The spatial representativeness error of in situ soil moisture (SM) is recognized as a major source of uncertainty when validating satellite SM products with a spatial resolution of tens of kilometers. Site underrepresentation is primarily caused by environmental heterogeneity, but their relationship remains poorly understood. Here, we assessed the spatial representativeness of in situ SM from 322 strictly screened stations worldwide relative to coarse-resolution (~0.25°) satellite footprint based on the extended triple collocation (ETC) method. We then evaluated the influence of the heterogeneity of four environmental factors (soil texture, land cover types, elevation, and vegetation coverage) on site representativeness. Moreover, we calculated SM variability within the satellite footprint based on 1-km SM data to explore its relationship with environmental heterogeneity. Results indicate that about 63% of the sites have relatively good spatial representativeness (ETC-derived correlation coefficient$\ge 0.7$). Soil texture and land cover exhibit greater heterogeneity across the mid and high latitudes of the Northern Hemisphere. The larger heterogeneity in elevation and vegetation coverage is primarily found in regions with significant ridges and dense vegetation, respectively. Land cover is the major factor influencing the spatial representativeness of SM sites, and the increase in the heterogeneity of land cover enhances SM variability, which negatively impacts site representativeness. The in situ SM can be more representative when the proportion of the land cover type where the site is located is higher or when there are fewer land cover types within the satellite footprint. Moreover, it is found that the newly proposed metric of the similar area ratio of sites, as a measure of land cover heterogeneity, can effectively reflect SM variability. This metric can also serve as a supplementary criterion for selecting representative sites, particularly in situations where sites are sparse and the ETC method is inapplicable. These findings provide useful references for robust evaluation of satellite SM products based on in situ measurements (e.g., in situ SM upscaling and SM site deployment).

Fault scarps have been demonstrated to preserve valuable information about past earthquakes. The slope breaks in the scarp morphology may indicate the number of surface-rupturing events on a fault. In this study, the morphology of fault scarps was used to investigate the earthquake rupturing history of the Wulashan Piedmont Fault (Northern China) based on high-resolution LiDAR topography. Through detecting the slope breaks in the fault scarp morphology, at least five individual surface-breaking events were identified, which is in good agreement with previous paleoseismic trenching records. Based on the fault slip rate determined by previous studies, an average recurrence interval of 1.3~1.8 kyr was estimated for the paleoseismic events, which is very close to the elapsed time since the most recent earthquake, indicating a high potential seismic hazard on the Wulashan Piedmont Fault.

The uncertainty inherent in validating satellite-derived soil moisture (SM) products is significantly attributed to the spatial underrepresentation of in situ SM measurements. The main reason for this phenomenon is the varying environmental conditions (called as environmental heterogeneity) within the satellite footprint. To better understand this issue, we assessed the spatial representativeness of in situ SM from 383 strictly screened stations worldwide relative to the coarse-resolution (~0.25°) satellite footprint and analyzed the effects of four environmental factors (i.e., soil texture, land cover, elevation, and vegetation coverage) using the extended triple collocation (ETC) technique. Results show about 63% of the sites have satisfactory levels of spatial representativeness (ETC derived correlation coefficient ⩾0.7). Land cover is the foremost factor affecting the spatial representativeness of SM sites. The in situ SM can better represent the true variability of SM when the proportion of land cover types where the site is located is higher or there are fewer land cover types within the satellite pixels.

The spatial resolution of existing satellite soil moisture products is very coarse (~25 km), and thus there is usually significant spatial heterogeneity in the land surface covered by satellite footprints. However, the effects of spatial heterogeneity on satellite soil moisture products are largely under-studied previously. The study firstly evaluated seven satellite soil moisture products comprising SMAP (DCA, IB, and MTDCA), SMOS-IC, AMSR2 (LPRM and JAXA) and FY-3C at a global scale using the extended triple collocation (ETC) method. Then, the skills of these products under a wide range of surface heterogeneity including heterogeneity in vegetation coverage, terrain, land cover, and soil texture were ascertained. The results indicate: (1) SMAP-IB and SMAP DCA products generally outperform others, followed by SMOS-IC and SMAP MTDCA which also exhibit satisfactory performance; (2) heterogeneity in vegetation coverage, terrain, and land cover generally decreases the R value of satellite soil moisture products, while heterogeneity in soil texture has an insignificant effect on product skills; (3) L-band products demonstrate greater stability compared to C/X-band datasets across various surface heterogeneity.

