Climate change and surface processes
Studies on the evolutionary process and the inter-relations of the earth surface elements are a front field in the world. West China is an ideal area for conducting researches on the earth surface processes. This paper reviews the progresses in researches on the earth surface process in West China and the growth of the relevant research groups under the supports by National Natural Science Foundation of China.
Urban heat islands resulting from land use and land cover change have become a major barrier to urbanization and sustainable development of ecological urban environments. Although many studies have focused on the interannual and seasonal characteristics of urban heat islands, there has been no comparative analysis of the urban surface thermal landscape at multiple spatio-temporal scales. This study described the spatio-temporal patterns of the urban surface thermal landscape in different seasons and by time of day (daytime/nighttime) in terms of quantity, shape, and structure using MODIS LST products, and revealed the evolution of the urban surface thermal landscape using mapping techniques and analysis of barycenter trajectories in metropolitan Beijing between 2003 and 2017. The conclusions were as follows: (1) The characteristics of the urban surface thermal landscape vary significantly in different seasons and by time of day. (2) The medium-temperature zone constitutes the largest proportion of the area of metropolitan Beijing, which is the most unstable area during the daytime and the instability of the sub-high-temperature and sub-low-temperature zones increased at night. (3) The stable zone is most important in terms of the change in the land surface thermal landscape, followed by the repeated-changes zone and the zone where the change occurred in the first 5 years. The changes of different temperature zones usually increased or decreased progressively. There was a cooling trend in the mountains. In the north mountain-transition zone, the process of transferring between sub-low temperature and medium temperature was repeated. There was a warming trend in the south. (4) The area of the high-temperature zone increased from 2003 to 2017 and its barycenter was concentrated in the city center; the barycenter of the low-temperature zone moved to the urban fringe. The ecological conservation development zone made the greatest contribution to the surface thermal landscape in metropolitan Beijing. The spatio-temporal distribution and evolution of the urban surface thermal landscape support management decisions aimed at alleviating the effect of the urban heat island.
Soil moisture is the link between the land surface and the atmosphere, which plays an important role in the hydrological cycle. As the "Third Pole" and "Asian Water Tower", the Tibetan Plateau has an important influence on the climate of the surrounding areas such as the formation and maintenance of the Asian monsoon and it also profoundly affects the availability of Asian water resources. Based on the measured soil moisture data from 100 stations distributed in the three climate zones on the Tibetan Plateau, this paper assesses the ECV, ERA, MERRA and Noah datasets, selects the best evaluated dataset for surface soil moisture, and analyzes the influence of various meteorological factors on spatial and temporal patterns of soil moisture changes. Finally, the paper evaluates the changes of surface soil moisture during the next about 100 years and explores possible climate causes. The results show that: (1) The Noah dataset has the best assessment of surface soil moisture in the Qinghai-Tibet Plateau during the historical period. Among all the regions, Naqu obtains the best assessment of surface soil moisture in each dataset. (2) Among various meteorological factors, precipitation is the most important factor affecting the temporal and spatial patterns of soil moisture in most areas, but the temperature and solar radiation have a relatively high impact in the Himalayas, especially on the north slope of the mountains. (3) The surface soil moisture had a significant downward trend from 1948 to 1970. However, it did not fluctuate obviously from 1970 to 1990. From 1990 to 2005, there existed a certain upward trend. Conversely, it has a rapid downward trend since 2005. (4) There is a downward trend for surface soil moisture in different future scenarios. Compared with the RCP2.6 and RCP4.5 scenarios, the soil moisture declines obviously with a more significant downward trend after 2080 under the RCP8.5 scenerio. (5) In the future, both precipitation and temperature show an upward trend. There was a downward trend for the drought index in the RCP8.5 scenario, whereas, there is no significant change under the RCP2.6 and RCP4.5 scenarios. The drought index can explain the change of surface soil moisture in the future to a certain extent.
Land surface temperature is one of the important parameters of scientific research such as resource environment, climate change and terrestrial ecosystem. MODIS LST (Land Surface Temperature, LST) products are important data sources for land surface temperature related research. The land surface temperature information of MODIS LST products is lost in the cloud coverage area. Therefore, the land surface temperature estimation of cloud coverage areas has become a frontier research problem of thermal infrared remote sensing. In order to solve the problem of missing land surface temperature information in the cloud occlusion area of MODIS LST products. In this paper, the Qinling area is used as the research area and the experimental data of MOD11A2 from 2001 to 2017 is selected. In the traditional Inverse Distance Weighting (IDW), Regular Spline (SPLINE), Ordinary Kriging (OK) and Trend Surface (TREND) spatial interpolation method, the important influence factor of elevation is introduced. Through a large number of spatial interpolation experiments, the traditional spatial interpolation method is improved, and a MODIS LST spatial interpolation method based on DEM correction is formed. Analysis of spatial interpolation results indicates: （1） The spatial interpolation accuracy is from high to low: OK> SPLINE > IDW>TREND, and the accuracy of the OK, SPLINE, IDW, and TREND methods based on DEM correction is increased by about 0.38°C, 0.31°C, 0.32°C, and 0.78°C, respectively; (2) The accuracy of spatial interpolation results shows seasonal differences. The interpolation accuracy is higher in summer, July, and August, and the interpolation accuracy is the lowest in January. （3）The interpolation accuracy has a certain relationship with the cloud area. When the cloud coverage area is less than 1.1km2, the interpolation error of the DEM+OK interpolation method is less than 0.55°C, and when the cloud coverage area is less than 3.1km2, the spatial interpolation error is less than 1°C. When the cloud coverage area is less than 2.7 km2, the interpolation error of the DEM+SPLINE method is less than 0.55°C, and the interpolation error of the DEM+SPLINE method is less than 1°C when the cloud coverage area is less than 10.4 km2. When the cloud coverage is 1.1~2.7 km2, the interpolation accuracy of DEM+SPLINE interpolation method is higher than of the DEM+OK interpolation method.
