Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model

Abstract

Identifying spatial distribution of crop planting in large-scale is one of the most significant applications of remote sensing imagery. As an active remote sensing system, synthetic aperture radar (SAR) provides high-resolution polarimetric information of land covers. Nowadays, it is possible to carry out continuous multi-temporal analysis of crops in large-scales since an increased number of spaceborne SAR systems has been launched. This paper formulates rice mapping as a semantic segmentation problem and proposes to use deep learning techniques to exploit the phenological similarity of rice production to identify the rice distribution in large-scales. The study area (i.e., about 58504 km2) located in Arkansas River Basin is selected to develop an adapted U-Net for large-scale rice mapping. The Sentinel-1 data in previous years (i.e., data collected in 2017 and 2018) are used to train and fine-tune the network, and current season data (i.e., data collected in 2019) is selected to test the robustness of the network. Experimental results show that the proposed method achieves the state-of-the-art performance as it benefits from the spatial characteristics and phenological similarity of rice. The experiments of rice extraction in different planting pattern regions and extracted features visual projection are conducted to explain the features mined by the adapted U-Net. Furthermore, the advantages of temporal generalization in large-scale are validated by the comparison between space migration and time migration, which indicates that the difference of rice in different years is smaller than that of rice in different spaces. Finally, the issues for operational implementation are discussed.