International Journal of Remote Sensing
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Monitoring spatial patterns and changes of surface net radiation in urban and suburban areas using satellite remote-sensing data Deyong Hu, Shisong Cao, Shanshan Chen, Lei Deng & Nan Feng To cite this article: Deyong Hu, Shisong Cao, Shanshan Chen, Lei Deng & Nan Feng (2017) Monitoring spatial patterns and changes of surface net radiation in urban and suburban areas using satellite remote-sensing data, International Journal of Remote Sensing, 38:4, 1043-1061, DOI: 10.1080/01431161.2016.1275875 To link to this article: http://dx.doi.org/10.1080/01431161.2016.1275875
Published online: 13 Jan 2017.
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Date: 17 January 2017, At: 09:16
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017 VOL. 38, NO. 4, 1043–1061 http://dx.doi.org/10.1080/01431161.2016.1275875
Monitoring spatial patterns and changes of surface net radiation in urban and suburban areas using satellite remote-sensing data Deyong Hu, Shisong Cao, Shanshan Chen, Lei Deng and Nan Feng College of Resource Environment and Tourism, Capital Normal University, Beijing, China ABSTRACT
ARTICLE HISTORY
The net radiation obtained by the earth’s surface drives the exchange and transmission of energy and material in earth systems. Accurately quantifying the surface net radiation (SNR) is a premise for climate change research. Satellite remote-sensing data can provide land-surface information on a regional scale, making it possible to monitor SNR spatial patterns and changes using a cost-effective methodology that is superior to traditional ground observation. Furthermore, it can address the problem that the local-scale observational data does not extend to large areas with sparsely distributed meteorological observation sites. In this article, urban and suburban areas of Beijing were selected as study areas, and the surface physical parameters were retrieved based on Landsat images. Auxiliary data were also applied to model SNR, including Moderate Resolution Imaging Spectroradiometer (MODIS) images and other land-surface observational data obtained from meteorological stations. In addition, the SNR spatial patterns and their changes in urban and suburban areas and the differences in different land-surface types, different years, and different seasons were analysed. The results showed the following: (1) the fractional vegetation cover was one of the principal factors affecting the surface radiation process, and the SNR value was high where the cover value was high. Comparing the SNR in urban areas with that in suburban areas, the value was higher, and there was generally a ‘plateau’ in the spatial distribution characteristics in urban areas. (2) After analysis of the mean SNR value for different land-surface types, the highest mean SNR was for water, followed by vegetation cover, artificial surface and bare land, and the deviation of the mean SNR values of all of the land-surface types in winter was smaller compared with those in summer. (3) With urban sprawl and rapid changes of land-surface cover, there was an increasing trend in the SNR value in urban areas that was more significant in summer than that in winter. According to the SNR values in 2004 and 2014, the areas of all of the land-surface types showed a small increase of approximately 35 W m–2 in summer and 25 W m–2 in winter.
Received 17 May 2016 Accepted 19 December 2016
CONTACT Deyong Hu University, Beijing, China
[email protected]
© 2017 Informa UK Limited, trading as Taylor & Francis Group
KEYWORDS
Satellite remote sensing; surface net radiation (SNR); urban and suburban areas; spatial pattern
College of Resource Environment and Tourism, Capital Normal
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1. Introduction The net radiation obtained by the earth’s surface is the result of the radiation budget balance and is the original power of the energy exchange and transmission in earth systems. With the rapid economic development and urbanization in the Beijing region, the natural land surface was converted to an urban surface covered by many artificial materials such as roads and buildings. These changes may result in an increase or decrease of the surface radiation budget and affect the surface net radiation (SNR) in urban areas, further impacting urban climate enormously. It is important to monitor spatial SNR patterns and their changes, as they are the bases of regional climate change (Mahmood et al. 2013; Wang and Dickinson 2013). Land-surface radiation can be quantified by field measurement or modelling estimation methods. Two types of models have been developed to estimate land-surface radiation flux. One is the empirical equation constructed of the surface radiation and conventional meteorological elements collected from observational stations (Bilbao and Miguel 2007; Lhomme, Vacher, and Rocheteau 2007; Wang and Liang 2008). The other is atmospheric radiative transfer models, such as Low Resolution Atmospheric Transmission (LOWTRAN) and Moderate Resolution Atmospheric Transmission (MODTRAN), by which the upward short-wave, the downward shot-wave and the long-wave radiation are calculated. Compared with traditional meteorological measurements of local-scale radiation, these models can be used to parameterize the surface radiation in large regions. Because the former requires many ground observation data and highly depends on the study area, it is inevitable to reconstruct the model for estimating land-surface radiation if the study area is changed. The latter models consider the radiation transfer process in different atmospheric conditions carefully but many input parameters, such as atmospheric visibility and compositions of aerosols, are required, and there may be difficulty in acquiring appropriate data. In short, they can realize the SNR parameterization in large regions but in some regions with sparsely distributed meteorological sites, these two types of models may be limited because of insufficient meteorological data. Satellite remote-sensing data can provide land-surface information for large regions, addressing the problem of the sparse distribution of meteorological observation stations (Zhang et al. 2015), and can be applied to parameterize SNR values. The methods for SNR estimation can be divided into two categories. One is the establishment of the empirical formula between the SNR (or its components) and its impact factors such as the solar irradiance at the top of the atmosphere, the atmospheric conditions, and land-surface parameters (Tang and Li 2008; Wang and Liang 2009), followed by application of remote-sensing images to retrieve these factors. The other is a direct retrieval of the SNR value from remote-sensing images based on the surface radiation balance equation. In this method, all of the land-surface parameters such as surface emissivity, albedo, and temperature, are retrieved from remote-sensing data. Then, the surface radiation budget is calculated, and the SNR value can be determined (George et al. 2004). The retrieval accuracy of land-surface parameters has a significant effect on SNR results, so it is necessary to promote parameterization accuracy (Bisht et al. 2005; Jin and Liang 2006; Kim and Liang 2010; Mira et al. 2016). The urban land surface is a typical non-natural surface type and its albedo and emissivity are obviously different from those of suburban areas that are primarily natural
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surface types (Barlow 2014). Many researchers have carried out comparative observation experiments between urban and suburban areas and their results showed that there were great differences in the surface radiation budget (Taha 1997). With rapid urbanization, the characteristics of the land surface and the atmospheric environment over the city are drastically changed, and these two aspects may affect the urban surface radiation budget. The effect of the atmospheric environment over the city led to less solar radiation obtained at the earth surface in urban areas than in suburban or rural areas because of a higher atmospheric attenuation effect (Robaa 2009). In addition, the surface albedo changed with urbanization to between approximately 0.10 and 0.30, with an average value of 0.15 (Miao et al. 2012). White and Christen found that there was less SNR in urban areas than in suburban areas (White, Eaton, and Auer 1978; Christen and Vogt 2004). Conversely, the simulation of the surface radiation balance in different landsurface types has been carried out, and the result showed that there was an interception effect to net radiation in urban areas that led to the continuous increase in net radiation with urban sprawl (Cui, Liu, Hu et al., 2012; Cui, Liu, Zhang et al., 2012). Due to seasonal differences and the vegetation cover change, the SNR was different in different seasons. With the urbanization, the changes of the land surface had a significant impact on the surface radiation budget. A more reliable surface radiation value can be obtained by station-observed data, but it can only characterize the surface radiation of the land surface at a local scale, not for an anisotropic region. Satellite remote-sensing technology has many advantages including a wide range observation, high resolution, and short revisit period, and it is a cost-effective way to study the surface net radiation at regional scales. The objective of this study is to monitor the spatial patterns and changes of SNR in urban and suburban areas using satellite remote-sensing data. Landsat time-series satellite remote-sensing images were collected, and the multi-period SNR values were retrieved from them. In addition, the spatial patterns and changes of SNR in urban and suburban areas of Beijing were analysed as well as the SNR differences in different land surfaces to describe the temporal and spatial variations of urban and suburban SNR values in Beijing in 10 recent years.
2. Methods and procedures 2.1. Methods The SNR can be decomposed into the short-wave net radiation and the long-wave net radiation: Rn ¼ RnS þ RnL ;
(1)
where Rn represents the SNR and RnS , RnL are the short-wave net radiation and the longwave net radiation, respectively. RnS is the final residue of the solar radiation received by the ground and the surface reflection to the sky, and it can be expressed as the function of the two variables of short-wave downward radiation and surface albedo: RnS ¼ R#S R"S ¼ ð1 αÞR#S ;
(2)
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where R#S and R"S are the short-wave downward radiation and the short-wave upward radiation, respectively, and α is the surface albedo. R#S is influenced by many factors including solar elevation angle, atmospheric transmittance, and the distance between sun and earth, and it can be estimated with the empirical formula (Bisht et al. 2005): R#S ¼
S0 cos2 θ ; 1:085cosθ þ e0 ð2:7 þ cosθÞ 103 þ β
(3)
where S0 is solar constant (1367 W m–2), θ is solar elevation angle, β is a constant (β ¼ 0:1), e0 is the vapour pressure of near surface that can be calculated using the Clausius–Clapeyron equation: Lv 1 1 e0 ¼ 6:11 exp ; Rv 273:15 T d
(4)
where Lv is the latent heat of vaporization (2.5 × 106 J kg–1), Rv represents the vapour constant (461 J kg–1 K–1), Td is the dew point temperature (K), which can be calculated using the atmospheric temperature and relative humidity. The land–atmosphere radiation transformation system consists not only of solar short-wave radiation but also of land-surface or atmospheric long-wave radiation, that is, (1) long-wave radiation emitted from the surface that is mainly affected by landsurface temperature and emissivity and (2) long-wave radiation emitted by the atmosphere, such as vapour, gas, and aerosol particles that absorb some solar radiation but reflect some of the radiation back to the sky. According to the Steffan–Boltzmann law, the long-wave net radiation can be calculated by RnL ¼ R#L ð1 εs ÞR#L R"L ¼ σεs εa T 4a σεs T 4s ;
(5)
where RnL is long-wave net radiation, R#L , and R"L are the long-wave downward radiation and upward radiation, respectively, Ta is atmospheric temperature (K), εa is atmospheric emissivity, εs is the surface emissivity, and σ is the Stefan–Boltzmann constant (5.6697 × 10–8 W m–2 K–4). From Equation (5), the parameters for modelling land-surface long-wave radiation are Ts, εs , Ta, and εa . The surface temperature and emissivity can be retrieved from remotesensing images, the atmospheric temperature can be replaced by near-surface temperature monitored by meteorological stations, and εa can be estimated with empirical formula (Prata 1996; Bisht et al. 2005): 46:5 εa ¼ 1 ð1 þ e0 Þ exp Ta
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! 46:5 1:2 þ 3 e0 ; Ta
(6)
where atmospheric temperature Ta can be obtained from meteorological data, and e0 can be calculated by formula (4).
