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.

Climate warming has significantly changed the near-surface soil freeze state, significantly impacting terrestrial ecosystems and regional agroforestry production. As Northeast China (NEC) is highly sensitive to climate change, this study introduces the concept of velocity to analyze the spatial pattern of frozen days (F-DAY), onset date of soil freeze (F-ON), offset date of soil freeze (F-OFF), and number of soil freeze/thaw cycles in spring (F-TC) in NEC from 1979 to 2020. We observed that the velocity changes of F-DAY, F-ON, and F-TC in croplands were significantly higher than those in forests (difference > 1 km yr(-1)), with the fastest velocity changes found in the cropland of the Songnen Plain. The highest velocity of FOFF was found in the forests of the Greater Khingan Range. In most study areas (> 60%), the isoline of F-DAY/F-ON/F-OFF/F-TC showed a northward movement. The isoline of F-DAY/F-ON/F-OFF/ F-TC moved in the cold direction in each cropland region (Sanjiang, Songnen, and Liaohe River Plains) and forest regions (Greater Khingan and Lesser Khingan Ranges, and the Changbai Mountains). The results of the quantitative analysis indicate that air temperature (T-A) had a more significant effect on the velocity change of F-DAY and F-ON in cropland, whereas snowpack is the dominant factor in forests. In both forests and croplands, the main factor affecting the velocity of F-OFF was snowpack, and T(A )mainly affected the F-TC. This study is significant for formulating regional climate change countermeasures and maintaining ecological security in cold regions.

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERAS-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i. e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m(3)/m(3), respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m(3)/m(3), respectively) performed better than ERAS-Land (R = 0.46; ubRMSE = 0.03 m(3)/m(3)). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs.

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERAS-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i. e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m(3)/m(3), respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m(3)/m(3), respectively) performed better than ERAS-Land (R = 0.46; ubRMSE = 0.03 m(3)/m(3)). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs.

Permafrost monitoring using remote sensing techniques is an effective approach at present. Permafrost mostly occurs below the land surface, which limits permafrost monitoring by optical remote sensing. Considering the specific hydrothermal relations between permafrost and its active layer, we developed a permafrost monitoring and classification method that integrated the ground surface soil freeze/thaw states determined by the dual-index algorithm (DIA) and the permafrost classification method based on thermal stability. The modified frost index was introduced into the method as a link between the DIA and the permafrost classification method. Northeastern China was selected to establish and verify the proposed method and to examine the changes in regional permafrost against the background of global warming from 2002 to 2017. The results showed that the ground surface soil freeze/thaw states were significantly correlated with the permafrost distribution. The spatial continuity of permafrost and its sensitivity to climate change could be effectively reflected by the modified frost index. The proposed method had a high accuracy with a classification error smaller than 3%, compared with static permafrost maps. Moreover, the proportion of permafrost decreased from 29% at the beginning of the 21st century to 22.5% at present in northeastern China over the study period. The southern permafrost boundary in the study area generally moved northward approximately 25-75 km. Additionally, the method was applied to the Northern Hemisphere (30 degrees N - 90 degrees N), which demonstrated its effectiveness and extended applicability.

At present, the glaciers in the Himalayas and the Tibetan Plateau (HTP) are retreating partly due to albedo reduction caused by deposited light-absorbing impurities such as mineral dust (MD) and black carbon (BC). Because BC also exists widely in MD from surface soil, it is necessary to further evaluate the contribution of BC from MD to the total BC at glacier region. This will help to improve the study of BC sources by considering the relative contributions from MD and direct combustion sources. Therefore, in this study, concentrations of total organic carbon (TOC) and fine particles of BC from 43 surface soil samples of the HTP were investigated. The contribution of BC from MD to total BC deposited at the glacier region was evaluated. The results showed strong correlations between TOC and BC of studied samples (R-2 = 0.70, p < 0.01), suggesting that they have similar sources and activity characteristics. The average BC concentration of studied samples was 2.02 +/- 1.55 mg g(-1), much lower than those of particles deposited at the glacier region and other regions with high soil TOC concentration. The contributions of BC from MD to total surface BC at two glaciers of the inner HTP (Zhadang and Xiaodongkemadi) were 17.66 +/- 10.84% and 20.70 +/- 16.35%, respectively. Therefore, the contribution of MD to glacier melting of the HTP is higher than that of previously assumed after BC coming along with MD is considered. Because MD concentration is higher at north and west part of the HTP, the contributions of MD at these glacier regions should be larger than previously assumed.

The ground surface soil heat flux (G(0)) is very important to simulate the changes of frozen ground and the active layer thickness; in addition, the freeze-thaw cycle will also affect G(0) on the Tibetan Plateau (TP). As G(0) could not be measured directly and soil heat flux is difficult to be observed on the TP in situ due to its high altitude and cold environment, most of previous studies have directly applied existing remote sensing-based models to estimate G(0) without assessing whether the selected model is the best one of those models for those study regions. We use in-situ observation data collected at 12 sites combined with Moderate Resolution Imaging Spectroradiometer (MODIS) data (MOD13Q1, MODLT1D, MOD09CMG, and MCD15A2H) and the China meteorological forcing dataset (CMFD-SRad and CMFD-LRad) to validate the main models during the freeze-thaw process. The results show that during the three stages (complete freezing (CF), daily freeze-thaw cycle (DFT), and complete thawing (CT)) of the freeze-thaw cycle, the root mean square error (RMSE) between the models' G(0) simulated value and the corresponding G(0) measured value is the largest in the CT phase and smallest in the CF phase. The simulated results of the second group schemes (SEBAL, Ma, SEBAL(adj), and Ma(adj)) were slightly underestimated, more stable, and closer to the measured values than the first group schemes (Choudhury, Clawson, SEBS, Choudhury(adj), Clawson(adj), and SEBSadj). The Ma(adj) scheme is the one with the smallest RMSE among all the schemes and could be directly applied across the entire TP. Then, four possible reasons leading to the errors of the main schemes were analyzed. The soil moisture affecting the ratio G(0)/R-n and the phase shift between G(0) and net radiation R-n are not considered in the schemes directly; the scheme cannot completely and correctly capture the direction of G(0); and the input data of the schemes to estimate the regional G(0) maybe bring some errors into the simulated results. The results are expected to provide a basis for selecting remote sensing-based models to simulate G(0) in frozen ground dynamics and to calculate evapotranspiration on the TP during the freeze-thaw process. The scheme Ma(adj) suitable for the TP was also offered in the study. We proposed several improvement directions of remote sensing-based models in order to enhance understanding of the energy exchange between the ground surface and the atmosphere.

