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

Int J Appl  Earth Obs Geoinformation 77 (2019) 119–128

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

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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, Peru Instituto Geofísico del Perú (IGP), Lima, Peru c Centro de Competencias del Agua (CCA), Lima, Peru d Univ. Grenoble Alpes, IRD, CNRS, Grenoble INP, Institut des Géosciences de l'Environnement (IGE, UMR 5001), 38000 Grenoble, France b

A R T I C LE I N FO

A B S T R A C T

Keywords: Tropical Andes Land surface warming Remote sensing High mountains

In this study, we report on the assessment of elevation-dependent warming processes in the Andean region between 7 °S and 20 °S, using Land Surface Temperature (LST). Remotely sensed LST data were obtained from Moderate 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 mean monthly daytime LST and mean monthly maximum air temperature. This relation is analysed for different types of coverage, obtaining a significant correlation that varies from 0.57 to 0.82 (p < 0.01). However, effects of change in land cover were ruled out by a previous comparative assessment of trends in daytime LST and normalized difference vegetation index (NDVI). The distribution of the winter daytime LST trend was found to be increasing in most areas, while decreasing in only a few areas. This trend shows that winter daytime LST is increasing at an average rate of 1.0 °C/decade. We also found that the winter daytime LST trend has a clear dependence on elevation, with strongest warming effects at higher elevations: 0.50 °C/decade at 1000–1500 masl, and 1.7 °C/decade above 5000 masl. However, the winter nighttime LST trend shows a steady increase with altitude increase. The dependence of rising temperature trends on elevation could have severe implications for water resources and high Andean ecosystems.

1. Introduction An upward trend in air temperature over high-elevation regions has attracted much attention within the scientific community in recent years (Pepin and Lundquist, 2008; Fan et al., 2015; Vuille et al., 2015). The Andes are the most important mountain range in the Southern Hemisphere, with a maximum elevation of more than 6000 masl in tropical and subtropical sections. The Andes represent, therefore, a formidable obstacle to the tropospheric flow promoting tropical-extratropical flow interactions, especially along their eastern flank (Garreaud, 2009; Garreaud et al., 2009). The limited availability of weather station data reveals several patterns of climate change in the Tropical Andes during the twentieth century. At the regional scale, substantial evidence is reported for the warming of air temperature by 0.01 °C/year between 1939 and 2006 (Vuille et al., 2008). This heating rate is similar to rates recorded in earlier studies, 0.010-0.011 °C/year for 1939–1998 and 0.015 °C/year

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for 1950–1994 (Vuille and Bradley, 2000; Vuille et al., 2003). In the Amazonian-Andean basin of Peru, Lavado et al. (2013) showed an upward trend in the average air temperature of 0.009 °C/year for the period 1965-2007. Huayao station (in Huancayo, Peru) has one of the longest records of maximum air temperature (1921-present) in the Central Peruvian Andes. The trend analysis of the referred variable recorded at this station showed an increase of 0.024 °C/year between 1950 and 2002 (IGP, 2005b) and of 0.03 °C/year between 1965 and 2006 (SENAMHI, 2011). Analysis of temperature as a function of elevation in the tropical Andes shows continuous warming at higher elevations during the period 1950–2010 (Vuille et al., 2015). These changes in air temperature could significantly modify the hydrological cycle in mountains (Nijssen et al., 2001). Thus, in the tropical Andes, a significant decrease in glacier mass balance has been reported and many glaciers could disappear in the coming decades, especially those below 5400 masl (Rabatel et al., 2013; Yarleque et al., 2018). Glacier retreat in the Andes

Corresponding author. E-mail address: [email protected] (J. Aguilar-Lome).

https://doi.org/10.1016/j.jag.2018.12.013 Received 16 April 2018; Received in revised form 22 December 2018; Accepted 28 December 2018 0303-2434/ © 2019 Elsevier B.V. All rights reserved.

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year). This decrease in snow cover is correlated with a decrease in precipitation and an increase in temperature; the correlation values vary according to latitude and elevation. Thus, it is possible to assess the effect of climate change on snow cover by using data processing from 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 the highest incidence of radiative frost across the region, associated with low cloud cover, low atmospheric humidity and low soil moisture (Saavedra and Takahashi, 2017; IGP, 2005a); whereas in other seasons the presence of clouds in the region generates numerous missing and abnormal values that affect the application of MODIS LST data (see Figure A.1 in the supplementary figures). Given these findings, and that little is known about LST trends in the tropical Andes, this paper aims to: i) assess the relationship between mean monthly LST and mean monthly maximum air temperature per land cover type, during the period 2000–2015 (JJA), ii) assess the austral winter (JJA) trends of daytime LST in the tropical Andes during the period 2000–2017, and iii) analyse the relationship between the LST trend and elevation, using winter daytime and nighttime LST data.

