Assessment of rice crop biophysical parameters using Sentinel-1 C-band SAR data. Issue 12 (15th December 2022)

• Record Type: Journal Article
• Authors: Kushwaha, Amit; Dave, Rucha; Kumar, Gaurav; Saha, Koushik; Khan, Armugha
• Is Part Of: Advances in space research. Volume 70:Issue 12(2022)
• Page Start: 3833
• Page End: 3844
• Publication Date: 2022-12-15
• Subjects: Sentinel–1 -- Backscatter -- Multi–temporal -- Biomass -- VWC; Space sciences -- Periodicals; Astronautics -- Periodicals; Geophysics -- Periodicals; 500.505
• DOI: 10.1016/j.asr.2022.02.021
• Languages: English
• ISSNs: 0273-1177

Abstract

Abstract: Evaluation of crop growth parameters is crucial to understand the crop phenology and its relation with Synthetic Aperture Radar (SAR) scattering mechanism. The study explores the potential of dual polarization Sentinel–1 backscatter parameters to investigate its sensitivity with various biophysical parameters of paddy crop. Temporal profile of crop growth parameters such as fresh biomass, dry biomass, vegetation water content (VWC) and plant height of paddy crop are assessed with respect to SAR parameters such as VV, VH, VH/VV and Radar Vegetation Index (RVI). Temporal Sentinel–1A SAR data during June to November 2018 was considered for this study. Regression analysis using linear and logarithmic models reveal that the sensitivity of Sentinel–1A backscatter parameters with crop biophysical parameters is best when the crop is at early vegetation stage i.e. between tillering to booting stages with maximum R 2 value of 0.68 for liner regression with VH and 0.69 for multiple regression for crop height followed by other biophysical parameters. At later vegetation stage i.e. ripening stage maximum R 2 value of 0.52 for liner regression with VH and multiple regression for crop biomass is observed. There is no significant correlation for peak vegetation stage. The results are indicative for the potential use of dual-polarimetric SAR parameters as input parameters for multiple regression models to improve the prediction of biophysical parameters at different phenolgical stages.