Soil moisture is a vital parameter for a variety of applications including hydrological modelling and climate change studies, particularly in permafrost regions where freeze-thaw processes and complex terrain pose significant monitoring challenges. This study evaluates the accuracy of seven surface soil moisture (SSM) products (SMOS-IC, ESA CCI, AMSR2 LPRM, SMAP-L3, SMAP-L4, ERA5-Land, GLDAS-Noah) and three root-zone soil moisture (RZSM) products (SMAP-L4, ERA5-Land, GLDAS-Noah) using in situ observations from 19 stations in the permafrost region of the Heihe River Basin, China, from 2012 to 2020. Focusing on the thawing season (July-October), the analysis employs statistical metrics including Pearson correlation coefficient (R), unbiased root mean square error (ubRMSE), bias, and slope. Results indicate that SMAP-L3 and SMAP-L4 exhibit the highest SSM accuracy (R = 0.24 and 0.23, respectively) with low ubRMSE (0.037-0.038), while ERA5-Land shows the best RZSM correlation (R = 0.43) but may indicate the presence of systematic biases, nonlinear responses, or limitations in dynamic range, among other issues (slope = 0.01). Environmental factors such as precipitation, land surface temperature, and normalised difference vegetation index significantly influence accuracy. Spatial variability and scale mismatches highlight the need for improved land surface models and data assimilation. This study provides critical insights for selecting and refining soil moisture products to enhance hydrological and climate research in permafrost regions.

