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Long-term high-frequency validation ofMODIS LST products in a Mediterraneanmountain environmentBogdan Paladea & Milagros Serranob

a European Union Satellite Centre, Torrejon de Ardoz, Spainb Departamento de Análisis Geográfico Regional y GeografíaFísica, Facultad de Geografía e Historia, Universidad Complutensede Madrid, Madrid, SpainPublished online: 13 Feb 2014.

To cite this article: Bogdan Palade & Milagros Serrano (2014) Long-term high-frequency validationof MODIS LST products in a Mediterranean mountain environment, International Journal of RemoteSensing, 35:3, 818-829, DOI: 10.1080/01431161.2013.873148

To link to this article: http://dx.doi.org/10.1080/01431161.2013.873148

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Long-term high-frequency validation of MODIS LST products in aMediterranean mountain environment

Bogdan Paladea* and Milagros Serranob

aEuropean Union Satellite Centre, Torrejon de Ardoz, Spain; bDepartamento de Análisis GeográficoRegional y Geografía Física, Facultad de Geografía e Historia, Universidad Complutense de

Madrid, Madrid, Spain

(Received 9 June 2013; accepted 9 November 2013)

The accuracy of Moderate-resolution Imaging Spectroradiometer (MODIS) level 31 km land surface temperature (LST) products was assessed through long-term valida-tion carried out in a mountainous site in Sierra Nevada, southeast Spain. A total of1458 day and night thermal images, acquired by Terra and Aqua satellites during 2008,were processed and compared to ground-truth data recorded at the meteorologicalstation of Robledal de Cañar with a frequency of one measurement every 10 min. Thepurpose of this investigation was to understand whether MODIS LST data can be usedas input for climate models to be constructed for mountainous environments. Severaltrends in the MODIS LST data were observed, including the underestimation ofdaytime values and the overestimation of night-time values. Although all the datasets (Terra and Aqua, diurnal and nocturnal) showed high correlation coefficients withground measurements, only night values maintained a relatively high accuracy ofapproximately 2°C of annual average error. Factors that may cause errors in theMODIS LST data, like acquisition angle, cloud, and snow cover, were analysedwithout conclusive results. High accuracy levels, i.e. close to 1°C, similar to othervalidation studies carried out over simpler and much more homogenous land-covertypes such as cultivated fields, have been achieved for night images acquired duringthe summer months, thus making these datasets reliable for their use in climatic modelsover mountainous regions.

1. Introduction

Land surface temperature (LST) is an important climatic factor in mountainous environ-ments due to its influence on air-surface radiation exchange (Wan 2008), which in turnimpacts the distribution and development of key landforms characteristic to high moun-tain environments such as rock glaciers and other permafrost-related features (Haeberliet al. 2006). Moreover, previous studies have shown that LST data can be used forestimates of daily minimum and maximum air temperature (Mostovoy et al. 2006) aswell as for the calculation of daily mean air temperatures (Colombi et al. 2007).

Traditionally, air temperature and LST values are provided by local meteorologicalstations. At high altitudes, above 2000 m a.s.l., two problems arise: first, altitude meteor-ological stations are scarce and, when they do exist, they very seldom register the groundtemperature. Second, the data provided by meteorological stations are point values,whereas the majority of climate models require spatially homogenous data (Neteler2010). Generally, because of the strong temperature–altitude relationship, the processing

*Corresponding author. Email: Bogdan.Palade@satcen.europa.eu

International Journal of Remote Sensing, 2014Vol. 35, No. 3, 818–829, http://dx.doi.org/10.1080/01431161.2013.873148

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of climate data from weather stations involves spatial interpolation based on the verticalthermal gradient (Colombi et al. 2007; Barry 2008). In this context, data provided byspace-borne sensors can be a useful alternative to deliver homogenous information overisolated mountain areas. A wide range of studies have dealt with the validation ofModerate-resolution Imaging Spectroradiometer (MODIS), Aster, and Landsat LST pro-ducts in lowland, flat areas with homogenous land cover such as cultivated fields(Akhoondzadeh and Saradjian 2008; Coll et al. 2005; Raj and Fleming 2008; Sobrino,Jiménez – Muñoz, and Paolini 2004; Kabsch, Olesen, and Prata 2008; Wan, Zhang, andZhang 2004; Wang, Liang, and Meyers 2008). However, there is a need for more researchaiming to validate long-term LST data in mountainous areas with strong relief, highslopes, and complex land cover.

