Impact of Rapid Urban Sprawl on the Local Meteorological Observational Environment Based on Remote Sensing Images and GIS Technology

remote sensing

Article Impact of Rapid Urban Sprawl on the Local Meteorological Observational Environment Based on Remote Sensing Images and GIS Technology

Yanhao Zhang 1,2,†, Guicai Ning 3,†, Shihan Chen 1,2 and Yuanjian Yang 1,2,4,*

1 Collaborative Innovation Centre on Forecast and Evaluation of Meteorological Disasters, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China; [email protected] (Y.Z.); [email protected] (S.C.) 2 School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China 3 Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong 999077, China; [email protected] 4 State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China * Correspondence: [email protected] † These authors contributed equally to this work.

Abstract: Rapid increases in urban sprawl affect the observational environment around meteorological stations by changing the land use/land cover (LULC) and the anthropogenic heat flux (AHF). Based on remote sensing images and GIS technology, we investigated the impact of changes in both LULC and AHF induced by urbanization on the meteorological observational environment in the Yangtze River Delta (YRD) during 2000–2018. Our results show that the observational environments around meteorological stations were significantly affected by the rapid expansion of built-up areas and the subsequent increase in the AHF, with a clear spatiotemporal variability. A positive correlation Citation: Zhang, Y.; Ning, G.; Chen, was observed between the proportion of built-up areas and the AHF around meteorological stations. S.; Yang, Y. Impact of Rapid Urban The AHF was in the order urban stations > suburban stations > rural stations, but the increases Sprawl on the Local Meteorological Observational Environment Based on in the AHF were greater around suburban and rural stations than around urban stations. Some Remote Sensing Images and GIS meteorological stations need to be relocated to address the adverse effects induced by urbanization. Technology. Remote Sens. 2021, 13, The proportion of built-up areas and AHF around the new stations decreased significantly after relo- 2624. https://doi.org/10.3390/ cation, weakening the urban heat island effect on the meteorological observations and substantially rs13132624 improving the observational environment. As a result, the observed daily mean temperature (relative humidity) decreased (increased) around the new stations after relocation. Our study comprehensively Received: 11 May 2021 shows the impact of rapid urban sprawl on the observational environment around meteorological Accepted: 30 June 2021 stations by assessing changes in both LULC and the AHF induced by urbanization. These findings Published: 4 July 2021 provide scientific insights for the selection and construction of networks of meteorological stations and are therefore helpful in scientifically evaluating and correcting the impact of rapid urban sprawl Publisher’s Note: MDPI stays neutral on meteorological observations. with regard to jurisdictional claims in published maps and institutional affil- Keywords: urban sprawl; anthropogenic heat; meteorological observation environment; remote iations. sensing; GIS technology

Copyright: © 2021 by the authors. 1. Introduction Licensee MDPI, Basel, Switzerland. This article is an open access article With the rapid increases in urban sprawl and economic development in China over distributed under the terms and the last 20 years, a large number of meteorological stations have gradually become sur- conditions of the Creative Commons rounded by urban areas and many of them are now located in city centers. This has led Attribution (CC BY) license (https:// to changes in the land use/land cover (LULC) around stations [1] and a deterioration in creativecommons.org/licenses/by/ the representativeness of the meteorological observational environment [2]. This has had 4.0/). a severe impact on the accuracy, representativeness and homogeneity of meteorological

Remote Sens. 2021, 13, 2624. https://doi.org/10.3390/rs13132624 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 2624 2 of 17

observational data [3–6]. Scientific and quantitative assessments of the changes in the meteorological observational environment are therefore important in weather and climate research [7–10]. Traditional surveys of the observational environment have mainly used methods such as field surveys, which often have poor accuracy and timeliness. Previous studies of regional climate change and urbanization effects usually distinguished urban and rural stations based on the population of the surrounding area and then used the results in comparative analyses of the temperature elements [11]. This discrimination method does not consider factors such as whether the station is located within the city, the natural conditions around the station or the characteristics of the underlying surface [12]. Satellite remote sensing has the characteristics of wide-area coverage and periodicity and is used as an important technical for LULC in various regions of the world. The method details are constantly updated, with more application areas and better results [13–15]. Satellite data can provide accurate and extensive real-time information about changes at meteorological stations and in the surrounding observational environment, facilitate the accurate classification of stations and effectively reproduce historical environmental changes such as changes in LULC around stations [16,17]. With recent rapid developments in remote sensing technology, satellite data have been increasingly used to evaluate observational environments. For example, high-resolution remote sensing images on Google Earth [3], the land surface temperature inversion prod- ucts of MODIS (the moderate resolution imaging spectroradiometer) [12] and nighttime light image [17] have all been used to obtain information about the land use types and thermal environments around meteorological stations to quantitatively evaluate the meteorological observational environment [12,18]. Shi et al. [6] analyzed the spatial distribution of the surface thermal environment in the buffer zone around stations and the temporal and spatial variation of the thermal environment contribution index. They showed that urban-type LULC is the main factor affecting the distribution of the thermal environment in the buffer zone around the station. This further consolidates the rationality for the assessment of urbanization-induced observational environment change around the meteorological station. Many studies have shown that the anthropogenic heat flux (AHF) can affect meteorological observations and climate within 5 km of the station through turbulence and advection [19–26]. Therefore, changes in the thermal environment around the station can- not be ignored when analyzing and evaluating the representativeness of the observational environment based on the attributes of the underlying surface alone. In particular, it is not clear whether the changes in the AHF around the station caused by urbanization have temporal or spatial effects on the observational environment. The distance between cities has decreased over the last 20 years and the rapid development of urban sprawl has greatly changed LULC and exacerbated the risk of extreme weather and climate events, posing serious threats to public health and agricultural production [27–30]. Therefore, assessing the impact of the rapid urban sprawl in the Yangtze River Delta (YRD) of China on the observational environment has important scientific significance and applications demonstration value [17,31]. Notably, YRD is one of the fastest urbanization areas in China and can typically represent the characteristics of the urbanization process in developing countries. Urbanization will bring the rapid growth of wealth, carbon emissions and urban population. These are the relevant indicators about globalization and the shifting centers of gravity of world’s human [32]. Therefore, the urbanization of the YRD is not only a domestic topic in China but also an international one. Exploring the impact of rapid urban sprawl on the local meteorological observational environment in the YRD is of a typical reference to the urbanization on a global scale. We selected 76 meteorological stations within the YRD, and then identified the proportion of built-up area (PBA) and obtained AHF data in a 5 km buffer zone around each station from Landsat and night-time illumination data from satellites. We systematically explored the impact of various factors of rapid urban sprawl on the local meteorological Remote Sens. 2021, 13, x FOR PEER REVIEW 3 of 17

Remote Sens. 2021, 13, 2624 3 of 17 explored the impact of various factors of rapid urban sprawl on the local meteorological observational environment, including the relocation of stations, changes in the underlying surface and the AHF. The rest of this paper is organized as follows. Section 2 introduces observational environment, including the relocation of stations, changes in the underlying the data and analysis methods. Section 3 summarizes the impact of urbanization, AHF surface and the AHF. The rest of this paper is organized as follows. Section2 introduces theand data station and relocat analysision methods. on the meteorological Section3 summarizes observation the impactal environment. of urbanization, Section AHF 4 anddis- stationcusses our relocation results onand the future meteorological research directions. observational Our conclusions environment. are provided Section4 discusses in Section our5. results and future research directions. Our conclusions are provided in Section5.

