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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 map- ping 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 Observa- tion 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 pos-processed using additional coarse res- olution datasets （i.e., MCD12Q1, GlobCover2009, MOD44W 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 ag- gregation 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 subse- quently 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.

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.

Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relatively standardized information from historical sources. Based on available data sources and a retrospective research, in this paper we review the approaches and methods of the digital reconstruction and analyze their advantages and possible constraints in the following aspects： （1） Historical documents contain qualitative or semi-quantitative information about past land use, which also usually include land-cover data, but preparation of archival documents is very time-consuming. （2） Historical maps and pictures offer visual and spatial quantitative land-cover information. （3） Natural archive has significant advantages as a method for reconstructing past vegetation and has its unique possibilities especially when historical records are missing or lacking, but it has great limits of rebuilding certain land-cover types. （4） Historical reconstruction models have been gradually developed from empirical models to mechanistic ones. The method does not only reconstruct the quantity of land use/cover in historical periods, but it also reproduces the spatial distribution. Yet there are still few historical land-cover datasets with high spatial resolution. （5） Reconstruction method based on multiple-source data and multidisciplinary research could build historical land-cover from multiple perspectives, complement the missing data, verify reconstruction results and thus improve reconstruction accuracy. However, there are challenges that make the method still in the exploratory stage. This method can be a long-term development goal for the historical land-cover reconstruction. Researchers should focus on rebuilding historical land-cover dataset with high spatial resolution by developing new models so that the study results could be effectively applied in simulations of climatic and ecological effects.

Earth’s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities (e.g., cropland, pastureland, urban land, wood harvest). Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments. However, due to unaccounted changes in land cover (resulting from both indirect anthropogenic and natural causes), heterogeneity in land-use/cover (LUC) conversions among grid cells, even for the same land use activity, and uncertainty associated with potential vegetation mapping and historical estimates of human land use result in land cover estimates that are substantially different compared to results acquired from remote sensing observations. Here, we present a method to implicitly account for the differences arising from these uncertainties in order to provide historical estimates of land cover that are consistent with satellite estimates for recent years. Due to uncertainty in historical agricultural land use, we use three widely accepted global estimates of cropland and pastureland in combination with common wood harvest and urban land data sets to generate three distinct estimates of historical land-cover change and underlying LUC conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and the extent to which different ecosystems have undergone changes. The annual land cover maps and LUC conversion maps are reported at 0.5°×0.5° resolution and describe the area of 28 landcover types and respective underlying land-use transitions. The reconstructed data sets are relevant for studies addressing the impact of land-cover change on biogeophysics, biogeochemistry, water cycle, and global climate.

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.

Land cover is one of the most basic input elements of land surface and climate models. Currently, the direct and indirect effects of land cover data on climate and climate change are receiving increasing attentions. In this study, a high resolution （30 m） global land cover dataset （GlobeLand30） produced by Chinese scientists was, for the first time, used in the Beijing Climate Center Climate System Model （BCC_CSM） to assess the influences of land cover dataset on land surface and climate simulations. A two-step strategy was designed to use the GlobeLand30 data in the model. First, the GlobeLand30 data were merged with other satellite remote sensing and climate datasets to regenerate plant functional type （PFT） data fitted for the BCC_CSM. Second, the up-scaling based on an area-weighted approach was used to aggregate the fine-resolution GlobeLand30 land cover type and area percentage with the coarser model grid resolutions globally. The GlobeLand30-based and the BCC_CSM-based land cover data had generally consistent spatial distribution features, but there were some differences between them. The simulation results of the different land cover type dataset change experiments showed that effects of the new PFT data were larger than those of the new glaciers and water bodies （lakes and wetlands）. The maximum value was attained when dataset of all land cover types were changed. The positive bias of precipitation in the mid-high latitude of the northern hemisphere and the negative bias in the Amazon, as well as the negative bias of air temperature in part of the southern hemisphere, were reduced when the GlobeLand30-based data were used in the BCC_CSM atmosphere model. The results suggest that the GlobeLand30 data are suitable for use in the BCC_CSM component models and can improve the performance of the land and atmosphere simulations.

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.

A method for the retrieval of land surface temperature (LST) from the two thermal bands of Landsat 8 data is proposed in this paper. The emissivities of vegetation, bare land, buildings, and water are estimated using different features of the wavelength ranges and spectral response functions. Based on the Planck function of the Thermal Infrared Sensor (TIRS) band 10 and band 11, the radiative transfer equation is rebuilt and the LST is obtained using the modified emissivity parameters. A sensitivity analysis for the LST retrieval is also conducted. The LST was retrieved from Landsat 8 data for the city of Zoucheng, Shandong Province, China, using the proposed algorithm, and the LST reference data were obtained at the same time from a geosensor network (GSN). A comparative analysis was conducted between the retrieved LST and the reference data from the GSN. The results showed that water had a higher LST error than the other land-cover types, of less than 1.2°C, and the LST errors for buildings and vegetation were less than 0.75°C. The difference between the retrieved LST and reference data was about 1°C on a clear day. These results confirm that the proposed algorithm is effective for the retrieval of LST from the Landsat 8 thermal bands, and a GSN is an effective way to validate and improve the performance of LST retrieval.

Remote sensing based land cover mapping at large scale is time consuming when using either supervised or unsupervised clas- sification approaches. This article used a fast clustering method---Clustering by Eigen Space Transformation （CBEST） to pro- duce a land cover map for China. Firstly, 508 Landsat TM scenes were collected and processed. Then, TM images were clus- tered by combining CBEST and K-means in each pre-defined ecological zone （50 in total for China）. Finally, the obtained clusters were visually interpreted as land cover types to complete a land cover map. Accuracy evaluation using 2159 test sam- pies indicates an overall accuracy of 71.7% and a Kappa coefficient of 0.64. Comparisons with two global land cover products （i.e., Finer Resolution Observation and Monitoring of Global Land Cover （FROM-GLC） and GlobCover 2009） also indicate that our land cover result using CBEST is superior in both land cover area estimation and visual effect for different land cover types.

Understanding the rate of snowmelt helps inform how water stored as snow will transform into streamflow. Data from 87 snow telemetry (SNOTEL) stations across the Southern Rocky Mountains were used to estimate spatio-temporal melt factors. Decreases in snow water equivalent were correlated to temperature at these monitoring stations for eight half-month periods from early March through late June. Time explained 70% of the variance in the computed snow melt factors. A residual linear correlation model was used to explain subsequent spatial variability. Longitude, slope, and land cover type explained further variance. For evergreen trees, canopy density was relevant to find enhanced melt rates; while for all other land cover types, denoted as non-evergreen, lower melt rates were found at high elevation, high latitude and north facing slopes, denoting that in cold environments melting is less effective than in milder sites. A change in the temperature sensor about mid-way through the time series (1990 to 2013) created a discontinuity in the temperature dataset. An adjustment to the time series yield larger computed melt factors.