From NDVI to Neural Networks: A Systematic Review of Satellite Remote Sensing Methods for Monitoring Vegetation Responses to Climate Change (2000–2025)

This systematic review, conducted following PRISMA 2020 guidelines, examines satellite remote sensing approaches used to monitor vegetation responses to climate change between 2000 and 2025. Of 757 peer-reviewed studies identified, 455 (60.1%) were available as open-access full text and were extracted using a deterministic, fully auditable regular-expression pipeline; the remaining 302 could be characterised only bibliographically and are excluded from the methodological denominators reported here. Output grew steeply after 2019, with 2021-2025 accounting for 480 studies (63.4%). Landsat (51.4% of full-text studies), the Moderate Resolution Imaging Spectroradiometer (44.6%) and Sentinel-2 (43.5%) dominated; 29.9% used synthetic aperture radar and 62.9% combined two or more sensor families. The Normalised Difference Vegetation Index remained the reference index (76.9%), ahead of the Enhanced Vegetation Index (28.1%), while solar-induced fluorescence appeared in only 4.0%. Random Forest (35.8%) and linear regression (29.5%) were the most common analytical methods; deep learning rose from absent before 2020 to 18.0% of studies published in 2023-2025. First-author institutions were split almost evenly between the Global North (52.4%) and Global South (47.6%), but the latter is dominated by one country: China contributed 208 studies (27.5%) against 152 (20.1%) for all other Global South countries combined. Drylands, shrublands and peatlands were markedly under-represented relative to their global extent. Validation reporting was incomplete, with the coefficient of determination given in 40.9% of full-text studies (within-study median 0.71). Only 8.8% provided a public code repository. The review identifies research gaps and priorities for multi-sensor integration, methodological standardisation, and reproducible practice.