Spatiotemporal Variations of Lake Surface Temperature Across the Tibetan Plateau Using MODIS LST Product

remote sensing

Article Spatiotemporal Variations of Lake Surface Temperature across the Tibetan Plateau Using MODIS LST Product

Kaishan Song *, Min Wang, Jia Du, Yue Yuan, Jianhang Ma, Ming Wang and Guangyi Mu Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China; [email protected] (M.W.); [email protected] (J.D.); [email protected] (Y.Y.); [email protected] (J.M.); [email protected] (M.W.); [email protected] (G.M.) * Correspondence: [email protected]; Tel.: +86-431-8554-2364

Academic Editors: Ruiliang Pu, Xiaofeng Li and Prasad S. Thenkabail Received: 13 June 2016; Accepted: 12 October 2016; Published: 17 October 2016 Abstract: Satellite remote sensing provides a powerful tool for assessing lake water surface temperature (LWST) variations, particularly for large water bodies that reside in remote areas. In this study, the MODIS land surface temperature (LST) product level 3 (MOD11A2) was used to investigate the spatiotemporal variation of LWST for 56 large lakes across the Tibetan Plateau and examine the factors affecting the LWST variations during 2000–2015. The results show that the annual cycles of LWST across the Tibetan Plateau ranged from −19.5 ◦C in early February to 25.1 ◦C in late July. Obvious diurnal temperature differences (DTDs) were observed for various lakes, ranging from 1.3 to 8.9 ◦C in summer, and large and deep lakes show less DTDs variations. Overall, a LWST trend cannot be detected for the 56 lakes in the plateau over the past 15 years. However, 38 (68%) lakes show a temperature decrease trend with a mean rate of −0.06 ◦C/year, and 18 (32%) lakes show a warming rate of (0.04 ◦C/year) based on daytime MODIS measurements. With respect to nighttime measurements, 27 (48%) lakes demonstrate a temperature increase with a mean rate of 0.051 ◦C/year, and 29 (52%) lakes exhibit a temperature decrease trend with a mean rate of −0.062 ◦C/year. The rate of LWST change was statistically signiﬁcant for 19 (21) lakes, including three (eight) warming and 17 (13) cooling lakes for daytime (nighttime) measurements, respectively. This investigation indicates that lake depth and area (volume), attitude, geographical location and water supply sources affect the spatiotemporal variations of LWST across the Tibetan Plateau.

Keywords: MODIS imagery; bulk temperature; Tibetan Plateau; water supply source

1. Introduction Lake water surface temperature (LWST) has been regarded as a critical parameter in controlling functioning processes in aquatic ecosystems, e.g., physical, chemical and biological processes [1,2], and serious ecological consequences may be caused by lake warming [3,4]. As demonstrated by [5], some of the large lakes across the world show obviously increasing temperature as a result of climate change [6]. For example, the surface temperature of Lake Baikal, the deepest lake in the world increased by about 1.2 ◦C since 1946 [7]. Lake temperature reflects its morphology, watershed and hydrological conditions and eventually influences aquatic organisms [8,9]. Conventional approaches to measurement of lake water temperature use in situ data loggers to measure LWST. While such measured information is temporally continuous, its application for capturing temperature variations at large scale is constrained because of its limited spatial coverage [9]. Synoptic information is needed to map the sources of thermal heterogeneity at the watershed scale and examine the influence of the thermal variation across lake surface that may influence biophysical-chemical processes for a specific water body [10–12].

Remote Sens. 2016, 8, 854; doi:10.3390/rs8100854 www.mdpi.com/journal/remotesensing Remote Sens. 2016, 8, 854 2 of 20

Lake water temperature is mainly influenced by the absorption of solar energy, which depends on an array of physical, chemical, and, to some extent, biotic properties of waters being concerned [1,13]. LWST changes seasonally due to the annual insulation and the variation of air temperature [1,9,14,15]. The amount of light absorbed by water increases exponentially with the distance of the light traveling through the water column, particularly those at wavelengths shorter than 750 nm [6,14]. High specific heat of water permits the dissipation of light energy and is accumulated as heat in the water column. However, heat retention is coupled with a number of factors that influence its distribution within lake systems, e.g., the wind speed, currents and other water movements, the morphology of watersheds, and gains and losses of water [13,14]. Thus, the pattern of thermal evolution and stratification fundamentally influences the physical and chemical cycles of lakes, which in turn govern lake production and decomposition processes [8]. Thermal remote sensing provides a synoptic context for evaluating the relationships between landscape and water thermal characteristics [4,16,17], and has been widely applied for mapping LWST and the circulation pattern of lakes [12,18]. For example, NOAA/AVHRR and Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra/Aqua has been widely used for monitoring lake surface temperature [19,20], while a bunch of investigations have used Landsat-TM/ETM+/OLI and ASTER for retrieving the thermal features of inland and coastal sea waters [4,18]. Known as the world “Third Pole” and the “Asian Water Tower”, the Tibetan Plateau has experienced obvious climatic change in the past several decades [21,22]. Consequently, the cryosphere and hydrologic environment have substantially changed on the plateau [22–24]). However, few studies have been conducted to map LWST in alpine environments like the Tibetan Plateau where the elevated land surface temperature may raise LWST of this region [9,20]. In this study, the LWST pattern of 56 lakes in the Tibetan Plateau was examined to determine its association with landscape variations, hydrologic conditions and relief gradients. The objectives of this study are to: (1) examine the LWST variations of lakes across the Tibetan Plateau by using time series MODIS LST data; (2) compare the seasonal behavior of WST for water bodies with various morphological conditions; and (3) examine the potential impact factors that regulate the spatial and seasonal WST variations.

2. Materials and Methods

2.1. Study Area The Tibetan Plateau is an extensive elevated plateau in China, covering an area about 2.5 million km2. Endorsed with high elevation and tens of thousands of glaciers, the plateau is the headwaters of the drainage basins for most of the rivers in the surrounding regions. The impact of global warming on the Tibetan Plateau is of intense scientiﬁc interest [20,22]. Annual precipitation ranges from 100 to 1300 mm and falls mainly as rainfall, snow and hailstorms [23]. Proceeding to the north and northwest, the plateau becomes progressively higher, colder and drier, until reaching the remote Changthang region in the northwestern part of the plateau, where the average elevation exceeds 5000 m and winter temperatures can drop to −40 ◦C. Thousands of closed lakes have developed on the Tibetan Plateau, and the total area is approximately half of the lake area in China [20], and most of which is sensitive to global warming [22,24]. Mainly fed with snow and glacier melting waters, some closed lakes are experiencing expansion in summer and autumn, which is more obvious when lakes are supplemented with glacier melting waters [22,24]. Lake surface temperature drops progressively from late autumn to winter, and begin to freeze in late November or December, and ice-off generally begins in April or May of the following year. Inﬂuenced by various geographical settings, elevation and climatic conditions, lake surface area and water volume show different responses to climate variability, while the response of LWST is yet not fully understood [25]. Remote Sens. 2016, 8, 854 3 of 20 Remote 2016, 8, 854 3 of 20

2.2. MODIS Imagery and Processing Due to the limited number of valid pixels in the MODIS daily LST product [[12],12], we use the MODIS LST 8-day composite product (level 3, MOD11A2),MOD11A2), which integrates the average of daily LST observations in eight days with a nominalnominal resolution of 11 kmkm22. The The data data temporal span span is 16 yearsyears starting fromfrom 1 1 June June 2000 2000 to 31to December31 December 2015. 2015. According According to previous to previous studies, studies, the MODIS the LSTMODIS products LST overproducts lake over surfaces lake were surfaces evaluated were evaluated with in situ with measured in situ temperature,measured temperature, and the absolute and the differences absolute betweendifferences MODIS between derived MODIS and derived in situ and measurements in situ meas rangedurements from ranged 0.8 tofrom 1.9 0.8 K [26to ,1.927]. K The[26,27]. MODIS The ReprojectionMODIS Reprojection Tool (MRT, Tool Land (MRT, Processes Land Processes DAAC, 2008) DAAC, was 2008) used was to extract used to 8-day extract composite 8-day composite LST data fromLST data 5 grid from tiles 5 and grid to tiles cover and the to Tibetan cover Plateau.the Tibeta Withn Plateau. limited directWith limited field temperature direct field measurements, temperature themeasurements, validation of the MODIS validation LST of products MODIS wasLST conductedproducts was by conducted following twoby following approaches. two Theapproaches. MODIS productsThe MODIS with products quasi-concurrent with quasi-co fieldncurrent survey were field validated survey were with validated direct temperature with direct measurements temperature overmeasurements lakes collected over inlakes 2014 collected and 2015. in In 2014 addition, and 2015. the reliabilityIn addition, of MODISthe reliability LST was of alsoMODIS validated LST was via comparingalso validated MODIS via comparing LST time seriesMODIS values LST time with series ground values surface with temperature ground surface (GST) temperature at 0 cm measured (GST) overat 0 cm five measured weather over stations five distributedweather stations across distributed the Tibetan across Plateau the (FigureTibetan1 ),Plateau which (Figure is proven 1), which to be anis proven effective to methodbe an effective to assess method LWST to over assess Lake LWST Siling over Co[ 12Lake]. Siling Co [12].

Figure 1.1.The The distribution distribution of lakesof lakes (>50 (>50 km2 )km concerned2) concerned and the and stations the stations for validation for validation dataset collection dataset acrosscollection the across Tibetan the Plateau. Tibetan Plateau.