is more evident during El Niño years, which induces higher air temperatures than normal (Rabatel et al., 2013). Nevertheless, apart from air temperature, the relationship between local/regional climate and glacier retreat responds to many other factors (Sicart et al., 2008, 2015). Rangwala and Miller (2012) discussed mechanisms which increase air temperature at high elevations; these include albedo-snow feedback, cloud feedback, water vapor feedback and aerosol feedback. As for the extent to which these mechanisms may be relevant to the Andean region in terms of future availability of water resources is not yet fully known (Vuille et al., 2015; Buytaert et al., 2017; Vuille et al., 2018). Land Surface Temperature (LST) is an essential parameter of physical processes occurring on the Earth's surface at global and regional scales. Variations in LST are a response to several surface-atmosphere interactions, which includes energy flows between the atmosphere and the surface of the Earth (Wan and Dozier, 1996). LST provides information about temporal and spatial variations in the balance of temperature of the soil surface and is important for environmental studies and management of water resources (Li et al., 2013). Due to its importance, the Intergovernmental Panel on Climate Change (IPCC) recommended including long-term LST data, based on satellite remote sensing, in global warming studies. This would help to overcome limitations typically found in conventional air temperature recorded at weather stations (Houghton et al., 2001) in a time period restricted to the satellite era. Quin et al. (2009) analysed warming as a function of the elevation using nighttime LST data from images gallery Terra/ MODIS on the Tibet Plateau (China) for the period of 2000-2006. This study showed a rise in the rate of temperature at 3000 to 4800 masl. Salama et al. (2012) analysed LST anomalies trend, retrieved from the SSM/I (Special Microwave Imager) sensor on the Tibet Plateau for the period 1987-2008. This trend analysis showed that the monthly and annual 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 this increase is insignificant. MODIS LST data has been frequently used to estimate air temperature (Vancutsem et al., 2010; Zhang et al., 2016; Yang et al., 2017). Shen and Leptoukh (2011) indicated that LST and air temperature have a close connection due to heat exchange between land and air. Mildrexler et al. (2011) found a strong positive correlation between the annual maximum LST from Aqua/MODIS and annual maximum air temperature globally. Further, they indicated that in barren areas like shrublands, grasslands, savannas and croplands the annual maximum LST values range from 10°C to 20°C, which is hotter than the corresponding annual maximum air temperature at higher elevations. Regarding the MODIS LST and the air temperature relationship in snow-capped sub-Artic mountains, Williamson et al. (2017) found that the LST average is 5–7 °C, which is colder than both the downscaled and MODIS 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 air temperature require a careful comparison before LST can be used as an approximation of air temperature in mountainous terrain. MODIS products have been used in other research studies in the Andean region. For example, Delbart et al. (2015) found that the decreasing trend in snow cover during winter, for the period 2001–2014, clearly explains the observed decreasing trend in the annual water discharge from four rivers (Mendoza, Tunuyán, Diamante, Atuel) in the Argentinian Cuyo region. In mountain regions between Chile and Argentina, Cornwell et al. (2016) related the melt-season fluvial flows with the snow water equivalent and obtained an R2 value of 0.80. The latter was estimated by a combination of instrumental records in remotely sensed snow-covered areas and a snow energy balance model. More recently, Saavedra et al. (2018) found that a large area (34 370 km2) of permanent snow coverage, between 29 °S and 36 °S, experienced a significant snow cover loss (2–5 less snowfall days per

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 American Altiplano (15° and 21 °S) and Lake Titicaca, which is on this plain. Between 3000 and 3500 masl in the study area, there is a biome zone called Puna, which is distinguished by a thermal and latitudinal limit from other biomes such as Páramo and Jalca (Cuesta et al., 2009; Josse et al., 2009). The dry highlands in the southern part of Peru and Bolivia are highly seasonal, with their hydrological behavior controlled by rainfall variability (Garreaud, 2009; Ochoa-Tocachi et al., 2016). Several studies have shown that there is no clear relationship between rainfall amount and altitude (Espinoza et al., 2009; Lavado et al., 2013). In addition, extreme precipitation events are observed on the eastern side of the Andes Mountain range, where precipitation ranges from 250 to 6000 mm/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 their ecosystems. In addition, they are constituted by three biogeographical zones (Josse et al., 2009): 1) Humid Puna, is distributed from northern Peru to the central-eastern side of the Andes mountains in Bolivia, and includes the Lake Titicaca basin. This biogeographic zone covers a wide altitudinal range, from 2000 masl in the inter-Andean valleys to more than 6000 masl at the highest peaks in the snow-capped mountains. As for the climate, there is a clear separation between the warm rainy season and the coldest and driest season. Anthropogenic activities have reduced the forested areas of Queñoa (Polylepis Spp), which have been replaced by shrubland and bushes in large areas. 2) Xerophytic Puna, is distributed mainly in the central-south of western Bolivia and northwest of Argentina. This area extends from 2000 masl in the high valleys in the east to more than 6000 masl in the snowy mountains and volcanoes in the western side of the Andean mountain range, and includes the vast high plain -Altiplanowhich is located in the widest part of the Andes mountain range. The climate in the Xerophytic Puna is tropical and markedly seasonal, with extreme arid conditions in the dry season, which is noticeably accentuated towards the south and the west, where the most extensive high mountain saline ecosystems of the Earth are found. 3) Yungas, extend from the north of Peru to the centre of Bolivia, 120

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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 triangles indicate the location of weather stations.

The first daily MODIS LST (MOD11_L2) product is calculated from the brightness temperature using the generalized split window algorithm proposed by Wan and Dozier (1996) only in cloud-free conditions. The first daily MODIS LST (MOD11_L2) product is calculated from the brightness temperature using the generalized split window algorithm proposed by Wan and Dozier (1996) only for cloud-free conditions.

passing through the mountainous slopes of the eastern watersheds in the tropical Andes, between the Humid Puna (in the West) and the Amazon lowland (in the East). This zone covers altitudes from 500 masl to more than 4000 masl. The vegetation is determined by the incidence of zonal trade winds coming from the Atlantic Ocean, which push against the great mountain barrier to generate almost constant cloudiness and fog through the process of convection, which in turn releases heavy rainfall throughout most of the year.

Δε T + T32 1−ε + B3 2 ⎞ 31 Ts = C + ⎛B1 + B2 ε ε ⎠ 2 ⎝ 1−ε Δε T31 − T32 + ⎛A1 + A2 + A3 2 ⎞ , ε ε ⎠ 2 ⎝

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 Geological Survey) database. The MOD11A2 product is an 8-day compound of daily LST values (MOD11A1) at a spatial resolution of 1 km. This product 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 the time of daytime and nighttime LST observation from Terra/MODIS on the station Huayao (Peru) (12.047 °S, 75.320 °W) are between 09:5410:54 and between 22:12-23:06 (local solar time), respectively. These have a sun-synchronous, and near-polar orbit so they can travel from the North Pole to the South Pole on the sunlit side as the Earth rotates below. As a result, they pass over Earth at approximately the same local time each day to ensure comparable daylight conditions during a day (Mao et al., 2017).

