Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial ...

17 Pages·2017·5.57 MB·English

remote sensing
Article
Quantifying Snow Cover Distribution in Semiarid
Regions Combining Satellite and Terrestrial Imagery
RafaelPimentel1,2,*,JavierHerrero1andMaríaJoséPolo1
1 FluvialDynamicsandHydrologyResearchGroup,AndalusianInstituteforEarthSystemResearch,
UniversityofCórdoba,CampusRabanales,EdificioLeonardodaVinci,ÁreadeIngenieríaHidráulica,
14017Córdoba,Spain;[email protected](J.H.);[email protected](M.J.P.)
2 HydrologyResearchUnit,SwedishMeteorologicalandHydrologicalInstitute,Folkborgsvägen17,
60176Norrköping,Sweden
* Correspondence:[email protected]
Received:29August2017;Accepted:22September2017;Published:26September2017
Abstract: Mediterraneanmountainousregionsconstituteaclimatechangehotspotwheresnowplays
acrucialroleinwaterresources. Thecharacteristicsnow-patcheddistributionovertheseareasmakes
spatialresolutionthelimitingfactorforitscorrectrepresentation. Thisworkassessestheestimation
ofsnowcoverareaandthecontributionofthepatchyareastotheseasonalandannualregimeof
the snow in a semiarid mountainous range, the Sierra Nevada Mountains in southern Spain, by
meansofLandsatimagerycombinedwithterrestrialphotography(TP).Twomethodologieswere
tested: (1)differenceindexestoproducebinarymaps;and(2)spectralmixtureanalysis(SMA)to
obtainfractionalmaps; theirresultswerevalidatedfrom“ground-truth”databymeansofTPin
asmallmonitoredcontrolarea. Bothmethodsprovidedsatisfactoryresultswhenthesnowcover
wasabove85%ofthestudyarea;belowthisthreshold,theuseofspectralmixtureanalysisisclearly
recommended. Mixed pixels can reach up to 40% of the area during wet and cold years, their
importancebeinglargerasaltitudeincreases,provingtheusefulnessofTPforassessingtheaccuracy
ofremotedatasources. Mixedpixelsidentificationallowsfordeterminingthemorevulnerableareas
facingpotentialchangesofthesnowregimeduetoglobalwarmingandclimatevariability.
Keywords: snow;semiaridregions;terrestrialphotography;LandsatTMandETM+
1. Introduction
In general, snowmelt-dominant regions are located in latitudes greater than 45◦ (North and
South). Intenseeffortstostudysnowdynamicshavebeendevelopedovertheseareas, inwhicha
continuousanddeepsnowpackcanbeusuallyfoundthroughoutlargeareas. However,therearemany
mountainousregionslocatedinlowerandwarmerlatitudes,whicharehighlydependentofsnow
waterresourcesforagricultureandwatersupply,despitesnowoccurrencebeingnon-uniformand
highlytimingduringthecoldseason. Overtheseareas,climatechangeeffectsarelikelytobemore
noticeableand,thus,theyconstituteanaturallaboratoryfortheirearlydetectionandevaluation[1,2].
Thisisthecaseofsemiaridmountainousregions,wherehighlevelofincomesolarradiation,mild
winters,andtorrentialprecipitationregimesareusualintrinsicfeaturesoftheirclimate[3,4];water
scarcityanddroughtperiodsarefoundtobecurrentlyincreasing,asmanyauthorshavestated[5–7].
Intheseregions,snowdoesnotfollowasingleandsomehowcontinuousaccumulation-snowmeltcycle,
morecommonlyfoundincolderregions,beingthespringseasonrepresentativeofasignificantinflow
ofmeltingwatertothesurfaceandgroundwaterbodies. Atypicalsemiaridsnowpackexhibitsavery
strongspatiotemporalvariability,andveryoftenundergoesdifferentaccumulation-snowmeltcycles
duringthecoldseasoninagivenyear. Theablationprocessusuallyresultsinpatcheddistributionof
snowaroundlocalsingularities,suchasrocks,vegetationbunches,depressions,amongothers[8–10].
RemoteSens.2017,9,995;doi:10.3390/rs9100995 www.mdpi.com/journal/remotesensing
RemoteSens.2017,9,995 2of17
These snow patches evolve into a mosaic-type spatial pattern with alternating snow-covered and
snow-free areas, whose size ranges between one to dozens of square meters. Their local spatial
evolutionisgovernedbytwomaindrivers: microscalepatterns(~1m)conditionedbytheinteraction
betweenthesnowandthemicro-topography[8]andmedium-sizedpatterns(~100m)highlyinfluenced
bywind,andterraincurvature[11].
Inrecentdecades,remotesensinghasbeenprovidingdistributedinformationaboutthesnowpack
all over the world and it is the major method for monitoring snow at medium–large scales,
complementingthetraditionalinsitufieldsurveysandgroundautomaticmeasurementstocover
largeareas. Snowcoverfraction(SCF),snowalbedo(SA),andsnowwaterequivalent(SWE)arethe
mostcommonvariablesmeasuredbyusingbothopticalandradarremotesensingimagery,based,
respectively, on the highly different brightness temperature between the visible and near-infrared
regions[12–15]andonthesignalattenuationduetothewaterpresentinthesnowpack[16,17]. Among
them,SCFisthemostusuallytargetedinformationfromwhichfurthervariablesarethenderived,
especiallyindistributedmodelingofthesnowprocesses[18,19].
Within the great amount of remote sensing information (e.g., NOAA, daily images with
1km×1kmcellsize;MODIS,dailyimageswith500×500mcellsize;LandsatThematicMapper,
16-dayimageswith30m×30mcellsize),theselectionofthedatasetiscloselyrelatedtothescaleofthe
targetedprocesses.Spatialresolutionisthelimitingfactorforthedetectionofsnowinsemiaridregions,
sincetheextremelychangingconditionsfavoraparticularlysnowdistribution,whichusuallyappears
asmedium-to-smallsizedpatches[20,21]. Hence,thosedatasetswithahigherspatialresolution,such
asLandsatThematicMapper(TM)andEnhancedThematicMapper(ETM+)data,arerecommendedin
thesestudies[22–24],since,forexample,MODISsnowproducts,witha500×500mspatialresolution,
arenotcapabletocapturethesignificantsub-griddistributioncurrentlyfound. Anotheralternative
forthestudyofthesnowextensionoverthesechangeableareasistheuseofterrestrialphotography
(TP),thatis,imagestakenfromtheEarthsurfaceandwhosespatialandtemporalresolutioncanbe
adaptedtothestudyproblem[8,9,18,25,26]. Despitetheadvantagesofthistechnique,easyadaptation
tothetemporalandspatialresolutionofthestudiedprocessesundermonitoring,itsuseoverlargeand
abruptmountainousareasislimitedsince,ononehand,agreatnumberofimageswouldberequired,
and,ontheotherhand,thereissomelackofinformationassociatedwiththenon-visibleareas,which
canbesignificantinsomecases.
