Abstract:A good knowledge of the quality of the satellite soil moisture products is of great importance for their application and improvement. This paper examines the performance of eight satellite-based soil moisture products, including the Soil Moisture Active Passive (SMAP) passive Level 3 (L3), the Soil Moisture and Ocean Salinity (SMOS) Centre Aval de Traitement des Données SMOS (CATDS) L3, the Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer 2 (AMSR2) L3, the Land Parameter Retrieval Model (LPRM) AMSR2 L3, the European Space Agency (ESA) Climate Change Initiative (CCI) L3, the Chinese Fengyun-3B (FY3B) L2 soil moisture products at a coarse resolution of~0.25 • , and the newly released SMAP enhanced passive L3 and JAXA AMSR2 L3 soil moisture products at a medium resolution of~0.1 • . The ground soil moisture used for validation were collected from two well-calibrated and dense networks, including the Little Washita Watershed (LWW) network in the United States and the REMEDHUS network in Spain, each with different land cover. The results show that the SMAP passive soil moisture product outperformed the other products in the LWW network region, with an unbiased root mean square (ubRMSE) of 0.027 m 3 m −3 , whereas the FY3B soil moisture performed the best in the REMEDHUS network region, with an ubRMSE of 0.025 m 3 m −3 . The JAXA product performed much better at 0.25 • than at 0.1 • , but at both resolutions it underestimated soil moisture most of the time (bias < −0.05 m 3 m −3 ). The SMAP-enhanced passive soil moisture product captured the temporal variation of ground measurements well, with a correlation coefficient larger than 0.8, and was generally superior to the JAXA product. The LPRM showed much larger amplitude and temporal variation than the ground soil moisture, with a wet bias larger than 0.09 m 3 m −3 . The underestimation of surface temperature may have contributed to the general dry bias found in the SMAP (−0.018 m 3 m −3 for LWW and 0.016 m 3 m −3 for REMEDHUS) and SMOS (−0.004 m 3 m −3 for LWW and −0.012 m 3 m −3 for REMEDHUS) soil moisture products. The ESA CCI product showed satisfactory performance with acceptable error metrics (ubRMSE < 0.045 m 3 m −3 ), revealing the effectiveness of merging active and passive soil moisture products. The good performance of SMAP and FY3B demonstrates the

Abstract:In this study, the Standardized Precipitation Evaporation Index (SPEI) was applied to characterize the drought conditions in Southwest China from 1982-2012. The SPEI was calculated by precipitation and temperature data for various accumulation periods. Based on the SPEI, the multi-scale patterns, the trend, and the spatio-temporal extent of drought were evaluated, respectively. The results explicitly showed a drying trend of Southwest China. The mean SPEI values at five time scales all decreased significantly. Some moderate and severe droughts were captured after 2005 and the droughts were even getting aggravated. By examining the spatio-temporal extent, the aggravating condition of drought was further revealed. To investigate the performance of SPEI, correlation analysis was conducted between SPEI and two remotely sensed drought indices: Soil Moisture Condition Index (SMCI) and Vegetation Condition Index (VCI). The comparison was also conducted with the Standardized Precipitation Index (SPI). The results showed that for both SMCI and VCI, the SPI and SPEI had approximate correlations with them. The SPEI could better monitor the soil moisture than the SPI in months with significant increase of temperature. The correlations between the VCI and SPI/SPEI were lower; nevertheless, the SPEI was slightly superior to the SPI.

Abstract:The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ • ) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson's correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m 3 /m 3 for the ascending data, and are 0.012, 0.026, and 0.055 m 3 /m 3 for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites.

This paper presents a microwave/optical synergistic methodology to retrieve soil moisture in an alpine prairie. The methodology adequately represents the scattering behavior of the vegetation-covered area by defining the scattering of the vegetation and the soil below. The Integral Equation Method (IEM) was employed to determine the backscattering of the underlying soil. The modified Water Cloud Model (WCM) was used to reduce the effect of vegetation. Vegetation coverage, which can be easily derived from optical data, was incorporated in this method to account for the vegetation gap information. Then, an inversion scheme of soil moisture was developed that made use of the dual polarizations (HH and VV) available from the quad polarization Radarsat-2 data. The method developed in this study was assessed by comparing the reproduction of the backscattering, which was calculated from an area with full vegetation cover to that with relatively sparse cover. The accuracy and sources of error in this soil moisture retrieval method were evaluated. The results showed a good correlation between the measured and estimated soil moisture (R 2 = 0.71, RMSE = 3.32 vol.%, p < 0.01). Therefore, this method has operational potential for estimating soil moisture under the vegetated area of an alpine prairie.

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The distribution of soil moisture is important for modeling hydrological and climatological processes to understand the Earth energy cycle and balance. The major difficulty for soil moisture retrieval in vegetated areas is how to separate the individual scattering contribution of soil moisture, vegetation, and surface roughness from the backscattered radar signal. In this paper, a semi-empirical method was proposed to retrieve soil moisture in the Ruoergai prairie using single temporal Radarsat-2 data. It was formulated by integrating the advanced integral equation model (AIEM), a semi-empirical ratio vegetation model, and optimum surface roughness parameters. The AIEM was run in the forward mode to simulate the backscattering coefficient of bare soil surface. The ratio vegetation model was applied for eliminating the vegetation effect from the observed backscattering coefficient. Meanwhile, four different vegetation parameters were used to characterize the change of vegetation: leaf area index, vegetation water content, normalized difference vegetation index, and enhanced vegetation index. A global search method was used to find out the optimum surface roughness parameters, which made the relationship between the observed and retrieved soil moisture reach up to the best. The collected in situ measurements and satellite data from the Ruoergai prairie were employed to validate the feasibility and effectiveness of the introduced method. From the analysis of experiment results, the optimum surface roughness parameters had a relative change when different vegetation parameters were used to parameterize the ratio vegetation model. In addition, the root-mean-square height had a significant impact on the accuracy of soil moisture retrieval compared with correlation length. The best retrieval result was obtained when EVI was used to remove the influence of vegetation, with a correlation coefficient of 0.84 and root-mean-square error of 4.05 vol · %. Therefore, compared with other three vegetation parameters, EVI was recommended to characterize the change of vegetation in this experiment. It was evident that optimum surface roughness parameters were validated to be a promising tool for soil moisture retrieval in prairie areas using Radarsat-2 data.Index Terms-Advanced integral equation model (AIEM), optimum surface roughness parameters, Radarsat-2, ratio vegetation model, soil moisture.

Active and passive microwave signatures respond differently to the land surface and provide complementary information on the characteristics of the observed scenes. The objective of this paper is to explore the synergy of active radar and passive radiometer observations at the same spatial scale to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands. The TVG model can simultaneously simulate the backscattering coefficient and emissivity with a set of input parameters. To calibrate this model, in situ soil moisture and temperature data collected from the Maqu area in the northeastern region of the Tibetan Plateau, interpolated leaf area index (LAI) data from the Moderate Resolution Imaging Spectroradiometer LAI eight-day products, and concurrent and coincident Soil Moisture Active Passive (SMAP) radar and radiometer observations are used. Because this model needs numerous input parameters to be driven, the extended Fourier amplitude sensitivity test is first applied to conduct global sensitivity analysis (GSA) to select the sensitive and insensitive parameters. Only the most sensitive parameters Manuscript

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