Spatiotemporal variation of heat fluxes in Beijing with land use change from 1997 to 2017

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Physics and Chemistry of the Earth journal homepage: www.elsevier.com/locate/pce

Spatiotemporal variation of heat fluxes in Beijing with land use change from 1997 to 2017 Menghui Guoa,b, Shaohui Chena,∗, Weimin Wangd, Hong Liangd, Guibin Haoa,b, Kai Liua a

Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China University of Chinese Academy of Sciences, Beijing, 100049, China d Shenzhen Environmental Monitoring Center, Shenzhen, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Urban heat flux Latent heat flux Sensible heat flux Land use Thermal infrared remote sensing model

It is meaningful to explore the influence of land use change on urban thermal environment for urban sustainable development and city livability improvement etc. Based on one pixel component arranging comparing algorithm (PCACA), this study estimates three instantaneous heat fluxes of Beijing city from meteorological data and the NDVI, land surface temperature (LST), and albedo products retrieved from Landsat data on September 21, 1997, September 22, 2009 and September 28, 2017. Then the temporal and spatial variation in the heat fluxes of the Beijing with land use change is discussed. The following key points have been found: 1) LST and heat fluxes have distinctly spatial heterogeneity, and significant differences between mountainous and plain areas, and among different land use types in plain area; 2) both LST and heat fluxes have a consistent order of arrangement for different land use types at different times. Forest has the highest latent heat flux (LHF) with an average of 265.7 W/m2, followed by cropland and grassland, and building land has the smallest average of 158.4 W/m2. LST has the reverse order, cropland has the highest average of 24.8 °C, followed by grassland and cropland, water body has the lowest average of 19.2 °C; 3) temporally, urban thermal fluxes vary greatly with land use transition. LHF and sensible heat flux (SHF) will respectively drastically reduce and increase when natural surface changes into building land. These analyses suggest that green spaces play an important role in easing urban heat environment.

1. Introduction With the gradual advancement of the Beijing city, impervious layer increase causes the thermal environmental problems like urban heat island have become the miniature of China's environmental pollution under the dual background of global climate change and rapid economic growth (Qiao et al., 2018). Albedo decrease due to the underlying surface change makes sensible heat flux (SHF) gradually dominate in surface energy budget, which results to the deterioration of urban thermal environment (Zheng et al., 2018). Heat island effect is one prominent sign of urban thermal environment deterioration associated with land use change. Because heat fluxes and thermal state are complicated at the land-atmosphere interface over urban underlying surface, up to now, the research on quantifying the relationship between land use change and urban thermal environment is not yet deep, which is not conducive to the scientific planning of the Beijing city to be an international famous metropolis. Domestic and foreign scholars have conducted in-depth and

∗

meticulous researchs on urban thermal environment from different aspects. Yan et al. (2018) study the influence of a large urban park on local urban thermal environment, and indicate as the distance from park border increases, ambient air temperature also gradually increases, and the mean air temperature difference between park and surrounding areas is in the range of 0.6–2.8 °C at different times. Amani-Beni et al. (2018) observe the impacts of urban park's tree, grass and waterbody on microclimate inside the Olympic park of Beijing during summer days. The results indicate that the park is generally 0.48–1.13 °C cooler during the day, as well as increases air humidity by 2.39–3.74% and generates more comfortable thermal environment. Cui et al. (2015) report the negative impact of impervious surface is greater than the positive impact of vegetation on urban thermal environment. Hao et al. (2015) find that the relative mean annual surface temperature (RMAST) between the south third ring and the south fifth ring is increased by more than 1.5 K through evaluating the relationship between percent impervious surface area and RMAST using the 1990–2014 multitemporal TM, ETM+, and OLI images. Liu et al.

