Browsing by Author "Jyoti Sharma"

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Artificial neural network for the estimation of soil moisture using earth observation datasets
(Elsevier, 2020) Sumit Kumar Chaudhary; Jyoti Sharma; Dileep Kumar Gupta; Prashant K. Srivastava; Rajendra Prasad; Dharmendra Kumar Pandey
Surface Soil Moisture (SSM) is an important variable in agricultural water management, required for irrigation water demand, scheduling, etc. In this chapter, the estimation of SSM is carried out using Artificial Neural Network (ANN) model trained by MODIS land surface temperature (LST) and normalised difference vegetation index (NDVI) feature spaces and validated using the in-situ data. The ANN model is trained, validated and tested using the three different combinations of input-output datasets. The first combination of datasets is considered as MODIS LST (input) and in-situ SSM (output) datasets for ANN-I model. The second combination of datasets is considered as MODIS NDVI (input) and in-situ SSM (output) datasets for ANN-II model. The third combination of datasets is considered as MODIS LST and NDVI (input) and in-situ SSM (output) datasets for ANN-III model. The performance of ANN-I, ANN-II and ANN-III models are evaluated in terms of correlation coefficient (r), bias and root mean squared error. In overall, the performance of ANN-II model was found good for SSM estimation. © 2021 Elsevier Inc. All rights reserved.
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.
Correction to: Impact of the indigenous rotavirus vaccine Rotavac in the Universal Immunization Program in India during 2016–2020 (Nature Medicine, (2025), 31, 11, (3871-3878), 10.1038/s41591-025-03998-9)
(Nature Research, 2025) Nayana P. Nair; Samarasimha Nusi Reddy; Sidhartha Giri; Tintu Varghese; Varunkumar Thiyagarajan; Jayaprakash Muliyil; Priya Hemavathy; Shainey Alokit Khakha; Rashmi Arora; Mohan Digambar Gupte; Jacqueline Elizabeth Tate; Umesh D. Parashar; Venkata Raghava Mohan; Gagandeep Kang; Mahima Mittal; Sunil Kumar Rao; Vineeta Gupta; Vipin M. Vashishtha; Sanjeev Kumar Verma; Kulandaivel Murugiah; Ramasubramaniam Pitchumani; Sridevi A. Naaraayan; Priyadarishini Dorairaj; Girish Kumar Chethrapilly Purusothaman; Senthilnathan Subramanian; Sumanth Amperayani; Sundaram Balasubramanian; Jayanta Kumar Goswami; Amrit Koirala; Shakti Lamichhane; Koshy C. George; Asolie Chase; Bhupesh Jain; Suresh C. Goyal; Dalpat Rajpurohit; Prabhu Prakash; Sunil Kothari; Vikash Katewa; Pramod Sharma; Shailja Vajpayee; Alok Kumar Goyal; Bharti Malhotra; R. K. Gupta; Prachi Chaudhary; Hemant Jain; Mannancheril Abraham Mathew; Asit Mansingh; Rashmi Patnaik; Samarendra Mahapatro; Subrat Kumar Majhi; Prasantajyoti Mohanty; Rajib Kumar Ray; Subrant Kumar Mohanty; Manas Kumar Nayak; Nirmal Kumar Mohakud; Mamata Devi Mohanty; J. Bikrant Kumar Prusty; Jasashree Choudhury; Mrutunjay Dash; Saroj Kumar Satpathy; Subal Kumar Pradhan; Jyoti Sharma; Sanjeev Chaudhary; Pancham Kumar; Shayam L. Kaushik; Rajesh Kumar; Bhavneet Bharti; Mini Pritam Singh; Muralidharan P. Jayashree; Akshay Kumar Saxena; Kushaljit Singh Sodhi; Arun Bansal; Ravi Prakash Kanojia; Adarsh Bansal; Madhu Gupta; Preeti Raikwar; Manoj Rawal; Anil Kumar Goel; Suraj Chawla; Poonam Dalal; Geeta Gathwala; Manohar Badur; Gorthi Rajendra Prasad; K. Kameswari; Padmalatha Pamu; Jeeru Bhaskara Reddy; J. Manikyamba; Krishna Babu Goru; G. S. Rama Prasad; G. V. Rama Devi; Suhasini Mekala; Sowmiya V. Senthamizh; Sunita Bidari
Correction to: Nature Medicinehttps://doi.org/10.1038/s41591-025-03998-9, published online 7 October 2025. In the version of the article initially published, Tintu Varghese (The Wellcome Trust Research Laboratory, Christian Medical College, Vellore, India) was missing from the author list and is now included, while the affiliation numbers shown for some Collaborators of the rotavirus vaccine effectiveness and impact assessment network members were incorrect. The author list and affiliations are now updated in the HTML and PDF versions of the article. © The Author(s) 2025.
