Abstract:

 Irrigation plays an important role in farmland management and timely and accurate access to irrigation information is important for modern agricultural production. With the continuous development of remote sensing technologies, optical remote sensing has surpassed traditional field measurement methods, which are known for their low quality and efficiency, and has found wide application. In this paper, the principles of inversing agricultural irrigation information using the vegetation indeices, soil moisture, and evapotranspiration methods based on optical remote sensing data were outlined, and the advantages and disadvantages of each method and their development trends were summarized. The results indicated that as research expands from small irrigation districts to larger provincial and national regions, the types of irrigation information required become more diverse, researchers were continuously optimizing existing methods to address their shortcomings, leading to the maturation of research techniques. Integration of multi-parameter inversion methodologies and machine learning algorithms could effectively improve the precision of agricultural irrigation information extraction, which was also the leading trend in optical remote sensing data extraction. This approach representd two major trends in the extraction of agricultural irrigation information based on optical remote sensing data. However, challenges such as time lags and limited penetration still remain. Future research should focus on developing models suitable for different spatial and temporal scales by utilizing the integration of multi-parameter inversion methodologies and machine learning algorithms, and should aim to deepen the understanding of underlying mechanisms and continuously improve the precision of agricultural irrigation information extraction from optical remote sensing data.

Key words: Irrigation, Optical remote sensing, Soil moisture content, Evapotranspiration