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2.2. Procedures According to formulas (1), (2), and (5), retrieval of parameters such as surface albedo, temperature, emissivity is needed before the SNR calculation. In this article, the surface albedo and surface emissivity were retrieved from visible and near-infrared data of Landsat satellite images and the atmospheric vapour content was retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) remote-sensing data. The surface temperature was retrieved by combining remote sensing data and meteorological data. Then, the short-wave downward radiation, short-wave upward radiation, long-wave downward radiation, and long-wave upward radiation were retrieved. In addition, the net radiation flux was calculated from remote-sensing images from around August and November, which represented summer and winter, respectively, and the accuracy of the result was validated by the field data. Finally, the spatial patterns and changes of SNR were analysed in urban and suburban areas, and the inter-annual results were compared between 2004 and 2014. Furthermore, the SNR changes were explored along with the urbanization and land-surface characteristics. The process is shown in Figure 1.
3. Study area and data 3.1. Study area Located in the Beijing region (39°40′–40°10′ N, 116°0′–116°45′ E), the study area primarily consists of plain terrain with very low altitude (less than 50 m) in addition to the mountains in the northwest. It is a warm temperate climate with typical semi-humid and semi-arid territoriality characteristics and its seasons are distinct. It is moderate in spring and fall but hot and rainy in summer with temperatures between 30℃ and 40℃ and cold and dry in winter with temperatures between – 10℃ and 0℃. Over the past 20 years, many natural surfaces have been replaced by urban surfaces such as roads, parks, and buildings, and the size of the city has expanded greatly. To compare the SNR differences between urban and suburban areas in the study area, the main ring roads of Beijing City were extracted from remotely sensed images, including second to sixth ring roads (Figure 2). In this article, the region inside the 5th ring road was defined as an urban area, and the region between the 5th and 6th ring roads was defined as a suburban area. According to the radiation data of Beijing meteorological stations, monthly mean SNR values reached the maximum in summer (about in July or August), while the lowest in winter (about in December or January), even it was negative. The hourly SNR data, measured by solar radiation observation stations on 19 May and 27 November 2004, was showed in Figure 3. As can be seen, there was a large SNR difference between summer and winter.
3.2. Data To compare the SNR between 2004 and 2014, cloud-free satellite images from 2004 and 2014 that cover the study area were collected, including summer and winter images. The data include the following: (1) Landsat 5 TM images from 19 May 2004 and 27 November 2004 and (2) Landsat 8 OLI and TIRS images from 20 November 2013 and 19 August 2014.
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Figure 1. Flow chart of the process.
Meanwhile, the contemporaneous MODIS data were collected for retrieving atmospheric water vapour. The meteorological observation data were collected from eight meteorological observation stations (MOS), 43 automatic meteorological observation stations (AMOS), and one solar radiation observation station (SROS), whose locations are shown in Figure 2. The conventional meteorological observation data included atmospheric pressure, atmospheric temperature, wind direction, wind speed, relative humidity, and rainfall. The radiation observation data included total radiation, net radiation, direct radiation, scattering radiation, and reflected radiation.
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Figure 2. Study area and locations of meteorological stations.
4. Data processing and results 4.1. Remotely sensing retrieval of land-surface parameters 4.1.1. Land-surface temperature There are three methods to retrieve land-surface temperature with remote-sensing data: the radiative transfer equation method, the mono-window algorithm, and the splitwindow algorithm. In Landsat 5 TM images, only one thermal infrared band is available; therefore, the last method cannot be used. There are two mono-window algorithms for Landsat 5 TM images, the Jiménez and Qin algorithm (Jiménez Muñoz and Sobrino 2003; Qin et al. 2001). Finally, the Qin algorithm is applied to the equation expressed as Ts ¼
ðað1 C DÞ þ ðbð1 C DÞ þ C þ DÞT B DT a Þ ; C
(7)
where the variable T s is the land-surface temperature (K). T B is the brightness temperature (K). T a is the atmospheric average temperature (K). a, b are coefficients, and C, D are temporary variables. C and D can be calculated by atmospheric transmittance (τ) and land-surface emissivity (ε) with the equations expressed as
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Figure 3. Hourly surface net radiation (SNR) data measured by the solar radiation observation station.
C ¼ ετ : D ¼ ð1 τÞð1 þ ð1 εÞτÞ
(8)
According to Equation (7) and (8), some quantitative parameters including T B , ε, τ, T a are required. The variable T B can be available from remote-sensing images, and T a can be estimated from air temperature data measured by meteorological observation stations, so two variables ε, τ must be parameterized appropriately.
4.1.2. Land-surface emissivity Empirical formula and image classification methods can parameterize ε. The former is based on land-surface bio-physical parameters, such as the normalized difference vegetation index (NDVI) (Van de Griend and Owe 1993), and it can be applied to a vegetated surface, but the value should be modified when the NDVI value is too high or too low. The latter is the assignment of quantitative ε values according to different surface cover types (Qin et al. 2003; Sobrino et al. 2004). In this article, the surface emissivity ε was estimated by the latter method. First, the land cover was classified into three types including the urban surfaces (including roads and buildings), the natural surfaces (including natural land surface, forest, and farmland), and the bare land. The spectral emissivity of building surface, vegetation and bare land were set as 0.970, 0.986, and 0.972, respectively, as estimated by Sobrino (Sobrino, Raissouni, and Li 2001) and Stathopoulou (Stathopoulou, Cartalis, and Petrakis 2007). The spectral emissivity of the urban surface can be regarded as mixed pixels of buildings and vegetated surface, and the natural surface consists of vegetated surface and bare land. Their surface emissivities can be calculated as follows:
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ε ¼ Pv Rv εv þ ð1 Pv ÞRm εm þ dε . . . ðurban surfaceÞ ; ε ¼ Pv Rv εv þ ð1 Pv ÞRs εs þ dε . . . ðnatural surfaceÞ
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(9)
where Pv is the proportion of vegetation in mixed pixel, that is, fractional vegetation cover. Rv , Rm and Rs represent the temperature ratio of vegetation, buildings, and bare land, respectively. εv , εm and εs represent the pure-pixel emissivity of vegetation, buildings, and bare land respectively. According to the proportion of vegetation in a mixed pixel, dε can be calculated by calculated as follows: dε ¼ 0:0038 Pv ; Pv 0:5 : (10) dε ¼ 0:0038 ð1 Pv Þ; Pv > 0:5 The fractional vegetation cover can be estimated based on the NDVI dimidiate pixel model expressed as Pv ¼
ðNDVIÞ ðNDVIÞs ðNDVIÞv ðNDVIÞs
2 ;
(11)
where ðNDVIÞs is the value of bare soil or building surface, and ðNDVIÞv is the value of fully vegetated surface. The temperature ratio Ri is defined as Ri ¼ ðTi =TÞ4 , and the temperature ratio of vegetation, buildings, and bare land can be calculated by the following empirical formula (Qin et al. 2004): 8 < Rv ¼ 0:9332 þ 0:0585Pv R ¼ 0:9886 þ 0:1287Pv ; (12) : m Rs ¼ 0:9902 þ 0:1068Pv where Rv , Rm and Rs represent the temperature ratio of vegetation, buildings and bare land respectively. Based on formulas (9)–(12), the surface emissivity of different land covers was retrieved from remote-sensing images of the study area.
4.1.3. Atmospheric transmittance The atmospheric transmittance is strongly influenced by the atmospheric water vapour. First, the atmospheric water vapour was retrieved from MODIS remote-sensing data as following equation, and then the atmospheric transmittance can be calculated by the empirical formulas corresponding to different band intervals as shown in Table 1. The equation is expressed as Table 1. Relationship between atmospheric transmittance and water vapour. Satellite Landsat 5 Landsat 8
Atmospheric water vapour(g m–2) 0.4–1.6 1.6–3 0.4–2 2–4
τ τ τ τ
Empirical formula = −0.0961w + 0.9820 = −0.1414w + 1.0537 = −0.0850w + 0.9650 = −0.1354w + 1.0539
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12 0 α ln ρρ19 2 A ; w¼@ β
(13)
where ρ2 and ρ19 represent the apparent reflectance of MODIS band 2 and 19, respectively (Meng, Lv, and Zhang 2007), and α and β are constants (α ¼ 0:02, β ¼ 0:651). The relationship between atmospheric transmittance and water vapour was simulated by MODTRAN, and the results are shown in Table 1.
4.1.4. Surface albedo The spectral reflectance can be retrieved from Landsat 5 or Landsat 8 images and must be converted to surface albedo. The conversion was implemented with the narrow-tobroadband (NTB) formula (Liang 2001). Due to the small difference in bandwidth between the Landsat 8 OLI sensor and Landsat TM, the NTB formula used for Landsat 8 OLI data is the same as for TM data and the equation is expressed as α ¼ 0:356r1 þ 0:13r3 þ 0:373r4 þ 0:085r5 þ 0:072r7 0:0018;
(14)
where variable α represents surface broadband albedo, ri (i = 1, 3, 4, 5, 7) represents reflectance values of the bands 1, 3, 4, 5, 7 of Landsat 5 TM data, or the bands 2, 4, 5, 6, 7 of Landsat 8 OLI data, respectively.
4.2. SNR retrieval results and accuracy validation The results of SNR were calculated based on Equations (1)–(5), as shown in Figure 4(a,b) are the results of SNR in the winter of 2004 and 2013, respectively. Figure 4(c,d) show the results of SNR in the summer of 2004 and 2014 respectively. The SNR value in urban areas is generally higher than that in suburban areas, whether in summer or winter. In summer, the value of SNR is generally above approximately 600 W m–2 in urban areas and even above 700 W m–2 in some central positions. Conversely, the value of SNR is below 600 W m–2 in suburban areas. In winter, the SNR is generally lower than that in summer; the SNR in urban areas is above 250 W m–2 and below 230 W m–2 in suburban areas. In the comparison of short-wave net radiation, long-wave net radiation and SNR, there were strong seasonal differences. In addition, there were some differences in different land surfaces; for instance, the differences between water and other land covers were very significant. The main factors that affected the differences of surface radiation are surface albedo, temperature, emissivity and solar elevation angle. The surface albedo is the principal factor affecting the surface short-wave radiation and surface temperature is the principal factor affecting the surface long-wave radiation. Thus, it is very important to verify their retrieval accuracy for the evaluation of the results. In this article, the accuracy of retrieval results such as surface albedo and radiation were verified with meteorological observation data. The verification data for surface albedo and radiation was from the Beijing radiation observation station located at 39°48′ N, 116°28′ E. The observation data included total radiation, net radiation, direct radiation, scattering radiation, and reflection radiation. According to the observation data, the total radiation, net radiation, direct radiation,
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Figure 4. Retrieval results of summer and winter SNR in different years: (a) and (b) are the results of SNR of 27 November 2004 and 20 November 2013, respectively; (c) and (d) are the results of SNR of 19 May 2004 and 19 August 2014, respectively.
scattering radiation, and reflection radiation flux were 827.78, 544.44, 627.78, 255.56, 150.00 W m–2, respectively. The retrieval value of surface net radiation was 552.65 W m–2 and its value exceeded approximately 8.21 W m–2 compared with the measured value. The retrieval value of the surface albedo was 0.29, exceeding approximately 0.05.