(1)

ε = 0.5(ε31 + ε32)

Δε = (ε31 − ε32) Where T31 and T32 , are the brightness temperatures in the strips 31 and 32; ε32 and ε32 are the emissivities of land surface in these two bands, which were estimated using the method based on emissivity classification processes (Snyder and Wan, 1998; Snyder et al., 1998) according to 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 multidimensional dataset into query tables. Validation of MODIS LST daily products (V005) with 47 in-situ measurements under clear skies (the LST value range being −10 °C to 58 °C and 0.4 cm–3.5 cm for the water vapour column) shows greater 121

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reliability (QC_day), it is an average error smaller than or equal to 0.01 or emissivity smaller than or equal to 1 K for LST (Wan et al., 2004). If this degree of accuracy is not achieved for the estimated values of LST and emissivity, the pixel is classified by another quality indicator and error estimator which is provided by 4–7 bits of QC_day code (Wan, 2007). Only the best quality pixels, having an error smaller than or equal to 2 K in the calculated LST, have been used for this study. Once these corrections are made, the average monthly LST is calculated as the sum of all 8-day products, provided for a particular month and divided by all nonzero occurrences. IFOV (Instantaneous Field of View) data in degrees Kelvin (K) was transformed to degrees Celsius (°C). To evaluate the accuracy of LST data, the monthly average of daytime MODIS LST data was selected for 20 areas of 3 km by 3 km, each with a weather station; this follows the methodology developed by François et al. (1999). The relationship between monthly daytime MODIS LST data and mean monthly maximum air temperature for the period of 2000–2015 (JJA) per each land cover type was evaluated using a linear regression. The Pearson correlation coefficient was used as a goodness-of-fit test. Trends for daytime and nighttime winter MODIS LST were calculated using Sen's method (Sen, 1968) for each pixel, with the following equation:

accuracy at 1 K in the majority of cases (at least 39 out of 47), and the root 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 USGS database and used to calculate dynamic vegetation activity. The NDVI data have spatial resolutions of 1 km and a monthly temporal resolution. The NDVI is calculated with the distribution function, using atmospheric correction of the surface reflectance (bidirectional reflectance distribution function - BRDF) masked by water, clouds, heavy aerosol and cloud shadows (Vermote and Vermeulen, 1999; Gao et al., 2002). MODIS Vegetation Index product (VI) was obtained using sophisticated methods to reduce atmospheric effects, clouds and observation angle variations (Huete et al., 2002). Besides the vegetation index, the MOD13A3 product contains a layer of pixel information on reliability which describes the NDVI quality values per pixel. This information on reliability is a decimal number that classifies the pixel into five 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).

x j − xk ⎞ β = Median ⎛⎜ ⎟, ∀ j > k ⎝ j−k ⎠

(2)

Where x j and xk represent data values at times j and k , respectively, and β is the time series slope (trend). β > 0 indicates a positive trend, while β < 0 indicates a negative trend. This non-parametric method does not require data to be normally distributed and is not sensitive to outliers. Mann-Kendall's test was used to evaluate the statistical significance of the trends. This test has been widely applied in studies on hydro-meteorological 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 important driver for the rising trend of LST (Jiang and Tian, 2010). In the plateau region of north-central Nigeria, urbanization and agricultural activities, including animal grazing, were responsible for the gradual loss of vegetation cover and rising average LST (Odunuga and Badru, 2015). Xu et al. (2013) indicates that the contribution of impervious surface to regional LST change can be up to six times higher than the sum of the contributions of vegetation and water. van Leeuwen et al. (2011) found

2.2.4. Air temperature data A 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 is the mean of daily maximum air temperatures in any given month. This data was collected from 2000 to 2015 during the austral winter (JJA) from 20 weather stations distributed above 1000 masl. Fig. 1 shows the locations of these weather stations, and Table 1 lists the name, geographic coordinates and elevation for each station. 2.3. Methodology The quality of the LST data is defined based on the layer of Table 1 Weather stations details. Station

Latitude

Longitude

Elevation (m)

Pixel elevation (m)

Land cover types

Paucaray Curahuasi Urubamba Yauri Ubinas Ayaviri Imata Puno Huancane Pampahuta Mañazo Moquegua Chuapalca Huayao Matucana Huamachuco Andahuaylas Cajabamba Huánuco La Angostura

−14.050 −13.552 −13.3106 −14.817 −16.382 −14.873 −15.836 −15.826 −15.201 −15.484 −14.8 −17.169 −17.305 −12.038 −11.839 −7.819 −13.649 −7.622 −9.952 −15.183

−73.634 −72.735 −72.124 −71.417 −70.857 −70.593 −71.088 −70.012 −69.754 −70.676 −70.067 −70.932 −69.644 −75.338 −76.378 −78.040 −73.367 −78.051 −76.249 −71.635

3206 2879 3863 3925 3551 3943 4519 3812 3890 4400 3920 1450 4177 3360 2348 3200 2865 2480 1947 4265

3206 2879 3041 3925 3551 3943 4449 3847 3898 4368 4084 1456 4220 3371 2699 3254 3137 2631 1988 4265

Cropland Shrubland Shrubland Grassland/shrubland Shrubland Urban areas Sparse vegetation/shrubland Urban areas Grassland or savannas Grassland or savannas Grassland Forest/shrubland Sparse vegetation Vegetation/cropland Shrubland Cropland/grassland Forest or shrubland/grassland Forest or shrubland Forest or shrubland/grassland Grassland/shrubland

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3.2. LST trend

that changes in day‐night LSTs were highly correlated to the spatial pattern of deforestation in different states of Brazil in the Brazilian Amazon. The Normalized Difference Vegetation Index (NDVI) is an indicator of vegetation health (Kinyanjui, 2011), and has been widely used to give an idea of vegetation cover status (Horion et al., 2013). We used NDVI to detect land cover changes. In order to eliminate any abnormal remnants of NDVI values in monthly images, these time series were filtered using spatial-temporal iteration methods and the SavitzkyGolay filter to replace the pixels eliminated by quality bands (Chen et al., 2014; Lili et al., 2015). The winter trend of NDVI was then calculated for each pixel using Sen's method (Sen, 1968) for the period 2000 - 2017. The NDVI trends provide information about changes in land cover during the study period (Hutchinson et al., 2015; Xu et al., 2016), which are explained by an abrupt increase or decrease in the daytime LST trend. In the case of the winter daytime LST trend, only areas 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 types around weather stations, and to exclude water bodies and permanent snow.

