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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. [ABSTRACT FROM AUTHOR]

Weeds can significantly reduce productivity in agriculture (Roslim et al., 2021). Mechanical weeding and pesticides for tomatoes under irrigation are some of the weed management methods which are time-consuming and environmentally hazardous (Gerhards et al., 2022). Remote sensing and machine learning techniques offer more precise weed detection and management, using high-resolution satellite data and drones to identify early weed infestations and employ precision agriculture approaches (Su et al., 2022). This study uses machine learning to analyze drone images and map weeds in the Ha-Mphaila tomato irrigation scheme in South Africa, showcasing the potential of drones and machine learning for sitespecific weed management in agriculture. [ABSTRACT FROM AUTHOR]