5. Spatial differences and changes of SNR in Beijing’s urban and suburban areas 5.1. Spatial pattern of SNR in urban and suburban areas To study the spatial pattern of SNR, the mean values of SNR and NDVI were calculated in urban and suburban areas, as shown in Table 2. (1) The SNR in urban areas was higher than that in suburban areas. The retrieval results showed that SNR value was approximately 20 W m–2 higher in urban areas
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Table 2. Comparison between the mean value of surface net radiation (SNR) and normalized difference vegetation index (NDVI) in different regions. The region inside second ring Rn (W m–2) NDVI Date 19 May 2004 624.3 0.07 27 November 2004 245.0 –0.02 20 November 2013 281.4 0.04 19 August 2014 663.3 0.10
The region between second ring and third ring
The region between third ring and fourth ring
The region between fourth ring and fifth ring
Rn (W m–2)
NDVI
Rn (W m–2)
NDVI
Rn (W m–2)
NDVI
Rn (W m–2)
NDVI
613.1 244.4 281.8 661.2
0.08 –0.02 0.04 0.12
595.7 235.9 275.9 651.6
0.06 –0.01 0.05 0.12
595.4 228.0 264.3 641.0
0.10 0.01 0.06 0.16
606.1 227.1 259.3 640.7
0.14 0.03 0.07 0.21
The region between fifth ring and sixth ring
than that in suburban areas. If the impact from complex mountainous terrain was ignored, the results showed that there was a ‘plateau’ feature of the spatial pattern of urban SNR value, in contrast to suburban areas. (2) The fractional vegetation cover was one of the principal factors affecting SNR. The mean values of NDVI in urban areas and suburban areas were calculated, and their trends showed that there was a close relationship between the NDVI value and the SNR. The SNR value was high where the NDVI value was high. Generally, the mean values of the NDVI in different regions of urban areas were higher than those in suburban areas, corresponding to the mean value of SNR in suburban areas, approximately 20 W m–2 lower than in urban areas. Inter-annual comparison between 2004 and 2014 showed that there was a positive correlation between NDVI and SNR value, that is, there were increasing trends in the mean value of both NDVI and SNR from 2004 to 2014. With the rapid urban sprawl, the natural surface had mainly been replaced by urban surface, and these conversions led to significant SNR change. A detailed analysis is presented in Section 6.
5.2. Differences of SNR in different land surfaces To compare the SNR differences, the land-surface types were divided into four types, including artificial surface (buildings and roads), vegetation surface (forest, shrub, and grassland), bare land (neither artificial surface nor vegetation, covered by bare soil), and water (surface water such as lakes, rivers, and ponds). The SNR results for different landsurface types are shown in Table 3. The SNR flux of all land surfaces at approximately 10:30 (the moment the satellite passes by) on 19 May 2004 and 19 August 2014 was calculated, and the results show that it was between approximately 560 and 770 W m–2 in the study area and that the SNR flux was between approximately 210 and 330 W m–2 on 19 May 2004 and 19 August 2014. The mean value of SNR of different seasons and different types of land surface were analysed and the results were as follows: (1) The mean SNR values from high to low were for water surface, vegetation cover, artificial surface and bare land. The SNR value is the highest for water surface and
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Table 3. Mean value of SNR of different land surfaces in different seasons. Season Summer (19 May 2004) Winter (27 November 2004) Winter (20 November 2013) Summer (19 August 2014)
Artificial surface (W m–2) 603.33 239.91 262.19 640.05
Vegetation surface (W m–2) 653.62 240.41 275.98 662.65
Bare land (W m–2) 568.56 214.66 247.76 613.63
Water (W m–2) 842.85 313.40 326.00 768.47
is the lowest for bare land in summer and winter. Although the sorting of landsurface type SNR values is the same in all seasons, there is less deviation of the SNR value in winter than in summer. (2) Analysing the differences of SNR between 2004 and 2014, the results show that there was growth in 2014 and it increased more in summer than winter.
5.3. Inter-annual variation of SNR To analyse the difference of SNR between 2004 and 2014, the statistical curves of the resulting maps of SNR were drawn, in which the horizontal axis is the value of SNR (W m–2), and the vertical axis is the number of pixels, as shown in Figure 5: (1) The peak value of the statistical curve in winter was higher than that in summer. There was a sharp waveform in winter and this indicated that the SNR value was comparatively concentrated in winter with a variation range smaller than that in summer. Most SNR values in winter were between approximately 220 and 270 W m–2, while between approximately 600 and 650 W m–2 in summer.
Figure 5. Statistical curve of SNR value in different seasons and different years.
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(2) Comparing the SNR statistical curve in winter in 2004 with that in 2013, the results showed that the wave peak on 27 November 2004 was higher than that on 20 November 2013, indicating that the former range of variation of the SNR value was smaller than the latter one and there was a high SNR value of approximately 20 to 30 W m–2 in 2014 compared with that in 2004. (3) Comparing the SNR statistical curve in summer in 2004 with that in 2013, the results showed that the wave width on 19 May 2004 was wider than that on 19 August 2014, indicating that the former range of variation of the SNR value was larger than the latter one and the SNR value on 19 August 2014 was generally higher, with a variation range smaller than in 2004. The SNR result depends on many factors, such as the solar zenith angle, atmospheric conditions, and physical properties of the land surface, and the analysis of the reasons for the SNR increase in 2014 is very complex. According to the analysis of the landsurface physical properties, the fractional vegetation cover affected the spatial distribution and the variation range of SNR value. Meanwhile, with the urban sprawl of the city of Beijing in the last 10 years, many natural surfaces have been replaced by artificial ones. This might accelerate the increase of SNR values as it might make SNR in urban areas higher than that in suburban areas, thus, a spatial distribution pattern like a ‘plateau’ of SNR was observed.
6. Discussion 6.1. Differences about short/long-wave upward /downward radiation in different surfaces and their effecting on SNR There were many factors affecting short-wave downward radiation flux, including solar zenith angle, and atmospheric transmissivity. In the same conditions of atmospheric transmissivity, its value was high when the solar elevation angle was high. Owing to a higher solar elevation angle at the time of the satellite passing through in summer than that in winter, the short-wave downward radiation flux received by earth’s surface was high. The short-wave upward radiation depended on surface albedo, which was affected by the composition and structure of land surface. In general case, the surface albedo of bare land and artificial surface with low fractional vegetation cover was higher than that in other land covers. While there are dense buildings in urban areas that have an interception effect on short-wave upward radiation, the surface albedo in urban areas might be lower than that in suburban areas. As a result, the short-wave net radiation in urban areas is higher than that in suburban areas. To analyse the impact of short-wave upward and downward radiation on short-wave net radiation, the land surface was divided into four types including artificial surface, vegetation, bare land, and water. The mean values of the short-wave net radiation, surface albedo, long-wave net radiation, and surface emissivity were calculated, and differences were revealed in different land surfaces (Table 4). The conclusions could be drawn as follows: (1) there was a large difference in the solar short-wave radiation received by the earth’s surface in different seasons, so the
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Table 4. Comparison of short-wave/long-wave net radiation and their influencing factors. Surface types Parameter Artificial surface Short-wave net radiation (W m–2) Surface albedo Long-wave net radiation (W m–2) Surface emissivity Vegetation Short-wave net radiation cover (W m–2) Surface albedo Long-wave net radiation (W m–2) Surface emissivity Bare land Short-wave net radiation (W m–−2) Surface albedo Long-wave net radiation (W m–2) Surface emissivity Water surface Short-wave net radiation (W m–2) Surface albedo Long-wave net radiation (W m–2) Surface emissivity
19 May 2004 704.54
27 November 2004 346.53
20 November 2013 370.55
19 August 2014 718.08
0.27 –100.20
0.12 –106.12
0.14 –107.86
0.18 –77.54
0.96 734.16
0.96 348.06
0.96 381.40
0.96 716.41
0.24 –79.56
0.12 –107.15
0.12 –104.91
0.19 –53.26
0.98 666.21
0.97 325.89
0.96 361.67
0.97 688.94
0.31 –96.56
0.17 –110.73
0.16 –113.41
0.22 –74.81
0.96 904.84
0.96 393.29
0.96 415.22
0.97 805.15
0.06 –61.48
0.02 –79.39
0.04 –88.71
0.08 –36.18
0.99
0.99
0.99
0.99
short-wave net radiation value in summer was much higher than that in winter. In the study area, the value in summer was over double that in winter. (2) According to the mean SNR value, the sorting of land-surface types from high to low was water surface, vegetation cover, artificial surface and bare land. In urban areas, the shortwave net radiation value was generally concentrated between approximately 700 and 900 W m–2 in summer, but it was between approximately 400 and 700 W m–2 in winter. In suburban areas, the short-wave net radiation value was generally concentrated between approximately 400 and 700 W m–2, but it is between approximately 200 and 400 W m–2 in winter. On the whole, short-wave net radiation in summer is higher than that in winter. According to formula (5), the long-wave upward radiation depended on the surface temperature and surface emissivity. The long-wave downward radiation depended on the atmospheric temperature and its emissivity. In addition, the long-wave net radiation was a budget balance of these radiations. As shown in Table 4, the conclusions could be drawn as follows: (1) the long-wave net radiation was negative, balancing the radiation transfer process and the interaction between land–atmosphere interface because the long-wave radiation from the land surface to the atmosphere was generally high, and there was a ‘loss’ on the land surface. (2) According to the sampling data, the results showed that the long-wave net radiation in summer was higher in urban areas than that in suburban areas, which was opposite to that in winter. The long-wave radiation was significantly affected by surface temperature, and it was higher in urban areas than in suburban areas in summer, while the surface temperature might be lower in urban areas than in suburban areas in cold and dry winters.
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6.2. Urban sprawl and its impact on SNR With urban sprawl, the natural surface is replaced by an urban surface. The changing of land surface significantly affected the surface physical parameters such as surface albedo and emissivity. In addition, it could affect short-wave upward radiation, short-wave downward radiation, and the SNR. The urban sprawl was calculated according to different regions separated by ring roads. The results showed that the percentage of the artificial surface for the landsurface area was 85% in the region inside the fourth ring, and 75% in the region between the fourth and fifth rings. The artificial surface in urban areas was closely saturated, so there were little differences between 2004 and 2014. There was a rapid urban sprawl in suburban areas between the 5th and 6th rings. From the remote-sensing image classification and statistical analysis, there was a significant increase from 46% in 2004 to 56% in 2014. Regarding the mean value of SNR in 2004 and 2014, there was an increase of approximately 35 W m–2 in summer and 25 W m–2 in winter. Generally, the change of surface cover with the rapid urban sprawl mainly led to the increase of SNR.
