论文标题
用于土壤水分检索的机器学习数据融合模型
A Machine Learning Data Fusion Model for Soil Moisture Retrieval
论文作者
论文摘要
我们开发了一个基于深度学习的卷积回归模型，该模型估计了土壤顶部〜5 cm中的体积土壤水分含量。输入预测因子包括Sentinel-1（活动雷达），Sentinel-2（光学图像）和SMAP（被动雷达）以及来自GLDAS的土壤格林和建模土壤水分领域的地球物理变量。该模型在2015 - 2021年期间对全球约1300个原位传感器的数据进行了训练和评估，并获得了0.727的平均每传感器相关性为0.727，ubrmse为0.054，可用于在名义320m分辨率下产生土壤湿度图。这些结果是针对不同位置的其他13种土壤水分厂的基准测试，并使用消融研究来识别重要的预测因子。
We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (active radar), Sentinel-2 (optical imagery), and SMAP (passive radar) as well as geophysical variables from SoilGrids and modelled soil moisture fields from GLDAS. The model was trained and evaluated on data from ~1300 in-situ sensors globally over the period 2015 - 2021 and obtained an average per-sensor correlation of 0.727 and ubRMSE of 0.054, and can be used to produce a soil moisture map at a nominal 320m resolution. These results are benchmarked against 13 other soil moisture works at different locations, and an ablation study was used to identify important predictors.
