Review of synthetic aperture radar with deep learning in agricultural applications

Abstract

Synthetic Aperture Radar (SAR) observations, valued for their consistent acquisition schedule and not being affected by cloud cover and variations between day and night, have become extensively utilized in a range of agricultural applications. The advent of deep learning allows for the capture of salient features from SAR observations. This is accomplished through discerning both spatial and temporal relationships within SAR data. This study reviews the current state of the art in the use of SAR with deep learning for crop classification/mapping, monitoring and yield estimation applications and the potential of leveraging both for the detection of agricultural management practices.

This review introduces the principles of SAR and its applications in agriculture, highlighting current limitations and challenges. It explores deep learning techniques as a solution to mitigate these issues and enhance the capability of SAR for agricultural applications. The review covers various aspects of SAR observables, methodologies for the fusion of optical and SAR data, common and emerging deep learning architectures, data augmentation techniques, validation and testing methods, and open-source reference datasets, all aimed at enhancing the precision and utility of SAR with deep learning for agricultural applications.