融合雷达与气候数据，用机器学习精准预测德国作物生长周期
A novel fusion of Sentinel-1 and Sentinel-2 with climate data for crop phenology estimation using Machine Learning
- 用LightGBM模型融合哨兵1/2遥感与高分辨率气候数据
- 全国尺度预测精度R²超0.43，平均误差仅6天
- 结果可直接用于作物模型校准，助力智慧农业
作物物候描述了作物从播种到收获的生理发育阶段，对农业管理决策至关重要。在海量地球观测数据背景下，已有研究尝试利用遥感（RS）和高分辨率气象数据精确检测物候。然而，多数研究集中于大尺度预测，缺乏针对作物模型社区的实用方法，未能有效整合哨兵1号、哨兵2号数据与高分辨率气候数据。为此，我们训练了一个机器学习（ML）LightGBM模型，在20米空间分辨率下预测德国八大主要作物的13个物候阶段。观测数据来自2017至2021年德国国家物候网络（德国气象局；DWD）。通过全面特征选择分析，确定了最优的遥感与气候数据组合。在全国尺度上，预测物候的R² > 0.43，平均绝对误差仅为6天，覆盖所有物候阶段与作物。时空分析显示模型具有跨空间与时间场景的迁移能力。结果表明，将雷达传感器与气候数据结合，能显著提升物候预测性能，为作物模型校准与评估提供高价值输入，支持科学农业决策，推动可持续粮食生产以应对日益增长的全球粮食需求。
原文摘要 · Abstract (English)
Crop phenology describes the physiological development stages of crops from planting to harvest which is valuable information for decision makers to plan and adapt agricultural management strategies. In the era of big Earth observation data ubiquity, attempts have been made to accurately detect crop phenology using Remote Sensing (RS) and high resolution weather data. However, most studies have focused on large scale predictions of phenology or developed methods which are not adequate to help crop modeler communities on leveraging Sentinel-1 and Sentinal-2 data and fusing them with high resolution climate data, using a novel framework. For this, we trained a Machine Learning (ML) LightGBM model to predict 13 phenological stages for eight major crops across Germany at 20 m scale. Observed phonologies were taken from German national phenology network (German Meteorological Service; DWD) between 2017 and 2021. We proposed a thorough feature selection analysis to find the best combination of RS and climate data to detect phenological stages. At national scale, predicted phenology resulted in a reasonable precision of R2 > 0.43 and a low Mean Absolute Error of 6 days, averaged over all phenological stages and crops. The spatio-temporal analysis of the model predictions demonstrates its transferability across different spatial and temporal context of Germany. The results indicated that combining radar sensors with climate data yields a very promising performance for a multitude of practical applications. Moreover, these improvements are expected to be useful to generate highly valuable input for crop model calibrations and evaluations, facilitate informed agricultural decisions, and contribute to sustainable food production to address the increasing global food demand.
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