Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects

Abstract

The use of neural network (NN) models for remote sensing (RS) retrieval of landscape biophysical and biochemical properties has become popular in the last decade. Recently, the emergence of "big data" that can be generated from remotely sensed data and innovative machine learning (ML) approaches have provided a platform for novel analytical approaches. Specifically, the advent of deep learning (DL) frameworks developed from traditional neural networks (TNN) offer unprecedented opportunities to improve the accuracy of SOC retrievals from remotely sensed imagery. This review highlights the use of TNN models and their evolution into DL architectures in remote sensing of SOC estimation. The review also highlights the application of DL, with a specific focus on its development and adoption in remote sensing of SOC mapping. The review concludes by highlighting future opportunities for the use of DL frameworks for the retrieval of SOC from remotely sensed data.

Keywords: Climate change; Deep learning; Hyperspectral; Multispectral; Radar; Remote sensing; Soil organic carbon.