2.3. Methods for Lake Skin Temperature CharacterizationCharacterization After mosaicking byby useuse ofof MRT,MRT, 8-day 8-day composite composite MODIS MODIS LST LST images images were were projected projected to to Albers Albers in GeoTIFFin GeoTIFF format format with with the the nearest nearest neighbor neighbor interpolation interpolation method. method. Four Four layers laye werers were extracted extracted from from LST product,LST product, i.e., daytime i.e., daytime LST (overpass LST (overpass time is about time 10.30 is about a.m. of10.30 local am time), of nighttimelocal time), LST nighttime (overpass timeLST is(overpass about 22.30 time p.m.is about of local 22.30 time) pm andof local corresponding time) and quality corresponding control images quality (QC-daytime control images and QC-nighttime(QC-daytime ).and Due QC-nighttime). to the strong contrast Due to between the strong land contrast and water between spectral land features and inwater near infraredspectral (NIR)features and in green near bands, infrared a threshold (NIR) and was usedgreen to bands, extract watera threshold body boundaries was used basedto extract on the water NIR/Green body ratioboundaries of Landsat based TM on images. the NIR/Green The boundary ratio shape of La filendsat was TM generated images. for The lakes boundary with surface shape area file greater was thangenerated 50 km for2 (56 lakes lakes) with using surface Landsat area greater TM images than 50 mainly km2 (56 acquired lakes) using in 2008 Landsat and making TM images reference mainly to theacquired MODIS in reflectance2008 and making product reference (MOD09Q1). to the To MODIS avoid errorsreflectance resulting product from (MOD09Q1). the fluctuation To ofavoid the land-watererrors resulting interface, from a the 1-km fluctuat bufferion zone of offshorethe land-water was generated interface, to excludea 1-km LSTbuffer pixels zone along offshore shoreline was zonesgenerated [12]. to exclude LST pixels along shoreline zones [12]. Although thethe 8-day8-day composite composite product product was was utilized utilized in thein study,the study, cloud cloud contamination contamination still exists still andexists an and appropriate an appropriate procedure procedure should should be taken be to ta removeken to problematicremove problematic pixels [12 pixels,26]. The[12,26]. method The proposedmethod proposed by [12] was by adopted [12] was to removeadopted unreliable to remove LST unreliable pixels over LST lakes pixels using over QC informationlakes using andQC medianinformation filtering and of median time series filtering LST data of time stacks. series With LST reference data stacks. to the LST With data reference quality flagto the files LST stored data in QCquality file, flag all pixels files stored that have in QC averaged file, all LST pixels errors that less have than averaged 1K (i.e., QCLST =errors 0, 1, 5, less 17, than 21), pixels 1K (i.e., with QC other = 0, QC1, 5, values 17, 21), were pixels removed. with other In the QC second values step, were a data removed. cube was In generated the second through step, stackinga data cube 46 layers was generated through stacking 46 layers of LST according to the day-of-year sequence, and then filtered by application of median filtering algorithms to eliminate problematic pixels in the LST images (see

Remote Sens. 2016, 8, 854 4 of 20 of LST according to the day-of-year sequence, and then filtered by application of median filtering algorithms to eliminate problematic pixels in the LST images (see supplementary Figure S1). For most of the cases considered here, the 8-day composite contains valid pixels greater than 70%, and only 37 out of 1472 images (2.5%) in the past 16 years contained valid pixels less than 70% (Figure S2). The presence of monthly effective images followed a similar temporal pattern when all the years considered (Figure S3). More effective images were identified from April to June, and September to mid-December. Furthermore, more effective images were also found from the nighttime MODIS LST product (Figure S3), which is consistent with the findings from other investigations [12,20,26].

2.4. Deﬁnition and Calculation of LWST The mean LWST is derived by averaging all pixels of daytime or nighttime LST images composited in a speciﬁc period for a given lake. With respect to monthly (e.g., freeze-up and break-up month) and annual LWST for the daytime or nighttime SWT, the mean was derived by aggregating the 8-day LWST composite for the corresponding time series [12]. Diurnal temperature differences (DTDs) of LWST were derived with daytime temperatures subtracted by nighttime ones. For each year, the ice break-up month starting point was determined through referencing to day-night averaged LWST of 56 lakes above 0 ◦C, and four 8-day LWST consecutive MODIS measurements were accounted since then. The warm month across the Tibetan Plateau starts from the end of July to mid of August with referencing to the highest day-night averaged LWST of 56 lakes in each year. The freeze-up month ending point was determined through referencing to day-night averaged LWST of 56 lakes about 0 ◦C for each year, four 8-day LWST consecutive measurements before that point were accounted as the freeze-up month. The intra-annual variation of LWST was analyzed by averaging daytime and nighttime SWT time series of ice-free seasons in the past 16 years. In each year, the annual average LST maps were the average of ice-free season 8-day maps calculating via pixel-by-pixel. In addition, ice break-up, freeze-up date, and ice-free duration having direct connection with LWST, were also calculated through counting the accumulative days of the mean temperature ((daytime + nighttime)/2) above, below 0 ◦C for each of the 56 lakes concerned, where the median of the Julian Day for each 8-day composite was considered for accumulative days counting. Further, to track the temporal trend of LSWT for each of the 56 lakes, the rate of temperature annual change from MODIS LST data was regressed against the data acquisition year using the following regression model [20]:

y = a + bx + e (1) where y represents the MODIS LST, x denotes the time series of years, e represents the residuals, a is the intercept, and b is the rate of temperature change; a and b are determined through least squares ﬁtting.

3. Results

3.1. Validation of LST MODIS Product

3.1.1. Validation by in Situ Measured Data In situ measurements were carried out in 2014 and 2015 in some of the lakes (Figure1) using YSI portable probes (YSI Inc., Yellow Spring, OH, USA) placed in open water at a depth of 0.3 m, and the sampling time ranged from 9:00 a.m. to 4:00 p.m. local time. Although in situ measurement of water surface temperatures at different stations were not coincident with the MODIS overpass time, the data were still used here due to the scarcity of the validation dataset. The MODIS images were selected if the ﬁeld survey day fell within one of the eight-day composite. Altogether, 26 stations match up with the MODIS LST products during these surveys, and the average of each 3 × 3 pixel window was extracted and compared with in situ measured temperatures. The scatter plot of LWST versus in situ measured lake water surface temperature shows a cool bias (LWST-in situ) of 1.74 ◦C Remote Sens. 2016, 8, 854 5 of 20

Remote 2016, 8, 854 5 of 20 and a root mean squared error (RMSE) of 2.8 ◦C (Figure2a). Apart from known potential error sources,effects [26,28], including a major instrumental source noisemight and be drift,due to sunlint, mismatch cloud up contamination, between the andtiming surface of the emissivity in situ effectsmeasured [26, 28data], a and major MODIS source over-passing, might be due towhich mismatch was further up between interfered the timing by the of eight-day the in situ composite. measured dataAs argued and MODIS by [25] over-passing, that the differences which was in further the ob interferedservation by time the eight-daybetween composite.satellites and As arguedin situ bymeasurements [25] that the may differences induce insome the observationdeviations, the time MO betweenDIS-derived satellites LWST and still in situshowed measurements its applicability may inducein characterizing some deviations, the thermodynamic the MODIS-derived features LWST of still the showed lakes its across applicability remote in areas characterizing with harsh the thermodynamicenvironmental settings. features Further, of the lakes considering across remote the va areaslidation with work harsh over environmental Lake Titicaca settings. by [26], Further, good consideringperformance theof MODIS validation LST work is expected over Lake over Titicaca the plateau by [ 26due], goodless aerosol performance interfering of MODIS with the LST path is expectedradiation.over Thus, the the plateau eight-day due MODIS less aerosol LST interferingcomposite is with applicable the path for radiation. investigating Thus, the the lake eight-day water MODIStemperature LST compositeover the plateau. is applicable for investigating the lake water temperature over the plateau.

Figure 2. MODISMODIS land land surface surface temperature temperature (LST) (LST) product product validation validation with with both both in situ in measured situ measured data data(a); and (a); andmeteorological meteorological data data (b) (wereb) were collected collected in infive ﬁve different different stations stations while while in in situ situ data werewere measured inin 1515 lakeslakes acrossacross thethe TibetanTibetan PlateauPlateau inin 20142014 andand 2015.2015.

