HOAL 2019 Daily 30 m Soil Moisture Dataset

This dataset provides soil moisture data for the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria, spanning the entire year of 2019. It has a temporal resolution of daily intervals and a spatial resolution of 30 meters. The dataset was generated using a multi-source satellite-ground data fusion perception method for soil moisture. Firstly, based on spatiotemporal fusion algorithms to obtain continuous spatiotemporal driving variables, an extreme random tree-based surface-to-surface fusion method was applied to downscale the two-layer soil moisture data to 30 m. Subsequently, using multi-layer in-situ measurements, a deep forest algorithm was employed to construct a point-to-surface fusion model for calibration. Ultimately, a high-precision daily soil moisture dataset at 30 m resolution was generated, covering the entire HOAL area for the year 2019. The dataset is stored in a common data format (GeoTIFF). This work was supported by the Open Fund of Hubei Luojia Laboratory (220100059).本数据集提供奥地利佩岑基兴水文开放空气实验室（Hydrological Open Air Laboratory，HOAL）2019年全年的土壤湿度数据。该数据集时间分辨率为逐日间隔，空间分辨率为30米。数据集采用多源星地数据融合感知方法生成土壤湿度产品：首先基于时空融合算法获取连续时空驱动变量，随后应用基于极端随机树的面-面融合方法将两层土壤湿度数据降尺度至30米；继而利用多层原位实测数据，通过深度森林算法构建点-面融合模型开展校准工作。最终生成覆盖2019年全年HOAL研究区的高精度30米分辨率逐日土壤湿度数据集。数据集以通用数据格式（GeoTIFF）存储。 本研究得到湖北珞珈实验室开放基金（220100059）资助。