In this study, long-term validation of MODIS LST data was undertaken, based on themeteorological data provided by the Spanish National Parks network through its GlobalChange Monitoring programme. One-year ground-truth data recorded at the Robledal deCañar meteorological station, situated on the southern slope of Sierra Nevada, southeastSpain, was used to calculate the bias and seasonal variability of space-borne MODIS LSTproducts. The general aim of the present work is to assess the accuracy of space-borneMODIS thermal images acquired over a mountainous area in Sierra Nevada and toanalyse the daily and seasonal variability of the resulting bias and its possible relationshipwith factors such as cloud cover, acquisition angle, emissivity, and snow cover. Also, thedifferences in accuracy between the images acquired by the two satellites that carry theMODIS payload (Terra and Aqua) are analysed and discussed.

At the time of the study, the MODIS sensor on board Terra and Aqua satellites wasregarded as the most suitable for climatic and geo-ecological applications due to the goodcombination of temporal, radiometric, and spatial resolution, and moderate acquisitionand processing costs.

2. Study area

Sierra Nevada is the highest mountain range in the Iberian Peninsula (Mulhacén Peak3475 m a.s.l., Veleta Peak 3398 m a.s.l.) and covers extensive areas within the provincesof Granada and Almería, southeast Spain (Figure 1). During the LGM (last glacialmaximum) the quaternary glaciers reached down to 1700 m a.s.l. on the northern slopes,being Europe’s southernmost glaciers of the Pleistocene epoch (Gómez Ortiz 2002).According to the majority of studies (Paschinger 1957; Messerli 1965; Lhénaff 1977;Gómez Ortiz et al. 2001), Sierra Nevada was affected by at least two glacial phases,possibly the European Riss and Würm stages. Presently, the areas above 1700 m a.s.l. areunder the dominion of a periglacial climate with specific landforms such as gelifluxionand solifluxion terraces, patterned ground, block streams, talus cones, protalus ramparts,and relict rock glaciers (Palade, Palacios Estremera, and Gómez Ortiz 2011). The lithol-ogy is dominated by materials of the Veleta complex formed by graphite schist, quartzite,and amphibolites (Puga et al. 2007).

For the purpose of the present research, the meteorological station situated in Robledalde Cañar was chosen as the source of the in situ validation data. Robledal de Cañar (36°57′02″N 003°25′44″W) is located on the southern slope of Sierra Nevada at an altitude of1735 m a.s.l. within the Río Sucio’s hydrological basin. The mean slope value of the siteis 8° facing 205°SSW. The climate could be classified as mountain dry Mediterranean,corresponding to Csa, Csb (Mediterranean climate), and Dsc (boreal climate due to highaltitudes), types of Koeppen (Martínez and Caballero 1996), with strong daily thermal

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contrast and thermal inversion mechanisms. It should be emphasized that the results ofthis study are strictly related to the singular space of Robledal de Cañar, as it uses onlypoint data corresponding to the meteorological station. The nearby meteorological stationof Soportujar (3.3 km SE, 1700 m a.s.l.) yields an average annual temperature of 12.8°Cwhile at Capileira meteorological station (6.4 km E), the average annual temperature is13.4°C. At Soportujar, the average minimum temperatures are registered in January–February (8.1°C) and the average maximum values are recorded in August (21.3°C)(AEMET 2010). The vertical thermal gradient was calculated by Martínez, Frontana,and Goicoechea (1981) to be 0.48°C/100 m. Average rainfall is less than 600 mm(Martínez and Caballero 1996), with maximum values recorded mainly in winter due tothe Atlantic air circulation. The land cover in the vicinity of the meteorological station ofRobledal de Cañar consists of young, low-density oak. The scarcity of the vegetalassociations is conditioned by the strong evapo-transpiration and the prolonged summerdrought.