3.1.2. Validation byby MeteorologicalMeteorological DataData The GSTs from from five five meteorological meteorological stations stations of of di differentfferent elevations elevations were were used used to validate to validate the theeight-day eight-day composited composited LST product LST product (see Figure (see Figure1 for locations).1 for locations). The averaged The averaged pixel values pixel of values a 3 × 3 ofwindowa 3 × 3 centeredwindow at centered each meteorological at each meteorological station were station extracted were and extracted were regressed and were against regressed the againstcorresponding the corresponding GST values. GST As values. shown As in shown Figure in Figure2b, a 2relativeb, a relative high high overall overall coefficient coefficient of 2 ofdetermination determination (R (R =2 0.88)= 0.88) and and low low RMSE RMSE (1.8) (1.8) were were achieved achieved for for the the pooled pooled GST GST dataset, with a bias 2 of 2.32.3 ◦°C.C. According to the regressionsregressions betweenbetween MODISMODIS derived LST and GST,GST, thethe slopesslopes andand RR2 variedvaried forfor differentdifferent stations. stations. The The Mangya Mangya station, station, located located in in west west of Lakeof Lake Gas Gas Hure Hure (see (see Figure Figure S4) andS4) theand lowest the lowest elevation elevation station station (3500 (3500 m) selected m) selected for validating for validating the MODIS the MODIS LST product, LST product, showed showed good consistencygood consistency between between MODIS MODIS derived derived LST and LST GST and (y = GST 1.091x (y += 0.87,1.091x R2 += 0.87, 0.977, R² Bias = 0.977, = 1.14). Bias Likewise, = 1.14). theLikewise, Chaka the station Chaka also station exhibited also good exhibited performance good performance (y = 0.925x + (y 0.968, = 0.925x R2 = 0.979,+ 0.968, Bias R² == 1.38),0.979, but Bias the = slope1.38), isbut not the close slope to unity.is not Accordingclose to unity. to the According result obtained to the fromresult the obtained Maduo from Station, the theMaduo MODIS Station, LST stillthe showedMODIS stableLST still performance showed stable (y = 1.053xperformance− 0.184, (y R 2== 1.053x 0.903, − Bias 0.184, = 2.57). R² = However,0.903, Bias both = 2.57). the Dangxiong However, (yboth = 0.772x the Dangxiong + 1.593, R 2(y= = 0.937, 0.772x Bias + 1.593, = 3.4) R² and = 0.937, Pulan Bias (y = = 0.769x 3.4) and− 4.729,Pulan R(y2 = 0.769x 0.917, Bias− 4.729, = 4.7 R²) stations= 0.917, exhibitedBias = 4.7) largestations biases exhibited by showing large biases lower by slope show valuesing lower far from slope unity values (see far Figure from1 unityand Figure (see Figure S4 for 1 location).and Figure The S4 large for discrepancylocation). The between large GSTdiscrepancy and MODIS between LST for GST stations and DangxiongMODIS LST and for Pulan stations may resultDangxiong from theand heterogeneity Pulan may result of the from landscape. the heterogene Further,ity the of instrument the landscape. deployment Further, depththe instrument may also causedeployment the discrepancy depth may between also cause GST andthe MODIS-deriveddiscrepancy between LST. AsGST argued and byMODIS-derived [12], it is reasonable LST. As to expectargued that by [12], the quality it is reasonable of MODIS to LST expect over that water the bodies quality is betterof MODIS than thatLST over over land water surfaces bodies since is better lake surfacesthan that are over rather land homogenous, surfaces since thus lake both spatialsurfaces and are temporal rather homogenous variations are, morethus stableboth spatial than that and of landtemporal due tovariations the larger are heat more capacity stable andthan less that varied of land emissivity due to the [25 larger]. Thereby, heat capacity the stable and performance less varied ofemissivity the MODIS [25]. LST Thereby, product the justifies stable this performa investigationnce of to thethe thermalMODIS characteristics LST product of justifies lakes across this theinvestigation Tibetan Plateau, to the wherethermal very characteristics limited field of surveys lakes across were conductedthe Tibetan for Plat monitoringeau, where the very lake limited water temperaturefield surveys features were conducted [21]. Further, for monitoring the relative the temperature lake water trend temperature rather than features absolute [21]. temperature Further, the is morerelative concerned temperature of lakes trend across rather the than plateau absolute in this temperature study. is more concerned of lakes across the plateau in this study.

RemoteRemote Sens. 20162016, 8, ,8548, 854 6 of6 of20 20

3.2. Spatial Pattern of Lake Surface Temperature 3.2. Spatial Pattern of Lake Surface Temperature 3.2.1. LWST Pattern of Lakes across the Tibetan Plateau 3.2.1. LWST Pattern of Lakes across the Tibetan Plateau The spatial temperature pattern of the month after ice break-up (Julian Day 114 to 144) is demonstratedThe spatial in temperature Figure 3. A patternlarge spatial of the variation month was after observed ice break-up for LWST, (Julian ranging Day 114from to −2.0 144) to is ◦ demonstrated14.0 °C (Figure in Figure 3a), the3. A coldest large spatial one was variation recorded was in observed Nam Co, for and LWST, the warmestranging fromone was−2.0 measured to 14.0 C (Figurewith 3ina), Lake the coldestYamdrok one (see was Figure recorded S4 for in Namlocations) Co, and. In thegeneral, warmest warmer one waslakes measured were situated with inin Lakethe Yamdroksouthern (see part Figure of the S4 plateau, for locations). while Inrelative general, low warmer temperature lakes werelakes situatedwere located in the in southern the Hoh part Xil of theregion. plateau, It can while be noted relative that low shallow temperature lakes exhibited lakes were relative located high in temperature, the Hoh Xil i.e., region. Lake It Dabsun, can be notedand thatLake shallow CuoE, lakes which exhibited are close relativeto Lake highSiling temperature, Co (Figure S4). i.e., With Lake respect Dabsun, to the and nighttime Lake CuoE, temperature which are closepattern, to Lake it can Siling be observed Co (Figure in Figure S4). With3b that respect low LWST to the was nighttime still exhibited temperature with lakes pattern, in the Hoh it can Xil be observedregion, inwhile Figure warm3b that temperature low LWST was was recorded still exhibited in Lake with Dabsun lakes and in the Gas Hoh Hure. Xil region,Lake Qinghai whilewarm and temperatureAyakekumu was in recordedthe north in and Lake those Dabsun lakes and locating Gas Hure. in the Lake southern Qinghai part and of Ayakekumu the plateau in showed the north andrelatively those lakes high locating temperature in the during southern the part nighttime of the plateau(see Figure showed S4 for relatively locations). high As temperature expected (Figure during the3c), nighttime large spatial (see Figurevariations S4 forof diurnal locations). temperatur As expectede differences (Figure 3(DTDs)c), large were spatial demonstrated variations offor diurnal lakes temperatureacross the plateau. differences Small (DTDs) DTDs were were observed demonstrated for large for and lakes deep across lakes, the e.g., plateau. Lake Qinghai, Small DTDs Nam Co, were observedSiling Co, for and large Ayakekumu, and deep lakes, large e.g., DTDs Lake were Qinghai, generally Nam found Co, for Siling small Co, ones and located Ayakekumu, in the south large DTDsand werewest generally of the plateau, found forand small those ones intermediate located in DTDs the south were and found west for of lakes the plateau, mainly located and those in the intermediate Hoh Xil region (see Figure S4 for locations). DTDs were found for lakes mainly located in the Hoh Xil region (see Figure S4 for locations).

FigureFigure 3. 3.Spatial Spatialpattern pattern ofof thethe monthlymonthly averagedaveraged wa waterter surface surface temperature temperature after after ice ice break-up break-up month:month: (a ()a daytime) daytime lake lake water water surface surface temperaturetemperature (LWST)(LWST) variation; ( (bb)) nighttime nighttime LWST LWST variation; variation; andand (c )(c lake) lake water water surface surface diurnal diurnal temperature temperature differencedifference (DTD).(DTD).

Remote Sens. 2016, 8, 854 7 of 20 Remote 2016, 8, 854 7 of 20

TheThe spatial spatial pattern pattern of of LWST LWST for for lakes lakes in in thethe warmwarm month (Julian Day Day 200–232, 200–232, a agood good indicator indicator forfor the the highest highest LWST LWST reached reached in the in region)the region) was demonstratedwas demonstrated in Figure in Figure4, with 4, temperature with temperature ranging fromranging 10 to from 28.7 10◦ Cto for28.7the °C for daytime, the daytime, and 3.0and to 3.0 19.0 to 19.0◦C °C for for nighttime. nighttime. AsAs shown in in Figure Figure 4a,4a, thethe warmest warmest temperature temperature waswas foundfound in in Lake Lake Dabsun Dabsun and and CuoE CuoE in the in thelow lowelevation elevation region region in the in thenorth north part, part, and and some some lakes lakes in the in southern the southern part of part the plateau. of the plateau. Lakes in the Lakes Hoh in Xil the region, Hoh and Xil Nam region, andCo, Nam PumuYumco Co, PumuYumco exhibited exhibited low temp lowerature temperature (10–12 °C). (10–12 In general,◦C). In general,the spatial the pattern spatial of pattern LWST of LWSTderived derived from from the thenighttime nighttime MODIS MODIS product product resemb resembledled that that in daytime; in daytime; however, however, Lake Lake Yamdrok Yamdrok showedshowed much much lower lower nighttime nighttime temperature temperature (Figure (Figure4 4b).b). CombinationCombination of both daytime daytime and and nighttime nighttime LWSTLWST patterns patterns indicates indicates that that large large and and deep deep lakeslakes withwith huge water storage storage showed showed less less variability variability forfor DTDs, DTDs,i.e., i.e., Lake Lake Qinghai Qinghai,, Nam Nam Co, Co, Siling Siling CoCo andand Ayakekumu.Ayakekumu. As As demonstrated demonstrated in in Figure Figure 4c,4 c, Lake Yamdrok Co, and Langa Co and other two relatively small lakes showed much larger DTD Lake Yamdrok Co, and Langa Co and other two relatively small lakes showed much larger DTD variability, ranging from 6 to 9 °C. variability, ranging from 6 to 9 ◦C.

FigureFigure 4. The4. The warm warm month month spatial spatial pattern patternof of waterwater surfacesurface temperature for for lakes lakes across across the the Tibetan Tibetan Plateau:Plateau: (a) ( daytimea) daytime LWST LWST variation; variation; (b) nighttime(b) nighttime LWST LWST variation; variation; and (andc) lake (c) waterlake water surface surface diurnal temperaturediurnal temperature difference difference (DTD). (DTD).

