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Int J Appl Earth Obs Geoinformation

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Elevation-dependent warming of land surface temperatures in the Andesassessed using MODIS LST time series (2000–2017)

Jaime Aguilar-Lomea,⁎, Raúl Espinoza-Villara,b,c, Jhan-Carlo Espinozab,d, Joel Rojas-Acuñaa,Bram Leo Willemsc, Walter-Martín Leyva-Molinaa,c

a Laboratorio de Teledetección, Facultad de Ciencias Físicas, Universidad Nacional Mayor de San Marcos, Lima, Perub Instituto Geofísico del Perú (IGP), Lima, Peruc Centro de Competencias del Agua (CCA), Lima, PerudUniv. Grenoble Alpes, IRD, CNRS, Grenoble INP, Institut des Géosciences de l'Environnement (IGE, UMR 5001), 38000 Grenoble, France

A R T I C L E I N F O

Keywords:Tropical AndesLand surface warmingRemote sensingHigh mountains

A B S T R A C T

In this study, we report on the assessment of elevation-dependent warming processes in the Andean regionbetween 7 °S and 20 °S, using Land Surface Temperature (LST). Remotely sensed LST data were obtained fromModerate Resolution Imaging Spectroradiometer (MODIS) sensor in an 8-day composite, at a 1 km resolution,and from 2000 to 2017 during austral winter (June-July-August, JJA). We analysed the relation between meanmonthly daytime LST and mean monthly maximum air temperature. This relation is analysed for different typesof coverage, obtaining a significant correlation that varies from 0.57 to 0.82 (p < 0.01). However, effects ofchange in land cover were ruled out by a previous comparative assessment of trends in daytime LST and nor-malized difference vegetation index (NDVI). The distribution of the winter daytime LST trend was found to beincreasing in most areas, while decreasing in only a few areas. This trend shows that winter daytime LST isincreasing at an average rate of 1.0 °C/decade. We also found that the winter daytime LST trend has a cleardependence on elevation, with strongest warming effects at higher elevations: 0.50 °C/decade at1000–1500masl, and 1.7 °C/decade above 5000masl. However, the winter nighttime LST trend shows a steadyincrease with altitude increase. The dependence of rising temperature trends on elevation could have severeimplications for water resources and high Andean ecosystems.

1. Introduction

An upward trend in air temperature over high-elevation regions hasattracted much attention within the scientific community in recentyears (Pepin and Lundquist, 2008; Fan et al., 2015; Vuille et al., 2015).The Andes are the most important mountain range in the SouthernHemisphere, with a maximum elevation of more than 6000 masl intropical and subtropical sections. The Andes represent, therefore, aformidable obstacle to the tropospheric flow promoting tropical-extra-tropical flow interactions, especially along their eastern flank(Garreaud, 2009; Garreaud et al., 2009).

The limited availability of weather station data reveals severalpatterns of climate change in the Tropical Andes during the twentiethcentury. At the regional scale, substantial evidence is reported for thewarming of air temperature by 0.01 °C/year between 1939 and 2006(Vuille et al., 2008). This heating rate is similar to rates recorded inearlier studies, 0.010-0.011 °C/year for 1939–1998 and 0.015 °C/year

for 1950–1994 (Vuille and Bradley, 2000; Vuille et al., 2003). In theAmazonian-Andean basin of Peru, Lavado et al. (2013) showed anupward trend in the average air temperature of 0.009 °C/year for theperiod 1965-2007. Huayao station (in Huancayo, Peru) has one of thelongest records of maximum air temperature (1921-present) in theCentral Peruvian Andes. The trend analysis of the referred variablerecorded at this station showed an increase of 0.024 °C/year between1950 and 2002 (IGP, 2005b) and of 0.03 °C/year between 1965 and2006 (SENAMHI, 2011).

Analysis of temperature as a function of elevation in the tropicalAndes shows continuous warming at higher elevations during theperiod 1950–2010 (Vuille et al., 2015). These changes in air tempera-ture could significantly modify the hydrological cycle in mountains(Nijssen et al., 2001). Thus, in the tropical Andes, a significant decreasein glacier mass balance has been reported and many glaciers coulddisappear in the coming decades, especially those below 5400 masl(Rabatel et al., 2013; Yarleque et al., 2018). Glacier retreat in the Andes

https://doi.org/10.1016/j.jag.2018.12.013Received 16 April 2018; Received in revised form 22 December 2018; Accepted 28 December 2018

⁎ Corresponding author.E-mail address: jaguilarlome@gmail.com (J. Aguilar-Lome).

