## 2 Global Open Earth Observations Satellite Missions for Monitoring Land Surface Phenology
### Moderate Resolution Imaging Spectroradiometer (MODIS)
In carbon cycling, there are two key parameters that indicate vegetation characteristics and are commonly extracted from remote sensing data: gross primary production (GPP) and net primary production (NPP). GPP is an annual measure of production activity or vegetation growth. NPP is an indicator of how much carbon dioxide is assimilated in the photosynthesis process after the deduction of carbon dioxide that is given off during respiration by organisms. NPP is often used as the indicator of global ecological conditions, such as large-scale climate shifts, deforestation, desertification, pollution damage, crop conditions, glacial retreats, flooding, wildfires, and urbanization ([[https://modis.gsfc.nasa.gov/data/dataprod/nontech/MOD17.php](https://modis.gsfc.nasa.gov/data/dataprod/nontech/MOD17.php)]([https://modis.gsfc.nasa.gov/data/dataprod/nontech/MOD17.php](https://modis.gsfc.nasa.gov/data/dataprod/nontech/MOD17.php)), accessed on 10 November 2024). GPP/NPP provides information that has important social and economic impacts, including crop yield and forest production. These two measures are very often used in various studies and reports related to climate change or land cover/land use change assessments, which prompted the generation of the corresponding MODIS products: gross primary productivity 8-day L4 global at 1 km and net primary production yearly L4 global 1 km spatial resolution ([[https://modis.gsfc.nasa.gov/data/dataprod/mod17.php](https://modis.gsfc.nasa.gov/data/dataprod/mod17.php)]([https://modis.gsfc.nasa.gov/data/dataprod/mod17.php](https://modis.gsfc.nasa.gov/data/dataprod/mod17.php)), accessed on 10 November 2024). These proxy indices of ecosystem exchange use for construction a very close connection between variations in spectral reflectance of high temporal MODIS data over vegetated surfaces and related carbon dynamics in the background. Spectral reflectance data or derived vegetation indices are very closely tied with ground measurements of carbon dynamics, commonly obtained from the eddy-covariance carbon flux towers associated into the global networks such as FLUXNET ([PERSON], 2018). Field Eddy-covariance measurements are therefore commonly used for functional mapping and calibration of remotely sensed observations to carbon-related products such as GPP and NPP. Existing studies across various vegetation sites commonly confirmed the mentioned relationship where spectral indices such as the MODIS enhanced vegetation index (EVI) most closely approximate the variance of daily GPP ([PERSON] et al., 2018). Satellite imagery also enables the detection of the occurrence and duration of the snow cover or snow cover phenology, which has been found to be closely related to the SOS ([PERSON] et al., 2018). Identification and recognition of the land processes over Europe, such as afforestation, agriculturalization, degradation, land abandonment, land restoration, shrubland increase, and urbanization using NDVI time series, were performed by [PERSON] et al. (2018). The effect of large afforestation activities on LSP was studied by [PERSON] et al. (2018), who detected prolongation of the growing season, with a large delay in senescence on restored areas in the loess plateau in China. Likewise, they found that air temperature is the main factor that affects the green-up and maturity in the first part of the vegetation season, and precipitation controls senescence and dormancy in the second part. Human activities such as large afforestation (forest expansion) of the agro-pastoral ecotone of northern China, together with climate change, caused the pronounced increase in vegetation greening (represented by NDVI) in almost 89% of the study area ([PERSON] et al., 2018). Similar results, where large human activities of afforestation contributed to the positive trends of vegetation dynamics, were obtained by [PERSON] et al. (2018). The effect of the large reservoirs and dam construction was analyzed by [PERSON] et al.