Remote 2016, 8, 854 8 of 20 Remote Sens. 2016, 8, 854 8 of 20 The spatial pattern of monthly LWST for lakes before completely frozen (304–336) in the TibetanThe spatialPlateau pattern was presented of monthly in Figure LWST for5. A lakes very beforeobvious completely spatial pattern frozen of (304–336) LWST was in theevident Tibetan in Plateauthe daytime was presented MODIS inLST Figure product.5. A very It can obvious be seen spatial that patternLake Qinghai of LWST had was a evident large storage in the daytime at low MODISelevation, LST and product. lakes in It canthe southern be seen that part Lake of the Qinghai plateau had exhibited a large warmer storage temperature, at low elevation, ranging and from lakes in1 the to 6.8 southern °C before part the of presence the plateau of ice. exhibited Lakes situat warmered across temperature, the Hoh Xil ranging region and from the 1 towestern 6.8 ◦C part before of thethe presence plateau had of ice. low Lakes temperatures, situated ranging across the from Hoh −9 to Xil −1 region °C. Almost and the a similar western pattern part of LWST the plateau was exhibited for lakes across the plateau during the nighttime. A comparable DTD pattern was revealed had low temperatures, ranging from −9 to −1 ◦C. Almost a similar pattern of LWST was exhibited as that from ice break-up and the warm periods, i.e., smaller DTDs were found with large and deep for lakes across the plateau during the nighttime. A comparable DTD pattern was revealed as that lakes, e.g., Lake Qinghai, Nam Co, Siling Co, MapamYumco, and Ayakekumu, while large DTDs from ice break-up and the warm periods, i.e., smaller DTDs were found with large and deep lakes, were recorded with Lake Yamdrok, and those situated in the Hoh Xil region (see Figure S4 for e.g., Lake Qinghai, Nam Co, Siling Co, MapamYumco, and Ayakekumu, while large DTDs were locations). recorded with Lake Yamdrok, and those situated in the Hoh Xil region (see Figure S4 for locations).

FigureFigure 5. 5.The The spatial spatial pattern pattern of of the the monthlymonthly averagedaveraged waterwater surface temperature before before freeze-up freeze-up periodperiod in in Tibetan Tibetan Plateau: Plateau: ( a(a)) LWST LWST daytime daytime variation;variation; ((b) LWSTLWST nighttime variation; variation; and and (c (c) )lake lake waterwater surface surface diurnal diurnal temperature temperature difference difference (DTD). (DTD).

Remote Sens. 2016, 8, 854 9 of 20

3.2.2. Water Surface Temperature Variation for Typical Lakes As shown in Figure6, apparent internal variations for LWST were demonstrated with respect to different lake sizes, attitudes and water supply sources. Located at the lower elevation (Table1), Lake Qinghai demonstrated higher temperature with obvious internal temperature variation (Figure6a). The temperature is high in the mid-north and northeast regions for the daytime and in the northeast region for the nighttime. The mean temperature generally resembles the temperature in nighttime. Likewise, the temperature in Siling Co displays high values in the south and north shoreline border regions (Figure6b), but less variability in the open water region. Located in the high elevation Hoh Xil region, the temperature of Lake Hoh Xil was low and had larger spatial variability (Figure6d). While Lake MapamYumco and Langa Co are spatially close, higher internal spatial variability was presented in Langa Co due to its shallow water depth and much complex shoreline (Figure6e). The temperature pattern in Lake Gyaring resembles that in Lake Ngoring, but statistical analysis suggested a little bit higher temperature presented in Lake Gyaring due to shallow water depth (Figure6f and Table1). Because of shallow water depth and high salinity, Lake Dabsun showed the highest temperature across the plateau (Figure6g). Located in the southern part of the plateau, Lake Yamdrok also exhibited high water temperature with strong internal variation (Figure6h).

Table 1. Limnological characteristics of typical lakes across the Tibetan Plateau for water surface temperature comparison.

Name WL (m) A (km2) AD (m) Supply V (108 m3) Salinity (PSU) LakeQinghai 3194 4340 21.3 R + P 738.6 14.7 Nam Co 4718 1961 57.6 G + R + P 863.7 0.9 Siling Co 4530 2175 33.5 G + R + P - 18.3 CuoE 4561 269 7.8 R + P - 261 Ayakekumu 3876 927 12.3 G + R - 145.9 Yamdrok 4441 638 43.5 R + P 151.0 1.7 Pumu Yumco 5010 290 47 G + R + P 133.5 0.37 Dangre Yumco 4528 835.3 93 G + R + P 576.5 9.7 Taruo Co 4566 515.6 - G + P - 110.7 Langa Co 4572 268 31.4 G + R + P 57.1 0.7 MapamYumco 4586 412 47.3 G + R + P 146.2 0.2 Gyaring Hu 4292 526 8.9 R + P 46.7 0.5 Ngoring Hu 4269 610 17.6 R + P 107.6 0.3 Hoh Xil 4878 300 30.7 G + R 58.6 13.4 UlanUla 4854 544 6.9 G + P - 10.9 Akesaiqin 4848 185 16 R + P - 54.8 Dabsun 2675 257 1.02 R + P 2.6 320.2 Note: WL, water level; A, area; AD, average depth; V, volume; R, river; G, glacier melting water; P, precipitation over lake surface; - not applicable.

As demonstrated in the right column of Figure6, the DTDs of the shoreline regions for different lakes had much larger variability, particularly for these with elongated shapes (e.g., Lake Hoh Xil and Lake Langa). It also revealed that lakes having large area and in deep basins showed less internal variability, e.g., Lake Qinghai and Siling Co. As an extreme case, Lake Dabsun also demonstrated a large DTD variation from shoreline toward the central part of the lake surface. Similarly, Lake Yamdrok, a narrow lake in a complex lake basin, demonstrated larger DTDs. Remote Sens. 2016, 8, 854 10 of 20 Remote 2016, 8, 854 10 of 20

Figure 6.The spatial LWST patterns for eight typical lakes during ice-free period, column one to three Figure 6. The spatial LWST patterns for eight typical lakes during ice-free period, column one to are averaged daytime, nighttime, and mean of day-nighttime temperature, while column four is the three are averaged daytime, nighttime, and mean of day-nighttime temperature, while column four averaged diurnal temperature difference (DTD): (a) Qinghai Lake; (b) Siling Co; (c) Nam Co; (d) is the averaged diurnal temperature difference (DTD): (a) Qinghai Lake; (b) Siling Co; (c) Nam Co; Hoh Xil Lake; (e) Mapam Yumco and Langa Co; (f) Ngoring Lake and Gyaring Lake; (g) Yamdrok; (d) Hoh(h) Dabsun Xil Lake; Lake. (e) Mapam Yumco and Langa Co; (f) Ngoring Lake and Gyaring Lake; (g) Yamdrok; (h) Dabsun Lake. 3.3. Temporal Variation of Lake Surface Temperature 3.3. Temporal Variation of Lake Surface Temperature 3.3.1. Intra-Annual Variation of LWST 3.3.1. Intra-AnnualAs demonstrated Variation in Figure of LWST 7, the overall temperature increased from the beginning of February, Asand demonstrated the daytime temperature in Figure7 was, the about overall 0 °C temperature in the mid of increased March (approximately from the beginning Julian Day of February, 70), and thewhile daytime the nighttime temperature temperature was reached about 0 0◦ °CC inin thethe beginning mid of March of May (approximately (approximately JulianJulian Day Day 70), 128). Overall, the warmest temperature of LWST reached in the early stage of August. The timing of while the nighttime temperature reached 0 ◦C in the beginning of May (approximately Julian Day 128). lake ice cover break-up and freeze-up is an important phenological proxy for biological and Overall,chemical the warmest processes temperature [8]. The timing of of LWST the maximum reached intemperature the early stageis the ofmost August. active period The timing for lake of lake ice coversurface break-up evaporation, and freeze-upand also probably is an important the most phenological productive period proxy of for a biologicallake for which and chemicalthe processesproductivity [8]. The is limited timing by of temperature. the maximum The temperature average overall is thelake mosttemperature active perioddrops to for 0 °C lake at the surface evaporation,end of November and also probablyor beginning the mostof December productive (approximately period of aJulian lake forDay which 336), theindicating productivity the is limitedformation by temperature. of ice. By averaging The average both overall daytime lake and temperaturenighttime MODIS drops product, to 0 ◦C the at themean end LWST of November of lakes or beginningranged of from December −10.3 to (approximately 15.7 °C. As shown Julian in DayFigure 336), 7, the indicating maximum the DTD formation (11.3 °C) of was ice. Byrecorded averaging bothduring daytime the and ice nighttimebreak-up period, MODIS gradually product, decreased the mean till LWST ice freeze-up of lakes occurs ranged (5.1 from °C), −and10.3 then to 15.7an ◦C. increase trend followed. Statistics revealed larger DTDs in the ice-covered season than ice-free As shown in Figure7, the maximum DTD (11.3 ◦C) was recorded during the ice break-up period, gradually decreased till ice freeze-up occurs (5.1 ◦C), and then an increase trend followed. Statistics revealed larger DTDs in the ice-covered season than ice-free season (8.7 vs. 6.2 ◦C). [12] argued that Remote Sens. 2016, 8, 854 11 of 20 Remote 2016, 8, 854 11 of 20

LWSTseason is probably (8.7 vs. 6.2 prone °C). to[12] large argued DTD that due LWST to change is probably in heat prone ﬂuxbetween to large DTD day anddue nightto change since in ice heat has muchflux less between speciﬁc day heat and capacity night since than ice water has much [27]. less specific heat capacity than water [27].

FigureFigure 7. Fifteen-year 7. Fifteen-year averaged averaged water water surface surface temperature temperature for daytime,for daytime, nighttime nighttime and theand meanthe mean value of day-nighttimevalue of day-nighttime temperature, temperature, and the diurnaland the temperaturediurnal temperature differences differences (DTD), the(DTD), ﬁrst the row first is derivedrow is fromderived 56 lakes from across 56 lakes the across plateau, the and plateau, the secondand the tosecond the fourth to the rowsfourth are rows results are results from typicalfrom typical lakes. E denoteslakes. E the denotes average theelevation average elevation of 56 lakes of 56 above lakes sea above level sea in level meter in (m).meter (m).