However, the combination of both data sources may overcome their individual scale-related
limitations provided that a long-time series is available. For this, the high-resolution TP images
overagivenareacanbeassumedastheground-truthinformationassociatedtothesnowcoverarea
distribution,againstwhichsnowdetectionalgorithmstobeappliedtothelowerresolutionsatellite
images,suchasLandsatTMandETM+.
The traditional classification algorithms for snow detection are based on normalized indexes
whichprovideabinaryclassificationassnowandno-snowpixelsthroughoutthestudyarea[15,27];
this simple classification may result in large error in heterogeneous and transitional areas within
the snow-dominated domain [14]. Alternatively, the spectral mixture analysis (SMA) approach
providesfractionofsnowcoverwithineachpixelandthus,constitutesastepforwardtocharacterize
heterogeneousandpatchysnowareasinsemiaridregions. Thismethodrequiresanexternaldataset,
withhigherspatialresolution,tocalibrateandvalidatethealgorithmsused. In[28]calculatedsnow
coverfractionvaluesfromMODIS(500×500m2)usingLandsatscenes(30×30m2)ascalibrationand
validationdatasetinSierraNevada(USA),RockyMountains,highplainsofColoradoandHimalaya,
andobtainedameanrelativeerrorintheanalyzedscenesof5%,rangingfrom1%to13%.Nevertheless,
thesignificantpatchsizescalesofthesnowdistributioninMediterraneanregionsrequiretheuseof
Landsatimageryasprimarysourcewhentheevolutionofthispatchysnowpackisthetargetedgoal
onthemidandlongterm.
Thisworkassessestheestimationofsnowcoverareainsemiaridregionsduringdifferentstages
inthesnowseasonbymeansofLandsatimageryandthecontributionofthepatchyareasinthese
RemoteSens.2017,9,995 3of17
regionstotheseasonalandannualregimeofthesnow. Forthis,a14-yearseriesofsnowcovermaps
(30×30m)wasobtainedfromLandsatimageryintheSierraNevadaMountainsinsouthernSpain.
Remote Sens. 2017, 9, 995 3 of 17
Two methodologies were tested for the estimation of the SCF: (1) the use of difference indexes to
obtainbinarymaps(coveredandnon-coveredpixels);and(2)aspectralmixtureanalysismodelto
(30 × 30 m) was obtained from Landsat imagery in the Sierra Nevada Mountains in southern Spain.
calcuTlawteo fmraectthioondaollosgnioesw wceorve etresmteadp fso(rf rthacet ieosntimofatcioonv eorfa gtheei nSCsiFd:e (1e)a cthhep uisxee lo).f Tdhifefepreenrcfeo rimndaenxcees otof both
methoobdtoalino gbiiensairsy amssaepsss (ecdovbeyremd eaannds noofnS-cCoFvemreadp psi(x1e0ls×); a1n0dm (2)) oab stpaeincterdal fmroimxtuTreP ainnaalyssmis amllomdeol ntoit ored
contrcoallcaurelaatei nfrtahcetiosntuadl synsoiwte .cover maps (fraction of coverage inside each pixel). The performance of
both methodologies is assessed by means of SCF maps (10 × 10 m) obtained from TP in a small
2. StumdoynSitoitreeda ncodnAtrovla ailraeab line tDhea tsatudy site.
ThisstudyiscarriedoutinSierraNevadaMountains,SouthernSpain(Figure1A,B).Theyarea
2. Study Site and Available Data
linearmountainrangeof90-kmlength,thatrunningparalleltothecoastlineofMediterraneanSea,
This study is carried out in Sierra Nevada Mountains, Southern Spain (Figure 1A,B). They are a
wheretheAlpineandMediterraneanclimateconditionscanbefoundinjusta40-kmdistance. Strong
linear mountain range of 90-km length, that running parallel to the coastline of Mediterranean Sea,
altitudinalgradientswithmarkeddifferencesbetweenthesouth(directlyaffectedtothesea)andthe
where the Alpine and Mediterranean climate conditions can be found in just a 40-km distance. Strong
northfacesarefoundinthearea. Itshighsummitsmakesnowthemainfactorthatconditionsmost
altitudinal gradients with marked differences between the south (directly affected to the sea) and the
ofthenohrythd rfoacloesg iacrael faonudnde icno ltohge iacraelap. Irtos cheisgshe ssuomvmerittsh meaakree as.nTowhe thpea rmtiaciunl faarctcohra trhaactt ceorinsdtiictisonms amkoesSt ierra
Nevaodf athae rhiycdhrorelosgeircvaol airndo feceonldogeimcailc pwroicledslsiefse osvpeerc tihees .arIetai.s Tchoen psairdtiecrueldar tchhearmacotesrtisitmicps omratkaen tSiceerrnat erof
biodiNveervsaitdya ian rtihceh wreessetrevronirM oef deintderermanice wanildreligfeio snp,ewciietsh. oItv iesr c2o1n0s0iddeirfefde rtehnet mvaossct uimlarpoprltaanntt tcaexnaterre coof rded,
accoubniotdinivgefrosirtyn eina rtlhye3 w0%estoefrnth Mevedaistceurrlaanreflano rraegoifotnh, eweintht iroeveIbr e2r1i0a0n dPieffneirnesnut lvaa[s2c9u,l3a0r ].plTahnet traexgai onis
recorded, accounting for nearly 30% of the vascular flora of the entire Iberian Peninsula [29,30]. The
alsoenvironmentallyprotected,thehighestelevationsoftherangeweredeclaredapartofaUNESCO
region is also environmentally protected, the highest elevations of the range were declared a part of
BiosphereReservein1986,aNaturalParkin1989,andaNationalParkin1999.
a UNESCO Biosphere Reserve in 1986, a Natural Park in 1989, and a National Park in 1999.

Figure 1. (A) Location of Sierra Nevada Mountain in Spain; (B) Sierra Nevada Mountain range, limits
Figure1.(A)LocationofSierraNevadaMountaininSpain;(B)SierraNevadaMountainrange,limits
of the Natural and National Parks (dark and light blue respectively) and 1500 m a.s.l. elevation line
oftheNaturalandNationalParks(darkandlightbluerespectively)and1500ma.s.l.elevationline
(black line); (C) Location of the control area monitored by terrestrial photography at Durcal hillside.
(blackline);(C)LocationofthecontrolareamonitoredbyterrestrialphotographyatDurcalhillside.
