Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates

2026-02-04

arXiv.org

AI Summary

This study addresses the challenges of sparse and irregular satellite NDVI observations caused by cloud cover and the difficulty of short-term forecasting of crop vegetation dynamics under heterogeneous climatic conditions. The authors propose a probabilistic forecasting framework that employs a deep learning architecture to separately encode historical NDVI and meteorological observations along with future exogenous covariates, fusing multimodal information for multi-step quantile prediction. A novel temporally distance-weighted quantile loss function is introduced, complemented by feature engineering that incorporates both cumulative and extreme weather metrics, effectively capturing the delayed vegetation response to meteorological drivers and temporal uncertainty. Experiments on European satellite data demonstrate that the proposed method outperforms existing statistical, deep learning, and time series baselines in both point and probabilistic forecasting metrics, with ablation studies confirming historical NDVI as the dominant predictor and meteorological covariates providing significant performance gains.

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 encoding of historical NDVI and meteorological observations from future exogenous covariates, fusing both representations for multi-step quantile prediction. To address irregular revisit patterns and horizon-dependent uncertainty, we introduce a temporal-distance weighted quantile loss that aligns the training objective with the effective forecasting horizon. In addition, we incorporate cumulative and extreme-weather feature engineering to capture delayed meteorological effects relevant to vegetation response. Experiments on European satellite data show that the proposed approach outperforms statistical, deep learning, and time-series baselines on both pointwise and probabilistic evaluation metrics. Ablation studies confirm that target history is the primary driver of performance, with meteorological covariates providing additional gains in the full multimodal setting. The code is available at https://github.com/arco-group/ndvi-forecasting.

Problem

• NDVI forecasting
• sparse satellite time series
• weather covariates
• irregular sampling
• vegetation dynamics

Innovation

• probabilistic forecasting
• NDVI prediction
• irregular time series
• quantile loss
• multimodal fusion