Spatial Distribution Pattern and Han Subgroup Characteristics of Traditional Villages in Guangdong

A total of 126 traditional villages in Guangdong were selected for research to analyze their spatial distribution pattern, spatial autocorrelation, and Han subgroup characteristics through the spatial analysis with GIS. The results showed that the traditional villages in Guangdong were distributed compactly, which was similar to that in other provinces; on the prefecture-level city scale, they were mainly distributed in Meizhou, Qingyuan, Zhanjiang, and Zhaoqing, showing a central distribution; on the regional scale, they were mainly distributed in northern Guangdong and the Pearl River Delta, indicating an unbalanced distribution; their density of spatial distribution showed a significant difference with a provincial distribution density of 0.59 per thousand km^2 and the highest distribution density of 2.14 per thousand km^2; there were four gathering areas accommodating many traditional villages, namely Meizhou-Chaoshan area, Guangfo-Dongguan area, Leizhou in Zhanjiang, and Lianzhou in Qingyuan; the spatial distribution of traditional villages, which presented a pattern of hot spot—sub-hot spot—sub-cold spot—cold spot, had a significant global spatial autocorrelation, and areas in similar sizes were tending towards gathering; traditional villages in Guangdong belonged to the Cantonese-speaking group and Hakka-speaking group with a density of 0.59 per thousand km^2 and 0.48 per thousand km^2 respectively, and Han subgroup with the highest distribution density was the minority areas of 1.31 per thousand km^2, followed by Shaozhou dialect area with a density of 1.00 per thousand km^2.

Quantifying foliar trait variation and covariation in sun and shade leaves using leaf spectroscopy in eastern North America

Characterizing foliar trait variation in sun and shade leaves can provide insights into inter-and intra-species resource use strategies and plant response to environmental change.However,datasets with records of multiple foliar traits from the same individual and including shade leaves are sparse,which limits our ability to investigate trait-trait,trait-environment relationships and trait coordination in both sun and shade leaves.We presented a comprehensive dataset of 15 foliar traits from sun and shade leaves sampled with leaf spectroscopy,including 424 individuals of 110 plant species from 19 sites across eastern North America.We investigated trait variation,covariation,scaling relationships with leaf mass,and the effects of environment,canopy position,and taxonomy on trait expression.Generally,sun leaves had higher leaf mass per area,nonstructural carbohydrates and total phenolics,lower mass-based chlorophyll a+b,carotenoids,phosphorus,and potassium,but exhibited species-specific characteristics.Covariation between sun and shade leaf traits,and trait-environment relationships were overall consistent across species.The main dimensions of foliar trait variation in seed plants were revealed including leaf economics traits,photosynthetic pigments,defense,and structural traits.Taxonomy and canopy position collectively explained most of the foliar trait variation.This study highlights the importance of including intra-individual and intra-specific trait variation to improve our understanding of ecosystem functions.Our findings have implications for efficient field sampling,and trait mapping with remote sensing.

Residents'environmental conservation behaviour in the mountain tourism destinations in China:Case studies of Jiuzhaigou and Mount Qingcheng

This study aimed at gaining an understanding of the formation of residents' Environmental Conservation Behaviour(ECB) to promote sustainable tourism development in Chinese mountain areas. In particular, a theoretical framework was constructed by merging residents' mountain attachment and their environmental values,and awareness of environmental consequences based on the Cognition Behaviour Theory. This research sampled 642 residents with convenience sampling method in Jiuzhaigou and Mount Qingcheng, and structural equation modelling analysis was conducted to identify the relationships among the above variables. Results verified that mountain attachment affected people's awareness of environmental consequences, which then affected their ECB.Environmental values and mountain attachment directly and positively influenced ECB. Moreover,awareness of environmental consequences played the most prominent role in predicting ECB among all the predictors in this study. In all, this study derived several significant implications which can not only contribute to environmental management policies in tourism field but also help to improve the levels of the antecedents of residents' ECB and promote sustainable tourism development in Chinese mountain areas.