Taking the Chengguan District of Lanzhou City as a research area in the slope disaster-prone area, the surface deformation rate of surface deformation points is extracted by PS-InSAR technology, and the deformation rate can effectively reflect the distribution and uplifting of geological disasters in the spatial range. Based on the coKriging interpolation, combined with the generalized linear model (GLM) and the particle swarm optimization (PSO) algorithm, the coKrigong interpolation is optimized by fitting the linear model to construct the PSO-GLM-coKriging interpolation model to the surface deformation rate. The main variables, DEM, geotechnical porosity and NDVI fitting parameters were covariates, and spatial interpolation simulations were performed. Compared with the co-Kriging model and the GLM-co-Kriging model, the PSO-GLM-coKriging interpolation model has higher precision and better simulation effect, eliminating the complexity of multi-dimensional generation and improving the small-scale range. Interpolation effect, the error of the three models is 1.25mm/year, 0.70mm/year, 0.47mm/year. By comparison, the PSO-GLM-coKriging interpolation model has higher simulation accuracy and better simulation results. In the overall range, the interpolation results of the three models are similar in spatial distribution, in line with the actual situation of the surface. Therefore, the interpolation simulation of the blank area of ??the deformation point is carried out by the PSO-GLM interpolation model to fill the gap that the PS-InSAR technology can not extract the surface information at the non-deformation point, and the ground subsidence and uplift with sudden degeneration and sudden landslides will be completed. Geological disasters have been effectively combined, and the coupling relationship between geological disasters with high uncertainty and the monitoring of surface deformation can be established, which provides certain data and theoretical support for the planning and construction of urbanization in Chengguan District.
The spatial correlation between urban land surface temperature (LST) and vegetation coverage (NDVI) has been widely studied, but its scale effect is often ignored, which brings uncertainty to the results. Taking Zhengzhou City as an example and based on four Landsat8 images, this study retrieved the land surface temperature by the radiation conduction method, and identified the spatial correlation analysis scale of the land surface temperature by using the semivariance function. It then combined the spatial correlation index Moran's I to discuss the spatial correlation between land surface temperature and vegetation coverage from three aspects: multi-scales, multi-seasons, and multi-adjacent ranges. The results show that: (1) Both the univariate spatial autocorrelation scale and bivariate spatial correlation scale of LST and NDVI are around 300 m; (2) Within the 300 m correlation scale, there is a significant scale effect in the univariate spatial autocorrelation, but the scale effect of bivariate spatial correlation is much weaker by comparison; (3) The univariate spatial autocorrelation and bivariate spatial correlation scale effects of LST and NDVI show significant seasonal differences; (4) With the increase of adjacent range, the spatial autocorrelation of LST and NDVI weakens, and the scale effect is more obvious. Therefore, to measure the spatial correlation between LST and NDVI, spatiotemporal scale effect should be taken into consideration. This study should be helpful for further understanding the scale effect of spatial correlation between LST and NDVI.
Estuarine region is one of the most densely populated and prosperous area around the world, and it is also an eco-environmental vulnerable area which is more fragile to human activities. The acceleration of urbanization have inevitably resulted in a series of ecological and environmental problems on estuarine region, the thermal environment is a severe part of them. Higher temperatures and extreme heat not only hamper air quality but also increase energy consumption for cooling, threatening the health of urban residents. Based on the multi-source remote sensing images, characteristics of land surface temperature under the urbanization in Minjiang River estuary area were analyzed by using remote sensing techniques and statistical methods. With the help of Moran's I index, spatial clustering characteristic and scale effect of LST were examined. Further, the correlations between LST and different landscapes were found in quantitative analysis. The results showed that: (1) Built-up land area increased sharply from 1993 to 2013, showing a slow-rapid-steady and increasing process. A large number of large-scale edge-expansion was the primary growth type, meanwhile urban sprawl was mainly in east, west and south directions. (2) The area of sub-high and high temperature zone increased markedly, while the sub-low and middle temperature zone reduced; and there was no significant change in low temperature zone. Moreover, the spatial distribution of the high temperature region was consistent with built-up land expansion. (3) The LST exhibited an obvious disturbance characteristic; the temperature near city center presented dramatic changes and the temperature fluctuation in suburb was relatively smoother than Fuzhou city proper. On the other hand, the LST had a significant spatial clustering characteristic, and the spatial pattern of LST had a scale effect. (4) The dominance of built-up land significantly strengthened surface temperature, while increasing the dominance of vegetation and water could cool temperature. Cropland displayed no sign of cooling effect, the LST tended to be stable as the percentage of cropland increased. The results of the study can provide a useful reference for improving urban thermal environment and developing sustainable cities in estuarine regions.