2. Materials and Methods 2.1. Study Areas As shown in in Figure Figure 1,1, the the YRD YRD is is an an alluvial alluvial plain plain loc locatedated in inthe the lower lower reaches reaches of the of theYangtze Yangtze River River and and ranges ranges between between 115 115–123–123°E,◦ E,32 32–29–29°N.◦N. It Itcovers covers 26 26 cities cities in Jiangsu, Zhejiang andand AnhuiAnhui provinces provinces and and Shanghai, Shanghai, covering covering an an area area of 225,000of 225,000 square square kilometers. kilome- Thisters. regionThis region has the has highest the highest density density of river of networks river networks in China in andChina dominated and dominated by plains, by withplains, an with average an average elevation elevation of about of about 50 m. 50 The m. The subtropical subtropical monsoon monsoon climate climate controls controls in thein the YRD YRD and and thus thus induces induces the the four four distinctdistinct seasons.seasons. The The annual annual average average temperature temperature is ◦ isabout about 14– 14–1818 °C, C,January January and and July July are are observed observed the the lowest lowest and and highest highest temperatures temperatures,, re- respectively.spectively. The The annual annual total total precipitation precipitation is is about about 100 1000–14000–1400 mm, mm, concentrated in springspring and summer seasons.

Figure 1. MapMap of of the the YRD YRD showing showing land land use, use, the theclassification classiﬁcation of meteorological of meteorological stations stations,, the rel theo- relocatedcated stations stations (black) (black) and andreference reference stations stations (white) (white) (initials (initials of the of station the station names names are given are givenabove the stations). The LULC maps in 2016 were from the MODIS MCD12Q1 product at a 0.5 km × 0.5 above the stations). The LULC maps in 2016 were from the MODIS MCD12Q1 product at a km resolution. 0.5 km × 0.5 km resolution.

2.2. Selection of Meteorological Stations Based onon thethe observational observational environment environment around around the the stations stations in YRD in YRD and high-resolutionand high-reso- satellitelution satellite remote sensingremote imaging,sensing weimaging, selected we 76 selected meteorological 76 meteorological stations as the stations example as samples the ex- (Figureample 1samples). The chosen(Figure stations 1). The met chosen the followingstations met requirements: the following (1) requirements: the station type (1 was) the thestation national type was benchmark the national climate benchmark station, national climate basicstation, weather national station basic or weather national station general or weathernational station;general (2) weather the meteorological station; (2) the observational meteorological dataset observational from 2000 to 2018dataset was from basically 2000 complete;to 2018 was(3) basically these meteorological complete; (3 stations) these weremeteorological evenly distributed stations throughoutwere evenly the distributed YRD and werethroughout surrounded the YRD by differentand were types surrounded of LULC. by The different selected types stations of LULC. can therefore The selected be used sta- totions explore can therefore the impact be of used rapidly to explore increasing the urban impact sprawl of rapidly on the increasing observational urban environment sprawl on aroundthe observational meteorological environment stations. Previousaround meteorological studies have shown stations. that thePrevious maximum studies range have of affectingshown that observational the maximum data range under of advection affecting andobservational turbulence data transport under conditions advection doesand tur- not usuallybulence exceedtransport 5 km conditions [6,7]. We thereforedoes not tookusually the meteorologicalexceed 5 km [6,7] stations. We astherefore the center took of thethe buffer area with a radius of 5 km to quantitatively evaluate the impact of urbanization on the observational environment.

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Seven groups of relocated stations were selected to compare the observational environment before and after relocation (also see Figure1). The chosen relocated stations met the following requirements [10]: (1) the relocation was mainly due to the deterioration of the meteorological observational environment; (2) the difference in elevation before and after relocation was < 50 m and the horizontal distance was 20 km; (3) there was no signiﬁcant difference in topography before and after relocation; and (4) the type of observational instruments had not changed. Among the seven groups of stations, Bozhou, Bengbu and Anqing stations were located in the northern plains, middle hills and southern mountainous area of the YRD, respectively, which shows that they had good regional representation. On the other hand, after checking the meteorological dataset, we found that only the data of the daily mean temperature and relative humidity data (old station minus new station) at Anqing Station in 2012, Bengbu Station in 2013 and Bozhou Station in 2014 were complete, accurate and comparable. We chose them to analyze the observational data from the old and new stations and to explore the impact of the rapid increase in urban sprawl on the meteorological observations. To compare the relationship between AHF and PBA of the relocated stations and other stations (also see Figure1), seven reference stations without relocation history were selected. The selected reference stations should be located around the relocated stations and belong to different types of stations (including two city stations, three suburban stations, and two rural stations), indicating that these reference stations can represent the whole area of YRD. We selected the daily mean temperature of all relocated stations and reference stations from 2010 to 2015 for considering whether the characteristics of the relocated stations’ meteorological data were contextualized in a more general trend in the whole YRD region. All the relocations took place during this period, and the data of the 14 stations during this period were complete and accurate.

2.3. Classification of Meteorological Stations The urban impervious surface area (UISA) [33] refers to the urban impervious surface features caused by artificial land use activities, such as building roofs, asphalt or cement roads and parking lots. The coverage of the impervious surface is the percentage of the existing impervious surface area to the total surface area. This is an important component of research on environmental change in urban areas and reflects the impact of urban sprawl on the underlying surface. We established a buffer zone with a radius of 5 km centered on the station and then calculated the proportion of impervious surface area in the buffer zone to the total area. Ranking all stations from the largest to smallest, the first to 26th (the top third) stations were classified as urban stations, the 27th to 51th (the middle third) were suburban stations and the last 26 stations (bottom third) were rural stations.