Directional Analysis of Urban Expansion Based on Sub-pixel and Regional Scale: A Case Study of Main Districts in Guangzhou, China

Multi-scale data have had a wide-ranging level of performance in the area of urban change monitoring. Herein we investigate the correlation between the impervious surface fraction(ISF) and the Defense Meteorological Satellite Program/Operational Linescan System(DMSP/OLS) nighttime stable light(NTL) data with respect to the urban expansion in the main districts of Guangzhou. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Image(OLI) data from 1988 to 2015 were used to extract the ISF using the linear spectral mixture analysis model and normal difference build-up index at the sub-pixel scale. DMSP/OLS NTL data from 1992 to 2013 were calibrated to illustrate the urban nighttime light conditions at the regional scale. Urban expansion directions were identified by statistics and kernel density analysis for the ISF study area at the sub-pixel scale. In addition, the correlation between the ISF and DMSP/OLS NTL data were illustrated by linear regression analysis. Furthermore, Profile Graph in ArcGIS was employed to illustrate the urban expansion from the differences in correlation in different directions. The conclusions are as follows: 1) The impervious surface(IS)in the study area has expanded to the northeast and the east, starting with the old urban zones, and the high-density IS area has increased by321.14 km^2. 2) The linear regression analysis reveals a positive correlation between the ISF and the DMSP/OLS NTL data. The multi-scale data changes are consistent with the actual urban planning of Guangzhou. 3) The DMSP/OLS NTL data overestimate the urban extent because of its saturation and blooming effects, causing its correlation with ISF to decrease. The pattern of urban expansion influences the saturation and blooming effects of the DMSP/OLS NTL data.

Impact of the Built Environment on the Spatial Heterogeneity of Regional Innovation Productivity:Evidence from the Pearl River Delta,China

With the global economy increasingly dependent on innovation,urban discourse has shifted to consider what kinds of spatial designs may best nurture innovation.We examined the relationship between the built environment and the spatial heterogeneity of regional innovation productivity(RIP)using the example of China's Pearl River Delta(PRD).Based on a spatial database of 522546 patent data from 2017,this study proposed an innovation-based built environment framework with the following five aspects:healthy en-vironment,daily interaction,mixed land use,commuting convenience,and technology atmosphere.Combining negative binomial regression and Geodetector to examine the impact of the built environment on RIP,the results show that the spatial distribution of innovation productivity in the PRD region is extremely uneven.The negative binomial regression results show that the built environment has a significant impact on the spatial differentiation of RIP,and,specifically,that healthy environment,mixed land use,commuting convenience,and technology atmosphere all demonstrate significant positive impacts.Meanwhile,the Geodetector results show that the built environment factor impacts the spatial heterogeneity of RIP to varying degrees,with technology atmosphere demonstrating the greatest impact intensity.We conclude that as regional development discourse shifts focus to the knowledge and innovation economy,the innovation-oriented design and updating of built environments will become extremely important to policymakers.

Prediction of Debris Flow Runoff Process Based on Hydrological Model： a Case Study at Shenxi Gully,Regarding a Long-Term Impact by Wenchuan Earthquake

Debris flow runoff process is one of key parameters for the design of emergency measures and control engineering. The Shenxi gully in Dujiangyan region,located in the meizoseismal areas of Wenchuan earthquake,was selected as the study area. Based on the research of hazard inducing environment,a soil conservation service( SCS) hydrological model was used to simulate the process of water flow,and then the debris flow runoff process was calculated using the empirical formula combining the results from the SCS hydrological model. Taking the debris flow event occurred on July 9th,2013 as an example,the peak discharges of water flow and debris flow were calculated as 162. 12 and 689. 22 m3/s,with error of 6. 03% compared to the measured values. The debris flow confluence process lasted 1. 8h, which was similar with the actual result. The proposed methodology can be applied to predict the debris flow runoff process in quake-hit areas of the Wenchuan earthquake and is of great importance for debris flow mitigation.

Influencing Factors of Housing Sales Vacancy in the Guangzhou Metropolitan Area,China

In this paper,nine indicators selected from three perspectives(convenience,environmental and location characteristics)and three regression models(OLS,SLM and SEM)are used to explore the influencing factors of housing sales vacancy in the Guangzhou Metropolitan Area,China.The results show that subway accessibility,peripheral aversion municipal facilities and distance from the CBD(Central Business District)are consistent with theoretical expectations.Subway accessibility is negatively correlated with the housing sales vacancy rates,while peripheral aversion municipal facilities and distance from the CBD are positively correlated with housing vacancy rates.

