Research on remote sensing monitoring methods for actual irrigated area based on spatiotemporal data fusion

Abstract

[Objective] To fully utilize the temporal information of remote sensing imagery, dynamically identify irrigated areas and their spatial distribution, and provide data support for enhancing irrigation water management capabilities. [Methods] Taking the Yongji Irrigation Area in the Hetao Irrigation District of Inner Mongolia as the study area, spatiotemporal fusion of MODIS and Sentinel-2 data was performed using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model(ESTARFM) to calculate and construct a daily time series of the Re-modified Perpendicular Drought Index(RPDI). To address the flattening of irrigation characteristics in spatiotemporal fused data, a Bi-directional Long Short-Term Memory(Bi-LSTM) network based on sliding window was used to identify irrigation events and dynamically monitor the irrigated area and its spatial distribution. [Results] From April to June in 2024, the actual irrigated areas in the Yongji Irrigation Area were 323.88 km², 462.67 km², 500.57 km², respectively. The irrigation event identification achieved an average overall accuracy of 89.82 % and an average kappa coefficient of 0.77, effectively reflecting the dynamic spatiotemporal variation of irrigation events within the irrigation area. [Conclusion] Spatiotemporal fusion provides a more continuous image data foundation for monitoring actual irrigated areas. The Bi-LSTM method based on sliding window effectively captures the temporal variations in soil moisture content during irrigation. It addresses the challenge of irrigation identification caused by the flattening of soil moisture changes in the fusion of different spatiotemporal resolution data, thereby improving the capability for continuous and dynamic monitoring of actual irrigated areas.

Keywords

actual irrigated area / remote sensing monitoring / spatiotemporal data fusion / RPDI time series / Bi-LSTM / Yongji Irrigation Area / influencing factors