat-generated classification maps for Western U.S. and for the Saratov, U.S.S.R. crop regions are given.

  20. 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.

  1. Temperature increase reduces global yields of major crops in four independent estimates.

    PubMed

    Zhao, Chuang; Liu, Bing; Piao, Shilong; Wang, Xuhui; Lobell, David B; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe; Durand, Jean-Louis; Elliott, Joshua; Ewert, Frank; Janssens, Ivan A; Li, Tao; Lin, Erda; Liu, Qiang; Martre, Pierre; Müller, Christoph; Peng, Shushi; Peñuelas, Josep; Ruane, Alex C; Wallach, Daniel; Wang, Tao; Wu, Donghai; Liu, Zhuo; Zhu, Yan; Zhu, Zaichun; Asseng, Senthold

    2017-08-29

    Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.

  2. Temperature Increase Reduces Global Yields of Major Crops in Four Independent Estimates

    NASA Technical Reports Server (NTRS)

    Zhao, Chuang; Liu, Bing; Piao, Shilong; Wang, Xuhui; Lobell, David B.; Huang, Yao; Huang, Mengtian; Yao, Yitong; Bassu, Simona; Ciais, Philippe; hide

    2017-01-01

    Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multi-method analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.

  3. [Prediction of winter wheat yield based on crop biomass estimation at regional scale].

    PubMed

    Ren, Jian-Qiang; Liu, Xing-Ren; Chen, Zhong-Xin; Zhou, Qing-Bo; Tang, Hua-Jun

    2009-04-01

    Based on the 2004 in situ data of crop yield, remote sensing inversed photosynthetically active radiation (PAR), fraction of photosynthetically active radiation (f(PAR)), climate, and soil moisture in 83 typical winter wheat sampling field of 45 counties in Shijiazhuang, Hengshui, and Xingtai of Hebei Province, a simplified model for calculating the light use efficiency (epsilon) of winter wheat in Huanghuaihai Plain was established. According to the crop accumulated biomass from March to May and corrected by harvest index, the quantitative relationship between crop biomass and crop yield for winter wheat was set up, and applied in the 235 counties in Huanghuaihai Plain region of Hebei Province and Shandong Province and validated by the official crop statistical data at county level in 2004. The results showed that the root mean square error (RMSE) of predicted winter wheat yield in study area was 238.5 kg x hm(-2), and the relative error was 4.28%, suggesting that it was feasible to predict winter wheat yield by crop biomass estimation based on remote sensing data.

  4. 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.

  5. 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

  6. A generic model for estimating biomass accumulation and greenhouse gas emissions from perennial crops

    NASA Astrophysics Data System (ADS)

    Ledo, Alicia; Heathcote, Richard; Hastings, Astley; Smith, Pete; Hillier, Jonathan

    2017-04-01

    Agriculture is essential to maintain humankind but is, at the same time, a substantial emitter of greenhouse gas (GHG) emissions. With a rising global population, the need for agriculture to provide secure food and energy supply is one of the main human challenges. At the same time, it is the only sector which has significant potential for negative emissions through the sequestration of carbon and offsetting via supply of feedstock for energy production. Perennial crops accumulate carbon during their lifetime and enhance organic soil carbon increase via root senescence and decomposition. However, inconsistency in accounting for this stored biomass undermines efforts to assess the benefits of such cropping systems when applied at scale. A consequence of this exclusion is that efforts to manage this important carbon stock are neglected. Detailed information on carbon balance is crucial to identify the main processes responsible for greenhouse gas emissions in order to develop strategic mitigation programs. Perennial crops systems represent 30% in area of total global crop systems, a considerable amount to be ignored. Furthermore, they have a major standing both in the bioenergy and global food industries. In this study, we first present a generic model to calculate the carbon balance and GHGs emissions from perennial crops, covering both food and bioenergy crops. The model is composed of two simple process-based sub-models, to cover perennial grasses and other perennial woody plants. The first is a generic individual based sub-model (IBM) covering crops in which the yield is the fruit and the plant biomass is an unharvested residue. Trees, shrubs and climbers fall into this category. The second model is a generic area based sub-model (ABM) covering perennial grasses, in which the harvested part includes some of the plant parts in which the carbon storage is accounted. Most second generation perennial bioenergy crops fall into this category. Both generic sub

  7. The Maximum Likelihood Estimation of Signature Transformation /MLEST/ algorithm. [for affine transformation of crop inventory data

    NASA Technical Reports Server (NTRS)

    Thadani, S. G.

    1977-01-01

    The Maximum Likelihood Estimation of Signature Transformation (MLEST) algorithm is used to obtain maximum likelihood estimates (MLE) of affine transformation. The algorithm has been evaluated for three sets of data: simulated (training and recognition segment pairs), consecutive-day (data gathered from Landsat images), and geographical-extension (large-area crop inventory experiment) data sets. For each set, MLEST signature extension runs were made to determine MLE values and the affine-transformed training segment signatures were used to classify the recognition segments. The classification results were used to estimate wheat proportions at 0 and 1% threshold values.

  8. Remote Estimation Of Net Ecosystem Carbon Dioxide Exchange In Crops: Principles, Algorithm Calibration And Validation

    NASA Astrophysics Data System (ADS)

    Gitelson, A. A.; Vina, A.; Verma, S. B.; Rundquist, D. C.; Keydan, G. P.; Leavitt, B.; Arkebauer, T. J.; Burba, G. G.; Suyker, A. E.

    2004-12-01

    Accurate estimation of spatially distributed CO2 fluxes is of great importance for regional and global carbon balance studies. Tower-based instruments provide flux data from a small footprint area and scaling beyond the footprint to the region is quite challenging. We developed a technique that relates tower-based mid-day CO2 exchange data with remotely sensed reflectances in the near infrared and either the green (around 550 nm) or the red-edge (near 700 nm) spectral ranges, to accurately estimate net ecosystem CO2 exchange (NEE) in commodity crops. The technique, which is solely based on remotely sensed data, was tested for mid-day NEE estimation in irrigated and rainfed maize and soybean during three seasons (2001 through 2003). The technique provides accurate estimations of mid-day NEE in crops, explaining more than 88% of NEE variation in maize and 86% in soybean, and shows great potential for remotely tracking crop NEE. The technique was validated by an independent data set; root mean square error in predicting mid-day NEE in the range 0-2.5 mgCm-2s-1 was 0.3 mgCm-2s-1 using NIR and red-edge bands and 0.38 mgCm-2s-1 using NIR and green bands. The developed technique will improve our understanding of how to retrieve crop ecosystem CO2 exchange synoptically. By improving the accuracy of retrievals, we will advance the understanding of regional and global carbon dynamics, reducing the uncertainties attendant to NEE estimation in crops.

  9. 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.

  10. 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.

  11. Health Service Areas (HSAs) - Small Area Estimates

    Cancer.gov

    Health Service Areas (HSAs) are a compromise between the 3000 counties and the 50 states. An HSA may be thought of as an area that is relatively self-contained with respect to hospital care and may cross over state boundries.

  12. 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

  13. Simplified triangle method for estimating evaporative fraction over soybean crops

    NASA Astrophysics Data System (ADS)

    Silva-Fuzzo, Daniela Fernanda; Rocha, Jansle Vieira

    2016-10-01

    Accurate estimates are emerging with technological advances in remote sensing, and the triangle method has demonstrated to be a useful tool for the estimation of evaporative fraction (EF). The purpose of this study was to estimate the EF using the triangle method at the regional level. We used data from the Moderate Resolution Imaging Spectroradiometer orbital sensor, referring to indices of surface temperature and vegetation index for a 10-year period (2002/2003 to 2011/2012) of cropping seasons in the state of Paraná, Brazil. The triangle method has shown considerable results for the EF, and the validation of the estimates, as compared to observed data of climatological water balance, showed values >0.8 for modified "d" of Wilmott and R2 values between 0.6 and 0.7 for some counties. The errors were low for all years analyzed, and the test showed that the estimated data are very close to the observed data. Based on statistical validation, we can say that the triangle method is a consistent tool, is useful as it uses only images of remote sensing as variables, and can provide support for monitoring large-scale agroclimatic, specially for countries of great territorial dimensions, such as Brazil, which lacks a more dense network of meteorological ground stations, i.e., the country does not appear to cover a large field for data.

  14. 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

  15. 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.

  16. 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

  17. Remote-Sensing Biophysical Models for Estimating Lai of Irrigated Crops in Murry Darling Basin

    NASA Astrophysics Data System (ADS)

    Wittamperuma, I.; Hafeez, M.; Pakparvar, M.; Louis, J.

    2012-07-01

    Remote sensing is a rapid and reliable method for estimating crop growth data from individual plant to crops in irrigated agriculture ecosystem. The LAI is one of the important biophysical parameter for determining vegetation health, biomass, photosynthesis and evapotranspiration (ET) for the modelling of crop yield and water productivity. Ground measurement of this parameter is tedious and time-consuming due to heterogeneity across the landscape over time and space. This study deals with the development of remote-sensing based empirical relationships for the estimation of ground-based LAI (LAIG) using NDVI, modelled with and without atmospheric correction models for three irrigated crops (corn, wheat and rice) grown in irrigated farms within Coleambally Irrigation Area (CIA) which is located in southern Murray Darling basin, NSW in Australia. Extensive ground truthing campaigns were carried out to measure crop growth and to collect field samples of LAI using LAI- 2000 Plant Canopy Analyser and reflectance using CROPSCAN Multi Spectral Radiometer at several farms within the CIA. A Set of 12 cloud free Landsat 5 TM satellite images for the period of 2010-11 were downloaded and regression analysis was carried out to analyse the co-relationships between satellite and ground measured reflectance and to check the reliability of data sets for the crops. Among all the developed regression relationships between LAI and NDVI, the atmospheric correction process has significantly improved the relationship between LAI and NDVI for Landsat 5 TM images. The regression analysis also shows strong correlations for corn and wheat but weak correlations for rice which is currently being investigated.

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. Technical Note: On the Matt-Shuttleworth approach to estimate crop water requirements

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

    The Matt-Shuttleworth method provides a way to make a one-step estimate of crop water requirements with the Penman-Monteith equation by translating the crop coefficients, commonly available in United Nations Food and Agriculture Organization (FAO) publications, into equivalent surface resistances. The methodology is based upon the theoretical relationship linking crop surface resistance to a crop coefficient and involves the simplifying assumption that the reference crop evapotranspiration (ET0) is equal to the Priestley-Taylor estimate with a fixed coefficient of 1.26. This assumption, used to eliminate the dependence of surface resistance on certain weather variables, is questionable; numerical simulations show that it can lead to substantial differences between the true value of surface resistance and its estimate. Consequently, the basic relationship between surface resistance and crop coefficient, without any assumption, appears to be more appropriate for inferring crop surface resistance, despite the interference of weather variables.

  5. 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.

  6. 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. Copyright © 2013 Elsevier Inc. All rights reserved.

  7. Estimating national crop yield potential and the relevance of weather data sources

    NASA Astrophysics Data System (ADS)

    Van Wart, Justin

    2011-12-01

    To determine where, when, and how to increase yields, researchers often analyze the yield gap (Yg), the difference between actual current farm yields and crop yield potential. Crop yield potential (Yp) is the yield of a crop cultivar grown under specific management limited only by temperature and solar radiation and also by precipitation for water limited yield potential (Yw). Yp and Yw are critical components of Yg estimations, but are very difficult to quantify, especially at larger scales because management data and especially daily weather data are scarce. A protocol was developed to estimate Yp and Yw at national scales using site-specific weather, soils and management data. Protocol procedures and inputs were evaluated to determine how to improve accuracy of Yp, Yw and Yg estimates. The protocol was also used to evaluate raw, site-specific and gridded weather database sources for use in simulations of Yp or Yw. The protocol was applied to estimate crop Yp in US irrigated maize and Chinese irrigated rice and Yw in US rainfed maize and German rainfed wheat. These crops and countries account for >20% of global cereal production. The results have significant implications for past and future studies of Yp, Yw and Yg. Accuracy of national long-term average Yp and Yw estimates was significantly improved if (i) > 7 years of simulations were performed for irrigated and > 15 years for rainfed sites, (ii) > 40% of nationally harvested area was within 100 km of all simulation sites, (iii) observed weather data coupled with satellite derived solar radiation data were used in simulations, and (iv) planting and harvesting dates were specified within +/- 7 days of farmers actual practices. These are much higher standards than have been applied in national estimates of Yp and Yw and this protocol is a substantial step in making such estimates more transparent, robust, and straightforward. Finally, this protocol may be a useful tool for understanding yield trends and directing

  8. Estimating evapotranspiration of reference crops using the remote sensing approach

    NASA Astrophysics Data System (ADS)

    Payero, Jose Oscar

    For this study, seasonal meteorological and multispectral measurements were made over grass and alfalfa fields at Kimberly, Idaho, with the purpose of assessing the validity of the remote sensing method for the determination of evapotranspiration (ET) of reference crops and to establish relationships to derive ET calculation parameters from remotely sensed data. Meteorological data were obtained with the Bowen ratio method, and a new procedure was first developed to validate these data. Empirical equations were derived to estimate diurnal variation of soil heat flux. Relationships were also developed to estimate plant height from remotely sensed information. Also, a methodology to obtain surface albedo, using a variable (P/T) ratio, was described and applied. The (P/T) ratio is the fraction of the total reflected short wave radiation sensed by discrete radiometer bands. The effects of using remotely sensed aerodynamic temperature and wind-speed-corrected roughness length were evaluated. Also, different methods to correct for atmospheric stability, and to extrapolate daily ET values from instantaneous measurements were compared. It was found that the performance of the remote sensing method for estimating evapotranspiration was a function of the evaporative ratio (ER), which is the ratio of the latent heat flux to available energy. For ER ≤ 1.2, the instantaneous noon sensible and latent heat fluxes obtained with the remote sensing method compared very well with those obtained using the Bowen ratio method. On the other hand, for ER > 1.2 the method was not useful. Aerodynamic temperature corrections and the use of wind- corrected roughness lengths did not improve the results. Stability correction was only necessary when the aerodynamic resistance values were above 100 seconds per meter. None of several methods to extrapolate daily ET values from instantaneous measurements performed acceptably under the advective conditions of Kimberly.

  9. Rabi cropped area forecasting of parts of Banaskatha District,Gujarat using MRS RISAT-1 SAR data

    NASA Astrophysics Data System (ADS)

    Parekh, R. A.; Mehta, R. L.; Vyas, A.

    2016-10-01

    Radar sensors can be used for large-scale vegetation mapping and monitoring using backscatter coefficients in different polarisations and wavelength bands. Due to cloud and haze interference, optical images are not always available at all phonological stages important for crop discrimination. Moreover, in cloud prone areas, exclusively SAR approach would provide operational solution. This paper presents the results of classifying the cropped and non cropped areas using multi-temporal SAR images. Dual polarised C- band RISAT MRS (Medium Resolution ScanSAR mode) data were acquired on 9thDec. 2012, 28thJan. 2013 and 22nd Feb. 2013 at 18m spatial resolution. Intensity images of two polarisations (HH, HV) were extracted and converted into backscattering coefficient images. Cross polarisation ratio (CPR) images and Radar fractional vegetation density index (RFDI) were created from the temporal data and integrated with the multi-temporal images. Signatures of cropped and un-cropped areas were used for maximum likelihood supervised classification. Separability in cropped and umcropped classes using different polarisation combinations and classification accuracy analysis was carried out. FCC (False Color Composite) prepared using best three SAR polarisations in the data set was compared with LISS-III (Linear Imaging Self-Scanning System-III) image. The acreage under rabi crops was estimated. The methodology developed was for rabi cropped area, due to availability of SAR data of rabi season. Though, the approach is more relevant for acreage estimation of kharif crops when frequent cloud cover condition prevails during monsoon season and optical sensors fail to deliver good quality images.

  10. 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

  11. Estimating winter wheat phenological parameters: Implications for crop modeling

    USDA-ARS?s Scientific Manuscript database

    Crop parameters, such as the timing of developmental events, are critical for accurate simulation results in crop simulation models, yet uncertainty often exists in determining the parameters. Factors contributing to the uncertainty include: a) sources of variation within a plant (i.e., within diffe...

  12. Crop residue inventory estimates for Texas High Plains cotton

    USDA-ARS?s Scientific Manuscript database

    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...

  13. 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.

    2012-10-01

    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 conceptual models of potential evaporation of Penman, Penman-Monteith and Priestley-Taylor, and discussions of reference crop evaporation and then 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) hard-wired evaporation estimates, (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 estimates. This paper is supported by supplementary material that includes

  14. 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

  15. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  17. 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

  18. Comparison Between the Use of SAR and Optical Data for Wheat Yield Estimations Using Crop Model Assimilation

    NASA Astrophysics Data System (ADS)

    Silvestro, Paolo Cosmo; Yang, Hao; Jin, X. L.; Yang, Guijun; Casa, Raffaele; Pignatti, Stefano

    2016-08-01

    The ultimate aim of this work is to develop methods for the assimilation of the biophysical variables estimated by remote sensing in a suitable crop growth model. Two strategies were followed, one based on the use of Leaf Area Index (LAI) estimated by optical data, and the other based on the use of biomass estimated by SAR. The first one estimates LAI from the reflectance measured by the optical sensors on board of HJ1A, HJ1B and Landsat, using a method based on the training of artificial neural networks (ANN) with PROSAIL model simulations. The retrieved LAI is used to improve wheat yield estimation, using assimilation methods based on the Ensemble Kalman Filter, which assimilate the biophysical variables into growth crop model. The second strategy estimates biomass from SAR imagery. Polarimetric decomposition methods were used based on multi-temporal fully polarimetric Radarsat-2 data during the entire growing season. The estimated biomass was assimilating to FAO Aqua crop model for improving the winter wheat yield estimation, with the Particle Swarm Optimization (PSO) method. These procedures were used in a spatial application with data collected in the rural area of Yangling (Shaanxi Province) in 2014 and were validated for a number of wheat fields for which ground yield data had been recorded and according to statistical yield data for the area.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  20. Estimating seed crops of conifer and hardwood species

    Treesearch

    Philip M. McDonald

    1992-01-01

    Cone, acorn, and berry crops of ponderosa pine (Pinus ponderosa Dougl. ex Laws. var. ponderosa), sugar pine (Pinus lambertiana Dougl.), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), California white fir (Abies concolor var. lowiana (Gord...

  1. 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.

  2. 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.

  3. 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

  4. An Adapted Regression Estimator Method to Assess the Crop Acreage in Mengcheng County on the North China Plain

    NASA Astrophysics Data System (ADS)

    Kerdiles, Herve; Dong, Qinghan; Spyratos, Sphyridon; Gallego, Javier

    2013-01-01

    Image classifications including sub pixel analysis are often used to estimate directly the crop acreage, while ground data collected during field surveys play a secondary role. This pixel counting approach often leads to a biased estimation due to non-representative selection of ground data and subjective a-priori knowledge of analysts. Instead regression estimator approach combining remote sensing information with a rigorous ground sampling can result in an accurate assessment of crop acreage. In this study to estimate the maize area, the point frame sampling approach is adapted to the strip-like cropping pattern on the North China Plain. Remote sensing information is used to perform a cost-efficient stratification from which no-agricultural areas are excluded from ground survey. This information is also included in a later stage as an auxiliary estimator in regression analysis. The results showed that the integration of remote sensing information as an auxiliary estimator can improve the confidence of estimation by reducing the variance of the estimates.

  5. 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

  6. 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.

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

    PubMed

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

    2007-10-16

    The results of previous studies have suggested that estimated daily globalradiation (RG) values contain an error that could compromise the precision of subsequentcrop model applications. The following study presents a detailed site and spatial analysis ofthe RG error propagation in CERES and WOFOST crop growth models in Central Europeanclimate conditions. The research was conducted i) at the eight individual sites in Austria andthe Czech Republic where measured daily RG values were available as a reference, withseven methods for RG estimation being tested, and ii) for the agricultural areas of the CzechRepublic using daily data from 52 weather stations, with five RG estimation methods. In thelatter case the RG values estimated from the hours of sunshine using the ångström-Prescottformula were used as the standard method because of the lack of measured RG data. At thesite level we found that even the use of methods based on hours of sunshine, which showedthe 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, deviationsgreater than ±10 per cent in winter wheat and spring barley yields were noted in 5 to 6 percent of cases. The precision of the yield estimates and other crop model outputs was lowerwhen 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 diurnal

  8. Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

    NASA Astrophysics Data System (ADS)

    Hoffman, A.; Forest, C. E.; Kemanian, A.

