A Semi-Empirical Inversion Model for Assessing Surface Soil Moisture using AMSR-E Brightness Temperatures

In 2004-2005, 2007 and 2009, three major drought disasters occurred in Guangdong Province of southern China, which caused serious economic losses. Hence, it has recently become an important research subject in China to monitor surface soil moisture (SSM) and the drought disaster quickly and accurately. SSM is an effective indicator for characterizing the degree of drought. First, using the brightness temperatures (T[subscript b]) of the Advanced Microwave Scanning Radiometer on the EOS Aqua Satellite (AMSR-E), a modified surface roughness index was developed to map the land surface roughness. Then by combining microwave polarization difference indices (MPDI)-based vegetation cover classification and the modified surface roughness index, a simple semi-empirical model of SSM was derived from the passive microwave radiative transfer equation using AMSR-E C-band T[subscript b] and observed surface soil temperature (T[subscript s]). The model was inverted to calculate SSM. The results show the ability to discriminate over a broad range of SSM (7%~73%) with an accuracy of 2.11% in bare ground and flat areas (R² =0.87), 2.89% in sparse vegetation and flat surface areas (R²=0.85), about 6%~9% in dense vegetation areas and rough surface areas (0.80≤R²≤0.83). The simulation results were also validated using in-situ SSM data (R²=0.87, RMSE=6.36%). Time series mapping of SSM from AMSR-E imageries further demonstrated that the presented method was effective to detect the initiation, duration and recovery of the drought events.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/journal-of-hydrology/.Keywords: Drought disaster, Brightness temperature (T[subscript b]), South China, Semi-Empirical model, Surface soil moisture (SSM), AMSR-E, Passive microwave remote sensin

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

Digitizing the land surface temperature (Ts) and surface soil moisture (mv) is essential for developing the intelligent Digital Earth. Here, we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving Ts and mv using the dual-polarized Tb 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 Ts and mv in southern China showed the average root mean square errors (RMSE) of Ts and mv retrievals reach 2.42 K (R2 = 0.61, n = 351) and 0.025 g cm−3 (R2 = 0.68, n = 663), respectively. The results were also validated using global in situ Ts (n = 2362) and mv (n = 1657) of International Soil Moisture Network. The corresponding RMSE are 3.44 k (R2 = 0.86) and 0.039 g cm−3 (R2 = 0.83), respectively. The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer Ts (R2 = 0.57; RMSE = 2.91 k) and ECV_SM mv (R2 = 0.51; RMSE = 0.045 g cm−3), respectively. Overall, this paper indicates an effective way to jointly modeling Ts and mv using passive microwave remote sensing

Relationship between Urban Floating Population Distribution and Livability Environment: Evidence from Guangzhou’s Urban District, China

The livability environment is an important aspect of urban sustainable development. The floating population refers to people without local hukou (also called ‘non-hukou migrants’). The floating population distribution is influenced by livability environment, but few studies have investigated this relationship. Especially, the influence of social environment on floating population distribution is rarely studied. Therefore, we study 1054 communities in Guangzhou’s urban district to explore the relationship between livability environment and floating population distribution. The purpose of this article is to study how livability environment affects floating population distribution. We develop a conceptual framework of livability environment, which consists of physical environment, social environment and life convenience. A cross-sectional dataset of the impact of livability environment on the floating population distribution is developed covering the proportion of floating population in the community as the dependent variable, eight factors of livability environment as the explanatory variables, and two factors of architectural characteristics and one factor of location characteristics as the control variables. We use spatial regression models to explore the degree of influence and direction of physical environment, social environment and life convenience on the floating population distribution in livability environment. The results show that the spatial error model is more effective than ordinary least squares and spatial lag model models. The five factors of the livability environment have statistical significance regarding floating population distribution, including four social environment factors (proportion of middle- and high-class occupation population, proportion of highly educated people in the population, proportion of rental households, and unemployment rate) and regarding life convenience factors (work and shopping convenience). The conclusion has value for understanding how the social environment affects the residential choice of the floating population. This study will help city administrators reasonably guide the residential pattern of the floating population and formulate reasonable management policies, thereby improving the city’s livability, attractiveness and sustainable development

Spatial-Temporal Dynamics of the Economic Efficiency of Construction Land in the Pearl River Delta Megalopolis from 1998 to 2012

Since the 1980s, the rapid, extensive, and dispersed urban expansion in the Pearl River Delta megalopolis (PRDM) has led to landscape fragmentation and the inefficient use of construction land. Like other developed regions in China that are subject to the dual challenges of shortages of construction land and deterioration of the ecological environment, it is becoming increasingly important in the PRDM to improve the land-use efficiency of urban construction. However, current methods for assessing land-use efficiency do not meet the emerging needs of land-use planning and policymaking. Therefore, using the American Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) nighttime light imagery and Landsat TM data, this study aims to develop a timely and efficient approach to model the high-resolution economic efficiency of construction land (EECL). With this approach, we mapped the reliable EECL of the PRDM at township level and with a one-kilometer grid. Next, the study compared the temporal changes and revealed the spatial-temporal dynamics in order to provide a scientific reference for informed land-use planning and policymaking. The results show that since 1998, the economic efficiency of construction land in the PRDM increased in general but varied significantly throughout the area. Further, these disparities widened from 1998 to 2012 between the PRDM’s inner and peripheral circles. Only one-fifth of the towns and subdistricts were categorized as fast-growth or ultrafast-growth, with the majority located in the most developed areas of the PRDM’s inner circle. In order to improve the efficiency of construction land in the PRDM and realize sustainable development, differentiated land-use policies for the inner and peripheral circles were proposed. The inner circle should focus on promoting the efficiency of existing construction land and encourage urban renewal, while the peripheral circle should enhance the control of new construction land and improve its efficiency