Based on the domestic and foreign published papers about heavy metal contents of urban surface dust, the spatial variation of heavy metal contents in surface dust in China was explored. Totally, 69 studies on dust Cr, 84 on Cu, 86 on Pb, 79 on Zn and 58 on Cd were collected. Firstly, the abnormal values were picked up and removed, then the spatial distribution was obtained through Kriging method and average values in the provinces were calculated. Meanwhile, compared with the soil background values, the accumulation values of heavy metal contents were calculated. The results showed that the spatial distribution of heavy metal contents was universally high and had obvious regional difference. On the whole, the spatial distribution of Cu, Pb and Zn contents was similar, which was generally dominated by south-north trend from high contents to low contents, while the spatial distribution of Cr contents was high in the central part of the country and low in other parts, and Cd contents had no obvious trends in China. Compared with the corresponding background values of soil heavy metal concentration, nearly all provinces had higher Pb, Cu, Zn and Cd concentrations, except for the fact that Cr had no accumulation in Shandong and Guizhou provinces. The highest enrichment of Cd and Pb was found in Hunan province with 277.95 and 42.82 times of the background values, and the highest enrichment of Cr was in Fujian province with 7.11 times of the background values. The highest Cu enrichment was in Guangdong province with 1.35 times of the background values; and the highest Zn enrichment was in Jiangxi province with 39.13 times of the background values, respectively.
Rapid urbanization has led to land cover pattern changes which alter the surface net radiation and eventually influence surface energy balance. This process has been accompanied by a series of ecological and environmental problems, one of which is the urban heat island effect. Therefore, research on the seasonal variations of urban surface net radiation and its relationship to land cover pattern can provide important insights for exploring the formation and evolution mechanism of urban heat island. Taking Xiamen city as a study area, this research retrieved surface net radiation using Landsat-5 TM remote sensing images and meteorological data of the four seasons. Then the seasonal variation characteristics of surface net radiation were further analyzed. Landscape metrics were used to characterize and describe the spatial composition and allocation of land cover pattern. The correlation analysis, partial correlation analysis, stepwise regression analysis and variance partitioning were applied to explore the relationship between surface net radiation and land cover pattern from multi-seasonal perspective. The results suggest that: (1) the highest mean value of surface net radiation was found in summer, followed by spring, fall and winter. Surface net radiation is higher for the land cover types of water and forestland, while lower for built-up land and bare land. (2) The spatial allocation of land cover pattern has no significant influence on surface net radiation. (3) The spatial composition of land cover pattern shows significant influence on surface net radiation. The proportions of bare land and the proportion of forest land are effective and important factors which affect the changes of surface net radiation all the year round. And the proportion of forest land is the most important and continuously effective factor which affects and explains the cross-seasonal differences of surface net radiation. This research expands our scientific understanding of the effects of land cover pattern on surface net radiation. And it is helpful in exploring the formation and evolution mechanism of urban heat island. In addition, it may provide theoretical hints and realistic guidance for urban planning and sustainable development.
Anthropogenic heat discharge not only constitutes the cause of urban heat island (UHI) formation, but also is an important indicator related to energy consumption. It is important to analysis the magnitude and variation of anthropogenic heat discharge in order to mitigate UHI effect and improve energy efficiency. This paper examined the spatio-temporal variation of anthropogenic heat discharge in the Xiamen Island, China using Landsat TM data and meteorological data. First, the anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model. Then, the urban functional regions derived from IKONOS data were combined with the anthropogenic heat discharge. The results indicate that the anthropogenic heat discharge in different types of urban functional regions reaches the maximum in summer and the minimum in spring. The anthropogenic heat discharge of industrial area was higher than those in the other regions for all seasons. The high anthropogenic heat discharge occurred in the old industrial bases in the west of Xiamen Island. In traffic area, high anthropogenic heat discharge was observed in the Changan Road, Jiahe Road, Chenggong Avenue, Xianyue Road, North Hubin Road-Lvling Road, South Hubin Road-East Lianqian Road. In residential area, high anthropogenic heat discharge was observed in the old town. The high anthropogenic heat discharge occurred in the large single buildings in commercial and public area. Overall, the anthropogenic heat discharge in the western part of Xiamen Island was higher than that in the east. The differences of spatial and seasonal distribution were closely related to land cover types, population and the degree of economic development. Moreover, the density and height of the buildings and materials of land cover change the amount of anthropogenic heat discharge by affecting other surface fluxes. This paper brings a more microscopic perspective by analyzing the spatio-temporal variation of anthropogenic heat discharge in different urban functional regions to study urban thermal environment and energy utilization, as well as to provide a theoretical basis for promoting urban sustainable development.
The surface water and groundwater are very important for the farmland irrigation, industrial production and living water. Normally, there is transformation relationship between the surface water and groundwater. The mechanism of transformation relationship between surface water and shallow groundwater is a key factor role for the regional water cycle and the formation and management of water recourse. The Haihe River is an important river of the northern China. The water crisis caused by over exploitation for the groundwater has become the most important limiting factor for the development of the regional economy. This research based on the δ18O, δD and chemical data of different water samples of two sampling events were collected from the southern upstream of the Haihe River Basin (headstream of the Zhanghe River). We analyzed the hydrochemical and isotopic characteristic of groundwater and surface water during the two seasons by using the methods of study statistics, spatial interpolation analysis, Gibbs and Piper third-line graphs. Based on the two element mixed isotope hydrology separate model, we quantitative analysis the transformation relationship between groundwater and surface water for some sampling sites. The results shown that: 1) δ18O, δD and TDS of groundwater and surface river water samples of the headstream of Zhanghe River during wet season have significant variation. However, in the dry season, only the δ18O, δD and TDS of groundwater show the significant variation. 2) Whether wet season or dry season, the mainly water type are the Ca-HCO3·SO4 and the Ca-HCO3 for the shallow groundwater in the headstream of the Zhanghe River of upstream of the Haihe River Basin. The river water chemical type has significant season variation. The water type of river water were Ca-HCO3 changed to Na-Cl type during wet season, in wet season, due to the stronger evaporation, the river water chemical type were Ca.Na-Cl type. In wet season, the water type of river water and groundwater are similar and further indicate the conversion between the surface water and groundwater. There is obviously different between the water chemical type for precipitation, river water and groundwater during the dry season of headstream of the Zhanghe River. The results of Gibbs analysis shown, the groundwater and river water were controlled by the interaction between rock and water. 3) In dry season, the groundwater and river water have not significant interaction, however, the groundwater and river water shown strong transformation during wet season. About 10.95%-82.90% groundwater discharge from river water and the mean about 48.72%. Western headstream of the Haihe River shown larger interaction between groundwater and river water. The knowledge of these can promote effective management of water resources, and add new trace element data to the world water geochemistry.
