NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019

Jichong Han,Yuchuan Luo,Huimin Zhuang,Fei Cheng,Fulu Tao,Zhao Zhang,Liangliang Zhang,Jing Zhang,Juan Cao

doi:10.5194/essd-13-5969-2021

Abstract

Abstract. An accurate paddy rice map is crucial for ensuring food security, particularly for Southeast and Northeast Asia. MODIS satellite data are useful for mapping paddy rice at continental scales but have a mixed-pixel problem caused by the 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 by generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (NESEA-Rice10). We compared the resultant paddy rice maps with available agricultural statistics at subnational levels and existing rice maps for some countries. The results demonstrated that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranged 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 was consistent with that of the rice map from the International Rice Research Institute. The paddy rice planting area may have been underestimated in the region in which the flooding signal was 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/10.5281/zenodo.5645344 (Han et al., 2021a). Small examples can be found from the following DOI: 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)
The estimated annual rice paddy areas were significantly correlated with the agricultural statistics at subnational levels
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)

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). 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). It is necessary to produce a paddy rice map dataset with high spatial resolution

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Methods

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Discussion

Conclusion