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is more evident during El Niño years, which induces higher air tem-peratures than normal (Rabatel et al., 2013). Nevertheless, apart fromair temperature, the relationship between local/regional climate andglacier retreat responds to many other factors (Sicart et al., 2008,2015). Rangwala and Miller (2012) discussed mechanisms which in-crease air temperature at high elevations; these include albedo-snowfeedback, cloud feedback, water vapor feedback and aerosol feedback.As for the extent to which these mechanisms may be relevant to theAndean region in terms of future availability of water resources is notyet fully known (Vuille et al., 2015; Buytaert et al., 2017; Vuille et al.,2018).

Land Surface Temperature (LST) is an essential parameter of phy-sical processes occurring on the Earth's surface at global and regionalscales. Variations in LST are a response to several surface-atmosphereinteractions, which includes energy flows between the atmosphere andthe surface of the Earth (Wan and Dozier, 1996). LST provides in-formation about temporal and spatial variations in the balance oftemperature of the soil surface and is important for environmentalstudies and management of water resources (Li et al., 2013). Due to itsimportance, the Intergovernmental Panel on Climate Change (IPCC)recommended including long-term LST data, based on satellite remotesensing, in global warming studies. This would help to overcome lim-itations typically found in conventional air temperature recorded atweather stations (Houghton et al., 2001) in a time period restricted tothe satellite era. Quin et al. (2009) analysed warming as a function ofthe elevation using nighttime LST data from images gallery Terra/MODIS on the Tibet Plateau (China) for the period of 2000-2006. Thisstudy showed a rise in the rate of temperature at 3000 to 4800 masl.Salama et al. (2012) analysed LST anomalies trend, retrieved from theSSM/I (Special Microwave Imager) sensor on the Tibet Plateau for theperiod 1987-2008. This trend analysis showed that the monthly andannual standardized anomalies are changing at a rate of 0.05 °C/year.Recently, Mao et al. (2017) reported that during the period 2001–2012,the LST is increasing slightly in the Southern Hemisphere, although thisincrease is insignificant.

MODIS LST data has been frequently used to estimate air tem-perature (Vancutsem et al., 2010; Zhang et al., 2016; Yang et al., 2017).Shen and Leptoukh (2011) indicated that LST and air temperature havea close connection due to heat exchange between land and air.Mildrexler et al. (2011) found a strong positive correlation between theannual maximum LST from Aqua/MODIS and annual maximum airtemperature globally. Further, they indicated that in barren areas likeshrublands, grasslands, savannas and croplands the annual maximumLST values range from 10°C to 20°C, which is hotter than the corre-sponding annual maximum air temperature at higher elevations.

Regarding the MODIS LST and the air temperature relationship insnow-capped sub-Artic mountains, Williamson et al. (2017) found thatthe LST average is 5–7 °C, which is colder than both the downscaled andMODIS temperature products for grid cells with> 90% snow cover.Although LST and air temperature are strongly correlated (Benali et al.,2012). Pepin et al. (2016) showed in their findings that LST and airtemperature require a careful comparison before LST can be used as anapproximation of air temperature in mountainous terrain.

MODIS products have been used in other research studies in theAndean region. For example, Delbart et al. (2015) found that the de-creasing trend in snow cover during winter, for the period 2001–2014,clearly explains the observed decreasing trend in the annual waterdischarge from four rivers (Mendoza, Tunuyán, Diamante, Atuel) in theArgentinian Cuyo region. In mountain regions between Chile and Ar-gentina, Cornwell et al. (2016) related the melt-season fluvial flowswith the snow water equivalent and obtained an R2 value of 0.80. Thelatter was estimated by a combination of instrumental records in re-motely sensed snow-covered areas and a snow energy balance model.More recently, Saavedra et al. (2018) found that a large area (34370 km2) of permanent snow coverage, between 29 °S and 36 °S, ex-perienced a significant snow cover loss (2–5 less snowfall days per

year). This decrease in snow cover is correlated with a decrease inprecipitation and an increase in temperature; the correlation valuesvary according to latitude and elevation. Thus, it is possible to assessthe effect of climate change on snow cover by using data processingfrom a weather station and remote sensors.

In this study, we analysed austral winter data (June-July-August,JJA) in the Peruvian Andes because this season is characterized by thehighest incidence of radiative frost across the region, associated withlow cloud cover, low atmospheric humidity and low soil moisture(Saavedra and Takahashi, 2017; IGP, 2005a); whereas in other seasonsthe presence of clouds in the region generates numerous missing andabnormal values that affect the application of MODIS LST data (seeFigure A.1 in the supplementary figures).