CRISPR/Cas9: efficient and emerging scope for Brassica crop improvement
(Springer Science and Business Media Deutschland GmbH, 2025) Shiv Shankar Sharma; Ashwani Pandey; Anamika Kashyap; Lakshay Goyal; Pooja Garg; Ranjeet Kushwaha; Jyoti Sharma; Shikha Tripathi; Sujata Kumari; George Thomas; Malkhey Verma; Navin Chandra Gupta; Ashish Kumar Gupta; Ramcharan C. Bhattacharya; Sandhya Sharma; Mahesh Rao
Main conclusion: CRISPR/Cas9 revolutionizes Brassica crop improvement by enhancing yield, quality, and stress resistance, providing a precise and versatile tool for genetic and agronomic advancements. Abstract: The rapidly advancing CRISPR/Cas9 (Clustered regularly interspaced short palindromic repeats/CRISPR associated protein 9) technologies are being employed in both diploid and polyploid species of Brassica for gene functions and precise genetic improvements. CRISPR/Cas technology has sparked significant attention among the scientific community due to its affordability, precision, and effectiveness compared to other genome editing techniques. The recent discoveries highlight the diverse applications of the CRISPR/Cas9 genome editing tool in enhancing agriculturally important traits in Brassica species. This technology has been utilized to improve yield, quality, and resistance to both biotic and abiotic stresses globally. Here, we present an overview that encourages researchers to explore and improve the functionality and genetic progress of Brassica U-triangle species utilizing genome editing technologies. In addition, ethical considerations and concerns associated with CRISPR technologies are addressed, providing valuable insight into how CRISPR/Cas9 tools and have revolutionized crop improvement with special emphasis on Brassica for various agronomically and nutritionally important traits. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Evaluation of Simulated AVIRIS-NG Imagery Using a Spectral Reconstruction Method for the Retrieval of Leaf Chlorophyll Content
(MDPI, 2022) Bhagyashree Verma; Rajendra Prasad; Prashant K. Srivastava; Prachi Singh; Anushree Badola; Jyoti Sharma
The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas hyperspectral images which have high spectral resolution are very limited in availability. In this work, hyperspectral imagery (AVIRIS-NG) is simulated using a multispectral image (Sentinel-2) and a spectral reconstruction method, namely, the universal pattern decomposition method (UPDM). UPDM is a linear unmixing technique, which assumes that every pixel of an image can be decomposed as a linear composition of different classes present in that pixel. The simulated AVIRIS-NG was very similar to the original image, and its applicability in estimating LCC was further verified by using the ground based measurements, which showed a good correlation value (R = 0.65). The simulated image was further classified using a spectral angle mapper (SAM), and an accuracy of 87.4% was obtained, moreover a receiver operating characteristic (ROC) curve for the classifier was also plotted, and the area under the curve (AUC) was calculated with values greater than 0.9. The obtained results suggest that simulated AVIRIS-NG is quite useful and could be used for vegetation parameter retrieval. © 2022 by the authors.
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.
Impact of the indigenous rotavirus vaccine Rotavac in the Universal Immunization Program in India during 2016–2020
(Nature Research, 2025) Nayana P. Nair; Samarasimha Nusi Reddy; Sidhartha Giri; Tintu Varghese; Varunkumar Thiyagarajan; Jayaprakash Muliyil; Priya Hemavathy; Shainey Alokit Khakha; Rashmi Arora; Mohan Digambar Gupte; Jacqueline Elizabeth Tate; Umesh D. Parashar; Venkata Raghava Mohan; Gagandeep Kang; Mahima Mittal; Sunil Kumar Rao; Vineeta Gupta; Vipin M. Vashishtha; Sanjeev Kumar Verma; Kulandaivel Murugiah; Ramasubramaniam Pitchumani; Sridevi A. Naaraayan; Priyadarishini Dorairaj; Girish Kumar Chethrapilly Purusothaman; Senthilnathan Subramanian; Sumanth Amperayani; Sundaram Balasubramanian; Jayanta Kumar Goswami; Amrit Koirala; Shakti Lamichhane; Koshy C. George; Asolie Chase; Bhupesh Jain; Suresh C. Goyal; Dalpat Rajpurohit; Prabhu Prakash; Sunil Kothari; Vikash Katewa; Pramod Sharma; Shailja Vajpayee; Alok Kumar Goyal; Bharti Malhotra; R. K. Gupta; Prachi Chaudhary; Hemant Jain; Mannancheril Abraham Mathew; Asit Mansingh; Rashmi Patnaik; Samarendra