Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.     

Using a global reference sample set and a cropland map for area estimation in China

A technically transparent and freely available reference sample set for validation of global land cover mapping was recently established to assess the accuracies of land cover maps with multiple resolutions. This sample set can be used to estimate areas because of its equal-area hexagon-based sampling design. The capabilities of these sample set-based area estimates for cropland were investigated in this paper. A 30-m cropland map for China was consolidated using three thematic maps (cropland, forest and wetland maps) to reduce confusion between cropland and forest/wetland. We compared three area estimation methods using the sample set and the 30 m cropland map. The methods investigated were: (1) pixel counting from a complete coverage map, (2) direct estimation from reference samples, and (3) model-assisted estimation combining the map with samples. Our results indicated that all three methods produced generally consistent estimates which agreed with cropland area measured from an independent national land use dataset. Areas estimated from the reference sample set were less biased by comparing with a National Land Use Dataset of China (NLUD-C). This study indicates that the reference sample set can be used as an alternative source to estimate areas over large regions.     

A multi-resolution global land cover dataset through multisource data aggregation

Recent developments of 30 m global land characterization datasets(e.g., land cover, vegetation continues field) represent the finest spatial resolution inputs for global scale studies. Here, we present results from further improvement to land cover mapping and impact analysis of spatial resolution on area estimation for different land cover types. We proposed a set of methods to aggregate two existing 30 m resolution circa 2010 global land cover maps, namely FROM-GLC(Finer Resolution Observation and Monitoring-Global Land Cover) and FROM-GLC-seg(Segmentation), with two coarser resolution global maps on development, i.e., Nighttime Light Impervious Surface Area(NL-ISA) and MODIS urban extent(MODIS-urban), to produce an improved 30 m global land cover map—FROM-GLC-agg(Aggregation). It was post-processed using additional coarse resolution datasets(i.e., MCD12Q1, GlobCover2009, MOD44 W etc.) to reduce land cover type confusion. Around 98.9% pixels remain 30 m resolution after some post-processing to this dataset. Based on this map, majority aggregation and proportion aggregation approaches were employed to create a multi-resolution hierarchy(i.e., 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km) of land cover maps to meet requirements for different resolutions from different applications. Through accuracy assessment, we found that the best overall accuracies for the post-processed base map(at 30 m) and the three maps subsequently aggregated at 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Our analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.     

Assessment of visual interpretation of thematic mapper (TM) images for land use and cover in Sudan

Three thematic mapper (TM) images, a band 3 image, a band 4 image and a 432 colour composite image of Khartoum were compared for detection, identification and recognition of land use and cover classes in hydrology in Sudan. Simple visual interpretational techniques were employed for this purpose. The results show that visual interpretation procedures can be very useful for an inventory of hydrologic land use and cover using Landsat TM photographic products. This is an important conclusion for developing countries where digital techniques are very expensive at the present and many developing countries cannot afford to make use of them.     

Improvement of ecological geographic regionalization based on remote sensing and canonical correspondence analysis: A case study in China

Ecological geographic regions, also called eco-regions, can be used to divide a remotely sensed image, which is helpful for reducing the complexity of land cover types within eco-regions and for improving the classification accuracy of land cover. In this case study in China, we improved a method of ecological geographic regionalization that is more suitable for remote sensing mapping of regional land cover, and we obtained new eco-regions. The canonical correspondence analysis (CCA) and k-means clustering were adopted in the ecological geographic regionalization using both seasonal remotely-sensed vegetation information and environmental data including climate, elevation and soil features. Our results show that the combination of seasonal vegetation information and the CCA performed well in the selection of the dominant environmental factor of the biogeographic pattern, and it can be used as regionalization indicators of eco-regions. We found that thermal factors are the most important driving forces of the biogeographic pattern in China, which followed by moisture factors. Two global land cover products (MODIS MCD12C1 and GlobCover) were used to assess our eco-regions. The results show that our eco-regions performed better than that of a previous study regarding the complexity of land cover types, such as in the number of types and the proportional area of the major/secondary type. These results indicate that the method of ecological geographic regionalization, which is based on environmental factors associated with seasonal vegetation features, is effective for reducing the regional complexity of land cover.     