The snow usually appears above 2000 m a.s.l. during winter and spring even though the
Tsnhoewsmnoewlt usesausaolnly gaepnpereaalrlys albasotvs efr2o0m00 Ampari.ls .tlo. dJuurnien. gTwhein ttyepriacnaldlys pmriilndg Meveednittehrroaungeahnt hweisnnteorws melt
produce several snowmelt cycles before the final melting phase, which distributes the snow in
seasongenerallylastsfromApriltoJune. ThetypicallymildMediterraneanwintersproduceseveral
patches over the terrain. Precipitation is heterogeneously distributed over the area because of the
snowmeltcyclesbeforethefinalmeltingphase,whichdistributesthesnowinpatchesovertheterrain.
steep orography, with a high annual variability (400–1500 mm). The average temperature during the
Precipitationisheterogeneouslydistributedovertheareabecauseofthesteeporography,withahigh
snow season can range from −5 °C to 5 °C, reaching values as low as −20 °C at certain times and
annualvariability(400–1500mm). Theaveragetemperatureduringthesnowseasoncanrangefrom
location during winter [31]. Table 1 shows some statistical descriptors of the most relevant
−5◦Cto5◦C,reachingvaluesaslowas−20◦Catcertaintimesandlocationduringwinter[31].Table1
meteorological variables on an annual basis.
showssomestatisticaldescriptorsofthemostrelevantmeteorologicalvariablesonanannualbasis.
Table 1. Mean and standard deviation, in brackets, of the main meteorological variables in Sierra
TableN1e.vaMdae aMnoaunndtaisntsa fnrdomar d20d09e vtoia 2t0io13n,, cianlcublraatcekde otvs,ero bfotthhe somuathin anmde nteoortrho lfoacgeisc aalndv aforira lbolwes airneaSs ierra
Neva(dbealoMwo 2u0n0t0a min as.sfr.lo.)m an2d0 h0i9ghto ar2e0a1s3 (,acbaolvceu 2la00te0d mo av.se.rl.)b. othsouthandnorthfacesandforlowareas
(below2000ma.s.l.)andhighareas(Parbeocivpeita2t0io0n0 ma.sT.le.m).perature Radiation Wind Speed
Facing Elevation
(mm) (°C) (MJ m‒2day‒1) (ms‒1)
High areas Pre7c8i3p.3it (a3t5i.o8n) Tem7p.8e (r6a.t9u) re R1a1d.1ia (t1i.o1)n Wi8n.2d (S0.p5)e ed
SFoauctihn fgace ElLeovwat aiorenas 50(m3.7m (1)4.8) 14(◦.8C ()7.4) (MJ9m.5- 2(1d.5a) y-1) 3(.m2 (s1-.14))
High areas - 6.8 (5.6) 10.9 (0.3) 5.1 (2.2)
North face Highareas 783.3(35.8) 7.8(6.9) 11.1(1.1) 8.2(0.5)
Southface Low areas 526.7 (116.3) 9.7 (4.5) 9.4 (1.2) 2.4 (1.3)
Lowareas 503.7(14.8) 14.8(7.4) 9.5(1.5) 3.2(1.4)
For this study, a total number of 132 Landsat TM and ETM+ scenes (temporal resolution of 16
days; spatial resolutHioignh oafr e3a0s × 30 m) wer-e selected dur6i.n8g(5 t.6h)e period (1200.090(–0.230)13) from t5h.1e( a2.v2a)ilable
Northface
Lowareas 526.7(116.3) 9.7(4.5) 9.4(1.2) 2.4(1.3)
cloud-free scenes (Table 2) to obtain 30 × 30 m snow cover maps of the study area by two different
RemoteSens.2017,9,995 4of17
For this study, a total number of 132 Landsat TM and ETM+ scenes (temporal resolution of
1R6emdoatey Sse;nssp. 2a0t1ia7,l 9r,e 9s9o5l u tionof30×30m)wereselectedduringtheperiod(2000–2013)fromtheava4i loafb 1l7e
cloud-freescenes(Table2)toobtain30×30msnowcovermapsofthestudyareabytwodifferent
aallggoorriitthhmmss.. AAddddiittiioonnaallllyy,, tthhee aavvaaiillaabbllee ddaaiillyy sseerriieess ooff TTPP ttaakkeenn wwiitthh aa ccoonnvveennttiioonnaall ddiiggiittaall ccaammeerraa
ffrroomm 22000099 ttoo 22001133 ccoovveerriinngg aann aapppprrooxxiimmaatteellyy 22--kkmm22 ccoonnttrrooll aarreeaa,, llooccaatteedd iinn tthhee wweesstteerrnn ppaarrtt ooff SSiieerrrraa
NNeevvaaddaa MMoouunnttaaiinnss ((FFiigguurree 11CC)) wweerree uusseedd ttoo eexxttrraacctt aa ssuubb--sseett ooff iimmaaggeess oonn tthhee sseelleecctteedd LLaannddssaatt ddaatteess
(Figure 2) from which validation of the resulting snow cover maps. This control area comprises a flat
(Figure2)fromwhichvalidationoftheresultingsnowcovermaps. Thiscontrolareacomprisesaflat
hillslope facing west with altitudes ranging from 1900 to 3011 m a.s.l. (the Caballo peak).
hillslopefacingwestwithaltitudesrangingfrom1900to3011ma.s.l. (theCaballopeak).
TTaabbllee2 2..S Snnoowwc coovveerra arereaac acalcluculalateteddf rforommL Lananddsasat-tb-bininaaryry( A(ABIN)),, LLaannddssaatt--ffrraaccttiioonnaall ((AAFRAC) )aanndd TTPP
BIN FRAC
((AATP)),, aanndd aabbssoolluuttee aanndd rreellaattiivvee eerrrroorr aassssoocciiaatteedd ttoo eeaacchh LLaannddssaatt mmaapp aanndd ddaattee aatt tthhee 22--kkmm22 vvaalildidaattioionn
TP
ccoonnttrrooll aarreeaa..
