arXiv:2602.17683v3 Announce Type: replace Abstract: Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support 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. The architecture separates the
The increasing availability of satellite data and advancements in AI/ML techniques for handling sparse time series enable more sophisticated agricultural forecasting models.
This development allows for more precise and proactive decision-making in agriculture, leading to improved resource allocation and potentially buffering against climate variability.
Field-level NDVI forecasting can now account for data sparsity and climatic heterogeneity with probabilistic outputs, moving beyond simpler deterministic models.
- · Precision agriculture companies
- · Farmers
- · Agricultural AI/ML developers
- · Satellite data providers
- · Traditional agricultural consultants
Improved crop yield predictions and optimized input usage for specific fields.
Enhanced food security and reduced agricultural waste through more efficient farming practices globally.
Potential for new financial instruments and insurance products based on highly granular, probabilistic yield forecasts.
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Read at arXiv cs.LG