Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates

TL;DR

This paper presents a probabilistic framework for short-term NDVI forecasting from sparse satellite data, integrating meteorological covariates and novel loss functions to improve accuracy and uncertainty estimation.

Contribution

It introduces a new architecture that separates encoding of NDVI and weather data, employs a horizon-aware loss, and demonstrates superior performance over existing methods.

Findings

Outperforms statistical, deep learning, and time-series baselines.

Target history is the main performance driver, with meteorological data providing additional benefits.

Code is publicly available at the specified GitHub repository.

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.