DateDate A((cid:1775)BkIm(cid:1776)N(cid:1783)(cid:1788)2) (cid:1775)(k(cid:1780)Am(cid:1792)F(cid:1775)2R(cid:1777)) AC(k(cid:1775)m(cid:1794)(cid:1790)2) AT((cid:1775)P(cid:1776)((cid:1783)(cid:1788)km−(2A(cid:1775)) (cid:1794)B(cid:1790)IN) −(A(cid:1775)T(cid:1776)P(cid:1783)(cid:1788)(cid:1775))−(cid:1794)(cid:1790)((cid:1775)A(cid:1794)B(cid:1790)IN)− AT(P(cid:1775))(cid:1780)(cid:1792)((cid:1775)k(cid:1777)m−(2A)(cid:1775) F(cid:1794)R(cid:1790)A) C−((cid:1775)A(cid:1780)T(cid:1792)(cid:1775)P(cid:1775)(cid:1777))(cid:1794)−(cid:1790)((cid:1775)A(cid:1794)F(cid:1790)RA)C −ATP)
4 May 2009 (km1.920) 1.(8k8m 2) 1.71 (km2) 0.19 (km2) 0.111 ATP 0.17 (km2) 0.099 ATP
4M2a9y J2u0n0e9 2009 1.09.006 0.014. 88 0.04 1.71 0.02 0.19 0.500 0.111 0.00 0.17 0.000 0.099
29Ju1n5 eM2a0y0 92010 0.10.690 1.808. 04 1.67 0.04 0.23 0.02 0.138 0.500 0.21 0.00 0.126 0.000
15M3a1y M2a0y1 02010 1.19.058 1.315. 88 1.24 1.67 0.34 0.23 0.274 0.138 0.11 0.21 0.089 0.126
31M8a yJu2n0e1 20010 1.15.803 0.810. 35 0.82 1.24 0.21 0.34 0.256 0.274 −0.02 0.11 −0.024 0.089
8Ju1n8e J2u0l1y0 2010 1.00.304 0.002. 80 0.02 0.82 0.02 0.21 1.000 0.256 0.00 −0.02 0.000 −0.024
189 JDuelyce2m01b0er 2010 0.10.464 1.303. 02 1.45 0.02 0.19 0.02 0.131 1.000 −0.12 0.00 −0.083 0.000
9December2010 1.64 1.33 1.45 0.19 0.131 −0.12 −0.083
10 January 2011 1.86 1.83 1.83 0.03 0.016 0.00 0.000
10January2011 1.86 1.83 1.83 0.03 0.016 0.00 0.000
3 February 2011 1.79 1.75 1.83 −0.04 −0.022 −0.08 −0.044
3February2011 1.79 1.75 1.83 −0.04 −0.022 −0.08 −0.044
31 March 2011 1.90 1.90 1.83 0.07 0.038 0.07 0.038
31March2011 1.90 1.90 1.83 0.07 0.038 0.07 0.038
8 April 2011 1.88 1.81 1.81 0.07 0.039 0.00 0.000
8April2011 1.88 1.81 1.81 0.07 0.039 0.00 0.000
10M1a0y M2a0y1 12011 1.16.767 1.013. 03 1.45 1.45 0.22 0.22 0.152 0.152 −0.42 −0.42 −0.290 −0.290
19Ju1n9 eJu2n0e1 12011 0.01.717 0.100. 10 0.17 0.17 0.00 0.00 0.000 0.000 −0.07 −0.07 −0.412 −0.412
12M1a2y M2a0y1 22012 1.13.131 1.011. 01 0.91 0.91 0.40 0.40 0.440 0.440 0.10 0.10 0.110 0.110
7Jan7u Jaarnyua2r0y1 32013 1.18.383 1.619. 69 1.83 1.83 0.00 0.00 0.000 0.000 −0.14 −0.14 −0.077 −0.077
24F2e4b rFueabrryu2ar0y1 32013 1.18.383 1.811. 81 1.83 1.83 0.00 0.00 0.000 0.000 −0.02 −0.02 −0.011 −0.011
13A1p3r Ailp2r0i1l 32013 1.19.090 1.818. 88 1.83 1.83 0.07 0.07 0.038 0.038 0.05 0.05 0.027 0.027
31M3a1y M2a0y1 32013 1.17.373 1.416. 46 1.08 1.08 0.65 0.65 0.602 0.602 0.38 0.38 0.352 0.352
16Ju1n6 eJu2n0e1 32013 0.07.474 0.406. 46 0.36 0.36 0.38 0.38 1.056 1.056 0.10 0.10 0.278 0.278
18Ju1l8y J2u0ly1 32013 0.00.202 0.002. 02 0.00 0.00 0.02 0.02 0.02 0.02

Figure 2. Landsat scenes, TM (grey dots) and ETM+ (black crosses) analyzed throughout the study
Figure2. Landsatscenes,TM(greydots)andETM+(blackcrosses)analyzedthroughoutthestudy
period 2000–2013 over Sierra Nevada Mountain. Available Terrestrial Photography (TP) used as
period 2000–2013 over Sierra Nevada Mountain. Available Terrestrial Photography (TP) used as
validation dataset (red dots) over the control area throughout the validation period (2009–2013).
validationdataset(reddots)overthecontrolareathroughoutthevalidationperiod(2009–2013).
3. Methods
3. Methods
Binary and fractional snow cover maps were derived, respectively, from (1) normalized
Binaryandfractionalsnowcovermapswerederived,respectively,from(1)normalizeddifference
difference indexes analysis and (2) spectral mixture analysis for each one of the selected Landsat
indexesanalysisand(2)spectralmixtureanalysisforeachoneoftheselectedLandsatscenesshowed
scenes showed in Figure 2. These images were pre-processed following the standard methods used
inFigure2. Theseimageswerepre-processedfollowingthestandardmethodsusedinthisareaby
in this area by Pimentel et al. (2012) [24].
Pimenteletal. (2012)[24].
3.1. Landsat Pre-Processing
The different stages in this pre-processing include a radiometric calibration, and atmospheric,
saturation and topographic corrections. Angular correction is not applied since the surface is
RemoteSens.2017,9,995 5of17
3.1. LandsatPre-Processing
Thedifferentstagesinthispre-processingincludearadiometriccalibration,andatmospheric,
saturation and topographic corrections. Angular correction is not applied since the surface is
consideredLambertian. AlthoughinrealitysnowsurfaceisnotLambertian,thisassumptiondoesnot
introducesignificanterrorswhentheremotesensorsarenadirviewingwithafixeddirection. Thisis
thecaseofTMandETM+sensor,whicharenadirviewingandhaveanfixedincident,approximately
30◦,beingtheanisotropycoefficientcloseto1[32–34].
The radiometric coefficients summarized in [35] were applied for the radiometric calibration.
TheatmosphericcorrectionwasperformedbytheDarkObjectSubtraction(DOS)methodusedin
manysimilaranalyses[36,37]. ThismethodretrievesthesurfacereflectancebyassumingaLambertian
surfaceandcloudlessatmosphericconditions,anditconsidersallthescatteringeffectsinthescenetobe
thesameasthoseofablackbodypresentinthescene[38];thedarkobjectisselectedfromtheminimum
radiancevalueofthehistogrammadeupfromatleasta200-pixelsub-setinthescene[39]thatmust
excludethosepixelsshadowedbytheterrainroughness. Moreover,fixedvaluesforthedownwelling
transmittanceparametersoftheatmosphereforeachbandareadopted[40],beingboththeupwelling
atmospherictransmittanceandthediffuseirradianceneglected. DOS-basedmethodsconstituteagood
alternativetothemorecomplexRadiativeTransferenceCodes(RTCs)whentheatmosphericoptical
propertiesthattheyrequirearenotfullyavailableorhaveaquestionablequality[41].