Surface runoff change is affected by climate change and human activities. Quantitative assessment the impacts of climate change and human activities on surface runoff changes is significant for water resources management. This paper analyzes the mechanism that climate change and human activities affect the change of surface runoff based on the process of hydrological cycle and compares the methods that separate the impacts of climate change and human activities. Then, the paper analyzes the differences of the contribution rates of climate change and human activities on surface runoff of some watersheds on globe. At present, integrating multiple mutation test methods is beneficial for improving the accuracy in identifying the abrupt change point of surface runoff. Eliminating the interference factors (such as the selection of meteorological and hydrological data, the parameter setting of model method and inherent uncertainty of methods) is of great significance to improve the consistency of the results of different quantitative methods. The key of future research is to find ways that could better couple the physical hydrological model methods and mathematical empirical methods to separate the impact of climate change and human activities on the change of surface runoff.
Owning to that the pixels in natural terrains are prone to spatial-temporal decorrelation during the long-term observation, using time-series InSAR (Synthetic Aperture Interferometry) technique to carry out deformation monitoring of natural terrains will face the challenge of lacking of available deformation measurement points. To solve this problem, an improved Small Baseline Subset (SBAS) method is proposed. It improves the selection process of initial high coherent pixels and phase filtering in conventional SBAS. Firstly, it uses the goodness of fit and the coherence threshold condition to identify statistically homogeneous pixels (SHP). After this, all pixels are divided into two parts base on the number of SHP, i.e. Persistent Scatterers (PS) candidates and Distributed Scatterers (DS) candidates. Then, initial high coherent PS and DS are selected from these two parts respectively. Finally those selected high coherent PS and DS are filtered by a weighted phase filter. The deformation monitoring experiment with 27 ENVISAT ASAR images, acquired over the northwest part of Beijing plain shows that: compared with StaMPS-PS (refers to the PS-InSAR in StaMPS) method and StaMPS-SBAS (refers to the SBAS in StaMPS) method, the improved method can effectively extend the quantity and coverage of deformation measurement points. The quantity of measurement points is increased by 22.6% and 27.6% respectively, and the deformation result of natural terrains is improved effectively. The deformation result of this study area is in good agreement with the displacement of 4 continuous GPS stations. Experimental results prove the effectiveness and superiority of this method in the inversion of ground deformation.
Comparison of surface radiation data of ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis data and data from station observation (China Meteorological Administration) is conducted at different time scales to check whether reanalysis data can reflect the characteristics of surface solar radiation over China. Based on the cluster analysis method, China is divided into 8 regions in order to study the regional differences of the surface radiation products of the ECMWF reanalysis data in China. Taking into account the influence of atmospheric factors on the earth's surface radiation and the spatial stratified heterogeneity of the atmospheric distribution, the geographical detector is used to find the causes of errors in different sites of reanalysis data. Overall, ECMWF is higher than the ground observation station data and the monthly deviation is 18.2835W/m2. ECMWF shows seasonal difference, greater deviation in spring and winter, less deviation in summer and autumn. Large relative deviation of the data mainly distributed in December, January, February and March while minor relative deviation of the data mainly concentrated in July, June, August and September. The dominant atmospheric factors in different regions are different in winter and summer. In summer, from zone 1 to 5 the dominant factors are aerosols and the power of determinant is larger. The dominant factors of the zone 6 are albedo and aerosol. The dominant factors of the zone 7 are cloud cover and aerosol but the power of determinant is small, merely 0.0228 and 0.0202, respectively. Failing significance test indicates that the four factors had no significant effect on the relative deviation in the zone 8. In winter, the dominant factors of zone 4, 6, 8 and zone 1, 3, 5, 7 are aerosol and cloud coverage, respectively.
