AI RESEARCH
Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates
arXiv CS.LG
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ArXi:2602.17683v2 Announce Type: replace Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision in precision agriculture. Normalized Difference Vegetation Index (NDVI) forecasting from satellite observations, however, remains challenging due to sparse and irregular sampling caused by cloud masking, as well as the heterogeneous climatic conditions under which crops evolve. In this work, we propose a probabilistic forecasting framework for field-level NDVI prediction under sparse, irregular clear-sky acquisitions.