    2016-12-01

    A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with

  9. 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

  10. Assessing wild bees in perennial bioenergy landscapes: effects of bioenergy crop composition, landscape configuration, and bioenergy crop area

    DOE PAGES

    Graham, John B.; Nassauer, Joan I.; Currie, William S.; ...

    2017-03-25

    Wild bee populations are currently under threat, which has led to recent efforts to increase pollinator habitat in North America. Simultaneously, U.S. federal energy policies are beginning to encourage perennial bioenergy cropping (PBC) systems, which have the potential to support native bees. Our objective was to explore the potentially interactive effects of crop composition, total PBC area, and PBC patches in different landscape configurations. Using a spatially-explicit modeling approach, the Lonsdorf model, we simulated the impacts of three perennial bioenergy crops (PBC: willow, switchgrass, and prairie), three scenarios with different total PBC area (11.7%, 23.5% and 28.8% of agricultural landmore » converted to PBC) and two types of landscape configurations (PBC in clustered landscape patterns that represent realistic future configurations or in dispersed neutral landscape models) on a nest abundance index in an Illinois landscape. Our modeling results suggest that crop composition and PBC area are particularly important for bee nest abundance, whereas landscape configuration is associated with bee nest abundance at the local scale but less so at the regional scale. Moreover, strategies to enhance wild bee habitat should therefore emphasize the crop composition and amount of PBC.« less

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

    NASA Astrophysics Data System (ADS)

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

    2017-03-01

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

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

    USDA-ARS?s Scientific Manuscript database

    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 ...

  13. 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%.

  14. 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.

  15. 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.

  16. An integrated crop and hydrologic modeling system to estimate hydrologic impacts of crop irrigation demands

    Treesearch

    R.T. McNider; C. Handyside; K. Doty; W.L. Ellenburg; J.F. Cruise; J.R. Christy; D. Moss; V. Sharda; G. Hoogenboom; Peter Caldwell

    2015-01-01

    The present paper discusses a coupled gridded crop modeling and hydrologic modeling system that can examine the benefits of irrigation and costs of irrigation and the coincident impact of the irrigation water withdrawals on surface water hydrology. The system is applied to the Southeastern U.S. The system tools to be discussed include a gridded version (GriDSSAT) of...

  17. Assessment of spectral indicies for crop residue cover estimation

    USDA-ARS?s Scientific Manuscript database

    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...

  18. ESTIMATING CROP WATER USE FOR CAMELINA WITH REMOTE SENSING

    USDA-ARS?s Scientific Manuscript database

    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...

  19. Cancer Related-Knowledge - Small Area Estimates

    Cancer.gov

    These model-based estimates are produced using statistical models that combine data from the Health Information National Trends Survey, and auxiliary variables obtained from relevant sources and borrow strength from other areas with similar characteristics.

  20. On semiautomatic estimation of surface area.

    PubMed

    Dvořák, J; Jensen, E B V

    2013-05-01

    In this paper, we propose a semiautomatic procedure for estimation of particle surface area. It uses automatic segmentation of the boundaries of the particle sections and applies different estimators depending on whether the segmentation was judged by a supervising expert to be satisfactory. If the segmentation is correct the estimate is computed automatically, otherwise the expert performs the necessary measurements manually. In case of convex particles we suggest to base the semiautomatic estimation on the so-called flower estimator, a new local stereological estimator of particle surface area. For convex particles, the estimator is equal to four times the area of the support set (flower set) of the particle transect. We study the statistical properties of the flower estimator and compare its performance to that of two discretizations of the flower estimator, namely the pivotal estimator and the surfactor. For ellipsoidal particles, it is shown that the flower estimator is equal to the pivotal estimator based on support function measurements along four perpendicular rays. This result makes the pivotal estimator a powerful approximation to the flower estimator. In a simulation study of prolate and oblate ellipsoidal particles, the surfactor also performs well for particles which are not extremely elongated. In particular, the surfactor is not very much affected by the singularity in the surfactor formula or by possible inaccuracies in the necessary angle measurements. We also assess the performance of the semiautomatic procedure in a study of somatostatin positive inhibitory interneurons from mice hippocampi. Only 35% of the cells needed to be analysed manually and an important decrease in workload was obtained by using the semiautomatic approach. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

  1. 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

  2. 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.

  3. Optical remote sensing for forest area estimation

    Treesearch

    Randolph H. Wynne; Richard G. Oderwald; Gregory A. Reams; John A. Scrivani

    2000-01-01

    The air photo dot-count method is now widely and successfully used for estimating operational forest area in the USDA Forest Inventory and Analysis (FIA) program. Possible alternatives that would provide for more frequent updates, spectral change detection, and maps of forest area include the AVHRR calibration center technique and various Landsat TM classification...

  4. 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.

  5. 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.

  6. [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.

  7. 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.

  8. 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.

  9. Coopers Rock Crop Tree Demonstration Area—20-year results

    Treesearch

    Arlyn W. Perkey; Gary W. Miller; David L. Feicht

    2011-01-01

    During the 1988/1989 dormant season, the Coopers Rock Crop Tree Demonstration Area was established in a 55-year-old central Appalachian hardwood forest in north-central West Virginia. After treatment, 89 northern red oak (Quercus rubra L.) and 147 yellow-poplar (Liriodentron tulipifera L.) crop trees were monitored for 20 years....

  10. 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.

    2016-11-01

    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.

  11. An evaluation of MODIS 250-m data for green LAI estimation in crops

    NASA Astrophysics Data System (ADS)

    Gitelson, Anatoly A.; Wardlow, Brian D.; Keydan, Galina P.; Leavitt, Bryan

    2007-10-01

    Green leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse applications. Remotely sensed data provide considerable potential for estimating LAI at local, regional, and global scales. The goal of this study was to retrieve green LAI from MODIS 250-m vegetation index (VI) data for irrigated and rainfed maize and soybeans. The performance of both MODIS-derived NDVI and Wide Dynamic Range Vegetation Index (WDRVI) were evaluated across three growing seasons (2002 through 2004) over a wide range of LAI and also compared to the performance of NDVI and WDRVI derived from reflectance data collected at close-range across the same field locations. The NDVI vs. LAI relationship showed asymptotic behavior with a sharp decrease in the sensitivity of the NDVI to LAI exceeding 2 m2/m2 for both crops. WDRVI vs. LAI relation was linear across the entire range of LAI variation with determination coefficients above 0.93. Importantly, the coefficients of the close-range WDRVI vs. LAI equation and the MODIS-retrieved WDRVI vs. LAI equation were very close. The WDRVI was found to be capable of accurately estimating LAI across a much greater LAI range than the NDVI and can be used for assessing even slight variations in LAI, which are indicative of the early stages of plant stress. These results demonstrate the new possibilities for analyzing the spatio-temporal variation of the LAI of crops using multi-temporal MODIS 250-m imagery.

  12. [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.

  13. Estimated annual agricultural pesticide use by crop group for states of the conterminous United States, 1992-2014

    USGS Publications Warehouse

    Baker, Nancy T.

    2017-01-01

    This dataset provides estimates of annual agricultural use of pesticide compounds by crop group at the state level for states in the conterminous United States, for the time period 1992-2014, compiled from data used to make county-level estimates by means of methods described in Thelin and Stone (2013) and Baker and Stone (2015). The source of this data is the same as the published county-level pesticide use estimates for 1992-2009 (Stone, 2013), estimates for 2008-2012 (Baker and Stone, 2015), and preliminary estimates for 2013 and 2014 respectively, Baker (2015), and Baker (2016). County level by-crop estimates are not published because of the increased uncertainty in estimating the geographic distribution of compounds applied to specific crops. County level estimates were aggregated to state level for high acreage crops such as corn and soybeans, and crop groups for lower acreage crops.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. Estimating Crop Growth Stage by Combining Meteorological and Remote Sensing Based Techniques

    NASA Astrophysics Data System (ADS)

    Champagne, C.; Alavi-Shoushtari, N.; Davidson, A. M.; Chipanshi, A.; Zhang, Y.; Shang, J.

    2016-12-01

    Estimations of seeding, harvest and phenological growth stage of crops are important sources of information for monitoring crop progress and crop yield forecasting. Growth stage has been traditionally estimated at the regional level through surveys, which rely on field staff to collect the information. Automated techniques to estimate growth stage have included agrometeorological approaches that use temperature and day length information to estimate accumulated heat and photoperiod, with thresholds used to determine when these stages are most likely. These approaches however, are crop and hybrid dependent, and can give widely varying results depending on the method used, particularly if the seeding date is unknown. Methods to estimate growth stage from remote sensing have progressed greatly in the past decade, with time series information from the Normalized Difference Vegetation Index (NDVI) the most common approach. Time series NDVI provide information on growth stage through a variety of techniques, including fitting functions to a series of measured NDVI values or smoothing these values and using thresholds to detect changes in slope that are indicative of rapidly increasing or decreasing `greeness' in the vegetation cover. The key limitations of these techniques for agriculture are frequent cloud cover in optical data that lead to errors in estimating local features in the time series function, and the incongruity between changes in greenness and traditional agricultural growth stages. There is great potential to combine both meteorological approaches and remote sensing to overcome the limitations of each technique. This research will examine the accuracy of both meteorological and remote sensing approaches over several agricultural sites in Canada, and look at the potential to integrate these techniques to provide improved estimates of crop growth stage for common field crops.

  3. Quantifying Uncertainty in Estimation of Potential Recharge in Tropical and Temperate Catchments using a Crop Model and Microwave Remote Sensing

    NASA Astrophysics Data System (ADS)

    Krishnan Kutty, S.; Sekhar, M.; Ruiz, L.; Tomer, S. K.; Bandyopadhyay, S.; Buis, S.; Guerif, M.; Gascuel-odoux, C.

    2012-12-01

    Groundwater recharge in a semi-arid region is generally low, but could exhibit high spatial variability depending on the soil type and plant cover. The potential recharge (the drainage flux just beneath the root zone) is found to be sensitive to water holding capacity and rooting depth (Rushton, 2003). Simple water balance approaches for recharge estimation often fail to consider the effect of plant cover, growth phases and rooting depth. Hence a crop model based approach might be better suited to assess sensitivity of recharge for various crop-soil combinations in agricultural catchments. Martinez et al. (2009) using a root zone modelling approach to estimate groundwater recharge stressed that future studies should focus on quantifying the uncertainty in recharge estimates due to uncertainty in soil water parameters such as soil layers, field capacity, rooting depth etc. Uncertainty in the parameters may arise due to the uncertainties in retrieved variables (surface soil moisture and leaf area index) from satellite. Hence a good estimate of parameters as well as their uncertainty is essential for a reliable estimate of the potential recharge. In this study we focus on assessing the sensitivity of crop and soil types on the potential recharge by using a generic crop model STICS. The effect of uncertainty in the soil parameters on the estimates of recharge and its uncertainty is investigated. The multi-layer soil water parameters and their uncertainty is estimated by inversion of STICS model using the GLUE approach. Surface soil moisture and LAI either retrieved from microwave remote sensing data or measured in field plots (Sreelash et al., 2012) were found to provide good estimates of the soil water properties and therefore both these data sets were used in this study to estimate the parameters and the potential recharge for a combination of soil-crop systems. These investigations were made in two field experimental catchments. The first one is in the tropical semi

  4. Evaluation of experiments for estimation of dynamical crop model parameters.

    PubMed

    Ioslovich, Ilya; Gutman, Per-Olof

    2007-07-01

    Planned experiments are usually expected to provide maximal benefits within limited costs. However, there are known difficulties in optimal design of experiments. They are related to the case when only limited number of parameters could be estimated, because available experiments are noninformative. The useful method for this case is considered based on the dominant parameters selection procedure (DPS). The methodology is illustrated here with data from five planned experiments related to the NICOLET lettuce growth model. The maximal number and the list of estimated parameters are determined while the conditional number of the information Fisher matrix (modified E-criterion) is kept below a given upper constraint.

  5. 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

  6. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  8. 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.

  9. 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.

  10. Estimating crop yields by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data

    NASA Astrophysics Data System (ADS)

    Reynolds, Curt Andrew

    The broad objective of this research was to develop a spatial model which provides both timely and quantitative regional maize yield estimates for real-time Early Warning Systems (EWS) by integrating satellite data with ground-based ancillary data. The Food and Agriculture Organization (FAO) Crop Specific Water Balance (CSWB) model was modified by using the real-time spatial data that include: dekad (ten-day) estimated rainfall (RFE) and Normalized Difference Vegetation Index (NDVI) composites derived from the METEOSAT and NOAA-AVHRR satellites, respectively; ground-based dekad potential evapo-transpiration (PET) data and seasonal estimated area-planted data provided by the Government of Kenya (GoK). A Geographical Information System (GIS) software was utilized to: drive the crop yield model; manage the spatial and temporal variability of the satellite images; interpolate between ground-based potential evapo-transpiration and rainfall measurements; and import ancillary data such as soil maps, administrative boundaries, etc. In addition, agro-ecological zones, length of growing season, and crop production functions, as defined by the FAO, were utilized to estimate quantitative maize yields. The GIS-based CSWB model was developed for three different resolutions: agro-ecological zone (AEZ) polygons; 7.6-kilometer pixels; and 1.1-kilometer pixels. The model was validated by comparing model production estimates from archived satellite and agro-meteorological data to historical district maize production reports from two Kenya government agencies, the Ministry of Agriculture (MoA) and the Department of Resource Surveys and Remote Sensing (DRSRS). For the AEZ analysis, comparison of model district maize production results and district maize production estimates from the MoA (1989-1997) and the DRSRS (1989-1993) revealed correlation coefficients of 0.94 and 0.93, respectively. The comparison for the 7.6-kilometer analysis showed correlation coefficients of 0.95 and 0

  11. [New index for crop canopy fresh biomass estimation].

    PubMed

    Chen, Peng-Fei; Nicolas, Tremblay; Wang, Ji-Hua; Philippe, Vigneault; Huang, Wen-Jiang; Li, Bao-Guo

    2010-02-01

    The objective of the present study is to propose a new vegetation index for corn canopy fresh biomass estimation, which improves the ability to accurately estimate high biomass levels by remote sensing technology. For this purpose, hyperspectral reflectance data of corn canopies were collected using a ground-based spectroradiometer during different field campaigns in a region of Quebec (Canada), from 2004 to 2008. Corresponding fresh biomass values were obtained by destructive measurements, and a hyperspectral image was also acquired using the Compact Airborne Spectrographic Imager (CASI) in 2005. A new biomass index named red-edge triangular vegetation index (RTVI) was designed and compared to existing indices used for fresh biomass estimation. The results showed that RTVI was the best vegetation index for predicting canopy fresh biomass, with sustained sensitivity at high fresh biomass levels. The best regression model between RTVI and canopy fresh biomass was the power fit, with determination coefficient (R2) of 0.96. With the validation by CASI imagery in 2005, good results were obtained. The relationship between CASI predicted biomass and actual biomass was 0.58 (R2), with the RMSE of 0.44 kg x m(-2).

  12. 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.

  13. 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.

  14. 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.

  15. Evaluation of six potential evapotranspiration models for estimating crop potential and actual evapotranspiration in arid regions

    NASA Astrophysics Data System (ADS)

    Li, Sien; Kang, Shaozhong; Zhang, Lu; Zhang, Jianhua; Du, Taisheng; Tong, Ling; Ding, Risheng

    2016-12-01

    Using potential evapotranspiration (PET) to estimate crop actual evapotranspiration (AET) is a critical approach in hydrological models. However, which PET model performs best and can be used to predict crop AET over the entire growth season in arid regions still remains unclear. The six frequently-used PET models, i.e. Blaney-Criddle (BC), Hargreaves (HA), Priestley-Taylor (PT), Dalton (DA), Penman (PE) and Shuttleworth (SW) models were considered and evaluated in the study. Five-year eddy covariance data over the maize field and vineyard in arid northwest China were used to examine the accuracy of PET models in estimating daily crop AET. Results indicate that the PE, SW and PT models underestimated daily ET by less than 6% with RMSE lower than 35 W m-2 during the four years, while the BC, HA and DA models under-predicted daily ET approximately by 10% with RMSE higher than 40 W m-2. Compared to BC, HA and DA models, PE, SW and PT models were more reliable and accurate for estimating crop PET and AET in arid regions. Thus the PE, SW and PT models were recommended for predicting crop evapotranspiration in hydrological models in arid regions.

  16. Estimation of net greenhouse gas balance using crop- and soil-based approaches: two case studies.

    PubMed

    Huang, Jianxiong; Chen, Yuanquan; Sui, Peng; Gao, Wansheng

    2013-07-01

    The net greenhouse gas balance (NGHGB), estimated by combining direct and indirect greenhouse gas (GHG) emissions, can reveal whether an agricultural system is a sink or source of GHGs. Currently, two types of methods, referred to here as crop-based and soil-based approaches, are widely used to estimate the NGHGB of agricultural systems on annual and seasonal crop timescales. However, the two approaches may produce contradictory results, and few studies have tested which approach is more reliable. In this study, we examined the two approaches using experimental data from an intercropping trial with straw removal and a tillage trial with straw return. The results of the two approaches provided different views of the two trials. In the intercropping trial, NGHGB estimated by the crop-based approach indicated that monocultured maize (M) was a source of GHGs (-1315 kg CO₂(-eq)ha(-1)), whereas maize-soybean intercropping (MS) was a sink (107 kg CO₂(-eq)ha(-1)). When estimated by the soil-based approach, both cropping systems were sources (-3410 for M and -2638 kg CO₂(-eq)ha(-1) for MS). In the tillage trial, mouldboard ploughing (MP) and rotary tillage (RT) mitigated GHG emissions by 22,451 and 21,500 kg CO₂(-eq)ha(-1), respectively, as estimated by the crop-based approach. However, by the soil-based approach, both tillage methods were sources of GHGs: -3533 for MP and -2241 kg CO₂(-eq)ha(-1) for RT. The crop-based approach calculates a GHG sink on the basis of the returned crop biomass (and other organic matter input) and estimates considerably more GHG mitigation potential than that calculated from the variations in soil organic carbon storage by the soil-based approach. These results indicate that the crop-based approach estimates higher GHG mitigation benefits compared to the soil-based approach and may overestimate the potential of GHG mitigation in agricultural systems. Copyright © 2013 Elsevier B.V. All rights reserved.

  17. OCO-2 Solar-induced Fluorescence Data Portal and Applications to Crop Yield Estimation

    NASA Astrophysics Data System (ADS)

    Zhai, A. J.; Jiang, J. H.; Frankenberg, C.; Yung, Y. L.; Choi, Y. S.

    2016-12-01

    Solar-induced fluorescence (SIF) is a direct byproduct of photosynthesis and is an index that can represent overall plant productivity level of any region around the globe. Recently, in 2014, NASA launched the Orbiting Carbon Observatory 2 (OCO-2) satellite, which collects SIF measurements at a higher spatial resolution than any previous instrument has. We have first assembled a web-based data portal, which can be easily utilized by both farmers and researchers, to allow convenient access to the SIF data from OCO-2. One possible use of SIF is to estimate agricultural status of crop fields anywhere in the world. We are using OCO-2 level 2 measurements in conjunction with the USDA's Cropland Data Layer and reported crop yield data to study how effectively SIF can estimate agricultural yield on various types of landscape and various species of crops. Results, methods, and future implications will be presented.

  18. 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.

  19. Evaluation of Multispectral Based Radiative Transfer Model Inversion to Estimate Leaf Area Index in Wheat

    USDA-ARS?s Scientific Manuscript database

    Leaf area index (LAI) is a critical variable for predicting the growth and productivity of crops. Remote sensing estimates of LAI have relied upon empirical relationships between spectral vegetation indices and ground measurements that are costly to obtain. Radiative transfer model inversion based o...

  20. 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.

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    USDA-ARS?s Scientific Manuscript database

    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 ...

