The precision of atmospheric dry delay model is closely correlated with the accuracy of GPS water vapor in the process of GPS (Global Position System) remote sensing. Radiosonde data (from 1996 to 2001) at Qingyuan ar...The precision of atmospheric dry delay model is closely correlated with the accuracy of GPS water vapor in the process of GPS (Global Position System) remote sensing. Radiosonde data (from 1996 to 2001) at Qingyuan are used to calculate the exact values of the atmospheric dry delay. Base on these calculations and the surface meteorological parameters, the local year and month correction models of dry delay at the zenith angle of 0° are established by statistical methods. The analysis result shows that the local model works better and is slight more sensitive to altitude angle than universal models and that it is not necessary to build models for each month due to the slight difference between year model and month model. Furthermore, when the altitude angle is less than 75°, the difference between curve path and straight path increases rapidly with altitude angle’s decrease.展开更多
Spatial scaling for net primary productivity （NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfe...Spatial scaling for net primary productivity （NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfer NPP at one scale by the algorithm with smaller error to at another is the urgent problem. Nonlinearity and effects from land cover type are two main problems in NPP scaling. In this paper, the contextural approach based on mixed pixels and support vector machine （SVM） algorithm are used to make the scaling model from the fine resolution （TM） to the coarse resolution （MODIS）. Spatial scaling from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed, and the correction of scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished. The result shows that the correlation between Rj_coereted of the correction factor for scale effect and 1-Fmiddle dessity grassland estimated by SVM regression model is higher （R2=0.81）. Before the correction for scale effect, the correlation between NPPMODIS and NPPTM is lower （R2=0.69; RMSE=3.47）, while the correlation between NPPTM and corrected NPPMODIS_corrected is higher （R2=0.84; RMSE= 1.87）. Therefore, NPP corrected for scale effect has been greatly improved in both correlation and error.展开更多
This paper used five years （2001-2006） time series of MODIS NDVI images with a 1-km spatial resolution to produce a land cover map of Qinghai Province in China. A classification approach for different land cover typ...This paper used five years （2001-2006） time series of MODIS NDVI images with a 1-km spatial resolution to produce a land cover map of Qinghai Province in China. A classification approach for different land cover types with special emphasis on vegetation, especially on sparse vegetation, was developed which synthesized Decision Tree Classification, Supervised Classification and Unsupervised Classification. The spatial distribution and dynamic change of vegetation cover in Qinghai from 2001 to 2006 were analyzed based on the land cover classification map and five grade elevation belts derived from Qinghai DEM. The result shows that vegetation cover in Qinghai in recent five years has been some improved and the area of vegetation was increased from 370,047 km^2 in 2001 to 374,576 km^2 in 2006. Meanwhile, vegetation cover ratio was increased by 0.63%. Vegetation cover ratio in high mountain belt is the largest （67.92%） among the five grade elevation belts in Qinghai Province. The second largest vegetation cover ratio is in middle mountain belt （61.80%）. Next, in the order of the decreasing vegetation cover ratio, the remaining grades are extreme high mountain belt （38.98%）, low mountain belt （25.55%） and flat region belt （15.46%）. The area of middle density grassland in high mountain belt is the biggest （94,003 km^2）, and vegetation cover ratio of dense grassland in middle mountain belt is the highest （32.62%）, and the increased area of dense grassland in high mountain belt is the greatest （1280 km^2）. In recent five years the conversion from sparse grass to middle density grass in high mountain belt has been the largest vegetation cover variation and the converted area is 15931 km^2.展开更多
The investigation of slow displacement in urban areas using the multi-baseline DInSAR technique has been a hot research topic in the field of DInSAR. The basic flow of this technique includes several steps such as the...The investigation of slow displacement in urban areas using the multi-baseline DInSAR technique has been a hot research topic in the field of DInSAR. The basic flow of this technique includes several steps such as the combination of interferometric image pairs, generation of differential interferograms, selection of high coherent points, generation of the Delaunay triangular network, calculation and integration of increments in network, unwrapping and calibration of the residual phase, and the estimation of both atmospheric and nonlinear displacement phase. Among these steps, the calculation of increments is the key to retrieve linear displacement, while unwrapping and calibration of the residual phase are the keys to retrieve nonlinear displacement. In order to improve the performance of these two steps, this paper proposes a modified model coherence function for increments estimation, and a triangular "circle" algorithm to deal with phase unwrapping and calibration. Based on the above algorithms, the subsidence of Suzhou City is investigated using 24 ERS scenes from February 1993 to December 2000. The results show that the linear subsidence velocity of the most urban area is about -20 to -30 mm/a during the time, with a yearly decrease in velocity. The displacement seems to be stable after 2000. Leveling data validate our results and demonstrate the reliability of the algorithm.展开更多
