Rice Flowering Date Retrieval from Sentinel-2 and Planet Fusion Imagery

Abstract

Abstract Estimating the timing of rice flowering is useful for predicting maturity, which subsequently informs management decisions such as field drainage and harvest timing that optimize yield and quality. This study utilized satellite-based remote sensing to identify the highest measured vegetation index (VI) value, or peak of the season (PoS), which was correlated with observed rice flowering dates. Sentinel-2 (S2) and daily Planet Fusion (PF) data were used to derive four VIs and evaluate their effectiveness in determining rice flowering dates using an offset-adjusted peak-VI approach. Our methodology was tested in the major rice-growing regions of New South Wales, Australia, including commercial and experimental fields, multiple varieties, during the 2021–22, 2022–23, and 2023–24 seasons. Additionally, we assessed the extent to which variations in flowering dates induced by nitrogen content differences can be estimated. The results show that the chlorophyll index green (CIG) derived from PF provided a consistent relationship between PoS and observed flowering dates, with an offset of approximately 13 days and a root mean squared error (RMSE) of 6.56 and 7 days for the seasons 2021–22 (n = 114 sample sites) and 2022–23 (n = 134 sample sites), respectively. Furthermore, the study found that the method was able to describe the variation in flowering dates due to differences in nitrogen uptake at most sites, with $$\hbox {R}^2$$ values ranging from 0.09 to 0.97; higher explained variance was observed at sites where there was a wider range of flowering dates. The methodology facilitates the estimation of flowering dates and is scalable from pixel, to field, to regional level analysis.