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Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and combined the Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products using a triple collocation (TC) analysis and the maximized Pearson correlation coefficient (R) method from April 2015 to December 2016. The Global Land Data Assimilation System (GLDAS) and global in situ observations were utilized to investigate and to compare the quality of satellite-based SSM products.The average R-values of SMAP, ASCAT, and AMSR2 were 0.74, 0.64, and 0.65 when they compared with in situ networks, respectively. The ubRMSD values were (0.0411, 0.0625, and 0.0708) m 3 m -3 ; and the bias values were (−0.0460, 0.0010, and 0.0418) m 3 m -3 for SMAP, ASCAT, and AMSR2, respectively. The highest average R-values from SMAP against the in situ results are very encouraging; only SMAP showed higher R-values than GLDAS in several in situ networks with low ubRMSD (0.0438 m 3 m -3 ). Overall, SMAP showed a dry bias (−0.0460 m 3 m -3 ) and AMSR2 had a wet bias (0.0418 m 3 m -3 ); while ASCAT showed the least bias (0.0010 m 3 m -3 ) among all the products. Each product was evaluated using TC metrics with respect to the different ranges of vegetation optical depth (VOD). Under vegetation scarce conditions (VOD < 0.10), such as desert and semi-desert regions, all products have difficulty obtaining SSM information. In regions with moderately vegetated areas (0.10 < VOD < 0.40), SMAP showed the highest Signal-to-Noise Ratio. Over highly vegetated regions (VOD > 0.40) ASCAT showed comparatively better 3 performance than did the other products.Using the maximized R method, SMAP, ASCAT, and AMSR2 products were combined one by one using the GLDAS dataset for reference SSM values. When the satellite products were combined, R-values of the combined products were improved or degraded depending on the VOD ranges produced, when compared with the results from the original products alone.The results of this study provide an overview of SMAP, ASCAT, and AMSR2 reliability and the performance of their combined products on a global scale. This study is the first to show the advantages of the recently available SMAP dataset for effective merging of different satellite products and of their application to various hydro-meteorological problems.

Passive microwave remotely sensed soil moisture products, such as Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) data, have been routinely used to monitor global soil moisture patterns. However, they are often limited in their ability to provide reliable spatial distribution data for soil moisture due to their coarse spatial resolutions. In this study, three machine learning approaches-random forest, boosted regression trees, and Cubist-were examined for the downscaling of AMSR-E soil moisture (25 9 25 km) data over two regions (South Korea and Australia) with different climatic characteristics using moderate resolution imaging spectroradiometer products (1 km), including surface albedo, land surface temperature (LST), Normalized Difference Vegetation Index, Enhanced Vegetation Index, Leaf Area Index, and evapotranspiration (ET). Results showed that the random forest approach was superior to the other machine learning models for downscaling AMSR-E soil moisture data in terms of the correlation coefficient [r = 0.71/0.84 (South Korea/Australia) for random forest, 0.75/0.77 for boosted regression trees, and 0.70/0.61 for Cubist] and root-mean-square error (RMSE = 0.049/0.057, 0.052/0.078, and 0.051/0.063, respectively) through crossvalidation. The ET and LST were identified as the most influential among the six input parameters when estimating AMSR-E soil moisture for South Korea, while ET, albedo, and LST were very useful for Australia. In overall, the downscaled soil moisture with 1 km resolution yielded a higher correlation with in situ observations than the original AMSR-E soil moisture data. The latter appeared higher than the downscaled data in forested areas, possibly due to the overestimation of soil moisture by passive microwave sensors over forests, which implies that downscaling can mitigate such overestimation of soil moisture.

Evapotranspiration (ET), or the sum of water released to the atmosphere from ground surfaces, intercepts canopy precipitation through evaporation and plant transpiration and is one of the most significant components in the water cycle. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) 16 global terrestrial ET products were validated at 17 flux tower locations in Asia. Overall, overestimations due to energy balance misclosure distorted the trend of the data at nine locations [r: 0.27-0.82; bias: -21.41-2.38 mm 8-d -1 ; Root Mean Square Error (RMSE): 6.12-21.81 mm 8-d -1 ]. Regardless of variation in the scattering patterns, good agreements between MODIS-based ET and ET measured at the flux towers were observed at five locations (r: 0.50-0.76; bias: -1.42-1.99 mm 8-d -1 ; RMSE: 1.99-8.96 mm 8-d -1 ). Underestimation at one site (r = 0.28, bias = -17.00 mm 8-d -1 , RMSE = 17.41 mm 8-d -1 ) was accompanied by mismatches at two sites (r = 0.12-0.18; bias = -4.19 − -0.04 mm 8-d -1 , RMSE = 5.76-7.66 mm 8-d -1 ). The best performances of the MOD16 ET algorithm were observed at sites with forested land cover, but no substantial differences were found under a variety of climate conditions. This study is the first comprehensive trial to validate global terrestrial MODIS ET in Asia, showing that a MODIS global terrestrial ET product can estimate actual ET with reasonable accuracy. We believe that our results can be used as baseline ET values for satellite image-based ET mapping research in South Korea.