The northward growth and expansion of the Shanxi rift system (SRS) are key to understanding the geodynamic evolution of the northeastern Ordos block. Previous studies have suggested that Shanxi rift activity weakens north of the Daihai-Huangqihai Basin under the influence of the EW-strike Zhangjiakou-Bohai tectonic zone. In this study, remote sensing and field investigations reveal an similar to 100 km long prominent linear structure north of the Jining Basin in the northeastern Ordos block. Three high-density electrical resistivity tomography (ERT) profiles across this structure identify this region as the North Jining Basin Fault, NE-striking, SW-dipping normal fault that structurally controls the northern margin of the basin, exhibiting typical fault depression characteristics and latest activity in the Holocene. Geological evidence confirms that the fault offsets mid-Holocene sediments with a vertical displacement of similar to 2.7 m. Combined with the spatiotemporal evolution of the Shanxi rift system, these findings suggest that the northeastern boundary of the rift remains active and continues to propagate northward, with the North Jining Basin Fault representing northward expansion of the extensional regime of the Shanxi rift.

Soil moisture (SM) with high precision and spatiotemporal resolution is crucial for crop yield estimation and water resource management, yet the spatial resolution of widely used passive microwave-based SM products remains low (tens of kilometers), making them inadequate for regional-scale applications. Spatial downscaling technique provides a viable solution to enhance the spatial resolution of passive microwave SM products. Despite extensive efforts made so far, surface heterogeneity which is an essential factor that causes differences in SM across coarse and fine scales by affecting processes such as water infiltration, evaporation, and storage has often been overlooked in previous algorithms, limiting the effectiveness of SM downscaling in heterogeneous regions. To address this knowledge gap, this study proposed a new SM downscaling method, termed heterogeneity-aware downscaling algorithm (HADA), which integrates surface heterogeneity including heterogeneity in land cover (LC), soil texture (ST), terrain, and vegetation coverage using the data-driven machine learning [i.e., random forest (RF)] approach. Moreover, a weighted scheme based on the importance ranking of heterogeneity parameters was developed to perform a more physically reasonable spatial correction of downscaling residuals. The proposed HADA was adopted to downscale the SM products generated by the newly developed microwave SM index (SMI) using SM active passive (SMAP) observations from 0.25 degrees to 0.05 degrees. Finally, the downscaling results were assessed using ground SM observations from 1260 sites across various regions worldwide and compared with existing methods and datasets. The results indicate the global distribution of the downscaled SM aligns well with that of the global aridity index (GAI), indicating a reasonable spatial behavior. Incorporating surface heterogeneity notably improves the fitting ability and estimation accuracy of the downscaling model. The downscaled products maintain accuracy comparable to the original data when validated by in situ SM but exhibits enhanced spatial details. Compared with the traditional DisPATCH approach, SMAP, ERA5-Land, and SiTHv2 SM datasets, HADA is superior in terms of both absolute accuracy and the capability to capture SM dynamics. This study not only provides an effective and feasible method to downscale passive microwave-based SM data but also introduces a potential approach to mitigate uncertainties caused by surface heterogeneity when downscaling other satellite derived products, thereby offering high-quality data support for diverse applications.

The Bogda Mountains, located in the middle-eastern section of the northern Tian Shan, are the forefront of its growth and expansion toward the Junggar Basin. Since the late Cenozoic (similar to 30 Ma), intense activity along piedmont faults has driven the rapid uplift of the Bogda Mountains and shaped the fluvial landscape. In this study, we used the bedrock channel stream-power erosion model and topographic analysis tools to extract 61 watersheds within the Bogda Mountains. Geomorphological parameters including the hypsometric integral (HI) and normalized steepness index (k(sn)) were also calculated. Results indicate that the landscape of the Bogda Mountains is primarily controlled by three active faults. The Fukang fault is currently the most active, whereas the North Bogda fault has gradually weakened. The South Bogda fault may have experienced a period of tectonic reactivation. The analysis of chi and Gilbert metrics suggest respectively different results of drainage divide migration, indicating a tectonically controlled pattern of non-uniform uplift in the Bogda Mountains and the differential activity of the boundary faults. The drainage divide currently maintains a state of dynamic equilibrium. In this study, knickpoint response times were calculated through reconstructing paleo-channel profiles, concluding that the Bogda Mountains have undergone two significant tectonic uplift events at approximately 25-20 Ma and 5 Ma.

