Discrimination and monitoring of rice cultural types using dense time series of Sentinel-1 SAR data

Abstract

Details about the spatial extent of rice cultivation in different seasons are essential for ensuring global food security as well as addressing environmental issues and climate change. This study aims to introduce a novel approach to discriminate between different rice cultural types and subtypes and identify their optimum stages to represent crop growth profiles using Synthetic Aperture Radar (SAR) data. Dense time series Sentinel-1 SAR backscatter data were analyzed at 12 days intervals throughout the growth period of summer and kharif rice. Statistical analysis using ANOVA at 5% level of significance was conducted to identify the critical stages of discrimination for all cultural types and subtypes. Knowledge-based decision tree algorithm was employed to classify all major types of rice grown during summer and kharif season. A comprehensive analysis revealed significant variations in backscatter profiles among different rice types. The most critical period for effectively distinguishing between various rice cultural types and subtypes was found to be from transplanting to tillering stage. Decision tree algorithm was able to discriminate between rice cultural types and subtypes with high overall accuracy and Kappa coefficient, viz., 94.74% and 0.94 for summer rice and 91.80% and 0.90 for kharif rice, respectively. The findings of this study revealed that C-band co and cross-polarized Sentinel-1 data have the potential to detect differences between different cultural types and subtypes of rice and, therefore, will be helpful in crop growth monitoring and agro-ecological change detection in the cultivation pattern.