Ramesh K. Singh

Estimation of crop coefficients using satellite remote sensing.

Crop coefficient ( Kc ) based estimation of crop evapotranspiration ( E Tc ) is one of the most commonly used methods for irrigation water management. The standardized FAO56 Penman-Monteith approach for estimating E Tc from reference evapotranspiration and tabulated generalized Kc values has been widely adopted worldwide to estimate E Tc . In this study, we presented a modified approach toward estimating Kc values from remotely sensed data. The surface energy balance algorithm for land model was used for estimating the spatial distribution of E Tc for major agronomic crops during the 2005 growing season in southcentral Nebraska. The alfalfa-based reference evapotranspiration ( E Tr ) was calculated using data from multiple automatic weather stations with geostatistical analysis. The Kc values were estimated based on E Tc and E Tr (i.e., Kc =E Tc /E Tr ). A land use map was used for sampling and profiling the Kc values from the satellite overpass for the major crops grown in southcentral Nebraska. Finally,...

Actual evapotranspiration (water use) assessment of the Colorado River Basin at the Landsat resolution using the operational simplified surface energy balance model

Accurately estimating consumptive water use in the Colorado River Basin (CRB) is important for assessing and managing limited water resources in the basin. Increasing water demand from various sectors may threaten long-term sustainability of the water supply in the arid southwestern United States. We have developed a first-ever basin-wide actual evapotranspiration (ETa) map of the CRB at the Landsat scale for water use assessment at the field level. We used the operational Simplified Surface Energy Balance (SSEBop) model for estimating ETa using 328 cloud-free Landsat images acquired during 2010. Our results show that cropland had the highest ETa among all land cover classes except for water. Validation using eddy covariance measured ETa showed that the SSEBop model nicely captured the variability in annual ETa with an overall R2 of 0.78 and a mean bias error of about 10%. Comparison with water balance-based ETa showed good agreement (R2 = 0.85) at the sub-basin level. Though there was good correlation (R2 = 0.79) between Moderate Resolution Imaging Spectroradiometer (MODIS)-based ETa (1 km spatial resolution) and Landsat-based ETa (30 m spatial resolution), the spatial distribution of MODIS-based ETa was not suitable for water use assessment at the field level. In contrast, Landsat-based ETa has good potential to be used at the field level for water management. With further validation using multiple years and sites, our methodology can be applied for regular production of ETa maps of larger areas such as the conterminous United States.

Treatment of anchor pixels in the METRIC model for improved estimation of sensible and latent heat fluxes

Abstract Reliable estimation of sensible heat flux (H) is important in energy balance models for quantifying evapotranspiration (ET). This study was conducted to evaluate the value of adding the Priestley-Taylor (PT) equation to the METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model. METRIC was used to estimate energy fluxes for 10 Landsat images from the 2005, 2006 and 2007 crop growing seasons in south-central Nebraska, USA, where each image owing to recent rainfall exhibited high residual moisture content even at the hot pixel. The METRIC model performed satisfactorily for net radiation (Rn ) and soil heat flux (G) estimation with a root mean square error (RMSE) of 52 and 24 W m-2, respectively. A RMSE of 122 W m-2 for H indicated the limitation of the METRIC model in estimating H for high residual moisture content of the hot pixel (Alfalfa reference ET fraction, ET r F > 0.15). The modified METRIC model (wet METRIC or wMETRIC) incorporating the PT equation was applied to calculate H at the anchor pixels (hot and cold) for high residual moisture content of the hot pixel. The α coefficient of the PT equation was locally calibrated using hourly meteorological data from an automatic weather station and Rn and G data from a Bowen ratio flux tower. The mean α coefficient value was 1.14. The wMETRIC model reduced the RMSE of H from 122 to 64 W m-2 and that of latent heat flux, LE, from 163 to 106 W m-2. The RMSE of daily ET decreased from 1.7 to 1.1 mm d-1 with wMETRIC. The results indicate that treatment of anchor pixels for high residual moisture content with the PT approach gives improved estimation of H, LE and daily ET. It is recommended that the wMETRIC model be used for estimating ET if the hot pixel has high residual moisture (i.e. reference ET fraction > 0.15). Citation Singh, R. K. & Irmak, A. (2011) Treatment of anchor pixels in the METRIC model for improved estimation of sensible and latent heat fluxes. Hydrol. Sci. J. 56(5), 895–906.