Sensitivity of surface air temperature change to land use/cover types in China

Using CRU high resolution grid observational temperature and ERA40 reanalysis surface air temperature data during 1960–1999, we investigated the sensitivity of surface air temperature change to land use/cover types in China by subtracting the reanalysis from the observed surface air temperature (observation minus reanalysis, OMR). The results show that there is a stable and systemic impact of land use/cover types on surface air temperature. The surface warming of each land use/cover type reacted differently to global warming. The OMR trends of unused land (⩾0.17 °C/decade), mainly comprised by sandy land, Gobi and bare rock gravel land, are obviously larger than those of the other land use/cover types. The OMR over grassland, farmland and construction land shows a moderate decadal warmingabout 0.12°C/decade, 0.10°C/decade, 0.12°C/decade, respectively. Woodland areas do not show a significant warming trend (0.06°C/decade). The overall assessment indicates that the surface warming is larger for areas that are barren and anthropogenically developed. The better the vegetation cover, the smaller the OMR warming trend. Responses of surface air temperature to land use/cover types with similar physical and chemical properties and biological processes have no significant difference. The surface air temperature would not react significantly until the intensity of land cover changes reach a certain degree. Within the same land use/cover type, areas in eastern China with intensive human activities exhibit larger warming trend. The results provide observational evidence for modeling research on the impact of land use/cover change on regional climate. Thus, projecting further surface climate of China in regional scale should not only take greenhouse gas increase into account, but also consider the impact of land use/cover types and land cover change. Supported by National Basic Research Program of China (Grant No. 2005CB422006), National Natural Science Foundation of China (Grant Nos. 90202012, 40771206)     

2000—2018年全国形变台站周边环境时空变化特征及对垂直摆观测影响分析

利用中国自主研制的全球首套30 m分辨率GlobeLand30地表覆盖产品调查全国定点形变台站周边5 km范围内的土地覆盖类型分布.应用地类面积/变化率、类型转入转出率等指标分析研究区各类型的转移规律,并辅以MODIS土地覆盖年数据集从全国和分区角度揭示2001—2018年间各土地类型时空变化格局,最后以垂直摆倾斜观测...     

Quantifying uncertainty in remotely sensed land cover maps

Remotely sensed land cover maps are increasingly used as inputs into environmental simulation models whose outputs inform decisions and policy-making. Risks associated with these decisions are dependent on model output uncertainty, which is in turn affected by the uncertainty of land cover inputs. This article presents a method of quantifying the uncertainty that results from potential mis-classification in remotely sensed land cover maps. In addition to quantifying uncertainty in the classification of individual pixels in the map, we also address the important case where land cover maps have been upscaled to a coarser grid to suit the users’ needs and are reported as proportions of land cover type. The approach is Bayesian and incorporates several layers of modelling but is straightforward to implement. First, we incorporate data in the confusion matrix derived from an independent field survey, and discuss the appropriate way to model such data. Second, we account for spatial correlation in the true land cover map, using the remotely sensed map as a prior. Third, spatial correlation in the mis-classification characteristics is induced by modelling their variance. The result is that we are able to simulate posterior means and variances for individual sites and the entire map using a simple Monte Carlo algorithm. The method is applied to the Land Cover Map 2000 for the region of England and Wales, a map used as an input into a current dynamic carbon flux model.     

On the significance of crop‐type information for the simulation of catchment hydrology

Spatially distributed hydrological models require information on the land cover pattern, as it directly influences the generation of run‐off. However, it is not clear which detail of land cover information is suitable for simulating the catchment hydrology. A better understanding of the relationship between the land cover detail and the hydrological processes is therefore required as it would enhance a successful application of the hydrological model. This study investigates the relevance of accurate information about the crop types in the catchment for the simulation of run‐off, baseflow and evapotranspiration. Results reveal that adding knowledge about the crop type in the model simulation is redundant when area‐averaged water flux predictions are intended. On the contrary, when a spatial distribution of water flux predictions is desired, it is meaningful to increase the land cover detail because an increased heterogeneity in the land cover map induces an increased heterogeneity in the hydrological response and locally reduces the uncertainty in the model prediction up to 50%. To assess the land cover uncertainty and to locate the regions for which the reduction in prediction uncertainty is the highest, the thematic uncertainty map (constructed through fuzzy logic) is shown to be a useful tool. Copyright © 2014 John Wiley & Sons, Ltd.     

