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.

翻译：对植被动态的短期预测是实现精准农业数据驱动决策支持的关键。然而，由于云掩膜造成的数据稀疏与不规则采样，以及作物生长的异质性气候条件，基于卫星观测的归一化植被指数（NDVI）预测仍具挑战。本文提出一种适用于稀疏、不规则晴空数据的田块级NDVI概率预测框架。该架构将历史NDVI与气象观测的编码过程与未来外生协变量分离，通过融合两类表征实现多步分位数预测。为应对不规则重访模式与预测时域相关的异质性不确定性，我们引入时间距离加权分位数损失函数，确保训练目标与有效预测时域对齐。此外，我们设计了累积与极端天气特征工程，以捕捉与植被响应相关的延迟气象效应。基于欧洲卫星数据的实验表明，所提方法在逐点评价与概率评价指标上均优于统计模型、深度学习模型及时间序列基线方法。消融研究证实，目标历史是性能的主要驱动因素，而气象协变量在全模态设置中进一步提升了效果。代码可通过 https://github.com/arco-group/ndvi-forecasting 获取。