Among these products, SMAP presents a better performance and has the highest spatial resolution (1–36 km) but it has a shorter time span (from 2015 until now) 9. These products differ in terms of spatiotemporal resolution, coverage, and data sources. AMSR2, ASCAT, Sentinel-1, SMAP, SMOS, ESA-CCI, and from LSM, e.g. There are soil moisture datasets at the global scale from satellites, e.g.
As a result, each type of soil moisture has its own advantages and limitations. LSM can be used to produce global soil moisture but there are big differences among different products due to different and uncertain parameterizations 1, 5, 8. However, satellite retrievals have spatiotemporal gaps, due to revisit time, land surface states, or complex topography 1. Satellite observations allow the retrieval of soil moisture at a global scale. The in-situ observations provide continuous observations from different soil depths at the point scale. There are three main sources of SSM 2, 4– 6: in-situ soil moisture, satellite observations, and soil moisture products from either Machine Learning (ML) algorithms or Land Surface Model (LSM) 2, 7. flood and drought monitoring, irrigation scheduling, and agricultural management.Īlthough SSM has such high importance from many perspectives, there is still a paucity of global-scale long-term high resolution SSM datasets with acceptable precision and accuracy. Therefore, a global high resolution, long-term, and spatiotemporally consistent SSM dataset is necessary for understanding the processes between the land surface and atmosphere, and is useful for numerous applications, e.g. SSM has impacts on climate processes by influencing the partitioning of the incoming energy in the latent and sensible heat fluxes and controlling the partitioning of precipitation into runoff, evapotranspiration, and infiltration 2, 3. Surface soil moisture (SSM) is a source of water for the atmosphere through processes leading to evapotranspiration from land 1– 3. GSSM1 km product can support the investigation of large-scale climate extremes and long-term trend analysis. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. The root mean square error of GSSM1 km in testing set is 0.05 cm 3/cm 3, and correlation coefficient is 0.9. The performance of the GSSM1 km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. Global Surface Soil Moisture (GSSM1 km) provides surface soil moisture (0–5 cm) at 1 km spatial and daily temporal resolution over the period 2000–2020. Here we use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics. Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited.
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