Over different lakes, the intra-annual variation of DTD followed a similar pattern, while the maximumOver different DTD and lakes, the the specific intra-annual dates reaching variation the of maximum DTD followed DTD varied a similar across pattern, different while lakes the maximum(Figure 9). DTD Lakes and with the large speciﬁc volume dates (large reaching area or the deep maximum depth, or DTDboth) generally varied across had adifferent smaller DTD, lakes (Figuree.g.,9 ).Lake Lakes Qinghai with largeand Nam volume Co (large(Table area1) both or deep exhibited depth, lower or both) DTDs, generally with the had maximum a smaller DTDs DTD, e.g.,taking Lake Qinghaiplace before and Namthe ice Co break-up (Table1 )period. both exhibited Lake Langa lower Co DTDs, and MapamYumco with the maximum are very DTDs close, taking but placethe before former the has ice much break-up less volume period. (Table Lake Langa1) and Coa much andMapamYumco larger DTD and are a longer very close, ice-water butthe duration former hasperiod much lesswas volumerecorded (Table with 1Lake) and Langa a much Co. larger In terms DTD of andvolume, a longer Lake ice-water Gyaring is duration about half period of Lake was recordedNgoring, with however, Lake Langa the two Co. lakes In had terms a similar of volume, trend for Lake both Gyaring daytime is and about nighttime half of DTDs. Lake Through Ngoring, however,close examination the two lakes of hadthe maximum a similar trendtemperature, for both it daytimecan be found and nighttimethat Lake DTDs.Gyaring Through is a little close bit examinationhigher than of theLake maximum Ngoring. temperature,It also should itbe can highlighted be found that Lakethe timing Gyaring of the is amaximum little bit higher DTDs thanfor Lakethese Ngoring. specific It lakes also shouldall occurred be highlighted before the ice that break-up the timing period, of the ranging maximum from Julian DTDs Day for these72 to 88. speciﬁc The lakestiming all occurred of the overall before DTD the ice was break-up around period, Julian Day ranging 72, but from it Julianis in the Day period 72 to when 88. The ice timing and water of the overallco-exists. DTD was around Julian Day 72, but it is in the period when ice and water co-exists.

3.3.2.3.3.2. Variations Variations of Iceof Ice Break-Up, Break-Up, Freeze-Up Freeze-Up and and Ice Ice Free Free Duration Duration IceIce break-up, break-up, freeze-up freeze-up and and ice-free ice-free duration duration have have strongstrong connectionconnection with with LWST. LWST. The The average average daytimedaytime and and nighttime nighttime temperatures temperatures were were derived derived fromfrom eacheach eight-dayeight-day composite for for 56 56 lakes lakes concerned,concerned, and and then then break-up, break-up, freeze-up freeze-up and and ice-free ice-fr durationee duration were were calculated. calculated. As As shown shown in in Figure Figure8a, 8a, the ice break-up dates varied significantly from lake to lake, ranging from Julian Day of 0 to 160 the ice break-up dates varied signiﬁcantly from lake to lake, ranging from Julian Day of 0 to 160

Remote Sens. 2016, 8, 854 12 of 20 Remote 2016, 8, 854 12 of 20

(standard(standard deviation deviation (SD) (SD) == 30.1 30.1 days). days). The The two two extreme extreme cases casesare Lake are Yamdrok Lake Yamdrok and Xuru and Co, Xuru which Co, whichare ice-free are ice-free during during the study the study period period according according to the to MODIS the MODIS LST product. LST product. As an Asoppositecase, an oppositecase, the thelakes lakes located located in the in theHoh Hoh Xil region Xil region in the innorthweste the northwesternrn part of the part plateau of the exhibited plateau late exhibited ice break-up late ice dates, e.g., Lake Hoh Xil (160), and Lake Jingyu (152). Comparatively, shallow lakes with high break-up dates, e.g., Lake Hoh Xil (160), and Lake Jingyu (152). Comparatively, shallow lakes with salinity generally had earlier ice break-up date, e.g., Lake Dabsun (64) and Lake CuoE (64) (see high salinity generally had earlier ice break-up date, e.g., Lake Dabsun (64) and Lake CuoE (64) Figure S4 for locations). (see Figure S4 for locations).

FigureFigure 8. The8. The timing timing variations variations of of (a )(a ice) ice break-up break-up date; date, (b ()b freeze-up) freeze-up date date and and (c ()c ice-free) ice-free durations durations for lakesfor acrosslakes across the Tibetan the Tibetan Plateau. Plateau.

Comparatively, the freeze-up dates of different lakes showed less variation (SD = 23.0 days), rangingComparatively, from Julian the Day freeze-up 304 to 32dates of the of following different year, lakes with showed the exception less variation of Lake (SD Yamdrok = 23.0 and days), rangingXuru Co from (Figure Julian 8b). Day Lake 304 toHara, 32 ofAkesaiqin, the following and Gas year, Hure with in the the exception northern ofpart Lake of Yamdrokthe plateau and Xuruexhibited Co (Figure earlier8b). ice Lake formation Hara, Akesaiqin,as air temperature and Gas we Hurent down in the at northern the end of part fall of and the the plateau beginning exhibited of earlierwinter. ice However, formation some as air lakes temperature in the southern went downpart of at the the plateau end of reveal fall and much the beginninglater ice formation of winter. However,dates, e.g., some PumuYumco, lakes in the Nam southern Co, and part MapamYumco of the plateau even revealshowed much freeze-up later dates ice formation in January dates, or e.g.,early PumuYumco, February in Namthe following Co, and year MapamYumco (see Figure S4 even for locations). showed freeze-up dates in January or early February in the following year (see Figure S4 for locations). Remote Sens. 2016, 8, 854 13 of 20

As shown in Figure8c, a signiﬁcant variation (S.D = 47.9 days) was demonstrated in the lake ice-freeRemote duration, 2016, 8, 854 ranging from 144 to 365 days. The lakes situated in the Hoh Xil region in the13 northwest of 20 with high elevation (exceeding 4500 m) exhibited shorter ice-free durations, e.g., Lakes Hoh Xil (144) As shown in Figure 8c, a significant variation (S.D = 47.9 days) was demonstrated in the lake and Jingyuice-free (144)duration, presented ranging the from shortest 144 to duration365 days. timeThe lakes for all situated the lakes in the investigated. Hoh Xil region As mentionedin the above,northwest Lake Yamdrok with high and elevation Xuru Co(exceeding were the 4500 two m) extreme exhibited cases shorter without ice-free ice durations, formation e.g., according Lakes to MODISHoh derived Xil (144) LST and measurements.Jingyu (144) presented Lakes the with shortest large duration volume time generally for all the exhibited lakes investigated. a longer ice-freeAs duration,mentioned e.g., Lakeabove, Qinghai, Lake Yamdrok Nam Co.and LikewiseXuru Co were lakes the situated two extreme in the cases southern without part ice offormation the plateau, e.g., MapamYumcoaccording to MODIS and DangreYumco,derived LST measurements. or lakes with Lakes high with salinity large also volume demonstrated generally exhibited longer ice-free a time,longer e.g., CuoE ice-free and duration, Taruo Coe.g., (see Lake Figure Qinghai, S4 forNam locations Co. Likewise and Tablelakes 1situated). in the southern part of the plateau, e.g., MapamYumco and DangreYumco, or lakes with high salinity also demonstrated 3.3.3.longer Inter-Annual ice-free time, Variation e.g., CuoE of WST and forTaruo Lakes Co (see across Figure the S4 Tibetan for locations Plateau and Table 1).

The3.3.3. inter-annual Inter-Annual variationsVariation of of WST the for averaged Lakes across LWST the of Tibetan lakes fromPlateau 2001 to 2015 are demonstrated in Figure9a. It can be seen that the annual daytime, nighttime co-varied during the past 15 years, The inter-annual variations of the averaged LWST of lakes from 2001 to 2015 are demonstrated with thein Figure highest 9a. beingIt can recordedbe seen that in the 2006 annual and thedaytime, lowest nighttime in 2012. co-varied Although during a slightly the past decrease 15 years, trend can bewith observed the highest during being the recorded study in period, 2006 and the the trend lowest of in daytime 2012. Although and nighttime a slightly weredecrease insignificant. trend The correspondingcan be observed parameters during the study for the period, ice break-up the trend periodof daytime during and thenighttime past 15 were years insignificant. are demonstrated The in Figurecorresponding9b. An obvious parameters decreasing for the ice trend break-up was observedperiod during (y = the− 0.110xpast 15 +years 3.827, are Rdemonstrated2 = 0.304, p < in 0.05), whileFigure obvious 9b. trendsAn obviou coulds decreasing not be observed trend was for observed nighttime (y = and −0.110x the mean+ 3.827, value. R² = 0.304, However, p < 0.05), an increasingwhile trendobvious was presented trends could for bothnot be nighttime observed for and nighttime the mean and during the mean 2001 value. to 2010, However, and an an opposite increasing trend followedtrend from was 2011presented to 2015. for both As shown nighttime in Figure and the9c, mean slight during and coincident 2001 to 2010, fluctuations and an opposite were observedtrend followed from 2011 to 2015. As shown in Figure 9c, slight and coincident fluctuations were observed for daytime and nighttime of the warm months during the past 15 years, but no trends could be traced. for daytime and nighttime of the warm months during the past 15 years, but no trends could be Compared to the averaged daytime and nighttime temperatures of ice break-up and the warm month traced. Compared to the averaged daytime and nighttime temperatures of ice break-up and the (Figurewarm9d), month a significant (Figure increasing9d), a significant trend increasing was observed trend during was observed the freeze-up during the period freeze-up during period the past 2 15 yearsduring (y = the 0.047x past 15− years0.906, (y R = 0.047x= 0.125, − 0.906,p < 0.1). R² = 0.125, p < 0.1).

