Annual paddy rice planting area and cropping intensity datasets and their dynamics in the Asian monsoon region from 2000 to 2020

Abstract

CONTEXTTimely information on the spatiotemporal trends in annual paddy rice planting areas (PRA) and cropping intensity (CI) in Asia is important for food security warnings and water use planning. However, long-time series datasets for these are generally unavailable. The spatiotemporal dynamics of annual paddy rice and cropping intensity over the last 20 years have rarely been investigated at the continental scale. OBJECTIVEThe objectives were to generate a publicly available database on annual PRA and CI and investigate the spatiotemporal dynamics of annual PRA and CI across the Asian monsoon region (AMR) over the last 20 years. METHODSThe annual PRA and cropping CI maps at 500-m resolution across the AMR from 2000 to 2020 were generated by the improved methods based on remote sensing data from multiple sources. Also, the spatiotemporal trends of paddy rice fields and cropping intensity were further analyzed based on Sen's slope and the Mann–Kendall test. RESULTS AND CONCLUSIONSThe results showed that the resultant annual PRA and CI were correlated with that based on field samples and census data. They were also consistent with the spatial distribution of existing high-resolution products in a specific year and region. The paddy rice planting area increased significantly (p < 0.05) in northeastern China and northwestern India and decreased significantly (p < 0.05) in southern China during 2000–2020. Single- and double-cropping were dominant in the AMR. Both double-cropping and triple-cropping increased significantly (p < 0.05) during 2001–2020. Single-cropping was converted to double-cropping in approximately 198.1 × 105 ha of areas, mainly in the North China Plain and northern India. And double-cropping was converted to triple-cropping in approximately 9.5 × 105 ha of areas, mainly in the Mekong Delta in Southeast Asia. However, the cropping intensity in some regions such as southern China declined from 2001 to 2020. SIGNIFICANCEThe results and datasets of this study will be useful for providing fundamental information for designing scientific policies, food security, and climate change, providing key data for yield forecasting, crop model building, and greenhouse gas estimating.