2.4. Anthropogenic Heat Environment around Meteorological Stations Assuming that all the energy consumed by humans is eventually converted into heat, the AHF density (Qa, units: J s−1 m−2) in a speciﬁc area and time can be approximated by:

E Q = c , (1) a t·A

where Ec, t and A represent the energy consumption, time and area, respectively. This method is based on the night-time satellite data being consistent with local economic development and energy consumption statistics and has been shown to help in estimating the AHF [16–20]. The high-resolution distribution of the AHF in the YRD was retrieved by acquiring data from two NOAA optical satellites operating during the night based on these principles. This study used inversion to obtain the high-resolution AHF in the YRD from the continuous, stable nighttime light data and energy statistics from 2000 to 2018 as described by Chen et al. [19]. We used two methods to determine the AHF around the meteorological stations. The ﬁrst method was based on a classiﬁcation system in which the AHF values in the buffer Remote Sens. 2021, 13, 2624 5 of 17

zone were divided into low AHF (L; <5 W m−2), sub-low AHF (SL; 5–10 W m−2), medium AHF (M; 10–15 W m−2), sub-high AHF (SH; 15–20 W m−2) and high AHF (H; >20 W m−2). The proportion of each AHF level in the buffer zone was calculated by:

PN = SN/ST, (2)

where N takes the ﬁve levels of L, SL, M, SH and H. For example, PL is the proportion corresponding to level L and PN meets the relationship PL + PSL + PM + PSH + PH = 1. SL is the area corresponding to level L in the buffer zone and ST is the total area of the buffer zone. Based on this method, a station with a higher proportion of SH and H levels indicates that the AHF surrounding the station is higher. The second method was based on the average value. This method counts the areas with different AHF values in the buffer zone, multiplies the AHF by the corresponding area, adds these values up and the divides the result by the total area of the buffer zone. The relationship can be written as:

Ave = 0 × S0 + 1 × S1 + 2 × S2 + . . . + N × SN/ST, (3)

where Ave is the AHF value corresponding to each station, 0 to N are all the AHF values that appear in the buffer zone, S0 to SN is the area corresponding to each AHF value and ST is the total area of the buffer zone. Based on this method, station with larger average values indicate that the level of AHF surrounding the station is higher.

2.5. Correlation between Built-up Areas and the Anthropogenic Heat Environment We used the following two methods to analyze the correlation between the PBA and the AHF in the buffer zone. In the ﬁrst method, the PBA in the buffer zone was used as the independent variable x and the proportion of different grades of AHF was used as the dependent variable y. The x value of the peak of each AHF grade curve represents the PBA corresponding to that grade of AHF. If the value of x corresponding to the peak value of the high-level (SH and H) AHF curve is larger, then the built-up area is positively correlated with the AHF. The second method used the linear equation y = ax + b, which with the PBA of buffer zone as the independent variable x and the average value of the AHF in the buffer zone as the dependent variable y. This equation was used to analyze the relationship between the change in the average AHF around the station and the variation in the built-up area.

3. Results 3.1. Urbanization Processes around Meteorological Stations Taking the meteorological stations located in four provincial cities as examples, we can see that the urbanization process was signiﬁcantly different at each station (Figure2). The PBA in the buffer zone around both Nanjing and Hefei stations were <10% in 2000, but > 60% in 2015 (Figure2b,c). The PBA in the buffer zone around Xujiahui Station in Shanghai had been at a very high level (>85%), reaching the highest value of 93.3% (Figure2d ). The development of the built-up area around Hangzhou Station was relatively small, from 34.3% in 2000 to 51.2% in 2018 (Figure2e). These temporal and spatial differences in urban sprawl were seen at all 76 stations and were strongly affected by the scale of the city in which the stations were located. Remote Sens. 2021, 13, x FOR PEER REVIEW 6 of 17 Remote Sens. 2021, 13, 2624 6 of 17 Remote Sens. 2021, 13, x FOR PEER REVIEW 6 of 17

FigureFigure 2. (a()a Map) Mapss of the of change the change in the extent in the of extent built-up of areas built-up in the areasYRD from in the 2000 YRD to 2018. from Annual 2000 to 2018. Figurechange 2.and (a )line Map graphss of the of thechange PBA in the bufferextent zone of built within-up a areas 5-km inradius the YRDcentered from on 2000 the meteor- to 2018. Annual Annualchangeological and changestations line of andgraphs the line four of graphsprovincial the PBA of incapital the the PBA buffercities in of the zone (b buffer) Hefei within ( zoneHF a) ,5 (- withinckm) Nanjing radius a 5-km ( NJcentered), ( radiusd) Xujiahui on centered the meteor- on theological(XJH meteorological) and stations (e) Hangzhou of stationsthe (fourHZ). of provincial the four capital provincial cities capital of (b) citiesHefei of(HF (b)), Hefei(c) Nanjing (HF), ( NJc) Nanjing), (d) Xujiahui (NJ), ((dXJH) Xujiahui) and (e (XJH)) Hangzhou and (e) ( HangzhouHZ). (HZ). The stations with the built-up area of 0%–5% in 2000 showed the largest changes in builtThe-Theup area stationsstations in their withwith subsequent thethe built-upbuilt development-up areaarea ofof 0%–5%(0Figure%–5% 3 inin). These 20002000 showedstationsshowed were thethelargest largestmainly changesru-changes inin built-upbuiltral or- upsuburban areaarea inin theirand their their subsequent subsequent development development development range was (Figure significantly(Figure3). 3 These). Theselarger stations stationsthan that were were of mainlymost mainly rural ru- city stations. Most of the built-up areas around stations showed their fastest development orral suburban or suburban and and their their development development range range was was signiﬁcantly significantly larger larger than than that ofthat most of most city from 2005 to 2010, which corresponds to the period of rapid urban sprawl in the YRD. The stations.city stations. Most Most of the of the built-up built-up areas area arounds around stations stations showed showed their their fastest fastest development development frombuilt- 2005up area to 2010,of a few which stations corresponds developed toslowly the period from 2015 of rapid to 2018 urban and only sprawl five in stations the YRD. The fromincreased 2005 the to PBA2010, by which >1% d correspondsuring this period. to the Another period 55 of stations rapid evenurban showed sprawl a negativein the YRD. The built-up area of a few stations developed slowly from 2015 to 2018 and only ﬁve stations builtgrowth-up in area the PBA.of a few This stations phenomenon developed was more slowly pronounced from 2015 for tostations 2018 and(mostly only urban five stations increased the PBA by >1% during this period. Another 55 stations even showed a negative increasedand suburban the stationsPBA by) >1%that accounted during this for period. >5% of theAnother built-up 55 areas stations in 2000 even—that showed is, after a negative growthgrowthurbanization inin thethe had PBA.PBA. reac ThishedThis a phenomenoncertainphenomenon level, the waswas expansion moremore pronounced pronouncedrate of the cities forfor and stationsstations towns (mostlyslowed(mostly urbanurban andanddown. suburbansuburban This was stations)stations also affected) thatthat by accountedaccounted factors such forfor as >5%>5% sponge ofof thethe city built-upbuilt and -urbanup areasareas greening inin 2000—that2000 projects—that. is,is, afterafter urbanizationurbanization hadhad reachedreached aa certaincertain level,level, thethe expansionexpansion raterate ofof thethe citiescities andand townstowns slowedslowed down.down. ThisThis waswas alsoalso affectedaffected byby factorsfactors suchsuch asas spongesponge citycity andand urbanurban greeninggreening projects.projects.

