Yuchen Wang

Bio: Yuchen Wang is an academic researcher from China University of Mining and Technology. The author has contributed to research in topics: Evapotranspiration & Impervious surface. The author has an hindex of 5, co-authored 7 publications receiving 61 citations. Previous affiliations of Yuchen Wang include Jiangsu Normal University.

Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data

Abstract: This paper presents a new assessment method for alleviating urban heat island (UHI) effects by using an urban land surface moisture (ULSM) index. With the aid of Landsat 8 OLI/TIRS data, the land surface temperature (LST) was retrieved by a mono-window algorithm, and ULSM was extracted by tasselled cap transformation. Polynomial regression and buffer analysis were used to analyze the effects of ULSM on the LST, and the alleviation effect of ULSM was compared with three vegetation indices, GVI, SAVI, and FVC, by using the methods of grey relational analysis and Taylor skill calculation. The results indicate that when the ULSM value is greater than the value of an extreme point, the LST declines with the increasing ULSM value. Areas with a high ULSM value have an obvious reducing effect on the temperature of their surrounding areas within 150 m. Grey relational degrees and Taylor skill scores between ULSM and the LST are 0.8765 and 0.9378, respectively, which are higher than the results for the three vegetation indices GVI, SAVI, and FVC. The reducing effect of the ULSM index on environmental temperatures is significant, and ULSM can be considered to be a new and more effective index to estimate UHI alleviation effects for urban areas.

Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China

Abstract: As an important energy absorption process in the Earth’s surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman–Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014–2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May–October) but not during the colder months (November–March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W·m−2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.

A Modified Multi-Source Parallel Model for Estimating Urban Surface Evapotranspiration Based on ASTER Thermal Infrared Data

Abstract: To date, little attention has been given to remote sensing-based algorithms for inferring urban surface evapotranspiration. A multi-source parallel model based on ASTER data was one of the first examples, but its accuracy can be improved. We therefore present a modified multi-source parallel model in this study, which has made improvements in parameterization and model accuracy. The new features of our modified model are: (1) a characterization of spectrally heterogeneous urban impervious surfaces using two endmembers (high- and low-albedo urban impervious surface), instead of a single endmember, in linear spectral mixture analysis; (2) inclusion of an algorithm for deriving roughness length for each land surface component in order to better approximate to the actual land surface characteristic; and (3) a novel algorithm for calculating the component net radiant flux with a full consideration of the fraction and the characteristics of each land surface component. HJ-1 and ASTER data from the Chinese city of Hefei were used to test our model’s result with the China–ASEAN ET product. The sensitivity of the model to vegetation and soil fractions was analyzed and the applicability of the model was tested in another built-up area in the central Chinese city of Wuhan. We conclude that our modified model outperforms the initial multi-source parallel model in accuracy. It can obtain the highest accuracy when applied to vegetation-dominated (vegetation proportion > 50%) areas. Sensitivity analysis shows that vegetation and soil fractions are two important parameters that can affect the ET estimation. Our model is applicable to estimate evapotranspiration in other urban areas.

Remote sensing estimation of urban surface evapotranspiration based on a modified Penman–Monteith model

Abstract: To date, remote sensing-based algorithms for inferring urban surface evapotranspiration (ET) remain little studied. Based on the modifications of the remote sensing Penman–Monteith (RS-PM) model, we propose an urban RS-PM model for estimating urban surface ET. Compared with the traditional RS-PM model, our urban RS-PM model is specifically developed for urban areas and is characterized by the following improvements: (1) excluding the interference of impervious surface components in urban areas by replacing the vegetation cover fraction index with land surface component fraction parameters inversed through linear spectral mixture analysis for calculating the area proportions of vegetation and soil; (2) considering the effect of the component fractions of vegetation or soil on all energy components of the surface energy balance by applying the modified multisource parallel model for estimating the component latent heat flux; and (3) optimizing the calculation of the component net radiation flux by considering the component surface characteristics. This urban RS-PM model was tested on an urban area of Xuzhou in the eastern Chinese province of Jiangsu. Landsat 8 operational land imager and thermal infrared sensor satellite images acquired between 2014 and 2016, together with their corresponding meteorological data and flux observation data, were used for estimating the ET of the study area for eight dates with the model. The results were validated by the latent heat flux data observed by an open path eddy covariance system. Validation shows the goodness of fit (R2), the root-mean-square error, the mean relative error, and the correlation coefficient (r) between estimated ET and observed ET for the eight dates were 0.8965, 24.14 W · m − 2, 18.5%, and 0.9546, respectively. The results prove that the urban RS-PM model is effective in estimating ET of urban areas with an acceptable accuracy.