Soil erosion risk assessment with CORINE model: case study in the Danjiangkou Reservoir region, China

Soil erosion is one of the major threats to the conservation of soil and water resources in the Danjiangkou Reservoir region (DRR), China. In order to describe the areas with high soil erosion risk (SER) and to develop adequate erosion prevention measures, SER in the DRR was assessed by integrating the CORINE model with GIS and RS. The main factors of soil erosion including erosivitiy, soil erodibility, topography and vegetation cover were determined from daily meteorological data, field survey soil profile data and soil sample analysis, digital elevation model (DEM), and land use and land cover (LULC), respectively. Landsat 5 TM imagery was used to generate a LULC classification. The results indicate that 59.1%, 31.2%, and 2.3% of the study area were under low, moderate, and high actual erosion risks, respectively. The results also indicate the study area is in low to moderate erosion risk level on the whole. The areas with moderate to high erosion risk continuously distributed in the southwest of the study area, and sporadically distributed in the north of the study area. Low erosion risk areas mainly located in the east. Up till now, most of the semi-quantitative models have not been applied extensively. The semi-quantitative CORINE model was mostly applied in the European and Mediterranean countries, while spatial comparison of actual SER map and field investigation in this study indicates that the CORINE model can be applicable in the monsoon region of China.     

MODIS Terra Collection 6 fractional snow cover validation in mountainous terrain during spring snowmelt using Landsat TM and ETM+

Daily swath MODIS Terra Collection 6 fractional snow cover (MOD10_L2) estimates were validated with two‐day Landsat TM/ETM + snow‐covered area estimates across central Idaho and southwestern Montana, USA. Snow cover maps during spring snowmelt for 2000, 2001, 2002, 2003, 2005, 2007, and 2009 were compared between MODIS Terra and Landsat TM/ETM + using least‐squared regression. Strong spatial and temporal map agreement was found between MODIS Terra fractional snow cover and Landsat TM/ETM + snow‐covered area, although map disagreement was observed for two validation dates. High‐altitude cirrus cloud contamination during low snow conditions as well as late season transient snowfall resulted in map disagreement. MODIS Terra's spatial resolution limits retrieval of thin‐patchy snow cover, especially during partially cloudy conditions. Landsat's image acquisition frequency can introduce difficulty when discriminating between transient and resident mountain snow cover. Furthermore, transient snowfall later in the snowmelt season, which is a stochastic accumulation event that does not usually persist beyond the daily timescale, will skew decadal snow‐covered area variability if bi‐monthly climate data record development is the objective. As a quality control step, ground‐based daily snow telemetry snow‐water‐equivalent measurements can be used to verify transient snowfall events. Users of daily MODIS Terra fractional snow products should be aware that local solar illumination and sensor viewing geometry might influence fractional snow cover estimation in mountainous terrain. Cross‐sensor interoperability has been confirmed between MODIS Terra and Landsat TM/ETM + when mapping snow from the visible/infrared spectrum. This relationship is strong and supports operational multi‐sensor snow cover mapping, specifically climate data record development to expand cryosphere, climate, and hydrological science applications. Copyright © 2013 John Wiley & Sons, Ltd.     

AntarcticaLC2000: The new Antarctic land cover database for the year 2000

Antarctica plays a key role in global energy balance and sea level change. It has been conventionally viewed as a whole ice body with high albedo in General Circulation Models or Regional Climate Models and the differences of land cover has usually been overlooked. Land cover in Antarctica is one of the most important drivers of changes in the Earth system. Detailed land cover information over the Antarctic region is necessary as spatial resolution improves in land process models. However, there is a lack of complete Antarctic land cover dataset derived from a consistent data source. To fill this data gap, we have produced a database named Antarctic Land Cover Database for the Year 2000 (AntarcticaLC2000) using Landsat Enhanced Thematic Mapper Plus (ETM+) data acquired around 2000 and Moderate Resolution Imaging Spectrometer (MODIS) images acquired in the austral summer of 2003/2004 according to the criteria for the 1:100000-scale. Three land cover types were included in this map, separately, ice-free rocks, blue ice, and snow/firn. This classification legend was determined based on a review of the land cover systems in Antarctica (LCCSA) and an analysis of different land surface types and the potential of satellite data. Image classification was conducted through a combined usage of computer-aided and manual interpretation methods. A total of 4067 validation sample units were collected through visual interpretation in a stratified random sampling manner. An overall accuracy of 92.3% and the Kappa coefficient of 0.836 were achieved. Results show that the areas and percentages of ice-free rocks, blue ice, and snow/firn are 73268.81 km² (0.537%), 225937.26 km² (1.656%), and 13345460.41 km² (97.807%), respectively. The comparisons with other different data proved a higher accuracy of our product and a more advantageous data quality. These indicate that AntarcticaLC2000, the new land cover dataset for Antarctica entirely derived from satellite data, is a reliable product for a broad spectrum of applications.     