Spatial Pattern of Housing Sales Vacancy in Guangzhou’s Urban District, China

Housing vacancy can reflect the destocking degree of the real estate market.Based on the data of 57 opened residential quarters(46,622 units)from 2015 to 2018,this paper constructs a calculation formula of the sales vacancy rate and then analyzes the spatial pattern in Guangzhou’s urban district.The results show that there is obvious differentiation in the spatial pattern of housing sales vacancy in Guangzhou’s urban district,showing a higher spatial pattern in the old area and urban district and a lower spatial pattern in the core area.Subdistricts with high vacancy rates are mainly located in the east of the old area,the south and east of the urban district and near Baiyun Mountain in the north.

Extent and spatial distribution of terrace abandonment in China

Driven by urbanization and industrialization,arable land in hilly and mountainous regions of China is gradually becoming marginalized,with the extent of arable land abandonment rapidly expanding from poor-quality sloping arable land to high-quality terraces.The abandonment of large-scale terraces will lead to a series of socio-economic and ecological effects.A national sample survey was used to investigate the extent and spatial distribution of terrace abandonment in China,and a total of 560 valid village questionnaires from 329 counties were collected in the mountainous areas of China.The main findings are as follows:(1)The phenomenon of terrace abandonment was widespread throughout the country,with 54% of the total surveyed villages exhibiting terrace abandonment,and the area of abandoned terraces accounting for 9.79% of the total.(2) The degree of terrace abandonment is high in the south and low in the north.The most serious region with abandonment was the hilly and mountainous areas in the south,especially in the middle and lower Yangtze River region.(3)The main driving factors of terrace abandonment were rural labor migration,agricultural mechanization level,irrigation conditions,and transportation conditions for cultivation.Targeted measures should be taken based on the specific conditions of each area to alleviate terrace abandonment.Measures such as improving terrace mechanization are universally applicable.Specifically,low-quality terraces can be withdrawn orderly,and for high-quality terraces,multiple measures are needed to consolidate agricultural production,such as adjusting the planting structure,strengthening agricultural infrastructure construction,and encouraging the transfer of land-use rights as well as large-scale operation.

UAVs as remote sensing platforms in plant ecology:review of applications and challenges

Aims Unmanned aerial vehicles(UAVs),i.e.drones,have recently emerged as cost-effective and flexible tools for acquiring remote sensing data with fine spatial and temporal resolution.It provides a new method and opportunity for plant ecologists to study issues from individual to regional scales.However,as a new method,UAVs remote sensing applications in plant ecology are still challenged.The needs of plant ecology research and the application development of UAVs remote sensing should be better integrated.Methods This report provides a comprehensive review of UAV-based remote sensing applications in plant ecology to synthesize prospects of applying drones to advance plant ecology research.Important Findings Of the 400 references,59%were published in remote sensing journals rather than in plant ecology journals,reflecting a substantial gap between the interests of remote sensing experts and plant ecologists.Most of the studies focused on UAV remote sensing’s technical aspects,such as data processing and remote sensing inversion,with little attention on answering ecological questions.There were 61%of studies involved community-scale research.RGB and multispectral cameras were the most used sensors(75%).More ecologically meaningful parameters can be extracted from UAV data to better understand the canopy surface irregularity and community heterogeneity,identify geometrical characteristics of canopy gaps and construct canopy chemical assemblies from living vegetation volumes.More cooperation between plant ecologists and remote sensing experts is needed to promote UAV remote sensing in advancing plant ecology research.

Dynamic features and driving mechanism of coal consumption for Guangdong province in China

Guangdong Province,as one of China’s fast-developing regions,an important manufacturing base,and one of the national first round low-carbon pilots,still faces many challenges in controlling its total energy consumption.Coal dominates Guangdong’s energy consumption and remains the major source of CO_(2).Previous research on factors influencing energy consumption has lacked a systematic analysis both from supply side(factors related to scale,structure,and technologies)and demand side(investment,consumption,and trade).This paper develops the logarithmic mean Divisia index(LMDI)method that focuses on the supply side and the structural decomposition analysis(SDA)method that focuses on the demand side to systematically identify the key factors driving coal consumption in Guangdong.Results are as follows:(1)Supply side analysis indicates that economic growth has always been the most important factor driving coal consumption growth,while energy intensity is the most important constraining factor.Industrial structure and energy structure have different impacts on coal consumption control during different development phases.(2)Demand side analysis indicates that coal is consumed mainly for international exports,inter-provincial exports,fixed capital formation,and urban household.(3)Industries with the fastest coal consumption growth driven by final demand have experienced significant shifts.Increments in industrial sectors were mainly driven by inter-provincial exports and urban household consumption in recent years.(4)Research on energy consumption in subnational regions under China’s new development pattern of“dual circulation”should not only focus on exports in the context of economic globalization but also pay more attention to inter-provincial exports on the background of strengthened interregional connections.