Assessing the Distribution of Urban Green Spaces and its Anisotropic Cooling Distance on Urban Heat Island Pattern in Baotou, China

Abstract: An essential part of urban natural systems, urban green spaces play a crucial role in mitigating the urban heat island effect (UHI). The UHI effect refers to the phenomenon where the temperature within a city is higher than that of the surrounding rural areas. The effects of the spatial composition and configuration of urban green spaces on urban land surface temperature (LST) have recently been documented. However, few studies have examined the effects of the directionality and distribution of green spaces on LST. In this study, we used a landscape index to describe the change in pattern of heat island intensity for the city of Baotou, China. We then used a semi-variable function and nearest neighbor algorithm to analyze the cooling effects of green spaces. We found that: (1) the cooling distance of an urban green space was not only influenced by its size, vegetation cover, and shape, but also showed anisotropy. In general, the larger the area of the urban green space and the higher the value of Normalized Difference Vegetation Index (NDVI; a measure of plant photosynthetic activity), the larger the cooling distance within a certain threshold. Green spaces with more regular shapes displayed higher LST mitigation; however, the cooling distance was directional, and cooling effects depended on the semi-major axis and semi-minor axis of the green space. (2) The distribution of the urban green space within the landscape played a key role in mitigating the UHI effect. Within a certain area, the cooling effect of green spaces that are evenly distributed was greater than that which was associated with either green spaces that were large in area or where greens spaces were aggregated in the landscape. Therefore, within urban areas, where space is limited, urban planning should account for green spaces that are relatively scattered and evenly distributed to maximize cooling effects. The results of this study have key implications for sustainable urban planning and development; to mitigate urban heat island effects it is important to not only increase canopy cover or the size of urban green spaces, but also to optimize their spatial configuration.

Local GPS tropospheric tomography

Abstract: A formulation of local GPS tropospheric tomography for determining 4-D distribution of refractivity in the troposphere is presented together with a preliminary analysis of local dense GPS campaign data. Dividing the modeling space up to a height of 10 km above GPS receivers into cells, the refractivity in each cell is estimated in a successive time window by a tomographic reconstruction method in a quite similar manner like the seismic velocity in each cell in Earth’s interior is estimated in seismic tomography. The basic data for tomography are GPS slant delays for respective pairs of station and satellite, which are the sum of postfit phase residual, hydrostatic and wet slant delay. On the other hand, the slant delay from a station to a satellite is expressed by the summation of the product of path length and refractivity in each cell along the ray path. In a given time window, we have numerous observed slant delays corresponding to different ray trajectories, and the refractivity in each cell can be estimated by discrete inversion and least squares methods. The observational equations are usually singular so that we use a damped least squares method popular in seismic tomography. An example of real data analysis is given for the 1995 Shigaraki GPS campaign data, which reveals the spatial and temporal change of refractivity corresponding to the passage of ‘cold front’.

Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data | NOVA. The University of Newcastle's Digital Repository

Abstract: This paper reviews methods for estimating evaporation from landscapes, regions and larger geographic extents, with remotely sensed surface temperatures, and highlights uncertainties and limitations associated with those estimation methods. Particular attention is given to the validation of such approaches against ground based flux measurements. An assessment of some 30 published validations shows an average root mean squared error value of about 50 W m−2 and relative errors of 15–30%. The comparison also shows that more complex physical and analytical methods are not necessarily more accurate than empirical and statistical approaches. While some of the methods were developed for specific land covers (e.g. irrigation areas only) we also review methods developed for other disciplines, such as hydrology and meteorology, where continuous estimates in space and in time are needed, thereby focusing on physical and analytical methods as empirical methods are usually limited by in situ training data. This review also provides a discussion of temporal and spatial scaling issues associated with the use of thermal remote sensing for estimating evaporation. Improved temporal scaling procedures are required to extrapolate instantaneous estimates to daily and longer time periods and gap-filling procedures are needed when temporal scaling is affected by intermittent satellite coverage. It is also noted that analysis of multi-resolution data from different satellite/sensor systems (i.e. data fusion) will assist in the development of spatial scaling and aggregation approaches, and that several biological processes need to be better characterized in many current land surface models.

Urban Heat Island Assessment Using the Landsat 8 Data: A Case Study in Shenzhen and Hong Kong

Abstract: In this study, we assess the urban heat island (UHI) effect using remote sensing data, a phenomenon emerging under the background of global warming and urbanization. With the rapid development of satellite technology, remote sensing images are widely applied to evaluate the UHI effect on rapidly-urbanized areas in recent years. In the study, we applied Landsat 8 data to estimate the land surface temperature (LST) in the case study of Shenzhen and Hong Kong. The methods of the mono-window algorithm (MWA), single-channel method (SCM), Qin’s split-window algorithm (SWA-Q) and Sobrino’s split-window algorithm (SWA-S) are used to calculate the LST from Landsat 8 data on 29 November 2013, 16 November 2014, 18 October 2015, and 7 February 2016. The results show that LST retrievals by SWA-Q are better than those of the other algorithms in the case study of Shenzhen and Hong Kong. From 2013 to 2016, the high-LST zones or UHIs in Shenzhen and Hong Kong are substantially identical. Although the LST is not obviously correlated with vegetation distribution, the growth condition of vegetation may impact the distribution of the UHI, and the high LST is slightly correlated to the high atmospheric particulate concentration. Additionally, in general, Shenzhen and Hong Kong are weak UHI regions and the UHI-affected area in Shenzhen is larger than that in Hong Kong from 2013 to 2016.