3. Data sets

3.1. Satellite imagery

For the purpose of this study we used the MODIS LST level 3 products, more preciselythe daily LST and Emissivity product resampled at 1 km of spatial resolution (MOD11A1and MYD11A1), available through the NASAWIST (Warehouse Inventory Search Tool)server (NASA 2012). Other MODIS products have been previously evaluated for theirpotential to predict surface temperatures, especially the MODIS LST 8 day composite,which has been found to be highly correlated with ground temperatures but with very lowaccuracy (Hengl et al. 2012). A total of 1458 night and day images acquired by both Terra

6° 0′ 0″W 4° 0′ 0″W 2° 0′ 0″W

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Figure 1. Geographic setting of the study region in SE Spain.

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and Aqua satellites during 2008 were processed and analysed. Both Terra and Aquaoverfly the Sierra Nevada Mountains twice a day. The precise time of the imagery variesupon the period of the year, but generally Terra has a day pass between 10:00 and 13:00and a night pass between 20:00 and 01:00, whereas Aqua has a day pass between 13:00and 15:00 and a night pass between 2:00 and 4:20, in terms of local time. Therefore, 363day and night images of Aqua (there were three days with unavailable data) and 366 dayand night images of Terra were used to cover the year 2008. As reference data we usedhigh-resolution aerial photography (50 cm of spatial resolution) and a 10 m DTM (digitalterrain model) produced by the Andalusian regional government.

3.2. In situ data

The ground-truth data consisting of soil surface temperature data were recorded with aninfrared thermometer each 10 min at the Robledal de Cañar meteorological station andlater validated by the Global Change monitoring programme running under the authorityof the Spanish National Parks network. The characteristics of the infrared thermometer aredetailed in Table 1. A total of 49,472 in situ soil surface temperature data were deliveredby the AEMET (Agencia Estatal de Meteorología) in order to be processed, analysed, andcorrelated with the satellite thermal imagery. The in situ temperature data were validatedconsidering the following criteria:

(1) recorded temperature must be within the range (–35°C)–(+50°C);(2) the hourly variation cannot exceed 25°C;(3) the 10 min variation cannot exceed 15°C; and(4) if the values exceed historical maximum and minimum, the data would be revised.

4. Methodology

Satellite imagery processing was executed partially within the ERDAS IMAGINE envir-onment (www.erdas.com), whereas initial spatial referencing and quality index extractionwere performed using LDOPE Tools (https://lpdaac.usgs.gov/tools/ldope_tools). The finalstatistics were calculated with Statgraphics (www.statgraphics.net).

MODIS LST level 3 data (MOD/MYD11A1) were used for the purpose of this study.The SDS (scientific data sets) data consist of 12 bands of information sampled at 1 km ofspatial resolution. The bands contain the night and day LST values, acquisition angle,quality assessment, time of acquisition, cloud cover, and emissivity values for bands 31and 32. Since the time of acquisition is embedded in the MODIS LST data as local solar

Table 1. Characteristics of the infrared thermometer.

Accuracy

± 0.2°C from (–10°) to (+65°C),± 0.5°C from (–40°C) to (+70°C),

± 0.1°C when the surface and the sensor have the same temperature

Spectral range 8 to 14 µmTemperature range (−55°C) to (+80°C)Field-of-view 22°Response time <1 s

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time and in situ data was recorded at the meteorological station with respect to the localSpanish time, time conversion was necessary and given by

UTC ¼ t þ IongðdegreesÞ=15; (1)

where t is the local solar time and UTC is Universal Time Coordinated.The UTC time was subsequently corrected for the Spanish time zone and the daylight

savings. The quality indices were extracted using LDOPE Tools. From the initial 1458thermal images, only 909 valid values have been produced over the site of Robledal deCañar, of which more than 92% were characterized by a maximum quality index. Withregard to the average assumed error at the satellite, 46.3% are less than 1°C, 51.1% areless than 2°C, and only 2.6% of the data are characterized by an error higher than 2°C.