Figure 3. (a) The ratio of PBA in 2005, 2010, 2015 and 2018 to that in 2000. (b) the PBA in the year 2000.

FigureFigure 3.3. ((aa)) T Thehe ratio ratio of of PBA PBA in in 2005, 2005, 2010, 2010, 2015 2015 and and 2018 2018 to tothat that in 2000. in 2000. (b) the (b) thePBA PBA in th ine year the year2000. 2000.

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3.2.3.2. Relationship Relationship between between Urban Urban Sprawl Sprawl and and the the Anthropogenic Anthropogenic Heat Heat Environment Environment TheThe AHFAHF increased increased significantlysignificantly fromfrom 20002000 to to 2018 2018 with with the the rapid rapid urban urban sprawl sprawl in in thethe YRD YRD (Figure(Figure4 ).4). TheThe spatialspatial distributiondistribution of the AHF generally generally exhibited exhibited two two types types:: 1) 1)If Ifwe we take take a large a large city city like like Hefei Hefei as a as center, a center, then then the theAHF AHF was washighest highest in a relativ in a relativelyely small smallarea, area,while while low AHF low AHFin the in surround the surroundinging areas areas of the of Hefei the Hefei city. city.2) In 2)contrast, In contrast, from from Nan- Nanjingjing along along the Yangtze the Yangtze River River to Shanghai, to Shanghai, and from andfrom Shanghai Shanghai along along the coastline the coastline to Hang- to Hangzhouzhou Bay, Bay,there there were were one or one more or more large largecities citiesand many and many smaller smaller cities, cities, forming forming an urban an urbanbelt. Under belt. Underthe influence the influence of these of cities, these the cities, AHF the was AHF high was over high a large over aarea. large In area. general, In general,the thermal the thermalenvironment environment around aroundstations stationschanged changed significantly significantly as a result as aof result the continu- of the continuousous increase increase in the inoverall the overall AHF in AHF the inYRD. the YRD.For example, For example, the average the average value value of AHF of AHF at 76 −2 −2 atstations 76 stations rapidly rapidly increased increased from from5.45 W 5.45 m−2 W in m 2000,in to 2000, 8.48 to W 8.48 m−2 Win m2005 inand 2005 to 12.44 and toW −2 −2 12.44m−2 in W 2010. m Thein 2010.n the Then AHF the increase AHF increasewas relatively was relatively slower in slower 2015 ( in13.84 2015 W (13.84 m−2) and W m 2018) and 2018 (14.74 W m−2) (Figure4f). (14.74 W m−2) (Figure 4f).

FigureFigure 4. 4.Maps Maps of of the the AHF AHF in in the the YRD YRD from from 2000 2000 to to 2018. 2018. ( a(a)) 2000, 2000, ( b(b)) 2005, 2005, ( c()c) 2010, 2010, ( d(d)) 2015 2015 and and (e(e)) 2018. 2018. ( f()f) The The average average AHFs AHFs in in a a 5-km 5-km buffer buffer zone zone around around 76 76 stations stations in in different different years. years.

FigureFigure5 5shows shows the the correlation correlation between between the the AHF AHF and and the the level level of of urbanization. urbanization. As As the the AHFAHF in in the the buffer buffer zone zone around around the stationthe station increased, increased, the x valuethe x ofvalue the curve’sof the peakcurve’s moved peak tomoved the right. to the This right. indicates This indicates a positive a correlationpositive correlation between thebetween AHF andthe AHF the built-up and the areas. built- Theup areas. difference The difference between the between five curves the five weakened curves weakened over time over and time this phenomenonand this phenome- was concentratednon was concentrated in the SL, Min the and SL, SH M levels. and SH The levels. three The curves three moved curves closer moved together, closer showingtogether, thatshow theing relationship that the relationship between thebetween distribution the distribution of different of different AHF levels AHF and levels urbanization and urban- wasization decreasing. was decreasing. However, However, the curve the of curve the levelsof the oflevels L and of HL and were H still were quite still differentquite dif- fromferent the from other the curves, other curves, the peak the height peak ofheight the L of level the curveL level was curve getting was lowergetting and lower lower, and whilelower, the whi Hle level the H was level gradually was gradually increasing. increasing. Among Among the average the average of the of 76 the stations, 76 stations, the Lthe level L level decreased decreased from from 52.28% 52.28% (2000) (2000 to) 11.39%to 11.39% (2018), (2018 whereas), whereas the the H H grade grade increased increased fromfrom 3.94% 3.94% (2000) (2000) to to 43.93% 43.93% (2018) (2018) (Figure (Figure5 ).5) This. This shows shows that that urbanization urbanization has has increased increased significantlysignificantly inin different different regions, regions, leading leadingto to an an increase increase in in the the AHF AHF in in the the buffer buffer zone zone of of differentdifferent stations; stations; mostmost ofof the the trends trends were were similar. similar. Only Only the the AHF AHF of of the the stations stations in in large large citiescities was was significantly significantly higher higher than than the the AHF AHF at at other other stations. stations.

