The biodiversity and resilience of ecosystems are increasingly threatened by land degradation, which affects food security and climate stability drastically by reducing the overall productivity in ecosystems. It is important to identify the difference between anthropogenic degradation such as deforestation, intensive agriculture, and urbanization, and natural climate variability. In this context, Earth Observation (EO) datasets offer significant capability for the detection of land degradation patterns on a global scale and their biophysical underpinnings, and climate interactions. The purpose of this study is to utilize EO datasets to monitor land degradation, by combining a satellite-based datacube of spectral indices together with primary productivity, biophysical indicators, and climate factors. Gross Primary Productivity (GPP) is one of the fundamental indicators of how well the ecosystem is functioning and is directly related to vegetation indices, such as NDVI and EVI, and the fraction of absorbed photosynthetically active radiation (fAPAR), which together provide insight into vegetation health and biomass dynamics. The integration of climate data, particularly soil moisture, precipitation, and temperature, with productivity maps aids in differentiating the changes caused by natural climate cycles and human-induced degradation. By identifying areas where productivity declines, we can detect possible human influences in situations where climate conditions alone cannot explain the observed variations. The goal of this integrated framework is to highlight the value of EO-derived GPP and related metrics for detecting, monitoring, and managing land degradation, ultimately supporting sustainable land use policies and climate resilience efforts globally.