  3. Estimating the Soil Thermal Conductivity in a Agricultural Crop Site in Southern Brazil

    NASA Astrophysics Data System (ADS)

    Zimmer, Tamíres; Roberti, Debora; Moreira, Virnei; Silveira, Marcos

    The thermal conductivity is higher when the heat storage is higher and the soil surface temperature is lower. The soil thermal conductivity is also dependant on the soil texture, porosity and moisture. Therefore, it varies from soil to soil and in the same soil, depending on its soil moisture. In the present work, it is shown soil thermal conductivity estimates in a agricultural crop located at the Cruz Alta city in southern Brazil. Also the dynamic of soil heat flux (G) is analyzed and the soil thermal conductivity (Ks) is estimated using experimental data form soil heat flux and soil temperature in a agricultural crop farm in a subtropical location in Southern Brazil. In this specific site, there is a crop rotation scheme along the year. The soil type is Rhodic Hapludox (FAO) or Typic Haplorthox (US Soil Taxonomy), characterized as a deep, clay soil. The experimental soil heat flux was compared with estimated soil heat flux by two forms: (1) using a known Ks from literature for this type of soil; (2) using Ks estimated using the inversion of the equation Qg=-ks* ((T2-T1)/ (Z2-Z1)), where T1 and T2 are the temperature in different layers above the soil and Z2-Z1 is the difference between the positions in temperature measurement. The general results agree with the literature for the specific agricultural crop for Ks values in the current study for the measurement period.

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    USDA-ARS?s Scientific Manuscript database

    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 ...

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. Comparing estimates of climate change impacts from process-based and statistical crop models

    NASA Astrophysics Data System (ADS)

    Lobell, David B.; Asseng, Senthold

    2017-01-01

    The potential impacts of climate change on crop productivity are of widespread interest to those concerned with addressing climate change and improving global food security. Two common approaches to assess these impacts are process-based simulation models, which attempt to represent key dynamic processes affecting crop yields, and statistical models, which estimate functional relationships between historical observations of weather and yields. Examples of both approaches are increasingly found in the scientific literature, although often published in different disciplinary journals. Here we compare published sensitivities to changes in temperature, precipitation, carbon dioxide (CO2), and ozone from each approach for the subset of crops, locations, and climate scenarios for which both have been applied. Despite a common perception that statistical models are more pessimistic, we find no systematic differences between the predicted sensitivities to warming from process-based and statistical models up to +2 °C, with limited evidence at higher levels of warming. For precipitation, there are many reasons why estimates could be expected to differ, but few estimates exist to develop robust comparisons, and precipitation changes are rarely the dominant factor for predicting impacts given the prominent role of temperature, CO2, and ozone changes. A common difference between process-based and statistical studies is that the former tend to include the effects of CO2 increases that accompany warming, whereas statistical models typically do not. Major needs moving forward include incorporating CO2 effects into statistical studies, improving both approaches’ treatment of ozone, and increasing the use of both methods within the same study. At the same time, those who fund or use crop model projections should understand that in the short-term, both approaches when done well are likely to provide similar estimates of warming impacts, with statistical models generally

  12. Methods of extending crop signatures from one area to another

    NASA Technical Reports Server (NTRS)

    Minter, T. C. (Principal Investigator)

    1979-01-01

    Efforts to develop a technology for signature extension during LACIE phases 1 and 2 are described. A number of haze and Sun angle correction procedures were developed and tested. These included the ROOSTER and OSCAR cluster-matching algorithms and their modifications, the MLEST and UHMLE maximum likelihood estimation procedures, and the ATCOR procedure. All these algorithms were tested on simulated data and consecutive-day LANDSAT imagery. The ATCOR, OSCAR, and MLEST algorithms were also tested for their capability to geographically extend signatures using LANDSAT imagery.

  13. Surface moisture estimation in urban areas

    NASA Astrophysics Data System (ADS)

    Jiang, Yitong

    Surface moisture is an important parameter because it modifies urban microclimate and surface layer meteorology. The primary objectives of this paper are: 1) to analyze the impact of surface roughness from buildings on surface moisture in urban areas; and 2) to quantify the impact of surface roughness resulting from urban trees on surface moisture. To achieve the objectives, two hypotheses were tested: 1) the distribution of surface moisture is associated with the structural complexity of buildings in urban areas; and 2) The distribution and change of surface moisture is associated with the distribution and vigor of urban trees. The study area is Indianapolis, Indiana, USA. In the part of the morphology of urban trees, Warren Township was selected due to the limitation of tree inventory data. To test the hypotheses, the research design was made to extract the aerodynamic parameters, such as frontal areas, roughness length and displacement height of buildings and trees from Terrestrial and Airborne LiDAR data, then to input the aerodynamic parameters into the urban surface energy balance model. The methodology was developed for comparing the impact of aerodynamic parameters from LiDAR data with the parameters that were derived empirically from land use and land cover data. The analytical procedures are discussed below: 1) to capture the spatial and temporal variation of surface moisture, daily and hourly Land Surface Temperature (LST) were downscaled from 4 km to 1 km, and 960 m to 30 m, respectively, by regression between LST and various components that impact LST; 2) to estimate surface moisture, namely soil moisture and evapotranspiration (ET), land surfaces were classified into soil, vegetation, and impervious surfaces, using Linear Spectral Mixture Analysis (LSMA); 3) aerodynamic parameters of buildings and trees were extracted from Airborne and Terrestrial LiDAR data; 4) the Temperature-Vegetation-Index (TVX) method, and the Two-Source-Energy-Balance (TSEB

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. On Estimating Crop Coefficients and Decoupling Factors of Forest Ecosystems in East Asia

    NASA Astrophysics Data System (ADS)

    Kang, M.; Kim, J.; Kwon, H.; Hirano, T.; Saigusa, N.; Takagi, K.; Yu, G.

    2012-12-01

    For effective water management, the quantification of evapotranspiration (ET) is imperative. Considering the difficulty of measurement and adequate simulation of ET in large areas, the crop coefficients (Kc=ETc adj/ETo/Ks; where ETc adj is the adjusted evapotranspiration, ETo is the reference crop evapotranspiration estimated by the FAO56-PM method, and Ks is the water stress coefficient) and the decoupling factors (Ω=ETa/ETp; where ETa is the actual evapotranspiration, and ETp is the potential evapotranspiration estimated by the Penman-Monteith equation) are valuable information for the assessment of regional ET. In this study, we determined the Kc and Ω of seven forest ecosystems in East Asia (i.e., temperate and coniferous forests in Gwangneung, Korea, GDK and GCK, respectively; cool temperate forests in Changbaishan, China, CBS, in Takayama and Teshio, Japan, TKY and TSE, respectively; subtropical forest in Xishuangbanna, Southern China, BNS; tropical forest in Palangkaraya, Indonesia, PDF) using the multi-year observed ET by eddy covariance technique. Annual ETo ranged from 443 mm at the TKY site to 1368 mm at the PDF site. Annual ETp ranged from 835 mm at the TSE site to 2466 mm at the PDF site. The Kc and Ω for the (cool) temperate forests showed a clear seasonality with the minimum (~0.3 of Kc and ~0 of Ω) in winter and the maximum in summer or early autumn. The maxima of Kc were different among the sites and ranged from 0.6 at the TSE site to 1.2 at the BNS site. Unlike the maximum Kc, the maxima of Ω were similar among the temperate forests (0.4~0.5) and between the tropical forests (~0.6). Implications of the results are discussed in terms of usefulness and accuracy of using Kc and Ω. Acknowledgement - This study was supported by the Korea Meteorological Administration Research and Development Program under Grant CATER 2012-3030. The flux database was provided by CarboEastAsia - A3 Foresight Program and KoFlux.

  19. An operational model to estimate hourly and daily crop evapotranspiration in hilly terrain: validation on wheat and oat crops

    NASA Astrophysics Data System (ADS)

    Rana, Gianfranco; Katerji, Nader; Ferrara, Rossana M.; Martinelli, Nicola

    2011-03-01

    In this paper, we present an operational model to estimate the actual evapotranspiration (ET) of crops cultivated on hilly terrains. This new model has the following three characteristics: (1) ET modelling is based on a Penman-Monteith (PM) type equation (Monteith 1965) where canopy resistance is simulated by following an approach already illustrated by Katerji and Perrier (Agronomie 3(6):513-521, 1983); (2) the estimation of ET, by means of the PM equation, is made by using meteorological variables simulated on sloped sites as input; (3) these variables are simulated by using simple relationships linking the variables measured at a reference site on plane to the topographic characteristics of the site (slope, orientation, altitude as difference between reference, and sloped sites). This approach presents two advantages if compared with previously proposed models: Not only computation steps are greatly simplified but also error sources due to the simulation of climatic variables in sloped sites and the ET estimation are well distinguished. This model was validated at hourly and daily scales at four sites cultivated with wheat and oats offering a wide range of slope and orientation values: a reference site on plane, site 1 (9° sloping, NW orientation, 7 m from the reference site in plane), site 2 (6°, SE, 12 m) and site 3 (1°, SE, 18 m). At hourly scale, the new model performed well at all sites studied. The observed slope of the linear relationships between estimated and measured ET values ranged between 0.93 and 1.03, with coefficients of determination, r 2, between 0.80 and 0.98. At daily scale, the slopes of the linear relationships between measured and estimated ET for the sites on plane and the sloped sites were practically the same (0.98 ± 0.01); however, the coefficient of determination r 2 observed in the site on plane was clearly greater (0.98) than that observed in the sloped sites (0.83). The presented analysis does not show any significant

  20. [Effect of plastic film mulching on crop yield and nitrogen efficiency in semiarid areas].

    PubMed

    Li, S; Li, F; Song, Q; Wang, J

    2001-04-01

    The effect of plastic film mulching, water storage in soil profile before sowing, and nitrogen fertilization on crop yield and nitrogen efficiency was examined in this paper. The study site was on the cultivated lossial soil in semiarid areas with 415 mm of annual rainfall and the test crop was spring wheat, Triticum aestivum. In order to study the effect of plastic film mulching, 4 levels of mulching were designed, including mulching of 0, 30 and 60 days after sowing and mulching over the whole growing period. The results showed that increase of soil water storage, plastic film mulching and nitrogen fertilization increased crop yield significantly(alpha < 0.01), and their effect followed in the order of nitrogen fertilization > increase of water storage > plastic film mulching. The effect of mulching on crop yield varied with water storage, nitrogen fertilization and mulching periods. When the water storage was low, there was no significant difference in crop yield between mulching and no mulching, although mulching increased crop yield slightly, and the nitrogen efficiency was higher for no mulching and mulching 30 days. When the water storage was high, the difference between the yield of mulching 60 days and no mulching was significant, but no difference in nitrogen efficiency was found for mulching 30 days, 60 days and over whole growing period. It was suggested that mulching over whole growing period was of less significance in practice.

  1. 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

  2. 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.

  3. Decreasing, not increasing, leaf area will raise crop yields under global atmospheric change.

    PubMed

    Srinivasan, Venkatraman; Kumar, Praveen; Long, Stephen P

    2017-04-01

    Without new innovations, present rates of increase in yields of food crops globally are inadequate to meet the projected rising food demand for 2050 and beyond. A prevailing response of crops to rising [CO2 ] is an increase in leaf area. This is especially marked in soybean, the world's fourth largest food crop in terms of seed production, and the most important vegetable protein source. Is this increase in leaf area beneficial, with respect to increasing yield, or is it detrimental? It is shown from theory and experiment using open-air whole-season elevation of atmospheric [CO2 ] that it is detrimental not only under future conditions of elevated [CO2 ] but also under today's [CO2 ]. A mechanistic biophysical and biochemical model of canopy carbon exchange and microclimate (MLCan) was parameterized for a modern US Midwest soybean cultivar. Model simulations showed that soybean crops grown under current and elevated (550 [ppm]) [CO2 ] overinvest in leaves, and this is predicted to decrease productivity and seed yield 8% and 10%, respectively. This prediction was tested in replicated field trials in which a proportion of emerging leaves was removed prior to expansion, so lowering investment in leaves. The experiment was conducted under open-air conditions for current and future elevated [CO2 ] within the Soybean Free Air Concentration Enrichment facility (SoyFACE) in central Illinois. This treatment resulted in a statistically significant 8% yield increase. This is the first direct proof that a modern crop cultivar produces more leaf than is optimal for yield under today's and future [CO2 ] and that reducing leaf area would give higher yields. Breeding or bioengineering for lower leaf area could, therefore, contribute very significantly to meeting future demand for staple food crops given that an 8% yield increase across the USA alone would amount to 6.5 million metric tons annually.

  4. 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

  5. Statistical properties of alternative national forest inventory area estimators

    Treesearch

    Francis Roesch; John Coulston; Andrew D. Hill

    2012-01-01

    The statistical properties of potential estimators of forest area for the USDA Forest Service's Forest Inventory and Analysis (FIA) program are presented and discussed. The current FIA area estimator is compared and contrasted with a weighted mean estimator and an estimator based on the Polya posterior, in the presence of nonresponse. Estimator optimality is...

  6. The Impact of Insects on Second-Year Cone Crops in Red Pine Seed-Production Areas

    Treesearch

    William J. Mattson

    1968-01-01

    Second-year cone crops in red pine seed-production areas have been severely damaged by five species of insects. Control of the two most destructive pests could increase present seed yields in most areas by at least 50 percent. Some seed-production areas may not produce harvestable seed crops until cone-insect populations are suppressed.

  7. 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.

  8. 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.

  9. 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.

  10. 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…

  11. Estimating Field Scale Crop Evapotranspiration using Landsat and MODIS Satellite Observations

    NASA Astrophysics Data System (ADS)

    Wong, A.; Jin, Y.; Snyder, R. L.; Daniele, Z.; Gao, F.

    2016-12-01

    Irrigation accounts for 80% of human freshwater consumption, and most of it return to the atmosphere through Evapotranspiration (ET). Given the challenges of already-stressed water resources and ground water regulation in California, a cost-effective, timely, and consistent spatial estimate of crop ET, from the farm to watershed level, is becoming increasingly important. The Priestley-Taylor (PT) approach, calibrated with field data and driven by satellite observations, shows great promise for accurate ET estimates across diverse ecosystems. We here aim to improve the robustness of the PT approach in agricultural lands, to enable growers and farm managers to tailor irrigation management based on in-field spatial variability and in-season variation. We optimized the PT coefficients for each crop type with available ET measurements from eddy covariance towers and/or surface renewal stations at six crop fields (Alfalfa, Almond, Citrus, Corn, Pistachio and Rice) in California. Good agreement was found between satellite-based estimates and field measurements of net radiation, with a RMSE of less than 36 W m-2. The crop type specific optimization performed well, with a RMSE of 30 W m-2 and a correlation of 0.81 for predicted daily latent heat flux. The calibrated algorithm was used to estimate ET at 30 m resolution over the Sacramento-San Joaquin Delta region for 2015 water year. It captures well the seasonal dynamics and spatial distribution of ET in Sacramento-San Joaquin Delta. A continuous monitoring of the dynamics and spatial heterogeneity of canopy and consumptive water use at a field scale, will help the growers to be well prepared and informed to adaptively manage water, canopy, and grove density to maximize the yield with the least amount of water.

  12. 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.

  13. Stable isotope ratios in irrigation water can estimate rice crop evaporation

    NASA Astrophysics Data System (ADS)

    Simpson, H. J.; Herczeg, A. L.; Meyer, W. S.

    1992-02-01

    Irrigated crops provide about one third of world food production, and the total area under irrigation has increased by more than a factor of three since 1950 [Brown, 1988]. Possibilities for further geographical expansion are limited; therefore future production increases are likely to require higher efficiency of water use. Of the major grain crops, lowland rice requires the most water, with total demand per unit area at least twice that for wheat and maize. Stable isotope abundance changes in irrigation water can provide direct indication of integrated evaporation losses exclusive of transpiration and thus provide a new tool to monitor a key parameter relevant to water use efficiency. Large enrichments of deuterium and oxygen-18 in rice field water compared to initial input water in a semi-arid region of southeastern Australia indicate high evaporation rates (7 mm day-1) during the first month following flooding. This contrasts with semi-mature rice crops which had small heavy isotope enrichments of field water, indicating low evaporation (<1 mm day-1), compared to transpiration (6 to 7 mm day-1). Over the entire rice-cropping season, evaporation accounted for about 40 per cent of total losses to the atmosphere, with transpiration providing the remainder.

  14. 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.

  15. 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

  16. 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.

  17. Estimated crop yield losses due to surface ozone exposure and economic damage in India.

    PubMed

    Debaje, S B

    2014-06-01

    In this study, we estimate yield losses and economic damage of two major crops (winter wheat and rabi rice) due to surface ozone (O3) exposure using hourly O3 concentrations for the period 2002-2007 in India. This study estimates crop yield losses according to two indices of O3 exposure: 7-h seasonal daytime (0900-1600 hours) mean measured O3 concentration (M7) and AOT40 (accumulation exposure of O3 concentration over a threshold of 40 parts per billion by volume during daylight hours (0700-1800 hours), established by field studies. Our results indicate that relative yield loss from 5 to 11% (6-30%) for winter wheat and 3-6% (9-16%) for rabi rice using M7 (AOT40) index of the mean total winter wheat 81 million metric tons (Mt) and rabi rice 12 Mt production per year for the period 2002-2007. The estimated mean crop production loss (CPL) for winter wheat are from 9 to 29 Mt, account for economic cost loss was from 1,222 to 4,091 million US$ annually. Similarly, the mean CPL for rabi rice are from 0.64 to 2.1 Mt, worth 86-276 million US$. Our calculated winter wheat and rabi rice losses agree well with previous results, providing the further evidence that large crop yield losses occurring in India due to current O3 concentration and further elevated O3 concentration in future may pose threat to food security.

  18. 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

  19. 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

  20. 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

  1. Stylet Penetration Estimates for a Suite of Phytophagous Hemipteran Pests of Row Crops.

    PubMed

    Esquivel, J F

    2015-06-01

    Members of the Miridae (Lygus lineolaris Palisot de Beauvois and Pseudatomoscelis seriatus Reuter) and Pentatomidae (Acrosternum hilare Say, Euschistus servus (Say), Euschistus tristigmus (Say), Euschistus quadrator Rolston, Oebalus pugnax (F.), Piezodorus guildinii (Westwood), and Thyanta custator accerra McAtee) comprise a piercing-sucking insect complex that continues to plague multiple crops, including cotton. All these species have been associated with pathogen transmission. Breaching of boll carpel walls facilitates introduction of pathogens, and it is unknown whether stylets of these pests can fully penetrate carpel walls. Thus, stylet penetration estimates are needed and have been the focus of the present work. Stylet penetration estimates for L. lineolaris were significantly deeper than P. seriatus. Among the Pentatomidae, highest mean penetration was estimated for E. servus followed by A. hilare, yet A. hilare possessed a longer rostrum. Similarly, O. pugnax showed deeper penetration estimates than P. guildinii yet P. guildinii possessed a longer rostrum. Thus, rostrum length should not be equated to penetration potential. Pseudatomoscelis seriatus and L. lineolaris both infest early-season cotton, and the ranges of observed penetration indicate these insects, as well as the Pentatomidae, can breach the walls of critical pinhead squares and smaller bolls. The insects addressed herein affect a myriad of crops globally, and penetration estimates allow identification of growth stages susceptible to feeding and disease transmission.

  2. Using thermal units for estimating critical period of weed competition in off-season maize crop.

    PubMed

    López-Ovejero, Ramiro Fernando; y Garcia, Axel Garcia; de Carvalho, Saul Jorge P; Christoffoleti, Pedro J; Neto, Durval Dourado; Martins, Fernando; Nicolai, Marcelo

    2005-01-01

    Brazilian off-season maize production is characterized by low yield due to several factors, such as climate variability and inadequate management practices, specifically weed management. Thus, the goal of this study was to determinate the critical period of weed competition in off-season maize (Zea mays L.) crop using thermal units or growing degree days (GDD) approach to characterize crop growth and development. The study was carried out in experimental area of the University of São Paulo, Brazil, with weed control (C), as well as seven coexistence periods, 2, 4, 6, 8, and 12 leaves, flowering, and all crop cycle; fourteen treatments were done. Climate data were obtained from a weather station located close to the experimental area. To determine the critical period for weed control (CPWC) logistic models were fitted to yield data obtained in both W and C, as a function of GDD. For an arbitrary maximum yield loss fixed in 2.5%, the CPWC was found between 301 and 484 GDD (7-8 leaves). Also, when the arbitrary loss yield was fixed in 5 and 10%, the period before interference (PBI) was higher than the critical weed-free period (CWFP), suggesting that the weeds control can be done with only one application, between 144 and 410 GDD and 131 and 444 GDD (3-8 leaves), respectively. The GDD approach to characterize crop growth and development was successfully used to determine the critical period of weeds control in maize sown off-season. Further works will be necessary to better characterize the interaction and complexity of maize sown off-season with weeds. However, these results are encouraging because the possibility of the results to be extrapolated and because the potential of the method on providing important results to researchers, specifically crop modelers.