Impervious surface is considered as an indicator of urban ecological environment and impervious surface area data, which is important to urban planning and environmental and resources management. The reconciliation between the V-I-S model and LSMA provided a continuum field model, which offered an alternative, effective approach for characterizing and quantifying the spatial and temporal changes of impervious surface. In this article, we extracted the impervious surface information from Landsat images of 2002, 2009 and 2015 within the metropolitan area of Wuhan by a fully constrained linear spectral mixture model based on the vegetation-impervious surface-soil (V-I-S) model. Here, gradient analysis was adopted to analyze spatial distribution and four different landscape indicators were chosen to analysis landscape patterns dynamics of impervious surface from 2002 to 2015. Rusults of this study were as follows: The average impervious surface coverage of Wuhan was respectively 27.53% in 2002, 34.65% in 2009 and 40.51% in 2015, which showed a trend of rising. The areas of high impervious surface coverage value of Wuhan are mainly distributed along the Yangtze River and Han River as well as in some secondary centers such as Jiangxia and Hannan and that of low value are mainly distributed in suburban counties. For the period 2002 to 2009, areas of new impervious surfaces mainly formed around existing urban areas and mostly concentrated in circumjacent areas of Wuhan Economy and Technology Development Zone and Donghu New Technology Development Zone as well. After 2009, it was observed that the distribution of new impervious surfaces was scattered. The impervious surface coverage of main urban area is obviously higher than new urban district, but the impervious surface coverage of new urban district increase rapidly. With the increase of the distance to downtown, impervious surface coverage tended to be stable after progressive decrease. The range of 10km outside the third-ring road and 4 km within it was the area of largest increment of average impervious surface coverage. Landscape pattern analysis results showed that natural surface and area of very high density impervious surface had a low degree of fragmentation, strong spatial continuity and a very simple shape. Conversely, low density and medium density impervious surface area had a high degree of fragmentation, weakest spatial continuity and a very complex shape. The patches shape shows that natural surface had a high dominant position in 2002, but after that changed to high density impervious surface after 2015.
Soil moisture is a key factor in the energy and water balance of the earth's surface, and it also plays an important role in the ecological environment. Soil moisture inversions based on Synthetic Aperture Radar （SAR） have shown promising progress but do not easily meet expected application requirements because a number of inversion algorithms cannot quantify the uncertainty of soil moisture inversions. Uncertainty of surface roughness is the main factor that causes uncertainty of SAR-retrieved soil moisture. Most of the existing studies focused on the uncertainty of single roughness parameter (correlation length), and seldom directly studied the uncertainty of surface combined roughness. The uncertainty was usually estimated by probability distribution of model parameter values in existing studies. Then, the probability distribution was propagated through the inversion process. Finally, the probability distribution of soil moisture inversion was obtained. The uncertainty was quantified by using skewness, kurtosis, and interquartile range in this paper. First of all, the range and distribution of the measured soil moisture data and roughness data in sampling area were counted and analyzed. Input values and scope of the AIEM model parameters were obtained. Then, effective correlation length was calculated by using the LUT (look up tables) method based on the measured soil moisture data and backscattering coefficients, and the effective combined roughness was obtained. The nonlinear relationship between the effective combined roughness and backscattering coefficients was constructed. By adding different levels of Gaussian noise to surface combined roughness, the uncertainty propagating of surface combined roughness in the process of retrieved soil moisture was studied, and the uncertainty of soil moisture retrieval was quantitatively analyzed. For each Gauss noise level, 1000 effective combined roughness sampling values with noise were obtained. By using the nonlinear relationship between the effective combined roughness and the backscattering coefficient, the backscattering coefficients corresponding to the sampling value of each effective combined roughness were derived. The soil moisture was obtained by using the empirical equation of soil moisture inversion. The skewness, kurtosis and interquartile range of the effective combined roughness and the inversion results were calculated. By using the AIEM （Advanced Integrated Equation Model） model and the limited range of input parameters, a large number of simulated data were obtained. The effective combined roughness of the simulation was introduced into different proportion error according to the initial value, and the soil moisture was obtained by the empirical equation of soil moisture inversion. Furthermore, according to the response characteristics between RMSE （Root Mean Square Error） of retrieved soil moisture and the error range of combined roughness, the error control range that meets the inversion accuracy requirement was obtained. The experimental results of sample area show that kurtosis range is -0.1984 to 1.2501, the deviation range is 0.0191 to 0.6791, and interquartile range is 0.0018 to 0.0167 when gaussian noise standard deviation range of composite roughness is 0 to 0.045. Also, these three quantitative indexes increase with the increase of combined roughness gaussian noise. Soil moisture inversion values tend to be concentrated near mode, and the tendency to underestimate soil moisture is more obvious than the overestimation tendency. The error range of combined roughness should be controlled within a certain range of the initial value to meet the inversion accuracy requirement, and it is negatively related to the incident angle. The error control range is suitable for bare soil with low surface roughness and low sparse vegetation coverage area.
Evapotranspiration (ET) plays an important role in the hydrological process as it is a major part in the ecological water balance. The surface ET can substantially influence on a regional scale the amount and spatial distribution of water resources. In arid lands like Xinjiang Uygur Autonomous Region located in Northwest China, ET is the main loss variable in water budget. It varies with land surface and local meteorological conditions. Quantitative estimation of spatio-temporal distribution and evolution of surface ET is essential for understanding the hydrological cycle and water resources management. Based on the measured data from MOD16 evapotranspiration product and meteorological stations, the spatio-temporal distribution and the evolution trend of land surface ET and potential evapotranspiration (PET) in Xinjiang during 2000-2014 were analyzed to further reveal the relationship between ET and PET. Results showed that: (1) the accuracy of the MOD16-ET (R2=0.83) in Xinjiang can meet the requirements, and can be used to examine the spatio-temporal distribution of surface ET; (2) the mean annual ET and PET were 364.29 mm and 1584.06 mm, respectively; the annual distribution was a unimodal pattern with an increase first then a decrease, the difference between ET and PET was the largest in summer, when there was a water shortage in the study area; (3) the mean ET and PET in northern Xinjiang are bigger than in southern Xinjiang, and that they are bigger in the west than in the east. The spatial distribution of PET was opposite to that of ET. The Altai Mountains, west shore of the Ili River Valley and western Tianshan Mountains had sufficient water supply, while the north and south of the Junggar Basin, the outer margin of eastern and western Xinjiang suffered from drought and water shortage; (4) during 2000-2014, ET was in a decreasing trend, and PET was in an increasing trend, which suggested that the drought was aggravated in Xinjiang in the past 15 years.
