Recent Advances in Land Surface Phenology Estimation with Multispectral Sensing

Vegetation phenology refers to changes in seasonal patterns of vegetation cycles, such as flowering and leaf fall, influenced by annual and seasonal fluctuations of biotic and abiotic drivers. Information about phenology is crucial for unravelling the underlying biological processes across vegetation communities in space and time. It is also important for ecosystem and resources management, conservation, restoration, policy and decision-making on local, national, and global scales. Numerous approaches to register Land Surface Phenology (LSP) appeared since Earth Observation from space became possible a few decades ago. This paper attempts to capture current progress and new capacities that arose with the advent of the free data policy, the Sentinel-era, new multispectral satellite sensors, cloud computing, and machine learning in LSP for natural and semi-natural environments. Spaceborne sensors' capacity to capture LSP, data fusion, and synergies are discussed. Information about retrieval methods through open-source tools and global LSP products and phenology networks are presented.