Fig. 3a shows the trends of winter daytime LST in the Tropical Andean region. In Fig. 3a, grey areas within the study region represent non-significant trend values (p > 0.05), or areas that were discriminated because of land cover change through the NDVI trend. The majority 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 an influence of coastal cloudiness at about 1200–1500 masl. Thus, an arid zone of variable extent is developed under the influence of the Pacific Anticyclone and large-scale atmospheric subsidence (Ubeda and Estremera, 2013). Moreover, a negative LST trend occurred in the western Andes and in the south-eastern part of the study area during the period of study. The LST trends in Fig. 3a increase by a rate of 0.11 °C per year, considering the whole of the studied area. This positive trend gradually increases to reach the highest regional value of 0.3 °C/year, for instance at high elevations of section A-A’ in the central Peruvian Andes (Fig. 3b). Over this region, there is a warming trend that reaches a 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 Andes between 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 Bolivian Altiplano 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 during the period 2000–2015 was used to verify the LST trend. Maximum air temperature during winter (average across all the 20 stations located in the 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-Moreno et al., 2016; Vicente-Serrano et al., 2018). However, LTS trends in the tropical Andes have not been provided before. To summarize the average trend of warming as a function of elevation, we show the average for the winter daytime LST trends for the period 2000–2017, at intervals of 500 m (Fig. 4). The LST trend increases at 1000–5000 masl, indicating that areas at higher-elevations exhibit higher rates of increasing LST (up to 0.18 °C/year). Fig. 5 shows the average for the winter nighttime LST trends for the period of 2000–2017, with an interval of 500 m at 1000 masl. Although a slight reduction in the increasing trend was observed at 3500–4000 masl, the warming trend increases slightly at higher elevations, particularly above 4000 masl.

3. Results 3.1. Relationship between daytime LST and maximum air temperature The relationship between mean monthly daytime LST (LSTd) and mean monthly maximum air temperature (Tmax) showed statistically significant linear correlations (0.57 ≤ R ≤ 0.82, p < 0.01) (Fig. 2a-d). The highest value of R corresponds to shrubland and the lowest value to grasslands. The relationship LSTd/Tmax is closely associated with each land cover type. The relationship LSTd/Tmax for grassland in all stations located below 4265 masl (black dots in Fig. 2c), and in stations located above 4400 masl (gray dots in Fig. 2c) are illustrated in scatterplots; the former is shifted toward a higher temperature distribution, compared to the latter. For grassland, the value of the slope is relatively low for stations located above 4400 masl (grey dots in Fig. 2c), while in shrubland the value 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 grassland areas (grey dots in Fig. 2c). The LSTd/Tmax relationship for urban areas (Fig. 2d) shows that Tmax is gradually increased from 12.4 °C to 19.4 °C. The weather station is located in urban areas between 3812 and 3927 masl and the average Tmax recorded is 16.1 °C (Fig. 2d).

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

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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) significance obtained 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.

4. Discussion

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

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

4.1.1. Different physical parameters Air temperature is measured with a thermometer exposed to air at 124

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may result in different local weather conditions (Mildrexler et al., 2011). The shrubland land cover type considered includes weather stations located within the forest biome where the LSTd/Tmax relationship is near 1:1 (Fig. 2a). The leaves of plants actively exchange absorbed solar radiation through evaporation. Therefore, during daylight hours, leaves maintain a temperature close to air temperature (Nemani et al., 1993). Mildrexler et al. (2011) found that the daytime LST data from Aqua was highly correlated (R = 0.92) with the maximum air temperature in shrubland areas globally, and Shen and Leptoukh (2011) estimated maximum air temperature using daytime LST data from Terra finding a mean absolute error (MAE) of 2.4 °C for shrubland areas in central and eastern Eurasia. The LSTd/Tmax scatterplot shows that grassland areas present warmer temperatures compared to shrubland areas. For land with scarce vegetation, IFOV of the remote sensing devices include both canopy and soil background elements. Consequently, the effective surface temperature is dependent on the relative proportions captured within the IFOV of the sensor for soil versus canopy cover (Friedl and Davis, 1994). Combined, these factors result in the potential for a large apportionment of incoming solar radiation into sensible heat, resulting in a high Bowen ratio (ratio of sensible heat flux to latent heat flux) and high daytime LST values (Mildrexler et al., 2011). However, Parida et al. (2008) indicated that among the different vegetation types, agriculture practiced in desert areas showed the highest surface temperature, followed by rainfed agriculture, irrigated agriculture and forest agriculture in the Gurajat state of India, with lower total rainfall (June-October). Several studies have shown that daytime LST data and maximum air temperature are correlated (Mildrexler et al., 2011; Shen and Leptoukh, 2011; Yang et al., 2017). In addition to land cover characteristics (Schwarz et al., 2012; Maeda and Hurskainen, 2014), there are many factors that can influence the complex relationship between daytime LST and maximum air temperature such as solar radiation, cloud cover, soil moisture and surface roughness (Vancutsem et al., 2010; Kloog et al., 2014; Xu et al., 2014). However, Zhang et al. (2016) indicated that cloud cover mainly affects the fundamental relationship between daytime LST and maximum air temperature, and Williamson et al. (2014) concluded that MODIS LST data may change its relationship with air temperature depending on the period of aggregation, since it is often aggregated into multi-day composites to mitigate data reductions caused by cloud cover.