Relationship between Urban Three-Dimensional Spatial Structure and Population Distribution: A Case Study of Kunming’s Main Urban District, China

The three-dimensional (3D) spatial structure within cities can reveal more information about land development than the two-dimensional spatial structure. Studying the relationship between the urban 3D spatial structure and the population distribution is a crucial aspect of the relationship between people and land within cities. However, a few relevant studies focus on the differences between employment population and night population distribution in relation to urban 3D spatial structure. Therefore, this study proposes a new concept of 3D space-filling degree (3DSFD), which is applicable to evaluate the city’s 3D spatial structure. We took 439 blocks in Kunming’s Main Urban District as a sample and analyzed the 3D spatial structure based on geographic information data at the scale of a single building. The characteristics and differences of the daytime and night population distribution in Kunming’s Main Urban District were identified using cell phone signaling big data. Accordingly, a cross-sectional dataset of the relationship between the city’s 3D spatial structure and the population distribution was constructed, with the 3D space-filling degree of the block as the dependent variable, two indicators of population distribution (daytime and night population density) as the explanatory variables, and seven indicators of distance from the city center, and building, road, and functional place densities, proportion of undevelopable land area, housing prices, and land use type as the control variables. We used spatial regression models to explore the significance, strength, and direction of the relationship between urban 3D spatial structure and population distribution. We found that the spatial error model (SEM) was the most effective. The results show that only night population distribution is significantly and positively related to 3DSFD. Every 1% increase in night population density in a block will increase the value of 3DSFD by 2.8307%. The night population distribution is the core factor affecting the 3D spatial structure of Kunming’s Main Urban District. The correlation between daytime population distribution and 3DSFD is not significant. This variability has been ignored in previous studies. The findings are informative for further understanding of the relationship between urban 3D space and population distribution, especially the difference between night and daytime populations. This study can help city managers reasonably plan urban land development intensity and construction height, guide the population layout and formulate management policies to improve urban population and space matching, enhancing the livability and attractiveness of cities

The novel microwave temperature vegetation drought index (Mtvdi) captures canopy seasonality across amazonian tropical evergreen forests

International audienceDespite its perennial canopy, the Amazonian tropical evergreen forest shows significant canopy growth seasonality, which has been represented by optical satellite-based observations. In this paper, a new Microwave Temperature–Vegetation Drought Index (MTVDI) based on Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sensors was used to capture the canopy seasonality from 2003 to 2010 in comparison with four climatic dryness indicators (Palmer Drought Severity Index (PDSI), Climatological Water Deficit (CWD), Terrestrial Water Storage (TWS), Vapor Pressure Deficit (VPD)) and two photosynthesis proxies (Enhanced Vegetation Index (EVI) and Solar-Induced chlorophyll Fluorescence (SIF)), respectively. Our results suggest that the MTVDI shows opposite seasonal variability with two photosynthesis proxies and performs better than the four climatic dryness indicators in reflecting the canopy photosynthesis seasonality of tropical forests in the Amazon. Besides, the MTVDI captures wet regions that show green-up during the dry season with mean annual precipitation higher than 2000 mm per year. The MTVDI provides a new way for monitoring the canopy seasonality of tropical forests from microwave signals

Low-Carbon Transportation Oriented Urban Spatial Structure: Theory, Model and Case Study

Optimising the spatial structure of cities to promote low-carbon travel is a primary goal of urban planning and construction innovation in the low-carbon era. There is a need for basic research on the structural characteristics that help to reduce motor traffic, thereby promoting energy conservation. We first review the existing literature on the influence of urban spatial structure on transport carbon dioxide emissions and summarise the influence mechanisms. We then present two low-carbon transportation oriented patterns of urban spatial structure including the traditional walking city and the modern transit metropolis, illustrated by case studies. Furthermore, we propose an improved model Green Transportation System Oriented Development (GTOD), which is an extension of traditional transit-oriented development (TOD) and includes the additional features of a walking city and an emphasis on the integration of land use with a green transportation system, consisting of the public transportation and non-auto travel system. A compact urban form, effective mix of land use and appropriate scale of block are the basic structural features of a low-carbon transportation city. However, these features are only effective at promoting low-carbon transportation when integrated with the green traffic systems. Proper integration of the urban structural system with the green space system is also required. The optimal land use/transportation integration strategy is to divide traffic corridors with wedge-shaped green spaces and limit development along the transit corridors. This strategy forms the basis of the proposed urban structural model to promote low-carbon transportation and sustainable urban growth management

Infections in hematologic malignancy patients treated by CD19 chimeric antigen receptor T‐cell therapy

Abstract Increasing use of chimeric antigen receptor‐T (CAR‐T) cell therapy has significantly improved the survival of hematologic malignancy patients, but CAR‐T cell treatment is also associated with increased risk of infection. Hence, understanding the characteristics of infection may improve disease prognosis. The data of post‐CAR‐T therapy infections were obtained from the VigiBase database. We identified a total of 554 infection reports (1001 infection events) involving CAR‐T therapy among the 3007 case reports. Infections occurred in 18.42% of cases reported in VigiBase with CAR‐T therapy and were most frequently occurred during the first month. Among cases reported in VigiBase, most of the infections were controllable, and only 4.4% of the cases were fatal. Bacteria (60.7%) and respiratory tract infection (50.9%) were the most common infection types. Compared with axicabtagene ciloleucel, infection in patients receiving tisagenlecleucel‐T therapy had a higher infection risk (ROR = 1.76; 95% CI = 1.46–2.12, p < 0.001). Meanwhile, fungus infection and mixed infection had poorer prognoses than virus infection. Concerning the disease prognoses, fungal and mixed infection should be given more attention, and extensive prospective studies are much needed to verify these findings