Monitoring irrigation dynamics in paddy fields using spatiotemporal fusion of Sentinel-2 and MODIS

Rapid and accurate monitoring of irrigation dynamics in paddy fields (the start, end, duration and irrigation peak, etc.) at field scales is crucial to the fine management of agricultural water resources, especially in typical areas with water shortages. However, there is still a lack of sufficient research to depict irrigation dynamics in paddy fields at high temporal and spatial levels. To this end, this study fused Sentinel-2 and MODIS images to map the spatio-temporal dynamics of irrigation events in paddy fields. A popular spatiotemporal fusion algorithm (enhanced spatial and temporal adaptive reflectance fusion model, ESTARFM) was used to generate 25 high-spatial resolution (10 m) remote sensing images based on 9 Sentinel-2 images and 24 MODIS images. Random forest algorithm was used to extract the spatial distribution of irrigated paddy fields. Water body index and vegetation index were employed to identify the start, end and duration of irrigation in paddy fields. Penman-Monteith model was used to estimate water surplus and deficit of irrigation during the critical irrigation period with daily observation data from meteorological stations. This study was carried out in rice-growing areas in the middle and lower reaches of the Yellow River in China. The results indicated that the spatial distribution difference of irrigation events in paddy fields with the shortest 3-day interval could be monitored in collaboration with Sentinel-2 and MODIS images. The start, end, and duration of irrigation in paddy fields presented significant spatial differences at field scales. A large amount of water from groundwater and Yellow River was needed, because the total water shortage of irrigation in paddy fields in the study area was about 80.79% of the total water demand. In addition, paddy fields with higher water demand were more concentrated spatially, as were paddy fields with lower water demand. The feasibility of spatiotemporal fusion of multi-source remote sensing data makes it possible to continuously monitor irrigation dynamics in paddy fields on high spatial resolution scales, which is conducive to the construction of spatiotemporal database and big data platform of agricultural irrigation information. This would not only help in promoting the high-quality development of agricultural water resources management but also alleviating the contradiction of regional water resources.