SeveralbandsofLandsatscenesareradiometric-saturatedduetotheradiometricconfiguration
ofthesensors. Snowconstitutesatmosta5%oftheanalyzedLandsatscenesandthus,thesensors
arenotspecificallycalibratedforsnow,whateverthemainlanduseis. Thissaturationisespecially
relevantduringthewinter,whenthehighestdifferencesbetweenthesnowandtherestoflandcovers
inthescenearefound. Tocorrectthissaturation,amultivariablecorrelationanalysisbetweenbandsis
employedtorecoverthesnowsaturatedpixels[42].
AC-correctionalgorithm[43]bymeansofalandcoverseparation[44]wasappliedastopographic
correctiontoremovetheeffectsoftheterrainroughnessontheimage. Again,theLambertiansurface
assumptionallowstheobtainingofalinearfitbetweentheilluminationangleandthedifferentbands
reflectance. Additionally,thediffuseirradianceistakenintoaccountbyasemi-empiricalestimation
oftheCfactor. Inordertoconsiderthemultiplereflectivepropertiesofthedifferentsoilcovers,the
pixelsareclassifiedasbaresoilorvegetatedareasbyusingtheNormalizedDifferenceVegetation
Index(NDVI).
3.2. SnowCoverMaps
3.2.1. BinarySnowCoverMaps
Thealgorithmforderivingbinarysnowcovermapsisbasedonthephysicalpropertiesofasnow
coverthroughouttheelectromagneticspectrum. Snowhasverydifferentextremevaluesinthevisible
(highreflectance)andinfraredregions(lowreflectance),sothataNormalizedDifferenceSnowIndex
(NDSI)hasbeendefinedforeachpixeloverathresholdaltitude(linkedtotheminimumheightin
whichsnowcanbefound)bycomparingtheindividualelectromagneticresponsesinthesetworegions
ofagivenpixel[45]. ForLandsatTMandETM+scenes,thespectralbandsthatcorrespondwiththese
tworegionsareband2(0.52–0.60µm)andband5(1.55–1.75µm). Theindexisdefinedasfollows:
band2−band5
NDSI= (1)
band2+band5
The discrimination between covered and non-covered pixels is done based on two threshold
values: NDSI>0.15andband1>0.06(0.45–0.52µm),adoptedtodetectsnowundertheparticular
characteristicsofsnowinMediterraneanregions[46].
RemoteSens.2017,9,995 6of17
3.2.2. FractionalSnowCoverMapsfromSpectralMixtureAnalysis
Thisalternativealgorithmprovidesanestimatedvalueofthesnowcoverfraction(SCF)ineach
pixel,basedontheassumptionoftheglobalreflectanceofamixedpixel(apixelmadeupofdifferent
typesofsurfaces)beingalinearcombinationoftheindividualreflectancevaluesofthesurfacespresent
inthepixel(endmembers). Thus,thisendmembers-spectralmixtureanalysis(SMA)[21,47]expresses
Remote Sens. 2017, 9, 995 6 of 17
thereflectanceineachpixelafterthepre-processing,asthelinearcombinationofthereflectancevalues
fromeacphresspenetc itnr athlee npdixmel e(emndbmeremgibveersn). bTyhuEsq, uthaist ieonndm(2e)m, bers-spectral mixture analysis (SMA) [21,47]
expresses the reflectance in each pixel after the pre-processing, as the linear combination of the
reflectance values from each spectral endmember gNiven by Equation (2),
∑
NDSIRS,λ = (cid:2898)FiRλ,i+ελ (2)
NDSIR =(cid:3533)i F R +ε (2)
(cid:2903),(cid:2971) (cid:2919) (cid:2971),(cid:2919) (cid:2971)
(cid:2919)
whereFiisthefractionoftheareaofendmemberiinagivenpixel;Rλ,iisthereflectanceofendmemberi
where F is the fraction of the area of endmember i in a given pixel; R is the reflectance of
atwavelengthλ(cid:2919)obtainedafterthepre-processingsteps;Nisthenumbero(cid:2971)f,(cid:2919)spectralendmemberstaken
endmember i at wavelength λ obtained after the pre-processing steps; N is the number of spectral
intoaccoeundnmt;eamnbderεsλ taiksetnh ientroe saicdcouuanlte; rarnodr εat itsh theew reasvideuleanl egrtrhorλ ata tshseo wciaavteeldentgotht hλe afsitsoocfiattheed Nto tehned fimt embers
(cid:2971)
consideroefd thien Nth eenadnmaelmysbiesr.s Tcohnesmideertehdo idn rtheeq uaniraelyss,ifsi.r Tsth,et omestehloedct rtehqeuisriegs,n fiifriscta, tnot seenledctm theem sbigenrisficinantth estudy
areaandenodbmtaeimnbtheresi rini nthdei vsitduudya lasrpeae catnrdu mob,taainnd thseeicro innddi,vtiodusaoll vspeetchtreumF, vaanldu esescfoonrd,e atoc hsoplvixe etlhien Fth escene.
i (cid:2919)
Thrveaeluseigs nfoifri ecaacnht peinxdelm ine tmheb secresnwe. ereidentifiedinthestudyarea:(a)puresnow,(b)vegetation(brush
Three significant endmembers were identified in the study area: (a) pure snow, (b) vegetation
creepingvegetation)and(c)rocks(predominantly,phyllites). Theirspectrumswereobtainedfroma
(brush creeping vegetation) and (c) rocks (predominantly, phyllites). Their spectrums were obtained
digitalspectrallibrary(http://speclab.cr.usgs.gov/)(Figure3). Theleast-squaresfittoF issolvedin
from a digital spectral library (http://speclab.cr.usgs.gov/) (Figure 3). The least-squares fit to iF is
(cid:2919)
eachpixseollwveidth ina eraecfhle pctixivele wNitehw at orneflmecetitvheo Nde.wTthoen rmeseuthltosdf. rTohme rtheseuSltMs fAromgo thbee ySMonAd gtoh ebedyiosncrdi mthien ationof
snow,sindcisecriitmailnloawtiosni dofe nsntiofwic,a stiinocne oitf amlloixwesd idpeinxteilfsica(wtiohne nof tmheixyedo cpciuxrelas n(wdhwenh ethreeyt hoecycuar raenldo cwahteedre) andthe
studyofththeye iarrceo lnotcraitbedu)t iaonnd ttoheth setusdnyo wof cthoevire rcoanrteraibiuntiothne tos ttuhde ysnsoitwe oconvderi fafererae nint ttihme estsucdayl essit.e on
different time scales.