7. Conclusion To monitor the spatial pattern and change of SNR in urban and suburban areas of the Beijing region, multi-resource remote-sensing data and observation data obtained from the meteorological station were used to retrieve surface physical parameters and radiation budget before verifying their accuracy with field survey data. The spatial pattern of SNR was analysed based on results retrieved from the multi-resource data and the differences in SNR of different land-surface types were compared, then the changes of SNR in different years and different seasons were analysed. The conclusions were as follows: (1) The fractional vegetation cover was one of the principal factors affecting SNR, and the SNR was higher in regions where vegetation cover was higher. Comparing the SNR in urban areas with that in suburban areas in summer and winter, it was higher in urban areas and there was generally a ‘plateau’ characteristic in the spatial pattern. In Beijing, there were high radiation values in the middle part of the city but low radiation values in the surrounding suburban area. (2) From high to low, the average SNR of different land-surface types was water surface, vegetation cover surface, artificial surface and bare land. The SNR of water surface was the highest of all the land-surface types, while the SNR of bare land was the lowest whether in summer or winter. The sorting order of SNR of different land surfaces in winter was the same as that in summer, but the deviation of SNR in all types decreased compared to that in summer. (3) With urban sprawl and surface cover changes, there was an increased trend of SNR value, and the trend was more significant in summer than in winter. According to the analysis of the differences between 2004 and 2014, there was a slow increase of SNR in all land-surface types of approximately 35 W m–2 in summer and 25 W m–2 in winter. The rapid urban expansion
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and the region’s surface cover change may be an important factor in the increase of SNR. With continuous urban sprawl, more and more natural surface in suburban areas was replaced by an artificial surface, and the enlargement of the extent of ‘plateau centre’ affected the surface radiation balance and regional climate significantly. Exploration of this effect is the next step in future research.
Acknowledgements This research is supported by National Natural Science Foundation of China (Parameterizing urban surface radiation and energy budget based on three-dimension modelling and sky view factor, No. 41671339) and Scientific Research Project of Beijing Municipal EducationCommission (Localization observation and remote sensing analysis of the influence of urbanization on surface radiation and energy balance No. KM201510028018)
Disclosure statement No potential conflict of interest was reported by the authors.
Funding This work was supported by the National Natural Science Foundation of China (Parameterizing urban surface radiation and energy budget based on three-dimension modelling and sky view factor) [41671339]; Scientific Research Project of Beijing Municipal Education Commission (Localization observation and remote sensing analysis of the influence of urbanization on surface radiation and energy balance) [KM201510028018].
References Barlow, J. F. 2014. “Progress in Observing and Modelling the Urban Boundary Layer.” Urban Climate 10 (10): 216–240. doi:10.1016/j.uclim.2014.03.011. Bilbao, J., and A. H. D. Miguel. 2007. “Estimation of Daylight Downward Longwave Atmospheric Irradiance Under Clear-Sky and All-Sky Conditions [J].” Journal of Applied Meteorology and Climatology 46 (6): 878–889. doi:10.1175/JAM2503.1. Bisht, G., V. Venturini, S. Islam, and L. Jiang. 2005. “Estimation of the Net Radiation Using Modis (Moderate Resolution Imaging Spectroradiometer) Data for Clear Sky Days [J].” Remote Sensing of Environment 97 (1): 52–67. doi:10.1016/j.rse.2005.03.014. Christen, A., and R. Vogt. 2004. “Energy and Radiation Balance of a Central European City [J].” International Journal of Climatology 24 (11): 1395–1421. doi:10.1002/(ISSN)1097-0088. Cui, Y. P., J. Y. Liu, Y. F. Hu, J. Wang, and W. Kuang. 2012. “Modeling the Radiation Balance of Different Urban Underlying Surfaces.” [In Chinese.] Chinese Science Bulletin 57 (9): 1046–1054. doi:10.1007/s11434-011-4933-x. Cui, Y. P., J. Y. Liu, X. Z. Zhang, H. Y. Feng, and J. B. Wang. 2012. “Modeling Urban Energy Balance and Temperature Differences of Different Underlying Surfaces [J].” [in Chinese.] Geographical Research, 31 (7): 1257–1268. George, R. D., R. M. John, C. Martha, M. N. John, P. K. William, and D. T. Ryan. 2004. “Estimating Land Surface Energy Budgets Froms Space: Review and Current Efforts at the University of Wisconsin-Madison and USDA-ARS [J].” Bulletin of the American Meteorological, 85 (1): 65–78.
1060
D. HU ET AL.
Jiménez Muñoz, J. C., and J. A. Sobrino. 2003. “A Generalized Single-Channel Method for Retrieving Land Surface Temperature from Remote Sensing Data [J].” Journal of Geophysical Research: Atmospheres (1984–2012) 108 (D22). doi:10.1029/2003JD003480. Jin, M., and S. Liang. 2006. “An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations [J].” Journal of Climate 19 (12): 2867–2881. doi:10.1175/JCLI3720.1. Kim, H.-Y., and S. Liang. 2010. “Development of a Hybrid Method for Estimating Land Surface Shortwave Net Radiation from MODIS Data [J].” Remote Sensing of Environment 114 (11): 2393– 2402. doi:10.1016/j.rse.2010.05.012. Lhomme, J., J. Vacher, and A. Rocheteau. 2007. “Estimating Downward Long-Wave Radiation on the Andean Altiplano [J].” Agricultural and Forest Meteorology 145 (3–4): 139–148. doi:10.1016/j. agrformet.2007.04.007. Liang, S. 2001. “Narrowband to Broadband Conversions of Land Surface Albedo I Algorithms [J].” Remote Sensing of Environment 76 (2): 213–238. doi:10.1016/S0034-4257(00)00205-4. Mahmood, R., R. A. Pielke Sr, K. G. Hubbard, D. Niyogi, P. A. Dirmeyer, C. McAlpine, A. M. Carleton, et al. 2013. “Land Cover Changes and Their Biogeophysical Effects on Climate.” International Journal of Climatology 34 (4): 929–953. doi:10.1002/joc.2014.34.issue-4. Meng, X. H., S. H. Lv, and T. T. Zhang. 2007. “Testing, Improvement and Application of MODIS near Infrared Water Vapor Products: Taking Jinta Oasis in Heihe River Basin as a Case Study [J].” Journal of Infrared and Millimeter Waves 26 (2): 107. Miao, S. G., J. X. Dou, F. Chen, J. Liu, and A. G. Li. 2012. “Analysis of Observations on the Urban Surface Energy Balance in Beijing [J].” [in Chinese.] Science China Earth Sciences 42 (9): 1394– 1402. Mira, M., A. Olioso, B. Gallego-Elvira, D. Courault, S. Garrigues, O. Marloie, O. Hagolle, P. Guillevic, and G. Boulet. 2016. “Uncertainty Assessment of Surface Net Radiation Derived from Landsat Images.” Remote Sensing of Environment 175: 251–270. doi:10.1016/j.rse.2015.12.054. Prata, A. J. 1996. “A New Long-Wave Formula for Estimating Downward Clear-Sky Radiation at the Surface.” Quarterly Journal of the Royal Meteorological Society 122: 1127–1151. doi:10.1002/ (ISSN)1477-870X. Qin, Z. H., W. J. Li, and B. Xu. 2004. “The Estimation of Land Surface Emissivity for Landsat TM6 [J].” [in Chinese.] Remote Sensing for Land & Resources 3 (61): 28–32. Qin, Z. H., W. J. Li, M. H. Zhang, A. Kamieli, and P. Berliner. 2003. “Estimating of the Essential Atmospheric Parameters of Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat TM6[J].” [in Chinese.] Remote Sensing for Land & Resources, 15 (2): 37–43. Qin, Z. H., M. H. Zhang, A. Kamieli, and P. Berliner. 2001. “Mono-Window Algorithm for Retrieving Land Surface Temperature from Landsat TM6 Data [J].” [in Chinese.] Acta Geographica Sinica, 56 (4): 456–466. Robaa, S. M. 2009. “Urban–Rural Solar Radiation Loss in the Atmosphere of Greater Cairo Region, Egypt [J].” Energy Conversion and Management 50 (1): 194–202. doi:10.1016/j. enconman.2008.06.024. Sobrino, J., N. Raissouni, and Z. L. Li. 2001. “A Comparative Study of Land Surface Emissivity Retrieval from NOAA Data.” Remote Sensing of Environment 75 (2): 256–266. doi:10.1016/S00344257(00)00171-1. Sobrino, J. A., J. C. Jiménez-Muñoz, J. El-Kharraz, M. Gómez, M. Romaguera, and G. Sòria. 2004. “Single-Channel and Two-Channel Methods for Land Surface Temperature Retrieval from DAIS Data and Its Application to the Barrax Site [J].” International Journal of Remote Sensing 25 (1): 215–230. doi:10.1080/0143116031000115210. Stathopoulou, M., C. Cartalis, and M. Petrakis. 2007. “Integrating Corine Land Cover Data and Landsat TM for Surface Emissivity Definition: Application to the Urban Area of Athens, Greece.” International Journal of Remote Sensing 28 (15): 3291–3304. doi:10.1080/01431160600993421. Taha, H. 1997. “Urban Climates and Heat Islands: Albedo, Evapotranspiration, and Anthropogenic Heat.” Energy & Buildings 25 (2): 99–103. doi:10.1016/S0378-7788(96)00999-1.
INTERNATIONAL JOURNAL OF REMOTE SENSING
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Tang, B., and Z. Li. 2008. “Estimation of Instantaneous Net Surface Longwave Radiation from MODIS Cloud-Free Data [J].” Remote Sensing of Environment 112 (9): 3482–3492. doi:10.1016/j. rse.2008.04.004. Van de Griend, A. A., and M. Owe. 1993. “On the Relationship between Thermal Emissivity and the Normalized Difference Vegetation Index for Natural Surfaces [J].” International Journal of Remote Sensing 14 (6): 1119–1131. doi:10.1080/01431169308904400. Wang, K., and S. Liang. 2008. “Estimation of Daytime Net Radiation from Shortwave Radiation Measurements and Meteorological Observations [J].” Journal of Applied Meteorology and Climatology 48 (3): 634–643. doi:10.1175/2008JAMC1959.1. Wang, K., and R. E. Dickinson. 2013. “Contribution of Solar Radiation to Decadal Temperature Variability over Land.” PNAS 110 (37): 14877–14882. doi:10.1073/pnas.1311433110. Wang, W. H., and S. L. Liang. 2009. “Estimation of High-Spatial Resolution Clear-Sky Longwave Downward and Net Radiation over Land Surfaces from MODIS Data [J].” Remote Sensing of Environment 113 (4): 745–754. doi:10.1016/j.rse.2008.12.004. White, J. M., F. D. Eaton, and A. H. Auer Jr. 1978. “The Net Radiation Budget of the St. Louis Metropolitan Area [J].” Journal of Applied Meteorology 17 (5): 593–599. doi:10.1175/1520-0450 (1978)0172.0.CO;2. Zhang, X., S. Liang, M. Wild, and B. Jiang. 2015. “Analysis of Surface Incident Shortwave Radiation from Four Satellite Products.” Remote Sensing of Environment 165: 186–202. doi:10.1016/j. rse.2015.05.015.
