Browsing by Author "Suraj A. Yadav"

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Challenges in Radar remote sensing
(Elsevier, 2022) Prashant K. Srivastava; Rajendra Prasad; Sumit Chaudhary Kumar; Suraj A. Yadav; Jyoti Sharma; Swati Suman; Varsha Pandey; Rishabh Singh; Dileep Kumar Gupta
This chapter provides different challenges that are generally faced by the radar remote sensing community. The different types of challenges of radar remote sensing in biochemical and biophysical parameter retrieval, flood detection and monitoring, soil moisture, snow, droughts, sensor development, and instrumentation are briefly provided. © 2022 Elsevier Inc. All rights reserved.
Far-field bistatic scattering simulation for rice crop biophysical parameters retrieval using modified radiative transfer model at X- and C-band
(Elsevier Inc., 2022) Suraj A. Yadav; Rajendra Prasad; Vijay P. Yadav; Bhagyashree Verma; Shubham K. Singh; Jyoti Sharma; Prashant K. Srivastava
Dual-polarimetric (i.e., HH and VV) scattering responses at X- and C-bands from indigenously designed far-field bistatic specular (bi-spec) scatterometer acquired over the entire rice crop phenology have been analyzed using a modified parametric radiative transfer model (MRTM). The scattering responses are examined over a wide-ranging bi-spec incidence angle varying from 20° to 60° at 10° intervals. Furthermore, optimization of the bi-spec scatterometer system showed high sensitivity at 40° specular angle of incidence based on the correlation analysis between the measured value of bi-spec scattering coefficient (σMeasured0) and vegetation biophysical parameters such as leaf area index (LAI) and plant water content (PWC). The MRTM implied to investigate the dominance of surface (σSurface0) and vegetation(σVegetation0) specular scattering components within the total value of simulated bi-spec scattering coefficient (σSimulated0) in forward scattering alignment (FSA) convention. The vegetation phase function (VPF) and a bi-directional reflectance distribution function (BRDF) are parameterized to approximate scattering responses from the vegetation volume layer and the surface beneath vegetation. In addition, empirical frequency-specific parameters (i.e., b1and b2) are used to simulate temporal dynamics of σSimulated0 using a linear relationship between vegetation optical depth (VOD) with LAI and PWC. The model and empirical frequency-specific parameters are calibrated using a constrained non-linear least square optimization algorithm, and the results are validated against the value of σMeasured0. According to the simulation findings, the total specular scattering decomposition offers a robust model for interpreting time-series microwave scattering scenarios through vegetation in the FSA convention. Moreover, as compared to C-band, the inverse modeling of MRTM showed high retrieval accuracies of LAI at VV polarization and PWC at HH polarization for the X-band. © 2022 Elsevier Inc.
Fusion of Optical and SAR Data Using Three Approaches for the Estimation of LAI With Modified Integral Equation Model
(Institute of Electrical and Electronics Engineers Inc., 2024) Shubham Kumar Singh; Rajendra Prasad; Suraj A. Yadav; Prashant K. Srivastava; Gulab Singh; Hari Shanker Srivastava
This research article presents a comprehensive investigation of leaf area index (LAI) estimation using Sentinel- 1 synthetic aperture radar (SAR) and Sentinel-2 Optical L2A datasets for the wheat crop. The water cloud model (WCM) and PROSAIL radiative transfer models (RTMs) are used to estimate LAI from SAR and optical data, respectively. To model the surface backscattering in WCM, the integral equation model (IEM) at VV and VH polarizations is used with the Gaussian correlation function. The results demonstrate that LAI derived from SAR at VH polarization (R2 = 0.72, RMSE = 0.60 m2m?2) exhibits superior accuracy compared with optical LAI (R2 = 0.70, RMSE = 0.82 m2m?2). A fusion approach incorporating deep learning, principal component analysis (PCA), and nonlinear regression techniques is applied to fuse the SAR and optical datasets to further enhance LAI estimation accuracy. The accuracy of these estimations is tested against the ground-truth LAI taken at different locations. Among the fusion methods tested, deep learning emerges as the most effective and accurate approach (R2 = 0.91, RMSE = 0.38 m2m?2). This study provides valuable insights into the estimation of LAI using multisource remote sensing data and highlights the potential of deep learning for improved accuracy in fusion applicationss. © 2024 IEEE.