FigureFigure 9. Inter-annual9. Inter-annual changes changes of of lake-averagedlake-averaged a annualnnual daytime daytime and and nighttime nighttime water water surface surface temperaturetemperature (WST) (WST) of lakes of lakes from from 2000 2000 to to 2015: 2015: ( a(a)) annualannual average; average; (b (b) break-up) break-up month; month; (c) freeze-up (c) freeze-up month;month; and (andd) warm(d) warm month. month.

The inter-annual variations of 56 lakes across the Tibetan Plateau from 2001 to 2015 are shown Thein Table inter-annual 2. As can variations be seen, ofthe 56 daytime lakes across and ni theghttime Tibetan trends Plateau for each from lake 2001 exhibited to 2015 are various shown in Tablepatterns,2. As can only be 23 seen, lakes the showed daytime consistent and nighttime temperature trends trends for in each both lakedaytime exhibited and nighttime various during patterns, only 23thelakes past showed15 years, consistentand the rest temperature exhibited an trends opposite in both trends daytime from daytime and nighttime and nighttime during LST the past 15 years,measurements. and the rest According exhibited to anthe oppositedaytime MODIS trends fromLST measurements, daytime and only nighttime 18 (32%) LST lakes measurements. showed Accordingincrease to in the temperature daytime with MODIS a mean LST rate measurements, of 0.04 °C/year, while only the 18 rest (32%) 38 (68%) lakes lakes showed decreased increase in in temperature with a mean rate of 0.04 ◦C/year, while the rest 38 (68%) lakes decreased in temperature

Remote Sens. 2016, 8, 854 14 of 20 with a mean rate of −0.06 ◦C/year. Regression analysis indicated that the daytime LWST trend of 19 lakes is significant, including three warming and 16 cooling lakes (Table2). With respect to nighttime MODIS LST measurements, 27 (48%) lakes increased in temperature with a mean rate of 0.051 ◦C/year, and 29 (52%) lakes decreased in temperature with a mean rate of −0.062 ◦C/year. The LWST change rate for 19 lakes was statistically significant, including seven warming and 12 cooling lakes for nighttime MODIS LST measurements. The number of lakes with warming rates is a little bit different from the findings by [20]. Altogether, 34 lakes were examined by both investigations; thus, further comparison was conducted on these lakes (Table S1). As seen in Table S1, 10 lakes increased consistently in temperature, while eight lakes consistently decreased in temperature. Sixteen lakes showed different temperature change rates, however, eight out of these lakes showed a consistent trend when the regression year was limited to 2001–2012 (Table S1 and reference [20]). Considering the low R-squares values in the regression models (Table S1), the remaining eight lakes with inconsistent trend can be ignored. Thus, the result from this investigation is consistent with the findings by [20].

Table 2. Lake water surface temperature change rates derived from MODIS LST regressing against year between 2001 and 2015 for both daytime and nighttime series data, these rates of temperature change in grey color are showing a cooling trend, while the rest showing warming trend.

Lake Name Rate of Daytime WST (◦C/year) R2 Rate of Nighttime WST (◦C/year) R2 Tsonag Lake 0.043 0.12 0.044 0.07 Hara Lake 0.021 0.04 0.081 0.13 Qinghai Lake 0.024 0.04 0.044 0.10 Lumaqangdon Co 0.033 0.04 0.048 0.08 ZigeTangco 0.013 0.02 0.074 * 0.29 Gyaring Lake 0.004 0.00 0.072 0.10 Hoh Xil Lake −0.143 ** 0.37 −0.091 ** 0.37 LexieWudan −0.073 * 0.27 −0.088 * 0.28 ZhariNam Co −0.041 * 0.23 −0.084 * 0.23 Ngangzi Co −0.04 4* 0.20 −0.012 0.00 Dore Sowdong Co −0.062 * 0.19 −0.044 0.05 Ringinyubu Co −0.040 * 0.18 −0.051 0.14 DogaicoringQangco −0.042 0.15 −0.013 0.01 Langa Co −0.093 0.13 −0.143 ** 0.30 Senli Co −0.122 0.11 −0.073 0.14 Palung Co −0.052 0.11 −0.087 * 0.20 PangongTso −0.032 0.08 −0.033 0.08 MapamYumco −0.061 0.07 −0.138 * 0.19 AngLaren Lake −0.014 0.02 −0.024 0.07 GyesarTso −0.013 0.01 −0.072 * 0.26 Gozha Co −0.013 0.01 −0.022 0.01 Nam Co −0.002 0.00 −0.054 0.08 Taro Co −0.0003 0.00 −0.053 * 0.19 Yamdrok 0.143 ** 0.55 −0.053 * 0.21 HuitenNur 0.171 ** 0.45 −0.173 ** 0.44 Xuru Co 0.054 * 0.28 −0.053 * 0.24 CuoE 0.041 0.10 −0.025 0.06 Mucuobingni 0.034 0.07 −0.083 * 0.22 Jargo Lake 0.034 0.06 −0.018 0.02 Urru Co 0.027 0.05 −0.044 0.13 Geren Co 0.011 0.02 −0.048 0.14 PumuYumco 0.013 0.02 −0.053 0.05 Zabuye Lake 0.023 0.02 −0.001 0.00 Dajia Lake 0.002 0.00 −0.124 * 0.24 DangreYumco 0.0023 0.00 −0.034 0.13 Akesaiqin −0.142 ** 0.63 0.032 0.12 Jingyu Lake −0.132 ** 0.46 0.024 0.02 Dogze Co −0.071 ** 0.42 0.044 0.20 Dabsun −0.251 ** 0.37 0.173 ** 0.45 UlanUla −0.088 ** 0.36 0.064 * 0.18 Remote Sens. 2016, 8, 854 15 of 20

Table 2. Cont.

Lake Name Rate of Daytime WST (◦C/year) R2 Rate of Nighttime WST (◦C/year) R2 Gas Hure −0.058 ** 0.34 0.003 0.00 CoNyi −0.134 * 0.29 0.138 ** 0.46 MemarTso −0.081 * 0.29 0.132 ** 0.44 Hoh Sai Lake −0.074 * 0.28 0.062 * 0.29 XijinUlan −0.081 * 0.25 0.013 0.01 Quemo Co −0.082 * 0.21 0.044 0.14 MirikGyangdramTso −0.044 0.09 0.024 0.03 AqqikKol −0.035 0.07 0.053 0.16 DonggiConag −0.044 0.06 0.014 0.09 Siling Co −0.013 0.04 0.034 0.09 Dogai Coring −0.024 0.03 0.023 0.02 Ngoring Lake −0.024 0.02 0.083 0.15 Dorge Co −0.023 0.02 0.004 0.00 Ayakekumu −0.009 0.01 0.011 0.01 Serlung −0.004 0.00 0.054 0.16 Kyebxang Co −0.0002 0.00 0.082 * 0.28 * Statistically signiﬁcant at the 0.05 level; ** statistically signiﬁcant at the 0.01 level.

4. Discussion

4.1. Water Volume (Depth and Area) As demonstrated in Figures3,5 and8, the area, depth and volume of water bodies have strong impacts on the variability of water temperature. According to [13], a strong relationship between heat budgets and water volume is possible for a speciﬁc lake. Thus, small temporal variations were revealed for lakes with large volume, e.g., Lake Qinghai, Nam Co and Siling Co, and Ayakekumu (Table1). Similar reasons can be attributed to the lower value of DTDs for large volume lakes. This can also explain why Lake Langa Co showed larger temporal variability and DTD than that for Lake MapamYumco though these two lakes have very similar environmental settings (closely situated, almost same elevation, Table2 and Figure S4). Lake Gyaring showed slightly higher variability and DTDs than Lake Ngoring, but the difference is insigniﬁcant. Thereby, other factors (e.g., the ratio of area to mean depth, and shoreline characteristic) may contribute to this phenomenon, and further investigation is required to determine the underlying reasons [29,30]. As the extreme case, Lake Dabsun exhibited the highest temperature (Max: 25.1 ◦C; Mean of ice-free season, MIFS: 15.54◦C), which is also the shallowest (around 1 m) lake at lowest elevation (2678 m). Given the elevation difference between Lake Qinghai and Dabsun (517 m), the air temperature is about 3 ◦C, which is much smaller than the corresponding averaged LWST difference during the ice-free season (15.54 − 9.09 = 6.45 ◦C). Thereby, the highest temperature observed for Lake Dabsun is also attributed to its shallow water depth with small water storage.

