A review of crop yield estimation on pixel and field scales from remotely sensed data

Abstract

Crop yield estimation over large regions can provide critical data support for regional agricultural management and global food security assessments. The previous reviews mainly focused on the technological advancements of methods in specific areas such as crop growth, data assimilation, and machine learning. No reviews have summarized the progress in all these areas, particularly at the pixel and field scales. This review comprehensively evaluates various methods for estimating global and regional crop yield from different remotely sensed data, particularly on the pixel and field scales, in the past two decades. All estimation methods are grouped into four categories: empirical statistical, light use efficiency (LUE), data assimilation, and machine learning. We also identify remaining challenges in data consistency, update frequency, and crop type coverage, particularly in data-scarce developing regions. This review provides valuable insights for researchers in the field of remotely sensed data-based crop yield estimation, enabling a deeper understanding of the current status of global and regional datasets, the characteristics and challenges of existing estimation methods, and future research directions.