Improved radar vegetation water content integration for SMAP soil moisture retrieval
(Elsevier B.V., 2025) J. Sharma; Rajendra B. Prasad; Prashant Kumar Srivastava; Shubham Kumar Singh; Suraj A. Yadav; Dharmendra Kumar Pandey
The Vegetation Water Content (VWC) serves as a crucial parameter within the framework of the Soil Moisture Active Passive (SMAP) satellite mission, particularly in its utilization for vegetation optical depth estimation in the Single Channel Algorithm (SCA) to determine soil moisture content. This study attempts to enhance the soil moisture estimation by estimating microwave VWC utilizing the Single Look Complex (SLC) format of dual-polarized Sentinel-1 data. This approach aims to refine the efficacy of the Single Channel Algorithm (SCA), thereby elevating the precision and reliability of soil moisture estimations. The Sentinel-1 datasets have been utilized to compute radar indices, particularly the Dual Polarimetric Radar Vegetation Index (DPRVI), Radar Vegetation Index (RVI), and Cross- and Co-Polarized Ratio (CCR). DPRVI reflects vegetation's growth and moisture properties, while RVI and CCR indicate vegetation water content and health status. The radar indices were employed within regression approaches such as random forest (RF), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and linear regression to estimate VWC. The performance of DPRVI was found better to capture aspects of vegetation dynamics and effectively estimates VWC values with a high correlation (R2) of 0.59. Furthermore, the DPRVI-estimated VWC values are integrated into the SCA, a renowned method for soil moisture retrieval. The results of SCA are compared to the ground-measured soil moisture along with the already available SMAP L2-enhanced passive soil moisture product. The soil moisture estimation via SCA integrated with the DPRVI-estimated VWC enhances the soil moisture estimations with an accuracy of (RMSE = 0.042 m3/m3 and ubRMSE = 0.039 m3/m3) compared to the SMAP L2 soil moisture. This integration allows for a more comprehensive understanding of soil-vegetation-atmosphere interactions and improves the accuracy of soil moisture assessments, critical for hydrological modeling, agricultural management, and environmental monitoring efforts. © 2024 Elsevier B.V.
Improving Spatial Representation of Soil Moisture Through the Incorporation of Single-Channel Algorithm with Different Downscaling Approaches
(Institute of Electrical and Electronics Engineers Inc., 2022) Jyoti Sharma; Rajendra Prasad; Prashant K. Srivastava; Suraj A. Yadav; Vijay P. Yadav
The use of microwave observations in the low-frequency range is a complementary tool for mapping surface soil moisture. The L-band (1-2 GHz) region is a lower frequency band of microwave radiations, and currently, only two satellite soil moisture data products are available in the L-band frequency range through the satellites Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity (SMOS). Both these have almost the same spatial resolution around 36-40 km. Although SMAP also provides an enhanced soil moisture product at 9 km, still finer scale information of soil moisture is required. The present study tries to enhance coarse-scale soil moisture by incorporating the single-channel algorithm (SCA). The results obtained by SCA were used as the inputs for the downscaling algorithms instead of directly using the satellite soil moisture product. Through this study, we implement and compare three approaches: approximation of thermal inertia (ATI), triangle, and dispatch methods. The results illustrated that the downscaling algorithms perform better with the estimated SMAP soil moisture through SCA in comparison to direct SMAP soil moisture, and the thermal inertia-based approach is the best performing among the three methods. © 1980-2012 IEEE.
