A systematic method for spatio-temporal phenology estimation of paddy rice using time series Sentinel-1 images

Abstract

Abstract Spatio-temporal phenological information is essential for regional crop monitoring and accurate management. This study aimed to obtain the spatio-temporal distribution of rice phenology using time series Sentinel-1A images. In this study, a systematic method for spatial and temporal phenological estimation was developed based on multi-temporal Sentinel data. This method consisted of: (1) the Spectral Similarity Measures (SSM) approach for rice mapping, and (2) the DTHT-PF approach, which includes a dynamic threshold algorithm (DT), a hierarchical tree (HT), and a particle filter (PF) used for phenology estimation. Thereinto, a dynamic threshold algorithm for transplanting date retrieval and a hierarchical tree for obtaining an initial phenology, which is then run through a PF, are proposed based on the SAR backscatter time series. In brief, we found that the VH backscatter has the potential to provide accurate phenological estimations for paddy rice. Furthermore, rice mapping at a high accuracy level of 92.97%, transplanting date retrieval, and initial phenology estimations lay a good foundation for understanding the spatio-temporal distribution of rice phenology. A determination factor (R2) and a root mean square error (RMSE) obtained using the DTHT-PF method were much higher (approximately 0.52 in the difference) and lower (approximately 14.67 in the difference), respectively, than those obtained using the PF approach with uniform initialization. R2 and RMSE values obtained using the real-time DTHT-PF method are slightly different from those obtained using the non-real-time DTHT-PF method. Map analysis revealed that the transplanting dates and the spatial distribution phenology were larger in the west and north of the study area near the South China Sea because of higher temperatures and earlier transplanting. Altogether, our results suggest that the DTHT-PF method and the VH backscatter obtained from the Sentinel-1A images have great potential for furthering our understanding of the spatio-temporal distribution of phenology estimation. This information can greatly contribute to the improvement of precision agriculture, including crop monitoring and yield predictions.