Our researchers Mateo Gašparović, Ivan Pilaš, Dorijan Radočaj and Dino Dobrinić published paper in Applied Science journal from MDPI: Monitoring and Prediction of Land Surface Phenology Using Satellite Earth Observations—A Brief Review
Abstract: Monitoring and predicting land surface phenology (LSP) are essential for understanding
ecosystem dynamics, climate change impacts, and forest and agricultural productivity. Satellite Earth
observation (EO) missions have played a crucial role in the advancement of LSP research, enabling
global and continuous monitoring of vegetation cycles. This review provides a brief overview
of key EO satellite missions, including the advanced very-high resolution radiometer (AVHRR),
moderate resolution imaging spectroradiometer (MODIS), and the Landsat program, which have
played an important role in capturing LSP dynamics at various spatial and temporal scales. Recent
advancements in machine learning techniques have further enhanced LSP prediction capabilities,
offering promising approaches for short-term prediction of vegetation phenology and cropland
suitability assessment. Data cubes, which organize multidimensional EO data, provide an innovative
framework for enhancing LSP analyses by integrating diverse data sources and simplifying data
access and processing. This brief review highlights the potential of satellite-based monitoring,
machine learning models, and data cube infrastructure for advancing LSP research and provides
insights into current trends, challenges, and future directions.
Keywords: remote sensing; land surface phenology; monitoring; prediction; Earth observations;
data cubes
Read full paper for free: https://www.mdpi.com/2076-3417/14/24/12020
Founding: This research was funded by Croatia Science Foundation, grant number IP-2022-10-5711