Incorporation of first-order backscattered power in Water Cloud Model for improving the Leaf Area Index and Soil Moisture retrieval using dual-polarized Sentinel-1 SAR data
(Elsevier Inc., 2023) Shubham Kumar Singh; Rajendra Prasad; Prashant K. Srivastava; Suraj A. Yadav; Vijay P. Yadav; Jyoti Sharma
The novel modification in the WCM (mWCM) is proposed in this study to simulate total backscattering contribution and to improve Leaf Area Index (LAI) and Soil Moisture (SM) retrieval using Sentinel-1 Single Look Complex (SLC) datasets at VV and VH polarizations for the wheat crop. The intended modification was achieved through two steps; (1) Proposing the scaling constants of vegetation (fveg), soil (fsoil) and vegetation-soil interaction (finter) within the traditional WCM. The scaling constants are dimensionless quantity and were derived utilizing the degree of polarization (which were computed using the Hermitian covariance matrix); (2) Incorporation of the first order scattering component derived from the novel Vegetation-Soil Scattering Model (VSSM) to the total backscattering within the traditional WCM. The aim of including the vegetation-soil interaction in land surface models is to predict the combined effect of vegetation and soil on the total backscattering. The model parameters (i.e., A, B, C, and E) were calibrated using a non-linear least square regression algorithm. The accuracy of the retrieved and measured LAI and SM is evaluated using the different statistical indicators, e.g., coefficient of determination (R2), Root Mean Square Error (RMSE), and Nash Sutcliffe Efficiency (NSE). The retrieval from mWCM produced better accuracy with lower error than traditional WCM. The forward simulation results of mWCM revealed a notably higher accuracy for the total simulated radar backscattered coefficient at the VH polarization (σ0VH). The VH results showed a high R2 = 0.86, a high NSE = 0.85, and a low RMSE = 0.51 dB, outperforming the simulated σ0VV with R2 = 0.84, NSE = 0.84, RMSE = 0.66 dB. Consequently, the inversion of the mWCM yielded significantly improved accuracy in retrieving LAI at the VH polarization. The VH retrieval results exhibited a R2 = 0.80, NSE = 0.78, and RMSE = 0.44 m2m−2, while the VV polarization achieved an R2 = 0.78, NSE = 0.77, and RMSE = 0.53 m2m−2 for LAI estimation. In SM retrieval, higher accuracy was observed at VV polarization with R2 value of 0.77, NSE value = 0.79, and RMSE = 0.048 m3m−3 than the VH polarization with R2 = 0.75, NSE = 0.76, and RMSE = 0.050 m3m−3. © 2023 Elsevier Inc.
Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms
(Elsevier B.V., 2022) Bhagyashree Verma; Rajendra Prasad; Prashant K. Srivastava; Suraj A. Yadav; Prachi Singh; R.K. Singh
With the availability of high-resolution data due to sensor technology advancement, it is now easier for researchers and scientists to detect or view the spectral variability of different crops. For this study, Leaf chlorophyll content (LCC) and Leaf area index (LAI) of the crops Maize (Zea mays), Mustard (Brassica), and pink Lentils (Lens esculenta) under different irrigation and fertilizer treatments have been analyzed. In total, rigorous assessment of 25-hyperspectral vegetation indices (VIs) at both leaf and canopy level for chlorophyll content, whereas 7- hyperspectral VIs for LAI at canopy level were computed to investigate the robustness of these VIs for LCC and LAI assessment. Variable importance in projection (VIP) using Partial Least Square regression (PLSR) and coefficient of determination (R2) were computed for all the VIs to extract the most sensitive information for the retrieval of LCC and LAI. As a result, the VIs using the red-edge reflectance bands at 705 and 750 nm were found highly responsive to LAI compared to other wavebands. In contrast, the VIs indices made of green (550 nm), red (670, 690, and 700 nm), and red-edge (705, 750 nm) bands were found highly sensitive to the temporal LCC values of lentils and maize crop beds. In addition, the temporal LCC values of Mustard crop beds’ were found sensitive to the VIs made of green (550 nm), red (670, 690, and 700 nm), and NIR (800 nm) wavebands. The three VIs having high VIP and R2 values were selected as optimum sets of input to build support vector regression models using radial (SVR-Rad), linear (SVR-Li), polynomial (SVR-Poly), Random Forrest Regression (RFR), Partial least square regression (PLSR), and Hybrid neural fuzzy inference system (HyFIS). The analysis showed that the SVR-Rad model outperformed the SVR-Li, SVR-Poly, RFR, PLSR, and HyFIS models in terms of robustness for biophysical and biochemical parameters retrieval using hyperspectral data. © 2021
