Estimation of Gross Primary Production and Net Primary Production of Vegetation Cover for Low Mountain Sub-Mediterranean Landscapes Using Remote Sensing and Geoinformation Modeling

Terrestrial vegetation cover is a critical component of the global carbon cycle, annually assimilating a substantial fraction of anthropogenic CO2 emissions. However, regional estimates of gross primary production (GPP) and net primary production (NPP) remain insufficiently studied, especially for ecologically sensitive areas such as the sub-Mediterranean landscapes of southeastern Crimea. The aim of this study is to calculate and map the spatio-temporal distribution of GPP and NPP across southeastern Crimea over the period 2001–2025 using Earth remote sensing data and geoinformation modeling. This study employed MODIS products (MOD17A2H collection 061) processed in the Google Earth Engine cloud platform, together with temperature and precipitation data (ClimateEU, CHIRPS). Statistical analysis included calculation of the carbon use efficiency (CUE) coefficient and correlation analysis. The results show that the mean GPP for southeastern Crimea is 1.13 kg C/m2 and the mean NPP is 0.61 kg C/m2, which exceed global average values. Maximum productivity is characteristic of natural forest communities (sessile oak, beech and juniper forests), whereas anthropogenically transformed landscapes (agricultural land, urban coenoses) exhibit the lowest values. The mean CUE is 0.54, with the highest values (0.63–0.66) recorded for agrocoenoses and steppes, and the lowest (0.49–0.57) for forests. A positive correlation between productivity and precipitation and a negative correlation with air temperature were identified, especially for forest ecosystems. This study fills a gap in regional primary productivity assessments and can serve as a basis for ecosystem monitoring under climate change and anthropogenic pressure.