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Ren H.,Peking University | Du C.,Peking University | Qin Q.,Peking University | Liu R.,Beijing Normal University | And 2 more authors.
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2014

This objective of this paper is to estimate atmospheric water vapor (wv) from the latest Landsat 8 Thermal InfRared Sensor (TIRS) image by using a new modified split-window covariance-variance ratio (MSWCVR) method. Model analysis showed that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.45g/cm2for most atmospheric moisture conditions. The MSWCVR was evaluated by using AERONET ground-measured data and cross-compared with MODIS products in 2013 at forty two ground sites, and results presented that the retrieved wv from TIRS data was highly correlated with but generally larger (about 1.0 g/cm2) than two others. The reasons for this uncertainty were mainly ascribed to data systematic noise and radiative calibration error. Future work must pay more attention to the data quality and radiative calibration of Landsat 8 TIRS data. © 2014 IEEE.


Du C.,Peking University | Ren H.,Peking University | Qin Q.,Peking University | Meng J.,Peking University | Li J.,Satellite Environmental Application Center
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2014

On the basis of the thermal infrared radiative transfer theory, this paper addressed the retrieval of Land Surface Temperature (LST) from Landsat 8-the latest satellite in the Landsat Data Continuity Mission (LDCM) project in two thermal infrared channels, using the Generalized Split-Window (GSW) algorithm. Meanwhile, a linear bidirectional reflectance distribution function (BRDF) models were used to estimate the emissivity according to different surface classification. A series of ranging of typical surface emissivity and the atmospheric water vapor content (WV) were used into an accurate atmospheric radiative transfer model MODTRAN 4.3 to derive the coefficients in the algorithm. The simulation result showed the LST estimated by the algorithm with the Root Mean Square Error (RMSE) is 1.26K for the all ranges of the atmospheric WV and the results could be better in lower atmospheric WV condition. © 2014 IEEE.


Du C.,Peking University | Ren H.,Peking University | Qin Q.,Peking University | Meng J.,Peking University | Zhao S.,Satellite Environmental Application Center
Remote Sensing | Year: 2015

This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance-variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future. © 2014 by the authors.


Sun G.,China Agricultural University | Sun G.,CAS Institute of Remote Sensing | Wan H.,Satellite Environmental Application Center | Wang C.,Satellite Environmental Application Center
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | Year: 2013

The "Golden Triangle" of MSGN (Inner Mongolia, Shaanxi, Gansu, Ningxia), a region with abundant energy resources, is a new energy base of China. To get the vegetation coverage status and its dynamic change, the 2000-2009 SPOT-VEGTATION NDVI (normalized difference vegetation index) of Golden Triangle energy in MSGN region was used to calculate the maximum vegetation coverage in one-year of every pixel, and the variation between ten years was analyzed. Result showed that the FVC (fractional vegetation cover) in Golden Triangle area was increased generally from 2000 to 2009. The highest average value was 45.45% in 2008, and the lowest average value was 33.74% in 2000. The vegetation coverage was declined slightly in 2005 and 2009, and the main reasons were attributed to city expansion and regional resources development.


Chen B.,Chinese National Engineering Research Center for Information Technology in Agriculture | Chen B.,Chinese Academy of Fishery Sciences | Pan Y.,Chinese National Engineering Research Center for Information Technology in Agriculture | Wang J.,Chinese National Engineering Research Center for Information Technology in Agriculture | And 4 more authors.
Mathematical and Computer Modelling | Year: 2013

Since van Groenigen and Stein (1998) proposed the SSA+MMSD (Spatial Simulated Annealing +Minimization of the Mean of Shortest Distances criterion) method, this method has found many applications in the optimization of sampling designs. However, it is computationally inefficient due to the complexity of this method itself. Initially in this paper, we analyze the computational complexity associated with this method from both SSA and MMSD aspects. And then, we propose some corresponding revisions (including the initial solution, perturbation rules, as well as the objective function) accordingly so as to reduce its computations. Finally, we evaluate the efficiency improvement via comparing some efficiency indexes of both original and modified methods (including the total perturbations needed, valid and better candidate designs generating rates of the perturbations, and the rate of objective function decline). Analysis and experimental results indicate that the modified method is much more efficient than the original one; in C++ implementations, the mean execution time needed for the modified method is only about 1/3 of that of the original. © 2011 Elsevier Ltd.