Given these findings, and that little is known about LST trends in thetropical Andes, this paper aims to: i) assess the relationship betweenmean monthly LST and mean monthly maximum air temperature perland cover type, during the period 2000–2015 (JJA), ii) assess theaustral winter (JJA) trends of daytime LST in the tropical Andes duringthe period 2000–2017, and iii) analyse the relationship between theLST trend and elevation, using winter daytime and nighttime LST data.

2. Materials and methods

2.1. Study area

The study area is the tropical Andean region between 7 °S and 20 °S,above 1000 masl (Fig. 1). This region includes the South AmericanAltiplano (15° and 21 °S) and Lake Titicaca, which is on this plain.Between 3000 and 3500masl in the study area, there is a biome zonecalled Puna, which is distinguished by a thermal and latitudinal limitfrom other biomes such as Páramo and Jalca (Cuesta et al., 2009; Josseet al., 2009). The dry highlands in the southern part of Peru and Boliviaare highly seasonal, with their hydrological behavior controlled byrainfall variability (Garreaud, 2009; Ochoa-Tocachi et al., 2016). Sev-eral studies have shown that there is no clear relationship betweenrainfall amount and altitude (Espinoza et al., 2009; Lavado et al.,2013). In addition, extreme precipitation events are observed on theeastern side of the Andes Mountain range, where precipitation rangesfrom 250 to 6000mm/year within a radius of a few kilometers(Espinoza et al., 2015; Chavez and Takahashi, 2017; Junquas et al.,2018).

The tropical Andes are characterized by a complex topography,diverse climatic conditions and extraordinary biodiversity in theirecosystems. In addition, they are constituted by three biogeographicalzones (Josse et al., 2009):

1) Humid Puna, is distributed from northern Peru to the central-easternside of the Andes mountains in Bolivia, and includes the LakeTiticaca basin. This biogeographic zone covers a wide altitudinalrange, from 2000 masl in the inter-Andean valleys to more than6000 masl at the highest peaks in the snow-capped mountains. Asfor the climate, there is a clear separation between the warm rainyseason and the coldest and driest season. Anthropogenic activitieshave reduced the forested areas of Queñoa (Polylepis Spp), whichhave been replaced by shrubland and bushes in large areas.

2) Xerophytic Puna, is distributed mainly in the central-south of wes-tern Bolivia and northwest of Argentina. This area extends from2000 masl in the high valleys in the east to more than 6000 masl inthe snowy mountains and volcanoes in the western side of theAndean mountain range, and includes the vast high plain -Altiplano-which is located in the widest part of the Andes mountain range. Theclimate in the Xerophytic Puna is tropical and markedly seasonal,with extreme arid conditions in the dry season, which is noticeablyaccentuated towards the south and the west, where the most ex-tensive high mountain saline ecosystems of the Earth are found.

3) Yungas, extend from the north of Peru to the centre of Bolivia,

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passing through the mountainous slopes of the eastern watersheds inthe tropical Andes, between the Humid Puna (in the West) and theAmazon lowland (in the East). This zone covers altitudes from500masl to more than 4000 masl. The vegetation is determined bythe incidence of zonal trade winds coming from the Atlantic Ocean,which push against the great mountain barrier to generate almostconstant cloudiness and fog through the process of convection,which in turn releases heavy rainfall throughout most of the year.

2.2. Data

2.2.1. Land surface temperature (LST)MODIS LST product (MOD11A2, version 006) obtained by Terra

(EOS) was downloaded from the USGS (United States GeologicalSurvey) database. The MOD11A2 product is an 8-day compound ofdaily LST values (MOD11A1) at a spatial resolution of 1 km. This pro-duct includes daytime and nighttime LST data, period of observation,zenith angle, quality reporting, amongst other layers (Wan et al., 2002;Wan, 2008). From view time layers (day and night), we obtain that thetime of daytime and nighttime LST observation from Terra/MODIS onthe station Huayao (Peru) (12.047 °S, 75.320 °W) are between 09:54-10:54 and between 22:12-23:06 (local solar time), respectively. Thesehave a sun-synchronous, and near-polar orbit so they can travel fromthe North Pole to the South Pole on the sunlit side as the Earth rotatesbelow. As a result, they pass over Earth at approximately the same localtime each day to ensure comparable daylight conditions during a day(Mao et al., 2017).

The first daily MODIS LST (MOD11_L2) product is calculated fromthe brightness temperature using the generalized split window algo-rithm proposed by Wan and Dozier (1996) only in cloud-free condi-tions.

The first daily MODIS LST (MOD11_L2) product is calculated fromthe brightness temperature using the generalized split window algo-rithm proposed by Wan and Dozier (1996) only for cloud-free condi-tions.