Figure3F.i(gAur)eE 3v. o(Alu)t iEovnolouftitohne oafv tehrea gaevdersangeodw scnoovwe rcofrvaecr tfiroanctiinonS iienr rSaieNrrae vNaedvaadMa oMuonutanitnaianb aobvoeveth teheth reshold
altitudetohfre1s0h0o0ldm alttihturdoeu gofh 1o0u0t0 tmhe thsrtouudgyhopuetr tihoed s(t2u0d0y0 p–e2r0io1d3 )(2f0r0o0m–20b1o3t)h frboimn abroyth( gbrienyaryd o(gtsre)ya dnodtsf)r actional
and fractional (black crosses) algorithms, and (B) the associated standard deviation values, together
(blackcrosses)algorithms,and(B)theassociatedstandarddeviationvalues,togetherwiththeirdifference.
with their difference.
3.2.3. Sn3o.2w.3.C Sonovwer CMovaepr sMVaaplsi dVaaltiidoantion
TheresTuhltes rfersoumlts fbroomth bsontho wsnocwov ceorverre rtertireivevaallm meetthhooddss frformom LaLnadnsadt sTaMt TanMd EaTnMd+E wTeMre+ vawlideraetevd alidated
using the 10-m snow cover maps estimated from TP over the control area (Figure 1C) selected in the
usingthe10-msnowcovermapsestimatedfromTPoverthecontrolarea(Figure1C)selectedinthe
study area.
studyarea.
RemoteSens.2017,9,995 7of17
HighResolutionSnowCoverMapsfromTerrestrialPhotography
Snow cover maps from TP are usually obtained in a two-step process [9]: (a) georeferencing
and (b) snow detection. The first one is achieved following some basic principles of computing
design[48,49]withthesupportofaDigitalElevationModel(DEM)ofthetargetarea. Theresultisa
functionthatrelatesthetwo-dimensionalelements(pixels)inthephotowiththethree-dimensional
elements(points,centersofeachcell)intheDEM[26]. Thesecondstepinvolvesanunsupervised
k-meansalgorithmthatdistinguishesbetweencoveredandnon-coveredpixels. Thefinalresultisa
snowcovermapstimeserieswiththesamespatialresolutionoftheDEMandthetemporalresolution
oftheTPacquisitionprocess. TPimagescanbeapowerfuldatasourcethatiseasilyadaptedtothe
temporalandspatialresolutionofthestudiedprocessesundermonitoring.
PerformanceIndicatorsoftheSnowCoverMapAlgorithms
Virtual fractional 30 × 30 m2 snow maps are obtained from the binary 10 × 10 m2 snow
maps resulting from the TP analysis, and they are used as “ground truth” to test the 30 × 30 m2
snowcovermapsobtainedfromLandsatanalysisbybothalgorithms,binaryandspectralmixture
approaches. Thedifferentmetricsemployedtoevaluatetheperformanceofeachmethod[28]aregiven
byEquations(3)–(6). Firstly,metricstotestthecorrectlocationofthepixelsareused:
RP
precision= (3)
RP+WP
RP
recall= (4)
RP+WN
RP+WN
accuracy= (5)
RP+RN+WP+WN
whereRPrepresentsthenumberofrightpositives(snowinbothTPandLandsatpixels);RNrepresents
thenumberofrightnegatives(nosnowinbothTPandLandsatpixels);WPrepresentsthenumber
ofwrongpositives(snowintheLandsatpixelandno-snowintheTPpixel);andWNrepresentsthe
numberofwrongnegatives(no-snowintheLandsatPixelandsnowintheterrestrialpixel).
Finally,theRootMeanSquareError(RMSE)isalsoprovidedintheanalysisoftheSMA:
(cid:118)
RMSE= (cid:117)(cid:117)(cid:116)1 ∑N[SCFTP(j)−SCFLandsat(j)]2 (6)
N
j=1
All these metrics are calculated for the area in each Landsat image associated to the mask
calculatedusingTPoverthecontrolarea(Figure1C).
4. Results
ThissectionshowstheSCFevolutioninthestudyareaduringthestudyperiodusingbothbinary
andfractionalanalysis. TheresultsofthevalidationofbothmethodologiesusingTPatthecontrol
areaarealsodescribed.
4.1. SnowCoverMapSeriesfromLandsatAnalysis
Figure3showstheaverage(Figure3A)andstandarddeviation(Figure3B)oftheSCFduringthe
studyperiodinthestudyarea(altitudesabove1500m). SCFaverageandstandarddeviationfollow
parallel trends for both methodologies, with mean values of 0.13 m2 m−2 and 0.11 m2 m−2 for the
averageSCF,and0.20m2m−2and0.23m2m−2forthestandarddeviation,forthebinaryandfractional
approaches,respectively. Ageneraloverestimationofthebinarymethodcanbeobserved,beinghigher
duringtheaccumulationphases,especiallyforheavysnowfalleventsthataffectareaswheresnowin
notusuallyfound,whichreachesareaswherethesnowisnotusuallypresent(e.g.,12January2003and
RemoteSens.2017,9,995 8of17
Remote Sens. 2017, 9, 995 8 of 17
6MarJcahnu2a0r0y5 2w00i3t handdi f6f eMreanrcche s20b0e5t wweitehn dbifofethremnceetsh boedtwoleoegni beostahb mouetth0o.d1omlo2gimes− a2b)o.uTth 0e.1t mhr2e msh−2o).l dThvea lues
usedttohrdesishcorlidm vinalauteest uhseepdr teos deniscceriomfisnnaotew thwe eprreesoebntcaei noef dsnionwth weesroeu otbhtearinnefda cine tinhet hsoeustthuedrny faarceea in[4 t6h]e, with
study area [46] ,with higher altitude predominance; this very likely overestimates the presence of
higheraltitudepredominance;thisverylikelyoverestimatesthepresenceofsnowonthewholedomain
snow on the whole domain of Sierra Nevada. These differences are smaller during the melting
ofSierraNevada. Thesedifferencesaresmallerduringthemeltingperiods,inwhichtheaverageSCF
periods, in which the average SCF seems to be more equally represented by both methodologies.
seemstobemoreequallyrepresentedbybothmethodologies. Nevertheless,thestandarddeviation
Nevertheless, the standard deviation over these periods (Figure 3B) shows a variable value, with a
overtheseperiods(Figure3B)showsavariablevalue,withageneraloverestimationofthebinarymaps,
general overestimation of the binary maps, with a mean difference of 0.04 m2 m−2. Despite the
with a mean difference of 0.04 m2 m−2. Despite the observed overestimation, both methodologies
observed overestimation, both methodologies produce similar average values during the study
produpceerisoidm tihlaatr satevmer afrgoemv dailfufeersendtu srpinatgiatlh deissttruibduytiopnesr,i owdhothsea tmsetaenm afnrdo mstadnidfaferdre dnetvsipataiotina lvdaliustersi baruet ions,
whossehmoweann ina nFidgusrtae n4d. a rddeviationvaluesareshowninFigure4.