Satellite soil moisture products have great potential for many applications, such as drought monitoring and landslide warning. However, these applications often require accurate and continuous soil moisture records, and the missing values in satellite soil moisture datasets often hamper the usefulness of these products for such applications. This study firstly proposed and compared three approaches, i.e., linear regression, linear rescaling, and random forest to fill the missing values in the SMAP soil moisture products in both temporal and spatial dimensions, based on the seamless ERA5 data from 2016 to 2019. Then, a total of twelve auxiliary data were incorporated into the training datasets of random forest to improve the accuracy of gap-filled SMAP data. Finally, the gap-filled SMAP data were compared with the original SMAP data and validated by in situ measurements from 1071 sites worldwide. The results indicate: 1) when using only the ERA5 datasets, the random forest performs better than linear regression and linear rescaling methods in the training phase, but its skill degrades noticeably in the validation phase; 2) by adding the auxiliary data, the performance of random forest improves significantly in the validation phase; 3) the gap-filled SMAP data maintain or even exceed the accuracy of the original SMAP soil moisture, demonstrating the effectiveness of the proposed gap-filling method.

The comprehensive and robust assessment of diverse global-scale satellite-based soil moisture products from various satellite data sources (e.g., different frequencies and incidence angles) and retrieval algorithms is essential for the refinements as well as applications of these products. To date, soil moisture retrieval algorithms and products are rapidly evolving and their updated iterations are ongoing. In support of the validation activities of recently developed/reprocessed satellite soil moisture products, the study firstly assessed eight commonly-employed satellite soil moisture datasets comprising SMAP (DCA, IB, and MTDCA), SMOS-IC, AMSR2 (LPRM and JAXA), FY-3C, and ESA CCI on a global scale using three different strategies, i.e., ERA5 reanalysis soil moisture dataset with similar spatial resolution to satellite products,in situmeasurements from densely-instrumented networks worldwide with mitigated spatial mismatch between ground site and satellite pixel, and the Extended Triple Collocation (ETC) method that can obtain error indicators relative to ground truth. The skills of these products under a broad range of vegetation density, land cover and climate types, and surface heterogeneity (heterogeneity in terrain, land cover, soil texture, and vegetation coverage) were also examined. The results indicate: (1) different soil moisture products show overall consistency in skill ranking under three different evaluation strategies, except for SMAP DCA, SMAP-IB, and SMAP MTDCA in terms ofRvalue; (2) ESA CCI, SMAP-IB, SMAP DCA products generally perform better than the others under three strategies, and SMOS-IC and SMAP MTDCA also show satisfactory performance concerning ubRMSD andRvalues; (3) vegetation density exerts visible influences on satellite soil moisture datasets. Specifically, the C/X-band (AMSR2 and FY-3C) and L-band (SMAP and SMOS) products display the optimal skills under sparse and moderate vegetation coverage respectively, and the impacts of vegetation density on C/X-band products are evidently stronger than those on L-band datasets. The errors of satellite soil moisture data also increase as the increase of heterogeneity in terrain, land cover, and vegetation coverage, while the effect of heterogeneity in soil texture on the skill of satellite soil moisture products is insignificant; (4) the skills of L-band products are more stable than those of C/X-band datasets under different ground conditions.

Soil moisture (SM) is influenced by changes in meteorological elements and vegetation, as well as by environmental heterogeneity. Due to the prevalence of extreme events in the past two decades, this complexity is growing in the 21st century. In this study, the global spatiotemporal trend of SM and its possible influencing factors were investigated by using the satellite-based ESA CCI SM from 2000-2021. The results reveal global SM generally declines at a rate of -1×10-4m3m-3yr-1, dominated by a drying trend in the southern hemisphere. From a global perspective, the driving force of precipitation and vegetation on SM fluctuation is stronger than that of temperature. Different environmental variables (e.g., land cover, soil texture, and terrain) have different regulatory effects on SM changes. In contrast to other types, there are general wetting trends of SM in croplands, savannas, forests, loam soils, and high elevations (> 1000 m). The response of SM to temperature and vegetation is greatly limited under barren and forests, respectively. The impact of temperature on SM is enhanced with increasing clay content or decreasing sand content. The high positive correlation between precipitation and SM is rarely influenced by environmental factors compared to temperature and vegetation.

