Early Season Mapping of Rice Using of Time Series Sentinel-1 SAR Images

Synthetic Aperture Radar (SAR) exhibits the capacity for comprehensive and continuous Earth observation, regardless of weather conditions and diurnal variations. Contemporary methodologies for rice field identification utilizing SAR rely upon the entirety of the rice growth cycle data, posing challenges in discerning rice cultivation within the ongoing cultivation cycle. In addressing this challenge, the present study introduces a novel metric, namely the 3-Sigmoid index (SSSI), designed to quantify the early season probability of land parcels planted rice. SSSI fully uses the crucial feature of intensity variation from low to high backscatter based on time series SAR data as paddy fields progress in their growth stages, so that we can identify in-season rice early. The method was validated in two experimental areas, and the experimental results indicate that the approach exhibits heigh accuracy in early-stage identification. Moreover, the method demonstrated successful recognition of rice fields during the tillering phase and even prior to it. In addition, the SSSI is independence from requisite prior knowledge, reference samples, and a plethora of pre-established parameters. This characteristic underscores its potential for widespread and extensive practical applications, particularly in regions characterized by persistent cloud cover, where optical remote sensing data is frequently inaccessible.