  3. 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

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

    USDA-ARS?s Scientific Manuscript database

    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...

  5. A STELLA model to estimate water and nitrogen dynamics in a short-rotation woody crop plantation

    Treesearch

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

    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., NH4...

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. 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

  12. Estimating forestland area change from inventory data

    Treesearch

    Paul Van Deusen; Francis Roesch; Thomas Wigley

    2013-01-01

    Simple methods for estimating the proportion of land changing from forest to nonforest are developed. Variance estimators are derived to facilitate significance tests. A power analysis indicates that 400 inventory plots are required to reliably detect small changes in net or gross forest loss. This is an important result because forest certification programs may...

  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. Estimating the Effect of Climate Change on Crop Yields and Farmland Values: The Importance of Extreme Temperatures

    EPA Pesticide Factsheets

    This is a presentation titled Estimating the Effect of Climate Change on Crop Yields and Farmland Values: The Importance of Extreme Temperatures that was given for the National Center for Environmental Economics

  15. 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

  16. Influence of resolution in irrigated area mapping and area estimation

    USGS Publications Warehouse

    Velpuri, N.M.; Thenkabail, P.S.; Gumma, M.K.; Biradar, C.; Dheeravath, V.; Noojipady, P.; Yuanjie, L.

    2009-01-01

    The overarching goal of this paper was to determine how irrigated areas change with resolution (or scale) of imagery. Specific objectives investigated were to (a) map irrigated areas using four distinct spatial resolutions (or scales), (b) determine how irrigated areas change with resolutions, and (c) establish the causes of differences in resolution-based irrigated areas. The study was conducted in the very large Krishna River basin (India), which has a high degree of formal contiguous, and informal fragmented irrigated areas. The irrigated areas were mapped using satellite sensor data at four distinct resolutions: (a) NOAA AVHRR Pathfinder 10,000 m, (b) Terra MODIS 500 m, (c) Terra MODIS 250 m, and (d) Landsat ETM+ 30 m. The proportion of irrigated areas relative to Landsat 30 m derived irrigated areas (9.36 million hectares for the Krishna basin) were (a) 95 percent using MODIS 250 m, (b) 93 percent using MODIS 500 m, and (c) 86 percent using AVHRR 10,000 m. In this study, it was found that the precise location of the irrigated areas were better established using finer spatial resolution data. A strong relationship (R2 = 0.74 to 0.95) was observed between irrigated areas determined using various resolutions. This study proved the hypotheses that "the finer the spatial resolution of the sensor used, greater was the irrigated area derived," since at finer spatial resolutions, fragmented areas are detected better. Accuracies and errors were established consistently for three classes (surface water irrigated, ground water/conjunctive use irrigated, and nonirrigated) across the four resolutions mentioned above. The results showed that the Landsat data provided significantly higher overall accuracies (84 percent) when compared to MODIS 500 m (77 percent), MODIS 250 m (79 percent), and AVHRR 10,000 m (63 percent). ?? 2009 American Society for Photogrammetry and Remote Sensing.

  17. Landsat based historical (1984-2014) crop water use estimates and trends in the Southwestern United States

    NASA Astrophysics Data System (ADS)

    Senay, G. B.; Schauer, M.; Friedrichs, M.; Velpuri, N. M.; Singh, R. K.

    2016-12-01

    Remote sensing-based field scale evapotranspiration (ET) maps are useful for characterizing water use patterns and assessing crop performance. Historical (1984-2014) Landsat-based ET maps were generated for major irrigation districts in the southwestern US. A total of 3,396 Landsat images were processed using the Operational Simplified Surface Energy balance (SSEBop) model that integrates weather and remotely sensed images to estimate monthly and annual ET within the study areas over the 31 years. Model output evaluation and validation using point-based eddy covariance flux tower, gridded-flux data and water balance ET approaches indicated relatively strong association between SSEBop ET and validation datasets. Historical trend analysis of seven agro-hydrologic variables using the Seasonal Mann-Kendall test showed interesting results. In a pair wise comparison, management influenced variables such as actual evapotranspiration (ETa), land surface temperature (Ts) and runoff (Q) were found to be more variable than their corresponding climate counterparts of atmospheric water demand (ETo), air temperature (Ta) and precipitation, responding to the impacts of management decisions. Our results indicated that only air temperature showed a consistent increase (up to 1.2 K) across all 9 irrigation sub-basins during the 31 years. District-wide ETa estimates were used to compute historical crop water use volumes and monetary savings for the Palo Verde Irrigation district (PVID). During the peak crop fallowing program in PVID, the water savings reached a maximum of 85,000 ac-ft per year which is equivalent to a dollar amount of $ 600 million. This study has many applications in planning water resource allocation, monitoring and assessing water usage and performance, and quantifying impacts of climate and land use/land cover changes on water resources. With increased computational efficiency and model development, similar studies can be conducted in other parts of the world.

  18. 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

  19. 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.

  20. Remote sensing applications for estimating changes on crop evapotranspiration of the most water intensive crops, due to climate change in Cyprus

    NASA Astrophysics Data System (ADS)

    Papadavid, G.; Neocleous, D.; Stylianou, A.; Markou, M.; Kountios, G.; Hadjimitsis, D.

    2016-08-01

    Water allocation to crops, and especially to the most water intensive ones, has always been of great importance in agricultural process. Deficit or excess water irrigation quantities could create either crop health related problems or water over-consumption situation which lead to stored water reduction and toxic material depletion to deeper ground layers, respectively. In this context, and under the current conditions, where Cyprus is facing effects of climate changes, purpose of this study is basically to estimate the needed crop water requirements of the past (1995-2004) and the corresponding ones of the present (2005-2015) in order to test if there were any significant changes regarding the crop water requirements of the most water intensive trees in Cyprus. Mediterranean region has been identified as the region that will suffer the most from climate change. Thus the paper refers to effects of climate changes on crop evapotranspiration (ETc) using remotely sensed data from Landsat TM/ ETM+ / OLI employing a sound methodology used worldwide, the Surface Energy Balance Algorithm for Land (SEBAL). Though the general feeling is that of changes on climate will consequently affect ETc, the results have indicated that there is no significant effect of climate change on crop evapotranspiration, despite the fact that some climatic factors have changed. Applying Student's T-test, the mean values for the most water intensive trees in Cyprus of the 1994-2004 decade have shown no statistical difference from the mean values of 2005-2015 decade's for all the cases, concluding that the climate change taking place the last decades in Cyprus have either not affected the crop evapotranspiration or the crops have manage to adapt into the new environmental conditions through time.

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

    USDA-ARS?s Scientific Manuscript database

    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 (...

  2. 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

  3. 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.

  4. Agricultural interventions for water saving and crop yield improvement, in a Mediterranean area - an experimental design

    NASA Astrophysics Data System (ADS)

    Morianou, Giasemi; Kourgialas, Nektarios; Psarras, George; Koubouris, George; Arampatzis, George; Karatzas, George; Pavlidou, Elisavet

    2017-04-01

    This work is a part of LIFE+ AGROCLIMAWATER project and the aim is to improve the water efficiency, increase the adaptive capacity of tree corps and save water, in a Mediterranean area, under different climatic conditions and agricultural practices. The experimental design as well as preliminary results at farm and river basin scales are presented in this work. Specifically, ten (10) pilot farms, both organic and conventional ones have been selected in the sub-basin of Platanias in western Crete - Greece. These ten pilot farms were selected representing the most typical crops in Platanias area (olive trees and citrus trees), as well as the typical soil, landscape and agricultural practices differentiation for each crop (field slope, water availability, soil type, management regime). From the ten pilot farms, eight were olive farms and the rest two citrus. This proportion correspond adequacy to the presentence of olive and citrus crops in the extended area of Platanias prefecture. Each of the ten pilot farm has been divided in two parts, the first one will be used as a control part, while the other one as the demonstration part where the interventions will be applied. The action plans for each selected farm are based on the following groups of possible interventions: a) reduction of water evaporation losses from soil surface, b) reduction of transpiration water losses through winter pruning and summer pruning, c) reduction of deep percolation water and nutrient losses, d) reduction of surface runoff, e) measures in order to maximize the efficiency of irrigation and f) rationalization of fertilizers and agrochemicals utilized. Preliminary results indicate that water saving and crop yield can be significantly improved based on the above innervations both at farm and river basin scale.

  5. 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

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

    PubMed

    Barney, Jacob N; DiTomaso, Joseph M

    2011-03-09

    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

  7. 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.

  8. Estimating Fuel Bed Loadings in Masticated Areas

    Treesearch

    Sharon Hood; Ros Wu

    2006-01-01

    Masticated fuel treatments that chop small trees, shrubs, and dead woody material into smaller pieces to reduce fuel bed depth are used increasingly as a mechanical means to treat fuels. Fuel loading information is important to monitor changes in fuels. The commonly used planar intercept method however, may not correctly estimate fuel loadings because masticated fuels...

  9. 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.

  10. Small Area Income and Poverty Estimates (SAIPE): 2010 Highlights

    ERIC Educational Resources Information Center

    US Census Bureau, 2011

    2011-01-01

    This document presents 2010 data from the Small Area Income and Poverty Estimates (SAIPE) program of the U.S. Census Bureau. The SAIPE program produces poverty estimates for the total population and median household income estimates annually for all counties and states. SAIPE data also produces single-year poverty estimates for the school-age…

  11. Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems

    USGS Publications Warehouse

    Glenn, E.P.; Neale, C. M. U.; Hunsaker, D.J.; Nagler, P.L.

    2011-01-01

    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 remotely sensed vegetation indices (VI) that measured the actual status of the crop on a field-by-field basis. VIs measure the density of green foliage based on the reflectance of visible and near infrared (NIR) light from the canopy, and are highly correlated with plant physiological processes that depend on light absorption by a canopy such as ET and photosynthesis. Reflectance-based crop coefficients have now been developed for numerous individual crops, including corn, wheat, alfalfa, cotton, potato, sugar beet, vegetables, grapes and orchard crops. Other research has shown that VIs can be used to predict ET over fields of mixed crops, allowing them to be used to monitor ET over entire irrigation districts. VI-based crop coefficients can help reduce agricultural water use by matching irrigation rates to the actual water needs of a crop as it grows instead of to a modeled crop growing under optimal conditions. Recently, the concept has been applied to natural ecosystems at the local, regional and continental scales of measurement, using time-series satellite data from the MODIS sensors on the Terra satellite. VIs or other visible-NIR band algorithms are combined with meteorological data to predict ET in numerous biome types, from deserts, to arctic tundra, to tropical rainforests. These methods often closely match ET measured on the ground at the global FluxNet array of eddy covariance moisture and carbon flux towers. The primary advantage of VI methods for estimating ET is that transpiration is closely related to radiation absorbed by the plant canopy, which is closely related to VIs. The primary disadvantage is that they cannot capture stress effects or soil

  12. 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

  13. Estimating areas contributing recharge to wells, lessons from previous studies

    USGS Publications Warehouse

    Franke, O. Lehn; Reilly, T.E.; Pollock, D.W.; LaBaugh, J.W.

    1998-01-01

    Factors relating to the estimation of areas contributing recharge to wells, such as complexity of the ground-water flow system, effects of changing hydrologic conditions, and effects of well-screen locations and pumping rates, are reviewed. The point of view that simulation is the best means to obtain physically based estimates of contributing areas is emphasized. An extensive list of USGS reports that include estimation of contributing areas is provided.

  14. 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.

  15. 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.

  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. 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.

  18. 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

  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. 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.

  1. 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.

  2. The simulation of cropping pattern to improve the performance of irrigation network in Cau irrigation area

    NASA Astrophysics Data System (ADS)

    Wahyuningsih, Retno; Rintis Hadiani, RR; Sobriyah

    2017-01-01

    Cau irrigation area located in Madiun district, East Java Province, irrigates 1.232 Ha of land which covers Cau primary channel irrigation network, Wungu Secondary channel irrigation network, and Grape secondary channel irrigation network. The problems in Cau irrigation area are limited availability of water especially during the dry season (planting season II and III) and non-compliance to cropping patterns. The evaluation of irrigation system performance of Cau irrigation area needs to be done in order to know how far the irrigation system performance is, especially based on planting productivity aspect. The improvement of irrigation network performance through cropping pattern optimization is based on the increase of water necessity fulfillment (k factor), the realization of planting area and rice productivity. The research method of irrigation system performance is by analyzing the secondary data based on the Regulation of Ministry of Public Work and State Minister for Public Housing Number: 12/PRT/M/2015. The analysis of water necessity fulfillment (k factor) uses Public Work Plan Criteria Method. The performance level of planting productivity aspect in existing condition is 87.10%, alternative 1 is 93.90% dan alternative 2 is 96.90%. It means that the performance of the irrigation network from productivity aspect increases 6.80% for alternative 1 and 9.80% for alternative 2.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  4. Can we use photography to estimate radiation interception by a crop canopy?

    PubMed

    Chakwizira, E; Meenken, E D; George, M J; Fletcher, A L

    2015-03-01

    Accuracy of determining radiation interception, and hence radiation use efficiency, depends on the method of measuring photosynthetically active radiation intercepted. Methods vary, from expensive instruments such as Sunfleck ceptometers to simple methods such as digital photography. However, before universal use of digital photography there is need to determine its reliability and compare it with conventional, but expensive, methods. In a series of experiments at Lincoln, New Zealand, canopy development for barley, wheat, white clover and four forage brassica species was determined using both digital photographs and Sunfleck ceptometer. Values obtained were used to calculate conversion coefficient (Kf/Ki) ratios between the two methods. Digital photographs were taken at 45° and 90° for barley, wheat and white clover and at only 90° for brassicas. There was an interaction of effects of crop and cultivar for the cereal crops. Barley closed canopies earlier than wheat, and 'Emir' barley and 'Stettler' wheat had consistently higher canopy cover than 'Golden Promise' and 'HY459', respectively. Canopy cover was consistently larger at 45° than 90° for cereals. However, for white clover, the angle of digital photography was not important. There was also an interaction between effects of species and method of determining canopy cover for brassicas. Photographs gave higher cover values than ceptometer for forage rape and turnip, but the relationship was variable for forage kale and swede. Kf/Ki ratios of 1.0-1.10 for cereals, white clover and forage rape and turnip show that digital photographs can be used to estimated radiation interception, in place of Sunfleck ceptometer, for these crops. © 2014 German Botanical Society and The Royal Botanical Society of the Netherlands.

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

    USDA-ARS?s Scientific Manuscript database

    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...

  6. A scalable satellite-based crop yield mapper: Integrating satellites and crop models for field-scale estimation in India

    NASA Astrophysics Data System (ADS)

    Jain, M.; Singh, B.; Srivastava, A.; Lobell, D. B.

    2015-12-01

    Food security will be challenged over the upcoming decades due to increased food demand, natural resource degradation, and climate change. In order to identify potential solutions to increase food security in the face of these changes, tools that can rapidly and accurately assess farm productivity are needed. With this aim, we have developed generalizable methods to map crop yields at the field scale using a combination of satellite imagery and crop models, and implement this approach within Google Earth Engine. We use these methods to examine wheat yield trends in Northern India, which provides over 15% of the global wheat supply and where over 80% of farmers rely on wheat as a staple food source. In addition, we identify the extent to which farmers are shifting sow date in response to heat stress, and how well shifting sow date reduces the negative impacts of heat stress on yield. To identify local-level decision-making, we map wheat sow date and yield at a high spatial resolution (30 m) using Landsat satellite imagery from 1980 to the present. This unique dataset allows us to examine sow date decisions at the field scale over 30 years, and by relating these decisions to weather experienced over the same time period, we can identify how farmers learn and adapt cropping decisions based on weather through time.

  7. Estimating life expectancies for US small areas: a regression framework

    NASA Astrophysics Data System (ADS)

    Congdon, Peter

    2014-01-01

    Analysis of area mortality variations and estimation of area life tables raise methodological questions relevant to assessing spatial clustering, and socioeconomic inequalities in mortality. Existing small area analyses of US life expectancy variation generally adopt ad hoc amalgamations of counties to alleviate potential instability of mortality rates involved in deriving life tables, and use conventional life table analysis which takes no account of correlated mortality for adjacent areas or ages. The alternative strategy here uses structured random effects methods that recognize correlations between adjacent ages and areas, and allows retention of the original county boundaries. This strategy generalizes to include effects of area category (e.g. poverty status, ethnic mix), allowing estimation of life tables according to area category, and providing additional stabilization of estimated life table functions. This approach is used here to estimate stabilized mortality rates, derive life expectancies in US counties, and assess trends in clustering and in inequality according to county poverty category.

  8. 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.

  9. 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.

  10. A method to estimate plant density and plant spacing heterogeneity: application to wheat crops.

    PubMed

    Liu, Shouyang; Baret, Fred; Allard, Denis; Jin, Xiuliang; Andrieu, Bruno; Burger, Philippe; Hemmerlé, Matthieu; Comar, Alexis

    2017-01-01

    Plant density and its non-uniformity drive the competition among plants as well as with weeds. They need thus to be estimated with small uncertainties accuracy. An optimal sampling method is proposed to estimate the plant density in wheat crops from plant counting and reach a given precision. Three experiments were conducted in 2014 resulting in 14 plots across varied sowing density, cultivars and environmental conditions. The coordinates of the plants along the row were measured over RGB high resolution images taken from the ground level. Results show that the spacing between consecutive plants along the row direction are independent and follow a gamma distribution under the varied conditions experienced. A gamma count model was then derived to define the optimal sample size required to estimate plant density for a given precision. Results suggest that measuring the length of segments containing 90 plants will achieve a precision better than 10%, independently from the plant density. This approach appears more efficient than the usual method based on fixed length segments where the number of plants are counted: the optimal length for a given precision on the density estimation will depend on the actual plant density. The gamma count model parameters may also be used to quantify the heterogeneity of plant spacing along the row by exploiting the variability between replicated samples. Results show that to achieve a 10% precision on the estimates of the 2 parameters of the gamma model, 200 elementary samples corresponding to the spacing between 2 consecutive plants should be measured. This method provides an optimal sampling strategy to estimate the plant density and quantify the plant spacing heterogeneity along the row.

  11. 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.

  12. 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.

  13. New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops

    NASA Astrophysics Data System (ADS)

    Viña, Andrés; Gitelson, Anatoly A.

    2005-09-01

    The fraction of absorbed photosynthetically active radiation, fAPAR, is an important biophysical characteristic in models of gas exchange between the terrestrial boundary layer and the atmosphere, as well as in the analysis of vegetation productivity. Synoptic estimation of fAPAR has been performed by using NDVI as a linear proxy of fAPAR, despite the saturation of NDVI at fAPAR beyond 0.7. This paper analyzes the NDVI/fAPAR relationship in row crops (i.e. maize and soybean), and evaluates alternative vegetation indices to overcome the loss of sensitivity of NDVI at moderate-to-high vegetation biomass. Red-edge NDVI, which uses NIR and a band around 700 nm and the recently proposed Wide Dynamic Range Vegetation Index, which uses red and NIR bands only, were found to be sensitive to fAPAR variation along its entire range and exhibited significant increase in sensitivity to fAPAR.

  14. Using seed purity data to estimate an average pollen mediated gene flow from crops to wild relatives.

    PubMed

    Lavigne, C; Klein, E K; Couvet, D

    2002-01-01

    Gene flow from crops to wild related species has been recently under focus in risk-assessment studies of the ecological consequences of growing transgenic crops. However, experimental studies addressing this question are usually temporally or spatially limited. Indirect population-structure approaches can provide more global estimates of gene flow, but their assumptions appear inappropriate in an agricultural context. In an attempt to help the committees providing advice on the release of transgenic crops, we present a new method to estimate the quantity of genes migrating from crops to populations of related wild plants by way of pollen dispersal. This method provides an average estimate at a landscape level. Its originality is based on the measure of the inverse gene flow, i.e. gene flow from the wild plants to the crop. Such gene flow results in an observed level of impurities from wild plants in crop seeds. This level of impurity is usually known by the seed producers and, in any case, its measure is easier than a direct screen of wild populations because crop seeds are abundant and their genetic profile is known. By assuming that wild and cultivated plants have a similar individual pollen dispersal function, we infer the level of pollen-mediated gene flow from a crop to the surrounding wild populations from this observed level of impurity. We present an example for sugar beet data. Results suggest that under conditions of seed production in France (isolation distance of 1,000 m) wild beets produce high numbers of seeds fathered by cultivated plants.