Olivine exposures at the central peak of Copernicus crater of the Earth＇s Moon have been confirmed by telescope observations and Clementine spectra data. Using these exposures as training sites, this study used a met...Olivine exposures at the central peak of Copernicus crater of the Earth＇s Moon have been confirmed by telescope observations and Clementine spectra data. Using these exposures as training sites, this study used a method of combining two spectral indices （950 nm/750 nm and 2000 nm/1500 nm）, one maturity index （Is/FeO）, and four chemical content indices （FeO, Mg, Al, Ca）, through a decision tree classifier, to map olivine-rich units on the west lunar surface based on mosaicked Clementine image （500 m pixel size）. Most classified olivine exposures are found inside craters or on their rays, suggesting that olivine exposures are directly associated with the impact processes. The results have been validated in several selected sites, though further validations with data from the newest missions are strongly needed.展开更多
Land surface all-wave net radiation(R_(n))is crucial in determining Earth’s climate by contributing to the surface radiation budget.This study evaluated seven satellite and three reanalysis long-term land surface R_(...Land surface all-wave net radiation(R_(n))is crucial in determining Earth’s climate by contributing to the surface radiation budget.This study evaluated seven satellite and three reanalysis long-term land surface R_(n)products under different spatial scales,spatial and temporal variations,and different conditions.The results showed that during 2000-2018,Global Land Surface Satellite Product(GLASS)-Moderate Resolution Imaging Spectroradiometer(MODIS)performed the best(RMSE=25.54 Wm^(-2),bias=-1.26 Wm^(-2)),followed by ERA5(the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis)(RMSE=32.17 Wm^(-2),bias=-4.88 Wm^(-2))and GLASS-AVHRR(Advanced Very-High-Resolution Radiometer)(RMSE=33.10 Wm^(-2),bias=4.03 Wm^(-2)).During 1983-2018,GLASS-AVHRR and ERA5 ranked top and performed similarly,with RMSE values of 31.70 and 33.08 Wm^(-2)and biases of-4.56 and 3.48 Wm^(-2),respectively.The averaged multi-annual mean R_(n)over the global land surface of satellite products was higher than that of reanalysis products by about 10~30 Wm^(-2).These products differed remarkably in long-term trends variations,particularly pre-2000,but no significant trends were observed.Discrepancies were more frequent in satellite data,while reanalysis products showed smoother variations.Large discrepancies were found in regions with high latitudes,reflectance,and elevation which could be attributed to input radiative components,meteorological variables(e.g.,cloud properties,aerosol optical thickness),and applicability of the algorithms used.While further research is needed for detailed insights.展开更多
The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles.Many studies have been conducted to estimate from satellite ...The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles.Many studies have been conducted to estimate from satellite top-of-atmosphere(TOA)data using various methods,particularly the application of machine learning(ML)and deep learning(DL).However,few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in retrieving.Based on extensive in situ measurements distributed at mid-low latitudes,the corresponding Moderate Resolution Imaging Spectroradiometer(MODIS)TOA observations,and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)used as a priori knowledge,this study assessed nine models for daily estimation,including six classic ML methods(random forest-RF,adaptive boosting-Adaboost,extreme gradient boosting-XGBoost,multilayer perceptron-MLP,radial basis function neural network-RBF,and support vector machine-SVM)and three DL methods(multilayer perceptron neural network with stacked autoencoders-SAE,deep belief network-DBN and residual neural network-ResNet).The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost,although they all performed poorly in certain conditions such as winter days,rugged terrain,and high elevation.ResNet had the most robust performance across different land cover types,elevations,seasons,and latitude zones,but it has disadvantages in practice because of its highly configurable implementation environment and low computational efficiency.The estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite(GLASS)product.展开更多