The use of remotely sensed land cover to derive floodplain friction coefficients for flood inundation modelling

Remotely sensed land cover was used to generate spatially‐distributed friction coefficients for use in a two‐dimensional model of flood inundation. Such models are at the forefront of research into the prediction of river flooding. Standard practice, however, is to use single (static) friction coefficients on both the channel and floodplain, which are varied in a calibration procedure to provide a “best fit” to a known inundation extent. Spatially‐distributed friction provides a physically grounded estimate of friction that does not require fitting to a known inundation extent, but which can be fitted if desired. Remote sensing offers the opportunity to map these friction coefficients relatively straightforwardly and for low cost. Inundation was predicted using the LISFLOOD‐FP model for a reach on the River Nene, UK. Friction coefficients were produced from land cover predicted from Landsat TM imagery using both ML and fuzzy c‐means classifiction. The elevetion data used were from combined contour and differential global positioning system (GPS) elevation data. Predicted inundation using spatially‐distributed and static friction were compared. Spatially‐distributed friction had the greatest effect on the timing of flood inundation, but a small effect on predicted inundation extent. The results indicate that spatially‐distributed friction should be considered where the timing of initial flooding (e.g. for early warning) is important. Copyright © 2007 John Wiley & Sons, Ltd.     

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.     

Land degradation assessment with the aid of geo‐information techniques

This research integrated selected land degradation indicators (vegetation cover, proportion of drifting sand area, desertification rate, and population pressure) with geo‐information techniques (remote sensing, geographic information system and global positioning system) to assess the severity of land degradation risk. The northern part of Shaanxi province in China was taken as a case study. A computerized land degradation severity assessment was implemented, and ERmapper ver.6.2 and ARC/INFO GIS ver.8.3 environments were used to manage and manipulate thematic data, and to process satellite images and tabular data. Two Landsat TM images in October 1987 and October 1999 were used to produce land use/cover maps of the study area based on the maximum likelihood classification method. These maps were then used to generate land use, land cover change, vegetation degradation and land degradation maps for the study area during the study period, and their corresponding data were integrated into a systematic analysis. Results showed that the overall severity of land degradation in the study area worsened during the study period from 1987 to 1999 with severely, highly and moderately degraded land accounting for 73·8 per cent of the total area. While the area affected by desertification has increased, the rate of desertification has also accelerated to reach 41·5 km² a^?1. Risk of land degradation in the study area has increased, on average, by 39·4 per cent since 1987. Incorporation of both natural and anthropogenic factors in the analysis provided realistic assessment of the risk of desertification. The study area, in general, is exposed to a high risk of land degradation. Copyright © 2006 John Wiley & Sons, Ltd.     

Methodology for credibility assessment of historical global LUCC datasets

Land use-induced land cover change(LUCC) is an important anthropogenic driving force of global change that has influenced, and is still influencing, many aspects of regional and global environments. Accurate historical global land use/cover datasets are essential for a better understanding of the impacts of LUCC on global change. However, there are not only evident inconsistencies in current historical global land use/cover datasets, but inaccuracies in the data in these global dataset revealed by historical record-based reconstructed regional data throughout the world. A focus in historical LUCC and global change research relates to how the accuracy of historical global land cover datasets can be improved. A methodology for assessing the credibility of existing historical global land cover datasets that addresses temporal as well as spatial changes in the amount and distribution of land cover is therefore needed. Theoretically, the credibility of a global land cover dataset could be assessed by comparing similarities or differences in the data according to actual land cover data(the "true value"). However, it is extremely difficult to obtain historical evidence for assessing the credibility of historical global land cover datasets, which cannot be verified through field sampling like contemporary global land cover datasets. We proposed a methodological framework for assessing the credibility of global land cover datasets. Considering the types and characteristics of the available evidence used for assessments,we outlined four methodological approaches:(1) accuracy assessment based on regional quantitative reconstructed land cover data,(2) rationality assessment based on regional historical facts,(3) rationality assessment based on expertise, and(4) likelihood assessment based on the consistency of multiple datasets. These methods were illustrated through five case studies of credibility assessments of historical cropland cover data. This framework can also be applied in assessments of other land cover types, such as forest and grassland.     