Optimization of dual-polarized bistatic specular scatterometer for studying microwave scattering response and vegetation growth parameters retrieval of paddy crop using a machine learning algorithm
(Elsevier B.V., 2020) Suraj A. Yadav; Rajendra Prasad; A.K. Vishwakarma; Jyoti Sharma; Bhagyashree Verma; Prashant K. Srivastava
Bistatic specular (Bi-spec) scatterometer measurement system was indigenously designed at X- and C-bands in the incidence angular range from 20° to 60° at the interval of 10° to study the scattering mechanism of vegetation growth parameters of paddy crop and their retrieval at HH and VV polarizations using machine learning algorithms. The contributions of coherent and incoherent scattering to the total reflected or scattered power was measured by the bi-spec scatterometer system to derive bi-spec scattering coefficient(σ0). The effect of vegetation growth parameters such as leaf area index (LAI), plant height (PH), vegetation water content (VWC) and fresh biomass (FBm) on the σ0was investigated. The values of σHH0 at C-band were observed to be higher at HH-polarization as compared to σHH0at X-band for all specular incidence angles due to higher penetrating ability than the X-band. An approach was made to find out the optimum parameters of the bi-spec scatterometer system by correlation analysis between the computed σ0and vegetation growth parameters of paddy crop and their retrieval by the SVR model using linear, polynomial and radial kernels. The optimum parameters of the bi-spec scatterometer system for the retrieval of LAI, FBm and VWC of paddy crop were found to be HH polarization, 40° angle of incidence at C-band. While, for PH retrieval, the optimum parameters were found to be VV polarization, 40° angle of incidence at X band. The potential of the developed SVR model was evaluated by computing centered root mean square error (CRMSE), standard deviation (SD) and correlation coefficient (R) between estimated and observed vegetation growth parameters. The retrieval of the vegetation growth parameters of paddy crop by the developed SVR model using radial kernel provided better results in comparison to linear and polynomial kernels for LAI, FBm and VWC at C-band and PH at X-band using bi-spec scatterometer data. © 2020 Elsevier B.V.
Roughness characterization and disaggregation of coarse resolution SMAP soil moisture using single-channel algorithm
(SPIE, 2021) Jyoti Sharma; Rajendra Prasad; Prashant K. Srivastava; Shubham K. Singh; Suraj A. Yadav; Vijay Pratap Yadav
Surface roughness is a crucial parameter for the estimation of soil moisture (SM). The present study attempted to optimize the surface roughness parameter (h) for the estimation of SM from Soil Moisture Active Passive (SMAP) using tau-omega (τ-ω) model and also downscaled the estimated SM product using a polynomial regression relation among Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and SM. The brightness temperature of SMAP available at two spatial resolutions (36 and 9 km) was used for two seasons intended for SM assessment. After assessment with in-situ SM data, 9-km SM data values were further used for spatial disaggregation to obtain the optimized downscaled soil moisture (ODSM) at 1 km. Results showed that the variation in the value of the roughness parameter strongly affects the performance of the τ-ω model and the downscaling performances. The investigation provided lowest values of root-mean-square error (RMSE) to be 0.0518 (at h = 0.35) and 0.0480 (at h = 0.25) for the SM estimation at 36 km for the different seasons used in this study while the lowest values of RMSE for ODSM were found to be 0.0365 (at h = 0.4) and 0.0252 (at h = 0.25, 0.3) for different seasons. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).