Satellite-derived soil moisture (SM) products frequently encounter extensive data gaps that significantly limit their practical utility, necessitating the development of robust gap-filling techniques to generate SM datasets with enhanced accuracy and continuous spatiotemporal coverage. Existing studies have typically relied on single machine learning or interpolation methods to fill SM gaps at regional scales. Machine learning approaches excel at filling missing values in large regions but tend to smooth out important local SM features, while the interpolation methods perform well in areas with low levels of missing data, but exhibit significant uncertainty in regions with large amounts of continuously missing data. These two kinds of approaches show potential complementarity and could together contribute to a more robust gap-filling method, which however have rarely been investigated. To fill this research gap, we established a novel SM gap-filling method by synergizing the advantages of machine learning for large-scale gap filling and the excellent gap-filling performance of interpolation in localized areas using the Stacking method at a global scale. The proposed approach integrates four base models including three machine learning techniques namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Feed-forward Neural Network (FNN), and one interpolation method known as Natural Neighbor Interpolation (NNI), and employs the Least Absolute Shrinkage and Selection Operator (LASSO) as the meta model. We compared the Stacking method and individual approaches in filling ESA CCI missing SM data, and validated the gap-filled SM using extensive ground SM from 1086 sites worldwide. The results indicate: (1) RF performs the best among the six selected machine learning methods, and its overall accuracy at a global scale is higher than that of interpolation methods. The feature importance analysis by SHapley Additive exPlanations (SHAP) indicates ERA5 SM, NDVI, and Global Aridity Index have high importance in the RF gap-filling model; (2) NNI is the best performing approach among the four selected interpolation methods, and it demonstrates better performance than machine learning methods in localized areas where the original SM data is relatively abundant; (3) Stacking is an effective method for SM gap filling on a global scale, with an averaged ubRMSE of 0.017 m3/ m3, RMSE of 0.022 m3/m3, Bias of 0.006 m3/m3, and R of 0.87 against the original ESA CCI SM, and it reduces the RMSE by 0.009 m3/m3, ubRMSE by 0.006 m3/m3, and improves R by 0.15 relative to the individual bestperforming RF method; (4) The gap-filled SM shows an improved skill than the original ESA CCI SM against global distributed ground SM, with Stacking displaying the lowest ubRMSE of 0.057 m3/m3 and the highest R of 0.63. The proposed Stacking method opens new avenues to fill the gaps in various satellite SM datasets.

To better constrain the paleoseismicity and assess the seismic hazard, we investigated the eastern segment of the Serteng Shan frontal fault along the northern margin of the Ordos Block. The Ordos Block in northern China has a stable interior but is surrounded by seismically active faults. Several historical earthquakes with magnitude >= M7 have ruptured along the northern boundary of the Ordos Block. Using excavated trenches, the eastern segment of the Serteng Shan frontal fault along the northern margin of the Ordos Block was investigated. Seven events were identified based on distinct geological markers, such as colluvial wedges. To constrain the timing of these events, 28 samples were collected and dated using the optically stimulated luminescence (OSL) method. The dating indicates show that all seven events occurred after similar to 90 ka, with the five most recent events occurring after 50 ka. Based on OxCal modeling results, the most recent event is inferred to have occurred at 7.0 +/- 1.1 ka. The penultimate and preceding events occurred at approximately 25.0 +/- 2.2 ka, 35.6 +/- 1.5 ka, 41.3 +/- 1.9 ka, and 46.2 +/- 2.4 ka, respectively. The minimum recurrence intervals are approximately 5 ka, or multiples thereof, resulting in longer intervals of up to similar to 20 ka. Combining the displacement of the T6 terrace and their corresponding ages, a uniform vertical slip rate of 0.15 +/- 0.02 mm/yr over the last 90 ka is estimated. The slip rates and recurrence intervals indicate that the eastern segment of the Serteng Shan fault experiences a low rate of surface-rupturing earthquakes. This behavior could be explained by the effect of unloading of a mega-paleolake in the Hetao Basin at similar to 50 ka. These results provide new constraints on long-term slip behavior and inform seismic hazard assessment.

The practicality of satellite soil moisture (SM) products is often limited by their missing values, and thus it is critical to develop gap-filling methods to obtain SM datasets with high precision and spatiotemporal coverage. Previous studies typically only employed single machine learning or interpolation method at regional scales for gap-filling purpose, and have not effectively utilized the strengths of different methods to fill the missing SM values on a global scale. To close this research gap, we established a new approach to fill the ESA CCI missing SM values globally through the Stacking method. Using LASSO as a meta model, it integrates five base models including four machine learning methods and one interpolation method. Specifically, the cubic interpolation (CI) was creatively integrated into the base model which enables Stacking to leverage the excellent performance of CI in localized areas, to improve the filling accuracy of machine learning technique. We compared the Stacking method and individual approaches in filling ESA CCI missing data, and also validated the gap-filled SM using ground SM worldwide. The results indicate: (1) the CI performs better than machine learning methods in localized areas where the original ESA CCI missing condition is slight, but it is not as effective as machine learning in global gap filling; (2) stacking reduces the RMSE by 0.009 m3/m3, ubRMSE by 0.006 m3/m3, and improves R by 0.16 relative to the individual best-performing random forest method, and thus is an effective approach to fill in missing SM values on a global scale.