ISSN: 0143-1161 (Print) 1366-5901 (Online) Journal homepage: http://www.tandfonline.com/loi/tres20
Monitoring spatial patterns and changes of surface net radiation in urban and suburban areas using satellite remote-sensing data Deyong Hu, Shisong Cao, Shanshan Chen, Lei Deng & Nan Feng To cite this article: Deyong Hu, Shisong Cao, Shanshan Chen, Lei Deng & Nan Feng (2017) Monitoring spatial patterns and changes of surface net radiation in urban and suburban areas using satellite remote-sensing data, International Journal of Remote Sensing, 38:4, 1043-1061, DOI: 10.1080/01431161.2016.1275875 To link to this article: http://dx.doi.org/10.1080/01431161.2016.1275875
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Date: 17 January 2017, At: 09:16
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017 VOL. 38, NO. 4, 1043–1061 http://dx.doi.org/10.1080/01431161.2016.1275875
Monitoring spatial patterns and changes of surface net radiation in urban and suburban areas using satellite remote-sensing data Deyong Hu, Shisong Cao, Shanshan Chen, Lei Deng and Nan Feng College of Resource Environment and Tourism, Capital Normal University, Beijing, China ABSTRACT
ARTICLE HISTORY
The net radiation obtained by the earth’s surface drives the exchange and transmission of energy and material in earth systems. Accurately quantifying the surface net radiation (SNR) is a premise for climate change research. Satellite remote-sensing data can provide land-surface information on a regional scale, making it possible to monitor SNR spatial patterns and changes using a cost-effective methodology that is superior to traditional ground observation. Furthermore, it can address the problem that the local-scale observational data does not extend to large areas with sparsely distributed meteorological observation sites. In this article, urban and suburban areas of Beijing were selected as study areas, and the surface physical parameters were retrieved based on Landsat images. Auxiliary data were also applied to model SNR, including Moderate Resolution Imaging Spectroradiometer (MODIS) images and other land-surface observational data obtained from meteorological stations. In addition, the SNR spatial patterns and their changes in urban and suburban areas and the differences in different land-surface types, different years, and different seasons were analysed. The results showed the following: (1) the fractional vegetation cover was one of the principal factors affecting the surface radiation process, and the SNR value was high where the cover value was high. Comparing the SNR in urban areas with that in suburban areas, the value was higher, and there was generally a ‘plateau’ in the spatial distribution characteristics in urban areas. (2) After analysis of the mean SNR value for different land-surface types, the highest mean SNR was for water, followed by vegetation cover, artificial surface and bare land, and the deviation of the mean SNR values of all of the land-surface types in winter was smaller compared with those in summer. (3) With urban sprawl and rapid changes of land-surface cover, there was an increasing trend in the SNR value in urban areas that was more significant in summer than that in winter. According to the SNR values in 2004 and 2014, the areas of all of the land-surface types showed a small increase of approximately 35 W m–2 in summer and 25 W m–2 in winter.
Received 17 May 2016 Accepted 19 December 2016
CONTACT Deyong Hu University, Beijing, China
[email protected]
© 2017 Informa UK Limited, trading as Taylor & Francis Group
KEYWORDS
Satellite remote sensing; surface net radiation (SNR); urban and suburban areas; spatial pattern
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1. Introduction The net radiation obtained by the earth’s surface is the result of the radiation budget balance and is the original power of the energy exchange and transmission in earth systems. With the rapid economic development and urbanization in the Beijing region, the natural land surface was converted to an urban surface covered by many artificial materials such as roads and buildings. These changes may result in an increase or decrease of the surface radiation budget and affect the surface net radiation (SNR) in urban areas, further impacting urban climate enormously. It is important to monitor spatial SNR patterns and their changes, as they are the bases of regional climate change (Mahmood et al. 2013; Wang and Dickinson 2013). Land-surface radiation can be quantified by field measurement or modelling estimation methods. Two types of models have been developed to estimate land-surface radiation flux. One is the empirical equation constructed of the surface radiation and conventional meteorological elements collected from observational stations (Bilbao and Miguel 2007; Lhomme, Vacher, and Rocheteau 2007; Wang and Liang 2008). The other is atmospheric radiative transfer models, such as Low Resolution Atmospheric Transmission (LOWTRAN) and Moderate Resolution Atmospheric Transmission (MODTRAN), by which the upward short-wave, the downward shot-wave and the long-wave radiation are calculated. Compared with traditional meteorological measurements of local-scale radiation, these models can be used to parameterize the surface radiation in large regions. Because the former requires many ground observation data and highly depends on the study area, it is inevitable to reconstruct the model for estimating land-surface radiation if the study area is changed. The latter models consider the radiation transfer process in different atmospheric conditions carefully but many input parameters, such as atmospheric visibility and compositions of aerosols, are required, and there may be difficulty in acquiring appropriate data. In short, they can realize the SNR parameterization in large regions but in some regions with sparsely distributed meteorological sites, these two types of models may be limited because of insufficient meteorological data. Satellite remote-sensing data can provide land-surface information for large regions, addressing the problem of the sparse distribution of meteorological observation stations (Zhang et al. 2015), and can be applied to parameterize SNR values. The methods for SNR estimation can be divided into two categories. One is the establishment of the empirical formula between the SNR (or its components) and its impact factors such as the solar irradiance at the top of the atmosphere, the atmospheric conditions, and land-surface parameters (Tang and Li 2008; Wang and Liang 2009), followed by application of remote-sensing images to retrieve these factors. The other is a direct retrieval of the SNR value from remote-sensing images based on the surface radiation balance equation. In this method, all of the land-surface parameters such as surface emissivity, albedo, and temperature, are retrieved from remote-sensing data. Then, the surface radiation budget is calculated, and the SNR value can be determined (George et al. 2004). The retrieval accuracy of land-surface parameters has a significant effect on SNR results, so it is necessary to promote parameterization accuracy (Bisht et al. 2005; Jin and Liang 2006; Kim and Liang 2010; Mira et al. 2016). The urban land surface is a typical non-natural surface type and its albedo and emissivity are obviously different from those of suburban areas that are primarily natural
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surface types (Barlow 2014). Many researchers have carried out comparative observation experiments between urban and suburban areas and their results showed that there were great differences in the surface radiation budget (Taha 1997). With rapid urbanization, the characteristics of the land surface and the atmospheric environment over the city are drastically changed, and these two aspects may affect the urban surface radiation budget. The effect of the atmospheric environment over the city led to less solar radiation obtained at the earth surface in urban areas than in suburban or rural areas because of a higher atmospheric attenuation effect (Robaa 2009). In addition, the surface albedo changed with urbanization to between approximately 0.10 and 0.30, with an average value of 0.15 (Miao et al. 2012). White and Christen found that there was less SNR in urban areas than in suburban areas (White, Eaton, and Auer 1978; Christen and Vogt 2004). Conversely, the simulation of the surface radiation balance in different landsurface types has been carried out, and the result showed that there was an interception effect to net radiation in urban areas that led to the continuous increase in net radiation with urban sprawl (Cui, Liu, Hu et al., 2012; Cui, Liu, Zhang et al., 2012). Due to seasonal differences and the vegetation cover change, the SNR was different in different seasons. With the urbanization, the changes of the land surface had a significant impact on the surface radiation budget. A more reliable surface radiation value can be obtained by station-observed data, but it can only characterize the surface radiation of the land surface at a local scale, not for an anisotropic region. Satellite remote-sensing technology has many advantages including a wide range observation, high resolution, and short revisit period, and it is a cost-effective way to study the surface net radiation at regional scales. The objective of this study is to monitor the spatial patterns and changes of SNR in urban and suburban areas using satellite remote-sensing data. Landsat time-series satellite remote-sensing images were collected, and the multi-period SNR values were retrieved from them. In addition, the spatial patterns and changes of SNR in urban and suburban areas of Beijing were analysed as well as the SNR differences in different land surfaces to describe the temporal and spatial variations of urban and suburban SNR values in Beijing in 10 recent years.
2. Methods and procedures 2.1. Methods The SNR can be decomposed into the short-wave net radiation and the long-wave net radiation: Rn ¼ RnS þ RnL ;
(1)
where Rn represents the SNR and RnS , RnL are the short-wave net radiation and the longwave net radiation, respectively. RnS is the final residue of the solar radiation received by the ground and the surface reflection to the sky, and it can be expressed as the function of the two variables of short-wave downward radiation and surface albedo: RnS ¼ R#S R"S ¼ ð1 αÞR#S ;
(2)
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where R#S and R"S are the short-wave downward radiation and the short-wave upward radiation, respectively, and α is the surface albedo. R#S is influenced by many factors including solar elevation angle, atmospheric transmittance, and the distance between sun and earth, and it can be estimated with the empirical formula (Bisht et al. 2005): R#S ¼
S0 cos2 θ ; 1:085cosθ þ e0 ð2:7 þ cosθÞ 103 þ β
(3)
where S0 is solar constant (1367 W m–2), θ is solar elevation angle, β is a constant (β ¼ 0:1), e0 is the vapour pressure of near surface that can be calculated using the Clausius–Clapeyron equation: Lv 1 1 e0 ¼ 6:11 exp ; Rv 273:15 T d
(4)
where Lv is the latent heat of vaporization (2.5 × 106 J kg–1), Rv represents the vapour constant (461 J kg–1 K–1), Td is the dew point temperature (K), which can be calculated using the atmospheric temperature and relative humidity. The land–atmosphere radiation transformation system consists not only of solar short-wave radiation but also of land-surface or atmospheric long-wave radiation, that is, (1) long-wave radiation emitted from the surface that is mainly affected by landsurface temperature and emissivity and (2) long-wave radiation emitted by the atmosphere, such as vapour, gas, and aerosol particles that absorb some solar radiation but reflect some of the radiation back to the sky. According to the Steffan–Boltzmann law, the long-wave net radiation can be calculated by RnL ¼ R#L ð1 εs ÞR#L R"L ¼ σεs εa T 4a σεs T 4s ;
(5)
where RnL is long-wave net radiation, R#L , and R"L are the long-wave downward radiation and upward radiation, respectively, Ta is atmospheric temperature (K), εa is atmospheric emissivity, εs is the surface emissivity, and σ is the Stefan–Boltzmann constant (5.6697 × 10–8 W m–2 K–4). From Equation (5), the parameters for modelling land-surface long-wave radiation are Ts, εs , Ta, and εa . The surface temperature and emissivity can be retrieved from remotesensing images, the atmospheric temperature can be replaced by near-surface temperature monitored by meteorological stations, and εa can be estimated with empirical formula (Prata 1996; Bisht et al. 2005): 46:5 εa ¼ 1 ð1 þ e0 Þ exp Ta
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! 46:5 1:2 þ 3 e0 ; Ta
(6)
where atmospheric temperature Ta can be obtained from meteorological data, and e0 can be calculated by formula (4).
