Over the past decade, satellite and airborne remote sensing of vegetation and crop fields has become a critical tool to monitor the state of vegetation around the world. Radiative Transfer (RT) in vegetation canopies plays an important role in data analysis and interpretation. This book provides an introduction to the problem of modeling RT in vegetation structures with applications to problem of estimating crop yields using airborne images. Theoretical and numerical implementation techniques are illustrated to show how to practically compute the radiative energy reflected by vegetation and crop fields. Subsequently, RT vegetation models are coupled with intelligent algorithms to estimate coffee cherry field percentages using airborne Visible and Near-Infrared (VNIR). Neural networks are designed and trained on RT models to learn the relationship between percentage of coffee cherries present in the field and VNIR reflectance collected by airborne sensors. This book is ideal for students and professionals interested in gaining a broad and practical perspective on how radiative transfer models can be used in vegetation monitoring and agricultural applications.

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