= + ⎛⎝

+ − + ⎞⎠

+

+ ⎛⎝

+ − + ⎞⎠

−

T C B B εε

B Δεε

T T

A A εε

A εε

T T

12

1 Δ2

,

s 1 2 3 231 32

1 2 3 231 32

(1)

= +ε ε ε0.5( )31 32

= −Δε ε ε( )31 32

Where T31 and T32, are the brightness temperatures in the strips 31 and32; ε32 and ε32 are the emissivities of land surface in these two bands,which were estimated using the method based on emissivity classifi-cation processes (Snyder and Wan, 1998; Snyder et al., 1998) accordingto land cover type derived from the ground cover product (MOD12Q1)and the snow cover product (MOD10L2); the coefficients Ai ( =i 1,2, 3),Bi ( =i 1,2, 3) and C are determined by interpolation of the multi-dimensional dataset into query tables.

Validation of MODIS LST daily products (V005) with 47 in-situmeasurements under clear skies (the LST value range being −10 °C to58 °C and 0.4 cm–3.5 cm for the water vapour column) shows greater

Fig. 1. Location map of the study area using the digital elevation model GTOPO30 (elevation≥ 1000 masl). Elevation ranges are shown in the legend; black trianglesindicate the location of weather stations.

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accuracy at 1 K in the majority of cases (at least 39 out of 47), and theroot mean square deviation (RMSD) is less than 0.7 K for all cases (Wan,2008).

2.2.2. Normalized difference vegetation index (NDVI)The MODIS product MOD13A3 (version 006) NDVI data for the

period January 2001 - December 2017 was obtained from the USGSdatabase and used to calculate dynamic vegetation activity. The NDVIdata have spatial resolutions of 1 km and a monthly temporal resolu-tion. The NDVI is calculated with the distribution function, using at-mospheric correction of the surface reflectance (bidirectional re-flectance distribution function - BRDF) masked by water, clouds, heavyaerosol and cloud shadows (Vermote and Vermeulen, 1999; Gao et al.,2002). MODIS Vegetation Index product (VI) was obtained using so-phisticated methods to reduce atmospheric effects, clouds and ob-servation angle variations (Huete et al., 2002). Besides the vegetationindex, the MOD13A3 product contains a layer of pixel information onreliability which describes the NDVI quality values per pixel. This in-formation on reliability is a decimal number that classifies the pixel intofive categories: -1, fill/no data; 0, good data; 1, marginal data; 2, snow/ice; and 3, cloud (Solano et al., 2010).

2.2.3. Digital elevation model (DEM)The GTOPO30 (Global Topographic Data) digital elevation model

was developed over three years by USGS, finalized in the late 1996.GTOPO30 was obtained from various topographic information sources,including vectors and DEM images. Grid spacing is 30 arc seconds(0.008333333 ° or 1 km) (EROS Data Center, 1996).

2.2.4. Air temperature dataA mean monthly maximum air temperature (Tmax) dataset was

provided by the National Meteorology and Hydrology Service of Peru(SENAMHI-Peru). The average monthly maximum air temperature isthe mean of daily maximum air temperatures in any given month. Thisdata was collected from 2000 to 2015 during the austral winter (JJA)from 20 weather stations distributed above 1000 masl. Fig. 1 shows thelocations of these weather stations, and Table 1 lists the name, geo-graphic coordinates and elevation for each station.

2.3. Methodology

The quality of the LST data is defined based on the layer of

reliability (QC_day), it is an average error smaller than or equal to 0.01or emissivity smaller than or equal to 1 K for LST (Wan et al., 2004). Ifthis degree of accuracy is not achieved for the estimated values of LSTand emissivity, the pixel is classified by another quality indicator anderror estimator which is provided by 4–7 bits of QC_day code (Wan,2007). Only the best quality pixels, having an error smaller than orequal to 2 K in the calculated LST, have been used for this study.

Once these corrections are made, the average monthly LST is cal-culated as the sum of all 8-day products, provided for a particularmonth and divided by all nonzero occurrences. IFOV (InstantaneousField of View) data in degrees Kelvin (K) was transformed to degreesCelsius (°C).

To evaluate the accuracy of LST data, the monthly average of day-time MODIS LST data was selected for 20 areas of 3 km by 3 km, eachwith a weather station; this follows the methodology developed byFrançois et al. (1999). The relationship between monthly daytimeMODIS LST data and mean monthly maximum air temperature for theperiod of 2000–2015 (JJA) per each land cover type was evaluatedusing a linear regression. The Pearson correlation coefficient was usedas a goodness-of-fit test.

