Progress and Perspectives of Crop Yield Forecasting With Remote Sensing: A review

Accurate and timely crop yield forecasts are critical to realizing global food security, balancing international grain trade, and promoting sustainable agricultural development. By providing consistent and large-scale observations, remote sensing technology has become indispensable in crop yield estimation across local, regional, and global scales. Over the past four decades, numerous crop yield forecasting approaches have been developed, including regression-based statistical models, machine learning, semi-empirical models, crop model-data assimilation (DA), and advanced deep learning (DL) approaches. This review comprehensively explores the latest advancements in these methodologies, critically evaluating their strengths and limitations in practical applications. In particular, this article highlights the challenges associated with spatiotemporal variability, environmental stress factors, and model scalability, offering potential solutions to enhance the accuracy and reliability of regional and global crop yield predictions. Besides, a selection strategy is also outlined, providing guidance on choosing the most appropriate yield estimation methods tailored to specific application objectives, data availability, and geographic scales. We also identify key factors affecting crop yield forecasting and offer insights into future trends and directions of development. Furthermore, we underscore the greatest potential of integrating artificial intelligence (AI) and remote sensing technologies with process-based crop growth models through DA techniques. This fusion holds significant promise for addressing the pressing need for accurate and scalable yield forecasts. As the global demand for food intensifies and the need for sustainable agriculture grows, the development and application of these advanced methodologies will be instrumental in ensuring resilient food systems and supporting sustainable agricultural practices.