Soil surface temperature data from the meteorological station of Robledal de Cañarwere compared to day and night thermal images acquired by Aqua and Terra satellites.The method used was the direct correlation between the satellite data and the correspond-ing in situ data available, i.e. no temporal average or physical–spatial up-scaling methodswere used. Several up-scaling methods exist and they are described in the recent literature(Guillevic et al. 2012). Generally they separate different emissivity classes at the sub-pixellevel in order to compensate for the land-cover heterogeneity within the coarse MODISpixel. Although good results have been achieved, it still remains to be investigated how asuitable scaling method could be applied in such a rapidly changing mountain environ-ment to compensate not only for spatial heterogeneity but also for temporal variety. The909 valid values were organized in four categories: Terra-day (Td), Terra-night (Tn), Aqua-day (Ad), and Aqua-night (An). Statistical analysis was performed on these four categoriesto assess the bias behaviour and its relationship with several technical or environmentalfactors such as acquisition angle, cloud cover, or snow cover.

In the case of MODIS LST level 3 data, LSTs are retrieved from thermal infraredimagery only in clear sky conditions (Wan 2008). However, in our study we alsoinvestigated the possible influence of adjacent cloud-contaminated pixels on the finalaccuracy of LST values. Moreover, the MOD10A1 snow product was used to assess theimportance of snow cover on the accuracy of final LST data. A discussion on the possibleinfluence of emissivity errors on temperature biases is also provided.

5. Results

The results showed only a slight difference in accuracy between Terra and Aqua satellites,but instead, a more important difference between day and night values was observed.

For the majority of the cases when night values were analysed, Terra showed a higherprecision than Aqua, with a 2.16°C of annual average bias compared to Aqua’s 2.8°C.The annual bias was homogeneously distributed during the year, although a slightlyhigher accuracy was recorded during the summer months. Figure 2 shows the monthlyaverage, and minimum and maximum bias recorded during 2008 at the Terra satelliteduring its night passes. If the July maximum bias outlier is disregarded, the three dataseries point to May–August as the most precise timeframe for satellite measurements. Themaximum accuracy was achieved for July (28 records), when the average monthly biaswas found to be 1.65°C. June and August have similar bias values of 1.68°C. The day andnight values showed different behaviour, which needs to be further analysed.

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While night values tended to be higher than the in situ data, the opposite happenedwith the day values. This means that as a general rule there is an overestimation of the insitu temperature data by the MODIS night images and an underestimation of the in situdata by the day values (Figure 3). The overall results showed that Terra night thermalimages produced the closest temperature values to in situ data with an average annualerror of 2.16°C (abs min: 0.02°C; abs max: 7.98°C). The Aqua night images had a slightlyhigher annual error of 2.8°C (abs min: 0.01°C; abs max: 6.95°C).

The day values showed very high average annual errors of 5.33°C and 5.03°C forTerra and Aqua, respectively. However, all the data sets showed high correlation coeffi-cients with the in situ data (Figure 4). Aqua night values had the highest correlation valueswith an R2 = 0.94 while Aqua day values were characterized by the lowest correlationcoefficient R2 = 0.78. The relationship between the Terra night values (Tn) and the in situdata (Tm) can be described by the following lineal regression equation:

Tm ¼ 1:05Tn � 2:95: (2)

As a general trend, the correlation values were higher for the night images than for the dayimages (Figure 4), irrespective of the satellite used. A similar conclusion was reached alsoby Colombi et al. (2007) for the Italian Alps.

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Figure 3. Overestimation of night values (left) and underestimation of day values (right) byMODIS sensor.