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2.2. Procedures According to formulas (1), (2), and (5), retrieval of parameters such as surface albedo, temperature, emissivity is needed before the SNR calculation. In this article, the surface albedo and surface emissivity were retrieved from visible and near-infrared data of Landsat satellite images and the atmospheric vapour content was retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) remote-sensing data. The surface temperature was retrieved by combining remote sensing data and meteorological data. Then, the short-wave downward radiation, short-wave upward radiation, long-wave downward radiation, and long-wave upward radiation were retrieved. In addition, the net radiation flux was calculated from remote-sensing images from around August and November, which represented summer and winter, respectively, and the accuracy of the result was validated by the field data. Finally, the spatial patterns and changes of SNR were analysed in urban and suburban areas, and the inter-annual results were compared between 2004 and 2014. Furthermore, the SNR changes were explored along with the urbanization and land-surface characteristics. The process is shown in Figure 1.
3. Study area and data 3.1. Study area Located in the Beijing region (39°40′–40°10′ N, 116°0′–116°45′ E), the study area primarily consists of plain terrain with very low altitude (less than 50 m) in addition to the mountains in the northwest. It is a warm temperate climate with typical semi-humid and semi-arid territoriality characteristics and its seasons are distinct. It is moderate in spring and fall but hot and rainy in summer with temperatures between 30℃ and 40℃ and cold and dry in winter with temperatures between – 10℃ and 0℃. Over the past 20 years, many natural surfaces have been replaced by urban surfaces such as roads, parks, and buildings, and the size of the city has expanded greatly. To compare the SNR differences between urban and suburban areas in the study area, the main ring roads of Beijing City were extracted from remotely sensed images, including second to sixth ring roads (Figure 2). In this article, the region inside the 5th ring road was defined as an urban area, and the region between the 5th and 6th ring roads was defined as a suburban area. According to the radiation data of Beijing meteorological stations, monthly mean SNR values reached the maximum in summer (about in July or August), while the lowest in winter (about in December or January), even it was negative. The hourly SNR data, measured by solar radiation observation stations on 19 May and 27 November 2004, was showed in Figure 3. As can be seen, there was a large SNR difference between summer and winter.
3.2. Data To compare the SNR between 2004 and 2014, cloud-free satellite images from 2004 and 2014 that cover the study area were collected, including summer and winter images. The data include the following: (1) Landsat 5 TM images from 19 May 2004 and 27 November 2004 and (2) Landsat 8 OLI and TIRS images from 20 November 2013 and 19 August 2014.
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Figure 1. Flow chart of the process.
Meanwhile, the contemporaneous MODIS data were collected for retrieving atmospheric water vapour. The meteorological observation data were collected from eight meteorological observation stations (MOS), 43 automatic meteorological observation stations (AMOS), and one solar radiation observation station (SROS), whose locations are shown in Figure 2. The conventional meteorological observation data included atmospheric pressure, atmospheric temperature, wind direction, wind speed, relative humidity, and rainfall. The radiation observation data included total radiation, net radiation, direct radiation, scattering radiation, and reflected radiation.
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Figure 2. Study area and locations of meteorological stations.
4. Data processing and results 4.1. Remotely sensing retrieval of land-surface parameters 4.1.1. Land-surface temperature There are three methods to retrieve land-surface temperature with remote-sensing data: the radiative transfer equation method, the mono-window algorithm, and the splitwindow algorithm. In Landsat 5 TM images, only one thermal infrared band is available; therefore, the last method cannot be used. There are two mono-window algorithms for Landsat 5 TM images, the Jiménez and Qin algorithm (Jiménez Muñoz and Sobrino 2003; Qin et al. 2001). Finally, the Qin algorithm is applied to the equation expressed as Ts ¼
ðað1 C DÞ þ ðbð1 C DÞ þ C þ DÞT B DT a Þ ; C
(7)
where the variable T s is the land-surface temperature (K). T B is the brightness temperature (K). T a is the atmospheric average temperature (K). a, b are coefficients, and C, D are temporary variables. C and D can be calculated by atmospheric transmittance (τ) and land-surface emissivity (ε) with the equations expressed as
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Figure 3. Hourly surface net radiation (SNR) data measured by the solar radiation observation station.
C ¼ ετ : D ¼ ð1 τÞð1 þ ð1 εÞτÞ
(8)
According to Equation (7) and (8), some quantitative parameters including T B , ε, τ, T a are required. The variable T B can be available from remote-sensing images, and T a can be estimated from air temperature data measured by meteorological observation stations, so two variables ε, τ must be parameterized appropriately.
4.1.2. Land-surface emissivity Empirical formula and image classification methods can parameterize ε. The former is based on land-surface bio-physical parameters, such as the normalized difference vegetation index (NDVI) (Van de Griend and Owe 1993), and it can be applied to a vegetated surface, but the value should be modified when the NDVI value is too high or too low. The latter is the assignment of quantitative ε values according to different surface cover types (Qin et al. 2003; Sobrino et al. 2004). In this article, the surface emissivity ε was estimated by the latter method. First, the land cover was classified into three types including the urban surfaces (including roads and buildings), the natural surfaces (including natural land surface, forest, and farmland), and the bare land. The spectral emissivity of building surface, vegetation and bare land were set as 0.970, 0.986, and 0.972, respectively, as estimated by Sobrino (Sobrino, Raissouni, and Li 2001) and Stathopoulou (Stathopoulou, Cartalis, and Petrakis 2007). The spectral emissivity of the urban surface can be regarded as mixed pixels of buildings and vegetated surface, and the natural surface consists of vegetated surface and bare land. Their surface emissivities can be calculated as follows:
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ε ¼ Pv Rv εv þ ð1 Pv ÞRm εm þ dε . . . ðurban surfaceÞ ; ε ¼ Pv Rv εv þ ð1 Pv ÞRs εs þ dε . . . ðnatural surfaceÞ
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(9)
where Pv is the proportion of vegetation in mixed pixel, that is, fractional vegetation cover. Rv , Rm and Rs represent the temperature ratio of vegetation, buildings, and bare land, respectively. εv , εm and εs represent the pure-pixel emissivity of vegetation, buildings, and bare land respectively. According to the proportion of vegetation in a mixed pixel, dε can be calculated by calculated as follows: dε ¼ 0:0038 Pv ; Pv 0:5 : (10) dε ¼ 0:0038 ð1 Pv Þ; Pv > 0:5 The fractional vegetation cover can be estimated based on the NDVI dimidiate pixel model expressed as Pv ¼
ðNDVIÞ ðNDVIÞs ðNDVIÞv ðNDVIÞs
2 ;
(11)
where ðNDVIÞs is the value of bare soil or building surface, and ðNDVIÞv is the value of fully vegetated surface. The temperature ratio Ri is defined as Ri ¼ ðTi =TÞ4 , and the temperature ratio of vegetation, buildings, and bare land can be calculated by the following empirical formula (Qin et al. 2004): 8 < Rv ¼ 0:9332 þ 0:0585Pv R ¼ 0:9886 þ 0:1287Pv ; (12) : m Rs ¼ 0:9902 þ 0:1068Pv where Rv , Rm and Rs represent the temperature ratio of vegetation, buildings and bare land respectively. Based on formulas (9)–(12), the surface emissivity of different land covers was retrieved from remote-sensing images of the study area.
4.1.3. Atmospheric transmittance The atmospheric transmittance is strongly influenced by the atmospheric water vapour. First, the atmospheric water vapour was retrieved from MODIS remote-sensing data as following equation, and then the atmospheric transmittance can be calculated by the empirical formulas corresponding to different band intervals as shown in Table 1. The equation is expressed as Table 1. Relationship between atmospheric transmittance and water vapour. Satellite Landsat 5 Landsat 8
Atmospheric water vapour(g m–2) 0.4–1.6 1.6–3 0.4–2 2–4
τ τ τ τ
Empirical formula = −0.0961w + 0.9820 = −0.1414w + 1.0537 = −0.0850w + 0.9650 = −0.1354w + 1.0539
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12 0 α ln ρρ19 2 A ; w¼@ β
(13)
where ρ2 and ρ19 represent the apparent reflectance of MODIS band 2 and 19, respectively (Meng, Lv, and Zhang 2007), and α and β are constants (α ¼ 0:02, β ¼ 0:651). The relationship between atmospheric transmittance and water vapour was simulated by MODTRAN, and the results are shown in Table 1.
4.1.4. Surface albedo The spectral reflectance can be retrieved from Landsat 5 or Landsat 8 images and must be converted to surface albedo. The conversion was implemented with the narrow-tobroadband (NTB) formula (Liang 2001). Due to the small difference in bandwidth between the Landsat 8 OLI sensor and Landsat TM, the NTB formula used for Landsat 8 OLI data is the same as for TM data and the equation is expressed as α ¼ 0:356r1 þ 0:13r3 þ 0:373r4 þ 0:085r5 þ 0:072r7 0:0018;
(14)
where variable α represents surface broadband albedo, ri (i = 1, 3, 4, 5, 7) represents reflectance values of the bands 1, 3, 4, 5, 7 of Landsat 5 TM data, or the bands 2, 4, 5, 6, 7 of Landsat 8 OLI data, respectively.
4.2. SNR retrieval results and accuracy validation The results of SNR were calculated based on Equations (1)–(5), as shown in Figure 4(a,b) are the results of SNR in the winter of 2004 and 2013, respectively. Figure 4(c,d) show the results of SNR in the summer of 2004 and 2014 respectively. The SNR value in urban areas is generally higher than that in suburban areas, whether in summer or winter. In summer, the value of SNR is generally above approximately 600 W m–2 in urban areas and even above 700 W m–2 in some central positions. Conversely, the value of SNR is below 600 W m–2 in suburban areas. In winter, the SNR is generally lower than that in summer; the SNR in urban areas is above 250 W m–2 and below 230 W m–2 in suburban areas. In the comparison of short-wave net radiation, long-wave net radiation and SNR, there were strong seasonal differences. In addition, there were some differences in different land surfaces; for instance, the differences between water and other land covers were very significant. The main factors that affected the differences of surface radiation are surface albedo, temperature, emissivity and solar elevation angle. The surface albedo is the principal factor affecting the surface short-wave radiation and surface temperature is the principal factor affecting the surface long-wave radiation. Thus, it is very important to verify their retrieval accuracy for the evaluation of the results. In this article, the accuracy of retrieval results such as surface albedo and radiation were verified with meteorological observation data. The verification data for surface albedo and radiation was from the Beijing radiation observation station located at 39°48′ N, 116°28′ E. The observation data included total radiation, net radiation, direct radiation, scattering radiation, and reflection radiation. According to the observation data, the total radiation, net radiation, direct radiation,
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Figure 4. Retrieval results of summer and winter SNR in different years: (a) and (b) are the results of SNR of 27 November 2004 and 20 November 2013, respectively; (c) and (d) are the results of SNR of 19 May 2004 and 19 August 2014, respectively.
scattering radiation, and reflection radiation flux were 827.78, 544.44, 627.78, 255.56, 150.00 W m–2, respectively. The retrieval value of surface net radiation was 552.65 W m–2 and its value exceeded approximately 8.21 W m–2 compared with the measured value. The retrieval value of the surface albedo was 0.29, exceeding approximately 0.05.
