Paper Title
Crop growth monitoring by dual polarimetric radar vegetation index (DpRVI) using Sentinel-1 SAR data for the soybean crop in Latur District of Maharashtra
Article Identifiers
Authors
GARGI GAYDHANE , UPASANA SINGH , ASHUTOSH PAWAR
Keywords
Soybean, DpRVI, PAI, VWC, NDVI, LSWI, Dual Polarization
Abstract
The utilization of Sentinel-1 Synthetic Aperture Radar (SAR) satellite data offers an exceptional opportunity for crop growth monitoring due to its better revisit frequency and extensive spatial coverage. This study leverages Sentinel-1 SAR data, specifically the dual-pol data comprising VV (Vertical-Vertical) polarization and VH (Vertical-Horizontal) polarization, to apply remote sensing and geospatial techniques in agriculture. These techniques harness Sentinel-derived information to support crop growth monitoring, particularly as an alternative to Sentinel 2A optical data, which can be hindered by cloud coverage. while previous literature has explored the use of backscatter data for crop characterization, this research takes a novel approach. It combines scattering information, including the degree of polarization and eigenvalue spectrum, to derive a new vegetation index known as the Dual Polarimetric Radar Vegetation Index (DpRVI) from dual-pol SAR data. This innovative index provides valuable insights into vegetation dynamics. Furthermore, this study focuses on assessing plant growth in the Bhada Revenue Circle, Latur district, Maharashtra. It does so by considering key crop biophysical parameters such as Plant Area Index (PAI), Vegetation Water Content (VWC), Normalized Difference Vegetation Index (NDVI), and Land Surface Water Index (LSWI) at various crop phenological growth stages. This approach allows for a comprehensive analysis of crop development throughout its lifecycle, emphasizing each critical growth stage. To validate the accuracy of these assessments, statistical analyses, including Linear regression, are employed. These analyses reveal correlations between each biophysical parameter and the Dual Polarimetric Radar Vegetation Index (DpRVI). These correlations provide valuable insights into Soybean crop performance, aiding in the prediction of crop yields and overall crop health. The study's findings indicate promising results for Soybean crop monitoring and highlight the potential of SAR data in agricultural applications. Indeed, the accumulation of key biophysical parameters including Plant Area Index (PAI), Vegetation Water Content (VWC), Normalized Difference Vegetation Index (NDVI), and Land Surface Water Index (LSWI) plays a significant role in plant growth development. In this context, The Dual Polarimetric Radar Vegetation Index (DpRVI) is a useful monitoring plant development during the kharif season, when microwave data may be used instead of optical data. Its correlation with biophysical parameters like PAI, VWC, NDVI, LSWI enhances yield predictions and supports informed decision-making, improving agricultural productivity and crop management practices.
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How To Cite
"Crop growth monitoring by dual polarimetric radar vegetation index (DpRVI) using Sentinel-1 SAR data for the soybean crop in Latur District of Maharashtra", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 10, page no.a589-a608, October-2023, Available :https://ijnrd.org/papers/IJNRD2310068.pdf
Issue
Volume 8 Issue 10, October-2023
Pages : a589-a608
Other Publication Details
Paper Reg. ID: IJNRD_206285
Published Paper Id: IJNRD2310068
Downloads: 000121230
Research Area: Science & Technology
Country: Pune, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2310068.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2310068
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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IJNRD is Scholarly open access journals, Peer-reviewed, and Refereed Journals, High Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool), Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) with Open-Access Publications.
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