FigurFeig4u.rDe i4s.t rDibisutrtiibountioofn tohfe thmee maneaann adndst astnadndaradrdd deevviiaattiioonn ooff tthhee SSCCFF (m(m2 2mm−2)− o2v)eor vtheer tshtuedsyt uardeya area
durindgutrhinegs tthued sytupdeyr ipoedri2o0d0 200–0200–12301u3s uinsignga ab bininaarryy aanndd aa ffrraaccttiioonnaal lalaglogroitrhitmh.m .
The local mean SCF value shows similar spatial patterns from both methodologies. In general,
ThelocalmeanSCFvalueshowssimilarspatialpatternsfrombothmethodologies. Ingeneral,
fractional maps present lower SCF values, being the higher differences between both approached
fractional maps present lower SCF values, being the higher differences between both approached
found at the higher elevations located in the southwestern area, and at the lower elevations in the
foundnoartththeaestheirgnh aerreae; ltehvea btiinoanrsy lmoceathteoddoilnogtyh eressouultsth inw eexscteesrsn snaorewa ,ina tnhde samttahlle vlaollweyesr. Oelne voantei ohnansdi,n the
northtehaes ftierrsnt zaorneea ;retphreesbeinntasr aynm areetah wodhoerloe gthyer sensouwlt spienrseisxtecnesces issn hoiwghienr; tchoenssemquaellnvtlayl,l leoynsg.eOr mneolntiengh and,
thefircsytclzeos ntaekree pplraecsee anntds tahneraer iesa aw hihgehreer tphreobsanboilwityp oefr oscisctuernrecnecies ohf imghixeerd; cpoixnesles q(puiexneltsl yc,olmonpogseerdm bye lting
cyclessntoawke anpdla ncoe sannodw)t.h Oenre thise oathheigr hhaenrdp, rtohbe asebciolintyd zoofnoec ccounrsrteitnuctees oafndm aixreead wphiexreel sth(ep sinxoewls icso vmerpy osed
sporadic and associated to heavy snowfalls, which enhances the presence of mixed pixels, too. The
bysnowandnosnow). Ontheotherhand,thesecondzoneconstitutesandareawherethesnowis
local standard deviation distribution for the study period confirm this hypothesis, and shows a high
verysporadicandassociatedtoheavysnowfalls,whichenhancesthepresenceofmixedpixels,too.
spatial variability for the fractional method; it also provides a clear elevation fringe where the
Thelocalstandarddeviationdistributionforthestudyperiodconfirmthishypothesis,andshowsa
appearance of mixed pixels dominates the distribution of the snow in this mountainous range.
highspatialvariabilityforthefractionalmethod;italsoprovidesaclearelevationfringewherethe
appea4r.2a.n Scneowof Cmovixere Mdappi xVeallsiddatoiomn ifnroamte Tsetrhreestdriiaslt rPihboutotgioranphoyf thesnowinthismountainousrange.
Landsat SCF maps were validated from the analysis of terrestrial pictures (assumed as “ground
4.2. SnowCoverMapValidationfromTerrestrialPhotography
truth”) obtained over a control area (Figure 1C) during 2009–2013, following the method described
Lina nSdecstaiotnS C3.F Fmigaupres 5wAe rsehovwalsi dtharteeed dfrifofmeretnhte eaxnamalpylseiss ooff ttheer rSeCstFr imalappisc toubrteasin(eads sfuromme dthae sth“rgereo und
truth”m)eothbotadisn (ebdinoavrye ranadc ofrnatcrtoiolnaarle aalg(oFriigthumres,1 aCn)dd tuerrriensgtr2ia0l 0p9i–ct2u0r1e3) ,fofor lrleopwreinsegnttahteivme settahgoeds odfe tshcer ibed
in Secstnioown s3e.asFoing:u croem5pAletseh sonwows tchorveeer, dbiefgfeinrnenintge oxfa tmhep slepsrinogf tmheeltSinCgF, amnda pensdo obft athine emdefltrionmg ctyhcele.t hree
The improvement in the snow cover detection is clearly observed from the left to the right of the
methods(binaryandfractionalalgorithms, andterrestrialpicture)forrepresentativestagesofthe
figure (binary-fractional-TP), being the TP capable of capturing small patched patterns, which are
snowseason: completesnowcover,beginningofthespringmelting,andendofthemeltingcycle. The
commonly found in these mountain regions. Figure 5A also shows the loss of accuracy of the binary
improvementinthesnowcoverdetectionisclearlyobservedfromthelefttotherightofthefigure
(binar y-fractional-TP),beingtheTPcapableofcapturingsmallpatchedpatterns,whicharecommonly
foundinthesemountainregions. Figure5Aalsoshowsthelossofaccuracyofthebinarymethodas
themeltingperiodevolves. Threespatialdistributionmetricsarecalculatedtonumericallyvalidate
Remote Sens. 2017, 9, 995 9 of 17
RemoteSens.2017,9,995 9of17
method as the melting period evolves. Three spatial distribution metrics are calculated to numerically
validate the performance of the Landsat maps using as “ground truth” TP maps: precision, recall and
theperformanceoftheLandsatmapsusingas“groundtruth”TPmaps: precision,recallandaccuracy
accuracy (Figure 5B).
(Figure5B).

FFiigguurree5 5.. ((AA)) SSeelleecctteedd eexxaammpplleess ooff ddiiffffeerreenntts sttaaggeessd duurrininggt thhees snnoowws seeaassoonn::a accccuummuullaattiioonn——ccoommpplleettee
ccoovveerr ((1100 JJaannuuaarryy 22001111)),, bbeeggiinnnniinngg oof fththee spsprirningg mmeletlintign g(1(61 6JuJnuen 2e021031) 3a)nadn ednden odf othfet hmeemltienlgti n(2g9
(J2u9nJeu n20e0290),0 9u)s,inugsi nthget LheanLdasnadt s(abtin(barinya arnyda nfrdacftriaocntaiol)n mal)etmhoedthoolodgoileosg aiensda TnPd aTtP thate tchoenctroonlt arorelaa.r e(Ba.)