With the rapid development of remote sensing technology, it is getting easier to obtain high-resolution topographic data, which is of great significance for quantitative investigations of active tectonics, particularly in inaccessible regions. The Tongkuang Shan Fault is a thrust fault located within the Yanqi Basin in the southeastern Tian Shan Mountain, and can be divided into the west and east segment based on the fault geometry. Although obvious fault scarps have developed along the fault, its activity has rarely been investigated since most part of the fault is located in a closed military area. In this study, the Worldview satellite stereoscopic images were utilized to create a 0.5 m resolution Digital Elevation Model (DEM) and orthoimage of the Tongkuang Shan Fault. Based on the high-resolution topographic data, we interpreted the geometric structure of the fault, classified the different geomorphic units, and measured the vertical displacements along the fault. A total of 296 vertical displacements were measured on different geomorphic surfaces along the fault trace. The results show six distinct clusters in the distribution of vertical displacements along the fault, with peak values at 1.4 f 0.4/1.5 f 0.5 m, 3.0 f 0.7/3.4 f 0.6 m, 5.7 f 0.9/5.5 f 0.7 m, 8.9 f 1.2/8.2 f 1.3 m, 15.4 f 1.0/16.2 f 0.9 m, and 28.3 f 1.8/26.5 f 1.5 m on the west and east segment, respectively, which are in good agreement with the vertical displacements acquired from different geomorphic units, indicating a multiple phase of activity on the fault. The fault may have undergone at least six strong paleoseismic events, and the smallest displacement cluster (1.4 f 0.4/1.5 f 0.5 m) is considered to be the coseismic displacement of the most recent event, yielding an earthquake magnitude of approximately Mw 7.0-7.2. Moreover, the coefficient of variation of the first four peak displacements are 0.39 and 0.24 for the two segments, indicating a high repeatability of paleoseismic displacements on the fault, potentially conforming to a characteristic earthquake model.

The utility of satellite soil moisture products is often limited by their missing values, and thus it is crucial to develop gap-filling methods to obtain soil moisture datasets with high-precision and spatiotemporal coverage. Previous studies often used a single gap-filling method in specific regions without analysis of the factors affecting the gap-filling accuracy. To narrow this research gap, this study first compared the correlation of SMAP soil moisture products with five spatially seamless model-based soil moisture datasets globally. Then based on the optimal ERA5 data from 2016 to 2019, the performance of four machine learning methods in filling the SMAP missing values was compared. The best-performing random forest (RF) method was compared with other five traditional bias-corrected methods. Subsequently, twelve auxiliary data were incorporated into the RF to improve the accuracy of gap-filled SMAP data, which were validated by ground measurements from 1071 sites worldwide. Finally, the environmental factors affecting the filling accuracy of SMAP data were analyzed on a global scale. The results indicate: 1) RF generally performs the best among the four machine learning approaches. When only using the ERA5 dataset for the model input, RF achieves higher accuracy compared to the other five bias-corrected methods during the training phase, but its skill degrades noticeably in the validation phase. The performance of RF improves significantly after adding auxiliary data; 2) against globally distributed in situ data, the gap-filled products show improved skill over the original SMAP data, with smaller ubRMSE of 0.049 m3m-3 (vs. 0.060 m3m- 3), demonstrating the RF method with auxiliary data can effectively fill the missing values of SMAP data; 3) the gap-filling accuracy is mainly affected by vegetation cover, soil moisture conditions, and land cover heterogeneity. Specifically, the filling accuracy is lower in denser vegetation coverage, wetter soil, and larger land cover heterogeneity.

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 (similar to 0.25 degrees) 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 >= 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).

