Reducing Climatic Bias in SDG 15.3.1 Land Degradation Assessments Using a Hybrid Productivity Approach: A Remote Sensing Analysis for Northern and Central Morocco (2000–2022)

Land degradation assessments for SDG 15.3.1 often misinterpret rainfall-driven vegetation fluctuations as human-induced decline, particularly in dryland environments where vegetation productivity responds strongly to precipitation variability. This study addresses this challenge by presenting a national-scale land degradation assessment (2000–2022) using a fully reproducible Google Earth Engine workflow that integrates 30 m Landsat time-series NDVI, precipitation, land cover, and soil organic carbon datasets. The core contribution is a precipitation-conditioned hybrid productivity indicator that adaptively selects among NDVI trends, Rain Use Efficiency (RUE), and Residual Trends (RESTREND) according to local precipitation dynamics. This framework operationalizes a climate-aware implementation of the land productivity (LP) sub-indicator within SDG 15.3.1 and enables systematic comparison among productivity metrics under varying rainfall conditions. Results for the 2015–2022 monitoring period, which included multiple drought years, indicate that 18% of land showed declining productivity, 75% remained stable, and 6% showed improvement. Decline was spatially concentrated in arid and semi-arid regions, whereas irrigated and managed landscapes exhibited localized improvements. The hybrid indicator provides an additional option for LP assessment that explicitly accounts for precipitation variability, supporting more climate-sensitive interpretation of productivity trends. This transferable, reproducible methodology strengthens national capacity for SDG 15.3.1 reporting and offers a scalable framework for land degradation assessments in other drought-prone regions.