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

4.2. LST trend The trends for winter daytime LST are consistent with the results presented by Vicente-Serrano et al. (2018), who showed that the average maximum air temperature in Peru has a positive trend during all months of the year, but becomes more pronounced during the winter months (JJA). López-Moreno et al. (2016) reported changes in maximum air temperature during the cold season (JJA), with more heterogeneous trends; while statistically significant trends were found mostly for the Bolivian Altiplano, during the period 1965-2012. Likewise, Vuille et al. (2015) showed that the air temperature trend has gradually increased between 1950 and 2010. The winter nighttime LST trend has a more homogeneous magnitude of change than the winter daytime LST trend. According to Vicente-Serrano et al. (2018), in Peru, minimum air temperature showed positive trends for every month of the year; nevertheless, these trends did not show an increase as a function of elevation. Falvey and Garreaud (2009) identified that temperatures on the coastal side of the Andes were decreasing at a rate of -0.02 °C/year for the period of 1979-2006. This cooling effect on the coastal side has been attributed to a shift towards a negative phase of the Interdecadal Pacific Oscillation (IPO) (Vuille et al., 2015). However, in the subtropical Andes it continues to experience positive temperature trends with respect to elevation (Vuille et al., 2015; Vicente-Serrano et al., 2018). This is most likely due to anthropogenic warming, as suggested

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

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

4.1.2. Differential spatial integration The meteorological information represents a punctual value of 10100 m2 (François et al., 1999), and the MODIS LST information is the product which integrates data for every 1 km2 into a unique value where each pixel may well contain several different land types. Each land cover type has distinct interactions with the atmosphere which 125

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

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. They attributed this amplified warming at high-elevations to a reduction in snow and glacier cover, which decreases the surface albedo and increases the absorption of solar radiation by the Earth's surface. Pepin et al. (2015) discussed important mechanisms that contribute towards elevation-dependent warming (EDW), such as snow albedo and surface-based feedbacks, water vapour changes and latent heat release, surface water vapour and radiative flux changes, surface heat loss and temperature change, and aerosols. All these mechanisms lead to increased warming with increased elevation, and it is believed that combinations of these mechanisms may account for contrasting regional patterns of EDW in many different mountain systems around the world (Pepin et al., 2015). Benali et al. (2012) described the LST as an indicative variable to the net surface energy balance driven by the emission of long-wave radiation from the surface. Consequently, it is likely that the surface water vapour and radiative flux mechanism contributes most to higher LST warming rates at high elevations, since outgoing longwave radiation is one major mechanism through which land surface loses heat, and is proportional to the fourth power of absolute temperature (the Stefan–Boltzmann law) (Pepin et al., 2015). Vuille et al. (2015) indicated that positive feedbacks, such as snow-albedo or download radiation feedback, may have also contributed to increased warming at higher elevations (see Figure A.2 in the supplementary figures). Peru has more than 70% of the world's tropical glaciers and is being affected by the accelerated retreat of most of these glaciers (LópezMoreno et al., 2014; Schauwecker et al., 2014; Vuille et al., 2008; Rabatel et al., 2013; Yarleque et al., 2018).

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.jag.2018.12.013. References Al Buhairi, M.H., 2010. Analysis of monthly, seasonal and annual air temperature variability and trends in Taiz City-Republic of Yemen. J. Environ. Prot. 1 (04), 401. Becker, F., Li, Z.L., 1995. Surface temperature and emissivity at various scales: definition, measurement and related problems. Remote. Sens. Rev. 12 (3-4), 225–253. Benali, A., Carvalho, A.C., Nunes, J.P., Carvalhais, N., Santos, A., 2012. Estimating air surface temperature in Portugal using MODIS LST data. Remote Sens. Environ. 124, 108–121. Bontemps, S., Defourny, P., Bogaert, E.V., Arino, O., Kalogirou, V., Perez, J.R., 2011. GLOBCOVER 2009-Products Description and Validation Report. Buytaert, et al., 2017. Glacial melt content of water use in the tropical Andes. Environ. Res. Lett. 12 (2017), 114014. Chattopadhyay, S., Edwards, D.R., 2016. Long-term trend analysis of precipitation and air temperature for kentucky, United States. Climate 4 (1), 10. Chavez, S.P.y, Takahashi, K., 2017. Orographic rainfall hotspots in the Andes-Amazon transition according to the TRMM precipitation radar and in situ data. J. Geophys. Res. Atmos. 122. Chen, L., Jin, Z., Michishita, R., Cai, J., Yue, J., Chen, D., Xu, B., 2014. Dynamic monitoring of wetland cover changes using time-series remote sensing imagery. Ecol. Inform. 24, 17–26. Cornwell, E., Molotch, N.P., McPhee, J., 2016. Spatio-temporal variability of snow water equivalent in the extra-tropical Andes Cordillera from distributed energy balance modeling and remotely sensed snow cover. Hydrol. Earth Syst. Sci. 20 (1), 411. Cuesta, F., Peralvo, M., Valarezo, N., 2009. Los bosques montanos de los Andes Tropicales. Una evaluación regional de su estado de conservación y de su vulnerabilidad a efectos del cambio climático. Serie investigación y sistematización. pp. 5. Dash, P., Göttsche, F.M., Olesen, F.S., Fischer, H., 2001. Retrieval of land surface temperature and emissivity from satellite data: physics, theoretical limitations and current methods. J. Indian Soc. Remote. Sens. 29 (1-2), 23. Delbart, N., Dunesme, S., Lavie, E., Madelin, M., Goma, R., 2015. Remote sensing of Andean mountain snow cover to forecast water discharge of Cuyo rivers. J. Alpine Res. Revue de géographie alpine 103–112. EROS Data Center, 1996. GTOP030 Documentation, Universal Resourse Locator, Global Land Information System. URL:. https://lta.cr.usgs.gov/GTOPO30. Espinoza, J.C., Ronchail, J., Guyot, J.L., Cochonneau, G., Naziano, F., Lavado, W., De Oliveira, E., Ponbosa, R., Vauchel, P., 2009. Spatio‐temporal rainfall variability in the Amazon basin countries (Brazil, Peru, Bolivia, Colombia, and Ecuador). Int. J. Climatol. 29 (11), 1574–1594. Espinoza, J.C., Chavez, S., Ronchail, J., Junquas, C., Takahashi, K., Lavado, W., 2015. Rainfall hotspots over the southern tropical Andes: spatial distribution, rainfall intensity, and relations with large-scale atmospheric circulation. Water Resour. Res. 51 (5), 3459–3475. Falvey, M., Garreaud, R.D., 2009. Regional cooling in a warming world: recent temperature trends in the southeast Pacific and along the west coast of subtropical South America (1979–2006). J. Geophys. Res. Atmos. 114, D4. Fan, X., Wang, Q., Wang, M., Jiménez, C.V., 2015. Warming amplification of minimum and maximum temperatures over high-elevation regions across the globe. PLoS One 10 (10), e0140213. https://doi.org/10.1371/journal.pone.0140213. François, C., Bosseno, R., Vacher, J.J., Seguin, B., 1999. Frost risk mapping derived from satellite and surface data over the Bolivian Altiplano. Agric. For. Meteorol. 95 (2), 113–137. Friedl, M.A., Davis, F.W., 1994. Sources of variation in radiometric surface temperature over a tallgrass prairie. Remote Sens. Environ. 48 (1), 1–17. Gao, F., Jin, Y., Schaaf, C.B., Strahler, A.H., 2002. Bidirectional NDVI and atmospherically resistant BRDF inversion for vegetation canopy. Ieee Trans. Geosci. Remote. Sens. 40 (6), 1269–1278. Garreaud, R.D., 2009. The Andes climate and weather. Adv. Geosci. 22, 3–11. Garreaud, R.D., Vuille, M., Compagnucci, R., Marengo, J., 2009. Present-day south american climate. Palaeogeogr. Palaeoclimatol. Palaeoecol. 281, 180–195. Giorgi, F., Hurrell, J.W., Marinucci, M.R., Beniston, M., 1997. Elevation dependency of the surface climate change signal: a model study. J. Clim. 10 (2), 288–296. Horion, S., Cornet, Y., Erpicum, M., Tychon, B., 2013. Studying interactions between climate variability and vegetation dynamic using a phenology based approach. Int. J. Appl. Earth Obs. Geoinf. 20, 20–32. Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., Van der Linden, P.J., Xiaosu, D., 2001. Climate Change 2001, The Scientific Basis. Cambridge University Press. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation índices. Remote Sens. Environ. 83, 195–213. Hutchinson, J.M., Jacquin, A., Hutchinson, S.L., Verbesselt, J., 2015. Monitoring vegetation change and dynamics on US Army training lands using satellite image time series analysis. J. Environ. Manage. 150, 355–366. Instituto Geofísico del Perú, 2005a. Diagnóstico de la Cuenca del Mantaro Bajo la Visión del Cambio Climático Vol. II. Fondo editorial del Concejo Nacional del Ambiente,