5. Spatial differences and changes of SNR in Beijing’s urban and suburban areas 5.1. Spatial pattern of SNR in urban and suburban areas To study the spatial pattern of SNR, the mean values of SNR and NDVI were calculated in urban and suburban areas, as shown in Table 2. (1) The SNR in urban areas was higher than that in suburban areas. The retrieval results showed that SNR value was approximately 20 W m–2 higher in urban areas
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Table 2. Comparison between the mean value of surface net radiation (SNR) and normalized difference vegetation index (NDVI) in different regions. The region inside second ring Rn (W m–2) NDVI Date 19 May 2004 624.3 0.07 27 November 2004 245.0 –0.02 20 November 2013 281.4 0.04 19 August 2014 663.3 0.10
The region between second ring and third ring
The region between third ring and fourth ring
The region between fourth ring and fifth ring
Rn (W m–2)
NDVI
Rn (W m–2)
NDVI
Rn (W m–2)
NDVI
Rn (W m–2)
NDVI
613.1 244.4 281.8 661.2
0.08 –0.02 0.04 0.12
595.7 235.9 275.9 651.6
0.06 –0.01 0.05 0.12
595.4 228.0 264.3 641.0
0.10 0.01 0.06 0.16
606.1 227.1 259.3 640.7
0.14 0.03 0.07 0.21
The region between fifth ring and sixth ring
than that in suburban areas. If the impact from complex mountainous terrain was ignored, the results showed that there was a ‘plateau’ feature of the spatial pattern of urban SNR value, in contrast to suburban areas. (2) The fractional vegetation cover was one of the principal factors affecting SNR. The mean values of NDVI in urban areas and suburban areas were calculated, and their trends showed that there was a close relationship between the NDVI value and the SNR. The SNR value was high where the NDVI value was high. Generally, the mean values of the NDVI in different regions of urban areas were higher than those in suburban areas, corresponding to the mean value of SNR in suburban areas, approximately 20 W m–2 lower than in urban areas. Inter-annual comparison between 2004 and 2014 showed that there was a positive correlation between NDVI and SNR value, that is, there were increasing trends in the mean value of both NDVI and SNR from 2004 to 2014. With the rapid urban sprawl, the natural surface had mainly been replaced by urban surface, and these conversions led to significant SNR change. A detailed analysis is presented in Section 6.
5.2. Differences of SNR in different land surfaces To compare the SNR differences, the land-surface types were divided into four types, including artificial surface (buildings and roads), vegetation surface (forest, shrub, and grassland), bare land (neither artificial surface nor vegetation, covered by bare soil), and water (surface water such as lakes, rivers, and ponds). The SNR results for different landsurface types are shown in Table 3. The SNR flux of all land surfaces at approximately 10:30 (the moment the satellite passes by) on 19 May 2004 and 19 August 2014 was calculated, and the results show that it was between approximately 560 and 770 W m–2 in the study area and that the SNR flux was between approximately 210 and 330 W m–2 on 19 May 2004 and 19 August 2014. The mean value of SNR of different seasons and different types of land surface were analysed and the results were as follows: (1) The mean SNR values from high to low were for water surface, vegetation cover, artificial surface and bare land. The SNR value is the highest for water surface and
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Table 3. Mean value of SNR of different land surfaces in different seasons. Season Summer (19 May 2004) Winter (27 November 2004) Winter (20 November 2013) Summer (19 August 2014)
Artificial surface (W m–2) 603.33 239.91 262.19 640.05
Vegetation surface (W m–2) 653.62 240.41 275.98 662.65
Bare land (W m–2) 568.56 214.66 247.76 613.63
Water (W m–2) 842.85 313.40 326.00 768.47
is the lowest for bare land in summer and winter. Although the sorting of landsurface type SNR values is the same in all seasons, there is less deviation of the SNR value in winter than in summer. (2) Analysing the differences of SNR between 2004 and 2014, the results show that there was growth in 2014 and it increased more in summer than winter.
5.3. Inter-annual variation of SNR To analyse the difference of SNR between 2004 and 2014, the statistical curves of the resulting maps of SNR were drawn, in which the horizontal axis is the value of SNR (W m–2), and the vertical axis is the number of pixels, as shown in Figure 5: (1) The peak value of the statistical curve in winter was higher than that in summer. There was a sharp waveform in winter and this indicated that the SNR value was comparatively concentrated in winter with a variation range smaller than that in summer. Most SNR values in winter were between approximately 220 and 270 W m–2, while between approximately 600 and 650 W m–2 in summer.
Figure 5. Statistical curve of SNR value in different seasons and different years.
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(2) Comparing the SNR statistical curve in winter in 2004 with that in 2013, the results showed that the wave peak on 27 November 2004 was higher than that on 20 November 2013, indicating that the former range of variation of the SNR value was smaller than the latter one and there was a high SNR value of approximately 20 to 30 W m–2 in 2014 compared with that in 2004. (3) Comparing the SNR statistical curve in summer in 2004 with that in 2013, the results showed that the wave width on 19 May 2004 was wider than that on 19 August 2014, indicating that the former range of variation of the SNR value was larger than the latter one and the SNR value on 19 August 2014 was generally higher, with a variation range smaller than in 2004. The SNR result depends on many factors, such as the solar zenith angle, atmospheric conditions, and physical properties of the land surface, and the analysis of the reasons for the SNR increase in 2014 is very complex. According to the analysis of the landsurface physical properties, the fractional vegetation cover affected the spatial distribution and the variation range of SNR value. Meanwhile, with the urban sprawl of the city of Beijing in the last 10 years, many natural surfaces have been replaced by artificial ones. This might accelerate the increase of SNR values as it might make SNR in urban areas higher than that in suburban areas, thus, a spatial distribution pattern like a ‘plateau’ of SNR was observed.
6. Discussion 6.1. Differences about short/long-wave upward /downward radiation in different surfaces and their effecting on SNR There were many factors affecting short-wave downward radiation flux, including solar zenith angle, and atmospheric transmissivity. In the same conditions of atmospheric transmissivity, its value was high when the solar elevation angle was high. Owing to a higher solar elevation angle at the time of the satellite passing through in summer than that in winter, the short-wave downward radiation flux received by earth’s surface was high. The short-wave upward radiation depended on surface albedo, which was affected by the composition and structure of land surface. In general case, the surface albedo of bare land and artificial surface with low fractional vegetation cover was higher than that in other land covers. While there are dense buildings in urban areas that have an interception effect on short-wave upward radiation, the surface albedo in urban areas might be lower than that in suburban areas. As a result, the short-wave net radiation in urban areas is higher than that in suburban areas. To analyse the impact of short-wave upward and downward radiation on short-wave net radiation, the land surface was divided into four types including artificial surface, vegetation, bare land, and water. The mean values of the short-wave net radiation, surface albedo, long-wave net radiation, and surface emissivity were calculated, and differences were revealed in different land surfaces (Table 4). The conclusions could be drawn as follows: (1) there was a large difference in the solar short-wave radiation received by the earth’s surface in different seasons, so the
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Table 4. Comparison of short-wave/long-wave net radiation and their influencing factors. Surface types Parameter Artificial surface Short-wave net radiation (W m–2) Surface albedo Long-wave net radiation (W m–2) Surface emissivity Vegetation Short-wave net radiation cover (W m–2) Surface albedo Long-wave net radiation (W m–2) Surface emissivity Bare land Short-wave net radiation (W m–−2) Surface albedo Long-wave net radiation (W m–2) Surface emissivity Water surface Short-wave net radiation (W m–2) Surface albedo Long-wave net radiation (W m–2) Surface emissivity
19 May 2004 704.54
27 November 2004 346.53
20 November 2013 370.55
19 August 2014 718.08
0.27 –100.20
0.12 –106.12
0.14 –107.86
0.18 –77.54
0.96 734.16
0.96 348.06
0.96 381.40
0.96 716.41
0.24 –79.56
0.12 –107.15
0.12 –104.91
0.19 –53.26
0.98 666.21
0.97 325.89
0.96 361.67
0.97 688.94
0.31 –96.56
0.17 –110.73
0.16 –113.41
0.22 –74.81
0.96 904.84
0.96 393.29
0.96 415.22
0.97 805.15
0.06 –61.48
0.02 –79.39
0.04 –88.71
0.08 –36.18
0.99
0.99
0.99
0.99
short-wave net radiation value in summer was much higher than that in winter. In the study area, the value in summer was over double that in winter. (2) According to the mean SNR value, the sorting of land-surface types from high to low was water surface, vegetation cover, artificial surface and bare land. In urban areas, the shortwave net radiation value was generally concentrated between approximately 700 and 900 W m–2 in summer, but it was between approximately 400 and 700 W m–2 in winter. In suburban areas, the short-wave net radiation value was generally concentrated between approximately 400 and 700 W m–2, but it is between approximately 200 and 400 W m–2 in winter. On the whole, short-wave net radiation in summer is higher than that in winter. According to formula (5), the long-wave upward radiation depended on the surface temperature and surface emissivity. The long-wave downward radiation depended on the atmospheric temperature and its emissivity. In addition, the long-wave net radiation was a budget balance of these radiations. As shown in Table 4, the conclusions could be drawn as follows: (1) the long-wave net radiation was negative, balancing the radiation transfer process and the interaction between land–atmosphere interface because the long-wave radiation from the land surface to the atmosphere was generally high, and there was a ‘loss’ on the land surface. (2) According to the sampling data, the results showed that the long-wave net radiation in summer was higher in urban areas than that in suburban areas, which was opposite to that in winter. The long-wave radiation was significantly affected by surface temperature, and it was higher in urban areas than in suburban areas in summer, while the surface temperature might be lower in urban areas than in suburban areas in cold and dry winters.
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6.2. Urban sprawl and its impact on SNR With urban sprawl, the natural surface is replaced by an urban surface. The changing of land surface significantly affected the surface physical parameters such as surface albedo and emissivity. In addition, it could affect short-wave upward radiation, short-wave downward radiation, and the SNR. The urban sprawl was calculated according to different regions separated by ring roads. The results showed that the percentage of the artificial surface for the landsurface area was 85% in the region inside the fourth ring, and 75% in the region between the fourth and fifth rings. The artificial surface in urban areas was closely saturated, so there were little differences between 2004 and 2014. There was a rapid urban sprawl in suburban areas between the 5th and 6th rings. From the remote-sensing image classification and statistical analysis, there was a significant increase from 46% in 2004 to 56% in 2014. Regarding the mean value of SNR in 2004 and 2014, there was an increase of approximately 35 W m–2 in summer and 25 W m–2 in winter. Generally, the change of surface cover with the rapid urban sprawl mainly led to the increase of SNR.