  15. 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.

  16. 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.

    2017-05-01

    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.

  17. Field size, length, and width distributions based on LACIE ground truth data. [large area crop inventory experiment

    NASA Technical Reports Server (NTRS)

    Pitts, D. E.; Badhwar, G.

    1980-01-01

    The development of agricultural remote sensing systems requires knowledge of agricultural field size distributions so that the sensors, sampling frames, image interpretation schemes, registration systems, and classification systems can be properly designed. Malila et al. (1976) studied the field size distribution for wheat and all other crops in two Kansas LACIE (Large Area Crop Inventory Experiment) intensive test sites using ground observations of the crops and measurements of their field areas based on current year rectified aerial photomaps. The field area and size distributions reported in the present investigation are derived from a representative subset of a stratified random sample of LACIE sample segments. In contrast to previous work, the obtained results indicate that most field-size distributions are not log-normally distributed. The most common field size observed in this study was 10 acres for most crops studied.

  18. Field size, length, and width distributions based on LACIE ground truth data. [large area crop inventory experiment

    NASA Technical Reports Server (NTRS)

    Pitts, D. E.; Badhwar, G.

    1980-01-01

    The development of agricultural remote sensing systems requires knowledge of agricultural field size distributions so that the sensors, sampling frames, image interpretation schemes, registration systems, and classification systems can be properly designed. Malila et al. (1976) studied the field size distribution for wheat and all other crops in two Kansas LACIE (Large Area Crop Inventory Experiment) intensive test sites using ground observations of the crops and measurements of their field areas based on current year rectified aerial photomaps. The field area and size distributions reported in the present investigation are derived from a representative subset of a stratified random sample of LACIE sample segments. In contrast to previous work, the obtained results indicate that most field-size distributions are not log-normally distributed. The most common field size observed in this study was 10 acres for most crops studied.

  19. 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.

  20. Impact of topography and soil factors on crop suitability in two Mediterranean areas (Egypt and Spain)

    NASA Astrophysics Data System (ADS)

    Abd-Elmabod, S. K.; Jordán, A.; Anaya-Romero, M.; Ali, R. R.; Muñoz-Rojas, M.; Zavala, L. M.; de la Rosa, D.

    2012-04-01

    The aim of this research is to study the influence of topography and soil factors on crop suitability two Mediterranean areas: Sevilla (southern Spain) and El-Fayoum (northern Egypt). The Shuttle Radar Topography Mission (SRTM) images were processed using ENVI 4.7 software to extract elevation data, slope gradient and slope direction. North-south toposequences from both areas were extracted and studied using Arc-GIS 9.3 software. Soil characteristics along these toposequences were extracted from regional soil maps, as well as land surveying and laboratory analyses. The Almagra model (included in the agro-ecological system MicroLEIS DSS) was used to evaluate agricultural soil suitability using soil factors of useful depth, texture, drainage, carbonate content, salinity, sodium saturation, and degree of development of the profile. Changes of soil characteristics through the toposequences are discussed. The results of Almagra model indicate that the crop suitability main limiting factors are soil texture, drainage, soil salinity and sodium saturation percent and topography factors elevation, slope gradient, slope direction.

  1. Method for estimating pesticide use for county areas of the conterminous United States

    USGS Publications Warehouse

    Thelin, Gail P.; Gianessi, Leonard P.

    2000-01-01

    Information on the amount and distribution of pesticide compounds used throughout the United States is essential to evaluate the relation between water quality and pesticide use. This information is the basis of the U.S. Geological Survey?s National Water-Quality Assessment (NAWQA) Program studies of the effects of pesticides on water quality in 57 major hydrologic systems, or study units, located throughout the conterminous United States. To support these studies, a method was devised to estimate county pesticide use for the conterminous United States by combining (1) state-level information on pesticide use rates available from the National Center for Food and Agricultural Policy, and (2) county-level information on harvested crop acreage from the Census of Agriculture. The average annual pesticide use, the total amount of pesticides applied (in pounds), and the corresponding area treated (in acres) were compiled for the 208 pesticide compounds that are applied to crops in the conterminous United States. Pesticide use was ranked by compound and crop on the basis of the amount of each compound applied to 86 selected crops. Tabular summaries of pesticide use for NAWQA study units and for the Nation were prepared, along with maps that show the distribution of selected pesticides to agricultural land.

  2. Estimating riparian area extent and land use in the Midwest.

    Treesearch

    Brian J. Palik; Swee May Tang; Quinn. Chavez

    2004-01-01

    This report quantifies the amount and land use/land cover of riparian area in the seven-State Midwest Region of the continental United States. We estimate that riparian areas cover 8.9 to 13.2 million hectares in the region and that approximately 72 percent of riparian areas support natural or semi-natural land cover.

  3. Estimating 20-year land-use change and derived CO2 emissions associated with crops, pasture and forestry in Brazil and each of its 27 states.

    PubMed

    Novaes, Renan M L; Pazianotto, Ricardo A A; Brandão, Miguel; Alves, Bruno J R; May, André; Folegatti-Matsuura, Marília I S

    2017-09-01

    Land-use change (LUC) in Brazil has important implications on global climate change, ecosystem services and biodiversity, and agricultural expansion plays a critical role in this process. Concerns over these issues have led to the need for estimating the magnitude and impacts associated with that, which are increasingly reported in the environmental assessment of products. Currently, there is an extensive debate on which methods are more appropriate for estimating LUC and related emissions and regionalized estimates are lacking for Brazil, which is a world leader in agricultural production (e.g. food, fibres and bioenergy). We developed a method for estimating scenarios of past 20-year LUC and derived CO2 emission rates associated with 64 crops, pasture and forestry in Brazil as whole and in each of its 27 states, based on time-series statistics and in accordance with most used carbon-footprinting standards. The scenarios adopted provide a range between minimum and maximum rates of CO2 emissions from LUC according to different possibilities of land-use transitions, which can have large impacts in the results. Specificities of Brazil, like multiple cropping and highly heterogeneous carbon stocks, are also addressed. The highest CO2 emission rates are observed in the Amazon biome states and crops with the highest rates are those that have undergone expansion in this region. Some states and crops showing large agricultural areas have low emissions associated, especially in southern and eastern Brazil. Native carbon stocks and time of agricultural expansion are the most decisive factors to the patterns of emissions. Some implications on LUC estimation methods and standards and on agri-environmental policies are discussed. © 2017 John Wiley & Sons Ltd.

  4. Estimation of leaf area with an integrating sphere.

    PubMed

    Serrano, Lydia; Gamon, J. A.; Berry, J.

    1997-01-01

    Relative absorptance of intact branches measured with an integrating sphere was compared to leaf area estimated by conventional methods (volume displacement and scanning area meter) for three conifer species: Picea mariana (Mill.) BSP, Pinus banksiana (Lamb.) and Pseudotsuga menziesii (Mirb.) Franco. A consistent relationship between relative absorptance and surface area emerged for the three species. The ability to predict leaf area from absorptance was further explored by measuring branches of Pseudotsuga menziesii grown in varying light and nutrient regimes. When a single equation was used to predict leaf area under all growth conditions, errors were as large as 40% primarily because of variation in leaf absorptivity, with the largest errors associated with extremely nutrient-deficient foliage. When separate empirical equations were developed for each growth treatment, predicted leaf surface area agreed to within 5% of the area determined by the volume displacement method. Leaf surface area estimated from theoretical principles was also in good agreement with total surface area estimated independently by conventional methods. With proper accounting for needle absorptivity, which varied with growth conditions, leaf area estimates obtained by the integrating sphere method were of similar accuracy to those obtained by conventional methods, with the added advantage that the method allowed intact foliage to be sampled nondestructively in the field. Because the integrating sphere method preserves branch structure during measurement, it could provide a useful measure of needle area for photosynthetic or developmental studies requiring repeated sampling of the same branch.

  5. The best alternative for estimating reference crop evapotranspiration in different sub-regions of mainland China.

    PubMed

    Peng, Lingling; Li, Yi; Feng, Hao

    2017-07-14

    Reference crop evapotranspiration (ET o) is a critically important parameter for climatological, hydrological and agricultural management. The FAO56 Penman-Monteith (PM) equation has been recommended as the standardized ET o (ET o,s) equation, but it has a high requirements of climatic data. There is a practical need for finding a best alternative method to estimate ET o in the regions where full climatic data are lacking. A comprehensive comparison for the spatiotemporal variations, relative errors, standard deviations and Nash-Sutcliffe efficacy coefficients of monthly or annual ET o,s and ET o,i (i = 1, 2, …, 10) values estimated by 10 selected methods (i.e., Irmak et al., Makkink, Priestley-Taylor, Hargreaves-Samani, Droogers-Allen, Berti et al., Doorenbos-Pruitt, Wright and Valiantzas, respectively) using data at 552 sites over 1961-2013 in mainland China. The method proposed by Berti et al. (2014) was selected as the best alternative of FAO56-PM because it was simple in computation process, only utilized temperature data, had generally good accuracy in describing spatiotemporal characteristics of ET o,s in different sub-regions and mainland China, and correlated linearly to the FAO56-PM method very well. The parameters of the linear correlations between ET o of the two methods are calibrated for each site with the smallest determination of coefficient being 0.87.

  6. 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.

  7. Assessment of the Broadleaf Crops Leaf Area Index Product from the Terra MODIS Instrument

    NASA Technical Reports Server (NTRS)

    Tan, Bin; Hu, Jiannan; Huang, Dong; Yang, Wenze; Zhang, Ping; Shabanov, Nikolay V.; Knyazikhin, Yuri; Nemani, Ramakrishna R.; Myneni, Ranga B.

    2005-01-01

    The first significant processing of Terra MODIS data, called Collection 3, covered the period from November 2000 to December 2002. The Collection 3 leaf area index (LAI) and fraction vegetation absorbed photosynthetically active radiation (FPAR) products for broadleaf crops exhibited three anomalies (a) high LAI values during the peak growing season, (b) differences in LAI seasonality between the radiative transfer-based main algorithm and the vegetation index based back-up algorithm, and (c) too few retrievals from the main algorithm during the summer period when the crops are at full flush. The cause of these anomalies is a mismatch between reflectances modeled by the algorithm and MODIS measurements. Therefore, the Look-Up-Tables accompanying the algorithm were revised and implemented in Collection 4 processing. The main algorithm with the revised Look-Up-Tables generated retrievals for over 80% of the pixels with valid data. Retrievals from the back-up algorithm, although few, should be used with caution as they are generated from surface reflectances with high uncertainties.

  8. Assessment of the Broadleaf Crops Leaf Area Index Product from the Terra MODIS Instrument

    NASA Technical Reports Server (NTRS)

    Tan, Bin; Hu, Jiannan; Huang, Dong; Yang, Wenze; Zhang, Ping; Shabanov, Nikolay V.; Knyazikhin, Yuri; Nemani, Ramakrishna R.; Myneni, Ranga B.

    2005-01-01

    The first significant processing of Terra MODIS data, called Collection 3, covered the period from November 2000 to December 2002. The Collection 3 leaf area index (LAI) and fraction vegetation absorbed photosynthetically active radiation (FPAR) products for broadleaf crops exhibited three anomalies (a) high LAI values during the peak growing season, (b) differences in LAI seasonality between the radiative transfer-based main algorithm and the vegetation index based back-up algorithm, and (c) too few retrievals from the main algorithm during the summer period when the crops are at full flush. The cause of these anomalies is a mismatch between reflectances modeled by the algorithm and MODIS measurements. Therefore, the Look-Up-Tables accompanying the algorithm were revised and implemented in Collection 4 processing. The main algorithm with the revised Look-Up-Tables generated retrievals for over 80% of the pixels with valid data. Retrievals from the back-up algorithm, although few, should be used with caution as they are generated from surface reflectances with high uncertainties.

  9. Continual observation on crop leaf area index using wireless sensors network

    NASA Astrophysics Data System (ADS)

    Jiao, Sihong

    2014-03-01

    Crop structural parameter, i.e. leaf area index(LAI), is the main factor that can effect the solar energy re-assignment in the canopy. An automatic measuring system which is designed on the basis of wireless sensors network(WSN) is present in this paper. The system is comprised of two types of node. One is the measurement nodes which measured solar irradiance and were deployed beneath and above the canopy respectively, and another is a sink node which was used to collect data from the other measurement nodes. The measurement nodes also have ability to repeater data from one node to another and finally transfer signal to the sink node. Then the collected data of sink node are transferred to the data center through GPRS network. Using the field data collected by WSN, canopy structural parameters can be calculated using the direct transmittance which is the ratio of sun radiation captured by the measurement node beneath and above the canopy on different sun altitude angles. The proposed WSN measurement systems which is consisted of about 45 measurement node was deployed in the Heihe watershed to continually observe the crop canopy structural parameters from 25 June to 24 August 2012. To validate the performance of the WSN measured crop structural parameters, the LAI values were also measured by LAI2000. The field preliminary validation results show that the designed system can capture the varies of solar direct canopy transmittance on different time in a day, which is the basis to calculate the target canopy structural parameters. The validation results reveal that the measured LAI values derived from our propose measurement system have acceptable correlation coefficient(R2 from 0.27 to 0.96 and averaged value 0.42) with those derived from LAI2000. So it is a promising way in the agriculture application to utilize the proposed system and thus will be an efficient way to measure the crop structural parameters in the large spatial region and on the long time series.

  10. 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...

  11. 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...

  12. 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

  13. 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.

  14. Crop Frequency Mapping for Land Use Intensity Estimation During Three Decades

    NASA Astrophysics Data System (ADS)

    Schmidt, Michael; Tindall, Dan

    2016-08-01

    Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, food security and sustainability of agricultural practices. Such spatial datasets are likely to support decisions on natural resource management, planning and policy. The complete Landsat Time Series (LTS) archive for 23 Landsat footprints in western Queensland from 1987 to 2015 was used in a multi-temporal mapping approach. Spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by on ground training data sampled across space and time for the classes Crop and No-Crop. Temporal information within summer and winter growing seasons for each year were summarised, and combined with various vegetation indices and band ratios computed from a mid-season spectral-composite image. All available temporal information was spatially aggregated to the scale of image segments in the mid- season composite for each growing season and used to train a random forest classifier for a Crop and No- Crop classification. Validation revealed that the predictive accuracy varied by growing season and region to be within k = 0.88 to 0.97 and are thus suitable for mapping current and historic cropping activity. Crop frequency maps were produced for all regions at different time intervals. The crop frequency maps were validated separately with a historic crop information time series. Different land use intensities and conversions e.g. from agricultural to pastures are apparent and potential drivers of these conversions are discussed.

  15. Separation of agroclimatic areas for optimal crop growing within the framework of the natural-agricultural zoning of Russia

    NASA Astrophysics Data System (ADS)

    Bulgakov, D. S.; Rukhovich, D. I.; Shishkonakova, E. A.; Vil'chevskaya, E. V.

    2016-09-01

    The separation of agroclimatic areas for optimal crop growing within is suggested within the framework of the natural-agricultural zoning of Russia developed under the supervision of I. Karmanov. Overall, 64 agroclimatic areas have been separated in Russia. They are specified by the particular soil and agroclimatic conditions and by the particular crops recommended for cultivation. The biological potential of these crops should correspond to the soil potential of the given area. A combined scheme of the natural-agricultural zoning of Russia and the separated agroclimatic areas is presented. It is argued that the information contained in this scheme can be used for developing landscape-adaptive farming systems, land cadaster, and land valuation; it is also helpful for terrain and remote sensing monitoring of soil fertility on arable lands and for soilecological monitoring.

  16. 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...

  17. A Bayesian approach to multisource forest area estimation

    Treesearch

    Andrew O. Finley

    2007-01-01

    In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply, demand is increasing for regional and national data layers depicting forest cover. These data layers must permit small area estimates of forest and, most importantly, provide associated error estimates. This paper presents a model-based approach for...

  18. The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields

    USDA-ARS?s Scientific Manuscript database

    Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and rep...

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Model-based Small Area Estimates of Cancer Risk Factors and Screening Behaviors - Small Area Estimates

    Cancer.gov

    These model-based estimates use two surveys, the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS). The two surveys are combined using novel statistical methodology.

  4. Characteristics of AVIRIS bands measurements in agricultural crops at Blythe area, California: II. Studies on kenaf, Hibiscus canabinus

    NASA Astrophysics Data System (ADS)

    Shakir Hanna, Safwat H.; Rethwisch, Michael D.

    2002-01-01

    AVIRIS data from Blythe,Calfornia , were acquired in June 1997 to study the agricultural spectra from different crops and for identification of crops in other areas with similar environmental factors and similar spectral properties. In this respect; the main objectives of this study are: 1) to compare the spectral and radiometric characteristics of AVIRIS data from agriculture crops with the spectra measured by FieldSpecR ASD radiometer; 2) to explore the use of AVIRIS images in identifying agriculture crops; and; 3) to build a spectral library for the crops that were studied. A long-term goal is to extend the spectral library for different vegetation or crops in different stages of growth. In order to support our study, on July 18-19, 2000 we collected spectra using the FieldSpecR ASD spectrometer from selected fields planted with different crops at Blythe area, California (at the Longitude 114 degree(s) 33.28 W and Latitude 33 degree(s) 25.42 N to Longitude 114 degree(s) 44.35 W and 33 degree(s) 39.77 N Latitude). The results of this study showed that there is a significant correlation between the data that were collected by AVIRIS image scene in 1997 and spectral data collected by the FieldSpecR spectrometer. This correlation allowed us to build a spectral library to be used in ENVI_IDL software. This leads to identification of different crops and in particular the visible part of the spectra. Furthermore, using IDL-ENVI algorithms of Spectral Angle Mapper classification (SAM), Spectral Feature Fitting (SFF) and Spectral Binary Encoding (SPE) showed that there is an excellent agreement between the predicted and the actual crop type (i.e. The correlation is between 85-90% match). Further use of the AVIRIS images can be of a value to crops identification or crops yield for commercial use. Kenaf crop spectra were studied. The kenaf varieties (Tainung 2, Everglades 41) were significantly differentiated by both the spectral data from AVIRIS and from the hand

  5. 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

  6. 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 λET.

  7. Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Jin, Xiuliang; Li, Zhenhai; Yang, Guijun; Yang, Hao; Feng, Haikuan; Xu, Xingang; Wang, Jihua; Li, Xinchuan; Luo, Juhua

    2017-04-01

    Timely and accurate estimation of winter wheat yield at a regional scale is crucial for national food policy and security assessments. Near-infrared reflectance is not sensitive to the leaf area index (LAI) and biomass of winter wheat at medium to high canopy cover (CC), and most of the vegetation indices displayed saturation phenomenon. However, LAI and biomass at medium to high CC can be efficiently estimated using imaging data from radar with stronger penetration, such as RADARSAT-2. This study had the following three objectives: (i) to combine vegetation indices based on our previous studies for estimating CC and biomass for winter wheat using HJ-1A/B and RADARSAT-2 imaging data; (ii) to combine HJ-1A/B and RADARSAT-2 imaging data with the AquaCrop model using the particle swarm optimization (PSO) algorithm to estimate winter wheat yield; and (iii) to compare the results from the assimilation of HJ-1A/B + RADARSAT-2 imaging data, HJ-1A/B imaging data, and RADARSAT-2 imaging data into the AquaCrop model using the PSO algorithm. Remote sensing data and concurrent LAI, biomass, and yield of sample fields were acquired in Yangling District, Shaanxi, China, during the 2014 winter wheat growing season. The PSO optimization algorithm was used to integrate the AquaCrop model and remote sensing data for yield estimation. The modified triangular vegetation index 2 (MTVI2) × radar vegetation index (RVI) and the enhanced vegetation index (EVI) × RVI had good relationships with CC and biomass, respectively. The results indicated that the predicted and measured yield (R2 = 0.31 and RMSE = 0.94 ton/ha) had agreement when the estimated CC from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model. When the estimated biomass from the HJ-1A/B and RADARSAT-2 data was used as the dynamic input variable for the AquaCrop model, the predicted yield showed agreement with the measured yield (R2 = 0.42 and RMSE = 0.81 ton/ha). These results show

  8. 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

  9. 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

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    USDA-ARS?s Scientific Manuscript database

    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...