Objective Absorbing aerosols, particularly black carbon (BC), exerts significant influence on the Earth's radiation budget by modifying both the amount and vertical distribution of solar radiation. Their climatic effects are especially pronounced in regions characterized by concentrated fossil fuel activities, such as large-scale coal mining areas. However, the spatial and temporal variability of their microphysical and optical properties introduces considerable uncertainty into regional radiative forcing assessments. The Zhundong Coalfield, located in eastern Xinjiang, China, is one such region where BC emissions from coal extraction and associated industrial activity are persistent yet under-characterized from a radiative perspective. This study aims to construct a rapid estimation framework for aerosol radiative forcing (ARF) over this region by integrating multi-band satellite observations with physically based scattering and radiative transfer models. The primary goal is to evaluate how aerosol optical depth (AOD), single scattering albedo (SSA), and particle size influence shortwave ARF at the top of the atmosphere (TOA), bottom of the atmosphere (BOA), and within the atmospheric column (ATM), and how ultraviolet-band data enhances the reliability of this estimation. Methods The research adopts a modular approach comprising aerosol property inversion and radiative transfer modeling. The aerosol inversion is based on a Mie scattering model incorporating a core-shell structure assumption, where BC forms the absorbing core and is coated by non-absorbing substances such as sulfate and nitrate. Satellite-derived aerosol products are used to constrain the model: MODIS provides AOD and SSA at visible wavelengths, while OMI contributes ultraviolet (UV) -band SSA and AOD information. Two experimental configurations are established-one based solely on MODIS data, and another integrating both MODIS and OMI-to assess the role of UV spectral information in constraining aerosol characteristics. Following inversion, the retrieved aerosol size and optical parameters are used as input to the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) model to simulate instantaneous ARF at TOA, BOA, and ATM under clear-sky conditions. Radiative forcing is calculated as the difference in net shortwave flux with and without aerosols. Multiple linear regression models are then constructed using different combinations of AOD, SSA, and core radius to quantify the relationship between these parameters and simulated ARF. Regression performance is evaluated using R (2) and RMSE statistics across both single-source and combined-source scenarios. Results and Discussions First, the inclusion of OMI UV-band data significantly improves the inversion accuracy of aerosol particle size characteristics. When only MODIS data are used, the retrieved BC core sizes are relatively narrow, mostly centered around 120 nm, and the shell diameters exhibit limited variation. However, when OMI UV observations are incorporated, the core size distribution broadens, capturing particles ranging from 90 to 160 nm, while the shell diameter spans a wider interval of 300?700 nm. This improved resolution stems from the stronger sensitivity of UVs to absorption by fine-mode particles, which enhances the model's ability to distinguish subtle differences in particle morphology. The resulting total particle size distributions-core plus shell-are more consistent with reported field measurements in coal-intensive regions. These results confirm that UV data not only improve inversion detail but also reduce the uncertainty in the wavelength in the representation of aerosol mixing states. Second, the quantitative relationship between optical parameters and ARF demonstrates clear physical consistency across TOA, BOA, and ATM layers. In both MODIS-only and MODIS-OMI configurations, AOD exhibits a strong negative correlation with TOA and BOA radiative forcing (R=-0.77 and -0.78, respectively), indicating a cooling effect due to enhanced scattering and absorption of incoming solar radiation. SSA also shows a strong negative correlation with TOA and BOA forcing (R=-0.78 and -0.62, respectively), suggesting that as the aerosol becomes more scattering-dominant, its net radiative cooling effect intensifies. Conversely, AOD shows weaker but positive correlations with ATM forcing (R=0.43), suggesting an increase in atmospheric heating when aerosol loading or absorption increases. This pattern aligns with physical expectations: absorbing aerosols like BC trap energy in the atmosphere, contributing to vertical energy redistribution. The analysis confirms that SSA has a stronger explanatory power than AOD, emphasizing its role as a key driver of radiative uncertainty forcing. Third, regression model performance improves markedly with the inclusion of SSA and core size as input parameters. Under the MODIS-only scenario, models using AOD alone yield limited explanatory power, withR (2) values of 0.59 (TOA), 0.61 (BOA), and 0.18 (ATM). Adding SSA improves the fits substantially, increasingR (2) to 0.78 (TOA) and 0.67 (BOA), and to 0.21 in the ATM. Incorporating core radius into the model yields additional gains, raisingR (2) in the ATM layer to 0.23 and lowering RMSE values across all layers. In the MODIS-OMI fusion scenario, even though the number of valid observation days decreases significantly (eg, from 2589 to 954 days at the Wucaiwan site), model performance continues to improve. For example,R (2) for ATM forcing increases from 0.18 to 0.29, and RMSE decreases from 2.04 to 1.85. These results suggest that high-spectral-resolution UV data provide greater constraint on aerosol absorption properties, thereby enabling more physically consistent radiative forcing estimates, even with reduced samples. This finding supports the robustness of UV-enhanced satellite inversion strategies in regional ARF modeling. Conclusions This study presents a data-model integration framework for estimating ARF over coal mining regions using multi-source satellite observations and physically based scattering and radiative transfer models. The combination of MODIS visible and OMI ultraviolet aerosol products improves the inversion of absorbing aerosol particle size distributions and enhances the retrieval of SSA, especially under complex mixing conditions. The constructed regression models reveal that SSA exerts a greater influence on radiative forcing than AOD, and that including particle size parameters further strengthens model reliability. Despite a reduction in observational frequency due to OMI's narrower sampling, the incorporation of UV-band information leads to consistently improved model performance across all atmospheric layers, particularly in the atmospheric column. These results highlight the critical role of spectral diversity in satellite remote sensing for accurately characterizing the radiative impacts of absorbing aerosols, and demonstrate the feasibility of applying such approaches to high-emission, data-scarce environments like the Zhundong Coalfield.