Corresponding author. E-mail address: [email protected] (S. Chen).

https://doi.org/10.1016/j.pce.2018.11.001 Received 3 September 2018; Received in revised form 20 October 2018; Accepted 1 November 2018 1474-7065/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

Please cite this article as: Guo, M., Physics and Chemistry of the Earth, https://doi.org/10.1016/j.pce.2018.11.001

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(2017) find that the Beijing city acts as heat island and cool island respectively in summer and winter based on HJ-1B data, and urban land surface temperature (LST) is greater by a maximum 7.14 K in July and August, and is 3.09 K lower in winter than suburban's LST. Surface energy budget varies widely across different land uses and surface materials. Feigenwinter et al. (2018) show that SHFs are highest for industrial areas, the surface-to-air temperature difference is largest for railway stations, and areas with high building density, and LHF is spatially related to the saturation deficit of vapor and the stomatal resistance of vegetation types. Naeem et al. (2018) consider that different land use types have different effects on energy partitioning and LSTs, and regulating land use have become a key initiative to reduce urban thermal environment and adapt to urban climate change. In assessing the surface heat fluxes in China's Suzhou using a sub-pixel remote sensing method (Liu et al., 2016), the percentage of impervious surface area and land-cover type are promising for delineating urban heat fluxes since statistically-significant correlations are found between LHF and VFC, and between SHF and the percentage of impervious surface area. The nonlinear relationship between heat regulation strength of vegetation defined as LHF/VFC and VFC is characterized as an exponential fitting by Kuang et al. (2015), which declines with the increase of VFC, and reveals that the LST, LHF, and Bowen ratio of Beijing increases along the outskirt-suburban-urban gradient. The results (Weng et al., 2013) show that SHF tends to change largely with LST, while LHF is largely modulated by vegetation abundance and the moisture condition at the same time. The response of net radiation flux (NRF) to land use type mainly attributes to surface albedo and LST, while the intra-class variation in the turbulent heat fluxes is more associated with the changes in vegetation, water bodies, and other surface factors. Since the energy exchange between land surface and atmosphere is influenced by underlying surface and atmospheric stability, heat fluxes in cities have very significant spatial heterogeneity. This requires a fundamental understanding of energy partition and the influence mechanism of different land use types on heat flux distributions (Kuang et al., 2015; Liu et al., 2017). Early urban thermal environment studies are limited by site observation data with limited spatial representation (Cui et al., 2015), or confined to the spatial analysis of LST, they cannot deeply analyze the pattern variations of surface heat fluxes and Bowen ratio caused by the change of land use type (Chen et al., 2014). Remote sensing is of great significance for the study of large-scale and long-term urban thermal environment. Remote sensing technology can simultaneously measure land surface situation in the form of visible bands and land surface thermal condition in the form of thermal infrared bands. The information obtained has good consistency and comparability, and gradually become the main source to estimate urban heat fluxes. A diversity of satellite remote sensing data has been extensively used to explore urban thermal patterns over a wide area (Liu et al., 2015). Since Brown and Rosenberg (1973) apply the thermal infrared remote sensing temperature to the calculation of LHF, a series of remote sensing heat flux models are developed. Early one-source models, such as SEBAL (Bastiaanssen et al., 1998), METRIC (Allen et al., 2007) and SEBS (Su 2002), taking the heterogenous underlying surface as a whole to reflect the total energy exchange between underlying surface and atmosphere, are easy to apply due to few input parameters, however, the difference of moisture content and thermal properties between vegetation and soil is neglected. Because of the straight substitution of remote sensing radiant temperature for the aerodynamic temperature, one-source models are necessary to correct residual aerodynamic resistance. At present, it is difficult to find a universal formula of residual resistance suitable for various underlying surfaces, so one-source models have a large error over urban areas with large spatial heterogeneity (Verhoef et al., 1997). Two-source models including SW (Shuttleworth and Wallace, 1985), SEBI (Menenti and Choudhury, 1993), N95 (Norman et al., 1995), ALEXI (Anderson et al., 1997), PM (Cleugh et al., 2007) and TTME