Remote sensing and GIS approach for assessment of the water balance of a watershed

Abstract Abstract The demand for water is increasing with growing population and industrialization. Water supply is considered to be one of the key factors for rapid development and urbanization. However, the overexploitation of water resources has resulted in a condition of unsustainability and environmental degradation. Hence, the information on spatial and temporal availability of water will be helpful for the optimum utilization of water resources. The water balance was used for computing seasonal and geographical patterns of water availability to facilitate better management of available water resources. The water balance study using the Thornthwaite and Mather (TM) model with the help of remote sensing and GIS is very helpful in finding out the periods of moisture deficit and moisture surplus for an entire basin. This study indicates that there is an annual deficit of 288.56 mm in the study basin and an annual surplus of 307.76 mm. The Nana Kosi watershed has a period of moisture surplus from June to August and the remaining months are a period of deficit.

On the Downscaling of Actual Evapotranspiration Maps Based on Combination of MODIS and Landsat-Based Actual Evapotranspiration Estimates

Downscaling is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for downscaling MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and downscaling. The regression with intercept method does have limitations in downscaling if the slope and intercept are computed over a large area. A good agreement was obtained between downscaled monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from −16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and downscaled monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between downscaled monthly AET using LinZI method and the gridded FLUXNET dataset. The downscaled monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed downscaling method will be very useful in advancing the application of remotely sensed images in water resources planning and management.

Comparison and Analysis of Empirical Equations for Soil Heat Flux for Different Cropping Systems and Irrigation Methods

We evaluated the performance of four models for estimating soil heat flux density (G) in maize (Zea mays L.) and soybean (Glycine max L.) fields under different irrigation methods (center-pivot irrigated fields at Mead, Nebraska, and subsurface drip irrigated field at Clay Center, Nebraska) and rainfed conditions at Mead. The model estimates were compared against measurements made during growing seasons of 2003, 2004, and 2005 at Mead and during 2005, 2006, and 2007 at Clay Center. We observed a strong relationship between the G and net radiation (Rn) ratio (G/Rn) and the normalized difference vegetation index (NDVI). When a significant portion of the ground was bare soil, G/Rn ranged from 0.15 to 0.30 and decreased with increasing NDVI. In contrast to the NDVI progression, the G/Rn ratio decreased with crop growth and development. The G/Rn ratio for subsurface drip irrigated crops was smaller than for the center-pivot irrigated crops. The seasonal average G was 13.1%, 15.2%, 10.9%, and 12.8% of Rn for irrigated maize, rainfed maize, irrigated soybean, and rainfed soybean, respectively. Statistical analyses of the performance of the four models showed a wide range of variation in G estimation. The root mean square error (RMSE) of predictions ranged from 15 to 81.3 W m-2. Based on the wide range of RMSE, it is recommended that local calibration of the models should be carried out for remote estimation of soil heat flux.

Novel approach for computing photosynthetically active radiation for productivity modeling using remotely sensed images in the Great Plains, United States

Gross primary production (GPP) is a key indicator of ecosystem performance, and helps in many decision-making processes related to environment. We used the Eddy covariancelight use efficiency (EC-LUE) model for estimating GPP in the Great Plains, United States in order to evaluate the performance of this model. We developed a novel algorithm for computing the photosynthetically active radiation (PAR) based on net radiation. A strong correlation ( R 2 = 0.94 , N = 24 ) was found between daily PAR and Landsat-based mid-day instantaneous net radiation. Though the Moderate Resolution Spectroradiometer (MODIS) based instantaneous net radiation was in better agreement ( R 2 = 0.98 , N = 24 ) with the daily measured PAR, there was no statistical significant difference between Landsat based PAR and MODIS based PAR. The EC-LUE model validation also confirms the need to consider biological attributes ( C 3 versus C 4 plants) for potential light use efficiency. A universal potential light use efficiency is unable to capture the spatial variation of GPP. It is necessary to use C 3 and C 4 based land use/land cover map for using EC-LUE model for estimating spatiotemporal distribution of GPP.