5. Final comments In this paper, we evaluate Land Surface Temperature (LST) trends observed for the tropical Andean region and the South American Altiplano. A linear correlation between the maximum air temperature and 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 Andes and south of the study area where the trend is negative. Time series of winter maximum air temperature (2000–2015) confirm this observation. Daytime and nighttime LST trends show a clear difference in their pattern of behaviour as a function of elevation. The daytime winter LST shows a stronger positive trend with elevation. Nevertheless, the strong warming pattern found in the daytime LST trend at higher elevations is not present for the nighttime winter LST trend at higher elevations. This study also confirms that the type of land cover has a notable influence in the relationship between daytime LST and maximum air temperature. Acknowledgments This work was supported by FONDECYT through the project: “Monitoreo de la dinámica de fenómenos ambientales y climáticos extremos en el Perú usando la teledetección por satélite”, funded by USAID through the PEER Project: “Strengthening resilience of Andean river basin headwaters facing global change” (PGA-084063) and “AGUA-ANDES: Ecological Infrastructure Strategies for Enhancing Water Sustainability in the Semi-Arid Andes” (PGA-174194), as well as by CONDESAN through the Project: "Investigación sobre una relación de dependencia entre la tasa de cambio de temperatura y la altitud". JCE received partial support of the AMANECER project funded by the MOPGA program of the French Government. Additionally, we extend our gratitude to the National Meteorology and Hydrology Service of Peru (SENAMHI-Peru) for providing data used in this study. Finally, the 126

