A High-Resolution (30m) Daily Soil Moisture Dataset via Causal Deep Learning (2000–2007)

This dataset provides an ultra-high-resolution daily soil moisture record specifically covering Hubei Province, China, spanning from 2000 to 2007. Generated utilizing advanced causal deep learning methodologies, it features an unprecedented spatial resolution of 30 meters and a daily temporal resolution, encompassing both Surface Soil Moisture (SSM) and Root Zone Soil Moisture (RZSM). This level of granular detail makes it highly valuable for regional precision agriculture, eco-hydrological modeling, and local-scale water resource management across the province. By employing a causal deep learning framework rather than relying solely on traditional statistical correlations, this dataset explicitly models the underlying cause-and-effect relationships between meteorological drivers and land surface conditions. This innovative approach ensures a physically robust, accurate, and reliable representation of complex, high-resolution soil moisture dynamics tailored to the specific environmental and geographical context of Hubei Province. The development of this dataset was financially supported by the Wuhan Natural Science Foundation Exploration Project (Chenguang Project) under grant number 2024040801020279. Researchers and practitioners utilizing this soil moisture data for academic or applied studies are kindly requested to properly cite the dataset and acknowledge this funding source in their related publications and research outputs. (Data Availability Note: Due to its large file size, the complete 2000–2023 soil moisture dataset is being uploaded in separate parts. This upload represents Part 1, covering the 2000–2007 period.)