Subit Chakrabarti

Assimilation of SMOS Soil Moisture for Quantifying Drought Impacts on Crop Yield in Agricultural Regions

This study investigates the effects of agricultural drought on crop yields, through integration of crop growth models and remote sensing observations. The soil moisture (SM) product from SM and Ocean Salinity (SMOS) mission obtained at 25 km was downscaled to a spatial resolution of 1 km, compatible with the crop models. The downscaling algorithm is based upon information theoretic learning and uses data-driven probabilistic relationships between high-resolution remotely sensed products that are sensitive to SM and in situ SM. The downscaled SM values are assimilated in the crop model using an Ensemble Kalman filter-based augmented state-vector technique that estimates states and parameters simultaneously. The downscaling and assimilation framework are implemented for predominantly agricultural region of the lower La-Plata Basin (LPB) in Brazil during two growing seasons. This rain-fed region was affected by agricultural drought in the second season, indicated by markedly lower precipitation compared to the first growing season. The downscaled SM was compared with the in situ SM at a validation site and the root mean square difference (RMSD) was 0.045 m3/m3. The crop yields estimated by the downscaling-assimilation framework were compared with those provided by the Companhia Nacional de Asastecimento (CONAB) and Instituto Brasileiro de Geografia e Estatistica (IBGE). The assimilated yields are improved during both seasons with increased improvement during the second season that was affected by agricultural drought. The differences between the assimilated and observed crop yields were 16.8% during the first growing season and 4.37% during the second season.

Downscaling Satellite-Based Soil Moisture in Heterogeneous Regions Using High-Resolution Remote Sensing Products and Information Theory: A Synthetic Study

In this study, a novel methodology based upon the information-theoretic measures of entropy and mutual information was implemented to downscale soil moisture (SM) observations from 10 km to 1 km. It included a transformation function that related auxiliary remotely sensed (RS) products at high resolution to in situ SM observations to obtain first estimates of SM at 1 km and merging this estimate with SM at coarse resolutions through Principle of Relevant Information (PRI). The PRI-based estimates were evaluated using synthetic observations in NC Florida for heterogeneous agricultural land covers (LC), with two growing seasons of sweet corn and one of cotton, annually. The cumulative density function showed an overall error in SM of <; 0.03 cubic meter/cubic meter in the region, with a confidence interval of 95% during the simulation period. The PRI estimates at 1 km were also compared with those from the method based upon Universal Triangle (UT). The spatially averaged root mean square error (RMSE) aggregated over the vegetative LC were 0.01 cubic meter/cubic meter and 0.15 cubic meter/cubic meter using the PRI and UT methods, respectively. The RMSE for downscaled estimates using the UT method increased to 0.28 cubic meter/cubic meter when Laplacian errors are used, while the corresponding RMSE for the PRI remains the same for both Laplacian or Gaussian errors. The Kullback-Liebler divergence (KLD) for estimates using PRI is about 50% lower than those using the method based upon UT indicating that the probability density function (PDF) of the PRI estimate is closer to PDF of the true SM, than the UT method.

Disaggregation of Remotely Sensed Soil Moisture in Heterogeneous Landscapes Using Holistic Structure-Based Models

In this paper, a novel machine learning algorithm is presented for disaggregation of satellite soil moisture (SM) based on self-regularized regressive models (SRRMs) using high-resolution correlated information from auxiliary sources. It includes regularized clustering that assigns soft memberships to each pixel at a fine scale followed by a kernel regression that computes the value of the desired variable at all pixels. Coarse-scale remotely sensed SM was disaggregated from 10 to 1 km using land cover (LC), precipitation, land surface temperature, leaf area index, and in situ observations of SM. This algorithm was evaluated using multiscale synthetic observations in NC Florida for heterogeneous agricultural LCs. It was found that the rmse for 96% of the pixels was less than 0.02 m 3/m3. The clusters generated represented the data well and reduced the rmse by up to 40% during periods of high heterogeneity in LC and meteorological conditions. The Kullback-Leibler divergence (KLD) between the true SM and the disaggregated estimates is close to zero, for both vegetated and bare-soil LCs. The disaggregated estimates were compared with those generated by the principle of relevant information (PRI) method. The rmse for the PRI disaggregated estimates is higher than the rmse for the SRRM on each day of the season. The KLD of the disaggregated estimates generated by the SRRM is at least four orders of magnitude lower than those for the PRI disaggregated estimates, whereas the computational time needed was reduced by three times. The results indicate that the SRRM can be used for disaggregating SM with complex nonlinear correlations on a grid with high accuracy.