4.2. Elevation and Air Temperature As shown in Figures3–5, the LWST of Lake Qinghai (15.8) is higher than that of Lake Nam Co (11.3) for both daytime and nighttime results. Similarly, Lake Yamdrok (Elevation: 4441 m; Area: 630 km2; Depth: 20–40 m) and Lake PumuYumco (Elevation: 5003 m; Area: 295 km2, 30–40 m) both are located in the southern part of the plateau, but the former has much higher temperature. According to [8] and [13], larger volume lakes exhibit higher heat budget, meaning that Lake Qinghai should show lower temperature than Lake Nam Co, the same conclusion holds for Lakes Yamdrok and PumuYumco. However, an opposite trend was observed mainly due to high elevation resulting in low air temperature, which leads to cool lake water. The air temperature lapse rate for the mountainous area with elevation ranging from 2000 to 6000 m is about 5–6 ◦C/1000 m [31], thus the difference of elevation for these two pairs of lakes may be one of the predominant reasons for the difference of temperature. To further investigate the relationship between air temperature over lakes and LWST, a regression model for air RemoteRemote Sens.2016,2016 8, 854, 8 , 854 1616 ofof 2020

and LWST, a regression model for air temperature over lake against MODIS derived lake surface temperaturetemperature overwas lakeobtained against for MODISeach lake derived (see Figure lake surface S6 for temperaturethe details of was the obtaineddata source for eachand lakedata (seeextraction Figure methods). S6 for the detailsAs shown of the in dataFigure source 10, clos ande datacorrelation extraction was methods). obtained Asfor showndaytime, in Figurenighttime 10, closeand the correlation mean temperature. was obtained Examination for daytime, of the nighttime correlati andon for the each mean lake temperature. revealed strong Examination effects of air of thetemperature correlation on for LWST each lakefor lakes revealed surrounded strong effects by homogeneous of air temperature landscape on LWST (Lake for Dabsun, lakes surrounded and Lake byQinghai), homogeneous while landscaperelatively (Lakeweak Dabsun, relationships and Lake were Qinghai), obtained while for relatively lakes weaksurrounded relationships with wereheterogeneous obtained forlandscape lakes surrounded (MapamYumco, with heterogeneous and Langa Co). landscape One extreme (MapamYumco, case was andLake Langa Xuru Co).Co, Onewhich extreme might case be wasinfluenced Lake Xuru by other Co, which factors, might e.g., be water inﬂuenced salinity by otherand factors,hot spring e.g., ground water salinity water andsupplies. hot spring Further ground investigation water supplies. was needed Further to investigationdetermine the was reasons. needed As to shown determine in Figure the reasons. 11, the Asmonthly shown air in Figuretemperature 11, the of monthly lakes distributed air temperature over ofthe lakes plateau distributed co-varies over very the well plateau with co-varies their LWST very well(see withFigure their S4 LWSTfor locations) (see Figure in most S4 for of locations) the cases. in However, most of the the cases. air temperatures However, the were air temperatures much lower werethan muchthe LWST lower derived than the from LWST the derived MODIS from LST the prod MODISuct, which LST product, is probably which due is probablyto narrow due lake to narrowshorelines lake and shorelines complex and reliefs complex around reliefs aroundthese lake theses lakes(Figure (Figure 11c,e,f). 11c,e,f). Thus, Thus, the the footprint footprint of of thethe interpolatedinterpolated airair temperature temperature may may contain contain land surfaceland surface exhibiting exhibiting gradient gradient air temperature air temperature variations, particularlyvariations, thoseparticularly lakes surrounded those lakes by surrounded elevated mountains, by elevated which mountains, showed a large which discrepancy showed betweena large airdiscrepancy temperature between and LWST air temperature derived from and MODIS LWST LSTderived product. from MODIS LST product.

Figure 10. The relationships between monthly air temperature over lakes and averaged monthly lake surface temperature (WST) derived from MODIS images from 2001 to 2015: (a) daytime; (b) nighttime; Figure 10. The relationships between monthly air temperature over lakes and averaged monthly lake and (c) mean WST of daytime and nighttime values. surface temperature (WST) derived from MODIS images from 2001 to 2015: (a) daytime; (b) nighttime; and (c) mean WST of daytime and nighttime values.

Remote Sens. 2016, 8, 854 17 of 20 Remote 2016, 8, 854 17 of 20

FigureFigure 11. 11.The The variations variations of of monthly monthly air air temperature temperature over over lakes lakes and and averaged averaged monthly monthly lake lake surface surface temperaturetemperature derivedderived fromfrom MODISMODIS images during during 2000 2000 to to 2015: 2015: (a ()a Siling) Siling Co; Co; (b) ( bAykekumu;) Aykekumu; (c) (cDangreYumco;) DangreYumco; (d (d) )Taruo Taruo Co; Co; (e (e) )PumuYumco; PumuYumco; ( (ff)) Yamdrok; Yamdrok; (g) Akesaiqin;Akesaiqin; ((hh)) UlanUla;UlanUla; ((ii)) HohHoh Xil; Xil; andand ( j()j) Dabsun. Dabsun.

4.3.4.3. Water Water Supply Supply Sources Sources and and Salinity Salinity LakeLake Ayakekumu Ayakekumu is is located located in in relatively relatively low low elevation elevation (3876 (3876 m), m), but but its its LWST LWST is is low low compared compared withwith lakes lakes in in the the similar similar elevation elevation with with the samethe sa depthme depth (10 m). (10 Feeding m). Feeding with snow with and snow glacier and melting glacier water,melting the water, lake has the shown lake has an obviousshown an areal obvious increase areal in theincrease past 26 in years the past (Figure 26 years S7). A (Figure low temperature S7). A low istemperature expected as theis expected mainly water as the supply mainly to water the lake supply originates to the from lake melting originates glaciers. from Likewise, melting glaciers. mainly suppliedLikewise, by mainly glacier andsupplied snow by melting glacier waters and [snow32,33], melting low temperatures waters [32,33], were recordedlow temperatures in Lake HolXil were ◦ ◦ ◦ ◦ (Max:recorded 8.47 C;in MeanLake ofHolXil ice-free (Max: season, 8.47 MIFS: °C; 6.20 MeanC) and of Lake ice-free LexieWudan season, (Max:MIFS: 8.52 6.20· C; MIFS:°C) and 5.51 LakeC) ◦ (seeLexieWudan Figure S4 (Max: for locations). 8.52·°C; MIFS: Situated 5.51 in °C) almost (see Figure the same S4 for elevation, locations). Lake Situated UlanUla in almost (Max: 10.25the sameC; ◦ ◦ ◦ MIFS:elevation, 7.32 LakeC) and UlanUla XijinUlan (Max: (Max: 10.25 10.69 °C; MIFS:C; MIFS: 7.32 °C) 7.71 andC) XijinUlan had higher (Max: temperature. 10.69 °C; MIFS: In addition, 7.71 °C) Lakehad HolXilhigher andtemperature. LexieWudan In hadaddition, a much Lake shorter HolX ice-freeil and period LexieWudan being 136 had and a 160much days, shorter respectively; ice-free comparatively,period being 136 Lake and UlanUla 160 days, and respectively; XijinUlan showed comparatively, a longer ice-freeLake UlanUla period (192and XijinUlan days and 168showed days, a longer ice-free period (192 days and 168 days, respectively). These four lakes have very similar elevation and climatic conditions, the water supply source may be the major reason for their

Remote Sens. 2016, 8, 854 18 of 20 respectively). These four lakes have very similar elevation and climatic conditions, the water supply source may be the major reason for their difference in temperature. Further, most the lakes with decreased temperature change rate are also more or less related to water supplying source, which is consistent with the ﬁndings by [20]. As known that the water salinity has effects on ice formation temperature, thus elongated ice-free duration may be expected for these high salinity lakes [34]. These hypothesis need to be further investigated on the saline effect on water temperature and ice-free duration.

5. Conclusions The inter-annual and intra-annual variations of WST for lakes across the Tibetan Plateau were derived from 1472 (7360 tiles) maps of daytime and nighttime MODIS LST eight-day composites during the period 2000–2015. The descriptive statistics of ice break-up, warm month, and ice freeze-up LWST and annual cycles of overall lakes and typical lakes were determined from the MOSDIS LST eight-day composite for 56 lakes greater than 50 km2 over the plateau. The annual cycle of LWST ranged from −19.5 ◦C in February to 25.1 ◦C in July. In terms of the spatial variation for typical seasons, the LWST ranged from −4 to 8.7 ◦C in the ice break-up season, from 8.3 to 25.1 ◦C in warm months (the end of July to beginning of August), and 13.7 to −2.4 ◦C in the freeze-up season. Much smaller DTDs were exhibited in summer and autumn, ranging from 1.3 to 8.9 ◦C, while larger DTDs were revealed in ice break-up season, ranging from 2.5 to 16.6 ◦C. This investigation also revealed that no obvious temperature change pattern can be determined for the 56 lakes being examined during the past 15 years. However, the inter-annual temperature variations measured from daytime and nighttime MODIS LST data showed different trends, and only 23 (41%) lakes showed either increasing or decreasing temperature trend from both daytime and nighttime measurements. According to the daytime MODIS LST measurements, 20 lakes exhibited a change rate of LWST, which was statistically significant, including three warming and 16 cooling lakes; 21 lakes showed a statistically significant rate of temperature change, but included eight warming and 13 cooling lakes for the nighttime MODIS LST measurements. It should also be highlighted that the spanning time may exert impacts on the inter-annual LWST change rate. According to this investigation, several factors, e.g., lake depth and area (volume), attitude, and water supply source can contribute to the spatiotemporal variation of LWST for lakes across the Tibetan Plateau. Shallow lakes contribute to a rapid response of SWT to solar and atmospheric forcing, while the response of large deep lakes is much slow. In addition, water salinity and ground water supply might be potential factors influencing LWST and ice-free duration, which need further investigation.