Recently,five Global LAnd Surface Satellite(GLASS)products have been released:leaf area index(LAI),shortwave broadband albedo,longwave broadband emissivity,incident short radiation,and photosynthetically active radiat...Recently,five Global LAnd Surface Satellite(GLASS)products have been released:leaf area index(LAI),shortwave broadband albedo,longwave broadband emissivity,incident short radiation,and photosynthetically active radiation(PAR).The first three products cover the years 19822012(LAI)and 19812010(albedo and emissivity)at 15 km and 8-day resolutions,and the last two radiation products span the period 20082010 at 5 km and 3-h resolutions.These products have been evaluated and validated,and the preliminary results indicate that they are of higher quality and accuracy than the existing products.In particular,the first three products have much longer time series,and are therefore highly suitable for various environmental studies.This paper outlines the algorithms,product characteristics,preliminary validation results,potential applications and some examples of initial analysis of these products.展开更多
In this study,the authors propose a model,called the Daily Downward Shortwave Radiation Random Forest Model over Rugged Terrain(DSRMT),to accurately calculate the downward shortwave radiation over a terrain without br...In this study,the authors propose a model,called the Daily Downward Shortwave Radiation Random Forest Model over Rugged Terrain(DSRMT),to accurately calculate the downward shortwave radiation over a terrain without bright surface on clear days at a daily scale(DSRdaily−rugged).It was built by using the random forest method based on the comprehensive samples from CERES4_SYN1deg_Ed4A within 17 typical mountainous regions.DSRMT could directly estimate DSRdaily-rugged from the instantaneous direct and diffuse solar radiation on a flat surface during 10:30–14:30hrs on each day by comparing with the terrain factors from a digital elevation model,broadband albedo from the Global Land Surface Satellite,and ancillary information.The in-situ validation results showed that it generally delivered superior performance in estimating DSRdailyrugged at any time during 10:30–14:30hrs,especially at noon,yielding a validated root mean-squared error(RMSE)of 24.90–29.22 Wm−2 and mean absolute error(MAE)of 19.16–22.94 Wm−2,and the average weighted DSRdaily-rugged were usually more accurate with the RMSE and MAE of 21.63 and 17.14 Wm−2.Overall,DSRMT was found to deliver satisfactory performance because of its high accuracy,robustness,ease of implementation,and efficiency,so it has the strong potential to be widely used in practice.展开更多
Although the surface energy budget is essential to determine Earth’s climate,site measurements of various radiative components are still too scarce to properly characterize their spatial and temporal variations.This ...Although the surface energy budget is essential to determine Earth’s climate,site measurements of various radiative components are still too scarce to properly characterize their spatial and temporal variations.This has led to the development of a growing number of surface radiation products,mainly including remotely sensed data,model reanalysis data,and simulations using General Circulation Models(GCMs).This collection of papers introduces new techniques,including the use of machine learning methods for radiation estimation,and evaluates and compares various radiation products,as well as their spatio-temporal variations.These studies show large discrepancies among various products across nearly all radiative parameters in either accuracy or spatio-temporal variations.However,remotely sensed radiation products perform relatively better than others.Despite this,there is an urgent need for further efforts to address these discrepancies and improve the accuracy of these estimates.Even though the major radiative parameters including downward shortwave radiation,net longwave radiation,and albedo,from most products show insignificant long-term variation trends on a global scale,only specific regions,such as the Yunnan-Kweichow Plateau(YKP)and regions with permafrost(i.e.Qinghai-Tibet Plateau and Arctic)and glaciers(i.e.Altai Mountains)exhibit remarkable trends.展开更多
基金Sino-Italian Cooperation Project "An Integrated System for the Planning, Monitoring and Real-time Forecasting of Floods Risks"
文摘The precision of atmospheric dry delay model is closely correlated with the accuracy of GPS water vapor in the process of GPS (Global Position System) remote sensing. Radiosonde data (from 1996 to 2001) at Qingyuan are used to calculate the exact values of the atmospheric dry delay. Base on these calculations and the surface meteorological parameters, the local year and month correction models of dry delay at the zenith angle of 0° are established by statistical methods. The analysis result shows that the local model works better and is slight more sensitive to altitude angle than universal models and that it is not necessary to build models for each month due to the slight difference between year model and month model. Furthermore, when the altitude angle is less than 75°, the difference between curve path and straight path increases rapidly with altitude angle’s decrease.