Estimation of surface soil temperature (ST) using passive microwave remote sensing is still a great challenge, especially under diverse land surface conditions. The microwave radiation imager (MWRI) on board Fengyun (FY)-3D satellite is the latest Chinese FY-3 series multiband microwave sensor in orbit, providing a valuable opportunity for estimating surface ST on a global scale. In this study, the FY-3D brightness temperature from 10.7 to 36.5 GHz and the topsoil temperature simulations from ERA5-Land and GEOS-FP from 2019 to 2020 were used to build the Multi-Band Temperature Retrieval Model (MBTRM). The universal global optimization algorithm was employed to establish the MBTRM on a per-pixel basis globally. Ground measurements from a total of 1221 sites worldwide were adopted to fully examine the performance of the proposed MBTRM. Moreover, four widely used passive microwave-based ST models/products were compared. The results reveal the proposed MBTRM performs the best with an averaged root mean square difference (RMSD) of 3.03 K and 3.79 K at descending and ascending overpass respectively and a correlation coefficient larger than 0.9. The advantage of the newly developed MBTRM is that it is user friendly (with explicit formula) and can be applied globally with satisfactory accuracy, and provides a good supplement to optical satellite based temperature products.

Through constraining the surface slip distribution of displaced landforms along a fault, we can gain important insights into the earthquake rupture histories and recurrence characteristics, thus enabling us to estimate the potential time and magnitude of future earthquakes on the fault. In this study, we constrained the surface slip distribution of displaced landforms along the Sertengshan Piedmont Fault in Northern China based on 1-m-resolution airborne Light Detection and Ranging (LiDAR) data. A total of 600 vertical displacement measurements were acquired along about 185 km stretch of the fault. Through statistically analyzing the displacement observations, we conclude that at least five to seven large surface-rupturing earthquakes have occurred on the Sertengshan Piedmont Fault, displaying a characteristic recurrence pattern with a remarkably regular slip increment of ∼2 m.

The Fengyun (FY)-3D satellite launched in November 2017, provides the latest multi-frequency brightness temperature (TB) of Chinese FY-3 series satellites. In this study, the FY-3D TB at five frequencies from 10.7 to 89 GHz during 2019 to 2020 were intercalibrated against AMSR2 TB using two intercalibration methods, i.e., the commonly used global linear regression method and the global per-pixel linear regression method proposed in the study. The effects of different environmental variables (e.g., land cover and its heterogeneity, climate types, water body fraction, terrain and vegetation coverage) on the calibration accuracy were investigated. The results reveal the global per-pixel linear regression method performs much better than global linear regression method with an averaged root mean square difference (RMSD) of 2.93 K at ascending overpass and 2.34 K at descending overpass. The water body fraction has the greatest influence on calibration accuracy, followed by terrain, land cover heterogeneity, and vegetation coverage. The RMSD is relatively lower in savannas and barren lands as well as in arid, cold and tropical climatic zones than that in other land cover and climate types.

As the second generation of Chinese polar-orbiting meteorological satellite missions, the Fengyun (FY)-3D satellite provides the latest multi-frequency brightness temperature (TB) of FY-3 series satellites. The microwave radiation imager (MWRI) boarded on FY-3D has similar sensor configuration as Advanced Microwave Scanning Radiometer 2 (AMSR2), and thus the intercalibration of these two sensors can make their TB data more consistent and continuous to facilitate their joint applications. In this study, the FY-3D H-pol and V-pol TB at five frequencies from 10.7 to 89 GHz during 2019 to 2020 were calibrated against AMSR2 TB over land. Two categories of intercalibration methods were compared, including global intercalibration method, i.e., global linear regression, and per-pixel-based intercalibration methods, i.e., per-pixel linear regression joint global linear regression, per-pixel linear regression joint inverse distance interpolation, per-pixel linear regression joint nearest neighbor interpolation, and global per-pixel linear regression. Furthermore, the effects of diverse environmental variables (i.e., land cover and its heterogeneity, climate types, water body fraction, terrain and its complexity, soil texture, and vegetation coverage) on FY-3D calibration accuracy were fully investigated. The results indicate that all five approaches can reduce the bias between FY-3D and AMSR2 TB, and the root-mean-square difference (RMSD) also reduces accordingly. Among them, the global per-pixel linear regression method performs the best with the lowest averaged RMSD of 2.93 K (at ascending overpass) and 2.34 K (at descending overpass), followed by the per-pixel linear regression joint inverse distance interpolation. The global linear regression method performs the worst with the largest RMSD of 4.69 and 3.82 K at ascending and descending overpass, respectively. The RMSD is relatively larger in temperate and polar climate zones, as well as in grasslands and croplands than in other climate and land cover types. The calibration errors generally decrease as the altitude increases, while they increase with the increase in land cover heterogeneity. The water body fraction exerts the greatest impact on the calibration accuracy, and the RMSD reaches 3 K when the water body fraction is greater than 15%. Soil texture, terrain complexity, and vegetation coverage generally have little influence on the calibration accuracy. These findings can provide a good reference for the intercalibration of satellites with similar configuration to generate long-term climate data records.