  13. Research on rice acreage estimation in fragmented area based on decomposition of mixed pixels

    NASA Astrophysics Data System (ADS)

    Zhang, H.; Li, Q. Z.; Lei, F.; Du, X.; Wei, J. D.

    2015-04-01

    Rice acreage estimation is a key aspect to guarantee food security and also important to support government agricultural subsidy system. In this paper, we explored a sophisticated method to improve rice estimation accuracy at county scale and we developed our approach with China Environment Satellite HJ-1A/B data in Hunan Province, a fragmented area with complex rice cropping patterns. Our approach improved the estimation accuracy by combing supervised and unsupervised classification upon decomposition of mixed pixels model, and the rice estimation results, validated by ground survey data, showed a close relationship (RMSE~3.40) with survey figures, the estimated accuracy (EA) reached 83.74% at county level according to the sub-pixel method, and the accuracy can be increased about 12% compared to the pure-pixel method. The results suggest that decomposition of mixed pixels method has great significance to the improvement of rice acreage estimation accuracy, and can be used in mountainous and broken planting area.

  14. 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.

  15. 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

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

    USDA-ARS?s Scientific Manuscript database

    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)...

  17. Clustering Information of Non-Sampled Area in Small Area Estimation of Poverty Indicators

    NASA Astrophysics Data System (ADS)

    Sundara, V. Y.; Kurnia, A.; Sadik, K.

    2017-03-01

    Empirical Bayes (EB) is one of indirect estimates methods which used to estimate parameters in small area. Molina and Rao has been used this method for estimates nonlinear small area parameter based on a nested error model. Problems occur when this method is used to estimate parameter of non-sampled area which is solely based on synthetic model which ignore the area effects. This paper proposed an approach to clustering area effects of auxiliary variable by assuming that there are similarities among particular area. A simulation study was presented to demonstrate the proposed approach. All estimations were evaluated based on the relative bias and relative root mean squares error. The result of simulation showed that proposed approach can improve the ability of model to estimate non-sampled area. The proposed model was applied to estimate poverty indicators at sub-districts level in regency and city of Bogor, West Java, Indonesia. The result of case study, relative root mean squares error prediction of empirical Bayes with information cluster is smaller than synthetic model.

  18. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000

    NASA Astrophysics Data System (ADS)

    Monfreda, Chad; Ramankutty, Navin; Foley, Jonathan A.

    2008-03-01

    Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover data sets from satellites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited. Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 min by 5 min (~10 km by 10 km) latitude-longitude grid. The resulting land use data sets depict circa the year 2000 the area (harvested) and yield of 175 distinct crops of the world. We aggregate these individual crop maps to produce novel maps of 11 major crop groups, crop net primary production, and four physiologically based crop types: annuals/perennials, herbaceous/shrubs/trees, C3/C4, and leguminous/nonleguminous.

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

    PubMed

    Hoekenga, Owen A

    2008-07-01

    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.

  20. 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

  1. Targeting Nitrous Oxide Reduction Efforts Using Crop-Specific, High-Resolution Emission Estimates from Synthetic Fertilizer

    NASA Astrophysics Data System (ADS)

    Gerber, J. S.; West, P. C.; Carlson, K. M.; Garcia de Cortazar-Atauri, I.; Launay, M.; Makowski, D.; Mueller, N. D.; O'Connell, C.

    2014-12-01

    Changes in global weather patterns are projected to have important impacts on regional food security. These impacts can be substantial, but how do they compare to other drivers of food supply vulnerability, including decreasing stocks, changing trade patterns, concentration of production, and changing demand patterns? We use multi-year global datasets of yield and area for staple crops to assess relationships between metrics of drought derived from high-resolution gridded weather data and crop yields. We then evaluate how these metrics are changing over time, and compare the resulting change in the likelihood of regional yield reduction events to other systemic factors.

  2. European-Scale Evaluation Of The Capacity Of Remote Sensing Indicators To Estimate Regional Crop Yields

    NASA Astrophysics Data System (ADS)

    Lopez-Lozano, Raul; Duveiller, Gregory; Seguini, Lorenzo; Garcia-Condado, Sara; Baruth, Bettina

    2013-12-01

    This study presents a systematic evaluation of how remote sensing indicators relate to official crop yield time series for wheat, barley and maize over Europe at the regional level. These indicators consist of cumulated values of 1-km fAPAR products from SPOT- VEGETATION that are currently being operationally used in the MARS Crop Yield Forecasting System (MCYFS) and which are compatible with future services that will be provided by the European Copernicus Programme. The study demonstrates how regional fAPAR time series are able to provide valuable information for many regions about crop yield potentials during the growing season.

  3. 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.

  4. 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.

  5. 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.

  6. 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).

  7. 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.

  8. 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

  9. Estimation of above ground biomass for multi-stemmed short-rotation woody crops

    Treesearch

    Brian A. Byrd; Wilson G. Hood; Michael C. Tyree; Dylan N. Dillaway

    2015-01-01

    With the increasing interest in short-rotation woody crop (SRWC) systems, an accurate yet quick, non-destructive means for determining aboveground biomass is necessary from both management and research perspectives.

  10. Estimating the Impact and Spillover Effect of Climate Change on Crop Yield in Northern Ghana.

    NASA Astrophysics Data System (ADS)

    Botchway, E.

    2016-12-01

    In tropical regions of the world human-induced climate change is likely to impact negatively on crop yields. To investigate the impact of climate change and its spillover effect on mean and variance of crop yields in northern Ghana, the Just and Pope stochastic production function and the Spatial Durbin model were adopted. Surprisingly, the results suggest that both precipitation and average temperature have positive effects on mean crop yield during the wet season. Wet season average temperature has a significant spillover effect in the region, whereas precipitation during the wet season has only one significant spillover effect on maize yield. Wet season precipitation does not have a strong significant effect on crop yield despite the rainfed nature of agriculture in the region. Thus, even if there are losers and winners as a result of future climate change at the regional level, future crop yield would largely depend on future technological development in agriculture, which may improve yields over time despite the changing climate. We argue, therefore, that technical improvement in farm management such as improved seeds and fertilizers, conservation tillage and better pest control, may have a more significant role in increasing observed crop productivity levels over time. So investigating the relative importance of non-climatic factors on crop yield may shed more light on where appropriate interventions can help in improving crop yields. Climate change, also, needs to be urgently assessed at the level of the household, so that poor and vulnerable people dependent on agriculture can be appropriately targeted in research and development activities whose object is poverty alleviation.

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    USDA-ARS?s Scientific Manuscript database

    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...

  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. Experience with basal area estimation by prisms in lodgepole pine.

    Treesearch

    James M. Trappe

    1957-01-01

    Estimation of basal area by prisms offers intriguing possibilities for reducing time and effort in making stand inventories. Increased inventory efficiency is a particular need in stands that are relatively low in value due to small stems, predominance of low value species or heavy defect. In the Pacific Northwest, lodgepole pine characteristically forms dense low-...

  15. 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...

  16. Grid-based sampling designs and area estimation

    Treesearch

    Joseph M. McCollum

    2007-01-01

    The author discusses some area and variance estimation methods that have been used by personnel of the U.S. Department of Agriculture Forest Service Southern Research Station and its predecessors. The author also presents the methods of Horvitz and Thompson (1952), especially as they have been popularized by Stevens (1997), and shows how they could be used to produce...

  17. Forest land area estimates from vegetation continuous fields

    Treesearch

    Mark D. Nelson; Ronald E. McRoberts; Matthew C. Hansen

    2004-01-01

    The USDA Forest Service's Forest Inventory and Analysis (FIA) program provides data, information, and knowledge about our Nation's forest resources. FIA regional units collect data from field plots and remotely sensed imagery to produce statistical estimates of forest extent (area); volume, growth, and removals; and health and condition. There is increasing...

  18. Estimating allowable-cut by area-scheduling

    Treesearch

    William B. Leak

    2011-01-01

    Estimation of the regulated allowable-cut is an important step in placing a forest property under management and ensuring a continued supply of timber over time. Regular harvests also provide for the maintenance of needed wildlife habitat. There are two basic approaches: (1) volume, and (2) area/volume regulation, with many variations of each. Some require...

  19. 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...

  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. 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.

  2. Using a basin-scale hydrological model to estimate crop transpiration and soil evaporation

    NASA Astrophysics Data System (ADS)

    Kite, G.

    2000-03-01

    Increasing populations and expectations, declining crop yields and the resulting increased competition for water necesitate improvements in irrigation management and productivity. A key factor in defining agricultural productivity is to be able to simulate soil evaporation and crop transpiration. In agribusiness terms, crop transpiration is a useful process while soil and open-water evaporations are wasteful processes. In this study a distributed hydrological model was used to compute daily evaporation and transpiration for a variety of crops and other land covers within the 17,200 km 2 Gediz Basin in western Turkey. The model, SLURP, describes the complete hydrological cycle for each land cover within a series of sub-basins including all dams, reservoirs, regulators and irrigation schemes in the basin. The sub-basins and land covers are defined by analysing a digital elevation model and NOAA AVHRR satellite data. In this study, the model uses the FAO implementation of the Penman-Monteith equation to simulate soil evaporation and crop transpiration. The results of the model runs provide time series of data on streamflow at many points along the river system, abstractions and return flows from crops within the irrigation schemes and areally distributed soil evaporation and crop transpiration across the entire basin on each day of an 11 year period. The results show that evaporation and transpiration vary widely across the basin on any one day and over the irrigation season and can be used to evaluate the effectiveness of the various irrigation strategies used in the basin. The advantages of using such a model as compared to deriving evapotranspiration from satellite data are that the model obtains results for each day of an indefinitely long period, as opposed to occasional snapshots, and can also be used to simulate alternate scenarios.

  3. 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.

  4. 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.

  5. Uptake and translocation of metals in different parts of crop plants irrigated with contaminated water from DEPZ area of Bangladesh.

    PubMed

    Goni, M A; Ahmad, J U; Halim, M A; Mottalib, M A; Chowdhury, D A

    2014-06-01

    Metal contamination in arable soils and crops grown in and around an industrial area of Bangladesh were measured, and the transfer factor from soils to crops was calculated accordingly. The highest concentration was observed for Fe and the order of metal concentration was Fe > Zn > Cr > Pb > Cu > Ni > Cd in soils. Bioaccumulation and translocation of metals from roots to edible parts of the crop plants were varied for almost all elements studied. Absorption of metals was significantly more in the roots compared to other plant parts. Accumulation of all metals in the edible parts of the plants was compared with the recommended maximum tolerable levels proposed by the Joint FAO/WHO Expert Committee on Food Additives. Bioconcentration factors values based on dry weights were below one for all metals except Cu in the rice roots and decreased in the order of Cu > Zn > Fe > Pb > Ni > Cd > Cr.

  6. Estimation of Carbon Budgets for Croplands by Combining High Resolution Remote Sensing Data with a Crop Model and Validation Ground Data

    NASA Astrophysics Data System (ADS)

    Mangiarotti, S.; Veloso, A.; Ceschia, E.; Tallec, T.; Dejoux, J. F.

    2015-12-01

    Croplands occupy large areas of Earth's land surface playing a key role in the terrestrial carbon cycle. Hence, it is essential to quantify and analyze the carbon fluxes from those agro-ecosystems, since they contribute to climate change and are impacted by the environmental conditions. In this study we propose a regional modeling approach that combines high spatial and temporal resolutions (HSTR) optical remote sensing data with a crop model and a large set of in-situ measurements for model calibration and validation. The study area is located in southwest France and the model that we evaluate, called SAFY-CO2, is a semi-empirical one based on the Monteith's light-use efficiency theory and adapted for simulating the components of the net ecosystem CO2 fluxes (NEE) and of the annual net ecosystem carbon budgets (NECB) at a daily time step. The approach is based on the assimilation of satellite-derived green area index (GAI) maps for calibrating a number of the SAFY-CO2 parameters linked to crop phenology. HSTR data from the Formosat-2 and SPOT satellites were used to produce the GAI maps. The experimental data set includes eddy covariance measurements of net CO2 fluxes from two experimental sites and partitioned into gross primary production (GPP) and ecosystem respiration (Reco). It also includes measurements of GAI, biomass and yield between 2005 and 2011, focusing on the winter wheat crop. The results showed that the SAFY-CO2 model correctly reproduced the biomass production, its dynamic and the yield (relative errors about 24%) in contrasted climatic, environmental and management conditions. The net CO2 flux components estimated with the model were overall in agreement with the ground data, presenting good correlations (R² about 0.93 for GPP, 0.77 for Reco and 0.86 for NEE). The evaluation of the modelled NECB for the different site-years highlighted the importance of having accurate estimates of each component of the NECB. Future works aim at considering

  7. 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.

  8. 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 P.; 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.

  9. Estimation of the Components of the Carbon and Water Budgets for Winter Wheat by Combining High Resolution Remote Sensing Data with a Crop Model

    NASA Astrophysics Data System (ADS)

    Veloso, A.; Ceschia, E.

    2014-12-01

    Croplands occupy more than one third of Earth's terrestrial surface contributing to climate change and also being impacted by those changes, since their production is conditioned by climatic conditions and water resources. It is thus essential to quantify and analyze the production and the main components of the carbon and water cycles for crop ecosystems. We propose here a regional modeling approach that combines: high spatial and temporal resolutions (HSTR) optical remote sensing data, a simple crop model and an extensive set of in-situ measurements for model's calibration and validation. The model, named SAFYE-CO2 (Simple Algorithm for Fluxes and Yield Estimates), is a daily time step model based on Monteith's light-use efficiency theory and coupled with a water budget module (FAO-56 method). SAFYE-CO2 estimates components of the carbon budget (gross primary production (GPP), ecosystem respiration (Reco), net ecosystem exchange (NEE), …) and of the crop water cycle (evaporation, transpiration, evapotranspiration (ETR) and soil water content) and also time courses of dry aboveground biomass and yield by assimilating Green Area Index (GAI) data obtained from HSTR satellite observations. For this work, we used a unique set of Formosat-2 and SPOT images acquired from 2006 to 2011 in southwest France. Crop and soil model parameters were set using both in-situ measurements and values found in the literature. Phenological parameters were calibrated by the GAI assimilation. The results indicate that the model correctly reproduces winter wheat biomass and yield production (relative error about 25%) for years with contrasted climatic conditions. The estimated net carbon flux components were overall in agreement with the flux measurements, presenting good correlations (R² about 0.9 for GPP, 0.77 for Reco and 0.84 for NEE). Regarding the ETR, a good correlation (R2 about 0.73) and satisfactory errors (RMSE about 0.47 mm.d-1) were found. Carbon and water budgets as well

  10. Methodology for the Model-based Small Area Estimates of Cancer Risk Factors and Screening Behaviors - Small Area Estimates

    Cancer.gov

    This model-based approach uses data from both the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS) to produce estimates of the prevalence rates of cancer risk factors and screening behaviors at the state, health service area, and county levels.

  11. 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.

  12. 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.

  13. 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.

  14. [Winter wheat area estimation with MODIS-NDVI time series based on parcel].

    PubMed

    Li, Le; Zhang, Jin-shui; Zhu, Wen-quan; Hu, Tan-gao; Hou, Dong

    2011-05-01

    Several attributes of MODIS (moderate resolution imaging spectrometer) data, especially the short temporal intervals and the global coverage, provide an extremely efficient way to map cropland and monitor its seasonal change. However, the reliability of their measurement results is challenged because of the limited spatial resolution. The parcel data has clear geo-location and obvious boundary information of cropland. Also, the spectral differences and the complexity of mixed pixels are weak in parcels. All of these make that area estimation based on parcels presents more advantage than on pixels. In the present study, winter wheat area estimation based on MODIS-NDVI time series has been performed with the support of cultivated land parcel in Tongzhou, Beijing. In order to extract the regional winter wheat acreage, multiple regression methods were used to simulate the stable regression relationship between MODIS-NDVI time series data and TM samples in parcels. Through this way, the consistency of the extraction results from MODIS and TM can stably reach up to 96% when the amount of samples accounts for 15% of the whole area. The results shows that the use of parcel data can effectively improve the error in recognition results in MODIS-NDVI based multi-series data caused by the low spatial resolution. Therefore, with combination of moderate and low resolution data, the winter wheat area estimation became available in large-scale region which lacks completed medium resolution images or has images covered with clouds. Meanwhile, it carried out the preliminary experiments for other crop area estimation.

  15. 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

  16. 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

  17. Estimation of the specific surface area for a porous carrier.

    PubMed

    Levstek, Meta; Plazl, Igor; Rouse, Joseph D

    2010-03-01

    In biofilm systems, treatment performance is primarily dependent upon the available biofilm growth surface area in the reactor. Specific surface area is thus a parameter that allows for making comparisons between different carrier technologies used for wastewater treatment. In this study, we estimated the effective surface area for a spherical, porous polyvinyl alcohol (PVA) gel carrier (Kuraray) that has previously demonstrated effectiveness for retention of autotrophic and heterotrophic biomass. This was accomplished by applying the GPS-X modeling tool (Hydromantis) to a comparative analysis of two moving-bed biofilm reactor (MBBR) systems. One system consisted of a lab-scale reactor that was fed synthetic wastewater under autotrophic conditions where only the nitrification process was studied. The other was a pre-denitrification pilot-scale plant that was fed real, primary-settled wastewater. Calibration of an MBBR process model for both systems indicated an effective specific surface area for PVA gel of 2500 m2/m3, versus a specific surface area of 1000 m2/m3 when only the outer surface of the gel beads is considered. In addition, the maximum specific growth rates for autotrophs and heterotrophs were estimated to be 1.2/day and 6.0/day, respectively.

  18. Interactive state-parameter estimation of a crop carbon mass balance model through the assimilation of observed winter wheat carbon flux and stock data

    NASA Astrophysics Data System (ADS)

    Sus, O.; Williams, M. D.; Gruenwald, T.

    2010-12-01

    Next to the consideration of land management practises, modelling the carbon balance of croplands requires a crop carbon budget model that realistically simulates photosynthesis, ecosystem respiration, soil carbon dynamics, and phenology dependant on crop-specific parameters and carbon allocation patterns. A crop carbon mass balance model is a tool which can aid to answer questions related to cropland carbon sequestration potential, best-practise recommendations, seasonal patterns and amplitude of net carbon exchange (NEE), and prediction of biomass growth and crop yield. However, land management complicates modelling of cropland NEE by largely determining the onset and length of the growing season of agricultural areas. Human decision making on crop cultivars, sowing and harvest dates, and management practices is difficult to simulate, and corresponding reliable data for larger spatial and temporal scales is still sparse. Crop carbon budget models require a specific set of parameters, some of which are poorly understood and are thus of empirical rather than mechanistic nature. Here, we present a study that deals with the assimilation of observations of both carbon flux and stock data into a crop C budget model (SPAc). Our data assimilation procedure (the Ensemble Kalman Filter, EnKF) aims at updating both model states and parameters, so that we will gain insight into optimized parameter values and carbon stock/flux estimates within quantified confidence limits. We obtained measured data of NEE, LAI, and leaf, root, stem, and storage organ dry mass for a winter wheat season in 2005/2006 from the CarboEurope Fluxnet site at Klingenberg/Germany. We conducted several model experiments, for each of which we assimilated a unique combination of data sources. We find that the assimilation of NEE data leads to reduced model error (observed vs. modelled NEE) compared to a model run without data assimilation (a reduction of ~15-20% of RMSE). The assimilated dry mass data on

  19. 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.

  20. 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

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    USDA-ARS?s Scientific Manuscript database

    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 ...

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    USDA-ARS?s Scientific Manuscript database

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

    PubMed

    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 R2=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 accurate than

  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. 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.

  8. 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.

  9. Small area estimation of child undernutrition in Ethiopian woredas

    PubMed Central

    Andrews, Colin; Khan, Qaiser

    2017-01-01

    Reducing child undernutrition is a key social policy objective of the Ethiopian government. Despite substantial reduction over the last decade and a half, child undernutrition is still high; with 48 percent of children either stunted, underweight or wasted, undernutrition remains an important child health challenge. The existing literature highlights that targeting of efforts to reduce undernutrition in Ethiopia is inefficient, in part due to lack of data and updated information. This paper remedies some of this shortfall by estimating levels of stunting and underweight in each woreda for 2014. The estimates are small area estimations based on the 2014 Demographic and Health Survey and the latest population census. It is shown that small area estimations are powerful predictors of undernutrition, even compared to household characteristics, such as wealth and education, and hence a valuable targeting metric. The results show large variations in share of children undernourished within each region, more than between regions. The results also show that the locations with larger challenges depend on the chosen undernutrition statistic, as the share, number and concentration of undernourished children point to vastly different locations. There is also limited correlation between share of children underweight and stunted across woredas, indicating that different locations face different challenges. PMID:28410435

  10. 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.