Trends for daytime and nighttime winter MODIS LST were calcu-lated using Sen's method (Sen, 1968) for each pixel, with the followingequation:

⎜ ⎟= ⎛⎝

−−

⎞⎠

∀ >β Medianx xj k

j k,j k

(2)

Where xj and xk represent data values at times j and k, respectively, andβ is the time series slope (trend). >β 0 indicates a positive trend, while

<β 0 indicates a negative trend. This non-parametric method does notrequire data to be normally distributed and is not sensitive to outliers.Mann-Kendall's test was used to evaluate the statistical significance ofthe trends. This test has been widely applied in studies on hydro-me-teorological trends and other environmental time series (Yu et al.,2002; Al Buhairi, 2010; Chattopadhyay and Edwards, 2016).

Human-induced land use and land cover changes are an importantdriver for the rising trend of LST (Jiang and Tian, 2010). In the plateauregion of north-central Nigeria, urbanization and agricultural activities,including animal grazing, were responsible for the gradual loss of ve-getation cover and rising average LST (Odunuga and Badru, 2015). Xuet al. (2013) indicates that the contribution of impervious surface toregional LST change can be up to six times higher than the sum of thecontributions of vegetation and water. van Leeuwen et al. (2011) found

Table 1Weather stations details.

Station Latitude Longitude Elevation(m)

Pixel elevation (m) Land cover types

Paucaray −14.050 −73.634 3206 3206 CroplandCurahuasi −13.552 −72.735 2879 2879 ShrublandUrubamba −13.3106 −72.124 3863 3041 ShrublandYauri −14.817 −71.417 3925 3925 Grassland/shrublandUbinas −16.382 −70.857 3551 3551 ShrublandAyaviri −14.873 −70.593 3943 3943 Urban areasImata −15.836 −71.088 4519 4449 Sparse vegetation/shrublandPuno −15.826 −70.012 3812 3847 Urban areasHuancane −15.201 −69.754 3890 3898 Grassland or savannasPampahuta −15.484 −70.676 4400 4368 Grassland or savannasMañazo −14.8 −70.067 3920 4084 GrasslandMoquegua −17.169 −70.932 1450 1456 Forest/shrublandChuapalca −17.305 −69.644 4177 4220 Sparse vegetationHuayao −12.038 −75.338 3360 3371 Vegetation/croplandMatucana −11.839 −76.378 2348 2699 ShrublandHuamachuco −7.819 −78.040 3200 3254 Cropland/grasslandAndahuaylas −13.649 −73.367 2865 3137 Forest or shrubland/grasslandCajabamba −7.622 −78.051 2480 2631 Forest or shrublandHuánuco −9.952 −76.249 1947 1988 Forest or shrubland/grasslandLa Angostura −15.183 −71.635 4265 4265 Grassland/shrubland

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that changes in day‐night LSTs were highly correlated to the spatialpattern of deforestation in different states of Brazil in the BrazilianAmazon.

The Normalized Difference Vegetation Index (NDVI) is an indicatorof vegetation health (Kinyanjui, 2011), and has been widely used togive an idea of vegetation cover status (Horion et al., 2013). We usedNDVI to detect land cover changes. In order to eliminate any abnormalremnants of NDVI values in monthly images, these time series werefiltered using spatial-temporal iteration methods and the Savitzky-Golay filter to replace the pixels eliminated by quality bands (Chenet al., 2014; Lili et al., 2015). The winter trend of NDVI was then cal-culated for each pixel using Sen's method (Sen, 1968) for the period2000 - 2017. The NDVI trends provide information about changes inland cover during the study period (Hutchinson et al., 2015; Xu et al.,2016), which are explained by an abrupt increase or decrease in thedaytime LST trend. In the case of the winter daytime LST trend, onlyareas where the NDVI trend did not change significantly were analysed.GLOBCOVER land cover product from the European Space Agency(ESA) (Bontemps et al., 2011) was used to identify land cover typesaround weather stations, and to exclude water bodies and permanentsnow.

3. Results

3.1. Relationship between daytime LST and maximum air temperature

The relationship between mean monthly daytime LST (LSTd) andmean monthly maximum air temperature (Tmax) showed statisticallysignificant linear correlations ( ≤ ≤ <R p0.57 0.82, 0.01) (Fig. 2a-d).The highest value of R corresponds to shrubland and the lowest value tograsslands. The relationship LSTd/Tmax is closely associated with eachland cover type. The relationship LSTd/Tmax for grassland in all sta-tions located below 4265masl (black dots in Fig. 2c), and in stationslocated above 4400 masl (gray dots in Fig. 2c) are illustrated in scat-terplots; the former is shifted toward a higher temperature distribution,compared to the latter.

