Intercomparison of Downscaling Techniques for Satellite Soil Moisture Products

During recent decades, various downscaling methods of satellite soil moisture (SM) products, which incorporate geophysical variables such as land surface temperature and vegetation, have been studied for improving their spatial resolution. Most of these studies have used least squares regression models built from those variables and have demonstrated partial improvement in the downscaled SM. This study introduces a new downscaling method based on support vector regression (SVR) that includes the geophysical variables with locational weighting. Regarding the in situ SM, the SVR downscaling method exhibited a smaller root mean square error, from 0.09 to 0.07m(3).m(-3), and a larger average correlation coefficient increased, from 0.62 to 0.68, compared to the conventional method. In addition, the SM downscaled using the SVR method had a greater statistical resemblance to that of the original advanced scatterometer SM. A residual magnitude analysis for each model with two independent variables was performed, which indicated that only the residuals from the SVR model were not well correlated, suggesting a more effective performance than regression models with a significant contribution of independent variables to residual magnitude. The spatial variations of the downscaled SM products were affected by the seasonal patterns in temperature-vegetation relationships, and the SVR downscaling method showed more consistent performance in terms of seasonal effect. Based on these results, the suggested SVR downscaling method is an effective approach to improve the spatial resolution of satellite SM measurement

Rain-Gauge Network Evaluations Using Spatiotemporal Correlation Structure for Semi-Mountainous Regions

A reliable network of rain gauges is a crucial component of rainfall estimation in a watershed. To provide a better evaluation method for rain-gauge networks, a new evaluation method using average inter-gauge correlation coefficients (averaged CC) for estimating an effective radius for each rain gauge was developed. In this study, averaged CCs were obtained from the values of inter-gauge correlation coefficients after choosing a minimum number of rainfall data sets as a threshold. The Nam River Basin (2400 km2) and its 24 rain gauges were selected with 8 years (2003 - 2010) rainfall data to validate a new evaluation method. In the spatial correlation coefficient fitting process for generating correlation distances, averaged CCs increased fitness accuracy (maximum 37%) in terms of coefficient of determination (R2) compared with a commonly used method (the last value of the inter-gauge correlation coefficient as the number of data sets is increased: last CC). In the evaluation of effective radii for 8 years, the robustness of the averaged CCs was supported by lower standard deviations for all rain gauges. For the optimum coverage of rainfall estimation in terms of effective radius, the Nam River Basin requires 20 rain gauges. Investigation of altitude effects presented that the effective radii were minimally influenced by the altitude of rain-gauge locations for this area

Development and Assessment of the Sand Dust Prediction Model by Utilizing Microwave-Based Satellite Soil Moisture and Reanalysis Datasets in East Asian Desert Areas

For several decades, satellite-based microwave sensors have provided valuable soil moisture monitoring in various surface conditions. We have first developed a modeled aerosol optical depth (AOD) dataset by utilizing Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Global Land Data Assimilation System (GLDAS) soil moisture datasets in order to estimate dust outbreaks over desert areas of East Asia. Moderate Resolution Imaging Spectroradiometer- (MODIS-) based AOD products were used as reference datasets to validate the modeled AOD (MA). The SMOS-based MA (SMOS-MA) dataset showed good correspondence with observed AOD (R-value: 0.56) compared to AMSR2- and GLDAS-based MA datasets, and it overestimated AOD compared to observed AOD. The AMSR2-based MA dataset was found to underestimate AOD, and it showed a relatively low R-value (0.35) with respect to observed AOD. Furthermore, SMOS-MA products were able to simulate the short-term AOD trends, having a high R-value (0.65). The results of this study may allow us to acknowledge the utilization of microwave-based soil moisture datasets for investigation of near-real time dust outbreak predictions and short-term dust outbreak trend analysis