PubMed | China University of Mining and Technology, University of Chinese Academy of Sciences, Satellite Environmental Application Center, China Institute of Water Resources and Hydropower Research and CAS Institute of Geographical Sciences and Natural Resources Research
Type: | Journal: BioMed research international | Year: 2015

Epidemiological studies around the world have reported that fine particulate matter (PM2.5) is closely associated with human health. The distribution of PM2.5 concentrations is influenced by multiple geographic and socioeconomic factors. Using a remote-sensing-derived PM2.5 dataset, this paper explores the relationship between PM2.5 concentrations and meteorological parameters and their spatial variance in China for the period 2001-2010. The spatial variations of the relationships between the annual average PM2.5, the annual average precipitation (AAP), and the annual average temperature (AAT) were evaluated using the Geographically Weighted Regression (GWR) model. The results indicated that PM2.5 had a strong and stable correlation with meteorological parameters. In particular, PM2.5 had a negative correlation with precipitation and a positive correlation with temperature. In addition, the relationship between the variables changed over space, and the strong negative correlation between PM2.5 and the AAP mainly appeared in the warm temperate semihumid region and northern subtropical humid region in 2001 and 2010, with some localized differences. The strong positive correlation between the PM2.5 and the AAT mainly occurred in the mid-temperate semiarid region, the humid, semihumid, and semiarid warm temperate regions, and the northern subtropical humid region in 2001 and 2010.


PubMed | China University of Mining and Technology, CAS Institute of Geographical Sciences and Natural Resources Research, Satellite Environmental Application Center and University of Chinese Academy of Sciences
Type: Journal Article | Journal: BMJ open | Year: 2015

To explore the association between Particulate Matter (PM)2.5 (particles with an aerodynamic diameter less than 2.5m) and lung cancer mortality rates and to estimate the potential risk of lung cancer mortality related to exposure to high PM2.5 concentrations.Geographically weighted regression was performed to evaluate the relation between PM2.5 concentrations and lung cancer mortality for males, females and for both sexes combined, in 2008, based on newly available long-term data. Lung cancer fatalities from long-term exposure to PM2.5 were calculated according to studies by Pope III et al and the WHO air quality guidelines (AQGs).31 provinces in China.PM2.5 was associated with the lung cancer mortality of males, females and both sexes combined, in China, although there were exceptions in several regions, for males and females. The number of lung cancer fatalities calculated by the WHO AQGs ranged from 531,036 to 532,004, whereas the number calculated by the American Cancer Society (ACS) reached 614,860 after long-term (approximately 3-4years) exposure to PM2.5 concentrations since 2008.There is a positive correlation between PM2.5 and lung cancer mortality rate, and the relationship between them varies across the entire country of China. The number of lung cancer fatalities estimated by ACS was closer to the actual data than those of the WHO AQGs. Therefore, the ACS estimate of increased risk of lung cancer mortality from long-term exposure to PM2.5 might be more applicable for evaluating lung cancer fatalities in China than the WHO estimate.


Wan H.-W.,Satellite Environmental Application Center | Kang J.,CAS Institute of Remote Sensing | Kang J.,University of Chinese Academy of Sciences | Gao S.,CAS Institute of Remote Sensing | Shen W.-M.,Satellite Environmental Application Center
Zhongguo Huanjing Kexue/China Environmental Science | Year: 2016

Water area dynamic change of the Hulun lake was analyzed from 2000 to 2013 using long time-series MODIS data and water index dynamic analysis method. The driving force of the change was also analyzed by combining meteorological data. The preliminary results showed that the water area of the lake decreased from 2286 km2 in 2000 to 1773 km2 in 2012 and the decreasing rate was 22.4%. The drastic changes mostly happened in the northeast and south of the lake. Due to the great increase of precipitation in 2013, the water area restored to 1883 km2 and the main growth happened in the south of the lake. The driving force analysis showed the variation of water area correlated negatively with an average annual temperature and positively with the annual total precipitation. However, the level of significance was for the precipitation higher than temperature. © 2016, Chinese Society for Environmental Sciences. All right reserved.