Downscaling microwave brightness temperatures using self regularized regressive models

An novel algorithm is proposed to downscale microwave brightness temperatures (TB), at scales of 10-40 km such as those from the Soil Moisture Active Passive mission to a resolution meaningful for hydrological and agricultural applications. This algorithm, called Self-Regularized Regressive Models (SRRM), uses auxiliary variables correlated to TB along-with a limited set of in-situ SM observations, which are converted to high resolution TB observations using biophysical models. It includes an information-theoretic clustering step based on all auxiliary variables to identify areas of similarity, followed by a kernel regression step that produces downscaled TB. This was implemented on a multi-scale synthetic data-set over NC-Florida for one year. An RMSE of 5.76 K with standard deviation of 2.8 K was achieved during the vegetated season and an RMSE of 1.2 K with a standard deviation of 0.9 K during periods of no vegetation.

Scattering modeling of dynamic soybean during SMAPVEX16-MicroWEX

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For SM studies, observations at L-band frequencies are more desirable due to larger penetration depths. The NASA Soil Moisture Active/Passive (SMAP) mission includes active and passive sensors at L-band to provide global observations of SM. The active observations are available from April–July 2015. In addition to the SM sensitivity, radar backscatter is highly sensitive to roughness of soil surface and scattering within the vegetation. Despite much progress in the development of backscattering models, there is still a gap in validating such models under dynamic vegetation conditions such agricultural crops. The goal of this study is to validate an incoherent model using season-long active observations over a soybean field at high temporal resolution during the SMAPVEX16-MicroWEX experiment.

Backscattering model for dynamic corn during SMAPVEX16-MicroWEX

Soil moisture (SM) is an important land surface variable for understanding the water cycle, ecosystem productivity, and linkages between water-carbon cycles. For SM studies, observations at L-band frequencies are more desirable due to larger penetration depths. The NASA Soil Moisture Active/Passive (SMAP) mission includes active and passive sensors at L-band to provide global observations of SM. The active observations are available from April–July 2015. In addition to the SM sensitivity, radar backscatter is highly sensitive to roughness of soil surface and scattering within the vegetation. Despite much progress in the development of backscattering models, there is still a gap in validating such models under dynamic vegetation conditions such agricultural crops. The goal of this study is to improve a coherent model and evaluate it using season-long active observations at high temporal resolution during the SMAPVEX16-MicroWEX experiment.

Spatial variability in microwave radiometric signatures of growing corn and soybean during SMAPVEX16-microwex

In this study, the impact of spatial variability due to the heterogeneity of vegetation in the agricultural region on passive microwave signatures available at various scales are explored using the brightness temperature (TB) observed from ground, air, and space. These observations were conducted during a growing season of corn and soybean in South Fork watershed, Iowa, as part of the NASA-Soil Moisture Active Passive Validation Experiment (SMAPVEX16). Both empirical and physically-based microwave emission models are used to understand the effects of vegetation on TB for corn and soybean using ground-based TB observations. The modeled TB will be upscaled based upon the USDA crop layer map to compare with the TB observed in the coarse scales.

A spatio-temporal data fusion algorithm for estimating high-resolution soil moisture in agricultural regions

In this study, a data-fusion algorithm is developed for estimation of high-resolution brightness temperatures (TB) at 1km from Soil Moisture Active Passive (SMAP) fine-grid TB product at 9km. It uses image segmentation to spatio-temporally cluster the study region based on meteorological and land cover similarity, followed by a support vector machine based regression that computes the value of the high-resolution TB at all pixels. High resolution remote sensing products such as land surface temperature, normalized difference vegetation index, enhanced vegetation index, precipitation, soil texture, and land-cover were used for disaggregation. The algorithm was implemented in Iowa, United States, from May to September 2016, and compared with the field observations of TB from Microwave Water and Energy Balance Experiment conducted as a part of the Soil Moisture Active Passive Validation Experiment (SMAPVEX16-MicroWEX). Additionally, they were also compared with the Sentinel downscaled SMAP TB at 1km. High resolution soil moisture is subsequently derived from high resolution TB using inverse models.