Ecosystem-dynamics link to hydrologic variations for different land-cover types

The soil moisture and evapotranspiration (ET) influence on ecosystem dynamics has been studied only in a limited way owing to the lack of large-scale measurements. The Normalized Difference Vegetation Index (NDVI) data retrieved using the Moderate Resolution Imaging Spectroradiometer (MODIS) was successfully used in this study to identify the ecological relationships that involve soil moisture and ET at 132 sites located on different continents around the world. Optimal relationships exist between NDVI and soil moisture within time lags of 10 days at forest and grassland sites, and 25 days at cropland and shrub land sites. The ecological correlations between NDVI and the hydrological variables are affected mainly by the land-cover type. The densely vegetated areas show shorter time lags for NDVI to ET owing to canopy evaporation and plant transpiration, which are almost simultaneous with NDVI

Assessment of drought conditions over Vietnam using standardized precipitation evapotranspiration index, MERRA-2 re-analysis, and dynamic land cover

Study region Vietnam. Study focus In recent years Vietnam has experienced historical drought events possibly affected by climate change, but the analysis is challenging due to lack of necessary observations for monitoring drought conditions. The goal of this study is to analyze the characteristics of droughts over a 30-year period, using three spatial-resolution MERRA-2 datasets in Vietnam. The Standardized Precipitation Evapotranspiration Index (SPEI) was used as an index for drought based on precipitation and temperature. We also estimated the impacts of drought on agriculture using annual land cover datasets. New hydrological insights for the regions Our results identified significant increasing trends in precipitation in Northern Vietnam and decreasing trends in Southern Vietnam. The increasing trends in temperature occurred mainly in Southern Vietnam. These trends in rainfall and temperature resulted in an increasing trend in drought frequency and severity in Southern Vietnam, especially in the South-Central Region and the Mekong Delta. The comparison between the observed drought records and modeled drought index demonstrated that the simulated drought conditions are better at higher spatial resolution. The area under drought in agricultural lands calculated using dynamic land-cover data sets resulted in a better agreement with observed records. Our findings reveal the feasibility of using a model-based drought index in data-sparse areas for long-term trend drought analysis, and for practical applications of advanced re-analysis products in water resource management

Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products

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) m3 m− 3; and the bias values were (− 0.0460, 0.0010, and 0.0418) m3 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 m3 m− 3). Overall, SMAP showed a dry bias (− 0.0460 m3 m− 3) and AMSR2 had a wet bias (0.0418 m3 m− 3); while ASCAT showed the least bias (0.0010 m3 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 0.40) ASCAT showed comparatively better 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.26027516National Research Foundation of Korea (NRF)National Research Foundation of Korea (NRF

Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure

There is a certain level of predictive uncertainty when hydrologic models are applied for operational purposes. Whether structural improvements address uncertainty has not well been evaluated due to the lack of observational data. This study investigated the utility of remotely sensed evapotranspiration (RS-ET) products to quantitatively represent improvements in model predictions owing to structural improvements. Two versions of the Soil and Water Assessment Tool (SWAT), representative of original and improved versions, were calibrated against streamflow and RS-ET. The latter version contains a new soil moisture module, referred to as RSWAT. We compared outputs from these two versions with the best performance metrics (Kling–Gupta Efficiency [KGE], Nash-Sutcliffe Efficiency [NSE] and Percent-bias [P-bias]). Comparisons were conducted at two spatial scales by partitioning the RS-ET into two scales, while streamflow comparisons were only conducted at one scale. At the watershed level, SWAT and RSWAT produced similar metrics for daily streamflow (NSE of 0.29 and 0.37, P-bias of 1.7 and 15.9, and KGE of 0.47 and 0.49, respectively) and ET (KGE of 0.48 and 0.52, respectively). At the subwatershed level, the KGE of RSWAT (0.53) for daily ET was greater than that of SWAT (0.47). These findings demonstrated that RS-ET has the potential to increase prediction accuracy from model structural improvements and highlighted the utility of remotely sensed data in hydrologic modeling