  11. New method to estimate the cycling frontal area.

    PubMed

    Debraux, P; Bertucci, W; Manolova, A V; Rogier, S; Lodini, A

    2009-04-01

    The purpose of this study was to test the validity and reliability of a new method to estimate the projected frontal area of the body during cycling. To illustrate the use of this method in another cycling speciality (i.e. mountain bike), the NM data were coupled with a powermeter measurement to determine the projected frontal area and the coefficient of drag in actual conditions. Nine male cyclists had their frontal area determined from digital photographic images in a laboratory while seated on their bicycles in two positions:Upright Position (UP) and Traditional Aerodynamic Position (TAP). For each position, the projected frontal area for the body of the cyclist as well as the cyclist and his bicycle were measured using a new method with computer aided-design software, the method of weighing photographs and the digitizing method. The results showed that no significant difference existed between the new method and the method of weighing photographs in the measurement of the frontal area of the body of cyclists in UP (p=0.43) and TAP (p=0.14), or between the new method and the digitizing method in measurement of the frontal area for the cyclist and his bicycle in UP (p=0.12) and TAP (p=0.31). The coefficients of variation of the new method and the method of weighing photographs were 0.1% and 1.26%, respectively. In conclusion, the new method was valid and reliable in estimating the frontal area compared with the method of weighing photographs and the digitizing method.

  12. Fire Scars Area Estimation Using CHRIS PROBA Satellite Data

    NASA Astrophysics Data System (ADS)

    Filchev, Lachezar; Dimitrov, Petar

    2013-12-01

    The dawn of 21st century is marked by severe and unpredictable natural and man - made hazards and disasters linked as to climate change as to human impact on environment. To study their effects on natural landscapes and protected areas it is important to perform, in some restrict regime protected areas, a continuous monitoring. Earth observation by satellites is one of the most promising instruments for this as it has the necessary time, spatial, and spectral resolution for this as well as it provides for non-contact estimation of the overall condition of the environment. This study presents preliminary results of fire scars area estimation on the territory of Bistrishko Branishte UNESCO Man and Biosphere (MAB) reserve in Vitosha Mountain, Bulgaria using CHRIS/PROBA satellite data. During the summer and early autumn of 2012 CHRIS/PROBA instrument was tasked to perform a series of acquisitions with a view to study the vegetation structure. The study uses two CHRIS/PROBA scenes acquired subsequently on 22 June 2012 and on 28 September 2012. The wildfire, which effects are studied, took place during the first two weeks of July 2012. After it was settled the second acquisition of CHRIS/PROBA instrument made possible the analysis of the post fire situation. The methods used for the study are the standard methods for image change detection based on spectral data employed in ENVI software (Academic license). In order to perform the change detection, the CHRIS/PROBA source data was geometrically and atmospherically corrected as well as co-registered. The multi angle product of CHRIS/PROBA Mode 1, consisting of 5 images, was used to check to what extent the five viewing angles affect the area estimation of the fire scars in the mountainous area following same procedures. The results from the analysis shown that almost 60 hectares from the coniferous vegetation (dead and healthy tree stands) were devastated by the wildfire.

  13. Radium-226 transfer factor from soils to crops and its simple estimation method using uranium and barium concentrations.

    PubMed

    Tagami, Keiko; Uchida, Shigeo

    2009-09-01

    Radium-226 ((226)Ra) should be assessed to determine the safety of geological disposal of high-level radioactive and transuranic wastes. Among the environmental transfer parameters that have been used in mathematical models for the environmental safety assessment, soil-to-plant transfer factor (F(v)) is of importance; it is defined as the plant/soil concentration ratio. Reported F(v) data for (226)Ra are still limited due to the low concentration of (226)Ra in plants in the natural environment. In this study, we collected F(v) of (226)Ra (F(v)-Ra) for crops and then applied a statistical approach to estimate F(v)-Ra instead of directly measuring the radionuclide. We found high correlations between (226)Ra and U concentrations in soils (because (226)Ra is a progeny in the (238)U series), and between (226)Ra and Ba concentrations in plants (because they are chemically similar in plant uptake). Using U in soil and Ba in plant values, we could estimate F(v)-Ra with good accuracy; the difference between estimated and measured F(v)-Ra values was a factor of 1.2 on average for crops. The method could estimate F(v)-Ra for the soil-to-plant systems where (226)Ra and Ba concentrations in soil are within the normal range, e.g. 8-100 Bq kg(-1)-dry for (226)Ra and 84-960 mg kg(-1)-dry for Ba.

  14. 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.

  15. 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.

  16. Rain volume estimation over areas using satellite and radar data

    NASA Technical Reports Server (NTRS)

    Doneaud, Andre A.; Vonderhaar, T. H.; Johnson, L. R.; Laybe, P.; Reinke, D.

    1987-01-01

    The analysis of 18 convective clusters demonstrates that the extension of the Area-Time-Integral (ATI) technique to the use of satellite data is possible. The differences of the internal structures of the radar reflectivity features, and of the satellite features, give rise to differences in estimating rain volumes by delineating area; however, by focusing upon the area integrated over the lifetime of the storm, it is suggested that some of the errors produced by the differences in the cloud geometries as viewed by radar or satellite are minimized. The results are good and future developments should consider data from different climatic regions and should allow for implementation of the technique in a general circulation model.

  17. 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

  18. 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.

  19. Analysis of scanner data for crop inventories

    NASA Technical Reports Server (NTRS)

    Horvath, R. (Principal Investigator); Cicone, R.; Crist, E.; Kauth, R. J.; Pont, W.

    1980-01-01

    Classification and technology development for area estimation of corn, soybeans, wheat, barley, and sunflowers are outlined. Supporting research for corn and soybean foreign commodity production forecasting is highlighted. Graphs profiling the greenness and brightness of the crops are presented.

  20. 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

  1. Effects of the density and homogeneity in NIRS crop moisture estimation

    NASA Astrophysics Data System (ADS)

    Lenzini, Nicola; Rovati, Luigi; Ferrari, Luca

    2017-06-01

    Near-infrared spectroscopy (NIRS) is widely used in fruits and vegetables quality evaluation. This technique is also used for the analysis of alfalfa, a crop that occupies a position of great importance in the agricultural field. In particular for the storage, moisture content is a key parameter for the crops and for this reason its monitoring is very important during the harvesting phase. Usually optical methods like NIRS are well suitable in laboratory frameworks where the specimen is properly prepared, while their application during the harvesting phase presents several diffculties. A lot of influencing factors, such as density and degree of homogeneity can affect the moisture evaluation. In this paper we present the NIRS analysis of alfalfa specimens with different values of moisture and density, as well as the obtained results. To study scattering and absorption phenomena, the forward and backward scattered light from the sample have been spectrally analyzed.

  2. 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.

  3. 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.

  4. Implementation of random forest algorithm for crop mapping across an aridic to ustic area of Indian states

    NASA Astrophysics Data System (ADS)

    Shukla, Gaurav; Garg, Rahul Dev; Srivastava, Hari Shanker; Garg, Pradeep Kumar

    2017-04-01

    The purpose of this study is to effectively implement random forest algorithm for crop classification of large areas and to check the classification capability of different variables. To incorporate dependency of crops in different variables namely, texture, phenological, parent material and soil, soil moisture, topographic, vegetation, and climate, 35 digital layers are prepared using different satellite data (ALOS DEM, Landsat-8, MODIS NDVI, RISAT-1, and Sentinel-1A) and climatic data (precipitation and temperature). The importance of variables is also calculated based on mean decrease in accuracy and mean decrease in Gini score. Importance and capabilities of variables for crop mapping have been discussed. Variables associated with spectral responses have shown greater importance in comparison to topographic and climate variables. The spectral range (0.85 to 0.88 μm) of the near-infrared band is the most useful variable with the highest scores. The topographic variable and elevation have secured the second place rank in the both scores. This indicates the importance of spectral responses as well as of topography in model development. Climate variables have not shown as much importance as others, but in association with others, they cause a decrease in the out of bag (OOB) error rate. In addition to the OOB data, a 20% independent dataset of training samples is used to evaluate RF model. Results show that RF has good capability for crop classification.

  5. 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.

  6. Multi-objective optimization of crop planting structure in irrigation area based on remote sensing technology

    USDA-ARS?s Scientific Manuscript database

    In the regions short of water, the adjustment of cropping structure is also an important measure to save water in agriculture besides water-saving irrigation techniques and cultivation techniques. This paper describes a method of water saving and high efficient water usage by adjusting the differen...

  7. 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

  8. Heavy metals effects on forage crops yields and estimation of elements accumulation in plants as affected by soil.

    PubMed

    Grytsyuk, N; Arapis, G; Perepelyatnikova, L; Ivanova, T; Vynograds'ka, V

    2006-02-01

    Heavy metals (Cu, Cd, Pb, Zn) effect on the productivity of forage crops (clover and perennial cereal grasses) and their accumulation in plants, depending on the concentration of these elements in a soil, has been studied in micro-field experiments on three types of soil. The principle objective was to determine regularities of heavy metals migration in a soil-plant system aiming the estimation of permissible levels of heavy metals content in soils with the following elaboration of methods, which regulate the toxicants transfer to plants. Methods of field experiments, agrochemical and atomic absorption analysis were used. Results were statistically treated by Statistica 6.0, S-Plus 6. Experimental results have shown that the intensity of heavy metals accumulation in plants depends on the type of the soil, the species of plants, the physicochemical properties of heavy metals and their content in the soil. Logarithmic interdependency of heavy metals concentration in soils and their accumulation in plants is suggested. However, the strong correlation between the different heavy metals concentrations in the various soils and the yield of crops was not observed. Toxicants accumulation in crops decreased in time.

  9. Equal Area Logistic Estimation for Item Response Theory

    NASA Astrophysics Data System (ADS)

    Lo, Shih-Ching; Wang, Kuo-Chang; Chang, Hsin-Li

    2009-08-01

    Item response theory (IRT) models use logistic functions exclusively as item response functions (IRFs). Applications of IRT models require obtaining the set of values for logistic function parameters that best fit an empirical data set. However, success in obtaining such set of values does not guarantee that the constructs they represent actually exist, for the adequacy of a model is not sustained by the possibility of estimating parameters. In this study, an equal area based two-parameter logistic model estimation algorithm is proposed. Two theorems are given to prove that the results of the algorithm are equivalent to the results of fitting data by logistic model. Numerical results are presented to show the stability and accuracy of the algorithm.

  10. Spectral estimation of green leaf area index of oats

    NASA Technical Reports Server (NTRS)

    Best, R. G.; Harlan, J. C.

    1985-01-01

    Green leaf area index (LAI) is a measure of vegetative growth and development and is frequently used as an input parameter in yield estimation and evapotranspiration models. Extensive destructive sampling is usually required to achieve accurate estimates of green LAI in natural situations. In this investigation, a statistical modeling approach was used to predict the green LAI of oats from bidirectional reflectance data collected with multiband radiometers. Stepwise multiple regression models based on two sets of spectral reflectance factors accounted for 73 percent and 65 percent of the variance in green LAI of oats. Exponential models of spectral data transformations of greenness, normalized difference, and near-infrared/red ratio accounted for more of the variance in green LAI than the multiple regression models.

  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

    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 for irrigation in the two counties in 1991, about 83,000 acre-feet, was about 5 percent greater than the quantity calculated from data reported by the ASCS.

  12. A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables

    Treesearch

    Michael E. Goerndt; Vicente J. Monleon; Hailemariam. Temesgen

    2011-01-01

    One of the challenges often faced in forestry is the estimation of forest attributes for smaller areas of interest within a larger population. Small-area estimation (SAE) is a set of techniques well suited to estimation of forest attributes for small areas in which the existing sample size is small and auxiliary information is available. Selected SAE methods were...

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Estimation of stand-level leaf area for boreal bryophytes.

    PubMed

    Bond-Lamberty, Ben; Gower, Stith T

    2007-04-01

    Bryophytes dominate the carbon and nitrogen cycling of many poorly drained terrestrial ecosystems and are important in the vegetation-atmosphere exchange of carbon and water, yet few studies have estimated their leaf area at the stand scale. This study quantified the bryophyte-specific leaf area (SLA) and leaf area index (LAI) in a group of different-aged boreal forest stands in well and poorly drained soils. Species-specific SLA (for three feather mosses, four Sphagnum spp. and Aulacomnium palustre mixed with Tomentypnum nitens) was assessed by determining the projected area using a flatbed scanner and cross-sectional geometry using a dissecting microscope. The hemisurface leaf area was computed as the product of SLA and live biomass and was scaled by coverage data collected at all stands. Pleurozium schreberi dominated the spatial coverage, biomass and leaf area in the well-drained stands, particularly the oldest, while S. fuscum and A. palustre were important in the poorly drained stands. Live moss biomass ranged from 47 to 230 g m(-2) in the well-drained stands dominated by feather mosses and from 102 to 228 g m(-2) in the poorly drained stands. Bryophyte SLA varied between 135 and 473 cm(2) g(-1), for A. palustre and S. capillifolium, respectively. SLA was strongly and significantly affected by bryophyte species, but did not vary between stands; in general, there was no significant difference between the SLA of non-Sphagnum mosses. Bryophyte LAI increased with stand age, peaking at 3.1 and 3.7 in the well and poorly drained stands, respectively; this represented approximately 40% of the overstory LAI in the well-drained stands and 100-1,000% in the poorly drained stands, underscoring the important role bryophytes play in the water and carbon budgets of these boreal forests.

  18. Estimating N2O processes during grassland renewal and grassland conversion to maize cropping using N2O isotopocules

    NASA Astrophysics Data System (ADS)

    Buchen, Caroline; Well, Reinhard; Flessa, Heinz; Fuß, Roland; Helfrich, Mirjam; Lewicka-Szczebak, Dominika

    2017-04-01

    Grassland break-up due to grassland renewal and grassland conversion to cropland can lead to a flush of mineral nitrogen from decomposition of the old grass sward and the decomposition of soil organic matter. Moreover, increased carbon and nitrogen mineralisation can result in enhanced nitrous oxide (N2O) emissions. As N2O is known to be an important greenhouse gas and a major precursor for ozone depletion, its emissions need to be mitigated by adjusting agricultural management practices. Therefore, it is necessary to understand the N2O processes involved, as well as the contribution of N2O reduction to N2. Apart from the widely used 15N gas flux method, natural abundance isotopic analysis of the four most abundant isotopocules of N2O species is a promising alternative to assess N2O production pathways. We used stable isotope analyses of soil-emitted N2O (δ18ON2O, δ15NN2Obulk and δ15NN2OSP= intramolecular distribution of 15N within the linear N2O molecule) with an isotopocule mapping approach to simultaneously estimate the magnitude of N2O reduction to N2 and the fraction of N2O originating from the bacterial denitrification pathway or fungal denitrification and/or nitrification. This approach is based on endmember areas of isotopic values for the N2O produced from different sources reported in the literature. For this purpose, we calculated two main scenarios with different assumptions for N2O produced: N2O is reduced to N2 before residual N2O is mixed with N2O of various sources (Scenario a) and vice versa (Scenario b). Based on this, we applied seven different scenario variations, where we evaluated the range of possible values for the potential N2O production pathways (heterotrophic bacterial denitrification and/or nitrifier denitrification and fungal denitrification and/or nitrification). This was done by using a range of isotopic endmember values and assuming different fractionation factors of N2O reduction in order to find the most reliable scenario

  19. 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.

  20. 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.

  1. 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.

  2. Networked Estimation with an Area-Triggered Transmission Method

    PubMed Central

    Nguyen, Vinh Hao; Suh, Young Soo

    2008-01-01

    This paper is concerned with the networked estimation problem in which sensor data are transmitted over the network. In the event-driven sampling scheme known as level-crossing or send-on-delta, sensor data are transmitted to the estimator node if the difference between the current sensor value and the last transmitted one is greater than a given threshold. The event-driven sampling generally requires less transmission than the time-driven one. However, the transmission rate of the send-on-delta method becomes large when the sensor noise is large since sensor data variation becomes large due to the sensor noise. Motivated by this issue, we propose another event-driven sampling method called area-triggered in which sensor data are sent only when the integral of differences between the current sensor value and the last transmitted one is greater than a given threshold. Through theoretical analysis and simulation results, we show that in the certain cases the proposed method not only reduces data transmission rate but also improves estimation performance in comparison with the conventional event-driven method. PMID:27879742

  3. 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.

  4. Digital cover photography for estimating leaf area index (LAI) in apple trees using a variable light extinction coefficient.

    PubMed

    Poblete-Echeverría, Carlos; Fuentes, Sigfredo; Ortega-Farias, Samuel; Gonzalez-Talice, Jaime; Yuri, Jose Antonio

    2015-01-28

    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 (LAI(D)), which was compared with LAI estimated by the proposed digital photography method (LAI(M)). Results showed that the LAI(M) was able to estimate LAI(D) 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 (f(f)) 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.

  5. 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

  6. 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.

  7. Vegetation-index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems

    USDA-ARS?s Scientific Manuscript database

    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...

  8. Use of a root zone soil moisture model and crop spectral characteristics to estimate sorghum yields in a dryland Alfisol toposequence

    NASA Astrophysics Data System (ADS)

    Mandal, Uttam Kumar; Victor, U. S.; Srivastava, N. N.; Sharma, K. L.; Ramesh, V.; Vanaja, M.; Korwar, G. R.; Ramakrishna, Y. S.

    2006-12-01

    This study investigated the relationship between sorghum grain yield over range of soil depth with seasonal crop water stress index based on relative evapotranspiration deficits and spectral vegetation indices. A root zone soil moisture model has been used to evaluate the seasonal soil moisture fluctuation and actual evapotranspiration within a toposequence having varying soil depth of 30 to 75 cm as well as different available water capacity ranging from 6.9% to 12.6% (V/V%). The higher r2 values between modeled and observed values of soil water (r2> 0.69 significant at <0.001) and runoff (r2 = 0.95, significant at P<0.001) indicated good agreement between model output and observed values. The spectral vegetation indices like simple ratio, normalized difference vegetation index (NDVI), green NDVI, perpendicular vegetation index, soil adjusted vegetation index (SAVI) and modified SAVI (MSAVI) was recorded through out the growth period of sorghum. The vegetation indices except perpendicular vegetation index measured during booting to anthesis stages were positively correlated (P<0.05) with leaf area index and yield. The MSAVI measured during booting to milk-grain stage have the highest positive correlation with yield. Variation was noticed when additive and multiplicative forms of water-production functions calculated from water budget model were used to predict crop yield. But the yield estimation was improved when spectral vegetation indices measured during booting to milk-grain is incorporated along with water production functions. The water budget model along with spectral vegetation indices gave satisfactory estimates of sorghum grain yields and appears to be a useful tool to estimate yield as a function of soil depth and available soil water.

  9. Estimating millet production for famine early warning: An application of crop simulation modelling using satellite and ground-based data in Burkina Faso

    USGS Publications Warehouse

    Thornton, P. K.; Bowen, W. T.; Ravelo, A.C.; Wilkens, P. W.; Farmer, G.; Brock, J.; Brink, J. E.

    1997-01-01

    Early warning of impending poor crop harvests in highly variable environments can allow policy makers the time they need to take appropriate action to ameliorate the effects of regional food shortages on vulnerable rural and urban populations. Crop production estimates for the current season can be obtained using crop simulation models and remotely sensed estimates of rainfall in real time, embedded in a geographic information system that allows simple analysis of simulation results. A prototype yield estimation system was developed for the thirty provinces of Burkina Faso. It is based on CERES-Millet, a crop simulation model of the growth and development of millet (Pennisetum spp.). The prototype was used to estimate millet production in contrasting seasons and to derive production anomaly estimates for the 1986 season. Provincial yields simulated halfway through the growing season were generally within 15% of their final (end-of-season) values. Although more work is required to produce an operational early warning system of reasonable credibility, the methodology has considerable potential for providing timely estimates of regional production of the major food crops in countries of sub-Saharan Africa.

  10. 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.

  11. Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region.

