Preprints
https://doi.org/10.5194/essd-2022-392
https://doi.org/10.5194/essd-2022-392
 
06 Dec 2022
06 Dec 2022
Status: this preprint is currently under review for the journal ESSD.

20 m Annual Paddy Rice Map for Mainland Southeast Asia Using Sentinel-1 SAR Data

Chunling Sun1,2,3, Hong Zhang1,2,3, Lu Xu1,2, Ji Ge1,2,3, Jingling Jiang1,2,3, Lijun Zuo1,2, and Chao Wang1,2,3 Chunling Sun et al.
  • 1Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • 2International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
  • 3College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Abstract. Over 90 % of the world’s rice is produced in the Asia-Pacific Region. Synthetic aperture radar (SAR) enables all-day and all-weather observations of rice distribution in tropical and subtropical regions. Rice growth patterns in tropical and subtropical regions are complex, and it is difficult to construct representative rice growth patterns, which makes it much more difficult to extract rice distribution based on SAR data. To address this problem, a rice mapping method based on time-series Sentinel-1 SAR data is proposed in this study for large regional tropical or subtropical areas. Based on the analysis of rice backscattering characteristics in mainland Southeast Asia, the combination of spatio-temporal statistical features with thegeneralization ability to complex rice growth patterns was selected, then input into the U-Net semantic segmentation model and combined with WorldCover data to eliminate false alarms, and finally the 20-meter resolution rice map of five countries in mainland Southeast Asia in 2019 was obtained. On the validation sample set, the proposed method achieved an accuracy of 92.20 %. Good agreement was obtained when comparing our rice map with statistical data and other rice maps at the national and provincial levels. The maximum coefficient of determination R2 was 0.93 at the national level and 0.97 at the provincial level. These results demonstrate the advantages of the proposed method in rice extraction with complex cropping patterns and the reliability of the generated rice maps. The 20 m annual paddy rice map for mainland Southeast Asia is available at https://doi.org/10.5281/zenodo.7315076 (Sun, 2022).

Chunling Sun et al.

Status: open (until 11 Feb 2023)

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  • RC1: 'Comment on essd-2022-392', Anonymous Referee #1, 28 Jan 2023 reply

Chunling Sun et al.

Data sets

20 m Annual Paddy Rice Map for Mainland Southeast Asia Using Sentinel-1 SAR Data Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, Chao Wang https://doi.org/10.5281/zenodo.7315076

Chunling Sun et al.

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Short summary
Over 90 % of the world’s rice is produced in the Asia-Pacific Region. In this study, a rice mapping method based on Sentinel-1 synthetic aperture radar data for mainland Southeast Asia is proposed. A combination of spatio-temporal features with strong generalization is selected and input into the U-Net model to obtain a 20-meter resolution rice map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice map is concordant with statistics and other rice maps.