Abstract

An accurate paddy rice map is crucial for food security, particularly for Southeast and Northeast Asia. The MODIS satellite data is useful for mapping paddy rice at continental scales but has a problem of mixed-pixel due to coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time-series Sentinel-1 and MODIS data. We demonstrated the method in generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (AsiaRiceMap10m). The resultant paddy rice maps were compared with available agricultural statistics at subnational levels and existing rice maps for some countries. The results show that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranges from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2 = 0.93) and Northeast China (R2 = 0.99). The spatial distribution of the 2017–2019 composite paddy rice map is consistent with the rice map from the International Rice Research Institute. The paddy rice planting area may be underestimated in the region where the flooding signal is not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.17632/j34b3jsvr9.1 (Han et al., 2021a). Find small examples here (https://doi.org/10.17632/cnc3tkbwcm.1) (Han et al., 2021b).

Highlights

  • Rice is one of the world’s main food sources, accounting for approximately 12 % of the global cropland area (Zhang et al, 2018; Singha et al, 2019)

  • We demonstrated the method by generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (NESEA-Rice10)

  • The main reason may be that our method reduced the mixed pixels in the paddy rice map and that the IRRI product from MODIS overestimated the area, as in previous studies (Fig. S13) (Chen et al, 2012; Li et al, 2020; Nelson and Gumma, 2015)

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Summary

Introduction

Rice is one of the world’s main food sources, accounting for approximately 12 % of the global cropland area (Zhang et al, 2018; Singha et al, 2019). 90 % of the world’s rice is produced in Asian countries (Chen et al, 2012; Yeom et al, 2021). Rice provides food for over 50 % of the world’s population (Minasny et al, 2019). The consumption of rice increases as the world’s population increases. Approximately 1/10 of CH4 emissions in the atmosphere come from methane emissions from rice paddies (Ehhalt et al, 2001; Xin et al, 2017; Zhang et al, 2020). An accurate planting area and spatial distribution information are the basis for monitoring paddy rice growth and predicting yield. It is necessary to produce a paddy rice map dataset with high spatial resolution

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