As direct geomorphic evidence and records of earthquakes on the surface, coseismic surface ruptures have long been a key focus in earthquake research. However, compared with strike-slip and normal faults, studies on reverse-fault surface ruptures remain relatively scarce. In this study, surface rupture characteristics of the most recent earthquake on the Kumysh thrust fault in eastern Tianshan were investigated using high-resolution topographic data, including 0.5 m- and 5 cm-resolution Digital Elevation Models (DEMs) generated from the WorldView-2 satellite stereo image pairs and Unmanned Aerial Vehicle (UAV) images, respectively. We carefully mapped the spatial geometry of the surface rupture and measured 120 vertical displacements along the rupture strike. Using the moving-window method and statistical analysis, both moving-mean and moving-maximum coseismic displacement curves were obtained for the entire rupture zone. Results show that the most recent rupture on the Kumysh Fault extends ~25 km with an overall NWW strike, exhibits complex spatial geometry, and can be subdivided into five secondary segments, which are discontinuously distributed in arcuate shapes across both piedmont alluvial fans and mountain fronts. Reverse fault scarps dominate the rupture pattern. The along-strike coseismic displacements generally form three asymmetric triangles, with an average displacement of 0.9–1.1 m and a maximum displacement of 2.8–3.2 m, yielding an estimated earthquake magnitude of Mw 6.6–6.7. This study not only highlights the strong potential of high-resolution remote sensing data for investigating surface earthquake ruptures, but also provides an additional example to the relatively underexplored reverse-fault surface ruptures.

Soil moisture plays a crucial role in terrestrial water, energy, and carbon cycles, and its spatiotemporal variations are key factors affecting and reflecting climate change. Passive microwave remote sensing is the most mature technique for monitoring large-scale soil moisture. However, its spatial resolution is often coarse (tens of kilometers) which cannot meet the application needs at regional and local scales. In light of this issue, this study utilized the newly developed microwave-based soil moisture index (SMI) to retrieve soil moisture at a global scale using the SMAP data (similar to 0.25 degrees). Then a novel approach was proposed to downscale SMI-derived soil moisture at a finer resolution (0.05 degrees) by incorporating surface heterogeneity including the heterogeneity in land cover, vegetation coverage, soil texture, and terrain along with the machine learning based random forest method. Both the original and downscaled SMI-derived soil moisture were validated by extensive in situ data worldwide. Results show SMI outperforms other satellite products with higher correlation coefficient (>0.7) and lower ubRMSE (< 0.05 m(3)m(-3)) across both dense and sparse soil moisture networks. The skill of downscaled soil moisture can be improved by adding information of surface heterogeneity. The downscaled soil moisture data shows comparable accuracy with the original SMI but exhibits more spatial details and effectively filled in strip-like missing areas of original data.

Strong earthquake activity along fault zones can lead to the displacement of geomorphic units such as gullies and terraces while preserving earthquake event data through changes in sedimentary records near faults. The quantitative analysis of these characteristics facilitates the reconstruction of significant earthquake activity history along the fault zone. Recent advancements in acquisition technology for high-precision and high-resolution topographic data have enabled more precise identification of displacements caused by fault activity, allowing for a quantitative assessment of the characteristics of strong earthquakes on faults. The 1920 Haiyuan earthquake, which occurred on the Haiyuan fault in the northeastern Tibetan Plateau, resulted in a surface rupture zone extending nearly 240 km. Although clear traces of surface rupture have been well preserved along the fault, debate regarding the maximum displacement is ongoing. In this study, we focused on two typical offset geomorphic sites along the middle segment of the Haiyuan fault that were previously identified as having experienced the maximum displacement during the Haiyuan earthquake. High-precision geomorphologic images of the two sites were obtained through unmanned aerial vehicle (UAV) surveys, which were combined with light detection and ranging (LiDAR) data along the fault zone. Our findings revealed that the maximum horizontal displacement of the Haiyuan earthquake at the Shikaguan site was approximately 5 m, whereas, at the Tangjiapo site, it was approximately 6 m. A cumulative offset probability distribution (COPD) analysis of high-density fault displacement measurements along the ruptures indicated that the smallest offset clusters on either side of the Ganyanchi Basin were 4.5 and 5.1 m long. This analysis further indicated that the average horizontal displacements of the Haiyuan earthquake were approximately 4–6 m. Further examination of multiple gullies and geomorphic unit displacements at the Shikatougou site, along with a detailed COPD analysis of dense displacement measurements within a specified range on both sides, demonstrated that the cumulative displacement within 30 m of this section of the Haiyuan fault exhibited at least five distinct displacement clusters. These dates may represent the results of five strong earthquake events in this fault segment over the past 10,000–13,000 years. The estimated magnitude, derived from the relationship between displacement and magnitude, ranged from Mw 7.4 to 7.6, with an uneven recurrence interval of approximately 2500–3200 years.