The synergy of active and passive microwave measurements has attracted considerable attention in recent years since they offer complementary information on the characteristics of the observed target (e.g., soil moisture), which motivates the launch of NASA’s Soil Moisture Active Passive (SMAP) mission. An assumption of a near-linear relationship between active and passive measurements has been made in the SMAP active–passive baseline algorithm, which is essential to downscale coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter ($\sigma ^{0}$) but has not yet been fully tested under a wide range of ground conditions. Motivated by this, we first examined the validity of the linear assumption by using concurrent and coincident SMAP active and passive observations under diverse environmental factors (e.g., land cover, climate types, terrain and its complexity, soil texture, vegetation coverage, soil moisture, and its dynamics). We also adopted SMAP enhanced TB to evaluate the performance of the disaggregated TB at the same grid resolution of 9 km. The results reveal there is a generally good linear relationship between$\sigma ^{0}$(no matter in dB or in linear unit) and TB at a global scale. There is no significant difference in the correlation among the four polarization combinations ($\sigma ^{0}_{\text {hh}}$versus TBh,$\sigma ^{{0}}_{\text {hh}}$versus TBv,$\sigma ^{{0}}_{\text {vv}}$versus TBh, and$\sigma ^{{0}}_{\text {vv}}$versus TBv) with the$\sigma ^{{0}}_{\text {vv}}$and TBhcombination displaying an overall slightly higher correlation. The linear relationship between$\sigma ^{{0}}$and TB is significantly affected by environmental factors. Particularly in bare soils and densely vegetated areas (e.g., large forest fraction and vegetation coverage), and arid and polar climate zones, the linear correlation between active and passive measurements worsens, whereas it is favorable in moderate vegetation and soil moisture as well as large soil moisture dynamic conditions. Interestingly, the linear correlation generally decreases as sand content increases while increases with the increase of clay content. The absolute linear correlation coefficient is higher with larger soil moisture dynamics. When compared to SMAP enhanced TB, it shows the linear assumption may have more influence on the correlation (i.e., temporal evolution) of downscaled TB than its absolute accuracy. These findings can enhance the understanding of the geophysical relationship between radar and radiometer signatures, and thus benefit active–passive joint algorithms for future satellite missions.

The Tibetan Plateau, known as the “Third Pole” of the world, is extremely sensitive to global climate change. Reliable soil moisture information is essential for understanding the impact of the Tibetan Plateau on the Asian monsoon. The study assessed a total of four satellite soil moisture products, namely the SMAP-SCA (V7), ESA CCI (V0.52), AMSR2 LPRM (V001), and FY-3C (V1) over the Tibetan Plateau using three densely-instrumented soil moisture networks (i.e., Maqu, Naqu, and Pali). Moreover, the possible error sources of these products were thoroughly investigated over the Tibetan Plateau, which had not yet been fully explored in previous studies. The results reveal the ESA CCI (V0.52) and SMAP-SCA (V7) generally perform better in the three networks with higher correlation coefficient ($R$) and lower standard deviation of the difference (STDD) compared to other products. The bias of ESA CCI (V0.52) is demonstrated to be dependent on the GLDAS Noah soil moisture, but it correlates much better with soil moisture measurements and also displays lower STDD value than the GLDAS Noah. SMAP brightness temperatures demonstrate much higher sensitivity to soil moisture than the AMSR$2~C$-band and FY-3C$X$-band measurements regardless of vegetation, surface roughness, and land cover heterogeneity. The auxiliary surface temperature used in SMAP-SCA (V7) also performs better than that used in AMSR2 LPRM (V001) and FY-3C (V1) though it is slightly colder than the ground measurements. These factors contribute to the better performance of SMAP-SCA (V7) soil moisture product compared to other satellite datasets. The AMSR2 LPRM (V001) product produces evidently larger absolute values than ground observations, but it can track the temporal trend of soil moisture in sparsely vegetated areas (Naqu and Pali). The FY-3C (V1) product exhibits some abnormal saturation values in Maqu with the highest vegetation biomass, while it performs better in Naqu and Pali with relatively lower vegetation coverage. The surface temperature derived from AMSR2 LPRM and FY-3C shows large uncertainties over the Tibetan Plateau which may be caused by the limited data used to calibrate the empirical surface temperature models.

