Surface Soil Moisture (SSM) information is needed for agricultural water resource management, hydrology and climate analysis applications. Temporal and spatial sampling by the space-borne instruments designed to retrieve SSM is, however, limited by the orbit and sensors of the satellites. We produced a Global Daily-scale Soil Moisture Fusion Dataset (GDSMFD) with 25 km spatial resolution (2011~2018) by applying the Triple Collocation Analysis (TCA) and Linear Weight Fusion (LWF) methods. Using five metrics, the GDSMFD was evaluated against in-situ soil moisture measurements from ten ground observation networks and compared with the prefusion SSM products. Results indicated that the GDSMFD was consistent with in-situ soil moisture measurements, the minimum of root mean square error values of GDSMFD was only 0.036 cm³/cm³. Moreover, the GDSMFD had a good global coverage with mean Global Coverage Fraction (GCF) of 0.672 and the maximum GCF of 0.837. GDSMFD performed well in accuracy and global coverage fraction, making it valuable in applications to the global climate change monitoring, drought monitoring and hydrological monitoring.

The daily AMSR-E/NASA(the Advanced Microwave Scanning Radiometer-Earth Observing System/the National Aeronautics and Space Administration) and JAXA (the Japan Aerospace Exploration Agency) soil moisture (SM) products from 2002 to 2011 at 25 km resolution were developed and distributed by the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) and JAXA archives, respectively. This study analyzed and evaluated the temporal changes and accuracy of the AMSR-E/NASA SM product and compared it with the AMSR-E/JAXA SM product. The accuracy of both AMSR-E/NASA and JAXA SM was low, with RMSE (root mean square error) > 0.1 cm³ cm^-3 against the in-situ SM measurements, especially the AMSR-E/NASA SM. Compared with the AMSR-E/JAXA SM, the dynamic range of AMSR-E/NASA SM is very narrow in many regions and does not reflect the intra- and inter-annual variability of soil moisture. We evaluated both data products by building a linear relationship between the SM and the Microwave Polarization Difference Index (MPDI) to simplify the AMSR-E/NASA SM retrieval algorithm on the basis of the observed relationship between samples extracted from the MPDI and SM data. We obtained the coe°cients of this linear relationship (i.e., A₀ and A₁) using in-situ measurements of SM and brightness temperature (T_B) data simulated with the same radiative transfer model applied to develop the AMSR-E/NASA SM algorithm. Finally, the linear relationships between the SM and MPDI were used to retrieve the SM monthly from AMSR-E T_B data, and the estimated SM was validated using the in-situ SM measurements in the Naqu area on the Tibetan Plateau of China. We obtained a steeper slope, i.e., A₁ = 8, with the in-situ SM measurements against A₁ = 1, when using the NASA SM retrievals. The low A₁ value is a measure of the low sensitivity of the NASA SM retrievals to MPDI and its narrow dynamic range. These results were confirmed by analyzing a data set collected in Poland. In the case of the Tibetan Plateau, the higher value A₁ = 8 gave more accurate monthly AMSR-E SM retrievals with RMSE = 0.065 cm₃ cm_-3. The dynamic range of the improved retrievals was more consistent with the in-situ SM measurements than with both the AMSR-E/NASA and JAXA SM products in the Naqu area of the Tibetan Plateau in 2011.

The ASCAT (Advanced SCATterometer) soil moisture product with 10-km spatial resolution was retrieved based on the soil water index (SWI) algorithm from the data acquired by the scatterometer on board the Meteorological OPerational (MetOP) satellites (MetOP-A, MetOP-B). In this study, the ASCAT product was downscaled from 10-km to 1-km spatial resolution based on the Apparent Thermal Inertia (ATI) estimated from MODIS Land Surface Temperature (LST) and Albedo retrievals in 54 grids (1 degree 1 degree) around 54 FLUXNET stations. First, the ATI was estimated at 1-km spatial resolution by using MODIS LST and Albedo data at the same spatial resolution and then resampled to 10-km. Second, the relationship between ASCAT soil moisture and ATI at 10-km spatial resolution was established. Finally, the spatiotemporally continuous soil moisture at 1-km spatial resolution was retrieved using the obtained relationship between ATI and ASCAT at 10-km spatial resolution, and the ATI data at 1-km spatial resolution. However, there were many missing values in the MODIS LST maps leading to spatiotemporal discontinuity in LST and calculated ATI data. To obtain spatiotemporal continuous ATI data, this study first reconstructed the MODIS LST data by finding similar points that had the same land cover type and similar NDVI (the Normalized Difference Vegetation Index) value. In this study, we found that the LST data of similar points in a pair of temporal adjacent LST images had a linear relationship. The LST data of these similar points in a pair of temporal adjacent LST images were used to establish a linear relationship and then used to reconstruct the pair of temporally adjacent LST images. The reconstructed LST data were used to obtain the spatiotemporal continuous ATI data at 1-km and 10-km spatial resolutions. In this study, downscaled 1-km spatial resolution soil moisture product within the 54 grids around the FLUXNET sites were obtained in 2013. Results indicated that the spatial distribution of the downscaled soil moisture using the reconstructed MODIS LST data is better than that using original MODIS LST data. Additionally, the downscaled soil moisture was evaluated against in-situ soil moisture measurements at 54 FLUXNET stations. The average of RMSE (the Root Mean Square Error) was 0.098 m³m^-3 and the average of MAE (the Mean Absolute Error) was 0.08 m³m^-3.