Song D.,CAS Institute of Geographical Sciences and Natural Resources Research | Zhuang D.,CAS Institute of Geographical Sciences and Natural Resources Research | Jiang D.,CAS Institute of Geographical Sciences and Natural Resources Research | Fu J.,CAS Institute of Geographical Sciences and Natural Resources Research | Wang Q.,Satellite Environmental Application Center
International Journal of Environmental Research and Public Health | Year: 2015

The purpose of this study was to assess soil heavy metal contamination and the potential risk for local residents in Suxian county of Hunan Province, southern China. Soil, rice and vegetable samples from the areas near the mining industrial districts were sampled and analyzed. The results indicate that the anthropogenic mining activities have caused local agricultural soil contamination with As, Pb, Cu and Cd in the ranges of 8.47–341.33 mg/kg, 19.91–837.52 mg/kg, 8.41–148.73 mg/kg and 0.35–6.47 mg/kg, respectively. GIS-based mapping shows that soil heavy metal concentrations abruptly diminish with increasing distance from the polluting source. The concentrations of As, Pb, Cu and Cd found in rice were in the ranges of 0.02–1.48 mg/kg, 0.66–5.78 mg/kg, 0.09–6.75 mg/kg, and up to 1.39 mg/kg, respectively. Most of these concentrations exceed their maximum permissible levels for contaminants in foods in China. Heavy metals accumulate to significantly different levels between leafy vegetables and non-leafy vegetables. Food consumption and soil ingestion exposure are the two routes that contribute to the average daily intake dose of heavy metals for local adults. Moreover, the total hazard indices of As, Pb and Cd are greater than or close to the safety threshold of 1. Long-term As, Pb and Cd exposure through the regular consumption of the soil, rice and vegetables in the investigated area poses potential health problems to residents in the vicinity of the mining industry. © 2015 by the authors; licensee MDPI, Basel, Switzerland.


PubMed | CAS Institute of Geographical Sciences and Natural Resources Research and Satellite Environmental Application Center
Type: Journal Article | Journal: International journal of environmental research and public health | Year: 2015

The purpose of this study was to assess soil heavy metal contamination and the potential risk for local residents in Suxian county of Hunan Province, southern China. Soil, rice and vegetable samples from the areas near the mining industrial districts were sampled and analyzed. The results indicate that the anthropogenic mining activities have caused local agricultural soil contamination with As, Pb, Cu and Cd in the ranges of 8.47-341.33 mg/kg, 19.91-837.52 mg/kg, 8.41-148.73 mg/kg and 0.35-6.47 mg/kg, respectively. GIS-based mapping shows that soil heavy metal concentrations abruptly diminish with increasing distance from the polluting source. The concentrations of As, Pb, Cu and Cd found in rice were in the ranges of 0.02-1.48 mg/kg, 0.66-5.78 mg/kg, 0.09-6.75 mg/kg, and up to 1.39 mg/kg, respectively. Most of these concentrations exceed their maximum permissible levels for contaminants in foods in China. Heavy metals accumulate to significantly different levels between leafy vegetables and non-leafy vegetables. Food consumption and soil ingestion exposure are the two routes that contribute to the average daily intake dose of heavy metals for local adults. Moreover, the total hazard indices of As, Pb and Cd are greater than or close to the safety threshold of 1. Long-term As, Pb and Cd exposure through the regular consumption of the soil, rice and vegetables in the investigated area poses potential health problems to residents in the vicinity of the mining industry.

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