At present, the glaciers in the Himalayas and the Tibetan Plateau (HTP) are retreating partly due to albedo reduction caused by deposited light-absorbing impurities such as mineral dust (MD) and black carbon (BC). Because BC also exists widely in MD from surface soil, it is necessary to further evaluate the contribution of BC from MD to the total BC at glacier region. This will help to improve the study of BC sources by considering the relative contributions from MD and direct combustion sources. Therefore, in this study, concentrations of total organic carbon (TOC) and fine particles of BC from 43 surface soil samples of the HTP were investigated. The contribution of BC from MD to total BC deposited at the glacier region was evaluated. The results showed strong correlations between TOC and BC of studied samples (R-2 = 0.70, p < 0.01), suggesting that they have similar sources and activity characteristics. The average BC concentration of studied samples was 2.02 +/- 1.55 mg g(-1), much lower than those of particles deposited at the glacier region and other regions with high soil TOC concentration. The contributions of BC from MD to total surface BC at two glaciers of the inner HTP (Zhadang and Xiaodongkemadi) were 17.66 +/- 10.84% and 20.70 +/- 16.35%, respectively. Therefore, the contribution of MD to glacier melting of the HTP is higher than that of previously assumed after BC coming along with MD is considered. Because MD concentration is higher at north and west part of the HTP, the contributions of MD at these glacier regions should be larger than previously assumed.

The accelerated warming of the Arctic climate may alter the local and regional surface energy balances, for which changing land surface temperatures (LSTs) are a key indicator. Modeling current and anticipated changes in the surface energy balance tequires an understanding of the spatio-temporal interactions between LSTs and land cover, both of which can be monitored globally by measurements from space. This paper investigates the accuracy of the MODIS LST/Emissivity Daily L3 Global 1 km V005 product and its spatio-temporal sensitivity to land surface properties in a Canadian High Arctic permafrost landscape. The land cover ranged from fully vegetated wet sedge tundra to barren rock. MODIS LSTs were compared with in situ radiometer measurements from wet tundra areas collected over a 2-year period from July 2008 to July 2010 including both summer and winter conditions. The accuracy of the MODIS LSTs was -1.1 degrees C with a root mean square error of 3.9 degrees C over the entire observation period. Agreement was lowest during the freeze-back periods where MODIS 1ST showed a cold bias likely due to the overrepresentation of clear-sky conditions. A multi-year analysis of LST spatial anomalies, i.e., the difference between MODIS LSTs and the MODIS 1ST regional mean, revealed a robust spatiotemporal pattern. Highest variability in LST anomalies was found during freeze-up and thaw periods as well as for open water surface in early summer due to the presence or absence of snow or ice. The summer anomaly pattern was similar for all three years despite strong differences in precipitation, air temperature and net radiation. Summer periods with regional mean ISTs above 5.0 degrees C showed the greatest spatial diversity with four distinct 2.0 degrees C classes. Summer anomalies ranged from -4.5 degrees C to 2.6 degrees C with an average standard deviation of 1.8 degrees C. Dry ridge areas heated up the most, while wetland areas and dry areas of sparsely vegetated bedrock with a high albedo remained coolest. The observed summer LST anomalies can be used as a baseline against which to evaluate both past and future changes in land surface properties that relate to the surface energy balance. Summer anomaly classes mainly reflected a combination of albedo and surface wetness. The potential to use this tool to monitor surface drying and wetting in the Arctic should therefore be further explored. A multi-sensor approach combining thermal satellite measurements with optical and radar imagery promises to be an effective tool for a dynamic, process-based ecosystem monitoring scheme. (C) 2015 Elsevier Inc. All rights reserved.

Hydrological processes in high altitude mountainous regions differ from those in more temperate regions, primarily due to such influences as cold temperatures, large and rapid change in surface energy balance during snowmelt, a long period at low-temperature environmental condition and the existence of permafrost. A physically based, semi-distributed water balance model to quantitatively simulate the hydrological processes and stream flow, as well as to estimate the potential consequences of projected global warming on stream Row for such high altitude mountainous regions was constructed. Distributed meteorological data from the interpolation of the point measurements by means of a digital elevation model (DEM) of the basin, such as air temperature, precipitation, snowfall ratio, wind speed, etc., have been used as model input. Several other hydrological parameters, such as soil moisture content and evapotranspiration, which are essential in simulation of river runoff in a water balance state, were estimated by the combination of Landsat TM and a DEM with the utilization of the distributed meteorological data. The model uses only a few crucial parameters for calibration, and the model structure is based upon estimating the stream flow components. Simulated results of spatially distributed soil moisture content, evapotranspiration and monthly discharge yield reasonable agreement, both spatially and temporally, to the field observations or the estimated results by the other approaches. This physically based model has the potential to project stream flow under the possible climate scenarios. Copyright (C) 2000 John Wiley & Sons, Ltd.