基金Foundation： Chinese Liaoning Province Education Bureau General Science Research Project （No. L2010226） Chinese Education Ministry Humanities and Social Sciences Key Research Base Project （No.08JJD790142）+1 种基金 Chinese Liaoning Province Education Bureau Innovation Team Project （No. 2007T095） Chinese Special Funds for Major State Basic Research Project （No. 2007CB714406）.
文摘Spatial scaling for net primary productivity （NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfer NPP at one scale by the algorithm with smaller error to at another is the urgent problem. Nonlinearity and effects from land cover type are two main problems in NPP scaling. In this paper, the contextural approach based on mixed pixels and support vector machine （SVM） algorithm are used to make the scaling model from the fine resolution （TM） to the coarse resolution （MODIS）. Spatial scaling from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed, and the correction of scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished. The result shows that the correlation between Rj_coereted of the correction factor for scale effect and 1-Fmiddle dessity grassland estimated by SVM regression model is higher （R2=0.81）. Before the correction for scale effect, the correlation between NPPMODIS and NPPTM is lower （R2=0.69; RMSE=3.47）, while the correlation between NPPTM and corrected NPPMODIS_corrected is higher （R2=0.84; RMSE= 1.87）. Therefore, NPP corrected for scale effect has been greatly improved in both correlation and error.
基金China’s Special Funds for Major State Basic Research Project, No.2007CB714406Knowledge Innovation Program of the Chinese Academy of Sciences, No.KZCX2-YW-313Foundation of the Chinese State Key Laboratory of Remote Sensing Science, No.KQ060006
文摘This paper used five years （2001-2006） time series of MODIS NDVI images with a 1-km spatial resolution to produce a land cover map of Qinghai Province in China. A classification approach for different land cover types with special emphasis on vegetation, especially on sparse vegetation, was developed which synthesized Decision Tree Classification, Supervised Classification and Unsupervised Classification. The spatial distribution and dynamic change of vegetation cover in Qinghai from 2001 to 2006 were analyzed based on the land cover classification map and five grade elevation belts derived from Qinghai DEM. The result shows that vegetation cover in Qinghai in recent five years has been some improved and the area of vegetation was increased from 370,047 km^2 in 2001 to 374,576 km^2 in 2006. Meanwhile, vegetation cover ratio was increased by 0.63%. Vegetation cover ratio in high mountain belt is the largest （67.92%） among the five grade elevation belts in Qinghai Province. The second largest vegetation cover ratio is in middle mountain belt （61.80%）. Next, in the order of the decreasing vegetation cover ratio, the remaining grades are extreme high mountain belt （38.98%）, low mountain belt （25.55%） and flat region belt （15.46%）. The area of middle density grassland in high mountain belt is the biggest （94,003 km^2）, and vegetation cover ratio of dense grassland in middle mountain belt is the highest （32.62%）, and the increased area of dense grassland in high mountain belt is the greatest （1280 km^2）. In recent five years the conversion from sparse grass to middle density grass in high mountain belt has been the largest vegetation cover variation and the converted area is 15931 km^2.
