Primary production measures the rate of energy fixation in organic compounds and is one of the most fundamental ecological processes. Indeed, more and more projects are focused on mapping Gross Primary Production (GPP), Net Primary Production (NPP) and Net Ecosystem Production (NEP), as well as related variables such as Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Leaf Area Index (LAI), Soil Organic Carbon (SOC) and albedo. Different modeling strategies are used to derive these variables from remotely sensed data, providing and increasing the availability of spatio-temporal maps of primary production. Most importantly, the temporal trends of primary productions can be used to detect declining marine and terrestrial areas. Available products differ in spatial and temporal resolution and extent, modeling strategies, including machine learning and mechanistic models, and validation strategies. In fact, besides the cross-comparison of these products with each other, their accuracy can be validated against in-situ measurements, such as eddy covariance flux measurements from FLUXNET and similar databases. We invite contributions related to primary productivity modeling and mapping, to compare methodologies, accuracy, spatial and temporal scales, resolution, and derived statistics. In addition to identifying biologically degrading areas, these works enable global-scale studies of the impact of climate change on primary productivity and ecological stability. Finally, we also encourage studies that assess the impact of climate variables on spatiotemporal variations in primary productivity, enhancing our understanding of how these factors shape primary productivity patterns across different ecosystems and under the effect of climate change.