The freeze-thaw erosion zone of the Tibetan Plateau (FTZTP) maintains an ecologically fragile system with enhanced thermal sensitivity under climate warming. Vegetation phenology in this cryosphere-dominated environment acts as a crucial biophysical indicator of climate variability, showing potentially amplified responses to environmental changes relative to other ecosystems. To investigate vegetation phenological characteristics and their climate responses, we derived key phenological parameters (the start, end and length of growing season-SOS, EOS, LOS) for the FTZTP from 2001 to 2021 using MODIS EVI data and analysed their spatiotemporal patterns and climatic drivers. Results indicated that the spatial distribution of phenology was highly heterogeneous, influenced by local climate, complex topography and diverse vegetation. SOS generally exhibited a delayed trend from east to west, while EOS was progressively later from the central plateau towards the southeast and southwest. Consequently, LOS shortened along both east-west and south-north gradients. Under sustained warming and wetting, the region experienced intensified freeze-thaw cycles, characterised by a delayed freeze-start, advanced thaw-end and shortened freeze-thaw duration. Both climate warming and freeze-thaw changes drove an overall significant advancement of SOS (-3.1 days/decade), delay of EOS (+2.2 days/decade) and extension of LOS (+5.3 days/decade) over the 21-year period. Notably, an abrupt phenological shift occurred around 2015. Prior to 2015, both SOS and EOS advanced, whereas afterward, SOS transitioned to a delaying trend and EOS exhibited a markedly stronger delay, leading to a pronounced extension of LOS. This regime shift was primarily attributed to changes in hydrothermal conditions controlled by climate warming and evolving freeze-thaw dynamics, with temperature being the dominant factor and precipitation exerting seasonally differential effects. Our findings elucidate the complex responses of alpine cryospheric ecosystems to climate change, revealing freeze-thaw processes as a key modulator of vegetation phenology.

Highlights What are the main findings? Permafrost in the Muri area responded to human disturbance without significant spatial expansion during 2000-2024. The semi-arid climate, rough terrain, thin root zone and gappy vertical structure underneath were the major factors. What are the implications of the main findings? Annual ALT estimated from 2000 to 2024 filled the data gap of high-resolution ALT in the Muri area. Knowledge was provided for a better understanding of alpine permafrost development.Highlights What are the main findings? Permafrost in the Muri area responded to human disturbance without significant spatial expansion during 2000-2024. The semi-arid climate, rough terrain, thin root zone and gappy vertical structure underneath were the major factors. What are the implications of the main findings? Annual ALT estimated from 2000 to 2024 filled the data gap of high-resolution ALT in the Muri area. Knowledge was provided for a better understanding of alpine permafrost development.Abstract Alpine permafrost plays a vital role in regional hydrology and ecology. Alpine permafrost is highly sensitive to climate change and human disturbance. The Muri area, which is located in the headwaters of the Datong River, northeast of the Tibetan Plateau, has undergone decadal mining, and the permafrost stability there has attracted substantial concerns. In order to decipher how and to what extent the permafrost in the Muri area has responded to the decadal mining in the context of climate change, daily MODIS land surface temperatures (LSTs) acquired during 2000-2024 were downscaled to 30 m x 30 m. The active layer thickness (ALT)-ground thaw index (DDT) coefficient was derived from in situ ALT measurements. An annual ALT of 30 m x 30 m spatial resolution was subsequently estimated from the downscaled LST for the Muri area using the Stefan equation. Validation of the LST and ALT showed that the root of mean squared error (RMSE) and the mean absolute error (MAE) of the downscaled LST were 3.64 degrees C and -0.1 degrees C, respectively. The RMSE and MAE of the ALT estimated in this study were 0.5 m and -0.25 m, respectively. Spatiotemporal analysis of the downscaled LST and ALT found that (1) during 2000-2024, the downscaled LST and estimated ALT delineated the spatial extent and time of human disturbance to permafrost in the Muri area; (2) human disturbance (i.e., mining and replantation) caused ALT increase without significant spatial expansion; and (3) the semi-arid climate, rough terrain, thin root zone and gappy vertical structure beneath were the major controlling factors of ALT variations. ALT, estimated in this study with a high resolution and accuracy, filled the data gaps of this kind for the Muri area. The ALT variations depicted in this study provide references for understanding alpine permafrost evolution in other areas that have been subject to human disturbance and climate change.

Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0-40 cm) with a satisfactory accuracy (R-2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

Forest growth in tropical regions is regulated in part by climatic factors, such as precipitation and temperature, and by soil factors, such as nutrient availability and water storage capacity. We examined a decade of growth data from Eucalyptus clonal plantations from over 113,000 forest inventory plots across a 10 million-ha portion of Mato Grosso do Sul in southwestern Brazil. From this full dataset, three subsets were screened: 71,000 plots to characterize growth and yield across water table depth classes, 17,000 plots to build generalized models, and 50,000 plots for clone-based analyses. Average precipitation varied little across the region (1150 to 1270 mm yr(-1)), but water table depth ranged from less than 10 m to over 100 m. Where the water table was within 10 m of the surface, about 20 % of the total water used by trees came from this saturated zone. Water tables deeper than 50 m contributed very little to tree water use. Sites with a water table within 10 m averaged 47 m(3) ha(-1) yr(-1) in stem growth (mean annual increment, MAI) across a full rotation, compared to less than 37 m(3) ha(-1) yr(-1) for sites with water tables deeper than 50 m. Drought-induced canopy damage rose from 7 % to 30 % along the water tables depth gradient, while tree mortality rose nearly fourfold. The optimal stocking level was about 1360 trees ha(-1) where water tables were accessible, declining to 1080 trees ha(-1) where they were not. Among the 15 most planted Eucalyptus clones, increases in MAI from the lowest to highest water table depths ranged from + 4.8 to + 16.8 m(3) ha(-1) yr(-1) , reflecting significant genotype-environment interactions. On average, MAI decreased by 0.8 m(3) ha(-1) yr(-1) (ranging from 0.4 to 1.4) for every 10 m increase in water table depth. Similarly, the Site Index at base age 7 years declined from 31 m to 27 m, with an average reduction of 0.25 m per 10 m increase in water table depth. Physiographic modeling of water table depths offers useful information for forest management practices like forest inventory and planning, clonal allocation, optimized planting densities, fertilization strategies, coppice techniques, and other landscape-specific strategies like tree breeding zones.

Early water stress detection is important for water use yield and sustainability. Traditional methods using the Internet of Things (IoT), such as soil moisture sensors, usually do not provide timely alerts, causing inefficient water use and, in some cases, crop damage. This research presents an innovative early water stress detection method in lettuce plants using Thermal Infrared (TIR) and RGB images in a controlled lab setting. The proposed method integrates advanced image processing techniques, including background elimination via Hue-Saturation- Value (HSV) thresholds, wavelet denoising for thermal image enhancement, RGB-TIR fusion using Principal Component Analysis (PCA), and Gaussian Mixture Model (GMM) clustering to segment stress regions. The leaves stressed areas annotated in the RGB image through yellow pseudo-coloring. This approach is predicated on the fact that when stomata close, transpiration decreases, which causes an increase in the temperature of the affected area. Experimental results reveal that this new approach can detect water stress up to 84 h earlier than conventional soil humidity sensors. Also, a comparative analysis was conducted where key components of the proposed hybrid framework were omitted. The results show inconsistent and inaccurate stress detection when excluding wavelet denoising and PCA fusion. A comparative analysis of image processing performed on a single- board computer (SBC) and through cloud computing over 5 G showed that SBC was 8.27% faster than cloud computing over a 5 G connection. The proposed method offers a more timely and accurate identification of water stress and promises significant benefits in improving crop yield and reducing water usage in indoor farming.

Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere. This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau, China, as well as in the related ecological and hydrological processes. However, the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques. Thus, this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected (GRD) data, the polarization decomposition parameters of Sentinel-1A single-look complex (SLC) data, the normalized difference vegetation index (NDVI) based on Sentinel-2B data, and the topographic factors based on digital elevation model (DEM) data. By combining these parameters with a machine learning model, we established a feature selection rule. A cumulative importance threshold was derived for feature variables, and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination (R2) and the unbiased root mean square error (ubRMSE). The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion, and the SHapley Additive exPlanations (SHAP) method was used to analyze the importance of these variables. The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion. Compared to the unfiltered model, the optimal feature combination led to a 0.09 increase in R2 and a 0.7% reduction in ubRMSE. Ultimately, the optimized model achieved a R2 of 0.87 and an ubRMSE of 5.6%. Analysis revealed that soil particle size had significant impact on soil water retention capacity. The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable, demonstrating a significant positive correlation. Moreover, the microtopographical features of hummocks interfered with soil moisture estimation, indicating that such terrain effects warrant increased attention in future studies within the permafrost regions. The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau, but also exhibits high computational efficiency (with a relative time reduction of 18.5%), striking an excellent balance between accuracy and efficiency. This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data, offering critical insights for ecological conservation, water resource management, and climate change adaptation on the Qinghai-Xizang Plateau.