For grassland, the value of the slope is relatively low for stationslocated above 4400 masl (grey dots in Fig. 2c), while in shrubland thevalue is higher (Fig. 2a). The average of the differences (LSTd-Tmax)varies from 3.7 °C in shrubland areas (Fig. 2a) to 14.8 °C in grasslandareas (grey dots in Fig. 2c). The LSTd/Tmax relationship for urban areas(Fig. 2d) shows that Tmax is gradually increased from 12.4 °C to19.4 °C. The weather station is located in urban areas between 3812 and3927 masl and the average Tmax recorded is 16.1 °C (Fig. 2d).

3.2. LST trend

Fig. 3a shows the trends of winter daytime LST in the TropicalAndean region. In Fig. 3a, grey areas within the study region representnon-significant trend values (p > 0.05), or areas that were dis-criminated because of land cover change through the NDVI trend. Themajority of areas show an increase in the daytime LST during winter,with highest values in the northern part (˜12 °S).

A negative LST trend is observed between zone boundaries with aninfluence of coastal cloudiness at about 1200–1500masl. Thus, an aridzone of variable extent is developed under the influence of the PacificAnticyclone and large-scale atmospheric subsidence (Ubeda andEstremera, 2013). Moreover, a negative LST trend occurred in thewestern Andes and in the south-eastern part of the study area during theperiod of study. The LST trends in Fig. 3a increase by a rate of 0.11 °Cper year, considering the whole of the studied area. This positive trendgradually increases to reach the highest regional value of 0.3 °C/year,for instance at high elevations of section A-A’ in the central PeruvianAndes (Fig. 3b). Over this region, there is a warming trend that reachesa peak rate of 0.29 °C per year on the eastern side of the Andes range.Fig. 3a also shows a significant downward trend west of the Andesbetween 1000 masl to 2000 masl, with an average rate of -0.12 °C/year.On the other hand, the cross-section over the Peruvian and BolivianAltiplano shows a significant warming trend in areas above 3000 masl.This trend varies between 0.07 °C/year and 0.21 °C/year (Fig. 3c).

Mean monthly maximum air temperature for winter season duringthe period 2000–2015 was used to verify the LST trend. Maximum airtemperature during winter (average across all the 20 stations located inthe Peruvian Andes) is increasing at a rate of 0.10 °C/year (p≤ 0.01),which is consistent with the results of previous studies (López-Morenoet al., 2016; Vicente-Serrano et al., 2018). However, LTS trends in thetropical Andes have not been provided before.

To summarize the average trend of warming as a function of ele-vation, we show the average for the winter daytime LST trends for theperiod 2000–2017, at intervals of 500m (Fig. 4). The LST trend in-creases at 1000–5000masl, indicating that areas at higher-elevationsexhibit higher rates of increasing LST (up to 0.18 °C/year). Fig. 5 showsthe average for the winter nighttime LST trends for the period of2000–2017, with an interval of 500m at 1000 masl. Although a slightreduction in the increasing trend was observed at 3500–4000 masl, thewarming trend increases slightly at higher elevations, particularlyabove 4000 masl.

Fig. 2. Observed LSTd/Tmax scatterplot for (a)shrubland, (b) cropland, (c) grassland for stations lo-cated below 4265masl (black dots) and above 4400masl (grey dots) and (d) urban areas. R values andlinear equations are indicated. (For interpretation ofthe references to colour in this figure legend, thereader is referred to the web version of this article).

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4. Discussion

4.1. Relationship between daytime LST and maximum air temperature

The month-to-month comparison of the relationship between themaximum air temperature and the corresponding daytime LST dataindicates that correlations vary from one land cover type to another.These correlations are significant and admissible for comparative pur-poses. Differences between LST data and maximum air temperatureamong weather stations may be attributed to:

4.1.1. Different physical parametersAir temperature is measured with a thermometer exposed to air at

1.25–2m, protected from direct solar radiation (OMM, 2008). WhereasLST is the temperature measured at surface level (Dash et al., 2001).LST is defined by the radiation emitted by the land surface observed bythe satellite sensors at the instantaneous viewing angle (Becker and Li,1995; Norman and Becker, 1995; Wan et al., 2004). LSTd tends to begreater than Tmax and the difference increases with rising temperature(Fig. 2a-d). During the same period of study, Tmax stayed below 30 °Cwhereas LSTd exceeded 30 °C. The LST can be 20 °C warmer than thecorresponding Tmax in bare soil with sparse vegetation. This variationof LSTd in relation to Tmax shows that LST and air temperature havedifferent physical meanings, magnitudes, responses to changing atmo-spheric conditions and diurnal cycle (Jin and Dickinson, 2010).Mildrexler et al. (2011) indicated that the large difference between

Fig. 3. (a) Spatial distribution of the daytime LST (°C/year) trend in the winter quarterly average for the period 2000–2017, with 95% (p≤ 0.05) significanceobtained using Sen’s method. Areas with land-cover change have been removed. In (b) A-A' and (c) B-B', two transects of temperature variation (points) and altitude(lines) are shown graphically.

