Optimal multi-temporal Sentinel-1A SAR imagery for paddy rice field discrimination; a recommendation for operational mapping initiatives

Abstract

Phenology-based rice mapping has mostly employed all satellite data over the growing season. This approach is not only time consuming but also requires huge data storage and is thus unsuitable for operational initiatives. Even with the use of feature selection algorithms that minimise data redundancy, the huge amount of time in data processing prior to optimal image subset selection is unavoidable. Thus, this study seeks to identify the rice development phase(s) whose Sentinel-1A data in vertical transmitted and horizontal received (VH) and vertical transmitted and vertical received (VV) polarizations offer the most accurate rice field discrimination. Using an area in southeast China during the 2017 growing season, VH + VV data of the vegetative and maturity phases recorded the highest overall accuracy (89.3%) based on Random Forest (RF). For the agro-ecology investigated in this study, therefore, potential users of Sentinel-1A in rice mapping can ex-ante select images acquired over the crop's vegetative and maturity phases to avoid the enormous computational time and storage attendant with entire-season data. These findings would complement algorithms like Support Vector Machine (SVM) that lack feature section tools. However, replications of this study at multiple years and ecologies, and comparison with classifications obtained from optimal data selection tools (variable importance) are strongly recommended to arrive at universally applicable conclusions.