Application and optimization algorithm of multi-source remote sensing data fusion in water resources monitoring

Water resources monitoring is very important to ensure human survival and social development. Traditional monitoring methods are limited by coverage, cost and real-time, and it is difficult to meet modern needs. With the development of remote sensing technology, multi-source remote sensing data fusion provides a new idea for water resources monitoring. In this paper, an optimized multi-source remote sensing data fusion algorithm is proposed. Aiming at the problems of data heterogeneity, temporal and spatial consistency and algorithm efficiency in water resources monitoring, a phased fusion strategy is adopted, which includes five core modules: data preprocessing and quality evaluation, adaptive weight distribution, multi-scale temporal and spatial alignment, lightweight fusion model construction and parallel computing optimization. Taking Henan section of the middle and lower reaches of the Yellow River as a case study, using multi-source remote sensing data such as Landsat-8, Sentinel-1, Sentinel-2 and MODIS, the results show that this method is superior to traditional methods in water extraction accuracy, soil moisture inversion accuracy and drought monitoring timeliness, which significantly improves the monitoring accuracy and efficiency, and provides a new technical means for water resources monitoring.