Taking Huoxi Coal Mine Area in Shanxi Province as the research area, we conducted numerical modeling and quantitative evaluation of landslide susceptibility using remote sensing and GIS technology. Based on the DEM with spatial resolution of 30 m × 30 m, five topographical parameters were derived: elevation, slope angle, slope aspect, plan curvature and profile curvature. Stratigraphic lithology was digitized based on the geological maps from Department of Geological Survey in 1:50 000 scale. Fault network, drainage network and road were digitized based on the geological maps and other thematic maps from Department of Land Resource in 1:50 000 scale. Then, buffer for faults, drainage, and road were done. Mining disturbance were digitized based on the planning maps of coal resources. If the point falls in the mine area, it is proved to be disturbed by the mining disturbance, otherwise is not affected. NDVI and land-use types interpreted and computed the Landsat TM images. Landslide data was collected by Bureau of Land and Resources and it is represented by the X, Y coordinates of its central point. Then, the correlation characteristics among evaluation factors and the spatial distribution of landslides were acquired by using remote sensing technology and GIS spatial analysis method. Repeated 5-fold cross validation method was adopted in this research and the landslide/non-landslide datasets were randomly split into a ratio of 80:20 for training and validating models. Based on the methods of the 5-fold cross-validation and the fitting accuracy to the constructed the landslide susceptibility assessment model-Radial Basis Function - Support Vector Machine (RBF-SVM), the precision of the models was quantitatively assessed. We calculated the importance of each evaluation factor in the RBF-SVM model. Meanwhile, we obtained landslide susceptibility map of Huoxi Coal Mine Area based on the RBF-SVM model. The landslide susceptibility of Huoxi Coal Mine Area was divided into four scales referencing the quantile law: low (0-0.02), medium (0.02-0.1), high (0.1-0.85) and very high (0.85-1) probability of landslide. The results show that: (1) the fitting accuracy was 87.22% in the modeling phase and 70.12% in the validation phase, respectively, for the RBF-SVM model; (2) it indicated that lithology, distance from road, slope aspect, elevation and land-use types have contribution to each model. Therefore, these five factors are most suitable conditioning factors for landslide susceptibility mapping in this area. Mining disturbance factors have little contribution to the model. The mining method in this area is underground mining and the mining depth is very deep affecting the stability of the slopes. (3) The number of landslides points in the very high region was 316, which account for 93.49% of the total number of landslides points and 50.99% of the total area. This study obtained the spatial distribution characteristics of the Huoxi Coalfield geological disasters and the quantitative evaluation of landslide susceptibility. It provides reference for the investigation about artificial slope in the research area monitoring the rational mining coal resources. It will also provide the reference for the related research in other similar coal region and management work.
Land surface temperature (LST) is an important parameter driving dynamics of biogeophysical processes on Earth surface. It has significant impacts on the distribution of permafrost and the change of the active layer depth. Conventional acquisition of LST data usually comes from weather station monitoring in a small and discrete scope. NASA's MOD11 A1 surface temperature product can provide a wide range of surface temperature data. In winter, however, the confusion of clouds and snow often leads to a large amount of data missing in the MOD11 A1 products in the permafrost region. In this paper, an improved split-window algorithm was selected to re-build the LST products in Northeast China, one of the major permafrost regions in China. Within the common land covers extracted from remote sensing classification results, such as vegetation, bare soil, water and snow. We extracted LST in each cover type from four cloud-free MODIS 1B satellite images in 2006. Both our results and the original MOD11 A1 products were statistically compared with ground measurements at weather stations. The average difference between our results and measurements at meteorological stations was small, reaching a room-mean-square error (RMSE) of 1.24 ℃. In comparison with the original MOD11 A1 products, our results took advantage of land covers and revealed better distributions of land surface temperature in snow area, and had a high consistency with the surface temperature products. This study provides a good approach to filling in the gaps of current land surface temperature products due to confusion caused by the cloud and snow.
Classification and regression tree (CART) algorithm was used to extract the impervious surface percentage (ISP) of Beijing six ring within the city in 2001 and 2011 based on QuickBird high-resolution images, Landsat TM and night light data. The method is suitable for typical temperate semi-arid climate area. We classified the ISP as three groups. The ISP region for 10%~60% is defined as a low-density region, 60%~80% is defined as a medium-density area and for the rest area which the ISP is more than 80% is high-density area. Meanwhile, based on the Landsat TM, the land surface temperature of 2001 and 2011 were retrieved by using the single window algorithm. The study area has been designated as a region within six rings of Beijing. According to the characteristic of Beijing urban layout, this paper analyzed the development trend of ISP in different ring road and its correlation with land surface temperature from 2001 to 2011. In order to seek an effective way to improve the ecological environment in Beijing and mitigate the urban heat island effect. The results are as follows: (1) The change of ISP in Beijing urban area mainly concentrate in the low-density area. In comparison with the low-density area, the ISP of middle-densitiy area and high-density area does not change so much. Due to the vigorous city construction within the fifth ring road from 2001 to 2011, the whole change of ISP is not obvious. The variation mainly concentrates in the region between the fifth ring road and the sixth ring road, in which the growth of low density area was significant, while the growth in the middle and high density area was mainly in the east part. From the above results, it can be concluded that the development within the fifth ring road and sixth ring road develops rapidly and the range of city construction continued in recent years. (2) Compared with 2001, the land surface temperature of the central areas in Beijing in 2011 increased dramatically, and the aggregation extent of high-temperature region is more evident. The temperature difference increased significantly between the region within the fourth ring road and the surrounding areas. (3) By comparing the average surface temperature of each density area in 2001 and 2011 we find that compared with 2001, the differences in land surface temperature in 2001 also increased between different ISP categories and the urban heat island effect was more and more remarkable. (4) In both 2001 and 2011, there is a positive correlation between the land surface temperature and the ISP in every ring road region of Beijing urban areas. In the regions between the fourth and sixth ring road, the land surface temperature and the ISP shares a similar change trend. In the regions with ISP between 10% and 20%, the rising rate of land surface temperature is obviously higher than other regions. In the regions with ISP higher than 20%, the rising rate of surface temperature decreases, and the change tends is uniform.