基金National Natural Science Foundation of China (Grant Nos. 40501044 and 40701106)
文摘The investigation of slow displacement in urban areas using the multi-baseline DInSAR technique has been a hot research topic in the field of DInSAR. The basic flow of this technique includes several steps such as the combination of interferometric image pairs, generation of differential interferograms, selection of high coherent points, generation of the Delaunay triangular network, calculation and integration of increments in network, unwrapping and calibration of the residual phase, and the estimation of both atmospheric and nonlinear displacement phase. Among these steps, the calculation of increments is the key to retrieve linear displacement, while unwrapping and calibration of the residual phase are the keys to retrieve nonlinear displacement. In order to improve the performance of these two steps, this paper proposes a modified model coherence function for increments estimation, and a triangular "circle" algorithm to deal with phase unwrapping and calibration. Based on the above algorithms, the subsidence of Suzhou City is investigated using 24 ERS scenes from February 1993 to December 2000. The results show that the linear subsidence velocity of the most urban area is about -20 to -30 mm/a during the time, with a yearly decrease in velocity. The displacement seems to be stable after 2000. Leveling data validate our results and demonstrate the reliability of the algorithm.
基金supported by the National High Technology Research and Development Program(No.2010AA12220101 and 2009AA12Z310)National Natural Science Foundation of China(No.40871202 and 41002120)
文摘Olivine exposures at the central peak of Copernicus crater of the Earth＇s Moon have been confirmed by telescope observations and Clementine spectra data. Using these exposures as training sites, this study used a method of combining two spectral indices （950 nm/750 nm and 2000 nm/1500 nm）, one maturity index （Is/FeO）, and four chemical content indices （FeO, Mg, Al, Ca）, through a decision tree classifier, to map olivine-rich units on the west lunar surface based on mosaicked Clementine image （500 m pixel size）. Most classified olivine exposures are found inside craters or on their rays, suggesting that olivine exposures are directly associated with the impact processes. The results have been validated in several selected sites, though further validations with data from the newest missions are strongly needed.
基金funded by the National Natural Science Foundation of China[grant numbers 42090012 and 41971291].
文摘Land surface all-wave net radiation(R_(n))is crucial in determining Earth’s climate by contributing to the surface radiation budget.This study evaluated seven satellite and three reanalysis long-term land surface R_(n)products under different spatial scales,spatial and temporal variations,and different conditions.The results showed that during 2000-2018,Global Land Surface Satellite Product(GLASS)-Moderate Resolution Imaging Spectroradiometer(MODIS)performed the best(RMSE=25.54 Wm^(-2),bias=-1.26 Wm^(-2)),followed by ERA5(the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis)(RMSE=32.17 Wm^(-2),bias=-4.88 Wm^(-2))and GLASS-AVHRR(Advanced Very-High-Resolution Radiometer)(RMSE=33.10 Wm^(-2),bias=4.03 Wm^(-2)).During 1983-2018,GLASS-AVHRR and ERA5 ranked top and performed similarly,with RMSE values of 31.70 and 33.08 Wm^(-2)and biases of-4.56 and 3.48 Wm^(-2),respectively.The averaged multi-annual mean R_(n)over the global land surface of satellite products was higher than that of reanalysis products by about 10~30 Wm^(-2).These products differed remarkably in long-term trends variations,particularly pre-2000,but no significant trends were observed.Discrepancies were more frequent in satellite data,while reanalysis products showed smoother variations.Large discrepancies were found in regions with high latitudes,reflectance,and elevation which could be attributed to input radiative components,meteorological variables(e.g.,cloud properties,aerosol optical thickness),and applicability of the algorithms used.While further research is needed for detailed insights.
基金supported by National Natural Science Foundation of China:[grant no 41971291,42090012]National Key Research and Development Program of China:[grant no 2020YFA0608704].
