The innovative application of precise geospatial vegetation forecasting holds immense potential across diverse sectors, including agriculture, forestry, humanitarian aid, and carbon accounting. To leverage the vast availability of satellite imagery for this task, various works have applied deep neural networks for predicting multispectral images in photorealistic quality. However, the important area of vegetation dynamics has not been thoroughly explored. Our study breaks new ground by introducing GreenEarthNet, the first dataset specifically designed for high-resolution vegetation forecasting, and Contextformer, a novel deep learning approach for predicting vegetation greenness from Sentinel 2 satellite images with fine resolution across Europe. Our multi-modal transformer model Contextformer leverages spatial context through a vision backbone and predicts the temporal dynamics on local context patches incorporating meteorological time series in a parameter-efficient manner. The GreenEarthNet dataset features a learned cloud mask and an appropriate evaluation scheme for vegetation modeling. It also maintains compatibility with the existing satellite imagery forecasting dataset EarthNet2021, enabling cross-dataset model comparisons. Our extensive qualitative and quantitative analyses reveal that our methods outperform a broad range of baseline techniques. This includes surpassing previous state-of-the-art models on EarthNet2021, as well as adapted models from time series forecasting and video prediction. To the best of our knowledge, this work presents the first models for continental-scale vegetation modeling at fine resolution able to capture anomalies beyond the seasonal cycle, thereby paving the way for predicting vegetation health and behaviour in response to climate variability and extremes.

翻译：精准的地理空间植被预测这一创新应用在农业、林业、人道主义援助和碳核算等多个领域具有巨大潜力。为充分利用卫星图像的海量可用性，已有诸多研究应用深度神经网络预测具有逼真质量的多光谱图像。然而，植被动态这一重要领域尚未得到深入探索。本研究开创性地引入了GreenEarthNet——首个专门为高分辨率植被预测设计的数据集，以及Contextformer——一种创新的深度学习方法，用于根据欧洲范围内精细分辨率的哨兵2号卫星图像预测植被绿度。我们的多模态变压器模型Contextformer通过视觉骨干网络利用空间上下文，并以参数高效的方式结合气象时间序列，预测局部上下文块上的时间动态。GreenEarthNet数据集包含学习到的云掩膜以及适用于植被建模的评估方案，同时与现有卫星图像预测数据集EarthNet2021保持兼容性，支持跨数据集模型比较。我们广泛的定性和定量分析表明，我们的方法在性能上优于一系列基准技术，包括超越EarthNet2021上先前的最先进模型，以及来自时间序列预测和视频预测的适配模型。据我们所知，本研究首次提出了能够在精细分辨率下实现大陆尺度植被建模、捕捉季节性周期之外异常的模型，从而为预测植被健康及其对气候变率和极端事件的响应行为铺平了道路。