GTGRI: A Gaussian Time-weighted Growth Rate Index for Multi-season Paddy Rice Mapping in Diverse Climates with SAR Time Series

Accurate paddy rice mapping remains challenging in multi-season cropping systems, where overlapping phenological stages introduce classification uncertainties. While Synthetic Aperture Radar-based (SAR) methods provide cloud-independent monitoring, they often misinterpret phenological transitions, leading to confusion between cropping cycles and spectrally similar vegetation. This study proposes GTGRI (Gaussian Time-weighted Growth Rate Index), a phenology-driven classification framework that integrates Growth Rate (GR) and Gaussian Time Decay (GTD) to enhance large-scale paddy rice mapping using Sentinel-1 SAR time-series data. Compared to benchmark methods (ARM-SARFS, PKI, SPRI), GTGRI demonstrates superior performance in single-season rice regions and is equally applicable to double-season rice systems. Because of the rice-specific phenological dynamics, GTGRI further improves rice classification by differentiating paddy rice from spectral similar vegetation, such as lotus/reeds. In mangrove-dominated landscapes, GTGRI significantly reduces misclassification, outperforming the PKI Index. By dynamically adjusting to temporal variations, GTGRI overcomes the limitations of static classification models, enhancing consistency and transferability across diverse rice-growing landscapes. Its robust performance in both structured and heterogeneous agricultural environments establishes GTGRI as a scalable and operational solution for global paddy rice mapping, with important implications for supporting food security and sustainable land use management.