Supplementary Materials: The following are available online at www.mdpi.com/2072-4292/8/10/854/s1, Figure S1: Before and after-filtering of MODIS LST 46 8-day composites from two typical pixels extracted from lake area; Figure S2: The percentage of valid pixels for day and night time MODIS LST images from 2000 to 2015 (1472 mosaics); Figure S3: Statistics of effective images for MODIS LST products across the Tibetan Plateau during 2000–2015, where the white gap indicates no MODIS LST data meets the valid pixels greater than 76; Figure S4: The names and locations of typical lakes concerned across the Tibetan Plateau in this investigation; Figure S5: (a) Inter-annual changes of lake-averaged annual daytime, nighttime and mean water surface temperature (WST) of Lake Siling Co from 2000 to 2015; and (b) nighttime trend from 2001 to 2013; Figure S6: Monthly air temperature product derived from data provided by China Meteorological Administration at a grid resolution of 0.5 degree, lake boundary shape file overlaid to the interpolated monthly air temperature product (the left column), and comparison of the extracted air temperature with lake surface temperature derived from MODIS LST product with example from Lake Ayakekumu (right column), (a)–(c) are air temperature versus daytime, nighttime and the average of daytime and nighttime MODIS LST over lake surface; Figure S7: Water surface area variation for Lake Ayakekumu from 1989 to 2015 retrieved with Landsat TM imagery data acquired in August or September for each year with data available. Table S1: There are 34 lakes match from both studies, and 10 lakes show lake surface temperature increase and 8 show lake surface temperature decrease from both studies; while the rest 16 lakes demonstrate different trend, however, if we narrow the LWST from 2001 to 2012, 8 more lakes show consistent LWST trend, and the remaining 8 lakes show different tend, however, these lakes generally did not show obvious trend according to the determination coefficient (R2) values. Remote Sens. 2016, 8, 854 19 of 20

Acknowledgments: The authors would like to thank financial supports from Natural Science Foundation of China (No. 41471290), and “One Hundred Talents” Program from Chinese Academy of Sciences granted to Kaishan Song. Thanks are extended to NASA for providing free MODIS LST imagery data. Many thanks also go to the editor and three anonymous reviewers for their valuable and instructive comments that have strengthened the manuscript. At last, the authors would like to thank Lin Li from Indiana Purdue University, Indianapolis (IUPUI) for improving the English expression, and Jingjing Zang, Yanqiu Li, and Junbin Hou for their valuable assistances in data collection and preprocessing. Author Contributions: K.S. and Y.Y. conceived and designed the analysis; Y.Y., M.W., J.D., J.M., M.W., and G.M. analyzed the data; K.S. wrote the paper; K.S. and M.W. revised the paper. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Li, R.N.; Chen, Q.W.; Zhang, X.Q.; Recknagel, R. Effects of temperature and macronutrients on phytoplankton communities across three largely different lakes identified by a time-space trade-off approach. Ecol. Inform. 2015, 29, 174–181. [CrossRef] 2. Ma, N.; Szilagyi, J.; Niu, G.Y.; Zhang, Y.S.; Zhang, T.; Wang, B.B.; Wu, Y.H. Evaporation variability of Nam Co Lake in the Tibetan Plateau and its role in recent rapid lake expansion. J. Hydrol. 2016, 537, 27–35. [CrossRef] 3. Verburg, P.; Hecky, R.E.; Kling, H. Ecological consequences of a century of warming in Lake Tanganyika. Science 2003, 301, 505–507. [CrossRef][PubMed] 4. Schneider, P.; Hook, S.J. Space observations of inland water bodies show rapid surface warming since 1985. Geophys. Res. Lett. 2010.[CrossRef] 5. Adrian, R.; O’Reilly, C.M.; Zagarese, H.; Baines, S.B.; Hessen, D.O.; Keller, W.; David, M.L.; Ruben, S.; Dietmar, S.; Ellen, V.D.; et al. Lakes as sentinels of climate change. Limnol. Oceanogr. 2009, 54, 2283–2297. [CrossRef][PubMed] 6. Livingstone, D.M. Impact of secular climate change on the thermal structure of a large temperate central European lake. Clim. Chang. 2003, 57, 205–225. [CrossRef] 7. Hampton, S.; Izmesteva, L.R.; Moore, M.V.; Katz, S.L.; Dennis, B.; Silow, E.A. Sixty years of environmental change in the world’s largest freshwater lake—Lake Baikal, Siberia. Glob. Chang. Biol. 2008, 14, 1947–1958. [CrossRef] 8. Wetzel, R.G. Limnology: Lake and River Ecosystems, 3rd ed.; Elsevier Academic Press: New York, NY, USA, 2001. 9. Hulley, G.C.; Hook, S.J.; Schneider, P. Optimized split-window coefficients for deriving surface temperatures from inland water bodies. Remote Sens. Environ. 2011, 115, 3758–3769. [CrossRef] 10. Alsdorf, D.E.; Lettenmaier, D.P. Tracking fresh water from space. Science 2003, 301, 1491–1494. [CrossRef] [PubMed] 11. Hook, S.; Vaughan, R.G.; Tonooka, H.; Schladow, S. Absolute radiometric in-flight validation of mid infrared and thermal infrared data from ASTER and MODIS on the terra spacecraft using the Lake Tahoe, CA/NV, USA, automated validation site. IEEE Trans. Geosci. Remote Sens. 2007, 45, 1798–1807. [CrossRef] 12. Ke, L.H.; Song, C.Q. Remotely sensed surface temperature variation of an inland saline lake over the central Qinghai—Tibet Plateau. ISPRS J. Photogram. Remote Sens. 2014, 98, 157–167. [CrossRef] 13. Gorham, E. Morphometric control of annual heat budgets in temperate lakes. Limnol. Oceanogr. 1964, 9, 529–533. [CrossRef] 14. Livingstone, D. Ice break-up on southern Lake Baikal and its relationship to local and regional air temperatures in Siberia and the North Atlantic Oscillation. Limnol. Oceanogr. 1999, 44, 1486–1497. [CrossRef] 15. Reinart, A.; Reinhold, M. Mapping surface temperature in large lakes with MODIS data. Remote Sens. Environ. 2008, 112, 603–611. [CrossRef] 16. Bussières, N.; Verseghy, D.; MacPherson, J.I. The evolution of AVHRR-derived water temperatures over boreal lakes. Remote Sens. Environ. 2002, 80, 373–384. [CrossRef] 17. Trumpickas, J.; Shuter, B.J.; Minns, C.K. Forecasting impacts of climate change on Great Lakes surface water temperatures. J. Gt. Lakes Res. 2009, 35, 454–463. [CrossRef] 18. Trumpickas, J.; Shuter, B.J.; Minns, C.K.; Cyr, H. Characterizing patterns of nearshore water temperature variation in the North American Great Lakes and assessing sensitivities to climate change. J. Gt. Lakes Res. 2015, 41, 53–64. [CrossRef] Remote Sens. 2016, 8, 854 20 of 20

19. Crosman, E.T.; Horel, J.D. MODIS-derived surface temperature of the Great Salt Lake. Remote Sens. Environ. 2009, 113, 73–81. [CrossRef] 20. Zhang, G.; Yao, T.; Xie, H.; Qin, J.; Ye, Q.; Dai, Y.; Guo, R. Estimating surface temperature changes of lakes in the Tibetan Plateau using MODIS LST data. J. Geophys. Res. Atmos. 2014, 119, 8552–8567. [CrossRef] 21. Zhou, B.; Xu, Y.; Wu, J.; Dong, S.; Shi, Y. Changes in temperature and precipitation extreme indices over China: Analysis of a high-resolution grid dataset. Int. J. Climatol. 2015.[CrossRef] 22. Song, C.Q.; Huang, B.; Ke, L.H.; Richards, K.S. Seasonal and abrupt changes in the water level of closed lakes on the Tibetan Plateau and implications for climate impacts. J. Hydrol. 2014, 514, 131–144. [CrossRef] 23. Rangwala, I.; Miller, J.R.; Xu, M. Warming in the Tibetan Plateau: Possible inﬂuences of the changes in surface water vapor. Geophys. Res. Lett. 2009.[CrossRef] 24. Zhang, G.; Xie, H.; Kang, S.; Yi, D.; Ackley, S.F. Monitoring lake level changes on the Tibetan Plateau using ICESat altimetry data (2003–2009). Remote Sens. Environ. 2011, 115, 1733–1742. [CrossRef] 25. Xiao, F.; Ling, F.; Du, Y.; Feng, Q.; Yan, Y.; Chen, H. Evaluation of spatial temporal dynamics in surface water temperature of Qinghai Lake. J. Arid Land 2013, 5, 452–464. [CrossRef] 26. Wan, Z.; Zhang, Y.; Zhang, Q.; Li, Z.L. Validation of the land surface temperature products retrieved from terra moderate resolution imaging spectroradiometer data. Remote Sens. Environ. 2002, 83, 163–180. [CrossRef] 27. Schneider, P.; Hook, S.J.; Radocinski, R.G.; Corlett, G.K.; Hulley, G.C.; Schladow, S.G.; Steissberg, T.E. Satellite observations indicate rapid warming trend for lakes in California and Nevada. Geophys. Res. Lett. 2009.[CrossRef] 28. Coll, C.; Caselles, V.; Galve, J.M.; Valor, E.; Niclos, R.; Sanchez, J.M.; Rivas, R. Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data. Remote Sens. Environ. 2005, 97, 288–300. [CrossRef] 29. Leblanc, M.; Lemoalle, J.; Bader, J.C.; Tweed, S.; Mofor, L. Thermal remote sensing of water under ﬂooded vegetation: New observations of inundation patterns for the ‘Small’ Lake Chad. J. Hydrol. 2011, 404, 87–98. [CrossRef] 30. Austin, J.A.; Colman, S.M. Lake Superior summer water temperatures are increasing more rapidly than regional air temperatures: A positive ice-albedo feedback. Geophys. Res. Lett. 2007.[CrossRef] 31. Jacobson, M.Z. Fundamentals of Atmospheric Modeling, 2nd ed.; Cambridge University Press: London, UK, 2005. 32. Lei, Y.B.; Yao, T.D.; Bird, B.W.; Yang, K.; Zhai, J.Q.; Sheng, Y.W. Coherent lake growth on the central Tibetan Plateau since the 1970s: Characterization and attribution. J. Hydrol. 2013, 483, 61–67. [CrossRef] 33. Magnuson, J.J.; Webster, K.E.; Assel, R.A.; Bowser, C.J.; Dillon, P.J.; Eaton, J.G.; Evans, H.E.; Fee, E.J.; Hall, R.I.; Mortsch, L.R.; et al. Potential effects of climate changes on aquatic systems: Laurentian Great Lakes and Precambrian Shield region. Hydrol. Process. 1997, 11, 825–871. [CrossRef] 34. Wang, S.M.; Dou, H.S. Chinese Lakes Inventory; Science Press: Beijing, China, 1998.

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).