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Figure 5. Scatter diagrams and graphs of the relationship between different levels of AHF and the Figure 5. ScatterFigurePBA diagrams in 5. aScatter 5 km andradius diagrams graphs buffer and of zone the graphs relationship around of the the relationship betweenmeteorological different between stations levels different in of different AHF levels and years: of the AHF PBA (a) and 2000, in athe 5(b km) radius buffer zonePBA around2005, in (ac the )5 2010, km meteorological radius (d) 2015 buffer and stations zone(e) 2018. around in different the meteorological years: (a) 2000, stations (b) 2005, in different (c) 2010, (years:d) 2015 (a and) 2000, (e) ( 2018.b) 2005, (c) 2010, (d) 2015 and (e) 2018. The slopes ofThe the lines slopes in ofFigure the lines6 are all in positive Figure6 andare allgenerally positive approach and generally unity, indi- approach unity, catingThe a slopes positiveindicating of thecorrelation lines a in positive Figurebetween correlation6 arethe all AHF positive betweenand rapid and thegenerally urban AHF sprawl. and approach rapid The unity,lines urban moved indi- sprawl. The lines catingupward a positive overmoved time, correlation which upward means between over that time, the the whichAHF AHF and meansincreased rapid that urban over the AHFtime.sprawl. increasedFor The example, lines over moved the time. av- For example, −2 upwarderage AHF over of time,the all averagestations which means AHFwas 5.45 of that all W stationsthe m −2AHF in 2000 was increased 5.45and Wit overhad m increasedtime.in 2000 For and example,to 14.74 it had W the increased m −2av- by to 14.74 W −2 erage2018. AHF By contrast, of amll stations theby slope 2018. was of By 5.45 the contrast, linesW m decreased−2 in the 2000 slope and slightly, of it the had linesindicating increased decreased that to 14.74the slightly, difference W m indicating−2 by in that the 2018.AHF By between contrast,difference stations the slope was in AHFof gradually the between lines decreased decreasing. stations slightly, was In graduallygeneral, indicating AHF decreasing. thatwas theclosely In difference general, related AHFin to was closely AHFthe changebetween of related stationsbuilt-up to was areas the gradually change around of the decreasing. built-up stations. areas Therefore, In general, around AHF theAHFstations. can was consider closely Therefore, and related reliably AHF to can consider thereflect change both of anthropogenicbuiltand- reliablyup areas reflect around emission both the anthropogeniceffects stations. and Therefore, land emission use AHFchange effects can effects consider and land related and use reliablyto change latent effects related reflectheat flux both and anthropogenicto sensible latent heatheat emission fluxflux. and sensibleeffects and heat land flux. use change effects related to latent heat flux and sensible heat flux.

Figure 6. ScatterFigure plots 6.andScatter linear plots plots and of linearthe relationship plots of the between relationship the average between AHF the averageand the AHFPBA andin the PBA in a Figurea 5-km 6. radius Scatter buffer5-km plots radius andzone linear around buffer plots zonethe of meteorological aroundthe relationship the meteorological stations. between the stations. average AHF and the PBA in a 5-km radius buffer zone around the meteorological stations.

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The changes in the thermal environment around the meteorological stations varied significantlyThe changes among in individual the thermal stations, environment with clear around temporal the meteorological and spatial differences. stations varied In mostsignificantly years, the among average individual AHF growth stations, was with in the clear order temporal urban andstations spatial > suburban differences. stations In most > years,rural stations the average and AHFthere growth was a wasperiod in the of orderrapid urbangrowth stations in the > AHF suburban fromstations 2000 to >2010 rural (Figurestations 7).and Thethere average was growth a period rate of rapidof suburban growth and in the rural AHF stations from 2000was >1.5 to 2010 over (Figure a five7- ). yearThe period, average significantly growth rate greater of suburban than that and of rural urban stations stations. was The >1.5 overall over a five-yeargrowth rate period, of significantly greater than that of urban stations. The overall growth rate of the AHF slowed the AHF slowed from 2010 to 2018. From 2010 to 2015, the growth rates of the three types from 2010 to 2018. From 2010 to 2015, the growth rates of the three types of station were 1.1, of station were 1.1, 1.1 and 1.2 and the growth rates were 1.0, 1.1 and 1.0 from 2015 to 2018. 1.1 and 1.2 and the growth rates were 1.0, 1.1 and 1.0 from 2015 to 2018. The difference in the The difference in the magnitude of the change also decreased. Although growth of the magnitude of the change also decreased. Although growth of the AHF from 2015 to 2018 AHF from 2015 to 2018 was the slowest, the average growth multiple of all meteorological was the slowest, the average growth multiple of all meteorological stations was always >1 stations was always >1 and there was no negative growth in the PBA in the buffer zone in and there was no negative growth in the PBA in the buffer zone in the corresponding years. the corresponding years.

FigureFigure 7. 7.AverageAverage of ofthe the rela relativetive multiples multiples of ofthe the AHF AHF at atthree three different different types types of ofstation station in ina 5 a km 5 km radiusradius buffer buffer zone zone around around the the meteorological meteorological stations stations (comparative (comparative analysis analysis every every five ﬁve years years).).

3.3.3.3. Impact Impact of ofthe the Relocation Relocation of ofMeteorological Meteorological Stations Stations on on the the Observational Observational Environment Environment TheThe observational observational environment environment around around the the stations stations improved improved with with the the relocation relocation of of thethe meteorological meteorological stations. stations. The The PBA PBA in inthe the buffer buffer zone zone decreased decreased overall overall after after relocation, relocation, withwith an an average average reduction reduction of of56. 56.8%8% compared compared with with the the old olderer stations stations (Figure (Figure 8)8. ).However, However, therethere were were differences differences in in the the change change of ofPBA PBA in inthe the buffer buffer zone zone between between different different stations. stations. ForFor example, example, the the PBA PBA ofof Anqing Anqing Station Station decreased decreased from from 54.7 54.7%% to to 0. 0.3%3% (Figure (Figure 8a8a),), the the PBAPBA of ofSheyang Sheyang Station Station changed changed from from 27. 27.8%8% to to18 18.9%.9% (Figure (Figure 8f8)f),, but but the the PBA PBA of of Taihu Taihu StationStation increased increased slightly slightly from from 6.4 6.4%% to 7.6% afterafter relocationrelocation (Figure (Figure8g). 8g Unlike). Unlike the the stations sta- tionsin large in large cities, cities, stations stations such such as Sheyang as Sheyang and Taihu and wereTaihu in were small in urban small areas urban and areas had and short distances for their relocations, they were also closer to the urban center after relocation. had short distances for their relocations, they were also closer to the urban center after The improvement at these stations was relatively small and the relationship between the relocation. The improvement at these stations was relatively small and the relationship relative position of the station and the built-up area changed marginally. between the relative position of the station and the built-up area changed marginally.