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LSTmax (annual maximum LST) and Tamax (annual maximum airtemperature) at higher temperatures captures the important distinctionbetween the radiative measurement, registered on the Earth’s surfacewhere thermal energy is more concentrated, and the air temperature,measured in a shelter at 1.5m above the ground.

4.1.2. Differential spatial integrationThe meteorological information represents a punctual value of 10-

100m2 (François et al., 1999), and the MODIS LST information is theproduct which integrates data for every 1 km2 into a unique valuewhere each pixel may well contain several different land types. Eachland cover type has distinct interactions with the atmosphere which

may result in different local weather conditions (Mildrexler et al.,2011). The shrubland land cover type considered includes weatherstations located within the forest biome where the LSTd/Tmax re-lationship is near 1:1 (Fig. 2a). The leaves of plants actively exchangeabsorbed solar radiation through evaporation. Therefore, during day-light hours, leaves maintain a temperature close to air temperature(Nemani et al., 1993). Mildrexler et al. (2011) found that the daytimeLST data from Aqua was highly correlated (R=0.92) with the max-imum air temperature in shrubland areas globally, and Shen andLeptoukh (2011) estimated maximum air temperature using daytimeLST data from Terra finding a mean absolute error (MAE) of 2.4 °C forshrubland areas in central and eastern Eurasia.

The LSTd/Tmax scatterplot shows that grassland areas presentwarmer temperatures compared to shrubland areas. For land withscarce vegetation, IFOV of the remote sensing devices include bothcanopy and soil background elements. Consequently, the effectivesurface temperature is dependent on the relative proportions capturedwithin the IFOV of the sensor for soil versus canopy cover (Friedl andDavis, 1994). Combined, these factors result in the potential for a largeapportionment of incoming solar radiation into sensible heat, resultingin a high Bowen ratio (ratio of sensible heat flux to latent heat flux) andhigh daytime LST values (Mildrexler et al., 2011). However, Paridaet al. (2008) indicated that among the different vegetation types,agriculture practiced in desert areas showed the highest surface tem-perature, followed by rainfed agriculture, irrigated agriculture andforest agriculture in the Gurajat state of India, with lower total rainfall(June-October). Several studies have shown that daytime LST data andmaximum air temperature are correlated (Mildrexler et al., 2011; Shenand Leptoukh, 2011; Yang et al., 2017). In addition to land covercharacteristics (Schwarz et al., 2012; Maeda and Hurskainen, 2014),there are many factors that can influence the complex relationshipbetween daytime LST and maximum air temperature such as solar ra-diation, cloud cover, soil moisture and surface roughness (Vancutsemet al., 2010; Kloog et al., 2014; Xu et al., 2014). However, Zhang et al.(2016) indicated that cloud cover mainly affects the fundamental re-lationship between daytime LST and maximum air temperature, andWilliamson et al. (2014) concluded that MODIS LST data may changeits relationship with air temperature depending on the period of ag-gregation, since it is often aggregated into multi-day composites tomitigate data reductions caused by cloud cover.

4.2. LST trend

The trends for winter daytime LST are consistent with the resultspresented by Vicente-Serrano et al. (2018), who showed that theaverage maximum air temperature in Peru has a positive trend duringall months of the year, but becomes more pronounced during the wintermonths (JJA). López-Moreno et al. (2016) reported changes in max-imum air temperature during the cold season (JJA), with more het-erogeneous trends; while statistically significant trends were foundmostly for the Bolivian Altiplano, during the period 1965-2012. Like-wise, Vuille et al. (2015) showed that the air temperature trend hasgradually increased between 1950 and 2010.

The winter nighttime LST trend has a more homogeneous magni-tude of change than the winter daytime LST trend. According toVicente-Serrano et al. (2018), in Peru, minimum air temperatureshowed positive trends for every month of the year; nevertheless, thesetrends did not show an increase as a function of elevation.