High-precision and high-resolution topographic data are the basis of active tectonics study. The rapid development of photogrammetry method provides an economical and effective means for obtaining such topographic data. Particularly in recent years, as the rapid development of computer vision theory and automatic feature-matching algorithm, a 3D reconstruction technique called "Structure from Motion" (SfM) was introduced into the photogrammetry method, greatly improving the flexibility and efficiency of the traditional photogrammetry method. In this study, we examined the applicability of SfM photogrammetry method in modeling the topography of fault zone by using images acquired with a low-cost digital camera mounted on an unmanned aerial vehicle (UAV) over the Altyn Tagh fault, which is located at the northern boundary of the Tibetan Plateau. The results show that the SfM photogrammetry method can obtain high-resolution topographic data of the fault zone, demonstrating its great potential in quantitative research of active tectonics.

This paper evaluated the consistency of four microwave remotely sensed sea ice concentration (SIC) products with respect to a reanalysis SIC dataset in polar regions during the period of 2015-2017. The remotely sensed SIC products include the Chinese Feng Yun-3B with enhanced NASA Team (NT2) sea ice algorithm (FY3B/NT2), the Chinese Feng Yun-3C with NT2 (FY3C/NT2), the DMSP SSMIS with Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) algorithm (SSMIS/ASI), and the GCOM-W AMSR2 with NASA Bootstrap (BT) algorithm (AMSR2/BT). The OISSTV2 (NOAA Optimum Interpolation 1/4 Degree Daily Temperature Analysis Version 2) dataset was adopted as a reference to compare and evaluate the performance of four satellite-based SIC products. The results show that remotely sensed SIC values are generally in good consistency with OISSTV2. Meanwhile, it is observed that different products have different bias in polar regions. Overall, the SSMIS/ASI product has better performance during the whole period, demonstrating that ASI algorithm may be more potential in SIC estimation. Our results also illustrate the spatial and temporal distribution characteristic of discrepancy between microwave remotely sensed SIC products and reanalysis dataset for the whole Arctic and Antarctic regions. The large difference for all the four SIC products mostly occurs in summer and marginal ice zone, indicating a great deal of uncertainty of satellite SIC products in this period and areas. The results will be useful to find possible errors in the satellite SIC products for further algorithm improvement.

Constraining along-fault displacement distribution provides an insight into the rupture process and strain release pattern on the fault, thus enabling us to make more accurate estimates of its potential future behavior. Recently, with the increasing availability of high-resolution stereo satellite images and the DEMs derived from them, both the accuracy and spatial density of the offset measurements can be significantly improved compared to traditional field surveys, which will greatly enhance our understanding of fault behavior. The South Heli Shan fault is an important component of the latest active faults on the northeast margin of the Tibetan Plateau. In this study, we built a 2 m resolution DEM of the South Heli Shan fault based on high-resolution GeoEye-1 stereo satellite images, and then acquired a total of 302 vertical displacement measurements along the fault, increasing the measurement density by nearly a factor of 5 compared to previous field surveys. Based on the displacement clusters on different segments, we conclude that at least four large earthquakes have ever occurred on the South Heli Shan fault, resulting in the variation of cumulative displacements on different geomorphic units. All of these events do not recur as the characteristic earthquake model along the whole fault, but they may follow a characteristic slip pattern on each individual segment.

