V
Viral A. Dave
Researcher at Dhirubhai Ambani Institute of Information and Communication Technology
Publications - 8
Citations - 53
Viral A. Dave is an academic researcher from Dhirubhai Ambani Institute of Information and Communication Technology. The author has contributed to research in topics: Land degradation & Vulnerability assessment. The author has an hindex of 3, co-authored 8 publications receiving 30 citations. Previous affiliations of Viral A. Dave include Anand Agricultural University.
Papers
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Journal ArticleDOI
Evaluation of full-polarimetric parameters for vegetation monitoring in rabi (winter) season
TL;DR: In this paper, the authors explored the use of polarimetric SAR data for crop classification by evaluating different polarIMR parameters and various decomposition techniques using single date fine quad-pol C-band RADARSAT-2 dataset over heterogeneous agricultural area of Mehsana district of Gujarat state in India.
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Cotton Crop Biophysical Parameter Study Using Hybrid/Compact Polarimetric RISAT-1 SAR Data
TL;DR: In this paper, a hybrid-polarity architecture consisting of transmitting circular polarisation and receiving two orthogonal linear polarisation was used to calculate four Stokes parameters, viz. plant height, plant age and plant biomass of cotton crops grown under two different environments, i.e., rainfed and irrigated in Guajrat, India were studied with respect to derived polarimetric parameters.
Journal ArticleDOI
Radar Vegetation Index for assessing cotton crop condition using RISAT-1 data
TL;DR: In this article, vegetation water content (VWC) is assessed using Rada for water stress management in the cotton belt of western India using a Rada-based water meter.
Journal ArticleDOI
Monitoring cotton crop condition through synergy of optical and radar remote sensing
Dipanwita Haldar,Rojalin Tripathy,Viral A. Dave,Rucha Dave,Bimal K. Bhattacharya,Arundhati Misra +5 more
TL;DR: Synergistic use of Optical and Microwave remotely sensed data for cotton condition based on biophysical traits, indices from ground measurements and satellite derived reflectance were assessed in this article, where the authors evaluated the performance of the combined data.
Proceedings ArticleDOI
Artificial Neural Network (ANN) based Soil Electrical Conductivity (SEC) prediction
TL;DR: In this article, the ANN-based model has been developed to predict the soil degradation in Soil Health Card (SHC) for the year 2014 and was incorporated in training the model.