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FigureFigure 8. Maps 8. Maps showing showing the 5 thekm 5radius km radius buffer buffer zone zonearound around the old the and old new and stations new stations and the and level the level of theFigure built 8. -Mapsup area showing in the the surrounding 5 km radius buffer environment zone around for the (a )old Anqing and new (AQ stations), (b) and Bengbu the level (BB ), (c) of the built-up area in the surrounding environment for (a) Anqing (AQ), (b) Bengbu (BB), (c) Bozhou Bozhouof the ( BZbuilt), -(upd) Huai’anarea in the (HA surrounding), (e) Pinghu environment (PH), (f) forSheyang (a) Anqing (SY) (andAQ) ,( g(b) )Taihu Bengbu (TH (BB) )stations, (c) in (BZ), (d) Huai’an (HA), (e) Pinghu (PH), (f) Sheyang (SY) and (g) Taihu (TH) stations in Figure1. FigureBozhou 1. (BZ), (d) Huai’an (HA), (e) Pinghu (PH), (f) Sheyang (SY) and (g) Taihu (TH) stations in Figure 1. The built-up area and anthropogenic heat environment around the stations improved The built-up area and anthropogenic heat environment around the stations improved andThe their built linear-up area changes and anthropogenic were consistent heatafter environment relocation around (Figure the9 stations). However, improved the difference and their linear changes were consistent after relocation (Figure 9). However, the differ- andin thetheir PBA linear in changes the buffer were zone consistent between after the relocation stations ( wasFigure relatively 9). However, signiﬁcant, the differ- whereas the ence in the PBA in the buffer zone between the stations was relatively significant, whereas encechanges in the PBA in the in AHFthe buffer were zone consistent. between the The stations PBA afterwas relatively relocation significant, averaged whereas 43.2% of that of the thechangesthe changes old station,in inthe the AHF AHF with were were a minimum consistent.consistent. of The 0.5%The PBA PBA and after aafter maximum relocation relocation ofaveraged 114.1%. averaged 43.2% For 43.2% comparison,of that of that the of theofaverage the old old station, station, AHF ofwith with the a olda minimum minimum station was ofof 0.0. 57.0%,55%% and and thea maximuma minimummaximum of was 114.of 114.1 41.5%%. 1For%. and comparison,For the comparison, maximum was the average AHF of the old station was 57.0%, the minimum was 41.5% and the maximum the average76.5%. TheAHF AHF of the was old mainly station affected was 57.0%, by athe combination minimum was of many 41.5% urban-driven and the maximum factors. waswas 76.5%. 76.5%. The The AHF AHF was was mainly mainly affected by by a acombination combination of manyof many urban urban-driven-driven factors. factors.

FigureFigure 9. Linear 9. Linear graph graph of the of changes the changes in (a) the in (PBAa) the and PBA (b) andthe average (b) the AHF average in the AHF 5 km in radius the 5 km radius buffer zone around the old and new stations. The solid (dotted) line after 2010 represents the sur- buffer zone around the old and new stations. The solid (dotted) line after 2010 represents the Figurerounding 9. Linear environment graph of of the the changesnew (old )in station (a) the after PBA relocation. and (b ) the average AHF in the 5 km radius surrounding environment of the new (old) station after relocation. buffer zone around the old and new stations. The solid (dotted) line after 2010 represents the surrounding environment of the new (old) station after relocation.

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To illustrate the reliability of the impact of station relocation on the observational environmentTo illustrate around the reliability stations, the of Suzhouthe impact station, of station Ningguo relocation station on and the Dongzhi observational station that environment around stations, the Suzhou station, Ningguo station and Dongzhi station were belong to urban station, suburban station and rural station, respectively, were selected that were belong to urban station, suburban station and rural station, respectively, were as the reference stations named non-relocated stations because they were not relocated selected as the reference stations named non-relocated stations because they were not re- during the study period. Compared with Figure9, the time series of PBA and AHF at these located during the study period. Compared with Figure 9, the time series of PBA and AHF three reference stations exhibited continuously increasing and the changes of the increasing at these three reference stations exhibited continuously increasing and the changes of the during the study period were much smaller than those at the relocated stations (Figure9). increasing during the study period were much smaller than those at the relocated stations During 2010 to 2015, the PBA and AHF at the relocated stations decreased about 43.2% and (Figure 9). During 2010 to 2015, the PBA and AHF at the relocated stations decreased 57.0%, respectively, while the PBA and AHF increased about 44.8% and 48.0%, respectively about 43.2% and 57.0%, respectively, while the PBA and AHF increased about 44.8% and at the reference stations. Compared with Figures9 and 10, we found that the curves of PBA 48.0%, respectively at the reference stations. Compared with Figure 9 and Figure 10, we and AHF at the reference stations were closer to the curves at the relocation stations before found that the curves of PBA and AHF at the reference stations were closer to the curves relocated. These results indicate that the impact of station relocation on the observational at the relocation stations before relocated. These results indicate that the impact of station relocationenvironment on the was observational obvious. To environment investigate was the impactobvious. of To improving investigate the the anthropogenic impact of improvingheat environment the anthropogenic on the meteorological heat environment observational on the meteorological data before and observational after relocation, data we beforeselected and the after daily relocation mean, temperature we selected the and daily relative mean humidity temperature data and (old relative station humidity minusnew datastation) (old forbasedstation minus on the new geographical station) forbased representation on the geographical of the stations representation and the corresponding of the stationsyear of relocation.and the corresponding That is, Bozhou, year Bengbuof relocation. and Anqing That is stations, Bozhou, are Bengbu located and in theAnqing northern stationsplains, are middle located hills in andthe northern southern plains, mountainous middle hills area and of thesouthern YRD, respectively.mountainous area of

FigureFigure 10. 10. LinearLinear graph graph of ofthe the changes changes in in(a) (thea) the PBA PBA and and (b) (theb) theaverage average AHF AHF in the in 5 the km 5 radius km radius bufferbuffer zone zone around around the the reference reference stations stations:: Suzhou Suzhou (SZ), (SZ), Ningguo Ningguo (NG) (NG) and and Dongzhi Dongzhi (DZ) (DZ) in inFigure Figure 1. 1. Figure 11 shows that the average temperature difference at Anqing Station was 0.78 ◦C, withFigure the maximum 11 shows valuethat the in average October temperature (1.08 ◦C) and difference the minimum at Anqing in March Station (0.45 was ◦0.78C). The °Caverage, with the temperature maximum differencevalue in October in autumn (1.08 °C was) and signiﬁcant the minimum and thein March difference (0.45 in°C) other. Theseasons average was temperature uniformlydistributed. difference in The autumn average was temperaturesignificant and difference the difference at Bengbu in other Station seasonswas just was 0.5 uniformly◦C and the distributed. differences The were average extremely temperature low in July, difference August at and Bengbu September, Station with was just 0.5 °C and the differences were extremely low in July, August and September, an average value of only 0.26 ◦C. After the relocation of Bozhou Station, the daily mean with an average value of only 0.26 °C. After the relocation of Bozhou Station, the daily temperature difference was 0.76 ◦C, with the minimum (0.06 ◦C) in February. However, mean temperature difference was 0.76 °C, with the minimum (0.06 °C) in February. How- the overall seasonal difference at Bozhou Station was small. The daily mean temperature ever, the overall seasonal difference at Bozhou Station was small. The daily mean temper- differences between the three groups of stations on most days were between 0 and 1.5 ◦C. ature differences between the three groups of stations on most days were between 0 and There were 22 days at Anqing, 63 days at Bengbu and 45 days at Bozhou stations when the 1.5 °C. There were 22 days at Anqing, 63 days at Bengbu and 45 days at Bozhou stations difference in the daily mean temperature between the new and old stations was ≤0 ◦C. By when the difference in the daily mean temperature between the new and old stations was contrast, there were 30 days at Anqing, 16 days at Bengbu and 57 days at Bozhou stations ≤0 °C. By contrast, there were 30 days at Anqing, 16 days at Bengbu and 57 days at Bozhou when the difference was >1.5 ◦C. stations when the difference was >1.5 °C. The humidity at the three groups of stations increased after relocation and the average difference was −5.9%. The average humidity difference at Anqing Station was −5.1% and was relatively large in the first half of the year. The difference in February was 0.6%; Remote Sens. 2021, 13, x FOR PEER REVIEW 12 of 17