This paper examines the quality of eight satellite soil moisture products at two typical spatial resolutions, including an intercomparison of SMAP passive, SMOS, JAXA AMSR2, LPRM AMSR2, ESA CCI and the Chinese FY3B soil moisture products at a coarse resolution of ~0.25°, and the newly released SMAP enhanced passive and JAXA AMSR2 soil moisture products at a medium resolution of ~0.1°. Insitu measurements from two representative dense networks, i.e., the Little Washita Watershed (LWW) in the United States and the REMEDHUS networks in Spain are used to compare and validate the eight soil moisture products. The results show that the SMAP passive and FY3B products outperform the other products with the lowest unbiased root mean square (ubRMSE) values of 0.027 m3m-3and 0.025 m3m-3in the LWW and REMEDHUS network regions respectively. SMOS slightly underestimates soil moisture with a dry bias, but it correlates well with insitu data with an average correlation value of 0.77. The JAXA product performs much better at 0.25° than at 0.1°, but both of them underestimate soil moisture (bias>-0.05 m3m-3) at most time. The SMAP enhanced passive soil moisture well captures the temporal variation of ground measurements with a correlation coefficient larger than 0.8, and is generally superior to the JAXA product. The LPRM shows much larger amplitude and temporal variation than the ground soil moisture with a wet bias larger than 0.09 m3m-3. The ESA CCI product shows satisfactory performance with acceptable error metrics (ubRMSE3m-3), revealing the effectiveness of merging active and passive soil moisture products. The good performance of SMAP and FY3B demonstrates the potential in integrating them into the existing long-term ESA CCI product to form a more complete product.

The quantitative study of active faults is highly dependent on high-precision and high-resolution topographic data. Though Light Detection and Ranging (LiDAR) technology can provide such data, its high cost greatly limits its use in many geoscience applications. Recently, the Structure from Motion (SfM) photogrammetry shows a great potential to provide topographic information with high precision, but at significantly lower costs than the laser scanning survey. In this study, the applicability of SfM photogrammetry method in modeling the topography of fault zone was investigated by using images acquired with a low-cost digital camera mounted on an UAV. The resolution and accuracy of the SfM-derived topographic data was evaluated in detail using existing airborne LiDAR data as a benchmark. The results show that the SfM photogrammetry method can produce a point cloud with the density seventy times higher than the airborne LiDAR. Furthermore, considering the errors in LiDAR data itself, and the precision of the SfM-derived point cloud is comparable to that of the LiDAR point cloud, demonstrating that the SfM photogrammetry method is an inexpensive and effective alternative to airborne LiDAR for the topography modeling of fault zone.

Theoretical modeling plays a significant role as forward and inverse problem in active and passive microwave remote sensing. Understanding the validity and limitations of the models is essential for model refinements and, perhaps more importantly, model applications. Motivated by these, this paper presents a comprehensive analysis of the scattering, both backscattering and bistatic scattering, and emission of rough soil surface predicted by the advanced integral equation model (AIEM), a well-established theoretical model. Numerically simulated data, covering a wide range of surface parameters, and in situ measurement data set of well-characterized bare soil surfaces were used to evaluate the performance of AIEM in predicting the scattering coefficient and microwave emissivity over a wide range of geometric parameters and ground surface conditions. The results show that the AIEM predictions are generally in good consistency with both numerical simulations and experiment measurements in terms of angular, frequency, and polarization dependences, except for some deviations in a few cases (e.g., at large incident angles and dry soil conditions). Extensive comparison confirms the effectiveness and practicability of AIEM for both scattering and emission of rough soil surface. Possible explanations for the discrepancy between the model prediction and data are given, together with suggestions for model usage and refinements.

Soil moisture is a key state variable which plays a significant role in many hydrological processes. The Soil Moisture Active Passive (SMAP) mission was launched on 31 January 2015 which can provide global information of soil moisture. Among the released SMAP data sets, the Level 4 Surface and Root Zone Soil Moisture Analysis Product (L4_SM) can not only provide information on surface soil moisture (top 5 cm of the soil column), but also provide estimates of root zone soil moisture (top 1 m of the soil column) which is very important for several key applications targeted by SMAP. However, since this product has been released only for a short time, its accuracy and reliability has not been validated so far. In this study, we evaluated the L4_SM soil moisture analysis product against in-situ soil moisture measurements collected from the Little Washita Watershed network located in southwest Oklahoma in the Great Plains region of the United States. The results show that both the surface and root zone soil moisture estimates in the L4_SM product are in good agreement with the in-situ measurements, and the RMSE is 0.027 m3/m3 and 0.032 m3/m3 for the surface and root zone soil moisture respectively which both have exceeded the RMSE requirement of 0.04 m3/m3 for this product.

The SMAP (soil moisture active passive) which is one of the satellites that specifically designed for soil moisture monitoring, was launched on 31 January 2015. Recently, the SMAP radiometer soil moisture product has been released to the public. It is very urgent to evaluate the reliability of this product before it can be widely used in hydrometeorological studies. In the study, we carried out an initial evaluation of SMAP radiometer soil moisture product against in-situ measurements from three networks. The three networks cover different land surface conditions, including two dense networks established in United States and Finland, and one sparse network set up in Romania. The results show that the SMAP soil moisture product agrees very well with the in-situ measurements although it sometimes exhibits dry or wet bias at different network regions. The overall ubRMSE of SMAP product is 0.036 m3m-3, well within the mission requirement of 0.04 m3m-3. Considering the algorithms are still under refinement, it can be reasonably expected that applications such as climate modeling and flood forecasting will benefit from the SMAP passive soil moisture product.