Urban shrinkage and growth: Measurement and determinants of economic resilience in the Pearl River Delta

In the aftermath of the global financial crisis of 2008, China witnessed gradual shrinkage of cities in the Pearl River Delta(PRD). In this study, we introduce the concept of economic resilience to analyse urban growth and shrinkage in the context of a rapidly-urbanising region. Multiple regression analysis is performed to explore the determinants of economic resilience in the PRD. By measuring resistance in the shrinking phase and recoverability in the growing phase in a group of cities in the PRD, this study distinguishes four scenarios and investigates their characteristics from a spatial perspective. The results demonstrate that the financial crisis had a severe and asymmetric influence on this area, indicating more than 15% of cities are faced with shrinking. The spatial distribution of economic resilience indicates a centre-periphery pattern, that is, high economic resilience in the inner ring and low economic resilience in the outer ring of the PRD. The service economy is found to play a significant role in promoting urban economic resilience. Results imply that sound economic policies for enhancing resilience: both poor local financial status and a high degree of export concentration adversely impact resistance, while upgrading the manufacturing economy and stimulating of industrial innovation are conducive to improve recoverability.

Spatial differences and driving forces of land urbanization in China

Land urbanization plays an important supporting and restriction role in the rapid and sustainable development of urbanization in China, and it shows distinctive spatial heteroge- neity. Applying urban area as the basic research unit and urban construction land area as the core indicator, this paper establishes the conceptual framework and calculation method for the quantity and rate of land urbanization process. The study evaluates the spatial differen- tiation pattern of absolute and relative process of land urbanization in 658 cities in China from 2000 to 2010. The spatial distribution of cities with rapid land urbanization process is dis- cussed, and the contribution rate and its spatial heterogeneity of major land use types are examined with the aid of GIS. The main conclusions are as follows： （1） Land urbanization in China shows a clear spatial difference. The greater the city scale, the faster its land urbani- zation. The cities with rapid land urbanization show a significant pattern of central distribution in coastal regions and a scattered distribution in the inland regions. （2） Over the last 10 years, the average quantity of land urbanization in the 656 cities was 3.82 km2, the quantity of land urbanization is differentiated by administrative grade. The average rate of land urbanization was 6.89%, obviously faster than the speed of population urbanization. The rate of land ur- banization reveals a pattern of differentiation between coastal and other cities. （3） In the past 10 years, the two primary land use types associated with land urbanization in China are residential and industrial, with a combined contribution rate of 52.49%. The greater the scale of the city, the more significant the driving effect of industrial land. In small- and medium-scale cities of the western and central regions, the growth of residential land is the primary driver of land urbanization, while in coastal urban agglomerations and cities on important communica- tion axes, the growth of industrial land is the main driver. （4） Overall, urban population ag- glomeration, industrial growth and investment are the three drivers of land urbanization in China, but cities of different scales have different drivers.

Effect of land prices on the spatial differentiation of housing prices:Evidence from cross-county analyses in China

This study analyzes the spatial patterns and driving forces of housing prices in China using a 2,872-county dataset of housing prices in 2014.Multiple theoretical perspec-tives on housing demand,supply,and market,are combined to establish a housing price model to explore the impact of land prices on housing prices.The relative impacts of land prices on housing prices at different administrative levels are then analyzed using the geo-graphical detector technique.Finally,the influencing mechanism of land prices on housing prices is discussed.The main conclusions are as follows.(1)Housing prices have a pyra-mid-ranked distribution in China,where higher housing prices are linked to smaller urban populations.(2)Land prices are the primary driver of housing prices,and their impacts on housing prices vary over different administrative levels.To be specific,the effect of land prices is the strongest in the urban districts of provincial capital cities.(3)The internal influ-ence mechanisms for land prices driving housing prices are:topographic factors,urban con-struction level,the agglomeration degree of high-quality public service resources,and the tertiary industrial development level.The urban land supply plan(supply policies)is the in-trinsic driver that determines land prices in cities;through supply and demand,cost,and market mechanisms,land prices then impact housing prices.