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5.1. Terra night values

The night thermal images acquired by the Terra satellite were characterized by highcorrelation coefficient with the in situ data. During 2008, 240 cloud-free images werecollected and processed. Generally, the number of images was well distributed along theentire year with a maximum of 28 values for July and a minimum of 14 values for Mayand December. Function (2) describes the relationship between 93% of the Terra nightvalues and the in situ data. With an RMS error of 0.93, the thermal data generated fromTerra night images are very promising for future climatic applications. The annual averagebias was 2.16°C for the whole of 2008 while more than 90% of the values had an errorunder 3.6°C. The daily bias values varied between 0.02°C and 7.98°C with a standarddeviation of 1.8°C. Among all the data sets analysed (Tn, Td, An, and Ad), Tn values hadthe highest accuracy. There was no correlation found between the bias values and the insitu data recorded at the Robledal de Cañar meteorological station (RMSE = 0.16).Comparing the in situ and the satellite values’ histograms, an obvious difference wasnoticed in the range of values. In situ data stretched from –16.5°C to 20.8°C whileMODIS LST data ranged only from –8.17°C to 20.89°C (Figure 5), thus indicatingpossible difficulties for the split window algorithm to calculate low LSTs.

Analysing both data series (in situ and MODIS LST), an important overestimation oftemperature by the MODIS sensor was observed, especially when negative values wereconcerned. The annual temperature average at the meteorological station was 5.8°Cwhereas the same parameter calculated for the satellite data was 7.5°C.

5.2. Terra day values

MODIS sensor on board Terra produced 212 cloud-free day thermal images over the siteof Robledal de Cañar during 2008. Compared to the in situ data the overall RMSE of the

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Figure 4. Graphs showing the correlation between MODIS sensor and ground-truth data.

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satellite data series was 0.82, thus indicating high correlation between both data series.The lineal regression function describes 82% of the in situ temperature values:

Tm ¼ 1:15Td — 0:12: (3)

However, although highly correlated, the Terra day images had an annual average error of5.33°C, i.e. substantially higher than the night values. The bias values stretched fromminimum values of 0.02°C to maximum values of 19.12°C with less than 25% of thevalues under 2.2°C of error, i.e. the average error of the night values.

As an overall trend, Terra day images tended to underestimate the in situ data, thistendency being most obvious during the spring and summer months. While the averageannual temperature at the meteorological station was 28.72°C, the Terra day LST yieldedan average temperature value of 24.94°C. Comparing the range of temperatures, MODISLST values stretched from 0.89°C to 43.93°C, whereas the recorded values at themeteorological station stretched from –1.98°C to 45.73°C. Again, no correlation wasfound between the annual distribution of the bias and the in situ temperature data(RMSE = 0.14).

5.3. Aqua night values

A total of 256 thermal images produced by the Aqua satellite during its night passes wereprocessed and compared to the ground-truth data. The annual average bias found was 2.8°C.The minimum bias found was 0.01°C, whereas the maximum bias was 6.95°C. The overallerror was homogeneously distributed during the entire year with a slow increase during thewinter months (December, January, and February) when the average monthly bias reachedup to 3.8°C. Similar to the case of Terra night values, the thermal images recorded on boardAqua tended to overestimate the real temperature values (ground-truth data), especiallyduring the winter months. As a matter of fact, the annual average An temperature was 2.7°Chigher than the temperature recorded at the meteorological station (6.8°C and 4.1°C,respectively). As expected, the range of in situ temperatures was bigger than the range ofsatellite temperatures.

Among all the data sets analysed, An values showed the highest correlation with the insitu data with an RMSE of 0.94. The linear model found is

Tm ¼ 1:04An — 2:95: (4)

Meteorological Station

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Figure 5. Temperature ranges recorded at satellite (Terra) and in situ (meteorological station).

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5.4. Aqua day values

Aqua day thermal images totalized 201 values that were processed and analysed. Theaverage annual bias was 5.03°C, with a minimum and a maximum error of 0.01°C and15.83°C, respectively. The error distribution trend was similar to the Td values, indicatingan underestimation of the ground-truth data, with the highest error during the winter–spring period (January to May). The average annual temperature at Robledal de Cañarmeteorological station was 29.29°C whereas the average calculated from An images was25.32°C. The range of values was slightly more limited for the satellite data than for themeteorological station (3.23–43.15°C and 0.11–43.99°C, respectively).