(Long and Singh, 2012) etc. can describe the energy exchange between soil-vegetation and atmosphere, but multi-angle thermal infrared data are needed to retrieve the component temperatures of vegetation and soil surfaces, and the accurate calculation of surface resistance which describes the evaporation drag of land surface is also difficult. Statistical models (Courault et al., 2005) are statistical regression equations between remote sensing products and ground observations. Since remote sensing products are good indicators of land surface thermal status, VFC and LST are respectively closely related to vegetation transpiration and soil evaporation. Statistical models are simple and easy to use, but lack enough physical basis, so they are not generally applied to different regions. The development of remote sensing technology makes the retrievals of surface parameters no longer difficult, but the accurate partition of available energy into LHF and SHF is still intractable. Moran et al. (1994) generalize the scatter plot of LST and VFC into one trapezoidal space and estimate the water deficit state of Alfalfa by linearly interpolating the slopes of trapezoidal dry and wet edges. Zhang et al. (2004) propose a pixel component arranging and comparing algorithm (PCACA) based on trapezoidal space and iso-slope line hypothesis. Linearly interpolating the slopes of trapezoidal dry and wet edges is used to characterize the change of mixed pixel's LST and albedo with VFC, and finally, LHF and SHF are estimated from the components of LST and albedo for vegetation and soil. The premises of PCACA are that all LSTs and albedos of mixed pixels fall into respective trapezoidal spaces, and iso-soil moisture line is matched with the slope derived from linearly interpolating the slopes of the dry and wet edges, more, the actual heat flux states of the four vertices of trapezoidal space are consistent with their theoretical situation. In urban, dry edge is determined by extremely dry surfaces with 0–1 VFCs while wet edge is composed of extreme moist surfaces with 0–1 VFCs. The trapezoidal dry and wet edges are linearly interpolated to capture the change rate of LST and albedo with VFC. For understanding the influence of Beijing's urban expansion on the spatiotemporal patterns of LST and heat fluxes, we must study the partition of available energy under different land use structures and quantitatively analyze the rise and fall of heat fluxes with land use transition. These will provide a reference for responding to urban thermal environment in Beijing. This study is mainly about the applicability of the two-layer remote sensing heat flux model in cities and the accuracy of energy segmentation using absolute wet and dry lines in complex underlying surfaces.

2. Material and methods 2.1. Study area Beijing is centered at (39.9ºN, 116.38ºE), surrounded by (39.43–41.05ºN, 115.42–117.5 ºE), and located on the northwestern edge of the North China Plain (Fig. 1). The area of Beijing city is 16801 km2, of which the impervious area is 1582 km2, accounting for 76% of the urban area (Kuang et al., 2014). Beijing is a typical warm temperate and semi-humid continental monsoon climate, which is hot and rainy in summer, cold and dry in winter, short in spring and autumn. The average annual air temperature is 11 °C, the highest air temperature is 42.6 °C, the lowest air temperature is −27.4 °C (http:// en.wikipedia.org/wiki/Beijing). Annual average rainfall is more than 600 mm, which is one of the most rainfall regions in North China, and 80% of the annual precipitation is concentrated in summer. By 2016, the permanent population of Beijing reaches 21.7 million, with a population density of 1324 people per km2. To effectively cope with the severe urban thermal environment and air pollution caused by rapid urbanization, it is necessary to study the evolvement of the heat fluxes with the urbanization.

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space, which reduces the uncertainty of previously subjective judgement. Thereby, the slopes of the trapezoidal dry and wet edges are calculated from the LSTs of the four vertices, then are linearly interpolated by the LST of the targeted pixel to obtain the slope for the targeted pixel. The interpolated slope as a substitution of Bowen ratio to partition the net radiation of the targeted pixel into SHF and LHF. The PCACA can be simply driven by only one angle remote sensing data which are provided from most of satellite data, more, the layered energy-separating way and the theoretical location of dry and wet lines can reduce the uncertainty in surface energy partitioning based on the Beer's law (Norman et al., 1995). In the PCACA, NRF is computed according to surface radiance balance:

Rn = (1 − α ) Rs ↓ + εa σTa4 − εs σTs4

(1)

In equation (1), Rn is NRF (W/m ), α is surface albedo, Rs ↓ is surface downward shortwave radiance (W/m2), εs is surface specific emissivity, Ts is LST (K), σ is Stephen-Boltzmann constant with a value of 5.67 × 10−8 W/(m2·K4). εa is cloudless atmosphere effective specific emissivity, and can be expressed as the function of atmosphere vapor pressure (ea ) and air temperature (Ta ) at reference height (2 m above ground) as: 2

Fig. 1. The study area surrounded by red curve. Each triangle represents an observation point for LST and VFC (Please refer to Fig. 3), where two black triangles represent the Daxing (S7) and Miyun (S6) flux observation stations. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