Global cultivated land mapping at 30 m spatial resolution

Cultivated land is one of the most important types of land cover in the global mapping of land cover, and its variation influences economic development, food security, and ecological environment protection. Existing products of global cultivated land mapping have a low resolution, and high spatial resolution products are in demand. This study uses global remote sensing image datasets in 2000/2010 with a spatial resolution of 30 m (Landsat TM/ETM+, HJ-1), MODIS 250 m NDVI time-serial data, and many types of reference data. An three-layer extraction method based on pixels, objects, and knowledge (POK) was adopted to ease cultivated land extraction in global-scale 30 m images, i.e., cultivated land classification based on pixel-scale multi-feature optimization, cultivated land automatic identification based on objects, and interactive object processing based on information service and priori knowledge. Global 30 m cultivated land mapping was accomplished for the two reference years (2000 and 2010), and statistical analysis was conducted on the data. Results showed that the total cultivated land area was 1.903 billion ha and 1.960 billion ha, respectively. Accuracy assessments showed that overall accuracy of global cultivated land mapping are higher than 92% for both the two reference years. The global cultivated land products in 2000/2010 developed in this research are superior to their international counterparts in terms of spatial resolution and classification accuracy. They also provide significant basic data on global food security, ecological environment supervision, and global change.     

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km²) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.     

Impact of lateral flow and spatial scaling on the simulation of semi‐arid urban land surfaces in an integrated hydrologic and land surface model

Understanding and representing hydrologic fluxes in the urban environment is challenging because of fine scale land cover heterogeneity and lack of coherent scaling relationships. Here, the impact of urban land cover heterogeneity, scale, and configuration on the hydrologic and surface energy budget (SEB) is assessed using an integrated, coupled land surface/hydrologic model at high spatial resolutions. Archetypes of urban land cover are simulated at varying resolutions using both the National Land Cover Database (NLCD; 30 m) and an ultra high‐resolution land cover dataset (0.6 m). The analysis shows that the impact of highly organized, yet heterogeneous, land cover typical of the urban domain can cause large variations in hydrologic and energy fluxes within areas of similar land cover. The lateral flow processes that occur within each simulation create variations in overland flow of up to ±200% and ±4% in evapotranspiration. The impact on the SEB is smaller and largely restricted to the wet season for our semi‐arid forcing scenarios. Finally, we find that this seasonal bias, predominantly caused by lateral flow, is displaced by a systematic diurnal bias at coarser resolutions caused by deficiencies in the method used for scaling of land surface and hydrologic parameters. As a result of this research, we have produced land surface parameters for the widely used NLCD urban land cover types. This work illustrates the impact of processes that remain unrepresented in traditional high‐resolutions land surface models and how they may affect results and uncertainty in modeling of local water resources and climate. Copyright © 2015 John Wiley & Sons, Ltd.     

利用卫星热红外遥感技术监测长白山天池火山活动性

介绍了卫星热红外遥感技术用于火山监测的国内外研究现状,阐述了热红外遥感技术的原理,分析了卫星热红外遥感技术用于火山活动性监测的可行性。以长白山天池火山为例,基于Landsat TM/ETM影像和ASTER影像反演获得了1999—2008年的温度场,并选取了其中的3种地面覆盖类型(森林植被、土壤和植被(矮草)以及裸露岩石),从而去除了地表环境因素的影响;从每种地面覆盖类型中扣除了当日天池气象站的平均气温,去除了气象因素的影响,得到了由火山热活动可能导致的温度热异常。结果显示,从1999—2005年,由火山活动导致的温度热异常伴随着扰动发生了明显的上升,自2005年以后逐渐下降,2006—2008年趋于稳定。这些结果与测震、GPS形变以及He同位素比值变化趋势保持了较好的一致性,表明卫星热红外遥感技术用于火山活动性监测的巨大潜力和优势,可以作为一种常规的监测手段尝试性地纳入日常的火山监测工作中