Remote sensing of soil moisture in a bistatic mode has attracted increasing attention in recent years. This paper investigates the bistatic radar response of soil moisture and surface roughness at L-band by using the advanced integral equation model (AIEM). To better explore the potential of bistatic scattering for soil moisture sensing, both polarized and angular scattering coefficients, and their combinations, were evaluated using a defined sensitivity index. Results show that sensitivity is enhanced in a bistatic mode compared to the monostatic case. Using a combination of dual polarized and angular data suppresses an undesired impact of the surface correlation function. Among the combinations, the dual angular observation reduces the influence of surface roughness, preserves a good sensitivity to soil moisture, and thus seems to be a good candidate for sensing soil moisture in bistatic configuration.

This letter investigates the bistatic radar response of soil moisture and surface roughness of bare soil surfaces at L-band using the advanced integral equation model (AIEM). It focuses on the use of bistatic geometries away from the specular region. To better explore the potential of bistatic scattering for soil moisture sensing, both polarized and angular scattering coefficients, and their combinations, are evaluated using a defined sensitivity index. The results show that sensitivity is enhanced in a bistatic mode compared with the monostatic case. Using a combination of dual polarized and angular data suppresses an undesired impact of the surface correlation function. Moreover, the forward region is preferred to soil moisture sensing regardless of the surface correlation function. Among the combinations investigated, the dual angular observation reduces the influence of surface roughness, preserves a good sensitivity to soil moisture response, and thus seems to be a good candidate for soil moisture sensing in bistatic configuration.

The Soil Moisture Active Passive (SMAP) mission, which is the newest L-band satellite that is specifically designed for soil moisture monitoring, was launched on January 31, 2015. A beta quality version of the SMAP radiometer soil moisture product was recently released to the public. It is crucial to evaluate the reliability of this product before it can be routinely used in hydrometeorological studies at a global scale. In this paper, we carried out a preliminary evaluation of the SMAP radiometer soil moisture product against in situ measurements collected from three networks that cover different climatic and land surface conditions, including two dense networks established in the U.S. and Finland, and one sparse network set up in Romania. Results show that the SMAP soil moisture product is in good agreement with the in situ measurements, although it exhibits dry or wet bias at different network regions. It well reproduces the temporal evolution and anomalies of the observed soil moisture with a favorable correlation greater than 0.7. The overall ubRMSE (unbiased root mean square error) of SMAP product is 0.036 m3· m-3, well within the mission requirement of 0.04 m3· m-3. The error sources of SMAP soil moisture product may be associated with the parameterization of vegetation and surface roughness but still needs to be tested and confirmed in more extent. Considering that the algorithms are still under refinement, it can be reasonably expected that hydrometeorological applications will benefit from the SMAP radiometer soil moisture product.

Soil moisture is a key state variable of land surface which plays a significant role in many hydrological processes. The Global Land Data Assimilation System (GLDAS) can produce global, high-resolution and continuous soil moisture data sets which have been used in many applications. However, before using the soil moisture products, it is crucial to validate their accuracy and reliability. In this study, we evaluate the simulated soil moisture from four land surface models (LSM) in GLDAS against in-situ soil moisture measurements collected from the Maqu network located on the Tibetan Plateau at different soil depths. The results show that all the four LSMs are able to capture the temporal variation of observed soil moisture well in the Maqu network region. However, four LSMs all display biases when compared with the in-situ measurements. The biases are mainly caused by the high soil organic carbon contents in the Tibetan Plateau, and may also come from uncertainties in model structure, model parameters, forcing data, and measurement errors, indicating that great efforts are still needed to further improve the simulation skill of LSMs on the Tibetan Plateau.