εa = 1.24(ea/ Ta)1/7 ea =

(2)

P∗q 0.622

(3)

Where P is atmosphere pressure (hPa), q is air specific humidity (g/g). In the PCACA, albedo is estimated by weighting the surface reflectances of the different bands in Landsat satellite. On September 21, 1997 and September 22, 2009, albedo is obtained by the method (Liang, 2001) as:

2.2. Data Three Landsat series data include Landsat 5 TM multispectral bands with 30 m spatial resolution and the thermal infrared band with 120 m spatial resolution on September 21, 1997 and September 22, 2009, and Landsat 8 OLI multispectral bands with 30 m spatial resolution and the thermal infrared band with 100 m spatial resolution on September 28, 2017. These data are used to calculate LSTs, VFCs, and surface albedos of the study area. The meteorological data are from China Meteorological Forcing Dataset (http://westdc.westgis.ac.cn/data/, accessed on August 3, 2018), which is produced through fusing Princeton reanalysis data, GLDAS data, GEWEX-SRB radiation data, TRMM precipitation data, and conventional meteorological observation data from the China Meteorological Administration. Its temporal and spatial resolutions are respectively 3 h and 0.1°, and time span is 1951–2016, including nearsurface air temperature, near-surface air pressure, near-surface air specific humidity, near-surface wind speed, ground downward shortwave radiation, ground downward longwave radiation, ground precipitation rate. There are 17 × 21 grid data in the study area. The meteorological data in 2017 come from the product “CLDASV2.0″ archived on the China Meteorological data sharing service network (http://cdc.cma.gov.cn, July 30, 2018). Flux observations are large aperture scintillation instrument and eddy correlator data of Daxing and Miyun sites from Multi-scale surface flux and meteorological elements observation dataset in the Hai River (http://westdc. westgis.ac.cn/data/af1f42 cb-241f-40ed-b01c-fca049d26241, accessed on August 3, 2018) archived at Cold and Arid Region Science Data Center. LST and VFC are validated by the results published by Kuang et al. (2014). In Fig. 1, each triangle represents an observation location for LST and VFC (Please refer to Fig. 2), where two black triangles represent the Daxing (S7) and Miyun (S6) flux observation stations.

α = 0.356ρ1 + 0.130ρ3 + 0.373ρ4 + 0.085ρ5 + 0.072ρ7 − 0.0018

(4)

??1, ??3, ??4, ??5 and ??7 respectively denote the surface reflectances of Landsat TM5 band 1, 3, 4, 5 and 7. On September 28, 2017, albedo is obtained using the method (Silva et al., 2016) by weighting the surface reflectances of Landsat 8 six different bands:

α = 0.3ρ2 + 0.277ρ3 + 0.233ρ4 + 0.143ρ5 + 0.036ρ6 − 0.012ρ7

(5)

??2, ??3, ??4, ??5, ??6 and ??7 respectively denote the surface reflectances of Landsat 8 band 2, 3, 4, 5, 6 and 7. In the PCACA, LST is retrieved by radiation transfer equation (Gillespie et al., 2002). The thermal infrared radiance at wavelength λ received by the satellite sensor can be expressed as:

Lλ = [ελ B (Ts ) + (1 − ελ ) L↓] τλ + L↑

(6)

Where λ denotes wavelength (μm), ελ denotes surface specific emissivity, Ts denotes LST (Κ), B(Ts) is the thermal radiance of the black body when its temperature equals Ts (W/(m2⋅sr⋅μm) ), τλ is the atmosphere transmittance at λ. L↓ and L↑ respectively denote the downward and upward atmosphere longwave radiance. B(Ts) can be derived from equation (7) as:

B (Ts ) = [Lλ − L ↑ − τλ (1 − ελ ) L↓]/(τλ ελ )

(7)

Ts can be obtained according to the Planck formula: Κ Ts = Κ2⋅ln ⎡ 1 + 1⎤ ⎢ ⎥ ⎣ Β (Ts ) ⎦

(8)