Spatially explicit surface energy budget and partitioning with remote sensing and flux measurements in a boreal region of Interior Alaska

Extrapolating energy fluxes between the ground surface and the atmospheric boundary layer from point-based measurements to spatially explicit landscape estimation is critical to understand and quantify the energy balance components and exchanges in the hydrosphere, atmosphere, and biosphere. This information is difficult to quantify and are often lacking. Using a Landsat image (acquired on 5 August 2004), the flux measurements from three eddy covariance flux towers (a 1987 burn, a 1999 burn, and an unburned control site) and a customized satellite-based surface energy balance model of Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC), we estimated net radiation, sensible heat flux (H), latent heat flux (LE), and soil heat flux (G) for the boreal Yukon River Basin of Interior Alaska. The model requires user selection of two extreme conditions present within the image area to calibrate and anchor the sensible flux output. One is the “hot” condition which refers to a bare soil condition with specified residual evaporation rates. Another one is the “cold” condition which refers to a fully transpiring vegetation such as full-cover agricultural crops. We selected one bare field as the “hot” condition while we explored three different scenarios for the “cold” pixel because of the absence of larger expanses of agricultural fields within the image area. For this application over boreal forest, selecting agricultural fields whose evapotranspiration was assumed to be 1.05 times the alfalfa-based reference evapotranspiration as the “cold” pixel could result in large errors. Selecting an unburned flux tower site as the “cold” pixel could achieve acceptable results, but uncertainties remain about the energy balance closure of the flux towers. We found that METRIC performs reasonably well in partitioning energy fluxes in a boreal landscape.

A Modified Approach for Estimation of Crop Coefficients using Satellite Remote Sensing Data

Crop coefficient (Kc) based estimation of crop evapotranspiration (ETc) is one of the most commonly used methods for irrigation water management. However the standard FAO Penman-Monteith approach for estimating ETc from reference evapotranspiration and tabulated generalized Kc values has some limitations. In this paper, we present a modified approach towards estimating Kc values from remotely sensed data. Surface Energy Balance Algorithm for Land (SEBAL) model was used for estimating spatial distribution of ETc during 2005 growing season in south-central Nebraska. The alfalfa based reference evapotranspiration (ETr) was calculated using multi-automatic weather station data with geostatistical analysis. Based upon the mean absolute error (MAE) and coefficient of determination (r2), the ordinary Kriging method resulted as the best interpolation technique for determining the reference evapotranspiration. The crop coefficient was estimated based on crop evapotranspiration and reference evapotranspiration. Land use map was used for sampling and profiling the crop coefficients on dates of satellite overpass for various major crops grown in south-central Nebraska. Finally a regression based model was developed to establish the relationship between the Normalized Difference Vegetation Index (NDVI) and the ETr based crop coefficient (Kcr) for corn, soybean, sorghum, and alfalfa under irrigated and dryland conditions. Validation of the model for the corn using Bowen ratio energy balance system based Kcr and estimated Kcr has shown good correlation (r2=0.73). This approach can be very useful for estimation of evapotranspiration using NDVI based crop coefficient and reference evapotranspiration.

Spectral Data-Based Estimation of Soil Heat Flux

Numerous existing spectral-based soil heat flux (G) models have shown wide variation in performance for maize and soybean cropping systems in Nebraska, indicating the need for localized calibration and model development. The objectives of this article are to develop a semi-empirical model to estimate G from a normalized difference vegetation index (NDVI) and net radiation (Rn) for maize (Zea mays L.) and soybean (Glycine max L.) fields in the Great Plains, and present the suitability of the developed model to estimate G under similar and different soil and management conditions. Soil heat fluxes measured in both irrigated and rainfed fields in eastern and south-central Nebraska were used for model development and validation. An exponential model that uses NDVI and Rn was found to be the best to estimate G based on r2 values. The effect of geographic location, crop, and water management practices were used to develop semi-empirical models under four case studies. Each case study has the same exponential model structure but a different set of coefficients and exponents to represent the crop, soil, and management practices. Results showed that the semi-empirical models can be used effectively for G estimation for nearby fields with similar soil properties for independent years, regardless of differences in crop type, crop rotation, and irrigation practices, provided that the crop residue from the previous year is more than 4000 kg ha-1. The coefficients calibrated from particular fields can be used at nearby fields in order to capture temporal variation in G. However, there is a need for further investigation of the models to account for the interaction effects of crop rotation and irrigation. Validation at an independent site having different soil and crop management practices showed the limitation of the semi-empirical model in estimating G under different soil and environment conditions.