The mechanism of farmland marginalization in Chinese mountainous areas:Evidence from cost and return changes

Farmland marginalization has become the main trend of land-use change in the mountainous areas of China. Using annual survey data of major agricultural production costs and earnings at national and provincial levels in China, this study aims to analyze the reasons and mechanism behind farmland marginalization in mountainous areas. We find that farmers on plains are able to reduce their per mu labor input effectively through intensive use of agricultural machinery, which has minimized the impact of the increase in labor price. However, it is extremely challenging for farmers in mountainous areas to use the same method owing to the rough terrain. Thus, per laborer farming area in these areas has increased relatively slowly, causing a widening gap in agricultural labor productivity between the two regions. With the rapid rise in labor costs since 2003, the marginalization of cultivated land in mountainous areas is evident. In 2013, the net profit of agricultural production in mountainous China fell below zero. Since 2000, the land-use and land-cover change in these areas was characterized by the reduction of farmland area, reforestation, and the enhancement of the NDVI value. The high correlation between the NDVI change rate and the ratio of change in farmland(r = –0.70) and forest(r = 0.91) in mountainous areas at provincial level further attests to the trend of farmland marginalization there. Finally, we summarize the mechanism of such marginalization against the backdrop of the rapid increase in the opportunity cost of farming and the rapid fall of agricultural labor forces in mountainous areas. This study contributes to a deep understanding of the development process of farmland marginalization and abandonment as well as forest transition in Chinese mountainous areas.

Influencing mechanism of energy-related carbon emissions in Xinjiang based on the input-output and structural decomposition analysis

Analysis of carbon emission mechanism based on regional perspectives is an im- portant research method capable of achieving energy savings and emission reductions. Xin- jiang, an important Chinese energy production base, is currently going through a period of strategic opportunities for rapid development. Ensuring stable socio-economic development while achieving energy savings and meeting emission reductions targets, is the key issue currently facing the region. This paper is based on the input-output theory, and conducts a structural decomposition analysis on the factors affecting energy-related carbon emissions in Xinjiang from 1997 to 2007; this analysis employs a hybrid input-output analysis framework of ＂energy - economy - carbon emissions＂. （1） Xinjiang＇s carbon emissions from energy con- sumption increased from 20.70 million tons in 1997 to 40.34 million tons in 2007; carbon emissions growth was mainly concentrated in the production and processing of energy re- sources, the mining of mineral resources, and the processing industry. （2） The analysis of the direct effects of the influencing factors on carbon emissions showed that the change in per capita GDP, the final demand structure, the population scale, and the production structure were the important factors causing an increase in carbon emissions, while the decrease in carbon emission intensity during this period was the important influencing factor in stopping the growth of carbon emissions. This showed that while the sizes of Xinjiang＇s economy and population were growing, the economic structure had not been effectively optimized and the production technology had not been efficiently improved, resulting in a rapid growth of carbon emissions from energy consumption. （3） The analysis of the indirect effects of the influencing factors of carbon emission showed that the inter-provincial export, fixed capital formation, and the consumption by urban residents had significant influence on the changes in carbon emissions from energy consumption in Xinjiang. （4） The growth of investments in fixed assets of carbon intensive industry sectors, in addition to the growth of inter-provincial exports ofenergy resource products, makes the transfer effect of inter-provincial ＂embodied carbon＂ very significant.

Spatio-temporal variations and influencing factors of energy-related carbon emissions for Xinjiang cities in China based on time-series nighttime light data

This essay combines the Defense Meteorological Satellite Program Operational Linescan System(DMSP-OLS)nighttime light data and the Visible Infrared Imaging Radiometer Suite(VIIRS)nighttime light data into a“synthetic DMSP”dataset,from 1992 to 2020,to retrieve the spatio-temporal variations in energy-related carbon emissions in Xinjiang,China.Then,this paper analyzes several influencing factors for spatial differentiation of carbon emissions in Xinjiang with the application of geographical detector technique.Results reveal that(1)total carbon emissions continued to grow,while the growth rate slowed down in the past five years.(2)Large regional differences exist in total carbon emissions across various regions.Total carbon emissions of these regions in descending order are the northern slope of the Tianshan(Mountains)>the southern slope of the Tianshan>the three prefectures in southern Xinjiang>the northern part of Xinjiang.(3)Economic growth,population size,and energy consumption intensity are the most important factors of spatial differentiation of carbon emissions.The interaction between economic growth and population size as well as between economic growth and energy consumption intensity also enhances the explanatory power of carbon emissions’spatial differentiation.This paper aims to help formulate differentiated carbon reduction targets and strategies for cities in different economic development stages and those with different carbon intensities so as to achieve the carbon peak goals in different steps.

