基于稀疏卫星时间序列和天气协变量的概率NDVI预测
Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates
AI总结：
本文提出一种概率预测框架，利用稀疏卫星数据和天气协变量进行田间NDVI预测，通过融合历史和未来数据实现多步分位数预测，实验表明优于传统和深度学习方法。
摘要：
短期植被动态预测是精准农业数据驱动决策支持的关键。然而，由于云遮蔽导致的稀疏不规则采样以及作物生长的异质气候条件，基于卫星观测的NDVI预测仍具挑战性。本文提出一种概率预测框架，用于在稀疏不规则晴空采集条件下进行田间NDVI预测。该框架将历史NDVI和气象观测编码与未来外生协变量分离，并融合两者进行多步分位数预测。为应对不规则重访模式和时间依赖性不确定性，引入了时间距离加权分位数损失，使训练目标与有效预测时间跨度对齐。此外，还纳入累积和极端天气特征工程，以捕捉与植被响应相关的延迟气象效应。在欧洲卫星数据上的实验表明，所提方法在点估计和概率评估指标上均优于统计、深度学习和时间序列基线方法。消融研究证实，目标历史是性能的主要驱动因素，气象协变量在全多模态设置中提供额外增益。代码可在https://github.com/arco-group/ndvi-forecasting获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
