The potential of Sentinel-1 for monitoring forage productivity in rangeland ecosystems : a review

Abstract

Rangelands are vital ecosystems that support forage production essential for livestock and biodiversity conservation, yet they face increasing degradation driven by anthropogenic pressures and climate variability. Remote sensing technologies offer scalable and non-destructive means to monitor forage productivity, with optical sensors limited by cloud cover and dense vegetation saturation. Synthetic Aperture Radar (SAR), particularly from the Sentinel-1 constellation, provides all-weather, high-resolution data capable of capturing structural and moisture-related vegetation attributes. This review evaluates the potential of Sentinel-1 SAR data for assessing and monitoring forage productivity in rangeland ecosystems. It highlights recent applications demonstrating Sentinel-1's effectiveness in forage productivity estimation and its integration with optical sensors like Sentinel-2 to enhance monitoring accuracy. Despite its advantages, challenges such as spatial resolution constraints, ecological sensitivity, and complex data processing impede full operational deployment. Future directions emphasize advanced data fusion techniques, machine learning approaches, and enhanced preprocessing algorithms to optimize Sentinel-1's utility. Integrating SAR with optical datasets promises to facilitate scalable, cost-effective, and reliable rangeland management strategies, supporting sustainable forage utilization and ecosystem resilience. Therefore, governments, the private sector, and NGOs should invest in Earth Observation infrastructure and capacity-building to translate remote sensing into actionable policies that promote sustainable rangeland management, climate change adaptation, and food security.

HIGHLIGHTS • Sentinel-1 SAR offers all-weather, high-resolution rangeland monitoring. • Combines with optical sensors to estimate vegetation structure and biomass. • Data fusion and advanced algorithms boost accuracy and usability. • Enables large-scale, cost-effective forage and ecosystem health assessments.