The Tongkuang Shan Fault is a thrust fault located within the Yanqi Basin in the southeastern Tian Shan Mountain. Although obvious fault scarps have developed along the fault, its activity has rarely been investigated since most part of the fault is located in a closed military area. In this study, Worldview satellite stereoscopic images were utilized to create a 0.5 m resolution Digital Elevation Model (DEM) and orthoimage of the Tongkuang Shan Fault. Based on the high-resolution topographic data, we interpreted the fault geometric structure, classified the different geomorphic units, and measured the vertical displacements along the fault. A total of 296 vertical displacements were measured on different geomorphic surfaces along the fault trace. The results show six distinct clusters in the distribution of vertical displacements along the fault, with peak values at 1.4 +/- 0.4/1.5 +/- 0.5 m, 3.0 +/- 0.7/3.4 +/- 0.6 m, 5.7 +/- 0.9/5.5 +/- 0.7 m, 8.9 +/- 1.2/8.2 +/- 1.3 m, 15.4 +/- 1.0/16.2 +/- 0.9 m, and 28.3 +/- 1.8/26.5 +/- 1.5 m on the west and east segment, respectively, which are in good agreement with the vertical displacements acquired from different geomorphic units. The fault may have undergone at least six strong paleoseismic events, and the smallest displacement cluster (1.4 +/- 0.4/1.5 +/- 0.5 m) is considered to be the coseismic displacement of the most recent event, yielding an earthquake magnitude of approximately M-w 7.0-7.2.

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 (similar to 0.25 degrees) 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 comprehensive and robust assessment of diverse global-scale satellite-based soil moisture (SM) 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 first assessed eight commonly employed satellite soil moisture datasets comprising soil moisture active passive (SMAP) (DCA, IB, and MTDCA), soil moisture and ocean salinity (SMOS)-IC, Advanced Microwave Scanning Radiometer-2 (AMSR2) [Land Parameter Retrieval Model (LPRM) and Japan Aerospace Exploration Agency (JAXA)], FY-3C, and European Space Agency (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 situ measurements 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 (LC) and climate types, and surface heterogeneity [heterogeneity in terrain, LC, soil texture (ST), 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 of $R$ value; 2) ESA CCI, SMAP-IB, and SMAP DCA products generally perform better than the others under three strategies, and SMOS-IC and SMAP MTDCA also show satisfactory performance concerning unbiased root mean square difference (ubRMSD) and $R$ values; 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, LC, and vegetation coverage, while the effect of heterogeneity in ST on the skill of satellite soil moisture products is insignificant; and 4) the skills of L-band products are more stable than those of C-/X-band datasets under different ground conditions.

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 spatial resolution of existing satellite soil moisture products is very coarse (similar to 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.

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 similar to 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.

Soil moisture (SM) plays a significant role in water, energy, and carbon cycles, and is a key variable related to multiple sustainable development goals. The frequent occurrence of extreme events in the 21st century complicates the understanding of terrestrial SM change and its drivers under diverse environmental conditions, particularly in the global scope. Here we globally explored the spatial-temporal trend of satellite-based SM (ESA CCI SM) and its possible drivers under a variety of environmental factors (land cover, soil texture, terrain, and vegetation coverage) during 2000-2021. Results indicate that global SM has generally declined with a rate of -0.10 x 10-3 m3m- 3 yr- 1 which is mainly dominated by the drying trend of the southern hemisphere. The moisture replenishment role of precipitation dominates in areas with relatively high SM values, and the positive influence of vegetation and precipitation on SM is prominent in regions where SM values are relatively low. But vegetation has a pronounced negative impact on SM north of 60 degrees N and in southeastern China. Compared with precipitation and vegetation, topsoil temperature has less impact on SM globally. Land cover types, soil properties, elevation, and vegetation cover influence SM variability and its response to climate and vegetation to varying degrees. For example, the overall wetting trend of SM is unique under loam soils and high elevations (> 1000 m) in terms of different soil texture and elevations. The increase in vegetation cover within a certain amount is accompanied by the wetting of SM, but in densely vegetated regions such as forests, the water retention effect of vegetation on SM is weakened. An evaluation of 'Dry gets drier, wet gets wetter' (DDWW) paradigm from the SM viewpoint shows the paradigm summarizes 41.06% of areas with significant SM changes. Precipitation replenishment and vegetation water retention profoundly impact the 'wet gets wetter' and 'dry gets wetter' patterns. Temperature is a strong influencing factor of the 'wet gets drier' pattern, while the reduction of vegetation and precipitation jointly affect the 'dry gets drier' pattern. These findings can deepen the understanding of SM changes in relation to climate, ecology, and land surface features, particularly in the 21st century with profound changes of the environment.