遥感技术驱动的作物产量估算方法研究进展OA

Under the multiple challenges of extreme climate events and resource constraints,developing high-precision and high-efficiency crop yield estimation methods is essential for ensuring food security and guiding agricultural policy.Leveraging its advantages in providing large-scale,dynamic,and rapid acquisition of spatially continuous crop information,remote sensing technology has become a core driving force in transforming yield estimation paradigms.This paper systematically reviews the principles,research progress,and characteristics of three mainstream categories of remote sensing-driven yield estimation methods,namely empirical,semi-empirical,and process-based models.It further conducts an in-depth analysis of existing challenges in the estimation process concerning sample availability,observational data,and modeling approaches.Corresponding strategies are proposed,including promoting the use of intelligent equipment and data-sharing mechanisms for sample acquisition,enhancing multi-source data integration and scale conversion,coupling process-based models with intelligent algorithms,and strengthening uncertainty quantification.Finally,the paper outlines future research priorities for crop yield estimation.This study aims to provide a reference framework for building efficient and robust crop yield estimation systems driven by remote sensing technology,thereby offering solid technical support for intelligent decision-making in global food security and sustainable agricultural development.