Browsing by Author "Shubham Kumar Singh"

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An improved volume power approach to estimate LAI from optimized dual-polarized SAR decomposition
(Taylor and Francis Ltd., 2023) Shubham Kumar Singh; Rajendra Prasad; Vivek Tiwari; Prashant K Srivastava
Synthetic Aperture Radar (SAR) imagery has proven to be a valuable tool for monitoring the Earth’s surface, particularly vegetation. However, many studies using SAR have focused solely on backscattering sensitivity without considering the structural and physical properties of the vegetation being analysed. This study proposes a new approach for vegetation monitoring using the Volume Power (VP) analysis technique. The proposed method aims to improve the accuracy of VP derived from the Freeman-Durden (FD) decomposition technique for dual polarimetric SAR data. To increase the sensitivity of vegetation scattering in SAR analysis, this study modified the VP using Depolarized Volume Power (DVP) and Anisotropic Volume Power (AVP). The first modification, DVP, was achieved by incorporating the degree of polarization ((Formula presented.)) in the analysis. The second modification, AVP, is achieved by considering the anisotropic scattering properties of vegetation. The modified VP is then used to estimate Leaf Area Index (LAI) using an empirical relationship between LAI and the modified VPs. The accuracy of the LAI estimation is evaluated using ground truth measurements. The results demonstrate that the proposed method provides more accurate LAI estimates than traditional methods. The approach also shows improved sensitivity to vegetation scattering compared to the original VP from the FD, indicating the effectiveness of the degree of polarization and anisotropic scattering in reducing the impact of unwanted scattering mechanisms from the surface. The accuracy (R 2) between in-situ LAI and the LAI retrieved from AVP and DVP was 0.85 and 0.82, whereas for FDVP, it was 0.74. These findings highlight the potential of the proposed approach for improving the accuracy of VP estimation in dual-polarimetric SAR data and enabling more accurate and efficient estimation of LAI. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
Ethylene in Abiotic Stress
(CRC Press, 2024) Rohit Kumar Mahto; Shikha Tripathi; Devendra Pratap Singh; Kishori Lal; Deepesh Kumar; Rahul Kumar; Ayyagari Ramlal; Shubham Kumar Singh; Shiv Shankar Sharma
Plants, being sessile organisms, confront various environmental stressors that profoundly impact their growth and development, ultimately affecting agricultural productivity. To thrive in adverse conditions, plants have evolved intricate mechanisms to sense and adapt to environmental changes. Plant hormones play a crucial role in mediating responses to these stresses, with ethylene emerging as a multifunctional phytohormone with pivotal roles in both growth and stress tolerance. Ethylene controls many aspects of the plant life cycle, including seed germination, root formation, flower growth, fruit ripening, senescence and responses to biotic and abiotic stresses. Ethylene exerts profound control over various facets of the plant life cycle, encompassing seed germination, root formation, flower development, fruit ripening, senescence and responses to both biotic and abiotic stressors. This multifaceted hormone plays a pivotal role in orchestrating plant responses to environmental cues, thereby influencing the resilience and reproductive success of plants. Understanding the processes is crucial for harnessing the potential of ethylene in optimizing crop production and ensuring the resilience of plants in response to environmental challenges. The chapter describes roles of ethylene in different stresses and emphasizes their roles for crop improvement programs. © 2024 selection and editorial matter, Dhandapani Raju, R Ambika Rajendran, Ayyagari Ramlal and Virendra Pal Singh; individual chapters, the contributors.
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.
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.
Life on the rocks: polyphasic evaluation of three epilithic cyanobacterial strains isolated from a single rock, with the description of Nostoc sikkimense sp. nov., from the northeastern region of India
(Oxford University Press, 2025) Sagarika Pal; Harsh Pant; Naresh Kumar; Priya; Shubham Kumar Singh; Nainshi Gupta; Prashant Singh
Three epilithic cyanobacterial strains were isolated from the scrapings of a single rock surface from the Reshi River in Sikkim, India. At the time of sampling, the rock surface did not show any visible cyanobacterial growth; however, the surface of the rock was glistering. Subsequent morphological analysis indicated that two out of three strains exhibited typical Nostoc-like morphology and the third strain had cell division in multiple planes showing typical morphology of a member of the family Hapalosiphonaceae. Further, 16S rRNA gene phylogeny indicated the strains to be members of the genera Nostoc, Desmonostoc, and Westiellopsis. For species-level demarcation, additionally, 16S-23S ITS (Internal Transcribed Spacer) region analysis was performed, which indicated that the strain RESHI-1B-PS was a novel cyanobacterial lineage of the genus Nostoc, while the strains RESHI-1A-PS and RESHI-1C-PS were representatives of Desmonostoc sp. and Westiellopsis prolifica, respectively. Thus, in the current investigation, we have described an undocumented species of the cyanobacteria, which we named Nostoc sikkimense in accordance with the guidelines outlined in the International Code of Nomenclature for algae, fungi, and plants (ICN). The study also enumerates and illustrates different life cycle stages of N. sikkimense RESHI-1B-PS along with further expanding the geographic distribution of W. prolifica and its substantial ecological adaptability. © 2025 The Author(s). Published by Oxford University Press on behalf of FEMS.
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.