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variations of land surface temperature anomalies observed over the Tibetan Plateau by the Special Sensor Microwave Imager (SSM/I) from 1987 to 2008. Clim. Change 114 (3-4), 769–781. Schauwecker, S., Rohrer, M., Acuña, D., Cochachin, A., Dávila, L., Frey, H., Giráldez, C., Gómez, J., Huggel, C., Jacques-Coper, M., Loarte, E., Salzmann, N., Vuille, M., 2014. Climate trends and glacier retreat in the Cordillera Blanca, Peru, revisited. Glob. Planet. Change 119, 85–97. Schwarz, N., Schlink, U., Franck, U., Großmann, K., 2012. Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators-An application for the city of Leipzig (Germany). Ecol. Indic. 18, 693–704. Sen, P.K., 1968. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389. SENAMHI, 2011. Escenario de cambio climático en la Cuenca del río Mantaro para el año 2100, Lima, Perú. Shen, S., Leptoukh, G.G., 2011. Estimation of surface air temperature over central and eastern Eurasia from MODIS land surface temperature. Environ. Res. Lett. 6 (4), 045206. Sicart, J.E., Hock, R., Six, D., 2008. Glacier melt, air temperature, and energy balance in different climates: the Bolivian Tropics, the French Alps, and northern Sweden. J. Geophys. Res. Atmos. 113, D24. Sicart, J.E., Espinoza, J.C., Quéno, L., Medina, M., 2015. Radiative properties of clouds over a tropical Bolivian glacier: seasonal variations and relationship with regional atmospheric circulation. Int. J. Climatol. 36, 3116–3128. Snyder, W.C., Wan, Z., 1998. BRDF models to predict spectral reflectance and emissivity in the thermal infrared. Ieee Trans. Geosci. Remote. Sens. 36 (1), 214–225. Snyder, W.C., Wan, Z., Zhang, Y., Feng, Y.Z., 1998. Classification-based emissivity for land surface temperature measurement from space. Int. J. Remote Sens. 19 (14), 2753–2774. Solano, R., Didan, K., Jacobson, A., Huete, A., 2010. MODIS vegetation index user’s guide (MOD13 series). Vegetation Index and Phenology Lab. The University of Arizona, pp. 1–38. Ubeda, J., Estremera, D.P., 2013. El clima de la vertiente del Pacífico de los Andes centrales y sus implicaciones geomorfológicas. Espacio y Desarrollo 20, 31–56. van Leeuwen, T.T., Frank, A.J., Jin, Y., Smyth, P., Goulden, M.L., van der Werf, G.R., Randerson, J.T., 2011. Optimal use of land surface temperature data to detect changes in tropical forest cover. J. Geophys. Res. Biogeosci. 116, G2. Vancutsem, C., Ceccato, P., Dinku, T., Connor, S.J., 2010. Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sens. Environ. 114 (2), 449–465. Vermote, E.F., Vermeulen, A., 1999. Atmospheric correction algorithm: spectral reflectances (MOD09). ATBD version 4. Vicente-Serrano, S.M., López‐Moreno, J.I., Correa, K., Avalos, G., Bazo, J., Azorin‐Molina, C., Domínguez‐Castro, F., Kenawy, A.E., Gimeno, L., Nieto, R., 2018. Recent changes in monthly surface air temperature over Peru, 1964–2014. Int. J. Climatol. 38 (1), 283–306. Vuille, M., Bradley, R.S., 2000. Mean annual temperature trends and their vertical structure in the tropical Andes. Geophys. Res. Lett. 27, 3885–3888. Vuille, M., Bradley, R.S., Werner, M., Keimig, F., 2003. 20th century climate change in the tropical Andes: observations and model results. Clim. Change 59 (1–2), 75–99. Vuille, M., Francou, B., Wagnon, P., Juen, I., Kaser, G., Mark, B., Bradley, R., 2008. Climate change and tropical Andean Glaciers: past, present and future. Earth-Science Review 89, 79–96. Vuille, M., Franquist, E., Garreaud, R., Lavado, W.S., Cáceres, B., 2015. Impact of the global warming hiatus on Andean temperature. J. Geophys. Res. Atmos. 120, 2169–8996. Vuille, M., Carey, M., Huggel, C., Buytaert, W., Rabatel, A., Jacobsen, D., Soruco, A., Villacish, M., Yarlequea, C., Timma, O.E., Condome, T., Salzmannc, N., Sicarte, J.E., 2018. Rapid decline of snow and ice in the tropical Andes–Impacts, uncertainties and challenges ahead. Earth. Rev. Wan, Z., 2007. Collection-5 MODIS Land Surface Temperature Products Users’ Guide. ICESS, University of California, Santa Barbara. Wan, Z., 2008. New refinements and validation of the MODIS land-surface temperature/ emissivity products. Remote Sens. Environ. 112, 59–74. Wan, Z., Dozier, J., 1996. A generalized Split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens. 34 (4), 892–905. Wan, Z., Zhang, Y., Zhang, Q., Li, Z., 2002. Validation of the land surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote Sens. Environ. 83, 163–180. Wan, Z., Zhang, Y., Zhang, Q., Li, Z., 2004. Quality assessment and validation of the MODIS global land surface temperature. Int. J. Remote Sens. 25, 261–274. Williamson, S.N., Hik, D.S., Gamon, J.A., Kavanaugh, J.L., Flowers, G.E., 2014. Estimating temperature fields from MODIS land surface temperature and air temperature observations in a sub-arctic alpine environment. Remote Sens. 6 (2), 946–963. Williamson, S.N., Hik, D.S., Gamon, J.A., Jarosch, A.H., Anslow, F.S., Clarke, G.K., Rupp, T.S., 2017. Spring and summer monthly MODIS LST is inherently biased compared to air temperature in snow covered sub-Arctic mountains. Remote Sens. Environ. 189, 14–24. Xu, H., Lin, D., Tang, F., 2013. The impact of impervious surface development on land surface temperature in a subtropical city: Xiamen, China. Int. J. Climatol. 33 (8), 1873–1883. Xu, Y., Knudby, A., Ho, H.C., 2014. Estimating daily maximum air temperature from MODIS in British Columbia, Canada. Int. J. Remote Sens. 35 (24), 8108–8121. Xu, L., Li, B., Yuan, Y., Gao, X., Zhang, T., Sun, Q., 2016. Detecting different types of directional land cover changes using MODIS NDVI time series dataset. Remote Sens. 8 (6), 495.