Despite the high average bias, the correlation with the in situ data was moderatelyhigh, with an RMSE of 0.78. The linear model found is

Tm ¼ 1:02Ad þ 3:29: (5)

As in the previous cases, no relationship was found between the distribution of the biasduring 2008 and the in situ data recorded at the meteorological station in Robledal deCañar.

Additional statistical analysis was performed to relate the LST bias with physical (atsatellite) or climate factors like acquisition angle, and snow and cloud cover. The snowcover was mapped from the MOD10A1 snow product and later spatially correlated withMODIS LST biases during the whole of 2008. No influence of snow cover on MODISLST error variation could be observed. The acquisition angle and cloud cover for everyLST recorded during both day and night by both satellites were extracted from bands 4, 8,11, and 12 of MOD/MYD11A1. Although MODIS LST values are extracted from thermalinfrared data only in clear sky conditions, adjacent pixels may as well be contaminatedsince thin clouds are not always detected by the cloud mask algorithm (Neteler 2010).However, none of these analyses revealed any correlation between cloud adjacency,acquisition angle, and MODIS LST bias.

6. Discussion

The main objective of this article was to analyse the possibility of using MODIS thermalimages acquired on board Terra and Aqua satellites for geomorphologic and climaticstudies in high mountain environment. Particularly, the behaviour of satellite thermal dataand the dimension of the associated biases in difficult topoclimatic conditions that are acharacteristic of high mountain environment, like relief displacement, shadow effects,mixed land-cover type (grass, bare soils, boulders) with different emissivity values, strongtemperature contrasts and wind effects, and frequent snow and cloud cover, were ana-lysed. This situation, combined with the moderate spatial resolution of the MODIS sensor(1 pixel = 1 km), can make the use of this type of data difficult for geo-climatic studies.Hence, we believe that the validation of MODIS data for mountainous environment isimportant.

One of the key facts revealed is the different behaviour of the MODIS sensor duringthe night and day passes. There was an obvious overestimation of the ground tempera-ture values during the night passes by 1.7°C in case of Terra and by 2.7°C in case ofAqua and a significant underestimation of the same parameter by 3.78°C in case ofTerra and by 3.97°C in case of Aqua during the day passes. The latter could be aconsequence of the high aridity index of the study area determined by the barrier effect

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of Sierra Nevada’s northern slope facing the humid Atlantic fronts. The particulartopoclimatic conditions favour important daily temperature differences that can reachup to 40°C during summer (Messerli 1965). At this point it should be emphasized thatMODIS technical papers warn of possible high errors of LST products over dry areasabove 30° latitude (Wan 2006).

It has also been discovered that during long-term data validation, the temperaturerange recorded on board both satellites was narrower than the temperature range registeredat the meteorological station. It is still to be discussed whether this is due to sensorsaturation issues or limitations of the split-window algorithm. Finally, an attempt wasmade to detect the possible factors that may affect the accuracy of MODIS LST data.Wang, Liang, and Meyers (2008) already analysed the factors that could cause errors inthe MODIS LST products, focusing on the zenith angle, surface air temperature, humidity,wind speed, and soil moisture. Among all, only the zenith angle was found to becorrelated to the LST errors. However, in our study, no conclusive remark could bedrawn on this matter after analysing LST errors and acquisition angle for the entire Tndata set, although, on average, the errors of LST derived from TIR images acquired at anangle superior to 30 degrees were 0.3°C higher than the rest. Regarding the influence ofcloud cover, theoretically, night LST images should be more affected by cloud contam-ination than the day LST images, since at night only TIR bands are used for clouddetection and masking. Nevertheless, amongst all data sets used for MODIS validation,night LST values yielded the highest accuracy in our study.