Falvey and Garreaud (2009) identified that temperatures on thecoastal side of the Andes were decreasing at a rate of -0.02 °C/year forthe period of 1979-2006. This cooling effect on the coastal side hasbeen attributed to a shift towards a negative phase of the InterdecadalPacific Oscillation (IPO) (Vuille et al., 2015). However, in the sub-tropical Andes it continues to experience positive temperature trendswith respect to elevation (Vuille et al., 2015; Vicente-Serrano et al.,2018). This is most likely due to anthropogenic warming, as suggested

Fig. 4. Averages of winter daytime LST trends for all study areas, at 500-mintervals, in the tropical Andes (7 °S and 20 °S). The values at the top of bars arethe numbers of MODIS pixels at each elevation range. The error bars representthe standard deviation of LST trend for each range of 500-m, mutiplied by 0.1 tobe visible.

Fig. 5. Average of winter nighttime LST trends for all study areas, at 500-mintervals, in the tropical Andes (7 °S and 20 °S). The values at the top of bars arethe numbers of MODIS pixels at each elevation range. The error bars representthe standard deviation of LST trend for each range of 500-m, multiplied by 0.1to be visible.

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by Vuille et al. (2015).Giorgi et al. (1997) found a similar altitude-related warming effect

for the Swiss Alps through the use of regional climate models. Theyattributed this amplified warming at high-elevations to a reduction insnow and glacier cover, which decreases the surface albedo and in-creases the absorption of solar radiation by the Earth's surface.

Pepin et al. (2015) discussed important mechanisms that contributetowards elevation-dependent warming (EDW), such as snow albedo andsurface-based feedbacks, water vapour changes and latent heat release,surface water vapour and radiative flux changes, surface heat loss andtemperature change, and aerosols. All these mechanisms lead to in-creased warming with increased elevation, and it is believed thatcombinations of these mechanisms may account for contrasting re-gional patterns of EDW in many different mountain systems around theworld (Pepin et al., 2015).

Benali et al. (2012) described the LST as an indicative variable tothe net surface energy balance driven by the emission of long-waveradiation from the surface. Consequently, it is likely that the surfacewater vapour and radiative flux mechanism contributes most to higherLST warming rates at high elevations, since outgoing longwave radia-tion is one major mechanism through which land surface loses heat, andis proportional to the fourth power of absolute temperature (the Ste-fan–Boltzmann law) (Pepin et al., 2015). Vuille et al. (2015) indicatedthat positive feedbacks, such as snow-albedo or download radiationfeedback, may have also contributed to increased warming at higherelevations (see Figure A.2 in the supplementary figures).

Peru has more than 70% of the world's tropical glaciers and is beingaffected by the accelerated retreat of most of these glaciers (López-Moreno et al., 2014; Schauwecker et al., 2014; Vuille et al., 2008;Rabatel et al., 2013; Yarleque et al., 2018).

5. Final comments

In this paper, we evaluate Land Surface Temperature (LST) trendsobserved for the tropical Andean region and the South AmericanAltiplano. A linear correlation between the maximum air temperatureand MODIS LST daytime was analized. Trends in winter daytime LST(2000–2017) demonstrated an increase in most parts of the study area,with the exception of the semi-arid area in the western side of the Andesand south of the study area where the trend is negative. Time series ofwinter maximum air temperature (2000–2015) confirm this observa-tion.

Daytime and nighttime LST trends show a clear difference in theirpattern of behaviour as a function of elevation. The daytime winter LSTshows a stronger positive trend with elevation. Nevertheless, the strongwarming pattern found in the daytime LST trend at higher elevations isnot present for the nighttime winter LST trend at higher elevations. Thisstudy also confirms that the type of land cover has a notable influencein the relationship between daytime LST and maximum air tempera-ture.

Acknowledgments

This work was supported by FONDECYT through the project:“Monitoreo de la dinámica de fenómenos ambientales y climáticos ex-tremos en el Perú usando la teledetección por satélite”, funded byUSAID through the PEER Project: “Strengthening resilience of Andeanriver basin headwaters facing global change” (PGA-084063) and“AGUA-ANDES: Ecological Infrastructure Strategies for EnhancingWater Sustainability in the Semi-Arid Andes” (PGA-174194), as well asby CONDESAN through the Project: "Investigación sobre una relaciónde dependencia entre la tasa de cambio de temperatura y la altitud".JCE received partial support of the AMANECER project funded by theMOPGA program of the French Government. Additionally, we extendour gratitude to the National Meteorology and Hydrology Service ofPeru (SENAMHI-Peru) for providing data used in this study. Finally, the

authors are grateful to Nicole Chabaneix and to the anonymous re-viewers for their contribution to improve this paper.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in theonline version, at https://doi.org/10.1016/j.jag.2018.12.013.

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