As one of the important outcomes of the National Geographic Census of China, the land cover classification reveals the information of both natural and artificial coverage elements, including vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various artificial structures. Obviously, it mainly focuses on profiling the natural characters of the land surface with temporal and distribution attributes, which has an obviously different classification system from other scene classification applications. In recent years, more and more high-resolution remote sensing platforms become available, it is possible to update and evaluate land cover classification quickly with the advantage of huge volume of data and more frequent data updates. Meanwhile, in practice we face with more and more challenges of the huge data. In this paper, we propose a novel approach for evaluating the land cover classification results by combining the object-oriented method with the Deep Convolutional Neural Network (D-CNN) model. With deeper structure and wilder receptive field, the deep neural network has the capability of abstract description from low-level features, and the deep learning has become one of the latest development trends in the artificial neural network field. The deep learning shows a completely different possibility in many fields, and it has been applied to the speech recognition, image recognition, information retrieval and so on. The newly-developed method of image recognition based on deep leaning has been preliminarily verified in the scene classification field. Traditionally, the land cover classification method is established on the pixel-based classifying. The latest improved method of the object-oriented classification frame has been proposed, but this new frame is hard to be achieved because of the lack of supports from efficient methods and algorithms. Nowadays, the deep neural network provides us an effective tool to achieve the object-oriented classification by clipping image spots from original images and inputting the clipped image spots to D-CNN. The D-CNN model can convolute and pool the image spots to realize the object-oriented classification of the land cover. By the combination of the object-oriented classification with the deep learning, the proposed method can extract more and better abstract features than the pixel-based approach, while the pixel-based method requires more manual interventions. When applying the deep learning method to land cover classification recognition, the prepared image spots as appropriate inputs will be automatically scored to its belonging classes. Thus, the score represent the degree of membership of the image spot matching to the corresponding class. By fine-tuning the D-CNN, we can obtain a new approach of judging the quality of the samples, in order to assure the reliability of the proposed approach. The fine-tuned D-CNN is required to be sufficiently robust, and we verify its robustness in the following experiment by employing the AlexNet. The experimental results show that the image spots of arable land and building can be recognized with the membership degree of 99.95% and 99.41%, but those of woodland and water area can be recognized only with the membership degree of 62.73% and 43.59%. Obviously, the proposed model can achieve the promising reliability that is related to the qualified and sufficient data set of the image spots which is used for fine-tuning of the net. The reason for poor robustness of the fine-tuned AlexNet in classifying the woodland and water area may be the insufficient size of data-set of these two classes. It shows that a fine-tuned deep convolutional neural network as a new model can be utilized in evaluating the land cover classification with high reliability.
IImpervious surface (IS) is often recognized as the indicator of regional ecosystems and environmental changes. Its spatio-temporal dynamics and ecological effects have been studied by many researchers, especially for the IS in Beijing municipality. However, most previous relevant studies examined Beijing as a whole without considering the differences and heterogeneity among the functional zones. In this study, the urban expansion in Beijing in some typical years (1991, 2001, 2005, 2011 and 2015) was analyzed by sub-pixel IS that obtained through the simulation of CART and change detection models. Then the spatio-temporal dynamics and variations of IS (1991, 2001, 2011 and 2015) in different functional zones and counties were analyzed based on the method of standard deviation ellipse, Lorenz curve, contribution index (CI) and landscape theory. It is found that the total area of impervious surface in Beijing increased dramatically from 1991 to 2015, increasing about 144.18%. The deflection angle of major axis of standard deviation ellipse decreased from 47.15° to 38.82°, indicating a trend that the major development axis in Beijing moved from the northeast-southwest orientation to the north-south orientation. Moreover, the heterogeneity of IS distribution in different counties weakened gradually but the CI values and landscapes in different zones differed greatly. Urban function extended zone (UFEZ) had the highest CI value, which means it played the most important role in the growth of IS in Beijing, and its lowest CI value was 1.79 during the study period, which is much greater than the highest CI values of other functional zones. Core functional zone (CFZ) contributed less than UFEZ, but it has the highest CONTAG value, showing a more connected IS landscape compared with other zones. The CI values of new urban developed zone (NUDZ) increased rapidly from 1991 to 2015, which increased from negative to positive and multiplied, indicating the NUDZ has become the main source for the growth of IS in Beijing gradually. However, the ecological conservation zone made a negative contribution at all times, and its CI value decreased constantly. In addition, the variations of landscape indices and centroids of impervious surface in different density classes indicate that the high-density impervious surface had a more compact configuration and a greater impact on the ecological environment.