文摘The all-wave net radiation(Rn)at the land surface represents surface radiation budget and plays an important role in the Earth's energy and water cycles.Many studies have been conducted to estimate from satellite top-of-atmosphere(TOA)data using various methods,particularly the application of machine learning(ML)and deep learning(DL).However,few studies have been conducted to provide a comprehensive evaluation about various ML and DL methods in retrieving.Based on extensive in situ measurements distributed at mid-low latitudes,the corresponding Moderate Resolution Imaging Spectroradiometer(MODIS)TOA observations,and the daily from the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)used as a priori knowledge,this study assessed nine models for daily estimation,including six classic ML methods(random forest-RF,adaptive boosting-Adaboost,extreme gradient boosting-XGBoost,multilayer perceptron-MLP,radial basis function neural network-RBF,and support vector machine-SVM)and three DL methods(multilayer perceptron neural network with stacked autoencoders-SAE,deep belief network-DBN and residual neural network-ResNet).The validation results showed that the three DL methods were generally better than the six ML methods except XGBoost,although they all performed poorly in certain conditions such as winter days,rugged terrain,and high elevation.ResNet had the most robust performance across different land cover types,elevations,seasons,and latitude zones,but it has disadvantages in practice because of its highly configurable implementation environment and low computational efficiency.The estimated daily values from all nine models were more accurate than the corresponding Global LAnd Surface Satellite(GLASS)product.
基金the‘Generation and Application of Global Products of Essential Land Variables’project funded and managed by the National Remote Sensing Center of China,Ministry of Science and Technology of China(2009AA122100)with the participation of about 20 universities and research institutes.
文摘Recently,five Global LAnd Surface Satellite(GLASS)products have been released:leaf area index(LAI),shortwave broadband albedo,longwave broadband emissivity,incident short radiation,and photosynthetically active radiation(PAR).The first three products cover the years 19822012(LAI)and 19812010(albedo and emissivity)at 15 km and 8-day resolutions,and the last two radiation products span the period 20082010 at 5 km and 3-h resolutions.These products have been evaluated and validated,and the preliminary results indicate that they are of higher quality and accuracy than the existing products.In particular,the first three products have much longer time series,and are therefore highly suitable for various environmental studies.This paper outlines the algorithms,product characteristics,preliminary validation results,potential applications and some examples of initial analysis of these products.
基金funded by the National Key Research and Development Program of China[2020YFA0608704]the National Natural Science Foundation of China[grant no 41971291].
文摘In this study,the authors propose a model,called the Daily Downward Shortwave Radiation Random Forest Model over Rugged Terrain(DSRMT),to accurately calculate the downward shortwave radiation over a terrain without bright surface on clear days at a daily scale(DSRdaily−rugged).It was built by using the random forest method based on the comprehensive samples from CERES4_SYN1deg_Ed4A within 17 typical mountainous regions.DSRMT could directly estimate DSRdaily-rugged from the instantaneous direct and diffuse solar radiation on a flat surface during 10:30–14:30hrs on each day by comparing with the terrain factors from a digital elevation model,broadband albedo from the Global Land Surface Satellite,and ancillary information.The in-situ validation results showed that it generally delivered superior performance in estimating DSRdailyrugged at any time during 10:30–14:30hrs,especially at noon,yielding a validated root mean-squared error(RMSE)of 24.90–29.22 Wm−2 and mean absolute error(MAE)of 19.16–22.94 Wm−2,and the average weighted DSRdaily-rugged were usually more accurate with the RMSE and MAE of 21.63 and 17.14 Wm−2.Overall,DSRMT was found to deliver satisfactory performance because of its high accuracy,robustness,ease of implementation,and efficiency,so it has the strong potential to be widely used in practice.
文摘Although the surface energy budget is essential to determine Earth’s climate,site measurements of various radiative components are still too scarce to properly characterize their spatial and temporal variations.This has led to the development of a growing number of surface radiation products,mainly including remotely sensed data,model reanalysis data,and simulations using General Circulation Models(GCMs).This collection of papers introduces new techniques,including the use of machine learning methods for radiation estimation,and evaluates and compares various radiation products,as well as their spatio-temporal variations.These studies show large discrepancies among various products across nearly all radiative parameters in either accuracy or spatio-temporal variations.However,remotely sensed radiation products perform relatively better than others.Despite this,there is an urgent need for further efforts to address these discrepancies and improve the accuracy of these estimates.Even though the major radiative parameters including downward shortwave radiation,net longwave radiation,and albedo,from most products show insignificant long-term variation trends on a global scale,only specific regions,such as the Yunnan-Kweichow Plateau(YKP)and regions with permafrost(i.e.Qinghai-Tibet Plateau and Arctic)and glaciers(i.e.Altai Mountains)exhibit remarkable trends.