this was the only month in which the difference was >0 among the three groups of stations, showing a relatively rare phenomenon of a reduction in humidity after relocation. The humidity at Bengbu Station increased slightly, with an average difference of −3.7%, and the difference between seasons was small. Bozhou Station had the largest increase in hu- Remote Sens. 2021, midity,13, 2624 with an average difference of −9.0%. The average difference in spring was 12 of 17 −11.9%, incl

Figure 11. FigureMonthly 11. differenceMonthly difference in the daily in meanthe daily temperature mean temperature (left—hand (left— panels)hand and panels relative) and humidityrelative hu- (right—hand panels) betweenmidity the (right new— andhand old panels stations) between (old station the new data and minus old stations new station). (old station (a,b) Anqingdata minus (AQ), new (c, dstation) Bengbu). (BB) and (e,f) Bozhou(a (BZ),b) Anqing stations. (AQ), (c,d) Bengbu (BB) and (e,f) Bozhou (BZ) stations.

In general, the observationalThe humidity environment at the three groups for most ofstations stations increased was improved after relocation as a result and the average of the reductiondifference in the built was-up− area5.9%. after The relocation average humidity and the AHF difference around at Anqingthe stations Station was was −5.1% and significantly reduced.was relatively Observational large in data the firstwas half significantly of the year. weakened The difference by the inurban February heat was 0.6%; this island (high temperaturewas the only) and month urban in d whichry island the (low difference humidity was) effect >0 amongs. That the is, threethe daily groups of stations, mean temperatureshowing of the a new relatively station rare decreased phenomenon relative of to a reductionthe old station, in humidity whereas after the relocation. The relative humidityhumidity increased. at Bengbu The improvement Station increased effect of slightly, station with relocation an average on the difference meteor- of −3.7%, and ological observationalthe difference environment between was seasons affected was by many small. factors, Bozhou includin Stationg hadthe distance the largest increase in between the oldhumidity, and new stations, with an the average form of difference urban expansion of −9.0%. and The the averagelocal geographical difference in spring was and climatic background−11.9%, including conditions. May This (−13.6%), led to the which large was difference the month in withthe effect the lowest of relo- humidity among cation on improvingthe three the groupsmeteorological of stations. observational Compared environme with the dailynt in mean different temperature, regions. the distribution of extreme days of relative humidity varied significantly among stations. 4. Discussion In general, the observational environment for most stations was improved as a result of the reduction in the built-up area after relocation and the AHF around the stations The findings reported here provide credible evidence that it is important to accu- was significantly reduced. Observational data was significantly weakened by the urban rately evaluate the impact of urbanization on the observational environment to improve heat island (high temperature) and urban dry island (low humidity) effects. That is, the the quality and representativeness of meteorological observational data. Most previous daily mean temperature of the new station decreased relative to the old station, whereas studies have only discussed the impact of a few factors—such as the impact of rapid urban the relative humidity increased. The improvement effect of station relocation on the sprawl on land use types, building heights and sky openness factors—around the station. meteorological observational environment was affected by many factors, including the It is not known how urbanization affects the anthropogenic heat environment around a distance between the old and new stations, the form of urban expansion and the local station, leading to uncertainties in the impact of urban sprawl on observations of temper- geographical and climatic background conditions. This led to the large difference in ature [30,34]. For example, the surrounding stations are often selected as climate reference the effect of relocation on improving the meteorological observational environment in stations to eliminatedifferent the regions. effects of climate change, but the significant differences in the

4. Discussion The ﬁndings reported here provide credible evidence that it is important to accurately evaluate the impact of urbanization on the observational environment to improve the quality and representativeness of meteorological observational data. Most previous studies have only discussed the impact of a few factors—such as the impact of rapid urban sprawl on land use types, building heights and sky openness factors—around the Remote Sens. 2021, 13, 2624 13 of 17

station. It is not known how urbanization affects the anthropogenic heat environment around a station, leading to uncertainties in the impact of urban sprawl on observations of temperature [30,34]. For example, the surrounding stations are often selected as climate reference stations to eliminate the effects of climate change, but the significant differences in the anthropogenic heat environment around the chosen stations and urban stations are not taken into account [30,35]. Many studies ignored the impact of anthropogenic heat on meteorological observations when selecting reference observation data [34,36,37]. Existing research on the influence of the AHF on meteorological elements has often paid more attention to the large-scale and high-level accumulation of the AHF and the effect of horizontal convection on the radiation at surrounding stations [38–42]. Our findings show that more attention should be paid to the effects of the local micro-scale anthropogenic heat environment around meteorological stations on the meteorological observational data. Relocated stations often still do not meet the basic requirements of the meteorological observational environment, which leads to multiple relocations and affects the homogeneity of the observational data. Most previous studies have only shown that indicators such as the urban heat island effect and the built-up area around the station have been improved by relocation, with increases in the area of water bodies and vegetation, and there have been few in-depth analyses of the critical factors affecting improvements in the observational environment after relocation [43–46], especially for anthropogenic heat environment. The relocation of stations is a complex project and needs to be coordinated to solve the contradictions between the representativeness of the meteorological observations and urban planning, economic development and the protection of farm and forest lands [47]. By comparing meteorological observational data before and after relocation, we have shown that changes in the observational environment will affect observational data, and even the inhomogeneity of its time series [2–47]. For instance, we selected four non-relocation stations (SZ and FN in the northern YRD; DZ and NG in the southern YRD), which can represent their background climate conditions, to match the four relocated stations (BB and SY in the northern YRD; AQ and TH in the southern YRD) during 2000–2018. These eight stations were divided in to four groups under similar climate zones, and their time series of annual temperatures and their differences were shown in Figure 12. During 2000–2018, generally, annual temperatures at all non-relocation stations exhibited increasing trends (0.13~0.36 ◦C/10a), while decreasing trends (−0.57~−0.05 ◦C/10a) for all relocated stations (Figure 12). Their annual temperature differences for each group showed sudden decreases when station-relocation occurred (Figure 12), corresponding well to the changes in AHF and PBA (Figure9). In contrast, through the mutation detection of the meteorological data of the relocated stations and the reference stations after relocations, the meteorological data of the relocated station was generally consistent with the trend of the data of all stations in the entire research area in same period (Figure 12), which shows that the stations after the relocation still had good regional representatives that can be used to analyze the situation of the entire study area. Therefore, the relocation of stations can reduce the impact of the urban heat island and dry island effects on meteorological observations, leading to observational data more accurately represents the local meteorological conditions. Our research evaluated the effect of the relocation of meteorological stations on improvements in the anthropogenic heat environment around the station and the impact on temperature observations, which showed clear seasonal differences [17,19,25], and can also explain discontinuities of time series of observations from the relocated stations with respect to the reference stations. Thus, it is the strong evidence that changes in the AHF affect meteorological elements [48–51]. Remote Sens. 2021, 13, 2624 14 of 17 Remote Sens. 2021, 13, x FOR PEER REVIEW 14 of 17