The emissivity of natural surfaces is a major parameter determining land surface temperature（LST）. In addition,the surface cover type influences emissivity and soil moisture is closely related to emissivity. Here,methods for obtaining the emissivity of bare soil using the MODIS generalized split-window algorithm and Landsat mono-window algorithm were improved. According to the empirical logarithm linear formula between soil moisture and soil emissivity,based on remote sensing data and ground observation data from the Soil Moisture Experiment 2004 （SMEX04）- Arizona study area,we discuss if the accuracy of land surface temperature retrieval can be improved when surface emissivity acquisition methods consider effects of soil moisture. We found that the accuracy of both improved algorithms considering soil moisture effects were better than algorithms not considering soil moisture effects. The mean error of LST retrieved by the improved MODIS generalized split-window algorithm reduced 1.0~1.5K,and the root mean square error reduced by 0.4~0.8K. Moreover,the mean error of LST retrieved by the improved Landsat mono-window algorithm reduced by 0.7K,and the root mean square error reduced by 0.9K. As a whole,the accuracy of land surface temperature retrieval can be improved when surface emissivity acquisition methods consider the effects of soil moisture,especially areas where vegetation coverage is less. Sensitivity analysis results show that the influence of remote sensing soil moisture data with a 0.04cm3/cm3 error on LST retrieval algorithms considering soil moisture effects is not obvious.
The response and feedback of land surface processes to climate change constitute a research priority in the field of geosciences. Previous studies have focused on the impacts of global climate change on land surface processes; however, the feedback of land surface processes to climate change remains unknown. It has become increasingly meaningful under the framework of Earth system science to understand systematically the relationships between agricultural phenology dynamics and biophysical processes, as well as their feedback to climate change. This study summarized research progress in this field, including agricultural phenology change, parameterization of phenology dynamics in land surface process models, and the influence of agricultural phenology dynamics on biophysical processes, as well as its feedback to climate. The results showed that the agricultural phenophase, represented by paramount phenological phases such as sowing, flowering, and maturity, has shifted significantly because of the impacts of climate change and agronomic management. Digital expressions of dynamic land surface processes, as well as biophysical and atmospheric processes, have been improved by coupling phenology dynamics in land surface models. Agricultural phenology dynamics influence net radiation, latent heat, sensible heat, the albedo, temperature, precipitation, and circulation, thus, play an important role in surface energy partitioning and climate feedback. Considering the importance of agricultural phenology dynamics in land surface biophysical processes and climate feedback, the following research priorities have been identified: (1) interactions between climate change and land surface phenology dynamics, (2) relationships between agricultural phenology dynamics and different land surface reflectivity spectra, (3) contributions of changes in crop physiological characteristics to land surface biophysical processes, and (4) regional differences of climate feedback from phenology dynamics in different climatic zones. This review will be helpful in accelerating the understanding of the role of agricultural phenology dynamics in land surface processes and climate feedback.
Land surface energy information of remote sensing describes the ecological process of regional ecosystem elements. The distribution and variation trends of land surface energy reflect structure and quality of regional ecosystem element. This study is based on the theory of ecology and aims to provide a scientific basis of preservation and restoration of forests in decision-making, prediction, implementation, verification and other aspects. In this study, we extracted the information about the comprehensive responses and interactive relationship between tropical rain forest and land surface energy in Sanya, using classes of vegetation greenness, land surface energy and the vegetation-energy relationship index to evaluate the quality of forest ecosystem. Vertical and horizontal distributions of tropical rain forest of 30 years (1987-2016) were used to discuss a change of spatial-temporal zonality. The following results are noted: (1) With around 90% of vegetation coverage in the past 30 years, classes of vegetation greenness are mainly composed of high and medium values, and has an increasing trend. (2) The low vegetation greenness and high land surface energy shifts to high vegetation greenness and low land surface energy from coastal area to mountain area. (3) The fluctuation of land surface energy distribution at all levels was less than 10%. Regions with medium energy expanded to low energy areas. (4) Tropical rain forest of high vegetation greenness increases with elevation increasing associated with land surface energy decreasing. (5) The ecological quality of the planted vegetation regions below 200 meters height, declined faster than that of planted vegetation regions above 400 meters height. Compared with planted vegetation regions, tropical rain forest regions have high spatial-temporally stability in both surface energy and vegetation greenness. In general, comprehensive response characteristics of remote sensing and their interactive relationship provide quantitative basis for evaluating the tropical rain forest ecosystems.
It is important to apply impervious surface product to the study of urban agglomeration spatial structure. Under the background of China's rapid urbanization, study on comparative analysis of urban agglomerations spatial structure between China and USA can provide policy proposals of urban agglomeration space optimization for Chinese government. Taking Beijing-Tianjin-Hebei (BTH) and Boswash as study areas, firstly, this paper maps the extent and density of impervious surface for BTH and Boswash respectively in seven periods of 1972, 1982, 1991, 1995, 2001, 2006 and 2011. Furthermore, at different scales of urban agglomeration and metropolitan region, landscape pattern index, gravitational index and spatial analysis were used to analyze differences of spatial structure between BTH and Boswash. The results showed that (1) impervious surface area increased rapidly in BTH, while it remained stable in Boswash. (2) BTH spatial structure had experienced different periods including isolated city development, dual-core city development, group city development and network-style city development, while Boswash spatial structure was more stable, and its spatial pattern had shown a "point-axis strip" feature. (3) The spatial pattern of high-high assembling region of impervious surface had showed a "standing pancake" feature in BTH, while a "multi-center gather and disperse group" in Boswash. (4) All of the percentages of impervious surface area in ecology, living, and production land in BTH were higher than those in Boswash. At last, from the perspective of urban agglomeration space optimization, the development proposals for BTH were proposed.
Climate change and surface processes
