Browsing by Author "Rajendra B. Prasad"

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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.
Retrieval of crop traits using PROSAIL-based hybrid radiative transfer model and EnMAP hyperspectral data
(Elsevier B.V., 2025) Prachi Singh; Prashant Kumar Srivastava; Prakash Kumar Jha; Jochem Verrelst; Pashupati Nath Singh; Rajendra B. Prasad
Implementing high spectral resolution imaging from the Environmental Mapping and Analysis Program (EnMAP) paved the way for detailed retrieval of agricultural traits for accurate crop monitoring and management. The proposed methodology involves the integration and detailed analysis of Radiative Transfer Modelling (RTM) with an integrated approach of machine learning (ML) and Active Learning (AL) algorithms for the retrieval of the Leaf Chlorophyll Content (LCC), Carotenoids (Car) and Leaf Area index (LAI) of wheat cropland from the continuous three years of the dataset. Reflectance values of leaf were collected using Analytical Spectral Device (ASD) − Spectroradiometer data ranging from 350-2500 nm and EnMAP satellite hyperspectral data extends spectral data range varies between 420 nm to 1000 nm in the visible and near-infrared (VNIR) of EMR region, and from 900 nm to 2450 nm in the shortwave infrared (SWIR) region for crop parameters mapping for a larger spatial area of Varanasi district, Uttar Pradesh, India. The PROSPECT + SAIL (PROSAIL) RTM was employed to simulate spectral (reflectance) data, and fourteen ML algorithms were assessed for implementation into a hybrid model. Kernel Ridge regression (KRR) was combined with Euclidean-based Diversity (EBD) algorithms to retrieve crop characteristics due to their exceptional accuracy and reduced uncertainty. Spectral profiles were further used to train hybrid models using PCA (Principal Component Analysis) feature selection, and combined techniques (ML + AL) were applied to retrieve LCC, Car, and LAI. Afterwards, biophysical and biochemical spatial large-scale estimation were provided through atmospherically corrected, and noise-removed EnMAP hyperspectral data with the help of a trained and tested hybrid (ML + AL) model and validated with the ground-measured datasets. The performance indicators showed significantly very high values of correlation during calibration (LCC = 0.99, Car = 0.74, and LAI = 0.99) and validation (LCC = 0.66, Car = 0.57, and LAI= 0.88). The work showed that the optimized hybrid (KRR + AL) models customized for EnMAP hyperspectral data can efficiently estimate the wheat biophysical and biochemical parameters in near-real time therefore, expanding this workflow to agricultural fields may enable more effective monitoring and management of wheat crops. © 2025 The Author(s)
Tracking Post-Fire Vegetation Regrowth and Burned Areas Using Bitemporal Sentinel-1 SAR Data: A Google Earth Engine Approach in Heath Vegetation of Mooloolah River National Park, Queensland, Australia
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Harikesh Singh; Prashant Kumar Srivastava; Rajendra B. Prasad; Sanjeev Kumar Srivastava
This study utilizes the unique capabilities of Sentinel-1 C-band synthetic aperture radar (SAR) data to map post-fire burned areas and monitor vegetation recovery in a heath-dominated Queensland National Park. Sentinel-1 SAR data were used due to their cloud-penetrating capability and frequent revisit times. Using Google Earth Engine (GEE), a bitemporal ratio analysis was applied to SAR data from post-fire periods between 2021 and 2023. SAR backscatter changes over time captured fire impacts and subsequent vegetation regrowth. This differentiation was further enhanced with k-means clustering. Validation was supported by Sentinel-2 dNBR and official fire history records. The dNBR provided a quantitative assessment of burn severity and was used alongside the fire history data to evaluate the accuracy of the burned area classification. While Sentinel-2 false-colour composite (FCC) imagery was generated for visualisation and interpretation purposes, the primary validation relied on dNBR and QPWS fire history records. The results highlighted significant vegetation regrowth, with some areas returning to near pre-fire biomass levels by March 2023. This approach demonstrates the sensitivity of Sentinel-1 SAR, especially in VV polarization, for detecting subtle changes in vegetation, providing a cost-effective method for post-fire ecosystem monitoring and informing ecological management strategies amid increasing wildfire events. © 2025 by the authors.