Mahapatro; Subrat Kumar Majhi; Prasantajyoti Mohanty; Rajib Kumar Ray; Subrant Kumar Mohanty; Manas Kumar Nayak; Nirmal Kumar Mohakud; Mamata Devi Mohanty; J. Bikrant Kumar Prusty; Jasashree Choudhury; Mrutunjay Dash; Saroj Kumar Satpathy; Subal Kumar Pradhan; Jyoti Sharma; Sanjeev Chaudhary; Pancham Kumar; Shayam L. Kaushik; Rajesh Kumar; Bhavneet Bharti; Mini Pritam Singh; Muralidharan P. Jayashree; Akshay Kumar Saxena; Kushaljit Singh Sodhi; Arun Bansal; Ravi Prakash Kanojia; Adarsh Bansal; Madhu Gupta; Preeti Raikwar; Manoj Rawal; Anil Kumar Goel; Suraj Chawla; Poonam Dalal; Geeta Gathwala; Manohar Badur; Gorthi Rajendra Prasad; K. Kameswari; Padmalatha Pamu; Jeeru Bhaskara Reddy; J. Manikyamba; Krishna Babu Goru; G. S. Rama Prasad; G. V. Rama Devi; Suhasini Mekala; Sowmiya V. Senthamizh; Sunita Bidari
In 2016, India introduced Rotavac (G9P[11]), an indigenous oral rotavirus vaccine administered at 6, 10 and 14 weeks of age through the Universal Immunization Program. Evaluating its effectiveness under routine programmatic conditions is critical, given the variable performance of rotavirus vaccines in low- and middle-income countries. Here we assessed Rotavac’s real-world effectiveness and impact across 31 hospitals in 9 states between 2016 and 2020 using a test-negative case–control design. Overall, 24,624 children were enrolled in surveillance (62% male and 38% female). Of 8,372 children aged 6–59 months eligible for effectiveness analysis (1,790 rotavirus-positive cases and 5,437 rotavirus-negative controls), 6,646 received 3 doses and 581 were unvaccinated. The adjusted vaccine effectiveness of 3 doses against severe rotavirus gastroenteritis was 54% (95% confidence interval (CI) 45% to 62%), with 1,574 vaccinated cases versus 5,072 vaccinated controls. Among children aged 6–23 months (1,486 vaccinated cases and 4,595 vaccinated controls), genotype-specific adjusted vaccine effectiveness was 51% (95% CI 36% to 62%) for G3P[8], 81% (95% CI 73% to 87%) for G1P[8] and 64% (95% CI 21% to 83%) for G1P[6]. Following vaccine introduction, rotavirus positivity among hospitalized children declined from 40% to 20%. These findings confirm that Rotavac provides substantial protection against severe rotavirus disease, including nonvaccine strains, and performs comparably to internationally licensed vaccines in similar settings. © The Author(s) 2025.
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.
Insights into early generation synthetic amphidiploid Brassica juncea: a strategy to harness maximum parental genomic diversity for improving Indian mustard
(Frontiers Media SA, 2025) Pooja Garg; Shikha Tripathi; Anamika Kashyap; A. Anil Kumar; Sujata Kumari; Mandeep L. Singh; Ranjeet Kushwaha; Shivani Shankar Sharma; Jyoti Sharma; Rashmi P. Yadav; Navin Chandra Gupta; Naveen Singh; Ramcharan C. Bhattacharya; Vinod Chhokar; Mahesh Rao
In India, amphidiploid Brassica juncea (AABB, 2n=36) is a significant oilseed crop, but its small gene pool limits its ability to develop traits of higher breeding and economic value. Through interspecific hybridization from various lines of the progenitor species, resynthesized B. juncea (RBJ) can provide breeders with additional resources for creating genetically diverse stress-tolerant and high-yielding cultivars. Three B. rapa accessions and eight B. nigra accessions were crossed in this study to develop 33 synthetic B. juncea lines. A total of 28 crosses were attempted, including the three-way crosses, but only the cross combinations with B. rapa cytoplasm led to successful embryonic development. Molecular diversity analysis of these lines in S2 generation revealed significant genetic diversity with higher levels of heterozygosity and allelic richness, along with significant variations for the yield-related traits. These results suggest that the synthesized lines could effectively enrich the genetic base of B. juncea and generate variability for agronomically important traits in a shorter time duration. The characterized variability in the synthetic lines needs to be utilized in hybridization, with already evolved genotypes, in early generations before it is lost due to chromosomal rearrangements, recombination and natural selection. © © 2025 Garg, Tripathi, Kashyap, Anil Kumar, Kumari, Singh, Kushwaha, Sharma, Sharma, Yadav, Gupta, Singh, Bhattacharya, Chhokar and Rao.
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.