Abstract
AbstractIn the context of climate change, crop mapping and yield estimation are critical for improving crop management and ensuring food security. Data-driven methods have succeeded in crop mapping and yield estimation, using a combination of multi-source training data and the application of advanced machine learning technologies. This chapter reviews various data-driven approaches that have been applied in crop mapping and yield estimation, with particular attention given to deep learning algorithms. A typical workflow for data analysis is systematically summarized; it includes multi-source data collection, data preprocessing, model development, model evaluation, and model improvement. The advanced data-driven technologies employed for model development in crop mapping and yield estimation are described and synthesized. This chapter introduces threshold-based methods to illustrate the challenges of intra-class variability and inter-class similarity in crop mapping. Various machine learning and deep learning algorithms are further highlighted in multi-temporal crop mapping analysis, with the consideration of multi-source remote sensing data. For crop yield estimation, typical mechanism models are introduced to clarify the challenges in the context of widely distributed yield heterogeneities across different regions. The data-driven statistical models in yield estimation are thoroughly summarized, with a focus on the utilization of remote sensing and environmental data. Challenges and possible future improvements in data-driven approaches are presented.KeywordsData-driven modelingCrop mappingYield estimationSatellite remote sensingData science
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