W/(m2⋅sr⋅μm) ,

Κ2 = 1260.56 K, For Landsat 5 Band 6, Κ1 = 607.76 for Landsat 8 band 10, Κ1 = 774.89 W/(m2⋅sr⋅μm) , K2 = 1321.08 K. In the website provided by NASA (http://atmcorr.gsfc.nasa.gov), the atmospheric profile parameters (L↑, L↓ and τλ) can be obtained by inputting imaging time and the central latitude and longitude, as shown in Table 1. It should be noted that this website only provides the

2.3. The PCACA For mapping the heat flux pattern of Beijing city, the PCACA (Zhang et al., 2004, 2008) is employed. The PCACA takes account of surface energy balance to determine the boundary of LST-VFC trapezoidal 3

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Fig. 2. The validation of LST and VFC.

the LSTs of the four vertices: the extreme LST for the extremely dry bare soil (Tsd) where evaporation equals zero, the extreme LST for the extremely wet bare soil (Tsw) where potential evaporation has occurred, the extreme LST for the water-stressed, full-cover vegetation (Tvd) where transpiration equals zero, the extreme LST for the water-saturated, full-cover vegetation (Tvw) where potential transpiration has occurred. According to surface energy balance equation, Tsd and Tvd are derived as (Zhang et al., 2008):

Table 1 Atmospheric profile parameters used in this study. Date

τλ

L↑

L↓

September 22, 2009 September 28, 2017

0.89 0.96

0.82 0.27

1.38 0.48

atmospheric profile data after 2000. For the retrieval of the LST in 1997, please refer to equation (2) in the literature (Shen et al., 2016) which follows the method proposed by Artis and Carnahan (1982). In the PCACA, surface specific emissivity is estimated using the classification threshold method (Qin et al., 2004) according to four land use types including impervious surface, vegetation, bare soil and water body as:

εs = VFC × Rv × ε v + (1 − VFC ) × Rm × εm + dε

Tsd =

0.9886 + 0.1287 × vfc Rm = ⎧ ⎨ ⎩ 0.9902 + 0.1068 × vfc

Tvd =

(9)

The specific emissivity of water in thermal infrared band is very high, which is close to the blackbody. It can be estimated by εm = 0.995.dε is empirically calculated as: (9.3)

dε = 0.0038 × (1 − vfc )⋅vfc>0.5

(9.4)

dε = 0.0019⋅vfc = 0.5

(9.5)

In the PCACA, VFC is computed as (Verger et al., 2009):

VFC =

NDVI − NDVImin NDVImax − NDVImin

(10)

NDVImin and NDVImax respectively denotes the minimum and maximum NDVIs in the study area. In this study, the NDVI values at 5% and 95% significant levels are taken as NDVImin and NDVImax for reducing the uncertainty caused by remote sensing data themselves. In the PCACA, ground heat flux (GHF) is calculated through its simplified relation with NRF:

G ≈ 0.3(1 − 0.9vfc ) Rn

3 + 0.7σε v Tvd

(12) ρCp T rvda vda

(13)

Where ?? is density of air (Kg/m³), Cp is the volumetric heat capacity of air (J/(Kg∙K)), rsda and rvda are the air dynamic resistances (s/m) in the case of extremely dry bare soil and water-stressed full-cover vegetation, αsd and αvd are the albedos in the case of extremely dry bare soil and water-stressed full-cover vegetation, and Tsda and Tvda are the air temperatures in the case of extremely dry bare soil and water4 stressed full-cover vegetation.σεsky Tsky represents downward longwave radiation which usually has small spatial variability for clear sky, and can be obtained from the conventional observations of meteorological stations at satellite overpass time. The detail calculations of other variables such as Tsda, Tvda, rsda, rvda can refer to the literature (Zhang et al., 2008) for interested readers. From equations (12) and (13), Tsd and Tvd can be iteratively computed. In the PCACA, the average air temperature at vfc = 1 is taken as Tvw considering that the surface radiant temperature of full-cover vegetation is very close to the ambient air temperature (Nishida et al., 2003). As for Tsw, the LST of water body is adopted as the Tsw of water-saturated bare soil. In this study, the LST of the Ao sea in Olympic park is used as Tsw. The Olympic forest park is an artificial green space occupying an area of 11.59 km2 in the urban area. In urban areas, Tsd represents the LST of dry impervious surface while Tvd represents the LST of the extremely dry closed canopy. In the PCACA, Rn is partitioned into SHF and LHF by Bowen ratio β:

(9.2)

dε = 0.0038 × vfc⋅vfc<0.5

ρCp

ρCp T rsda sda

3 + 0.7σεs Tsd

4 0.7[S0 (1 − α vd ) + σεsky Tsky ]+ rvda

(9.1)

bare soil impervious surface

ρCp rsda

Where εv = 0.986 is the average specific emissivity of the typical vegetation canopy, and εm is the specific emissivity of water body, bare soil or impervious surface, and respectively is 0.995, 0.972, or 0.970. In this study, Rv and Rm are obtained from the empirical formula proposed by Qin et al. (2004):

Rv = 0.9332 + 0.0585 × vfc

4 0.7[S0 (1 − αsd ) + σεsky Tsky ]+

LE =

Rn − G 1+β

(14)

By the relationship between water deficit index, evapotranspiration and potential evapotranspiration (Moran et al., 1994), β is derived:

(11)

β=

Where G denotes GHF. In the PCACA, the trapezoidal dry and wet edges are determined by

LSTmax − LSTmin −1 LSTmax − LST

(15)

LSTmax and LSTmin are estimated by linearly interpolating Tsd and 4

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and SHF are lower and the LHF is higher than those around, and similar phenomenon also exists around water body. Hence, urban green areas and water bodies are of great significance for alleviating urban thermal environment and improving the life qualities of residents. 41 As shown in Fig. 8, the averages of the LST and heat fluxes for different land use types show obvious differences. At the selected research moment, the average LST of building land is the highest, and is averagely 4.4 °C higher than that of natural surface. Water body has the lowest average LST with 17.3 °C in 1997 while forest has the lowest average LST with 19.3 °C in 2017. In different years, the average LST of building land is the highest, followed by arable land or grassland, and the average LST of woodland or water body is the lowest. 42 The order of NRF from large to small is water body, forest land, cultivated land, grassland, and building land. Since NRF is greatly affected by surface albedo, and the albedo and LST of water body are very low, the NRF for water body is the highest 558.1–689.3 W/m2. Vegetation has high albedo and low LST, so its NRF is the second highest. The albedo and LST of building land are both high, which leads to its NRF medium level 472.39–550.98 W/m2. The difference in NRF between water body and building land is about 90 W/m2. LHF is affected by energy, wind and soil moisture. Because of the poor water storage capacity of impervious layer, the soil water supply for heat consumption is insufficient and the LHF of building land is lower than those of natural surfaces, such as woodland, grassland, cultivated land, where the heat carried by water evaporation is very small, and most of the net radiation is used to heat the impervious layer, resulting in a large temperature difference between land and atmosphere which means a significant SHF. Natural surfaces including woodland, grassland and cultivated land are higher than the impervious layer in the permeability and porosity of underlying surface, and sufficient water is used to consume the heat stored in the soil, so the LSTs of natural surfaces are lower than that of the impervious layer. Though affected by aerodynamics and air humidity, the LHF of water body is higher because of water sufficiency, but is averagely lower 15.4 W/m2 than that of the forest land. SHF exhibits a law opposite to LHF and similar to LST. This is because urban surface energy is basically conserved ignoring the relatively less anthropogenic heat (Kuang et al., 2014), when NRF and GHF are certain, the greater the LHF, the smaller the SHF, and vice versa. At different times, the SHF of building land is very high from 247.5 to 270.5 W/m2, the SHF of water body is the lowest, which is from 185.1 to 236.57 W/m2. In addition, it can be seen from Fig. 8a that the LST has increased by 0.9–3.2 °C from 1997 to 2017. From Fig. 8c, the LHF is respectively increased by 5.53–24.18 W/m2 from 1997 to 2009 and by 40.88–86.77 W/m2 from 2009 to 2017. Since the physical properties of the impervious layer have not changed (Xiao et al., 2007), the variation of the LHF for building land is stable at 21.2 W/m2. The LHF of water body changes relatively small at 18.53 W/m2 and 24.67 W/m2 respectively from 1997 to 2009 and from 2009 to 2017. The most significant change of LHF takes place on natural surfaces, which is probably

Table 2 The validation of surface heat fluxes. 3 Flux (W/m2)

