AsiaRiceMap10m: high-resolution annual paddy rice maps for Southeast and Northeast Asia from 2017 to 2019

Abstract

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).

Challenges and opportunities in remote sensing-based crop monitoring: a review

“…Methods for exploiting all-weather imaging SAR data to eliminate cloud impacts in optical data have increased in popularity in crop areas where these data are available. SAR has already been used to identify paddy rice fields [ 80 ]. The coupling of the interferometric and backscattering information of SAR data can significantly improve crop type mapping.…”

High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine

“…The accuracy of the produced maps in this study was evaluated using three types of assessment: (1) validation using virtual observations based on Google Earth VHR (very high resolution) and street view images, (2) comparison with existing rice map by NESEA-Rice10 [40], and (3) comparison with known planting and harvesting schedule.…”

“…In particular, rice fields can be differentiated from other crops due to the presence of standing water during crop transplanting. Based on limited ground-based data, phenology-based classification can detect plant growth stages based on their vegetative cover changes (i.e., [22,40]). Most studies that use the phenologybased method are based on spectral indices and classify such indices using supervised or manual classification.…”

“…Furthermore, the GEE offers an interactive platform for geospatial and machine learning algorithms. Several studies have utilized the GEE platform for rice field mapping [22,29,30,34,35,40].…”

“…In a recent study, NESEA-Rice10 [40] produced a rice field extent map covering Southeast Asia (including Malaysia) and North-East Asia. The map was a new achievement because it covered a large area with high resolution (10 m).…”