Perhaps the most problematic aspect of deriving LST from satellite infrared imagery isthe assumed emissivity values. The infrared thermometer at Robledal de Cañar lies at 2 mabove the surface and has a view angle of 22°, generating a field of view of 0.8 m indiameter. This results in a much more homogenous land cover than in the case of the1 km × 1 km MODIS pixel. The surface viewed by the infrared thermometer could becharacterized as a mixture of dry grass and bare soil during summer, but snow cover isfrequent during winter. At the level of the MODIS pixel, from a supervised classificationexecuted on a September image, the land cover is composed mainly by dry grass (25%),wetter grass (10%), bare soil (35%), and young, sparse oak (30%), although two smallwater ponds and some sparse buildings do exist. However, their influence is minimal. Theoak phenological cycle varies during the year, from periods with green leaves to dryleaves and no leaves at all during winter. Also the percentage of wet grass/dry grass variesthroughout the year. For our study, MODIS emissivity values in bands 31 and 32 werecalculated for every valid Terra diurnal LST measurement during 2008. For band 31, themean emissivity value was 0.983 with minimum and maximum values of 0.982 and0.994, respectively. These values do not vary according to the phenological cycle of thevegetation at the site of interest (there are only five different emissivity values that repeatduring the year), but rather at intervals of several days. Actually, 99% of the assumedMODIS emissivity values are just two: 0.982 and 0.984. This causes constant errors inLST measurements throughout the year and it is still unknown how this variationaccommodates winter snow days.

For band 32, mean emissivity was found to be 0.986. Again, two values account formore than 98% of the measurements: 0.986 and 0.988. One of the drawbacks of the splitwindow algorithm is that integrated emissivity values are classification based (stored inone lookup table) and later actively modified with snow cover, vegetation index, andviewing angle coefficients (Snyder et al. 1998). During winter, it is unclear how themixture of sparse trees and snow cover can be successfully modelled. Perhaps this couldaccount for the percentage of higher biases found during winter LST measurements.

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7. Conclusion

A number of studies used MODIS products, especially the 8 day composite LST, in orderto fine-tune the prediction of ground or atmospheric temperature by statistical andphysical modelling (Pape, Wundram, and Löffler 2009; Hengl et al. 2012). The presentstudy tried to investigate whether daily MODIS LST measurements alone could serve asthe foundation for climate modelling in high mountain environment.

Long-term MODIS LST validation during 2008 gave overall variable results. On theone hand, the calculated biases for night-time and summer month measurements were closeto 1°C, the results being promising for future integration in climate models over mountai-nous environment. On the other hand, daytime LST values yielded concerning errors withan average annual bias superior to 5°C, therefore making problematic the use of thisinformation as input data in local or regional climate models. Mixed pixel problem(different land-cover types within the same pixel), emissivity, shadow effects, and hightemperature contrast during the day could explain the important difference between thenight images and the day images. As explained by Wang, Liang, and Meyers (2008), duringnight, the earth behaves as an isothermal body, meaning that the temperature variationswithin a relatively small area are limited. During the day, however, principally due toshadow effects, the thermal contrast in Sierra Nevada could reach up to 40°C duringsummer months. Consequently, the point measurements taken at Robledal de Cañar maynot reflect all the shadow conditions present within the MODIS 1 km × 1 km pixel.

Nevertheless, more research is needed to explain the overestimation of night-timemeasurements and the underestimation of daytime measurements in the case of both Terraand Aqua satellites. As stated by Wan (2010), the explanation may reside in the fact thatthe split-window algorithm underestimates daytime temperatures in bare soil areas, gen-erally because high LST values in these conditions were not considered in the develop-ment of the split-window algorithm. On the other hand, landform’s heterogeneity and thesampling of the 5 km emissivity product on the 1 km MODIS LST grid could alsointroduce important errors in semi-arid areas (Hulley and Hook 2010). In addition, theemissivity values used to calculate LST by MODIS are found to be constant; thus theircorrelation with the vegetation phenological cycle is very low.

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