Figure 12. TimeFigure series 12. Time of annual series temperatures of annual temperatures and their differences and their for differences four group for stations four group in the stat YRDions during in the 2000–2018. (a) BB and SZ,YRD (b )during SY and 200 FN0— in2018 the. northern (a) BB and YRD; SZ, ((cb)) AQ SY and FN NG, in (d the) TH northern and DZ YRD; in the (c southern) AQ and YRD.NG, (d) TH and DZ in the southern YRD. Our work provides a scientiﬁc reference for the development of future station reloca- Our worktion provides plans. a Follow-up scientific reference work will for further the development classify relocation of future into station various relo- types according to cation plans. Followthe built-up-up work area will and further the AHF classify level relocation around the into station. various For types example, according light detection and to the built-upranging area and (LIDAR) the AHF allows level for around extracting the station. really accurate For example, 3D data light on urbanization detection in the study and ranging (LIDARareas,) with allow greats for advantages extracting really in a very accurate dynamic 3D data environment on urbanization with fast in changesthe related to study areas, withrapid great urban advantages sprawl, especiallyin a very dynamic for obtaining environment the accurate with thefast classiﬁcation changes re- of LULC [52]. lated to rapid Therefore,urban sprawl, LIDAR especially techniques for shouldobtaining be applied the accurate for investigating the classificatio the impactsn of of urbaniza- LULC [52]. Therefore,tion on observationalLIDAR techniques environmental should be of applied meteorological for investigating stations inthe the impacts future. Additionally, of urbanizationrelevant on observational numerical environmental models will be of established meteorological to optimize stations the in rationality the future. of the layout of Additionally, relevantthe stations numerical based onmodels the morphology will be established of the urban to optimize sprawl, the the rationality local background of climate the layout of theconditions stations andbased the on station the morphology location. This of isthe essential urban sprawl, to resolve the the local contradictions back- between ground climateurban conditions sprawl and and the the station representation location. ofThis meteorological is essential to observations. resolve the contradictions between urban sprawl and the representation of meteorological observations. 5. Conclusions 5. Conclusions We comprehensively investigated the impact of rapid urban sprawl on the observa- We comprehensivelytional environment investigated around the meteorologicalimpact of rapid stationsurban sprawl in the on YRD the duringobserva- the time period 2000–2018 using remote sensing and GIS technology. We focused on the effects of the tional environment around meteorological stations in the YRD during the time period changes on both the built-up area and the AHF.

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Rapid changes in urban sprawl can lead to significant changes in both the land use/land cover and the AHF around meteorological stations. A positive correlation was found between the PBA and the AHF and this correlation was in the order urban stations > suburban stations > rural stations. The impact of the AHF on the local observational environment around meteorological stations showed a clear spatiotemporal variability. The level of urbanization around some meteorological stations has increased significantly with the rapid development of urbanization in the YRD, leading to a continuous increase in the AHF. For instance, the spatial average AHF of all the meteorological stations in the YRD increased from a value of 5.45 W m−2 in 2000 to 14.74 W m−2 in 2018. The AHF around the meteorological stations within large cities was higher than that at meteorological stations outside large cities, although the differences in the AHF among these stations gradually decreased with the development of urbanization in the YRD. Some meteorological stations need to be relocated to cope with the adverse effects induced by urbanization. After relocation, both the PBA and AHF around the new stations decreased significantly, weakening the urban heat island and urban dry island effects in the meteorological observations and substantially improving the observational environment, which had a huge difference from the stations without relocation. As a result, the observed daily mean temperature (relative humidity) decreased (increased) around the new stations after relocation. The improvement in the observational environment resulting from the relocation of the stations is closely related to the distance between the new station and the city, the rate of urban sprawl and the background conditions of the local geography and climate. As a consequence, the improvement in the observational environment resulting from relocation of the stations clearly varied in different regions of the YRD, all of them can contextualize in a more general trend in the whole YRD region, but also have own uniqueness from the stations without relocation. Many developing counties are undergoing similar rapid urbanization to China. The findings reported here are important in understanding the changes in the AHF induced by the effects of urbanization on the observational environment around meteorological stations in other regions of China. Our findings provide scientific insights for the selection and construction of networks of meteorological stations and thus are helpful in scientifically evaluating and correcting the impact of rapid urban sprawl on meteorological observations.

Author Contributions: Conceptualization, Y.Y.; methodology, Y.Z. and Y.Y.; software, Y.Z.; validation, G.N. and Y.Y.; formal analysis, Y.Z., G.N. S.C., and Y.Y.; writing—original draft preparation, Y.Z. and G.N; writing—review and editing, G.N., Y.Z. and Y.Y.; visualization, Y.Z. and S.C.; supervision, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript. Funding: This study was jointly supported by the National Key Research and Development Pro- gram of China (2018YFC1506502), the NSFC-DFG (42061134009), open funding of Guangdong Key Laboratory of Ocean Remote Sensing (2017B030301005-LORS2001), and open funding of the State Key Laboratory of Loess and Quaternary Geology (SKLLQG2010). Institutional Review Board Statement: “Not applicable” for studies not involving human. Data Availability Statement: Urban impervious surface area datasets (https://zenodo.org/record/ 4034161#.YJleSofitPZ (accessed on 10 April 2021)) were used to retrieve built-up area, nighttime light satellite datasets (http://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html (accessed on 10 April 2021)) were used to retrieve anthropogenic heat flux (AHF), surface meteorological observations were collected from the China Meteorological Data Service Center (http://data.cma.cn/en(accessed on 10 April 2021)). Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2021, 13, 2624 16 of 17

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