7. Conclusion To monitor the spatial pattern and change of SNR in urban and suburban areas of the Beijing region, multi-resource remote-sensing data and observation data obtained from the meteorological station were used to retrieve surface physical parameters and radiation budget before verifying their accuracy with field survey data. The spatial pattern of SNR was analysed based on results retrieved from the multi-resource data and the differences in SNR of different land-surface types were compared, then the changes of SNR in different years and different seasons were analysed. The conclusions were as follows: (1) The fractional vegetation cover was one of the principal factors affecting SNR, and the SNR was higher in regions where vegetation cover was higher. Comparing the SNR in urban areas with that in suburban areas in summer and winter, it was higher in urban areas and there was generally a ‘plateau’ characteristic in the spatial pattern. In Beijing, there were high radiation values in the middle part of the city but low radiation values in the surrounding suburban area. (2) From high to low, the average SNR of different land-surface types was water surface, vegetation cover surface, artificial surface and bare land. The SNR of water surface was the highest of all the land-surface types, while the SNR of bare land was the lowest whether in summer or winter. The sorting order of SNR of different land surfaces in winter was the same as that in summer, but the deviation of SNR in all types decreased compared to that in summer. (3) With urban sprawl and surface cover changes, there was an increased trend of SNR value, and the trend was more significant in summer than in winter. According to the analysis of the differences between 2004 and 2014, there was a slow increase of SNR in all land-surface types of approximately 35 W m–2 in summer and 25 W m–2 in winter. The rapid urban expansion
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and the region’s surface cover change may be an important factor in the increase of SNR. With continuous urban sprawl, more and more natural surface in suburban areas was replaced by an artificial surface, and the enlargement of the extent of ‘plateau centre’ affected the surface radiation balance and regional climate significantly. Exploration of this effect is the next step in future research.
Acknowledgements This research is supported by National Natural Science Foundation of China (Parameterizing urban surface radiation and energy budget based on three-dimension modelling and sky view factor, No. 41671339) and Scientific Research Project of Beijing Municipal EducationCommission (Localization observation and remote sensing analysis of the influence of urbanization on surface radiation and energy balance No. KM201510028018)
Disclosure statement No potential conflict of interest was reported by the authors.
Funding This work was supported by the National Natural Science Foundation of China (Parameterizing urban surface radiation and energy budget based on three-dimension modelling and sky view factor) [41671339]; Scientific Research Project of Beijing Municipal Education Commission (Localization observation and remote sensing analysis of the influence of urbanization on surface radiation and energy balance) [KM201510028018].
References Barlow, J. F. 2014. “Progress in Observing and Modelling the Urban Boundary Layer.” Urban Climate 10 (10): 216–240. doi:10.1016/j.uclim.2014.03.011. Bilbao, J., and A. H. D. Miguel. 2007. “Estimation of Daylight Downward Longwave Atmospheric Irradiance Under Clear-Sky and All-Sky Conditions [J].” Journal of Applied Meteorology and Climatology 46 (6): 878–889. doi:10.1175/JAM2503.1. Bisht, G., V. Venturini, S. Islam, and L. Jiang. 2005. “Estimation of the Net Radiation Using Modis (Moderate Resolution Imaging Spectroradiometer) Data for Clear Sky Days [J].” Remote Sensing of Environment 97 (1): 52–67. doi:10.1016/j.rse.2005.03.014. Christen, A., and R. Vogt. 2004. “Energy and Radiation Balance of a Central European City [J].” International Journal of Climatology 24 (11): 1395–1421. doi:10.1002/(ISSN)1097-0088. Cui, Y. P., J. Y. Liu, Y. F. Hu, J. Wang, and W. Kuang. 2012. “Modeling the Radiation Balance of Different Urban Underlying Surfaces.” [In Chinese.] Chinese Science Bulletin 57 (9): 1046–1054. doi:10.1007/s11434-011-4933-x. Cui, Y. P., J. Y. Liu, X. Z. Zhang, H. Y. Feng, and J. B. Wang. 2012. “Modeling Urban Energy Balance and Temperature Differences of Different Underlying Surfaces [J].” [in Chinese.] Geographical Research, 31 (7): 1257–1268. George, R. D., R. M. John, C. Martha, M. N. John, P. K. William, and D. T. Ryan. 2004. “Estimating Land Surface Energy Budgets Froms Space: Review and Current Efforts at the University of Wisconsin-Madison and USDA-ARS [J].” Bulletin of the American Meteorological, 85 (1): 65–78.
1060
D. HU ET AL.
Jiménez Muñoz, J. C., and J. A. Sobrino. 2003. “A Generalized Single-Channel Method for Retrieving Land Surface Temperature from Remote Sensing Data [J].” Journal of Geophysical Research: Atmospheres (1984–2012) 108 (D22). doi:10.1029/2003JD003480. Jin, M., and S. Liang. 2006. “An Improved Land Surface Emissivity Parameter for Land Surface Models Using Global Remote Sensing Observations [J].” Journal of Climate 19 (12): 2867–2881. doi:10.1175/JCLI3720.1. Kim, H.-Y., and S. Liang. 2010. “Development of a Hybrid Method for Estimating Land Surface Shortwave Net Radiation from MODIS Data [J].” Remote Sensing of Environment 114 (11): 2393– 2402. doi:10.1016/j.rse.2010.05.012. Lhomme, J., J. Vacher, and A. Rocheteau. 2007. “Estimating Downward Long-Wave Radiation on the Andean Altiplano [J].” Agricultural and Forest Meteorology 145 (3–4): 139–148. doi:10.1016/j. agrformet.2007.04.007. Liang, S. 2001. “Narrowband to Broadband Conversions of Land Surface Albedo I Algorithms [J].” Remote Sensing of Environment 76 (2): 213–238. doi:10.1016/S0034-4257(00)00205-4. Mahmood, R., R. A. Pielke Sr, K. G. Hubbard, D. Niyogi, P. A. Dirmeyer, C. McAlpine, A. M. Carleton, et al. 2013. “Land Cover Changes and Their Biogeophysical Effects on Climate.” International Journal of Climatology 34 (4): 929–953. doi:10.1002/joc.2014.34.issue-4. Meng, X. H., S. H. Lv, and T. T. Zhang. 2007. “Testing, Improvement and Application of MODIS near Infrared Water Vapor Products: Taking Jinta Oasis in Heihe River Basin as a Case Study [J].” Journal of Infrared and Millimeter Waves 26 (2): 107. Miao, S. G., J. X. Dou, F. Chen, J. Liu, and A. G. Li. 2012. “Analysis of Observations on the Urban Surface Energy Balance in Beijing [J].” [in Chinese.] Science China Earth Sciences 42 (9): 1394– 1402. Mira, M., A. Olioso, B. Gallego-Elvira, D. Courault, S. Garrigues, O. Marloie, O. Hagolle, P. Guillevic, and G. Boulet. 2016. “Uncertainty Assessment of Surface Net Radiation Derived from Landsat Images.” Remote Sensing of Environment 175: 251–270. doi:10.1016/j.rse.2015.12.054. Prata, A. J. 1996. “A New Long-Wave Formula for Estimating Downward Clear-Sky Radiation at the Surface.” Quarterly Journal of the Royal Meteorological Society 122: 1127–1151. doi:10.1002/ (ISSN)1477-870X. Qin, Z. H., W. J. Li, and B. Xu. 2004. “The Estimation of Land Surface Emissivity for Landsat TM6 [J].” [in Chinese.] Remote Sensing for Land & Resources 3 (61): 28–32. Qin, Z. H., W. J. Li, M. H. Zhang, A. Kamieli, and P. Berliner. 2003. “Estimating of the Essential Atmospheric Parameters of Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat TM6[J].” [in Chinese.] Remote Sensing for Land & Resources, 15 (2): 37–43. Qin, Z. H., M. H. Zhang, A. Kamieli, and P. Berliner. 2001. “Mono-Window Algorithm for Retrieving Land Surface Temperature from Landsat TM6 Data [J].” [in Chinese.] Acta Geographica Sinica, 56 (4): 456–466. Robaa, S. M. 2009. “Urban–Rural Solar Radiation Loss in the Atmosphere of Greater Cairo Region, Egypt [J].” Energy Conversion and Management 50 (1): 194–202. doi:10.1016/j. enconman.2008.06.024. Sobrino, J., N. Raissouni, and Z. L. Li. 2001. “A Comparative Study of Land Surface Emissivity Retrieval from NOAA Data.” Remote Sensing of Environment 75 (2): 256–266. doi:10.1016/S00344257(00)00171-1. Sobrino, J. A., J. C. Jiménez-Muñoz, J. El-Kharraz, M. Gómez, M. Romaguera, and G. Sòria. 2004. “Single-Channel and Two-Channel Methods for Land Surface Temperature Retrieval from DAIS Data and Its Application to the Barrax Site [J].” International Journal of Remote Sensing 25 (1): 215–230. doi:10.1080/0143116031000115210. Stathopoulou, M., C. Cartalis, and M. Petrakis. 2007. “Integrating Corine Land Cover Data and Landsat TM for Surface Emissivity Definition: Application to the Urban Area of Athens, Greece.” International Journal of Remote Sensing 28 (15): 3291–3304. doi:10.1080/01431160600993421. Taha, H. 1997. “Urban Climates and Heat Islands: Albedo, Evapotranspiration, and Anthropogenic Heat.” Energy & Buildings 25 (2): 99–103. doi:10.1016/S0378-7788(96)00999-1.
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Tang, B., and Z. Li. 2008. “Estimation of Instantaneous Net Surface Longwave Radiation from MODIS Cloud-Free Data [J].” Remote Sensing of Environment 112 (9): 3482–3492. doi:10.1016/j. rse.2008.04.004. Van de Griend, A. A., and M. Owe. 1993. “On the Relationship between Thermal Emissivity and the Normalized Difference Vegetation Index for Natural Surfaces [J].” International Journal of Remote Sensing 14 (6): 1119–1131. doi:10.1080/01431169308904400. Wang, K., and S. Liang. 2008. “Estimation of Daytime Net Radiation from Shortwave Radiation Measurements and Meteorological Observations [J].” Journal of Applied Meteorology and Climatology 48 (3): 634–643. doi:10.1175/2008JAMC1959.1. Wang, K., and R. E. Dickinson. 2013. “Contribution of Solar Radiation to Decadal Temperature Variability over Land.” PNAS 110 (37): 14877–14882. doi:10.1073/pnas.1311433110. Wang, W. H., and S. L. Liang. 2009. “Estimation of High-Spatial Resolution Clear-Sky Longwave Downward and Net Radiation over Land Surfaces from MODIS Data [J].” Remote Sensing of Environment 113 (4): 745–754. doi:10.1016/j.rse.2008.12.004. White, J. M., F. D. Eaton, and A. H. Auer Jr. 1978. “The Net Radiation Budget of the St. Louis Metropolitan Area [J].” Journal of Applied Meteorology 17 (5): 593–599. doi:10.1175/1520-0450 (1978)0172.0.CO;2. Zhang, X., S. Liang, M. Wild, and B. Jiang. 2015. “Analysis of Surface Incident Shortwave Radiation from Four Satellite Products.” Remote Sensing of Environment 165: 186–202. doi:10.1016/j. rse.2015.05.015.