Spatiotemporal analysis of COVID-19 risk in Guangdong Province based on population migration

Population migration,especially population inflow from epidemic areas,is a key source of the risk related to the coronavirus disease 2019(COVID-19)epidemic.This paper selects Guangdong Province,China,for a case study.It utilizes big data on population migration and the geospatial analysis technique to develop a model to achieve spatiotemporal analysis of COVID-19 risk.The model takes into consideration the risk differential between the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities of population migration.It further incorporates a time-lag process based on the time distribution of the onset of the imported cases.In theory,the model will be able to predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and provide support for advanced planning and targeted prevention measures.The research findings indicate the following:(1)The COVID-19 epidemic in Guangdong Province reached a turning point on January 29,2020,after which it showed a gradual decreasing trend.(2)Based on the time-lag analysis of the onset of the imported cases,it is common fora time interval to exist between case importation and illness onset,and the proportion of the cases with an interval of 1-14 days is relatively high.(3)There is evident spatial heterogeneity in the epidemic risk;the risk varies significantly between different areas based on their imported risk,susceptibility risk,and ability to prevent the spread.(4)The degree of connectedness and the scale of population migration between Guangdong’s prefecture-level cities and their counterparts in the source regions of the epidemic,as well as the transportation and location factors of the cities in Guangdong,have a significant impact on the risk classification of the cities in Guangdong.The first-tier cities-Shenzhen and Guangzhou-are high-risk regions.The cities in the Pearl River Delta that are adjacent to Shenzhen and Guangzhou,including Dongguan,Foshan,Huizhou,Zhuhai,Zhongshan,are medium-risk cities.The eastern,northern,and western parts of Guangdong,which are outside of the metropolitan areas of the Pearl River Delta,are considered to have low risks.Therefore,the government should develop prevention and control measures that are specific to different regions based on their risk classification to enable targeted prevention and ensure the smooth operation of society.

A comprehensive framework for seasonal controls of leaf abscission and productivity in evergreen broadleaved tropical and subtropical forests

Relationships among productivity,leaf phenology,and seasonal variation in moisture and light availability are poorly understood for evergreen broadleaved tropical/subtropical forests,which contribute 25% of terrestrial productivity.On the one hand,as moisture availability declines,trees shed leaves to reduce transpiration and the risk of hydraulic failure.On the other hand,increases in light availability promote the replacement of senescent leaves to increase productivity.Here,we provide a comprehensive framework that relates the seasonality of climate,leaf abscission,and leaf productivity across the evergreen broadleaved tropical/subtropical forest biome.The seasonal correlation between rainfall and light availability varies from strongly negative to strongly positive across the tropics and maps onto the seasonal correlation between litterfall mass and productivity for 68 forests.Where rainfall and light covary positively,litterfall and productivity also covary positively and are always greater in the wetter sunnier season.Where rainfall and light covary negatively,litterfall and productivity are always greater in the drier and sunnier season if moisture supplies remain adequate;otherwise productivity is smaller in the drier sunnier season.This framework will improve the representation of tropical/subtropical forests in Earth system models and suggests how phenology and productivity will change as climate change alters the seasonality of cloud cover and rainfall across tropical/subtropical forests.

Digitizing the thermal and hydrological parameters of land surface in subtropical China using AMSR-E brightness temperatures

Digitizing the land surface temperature(T_(s))and surface soil moisture(m _(v))is essential for developing the intelligent Digital Earth.Here,we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_(s) and m_(v) using the dual-polarized T_(b) of Aqua satellite advanced microwave scanning radiometer(AMSR-E)C-band(6.9 GHz)based on the simplified radiative transfer equation.Validation using in situ T_(s) and m_(v) in southern China showed the average root mean square errors(RMSE)of T s and m_(v) retrievals reach 2.42 K(R^(2)=0.61,n=351)and 0.025 g cm^(−3)(R^(2)=0.68,n=663),respectively.The results were also validated using global in situ T_(s)(n=2362)and m_(v)(n=1657)of International Soil Moisture Network.The corresponding RMSE are 3.44 k(R 2=0.86)and 0.039 g cm^(−3)(R^(2)=0.83),respectively.The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_(s)(R^(2)=0.57;RMSE=2.91 k)and ECV_SM m_(v)(R^(2)=0.51;RMSE=0.045 g cm^(−3)),respectively.Overall,this paper indicates an effective way to jointly modeling T_(s) and m_(v) using passive microwave remote sensing.