A5076971547- Soil Moisture and Remote Sensing
- Remote Sensing in Agriculture
- Precipitation Measurement and Analysis
- Remote Sensing and Land Use
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Parasite Biology and Host Interactions
- Remote-Sensing Image Classification
- Bird parasitology and diseases
- Microwave Engineering and Waveguides
- Smart Agriculture and AI
- Spectroscopy and Chemometric Analyses
- Vector-borne infectious diseases
- Atmospheric chemistry and aerosols
- Helminth infection and control
- Climate change and permafrost
- Land Use and Ecosystem Services
- Trypanosoma species research and implications
- Cryospheric studies and observations
- Advancements in Semiconductor Devices and Circuit Design
- Plant Water Relations and Carbon Dynamics
- Landslides and related hazards
- Parasites and Host Interactions
- Advanced Computational Techniques and Applications
- Fish Biology and Ecology Studies
- Flood Risk Assessment and Management
Institute of Electrical and Electronics Engineers
2025
Banaras Hindu University
2015-2024
Bundelkhand University
2024
Ansal University
2024
Weatherford College
2022
M.J.P. Rohilkhand University
1988-2021
University of Amsterdam
2019
Indian Institute of Technology BHU
1969-2018
St. Aloysius (Deemed to Be University)
2017
Aligarh Muslim University
1979-2010
The Resourcesat-2 is a highly suitable satellite for crop classification studies with its improved features and capabilities. Data from one of sensors, the linear imaging self-scanning (LISS IV), which has spatial resolution 5.8 m, was used to compare relative accuracies achieved by support vector machine (SVM), artificial neural network (ANN), spectral angle mapper (SAM) algorithms various crops non-crop covering part Varanasi district, Uttar Pradesh, India. separability analysis performed...
In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 stages from tillering ripening in Varanasi district, India. The parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry (DB) and plant height (PH) estimated using random forest regression (RFR), support vector (SVR), artificial...
In the present study, random forest regression (RFR), support vector (SVR) and artificial neural network (ANNR) models were evaluated for retrieval of soil moisture covered by winter wheat, barley corn crops. SVR with radial basis function kernel was provided highest adj. R2 (0.95) value wheat crop at VV polarization. However, RFR (0.94) polarization using Sentinel-1A satellite data. The values found RFR, linear kernels. least performance reported ANNR model almost all crops under...
Crop classification is needed to understand the physiological and climatic requirement of different crops. Kernel-based support vector machines, maximum likelihood normalised difference vegetation index schemes are attempted evaluate their performances towards crop classification. The linear imaging self-scanning (LISS-IV) multi-spectral sensor data was evaluated for types such as barley, wheat, lentil, mustard, pigeon pea, linseed, corn, sugarcane other crops non-crop water, sand, built up,...
This study involved an investigation of the long-term seasonal rainfall patterns in central India at district level during period from 1991 to 2020, including various aspects such as spatiotemporal trend patterns, variability, trends rainy days with different intensities, decadal percentage deviation and events along their respective intensities. The region was meticulously divided into distinct subparts, namely, Gujarat, Daman Diu, Maharashtra, Goa, Dadra Nagar Haveli, Madhya Pradesh,...
Monitoring water resources globally is crucial for forecasting future geo-hydro disasters across the Earth. In present study, an attempt was made to assess functional dimensionality of multi-satellite precipitation products, retrieved from CHIRPS, NASA POWER, ERA-5, and PERSIANN-CDR with respect gridded India Meteorological Department (IMD) dataset over a period 30+ years (1990–2021) on monthly yearly time scales at regional, sub pixel levels. The study findings showed that performance...
The mountain systems of the Himalayan regions are changing rapidly due to climatic change at a local and global scale. Indian Western Himalaya ecosystem (between tree line snow line) is an underappreciated component. Yet, knowledge vegetation distribution, rates change, interactions with snow-hydroclimatic elements lacking. purpose this study investigate linkage between spatiotemporal variability (i.e., greenness forest) related parameters cover, land surface temperature, Tropical Rainfall...