Rice ponding date detection in Australia using Sentinel-2 and Planet Fusion imagery

Abstract

Rice is unique, in that yields are maximized when it is grown under ponded (or flooded) conditions. This however has implications for water use (an important consideration in water-scarce environments) and greenhouse gas emissions. This work aimed to provide precise predictions of the date when irrigated rice fields were ponded, on a per-field basis. Models were developed using Sentinel-2 data (with the advantage of inclusion of water-sensitive shortwave infrared bands) and Planet Fusion data (which provides daily, temporally consistent, cross-calibrated, gap-free data). Models were trained with data from both commercial farms and research sites in New South Wales, Australia, and over four growing seasons (harvest in 2018–2021). Predictions were tested on the 2022 harvest season, which included a variety of sowing and water management strategies. A time-series method was developed to provide models with features including satellite observations from before and after the date being classified (as ponded or non-ponded). Logistic regression models using time-series features produced mean absolute errors for ponding date prediction of 4.9 days using Sentinel-2 data, and 4.3 days using Planet Fusion data. The temporal frequency of the Planet Fusion data compensated for the lack of spectral bands relative to Sentinel-2.