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.

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.