Synergy of dual–polarimetric radar vegetation descriptor and Gaussian processes regression algorithm for estimation of leaf area index
(Taylor and Francis Ltd., 2022) Shubham Kumar Singh; Rajendra Prasad; Vijay Pratap Yadav; Suraj A. Yadav; Jyoti Sharma; Prashant K Srivastava
Gaussian Process Regression (GPR) emerged as a powerful algorithm since last decade in many applications. However, it is not fully explored in remote sensing applications, especially for predicting plant biophysical variables in the heterogeneous environment of India. This kernel-based machine learning technique has effectively replaced conventional approaches for estimating vegetation characteristics from remotely sensed data. In this work, an attempt has been made to test the ability of GPR to estimate the leaf area index (LAI) of wheat crops using the Sentinel-1 (S1) derived Dual-Polarized Radar Vegetation Index (DpRVI) and Sentinel-2 (S2) Top of Atmosphere (TOA) products. Further, the ability of the atmospheric correction procedure was tested by S2 Bottom of Atmosphere (BOA) images. To accomplish this, the field measurements of LAI were carried out from January to March 2020. Further comparisons of the GPR’s performance were made with the Artificial Neural Network (ANN) coupled with the PROSAIL (PROSPECT+SAIL) radiative transfer model, available through the Sentinel Application Platform (SNAP) Biophysical processor. The accuracy of the estimated LAI was evaluated using the statistical indicators, for example, coefficient of determination (R 2), Root Mean Square Error (RMSE), and Nash Sutcliffe Efficiency (NSE). The results showed that the synergy of the GPR and DpRVI provided the most accurate result (R 2 = 0.822, RMSE = 0.503 m2 m−2, NSE = 0.831) as compared to the GPR and TOA (R 2 = 0.816, RMSE = 0.596 m2 m−2, NSE = 0.803) and SNAP biophysical processor (based on ANN) (R 2 = 0.696, RMSE = 0.760 m2 m−2, NSE = 0.578). Therefore, the study demonstrated the importance of S1 SAR images and GPR as an alternative tool among the other well-established machine learning algorithms to estimate the crop biophysical parameters. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Theory of monostatic and bistatic radar systems
(Elsevier, 2022) Suraj A. Yadav; Dileep Kumar Gupta; Rajendra Prasad; Jyoti Sharma; Prashant K. Srivastava
This chapter discusses radar bands and radar system configurations used for various applications of earth exploration. Because theoretical modeling in radar sensing deals with the modeling of radar cross-section or scattering coefficients, fundamental mathematical concepts for radar cross-section or scattering coefficients measurements of point targets and distributive targets using monostatic (i.e., backward scatter alignment convention) and bistatic (i.e., forward scatter alignment convention) radar system configurations are discussed in detail. In defining the radar cross-section or scattering coefficients of target, the amplitude calibration and characterization of radar system are necessary to obtain a meaningful radar cross-section. Four well-known and most common amplitude calibrations are discussed in this chapter. The subsystem characterization of radar system parameters is also discussed. Finally, we discuss the basic procedures of the measurement system and important precautions to take for meaningful radar data acquisition. © 2022 Elsevier Inc. All rights reserved.
Time-series polarimetric bistatic scattering decomposition using comprehensive modified first-order radiative transfer model at C-band for vegetative terrain and validation
(Taylor and Francis Ltd., 2022) Suraj A. Yadav; Rajendra Prasad; Prashant K. Srivastava; Shubham K. Singh; Jyoti Sharma; Sumana Khamrai
The sensitivity of the bistatic scattered signal to both the soil and vegetation physical properties in microwave sensing of vegetation is subject to uncertainties. A multi-angular and fully polarimetric data acquisition from a bistatic system increases the number of observations. Thus, optimum bistatic system parameters for vegetation monitoring are necessary to develop an understating of microwave interactions with the surface and vegetation properties. In this study, C-band fully polarimetric bistatic scatterometer (BiSCAT) system was designed to measure the scattering response of vegetated terrain in the forward specular plane. The correlation analysis between the measured bistatic scattering coefficient ((Formula presented.)) and in-situ soil/vegetation properties, such as plant water content (PWC) and soil moisture ((Formula presented.)), was used to find the optimum specular incidence angle of the BiSCAT system. The optimum parameters of the BiSCAT system were used as input to the modified first-order radiative transfer model (MRTM) for the (Formula presented.) simulation for vegetation. Kirchhoff’s approximate (KA) model for soil surface in forward specular plane was simplified for soil contribution within MRTM. Additionally, the temporal patterns of (Formula presented.) are modelled by employing the empirical formulation between the vegetation optical depth and PWC. The MRTM offers an understanding of co-polarized electromagnetic signal interaction with the temporal change in vegetation constituents and soil surface parameters. The model is limited to providing insights into co-polarized radar return from the target due to the incapability of yielding the cross-polarization factor from the KA model in the forward specular direction. The contributions of surface, vegetation, surface-vegetation, vegetation-surface and surface-vegetation-surface were quantified to better understand microwave’s interaction with the vegetation. The model is calibrated using a constrained non-linear least-square optimization algorithm. The performance indices of simulating (Formula presented.) yields good agreement with the BiSCAT measurements. © 2022 Informa UK Limited, trading as Taylor & Francis Group.