4 NRF

5 LHF

6 SHF

7 GHF

8 Daxing

10 476.1 16 487.3 22 521.2 28 492.5 34 21.8

11 134.3 17 187.2 23 257.0 29 182.8 35 64.5

12 209.4 18 239.4 24 213.0 30 255.3 36 36.7

13 19 25 31 37

20 Miyun

9 Observation 15 Estimation 21 Observation 27 Estimation 33 RMSE

33.6 60.7 29.1 54.5 26.3

Tvd, and Tsw and Tvw. 3. Results and discussion 3.1. The PCACA validation Before PCACA is applied to analyze the temporal and spatial patterns of surface heat fluxes in Beijing, it is first evaluated with observations. LST and VFC are verified by the data of September 22, 2009 published by Kuang et al. (2014), shown in Fig. 2. NRF, LHF, SHF and GHF are verified by the large aperture scintillation instrument and eddy correlation data of the Daxing and Miyun sites archived at Cold and Arid Region Science Data Center, shown in Table 2. From Fig. 2, the RMSEs of LST and VFC show that their estimation errors for the PCACA are relatively small as 1.15 °C and 0.06, respectively. From Table 2, the total error of NRF is 21.8 W/m2, and is 28.7 W/m2 lower than the observation at the Miyun site. LHF is estimated with a RMSE of 64.5 W/ m2, SHF is highly estimated with a RMSE of 36.7 W/m2, and GHF is highly estimated with a RMSE of 26.3 W/m2. 3.2. Results Figs. 3–7 respectively show the three VFCs, LSTs, albedos, SHF and LHFs of the study region. Figs. 4, 6 and 7 show the spatial patterns of the LST and heat fluxes inside the 6th ring of the Beijing city. It can be seen from Figs. 4, 6 and 7 that the LST and heat fluxes have a strong spatial heterogeneity. The difference between mountainous areas and plains is obvious, and the difference among different land use types in plain areas is also obvious. 38 Taking the urban area within the 6th ring as an example, the results for different land usage types are significantly different, which is shown in the 3rd column in Figs. 4, 6 and 7, and please refer to Fig. 10 for the land use map of the Beijing city. The LST and SHF are obviously higher than surrounding natural surfaces and water bodies while LHF is just the opposite for building lands, moreover, the LST and heat fluxes in the south are different from the north for building lands. The LST and SHF in the south are higher than those in the north while its LHF is lower than that in the north. This may be due to the eco-effect of many urban green areas, such as the Olympic Forest Park, Yuanming yuan Park and the Summer Palace in the northern urban. In the main city, the Taoranting and Tiantan Parks also have the phenomena that the LST

Fig. 3. The three VFCs inside the 6th ring of Beijing, (a) 1997, (b) 2009, (c) 2017. 5

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Fig. 4. The three LSTs inside the 6th ring of Beijing, (a) 1997, (b) 2009, (c) 2017.

Fig. 5. The three albedos inside the 6th ring of Beijing, (a) 1997, (b) 2009, (c) 2017.

Fig. 6. The three SHFs inside the 6th ring of Beijing, (a) 1997, (b) 2009, (c) 2017.

Fig. 7. The three LHFs inside the 6th ring of Beijing, (a) 1997, (b) 2009, (c) 2017.

Water body has the largest GHF because VFC in Equation (11) equals 0 for large water body, which results in a largest GHF. The second highest is building land. Although the impervious layer has a poor water storage capacity, it has a good heat storage capacity, which causes the LST rapidly rise and slowly lower, and leads to high GHF while GHF is relatively small for forest land, cultivated land, and grassland which have

attributed to the complicated comprehensive effect of heat, moisture and power. The urban heat island effect has a certain positive feedback on the increase of the LSTs of cultivated land and urban forest land (Du et al., 2016), which indirectly weakens urban thermal environment and accelerates the evapotranspiration of canopy. Fig. 9 reflects the tradeoff relationship between LHF, SHF, and GHF.

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Fig. 8. Averages of LST and heat fluxes over different land use types, (a) LST, (b) NRF, (c) LHF, (d) SHF.

Fig. 9. Comparison of LHF, SHF, GHF and β across different land use types in a: 1997, b: 2009, c:2017.