Lima-Perú, pp. 90. Instituto Geofísico del Perú, 2005b. Vulnerabilidad actual y futura ante el cambio climático y medidas de adaptación en la cuenca del río Mantaro Vol. III Fondo Editorial CONAM, Lima, Perú. Jiang, J., Tian, G., 2010. Analysis of the impact of land use/land cover change on land surface temperature with remote sensing. Procedia Environ. Sci. 2, 571–575. Jin, M., Dickinson, R.E., 2010. Land surface skin temperature climatology: benefitting from the strengths of satellite observations. Environ. Res. Lett. 5 (4), 044004. Josse, C., Cuesta, F., Navarro, G., Barrena, V., Cabrera, E., Chacón-Moreno, E., Tovar, A., 2009. Ecosistemas de los Andes del Norte y Centro. Secretaría General de la Comunidad Andina, Programa Regional ECOBONA-Intercooperation, CONDESANProyecto Páramo Andino, Programa BioAndes, EcoCiencia, NatureServe, IAvH, LTAUNALM, ICAE-ULA, CDC-UNALM, RUMBOL SRL. Lima, Bolivia, Colombia, Ecuador, Perú y Venezuela. Junquas, C., Takahashi, K., Condom, T., Espinoza, J.C., Chavez, S., Sicart, J.E., Lebel, T., 2018. Understanding the influence of orography over the precipitation diurnal cycle and the associated atmospheric processes in the central Andes. Clim. Dyn. Kinyanjui, M.J., 2011. NDVI‐based vegetation monitoring in Mau forest complex, Kenya. Afr. J. Ecol. 49 (2), 165–174. Kloog, I., Nordio, F., Coull, B.A., Schwartz, J., 2014. Predicting spatiotemporal mean air temperature using MODIS satellite surface temperature measurements across the Northeastern USA. Remote Sens. Environ. 150, 132–139. Lavado, W., Labat, D., Ronchail, J., Espinoza, J.C., Guyot, J.L., 2013. Trends in rainfall and temperature in the Peruvian Amazon-Andes basin over the last 40 years (19652007). Hydrol. Process. 41, 2944–2957. Li, Z., Tang, H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I., Sobrino, J., 2013. Satellitederived land surface temperature: current status and perspectives. Remote Sens. Environ. 131, 14–37. Lili, X., Baolin, L., Yecheng, Y., Xizhang, G., Tao, Z., 2015. A temporal-spatial iteration method to reconstruct NDVI time series datasets. Remote Sens. (Basel) 8906–8924. López-Moreno, J.I., Fontaneda, S., Bazo, J., Revuelto, J., Azorin-Molina, C., ValeroGarcés, B., Morán-Tejeda, E., Vicente-Serrano, S.M., Zubieta, R., Alejo-Cochachín, J., 2014. Recent glacier retreat and climate trends in Cordillera Huaytapallana. Peru. Global and Planetary Change 112, 1–11. López-Moreno, J.I., Morán‐Tejeda, E., Vicente‐Serrano, S.M., Bazo, J., Azorin‐Molina, C., Revuelto, J., Sánchez‐Lorenzo, A., Navarro‐Serrano, F., Aguilar, E., Chura, O., 2016. Recent temperature variability and change in the Altiplano of Bolivia and Peru. Int. J. Climatol. 36 (4), 1773–1796. Maeda, E.E., Hurskainen, P., 2014. Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data. Theor. Appl. Climatol. 118 (3), 497–509. Mao, K.B., Ma, Y., Tan, X.A., Shen, X.Y., Liu, G., Li, Z.L., Chen, J.M., Xia, L., 2017. Global surface temperature change analysis based on MODIS data in recent twelve years. Adv. Space Res. 59 (2), 503–512. Mildrexler, D.J., Zhao, M., Running, S.W., 2011. A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests. J. Geophys. Res. Biogeosci. 116, G3. Nemani, R., Pierce, L., Running, S., Goward, S., 1993. Developing satellite-derived estimates of surface moisture status. J. Appl. Meteorol. 32 (3), 548–557. Nijssen, B., O’Donnell, G.M., Hamlet, A.F., Lettenmaier, D.P., 2001. Hydrologic sensitivity of global rivers to climate change. Clim. Change 50, 143–175. Norman, J.M., Becker, F., 1995. Terminology in thermal infrared remote sensing of natural surfaces. Remote. Sens. Rev. 12 (3-4), 159–173. Ochoa-Tocachi, B.F., Buytaert, W., De Bièvre, B., Célleri, R., Crespo, P., Villacís, M., Gil‐Ríos, J., 2016. Impacts of land use on the hydrological response of tropical Andean catchments. Hydrol. Process. 30 (22), 4074–4089. Odunuga, S., Badru, G., 2015. Landcover change, land surface temperature, surface albedo and topography in the Plateau Region of North-Central Nigeria. Land 4 (2), 300–324. Organización Mundial de Meteorología, 2008. Guía de Instrumentos y Métodos de Observación Meteorológica, vol. 8. pp. 773. Parida, B.R., Oinam, B., Patel, N.R., Sharma, N., Kandwal, R., Hazarika, M.K., 2008. Land surface temperature variation in relation to vegetation type using MODIS satellite data in Gujarat state of India. Int. J. Remote Sens. 29 (14), 4219–4235. Pepin, N.C., Lundquist, J.D., 2008. Temperature trends at high elevations: patterns across the globe. Geophys. Res. Lett. 35, L14701. Pepin, N., Bradley, R.S., Diaz, H.F., Baraër, M., Caceres, E.B., Forsythe, N., Fowler, H., Greenwood, G., Hashmi, M.Z., Liu, X.D., Miller, J.R., 2015. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Chang. 5 (5), 424. Pepin, N.C., Maeda, E.E., Williams, R., 2016. Use of remotely sensed land surface temperature as a proxy for air temperatures at high elevations: findings from a 5000 m elevational transect across Kilimanjaro. J. Geophys. Res. Atmos. 121 (17), 9998. Quin, J., Yang, K., Liang, S., Guo, X., 2009. The altitudinal dependence of recent rapid warning over the Tibetan Plateau. Clim. Change 97, 321–327. Rabatel, A., Francou, B., Soruco, A., Gomez, J., Cáceres, B., Ceballos, J.L., Basantes, R., Vuille, M., Sicart, J.E., Huggel, C., Scheel, M., 2013. Current state of glaciers in the tropical Andes: a multi-century perspective on glacier evolution and climate change. Cryosphere 7, 81–102. Rangwala, I., Miller, J.R., 2012. Climate change in mountains: a review of elevationdependent warming and its possible causes. Clim. Change 114 (3-4), 527–547. Saavedra, M., Takahashi, K., 2017. Physical controls on frost events in the central Andes of Peru using in situ observations and energy flux models. Agric. For. Meteorol. 239, 58–70. Saavedra, F.A., Kampf, S.K., Fassnacht, S.R., Sibold, J.S., 2018. Changes in Andes snow cover from MODIS data, 2000–2016. Cryosphere 12 (3), 1027. Salama, M.S., Van der Velde, R., Zhong, L., Ma, Y., Ofwono, M., Su, Z., 2012. Decadal

127

Int J Appl  Earth Obs Geoinformation 77 (2019) 119–128

J. Aguilar-Lome et al.

Yu, P.S., Yang, T.C., Wu, C.K., 2002. Impact of climate change on water resources in southern Taiwan. J. Hydrol. 260 (1), 161–175. Zhang, H., Zhang, F., Zhang, G., He, X., Tian, L., 2016. Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau. Atmos. Chem. Phys. 16 (21), 13681–13696.

Yang, Y.Z., Cai, W.H., Yang, J., 2017. Evaluation of MODIS land surface temperature data to estimate near-surface air temperature in northeast China. Remote Sens. 9 (5), 410. Yarleque, C., Vuille, M., Hardy, D.R., Timm, O.E., De la Cruz, J., Ramos, H., Rabatel, A., 2018. Projections of the future disappearance of the quelccaya ice cap in the Central Andes. Sci. Rep. 8 (1), 15564.

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