Four decades of remote sensing for monitoring terrestrial ecosystems: a global review and future challenges
José Manuel Álvarez‐Martínez, Tijana Nikolić Lugonja, Alicia Valdés, Jorge González Le Barbier, Marta Suárez, Gonzalo Romero, Mirjana Radulović, Maja Knežević, Sonja Tarčak, Branko Brkljač, Bojana Bokić, Boris Radak, Andrijana Andrić, Miljana Marković, Sanja Brdar, Predrag Lugonja, Isidora Simović, Lori Giagnacovo, Borja Jiménez‐Alfaro
Abstract
Remote sensing (RS) has evolved from occasional mapping to continuous, indicator-based monitoring of terrestrial ecosystems. This review synthesizes four decades of global progress in RS to characterize natural and semi-natural ecosystems, examining how study purposes, sensor types and analytical methods have diversified from 1985 to 2025. A systematic literature review of 6856 publications (1567 selected) documents the transition from expert-based visual interpretation using aerial photography and early Landsat missions, to harmonized, AI-driven workflows that enable scalable and replicable ecosystem assessments. Advances in cloud computing, data cubes and open-access archives now allow wall-to-wall time series of analyses across regions and biomes. Yet, important challenges persist, including the underrepresentation of biodiversity-rich areas, limited in-situ calibration data and uncertainties related to phenological variability, image correction, or temporal mosaicking pipelines. Building on case studies from a global perspective, we outline design principles for policy-ready ecosystem indicators traceable to raw observations, comparable through time and space, and aligned with biodiversity policy frameworks. Integrating multi-sensor data (optical, radar, LiDAR, thermal), standardized in-situ observations and artificial intelligence/machine learning algorithms, RS provides a robust pathway towards operational ecosystem accounting and large-scale functional mapping and monitoring, strengthening conservation planning and ecosystem management worldwide. • We reviewed 1567 remote sensing studies on ecosystem monitoring (1985-2025) • We document the evolution of purpose, sensors and algorithms through time • Machine learning dominates mapping, while time-series analyses expand monitoring • Data gaps persist in biodiversity-rich but under-monitored regions • Reliable field data, multi-sensor fusion and AI will drive next-generation models