    PubMed

    Wei, Xiangqin; Gu, Xingfa; Meng, Qingyan; Yu, Tao; Zhou, Xiang; Wei, Zheng; Jia, Kun; Wang, Chunmei

    2017-07-08

    Leaf area index (LAI) is an important vegetation parameter that characterizes leaf density and canopy structure, and plays an important role in global change study, land surface process simulation and agriculture monitoring. The wide field view (WFV) sensor on board the Chinese GF-1 satellite can acquire multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for dynamic monitoring of LAI. Therefore, an automatic LAI estimation algorithm for GF-1 WFV data was developed based on the radiative transfer model and LAI estimation accuracy of the developed algorithm was assessed in an agriculture region with maize as the dominated crop type. The radiative transfer model was firstly used to simulate the physical relationship between canopy reflectance and LAI under different soil and vegetation conditions, and then the training sample dataset was formed. Then, neural networks (NNs) were used to develop the LAI estimation algorithm using the training sample dataset. Green, red and near-infrared band reflectances of GF-1 WFV data were used as the input variables of the NNs, as well as the corresponding LAI was the output variable. The validation results using field LAI measurements in the agriculture region indicated that the LAI estimation algorithm could achieve satisfactory results (such as R² = 0.818, RMSE = 0.50). In addition, the developed LAI estimation algorithm had potential to operationally generate LAI datasets using GF-1 WFV land surface reflectance data, which could provide high spatial and temporal resolution LAI data for agriculture, ecosystem and environmental management researches.

  12. Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region

    PubMed Central

    Wei, Xiangqin; Gu, Xingfa; Meng, Qingyan; Yu, Tao; Zhou, Xiang; Wei, Zheng; Wang, Chunmei

    2017-01-01

    Leaf area index (LAI) is an important vegetation parameter that characterizes leaf density and canopy structure, and plays an important role in global change study, land surface process simulation and agriculture monitoring. The wide field view (WFV) sensor on board the Chinese GF-1 satellite can acquire multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for dynamic monitoring of LAI. Therefore, an automatic LAI estimation algorithm for GF-1 WFV data was developed based on the radiative transfer model and LAI estimation accuracy of the developed algorithm was assessed in an agriculture region with maize as the dominated crop type. The radiative transfer model was firstly used to simulate the physical relationship between canopy reflectance and LAI under different soil and vegetation conditions, and then the training sample dataset was formed. Then, neural networks (NNs) were used to develop the LAI estimation algorithm using the training sample dataset. Green, red and near-infrared band reflectances of GF-1 WFV data were used as the input variables of the NNs, as well as the corresponding LAI was the output variable. The validation results using field LAI measurements in the agriculture region indicated that the LAI estimation algorithm could achieve satisfactory results (such as R2 = 0.818, RMSE = 0.50). In addition, the developed LAI estimation algorithm had potential to operationally generate LAI datasets using GF-1 WFV land surface reflectance data, which could provide high spatial and temporal resolution LAI data for agriculture, ecosystem and environmental management researches. PMID:28698464

  13. 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

  14. Establishing Crop Productivity Using RADARSAT-2

    NASA Astrophysics Data System (ADS)

    McNairn, H.; Shang, J.; Jiao, X.; Deschamps, B.

    2012-07-01

    Crop productivity is influenced by a number of management and environmental conditions, and variations in crop growth can occur in-season due to, for example, unfavourable meteorological conditions. Consequently information on crop growth must be temporally frequent in order to adequately characterize crop productivity. Leaf Area Index (LAI) is a key indicator of crop productivity and a number of methods have been developed to derive LAI from optical satellite data. Integration of LAI estimates from synthetic aperture radar (SAR) sensors would assist in efforts to monitor crop production through the growing season, particularly during periods of persistent cloud cover. Consequently, Agriculture and Agri-Food Canada has assessed the capability of RADARSAT-2 data to estimate LAI. The results of a sensitivity analysis revealed that several SAR polarimetric variables were strongly correlated with LAI derived from optical sensors for small grain crops. As the growing season progressed, contributions from volume scattering from the crop canopies increased. This led to the sensitivity of the intensity of linear cross-polarization backscatter, entropy and the Freeman-Durden volume scattering component, to LAI. For wheat and oats, correlations above 0.8 were reported. Following this sensitivity analysis, the Water Cloud Model (WCM) was parameterized using LAI, soil moisture and SAR data. A look up table inversion approach to estimate LAI from SAR parameters, using the WCM, was subsequently developed. This inversion approach can be used to derive LAI from sensors like RADARSAT-2 to support the monitoring of crop condition throughout the cropping season.

  15. 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.

  16. 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.

  17. 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.

  18. SOIL-DIFFUSIVE GRADIENT IN THIN FILMS PARTITION COEFFICIENTS ESTIMATE METAL BIOAVAILABILITY TO CROPS AT FERTILIZED FIELD SITES

    PubMed Central

    PEREZ, ANGELA L.

    2014-01-01

    Field trials in four distinct agricultural soils were conducted to examine changes to total recoverable and labile soil Cd and Ni concentrations with applications of commercial phosphate fertilizers. The edible portion of wheat and potato crops grown at the field plots were analyzed for recoverable Cd and Ni. Total recoverable Ni and Cd concentrations in agricultural soils increased by 10 and 22%, respectively, each year of the study at recommended application rates. Labile Cd and Ni were measured using diffusive gradients in thin films (DGT), a passive sampling device reported to estimate the plant bioavailable metal fraction. Nickel concentrations measured with DGT did not significantly change with treatment nor did they change over time. Cadmium concentrations measured with DGT increased with application rate and over time from 2003 to 2005, then decreased in 2006. Wheat grain Cd concentrations and Cd and Ni levels in tubers increased significantly with fertilizer treatment level. Grain and tuber Cd values exceeded the minimal risk levels for chronic oral exposure. At agronomical P-fertilizer application rates, 25% of plant samples deviated from the Cd minimal risk levels. The present study reports the use of Kd-BIO, defined as the ratio of total recoverable metal to GT measured metal, as a significant indicator of crop metal accumulation in the edible portion. The Kd-BIO values were well correlated with both grain and tuber concentrations over multiple growing seasons. Results from long-term field trials emphasize Kd-BIO as a dynamic term that provides risk characterization information about the fate of Cd and Ni in aged, fertilized agricultural soils and crops. PMID:19432507

  19. 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

  20. A theory of ventilation estimate over hypothetical urban areas.

    PubMed

    Liu, Chun-Ho; Ng, Chi-To; Wong, Colman C C

    2015-10-15

    Urban roughness is a major factor governing the flows and scalar transport in the atmospheric boundary layer (ABL) but our understanding is rather limited. The ventilation and pollutant removal of hypothetical urban areas consisting of various types of street canyons are examined using computational fluid dynamics (CFD). The aerodynamic resistance, ventilation efficiency, and pollutant removal are measured by the friction factor f, air exchange rate (ACH), and pollutant exchange rate (PCH), respectively. Two source configurations of passive tracer, ground-level-only (Tracer 0) and all-solid-boundary (Tracer 1) are employed to contrast their transport behavior. It is found that the ventilation and pollutant removal are largely attributed to their turbulent components (over 60%). Moreover, with a consistent support from analytical solution and CFD results, the turbulent ACH is a linear function of the square root of the friction factor (ACH'∝f(1/2)) regardless of building geometry. Tracer 0 and Tracer 1 exhibit diversified removal behavior as functions of friction factor so analytical parameterizations have not yet been developed. In view of the large portion of aged air removal by turbulence, it is proposed that the aerodynamic resistance can serve as an estimate to the minimum ventilation efficiency of urban areas.

  1. Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

    PubMed Central

    Din, Mairaj; Zheng, Wen; Rashid, Muhammad; Wang, Shanqin; Shi, Zhihua

    2017-01-01

    Hyperspectral reflectance derived vegetation indices (VIs) are used for non-destructive leaf area index (LAI) monitoring for precise and efficient N nutrition management. This study tested the hypothesis that there is potential for using various hyperspectral VIs for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over 2 years (2015 and 2016) in Meichuan, Hubei, China. Different N fertilization, 0, 45, 82, 127, 165, 210, 247, and 292 kg ha-1, were applied to generate various scales of VIs and LAI values. Regression models were used to perform quantitative analyses between spectral VIs and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine VIs, the ratio vegetation index, normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index (MTVI2) and exhibited strong and significant relationships with the LAI estimation at different phenological stages. The enhanced vegetation index performed moderately. However, the green normalized vegetation index and blue normalized vegetation index confirmed that there is potential for crop LAI estimation at early phenological stages; the soil-adjusted vegetation index and optimized soil-adjusted vegetation index were more related to the soil optical properties, which were predicted to be the least accurate for LAI estimation. The noise equivalent accounted for the sensitivity of the VIs and MSAVI, MTVI2, and NDVI for the LAI estimation at phenological stages. The results note that LAI at different crop phenological stages has a significant influence on the potential of hyperspectral derived VIs under different N management practices. PMID:28588596

  2. 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

  3. Estimating potential productivity cobenefits for crops and trees from reduced ozone with U.S. coal power plant carbon standards

    NASA Astrophysics Data System (ADS)

    Capps, Shannon L.; Driscoll, Charles T.; Fakhraei, Habibollah; Templer, Pamela H.; Craig, Kenneth J.; Milford, Jana B.; Lambert, Kathleen F.

    2016-12-01

    A standard for carbon dioxide emissions from power plants in the United States, known as the Clean Power Plan, has been finalized by the Environmental Protection Agency. Decreases in carbon dioxide emissions from fossil fuel combustion have the potential cobenefit of reductions in emissions of oxides of nitrogen, which contribute to the formation of ground-level ozone. Emissions of ozone precursors may result in elevated ozone concentrations nearby or downwind. Chronic exposure of sensitive vegetation to tropospheric ozone reduces its potential productivity. To evaluate the cobenefits of the Clean Power Plan to sensitive vegetation, we estimate ozone concentrations in the continental U.S. in 2020 with a chemical transport model in accordance with reference and alternative Clean Power Plan policy scenarios, which represent a range of possible approaches to reducing carbon dioxide emissions from power plants. The reductions in biomass, or the potential productivity losses, due to the exposure of 4 crops and 11 tree species to ozone are as large as 1.9% and 32%, respectively, in the reference scenario. The least stringent policy scenario reduces these losses by less than 3% for any given species; however, the scenarios consistent with policies resulting in more rigorous nitrogen oxide reductions produce potential productivity losses lower than the reference scenario by as much as 16% and 13% for individual crops or tree species, respectively. This analysis affords the opportunity to consider public welfare cobenefits of a regulation that is designed to reduce carbon dioxide emissions from power plants.

  4. Ethiopian wheat yield and yield gap estimation: A spatially explicit small area integrated data approach.

    PubMed

    Mann, Michael L; Warner, James M

    2017-02-01

    Despite the routine collection of annual agricultural surveys and significant advances in GIS and remote sensing products, little econometric research has integrated these data sources in estimating developing nations' agricultural yields. In this paper, we explore the determinants of wheat output per hectare in Ethiopia during the 2011-2013 principal Meher crop seasons at the kebele administrative area. Using a panel data approach, combining national agricultural field surveys with relevant GIS and remote sensing products, the model explains nearly 40% of the total variation in wheat output per hectare across the country. Reflecting on the high interannual variability in output per hectare, we explore whether these changes can be explained by weather, shocks to, and management of rain-fed agricultural systems. The model identifies specific contributors to wheat yields that include farm management techniques (e.g. area planted, improved seed, fertilizer, and irrigation), weather (e.g. rainfall), water availability (e.g. vegetation and moisture deficit indexes) and policy intervention. Our findings suggest that woredas produce between 9.8 and 86.5% of their locally attainable wheat yields given their altitude, weather conditions, terrain, and plant health. In conclusion, we believe the combination of field surveys with spatial data can be used to identify management priorities for improving production at a variety of administrative levels.

  5. Improving root-zone soil moisture estimations using dynamic root growth and crop phenology

    USDA-ARS?s Scientific Manuscript database

    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) observatio...

  6. Use of UAS remote sensing data to estimate crop ET at high spatial resolution

    USDA-ARS?s Scientific Manuscript database

    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...

  7. A comparison of operational remote sensing-based models for estimating crop evapotranspiration

    USDA-ARS?s Scientific Manuscript database

    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...

  8. Evapotranspiration measurement and crop coefficient estimation over a spring wheat Farmland ecosystem in the Loess Plateau.

    PubMed

    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.

  9. 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

  10. 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. Copyright © 2012 Elsevier Ltd. All rights reserved.

  11. 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.

  12. 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.

  13. 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

  14. 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

  15. Accuracy in the estimation of quantitative minimal area from the diversity/area curve.

    PubMed

    Vives, Sergi; Salicrú, Miquel

    2005-05-01

    The problem of representativity is fundamental in ecological studies. A qualitative minimal area that gives a good representation of species pool [C.M. Bouderesque, Methodes d'etude qualitative et quantitative du benthos (en particulier du phytobenthos), Tethys 3(1) (1971) 79] can be discerned from a quantitative minimal area which reflects the structural complexity of community [F.X. Niell, Sobre la biologia de Ascophyllum nosodum (L.) Le Jolis en Galicia, Invest. Pesq. 43 (1979) 501]. This suggests that the populational diversity can be considered as the value of the horizontal asymptote corresponding to the curve sample diversity/biomass [F.X. Niell, Les applications de l'index de Shannon a l'etude de la vegetation interdidale, Soc. Phycol. Fr. Bull. 19 (1974) 238]. In this study we develop a expression to determine minimal areas and use it to obtain certain information about the community structure based on diversity/area curve graphs. This expression is based on the functional relationship between the expected value of the diversity and the sample size used to estimate it. In order to establish the quality of the estimation process, we obtained the confidence intervals as a particularization of the functional (h-phi)-entropies proposed in [M. Salicru, M.L. Menendez, D. Morales, L. Pardo, Asymptotic distribution of (h,phi)-entropies, Commun. Stat. (Theory Methods) 22 (7) (1993) 2015]. As an example used to demonstrate the possibilities of this method, and only for illustrative purposes, data about a study on the rocky intertidal seawed populations in the Ria of Vigo (N.W. Spain) are analyzed [F.X. Niell, Estudios sobre la estructura, dinamica y produccion del Fitobentos intermareal (Facies rocosa) de la Ria de Vigo. Ph.D. Mem. University of Barcelona, Barcelona, 1979].

  16. 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

  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. 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.

  19. A sampling strategy to estimate the area and perimeter of irregularly shaped planar regions

    Treesearch

    Timothy G. Gregoire; Harry T. Valentine

    1995-01-01

    The length of a randomly oriented ray emanating from an interior point of a planar region can be used to unbiasedly estimate the region's area and perimeter. Estimators and corresponding variance estimators under various selection strategies are presented.

  20. 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.

  1. 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. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. 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

  3. 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.

  4. Methodology for completing Hanford 200 Area tank waste physical/chemical profile estimations

    SciTech Connect

    Kruger, A.A.

    1996-04-29

    The purpose of the Methodology for Completing Hanford 200 Area Tank Waste Physical/Chemical Profile Estimations is to capture the logic inherent to completing 200 Area waste tank physical and chemical profile estimates. Since there has been good correlation between the estimate profiles and actual conditions during sampling and sub-segment analysis, it is worthwhile to document the current estimate methodology.

  5. Quantitative Estimation of Above Ground Crop Biomass using Ground-based, Airborne and Spaceborne Low Frequency Polarimetric Synthetic Aperture Radar

    NASA Astrophysics Data System (ADS)

    Koyama, C.; Watanabe, M.; Shimada, M.

    2016-12-01

    Estimation of crop biomass is one of the important challenges in environmental remote sensing related to agricultural as well as hydrological and meteorological applications. Usually passive optical data (photographs, spectral data) operating in the visible and near-infrared bands is used for such purposes. The virtue of optical remote sensing for yield estimation, however, is rather limited as the visible light can only provide information about the chemical characteristics of the canopy surface. Low frequency microwave signals with wavelength longer 20 cm have the potential to penetrate through the canopy and provide information about the whole vertical structure of vegetation from the top of the canopy down to the very soil surface. This phenomenon has been well known and exploited to detect targets under vegetation in the military radar application known as FOPEN (foliage penetration). With the availability of polarimetric interferometric SAR data the use PolInSAR techniques to retrieve vertical vegetation structures has become an attractive tool. However, PolInSAR is still highly experimental and suitable data is not yet widely available. In this study we focus on the use of operational dual-polarization L-band (1.27 GHz) SAR which is since the launch of Japan's Advanced Land Observing Satellite (ALOS, 2006-2011) available worldwide. Since 2014 ALOS-2 continues to deliver such kind of partial polarimetric data for the entire land surface. In addition to these spaceborne data sets we use airborne L-band SAR data acquired by the Japanese Pi-SAR-L2 as well as ultra-wideband (UWB) ground based SAR data operating in the frequency range from 1-4 GHz. By exploiting the complex dual-polarization [C2] Covariance matrix information, the scattering contributions from the canopy can be well separated from the ground reflections allowing for the establishment of semi-empirical relationships between measured radar reflectivity and the amount of fresh-weight above

  6. [Characteristics of evapotranspiration and crop coefficient of agroecosystems in semi-arid area of Loess Plateau, Northwest China].

    PubMed

    Yang, Fu-Lin; Zhang, Qiang; Wang, Run-Yuan; Wang, Sheng; Yue, Ping; Wang, He-Ling; Zhao, Hong

    2013-05-01

    Evapotranspiration (ET) is an important component of ground surface energy balance and water balance, and closely related to water cycle. By using eddy covariance technique, this paper studied the ET characteristics of agroecosystems in the semi-arid area of Loess Plateau in growth season (from April to September), 2010, and analyzed the relationships between crop coefficient and environmental factors. During the observation period, the diurnal variation of latent heat flux (LE) in each month was similar to single-peak curve, and the peak value (151.4 W x m(-2)) occurred in August. The daytime energy partitioning manner showed a significant seasonal variation, with LE/R(n) < H/R(n) (R(n) was net radiation, and H was sensible heat flux) from April to June, and LE/R(n) > H/R(n) from July to September. The daily ET rate also showed a significant seasonal variation, with the maximum of 4.69 mm x d(-1). The wind speed (W(s)), relative humidity (RH), soil water content (theta), and atmospheric vapor pressure deficit (D) were the major factors affecting the crop coefficient K(c) which was exponentially decreased with increasing W(s), exponentially increased with increasing RH and theta, and linearly decreased with increasing D.

  7. 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.

  8. 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

  9. Spatial distribution of unspecified chronic kidney disease in El Salvador by crop area cultivated and ambient temperature.

    PubMed

    VanDervort, Darcy R; López, Dina L; Orantes, Carlos M; Rodríguez, David S

    2014-04-01

    Chronic kidney disease of unknown etiology is occurring in various geographic areas worldwide. Cases lack typical risk factors associated with chronic kidney disease, such as diabetes and hypertension. It is epidemic in El Salvador, Central America, where it is diagnosed with increasing frequency in young, otherwise-healthy male farmworkers. Suspected causes include agrochemical use (especially in sugarcane fields), physical heat stress, and heavy metal exposure. To evaluate the geographic relationship between unspecified chronic kidney disease (unCKD) and nondiabetic chronic renal failure (ndESRD) hospital admissions in El Salvador with the proximity to cultivated crops and ambient temperatures. Data on unCKD and ndESRD were compared with environmental variables, crop area cultivated (indicator of agrochemical use) and high ambient temperatures. Using geographically weighted regression analysis, two model sets were created using reported municipal hospital admission rates are per thousand population for unCKD 2006-2010 and rates of ndESRD 2005-2010 [corrected]. These were assessed against local percent of land cultivated by crop (sugarcane, coffee, corn, cotton, sorghum, and beans) and mean maximum ambient temperature, with Moran's indices determining data clustering. Two-dimensional geographic models illustrated parameter spatial distribution. Bivariate geographically weighted regressions showed statistically significant correlations between percent area of sugarcane, corn, cotton, coffee, and bean cultivation, as well as mean maximum ambient temperature with both unCKD and ndESRD hospital admission rates. Percent area of sugarcane cultivation had greatest statistical weight (p ≤ 0.001; Rp2 = 0.77 for unCKD). The most statistically significant multivariate geographically weighted regression model for unCKD included percent area of sugarcane, cotton and corn cultivation (p ≤ 0.001; Rp2 = 0.80), while, for ndESRD, it included the percent area of sugarcane, corn

  10. 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.

  11. 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 accur