Background and aimsAlpine swamp meadows play a vital role in water conservation and maintaining ecological balance. However, the response mechanisms of its area and hydrological functions under global climate change remain unclear, particularly the impact of permafrost degradation on water storage capacity, which urgently requires quantification.MethodsWe integrated multi-temporal Landsat data (2000-2023) and phenological features to construct a classification framework for alpine swamp meadows. A multi-source remote sensing-based water balance assessment method was developed. Random forest importance evaluation and piecewiseSEM were employed to quantify the impacts and pathways of multidimensional driving factors on changes in alpine swamp meadow area and water storage.ResultsThe phenology-based classification method effectively extracted alpine swamp meadows with a mean producer's accuracy of 92.84%, user's accuracy of 92.14%, and a Kappa coefficient of 0.95. The study found that the spatial expansion of alpine swamp meadows in the watershed showed an initial decrease followed by an increase trend, while the water storage capacity continued to decline, indicating a significant decoupling between the two.ConclusionUnder climate change, increased precipitation and reduced snow cover albedo have led to the expansion of alpine swamp meadows, while enhanced evapotranspiration and the degradation of permafrost aquicludes have caused a systematic decline in their water storage capacity. These findings provide a scientific basis for assessing the health of alpine ecosystems and managing water resources under climate change.

Soil freeze-thaw state influences multiple terrestrial ecosystem processes, such as soil hydrology and carbon cycling. However, knowledge of historical long-term changes in the timing, duration, and temperature of freeze-thaw processes remains insufficient, and studies exploring the combined or individual contributions of climatic factors-such as air temperature, precipitation, snow depth, and wind speed-are rare, particularly in current thermokarst landscapes induced by abrupt permafrost thawing. Based on ERA5-Land reanalysis, MODIS observations, and integrated thermokarst landform maps, we found that: 1) Hourly soil temperature from the reanalysis effectively captured the temporal variations of in-situ observations, with Pearson' r of 0.66-0.91. 2) Despite an insignificant decrease in daily freeze-thaw cycles in 1981-2022, other indicators in the Qinghai-Tibet Plateau (QTP) changed significantly, including delayed freezing onset (0.113 d yr- 1), advanced thawing onset (-0.22 d yr- 1), reduced frozen days (-0.365 d yr- 1), increased frozen temperature (0.014 degrees C yr- 1), and decreased daily freeze-thaw temperature range (-0.015 degrees C yr- 1). 3) Total contributions indicated air temperature was the dominant climatic driver of these changes, while indicators characterizing daily freeze-thaw cycles were influenced mainly by the combined effects of increased precipitation and air temperature, with remarkable spatial heterogeneity. 4) When regionally averaged, completely thawed days increased faster in the thermokarstaffected areas than in their primarily distributed grasslands-alpine steppe (47.69%) and alpine meadow (22.64%)-likely because of their stronger warming effect of precipitation. Locally, paired comparison within 3 x 3 pixel windows from MODIS data revealed consistent results, which were pronounced when the thermokarst-affected area exceeded about 38% per 1 km2. Conclusively, the warming and wetting climate has significantly altered soil freeze-thaw processes on the QTP, with the frozen soil environment in thermokarstaffected areas, dominated by thermokarst lakes, undergoing more rapid degradation. These insights are crucial for predicting freeze-thaw dynamics and assessing their ecological impacts on alpine grasslands.