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Fig. 10. Three land use maps in 1997, 2009 and 2017.

Fig. 11. The LHF variation with land use change from 1997 to 2017.

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relatively high VFCs. Bowen ratio reflects the partition of available energy (Rn-G) into LHF and SHF. The Bowen ratio of building land is the largest, which is averagely 2.0, while forest land and water body have the smallest Bowen ratio, which is around 1.0. In the PCACA, the straight lines between the dry and wet edges of the trapezoidal space are assumed to coincide with the iso-soil moisture lines. Since the dry and wet edges obtained from the LST-VFC trapezoid have a difference from the ideal dry and wet edges, the dry edge obtained in the PCACA by energy balance equation is lower than the theoretical dry edge, and this approximation will bring uncertainty to the PCACA, then the Bowen ratio obtained becomes small, leading to a large LHF and a small SHF, which is also to be improved in the future research. Taking the pair of the LHFs on 21/09/1997 and 28/09/2017 as an example, the impact of land use change on LHF is discussed. Fig. 10 shows the three land use maps in 1997, 2009 and 2017. Fig. 11 shows the change of LHF with land usage change from 1997 to 2017.

This work is supported by Strategic Priority Research Program A of the Chinese Academy of Sciences [XDA20010301], General Program of National Natural Science Foundation of China [41671368, 41371348] and National Key R&D Program of China [2017YFB0203101]. References Allen, R.G., Tasumi, M., Trezza, R., 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model. Journal of Irrigation and Drainage Engineering-Asce 133 (4), 380–394. Amani-Beni, M., Zhang, B., Xie, G.D., Xu, J., 2018. Impact of urban park's tree, grass and waterbody on microclimate in hot summer days: a case study of Olympic Park in Beijing, China. Urban For. Urban Green. 32, 1–6. Anderson, M.C., Norman, J.M., Diak, G.R., Kustas, W.P., et al., 1997. A two-source timeintegrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ. 60 (2), 195–216. Artis, D.A., Carnahan, W.H., 1982. 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3.3. Discussion Fig. 11 shows that the western mountainous areas are basically unchanged, and still dominated by woodland. The areas where LHF changes drastically are distributed in plains. The areas where LHF is drastically reduced lie in the edges of the main urban, and the place where crop land is converted into building land, such as capital airport area, where the LHF is averagely reduced by 200 W/m2. The areas with a rapid increase in LHF are around the Yongding River. In 1997, the banks of the Yongding River are bare land. After the large afforestation project in the Beijing plain in 2012, it becomes forest and crop land. The increase in LHF is also evident in the arable land located on the southern outskirt of Beijing city. Affected by the thermal radiation of central urban, the air temperature rises and water evaporates quickly. In addition, the LHF has been increased dramatically by some new added green spaces in Beijing (Maimaitiyiming et al., 2014), such as the Olympic Forest Park developed from the wasteland, on the one hand, the Olympic Forest Park has increased the green area of the North City from 1997 to 2017 by 12 square kilometers. On the other hand, urban thermal environment has also promoted water evapotranspiration, making LHF increase obviously. Kuang et al. (2014) have got similar conclusion.

4. Conclusion For the Beijing city, LST and heat fluxes have a strong spatial heterogeneity, and differ obviously between mountains and plains and between the southern and northern parts of urban areas. Parks and water bodies inside the city can reduce air temperature and SHF and increase LHF. There are significant differences in LST and LHFs among different land use types. The LST is the highest and the LHF is the lowest for building land, on the contrary, forest land and water body are the highest in LHF and the lowest in SHF. Grassland and cultivated land are between them. The LST and heat fluxes of cultivated land are affected by the status and type of crops, and changes with time to a certain extent. Land use changes will also change the spatiotemporal patterns of LST and heat fluxes. From suburban farmland to building land, LHF will decrease, and urban expansion will cause LST to increase, which promotes the evapotranspiration of the surrounding arable land and makes LHF become big. The appearance of many green spaces in Beijing has changed the surrounding thermal environment. The LHF of green space is significantly higher than building land. Parks and green areas are of great significance for alleviating urban thermal environment and increasing air humidity, hence, the layout and scale of parks and green areas should be the centre of attention during urban land use planning.

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