Validation of the remotely sensed soil moisture products is very important for data application and the refinement of the retrieval algorithms. In the study, we evaluated three newest soil moisture products that are the essential climate variable (ECV) soil moisture products (active-only, passive-only, and merged active-passive) which are the first multi-decadal satellite-based soil moisture data sets released by the European Space Agency recently. In-situ measurements used for validation are from three networks established in the Tibetan Plateau. The results show that all the ECV products can capture the soil moisture dynamics well. The active product performs better than the passive product when adding the Advanced Scatterometer (ASCAT) data set and is less sensitive to vegetation cover, but both of them overestimate the ground measurements and exhibit higher variation than in-situ data. Overall, the combined product outperforms other two products. It can preserve the relative dynamics of the active and passive products very well and the number of observations is also improved, indicating a positive effect by merging both active and passive data sets in the Tibetan Plateau.

A method for soil moisture and surface temperature estimation in the Tibetan Plateau (TP) using spaceborne radiometer observations was presented. Based on the physical basis that the 36.5-GHz (Ka-band) vertical brightness temperature is highly sensitive to the topsoil temperature, a new surface temperature model was developed using all ground measurements available from three networks named CAMP/Tibet, Maqu, and Naqu, established in the TP, which can significantly improve the accuracy of surface temperature derived from the land parameter retrieval model (LPRM). Then, the new surface temperature model, which was calibrated with in situ data, was integrated into the soil moisture retrieval algorithm proposed in this letter using Advanced Microwave Scanning Radiometer (AMSR-E) observations. The algorithm combines the vegetation optical depth and roughness into an integrated factor to avoid making unreliable assumptions and using auxiliary data to get these two parameters. Finally, the algorithm was validated by ground measurements from the dense Naqu network and was compared with NASA AMSR-E and Soil Moisture and Ocean Salinity (SMOS) official algorithms. The results show that the proposed algorithm can provide much more accurate soil moisture retrievals than the other two satellite algorithms in the Naqu network region. The algorithm can be applied to the areas with spare vegetation but may not be very suitable for densely vegetated surfaces.

Soil moisture is one of the key environmental variables in the Earth science. Data assimilation (DA) provides a way to effectively combine model simulations and observations, thus can yield superior soil moisture estimations. Among various DA methods, the particle filter (PF) is free from the constraints of linear models and Gaussian error distributions, thus receiving increasing attention in DA. However, the particle degeneracy still remains a major problem in practical application of PF. In this paper, an improved PF is proposed based on ensemble Kalman filter (EnKF) and the Markov Chain Monte Carlo (MCMC) method. The improved PF is tested by assimilating brightness temperatures from the Advanced Microwave Scanning Radiometer (AMSR-E) into the variance infiltration capacity (VIC) model to estimate soil moisture in the NaQu network region at the Tibetan Plateau. The experiment results show that the improved PF can provide more accurate soil moisture estimations than the EnKF and standard PF, thus demonstrating the effectiveness of the improved PF.

A new model for land surface temperature (LST) estimation in the Tibetan Plateau using passive microwave observations was presented. The new LST retrieval model was developed based on the strong linear relationship between the 36.5 GHz vertical polarized brightness temperature observations from Advanced Microwave Scanning Radiometer (AMSR-E) and the topsoil measurements from three networks named CAMP/Tibet, Maqu, and Naqu, established in the Tibetan Plateau. These networks were located in regions of different climatic conditions and vegetation cover. Then the new LST retrieval model was validated by ground measurements from the three networks and was also compared with the well-know land parameter retrieval model (LPRM). The results show that the new model can significantly improve the estimation accuracy of LST compared with LPRM in the Tibetan Plateau, and better results are achieved at the AMSR-E descending pass. The LPRM underestimates the LST at AMSR-E descending pass while it overestimates the LST at AMSR-E ascending pass in the Tibetan Plateau.

A physically-based algorithm for surface soil moisture retrieval in the Tibetan Plateau using passive microwave remote sensing was presented. The algorithm is based on a radiative transfer model and the assumption that the vegetation optical depth is polarization independent. It combines the effects of vegetation and roughness as a single parameter and uses the microwave polarization difference index (MPDI) to eliminate the effects of surface temperature and obtain soil moisture through a nonlinear iterative procedure. The advantage of this algorithm is that it needs only one frequency brightness temperature observations, and requires no field observations of roughness, soil moisture or vegetation data sets during the whole retrieval process. Finally, the algorithm was tested with the 6.9 GHz dual-polarized brightness temperature data from the Advanced Microwave Scanning Radiometer (AMSR-E) and compared with NASA official algorithm using in situ soil moisture from 20 stations in the Tibetan Plateau. The results show that the soil moisture retrieved by the algorithm is more consistent with ground measurements than the NASA soil moisture products.