The 20 m Africa rice distribution map of 2023 - J. Jiang; J. Jiang; J. Jiang; H. Zhang; H. Zhang; H. Zhang; J. Ge; J. Ge; J. Ge; L. Zuo; L. Xu; L. Xu; et al. (Earth System Science Data)
In recent years, the demand for rice in Africa has been growing rapidly, and, in order to meet this demand, the rice cultivation area is also expanding rapidly; thus, it is of great significance to monitor the rice cultivation in Africa. The spatial and temporal distribution of rice cultivation in Africa is complex, making it difficult to use phenology-based rice identification methods, and the existing rice distribution products of Africa are all made up of grid-based statistical data with a low resolution, unable to obtain accurate rice field location and available labels. To address these two difficulties, based on time series optical and dual-polarization synthetic aperture radar (SAR) data, this study proposes a sample set construction method by means of fast-coarse-positioning-assisted visual interpretation and a feature-importance-guided supervised classification combining multiple temporal optical and SAR features to reduce the impact of rice diversity in Africa. Firstly, we use the time series statistical features of vertical transmit, horizontal receive (VH) data for fast coarse positioning and screening of possible rice areas and combine multiple auxiliary data for visual interpretation to construct the sample set; secondly, based on the complementary information in SAR data and optical data, the 20 m Africa rice distribution map of 2023 was completed by combining the object-oriented segmentation results of temporal optical images and the pixel-based classification results of temporal SAR data features after feature selection. The average classification accuracy of the proposed method for the validation set is more than 85 %, and the R2 of the linear fit to various existing statistical data is more than 0.9, which proves that the proposed method can achieve the spatial distribution mapping of rice under complex climatic conditions in a large region, providing crucial data support for rice monitoring and agricultural policy development. The dataset is available at https://doi.org/10.5281/zenodo.13729353 (Jiang et al., 2024).