Deep Learning-Based Image Processing of Multi-Sensor Satellite Imagery for Precision Agriculture and Environmental Monitoring: Current Trends and Techniques

Abstract

The recent development of archives of Earth-observation imagery from satellites like Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (multispectral optical) has led to the need for automated, scalable image-processing techniques for the extraction of agricultural and environmental information at continental scale. The shift from classical pixel-based and shallow machine learning classifiers to deep-learning networks, like Convolutional Neural Networks (CNNs), encoder-decoder segmentation networks (U-Net and its attention-augmented counterparts), CNN-recurrent networks for multi-temporal sequences, Generative Adversarial Networks (GANs) for super-resolution, and vision transformer networks trained using self-supervised objectives, are reviewed. Based on the latest peer-reviewed literature (2015-2025), the paper synthesizes reported accuracy, F1-score, and Intersection-over-Union trends across different applications of crop classification, land-cover mapping and change detection, and evaluates methodological evolution from single-image classification to the use of multi-sensor pipelines temporally and spectrally fused. Current problems are outlined, such as optical contamination and atmospheric effects; limited and geographically imbalanced labeled datasets; high computational requirements for transformer-based inference; and lack of cross-region generalization. The review suggests that the most viable path forward for operational, weather-resilient agricultural and climate monitoring in the operational phase is using Sentinel-1 SAR with Sentinel-2 optical data through self-supervised and attention-based architectures, especially for regions of the Global South with little data.