(EBv)oElvuotilounti oonf othfeth tehtrheere memeteritcrsic s(p(rperceicsiisoino,n r,erceaclall laanndd aaccccuurraaccyy)) uusseedd dduurriinngg tthhee vvaalliiddaattiioonn ppeerrioiodd
((22000099––22001133)) ffoorr bbootthh mmeetthhooddoollooggiieess ((rriigghhtt));; ddiissttrriibbuuttiioonn ffuunnccttiioonn ((bbooxxpplloott,, tthhee cceennttrraall lliinnee aanndd tthhee
uuppppeerr aanndd lloowweerr eeddggeess ooff tthhee bbooxxr reepprreesseennttt thheem meeddiaiann,,7 755tthha anndd2 255ththp peerrcceennttilielessr reessppeeccttiviveellyy;; tthhee
bbllaacckk wwhhiisskkeerrss eexxtteenndd ttoo tthhee eexxttrreemmee vvaalluueess ccoonnssiiddeerreedd iinn tthhee aannaallyyssiiss;; aanndd tthhee rreedd ccrroosssseess aarree tthhee
oouuttlliieerrss))o offe eaacchhm meettrricicf foorre eaacchhm meeththoodd( l(eleftf)t.).
PPrreecciissiioonn ddeessccrriibbeess tthhee ffrraaccttiioonn ooff ppoossiittiivvee ssnnooww mmaappppiinngg rreessuullttss ppiixxeellss tthhaatt ttrruullyy iiddeennttiififieedd
ssnnooww)),, tthhaatt iiss,, tthhee rraattiioo bbeettwweeeenn tthhee ppiixxeellss ddeetteecctteedd aass ssnnooww iinn LLaannddssaatt aanndd tthhee ssnnooww ppiixxeellss iinn TTPP..
DDuurriinngg tthhee ssttuuddyy ppeerriioodd,, iittss vvaalluueess rraannggee ffrroomm 00..1155 ttoo 11,, wwiitthh aa mmeeaann vvaalluuee ooff 00..7755,, ffoorr tthhee bbiinnaarryy
mmaappss,, aanndd ffrroomm 00 ttoo 11,, wwiitthh aa mmeeaann vvaaluluee oof f0.07.97,9 f,ofro trhteh ferafrcaticotinoanl amlampasp. Ss.imSiimlaril parrepcriesicoisni ovnalvuaelsu aerse
obtained using both methodologies during the winter period, which is a slightly higher for the
fractional maps. There is not a clear behavior of this metric during the melting period.
Remote Sens. 2017, 9, 995 10 of 17
RemoteSens.2017,9,995 10of17
Recall describes the fraction of snow in all the scene that is identified by each method, that is,
what fraction of the pixels detected as snow in TP are also detected by Landsat. It ranges from 0 to 1,
warietho ba tmaineaend vuasliuneg obfo 0th.79m, efothr othdeo lboigniaersyd muraipnsg, athned wfrionmte r0.p1e5r tioo d1,, wwhitihc ha imsaeasnli gvhaltulye hoifg 0h.e8r6,f oinr tthhee
cfraascet ioofn athlem farpacst.iTonhaerl emisapnos.t Raeccleaallr vbaelhuaevsi oarreo fatghaiisnm heitgrhicerd udruinrigngth tehme ewltiinntgerp, ebruiot di.n this case the
fractiRonecaal lmldaepssc srhiboewsst hbeetftrearc mtioentriocf psrnaocwticianllayl lint haell socfe tnhee tdhaatteiss. i dentifiedbyeachmethod,thatis,
whatFfirnaacltliyo,n acocfutrhaecyp irxeeplrsedseentetcs taeldl tahses pnioxwelsi ncoTrPreactrley aidlseondtiefiteedct, ewdhbayteLvaenr dthsea ts.itIutaratinogne iss f(rsonmow0 otor
1n,ow-sintohwa)m; seoa, nthvisa lmueetorifc0 a.7ls9o, ftoakretsh einbtoin aacrcyomunatp tsh,ea nnod-sfrnoomw 0p.i1x5eltso. 1A,cwcuitrhacaym raenagnevs aflruoemo 0f.055.8 t6o, i1n,
wthiethc aas emoefatnh eoff r0a.c8t5i,o fnoarl tmhea pbsi.nRareyc amllavpasl,u aensda rferoamga i0n.8h tiog h1e, rwdiuthri na gmtheaenw oifn t0e.9r,6b, ufotri nththe ifsracactsieonthael
mfraacptsio. nFaralcmtiaopnsals hmoawpss beexthteibrimt ae tcrliecapr rbaectttiecra lalycciunraalclyo tfhtahne dbianteasr.y maps, not only during winter but
also dFuinrainllgy ,tahcec murealctyinrge ppreersioendtss. aTlhlitsh ies ppiaxretliscucolarrrleyc trleyleidveanntti fiine dre,gwiohnaste wvehretrhe ethsiet upaetrisoinstiesn(csen oofw thoer
snnoo-swnpoawck); isso n,toht icsomnteitnruicoaulss,o atnadk edsififnetroenatc csonuonwt athcceunmou-slnaotiwonp/mixeelltsi.ngA cccyuclreasc yocrcaunrg desurfirnogm a0 g.5i5vetno
1se,awsoitnh. ameanof0.85,forthebinarymaps,andfrom0.8to1,withameanof0.96,forthefractional
mapsF.igFurarcet i6o snhaolwmsa pthsee exvhoibluittiaocnl eoafr tbheet taevrearaccguer SaCcyF tvhaalnueb ionvaerry tmhea pcos,nntrootlo anrleya dfruormin gthwe iLnatenrdbsuatt
malsaopsd aunrdin gthteh veamlidealttiinogn pTePr idoadtsa.seTt,h tiosgiesthpearr twiciuthla rtlhye raeslseovcainatteidn dreisgpioernssiown hgerraepthh. eLpanerdssiastte bnicnearoyf
athned sfnraocwtiponacakl misanpos tpcroensetinntu goloubs,ala RndMdSEif fvearelunetss onfo 0w.1a2c mcu2 mmu−2l aatniodn 0/.0m9e mlti2n mg−2c,y rcelsepseoccticvuerlyd, udruinrignga
tghive esntusdeya spoenr.iod. Table 2 shows the absolute and relative error values for each Landsat map and date.
GFiegnuerreal6lys,h tohwe sfrtahcetieovnoallu mtioenthoofdt hreesauvletsr ahgaevSeC aF lovwaleure eorvreorr tthheanco tnhter oblianraeray,f rboemintgh ethLisa nmdosraet
rmelaepvsanant dfotrh senvoawli dcoavtieorn aTrePad laotwaseert ,thtoagne 8th5%er owf itthhet hceonatsrsoolc aiaretead. Tdhisep leartsei omnegltrianpgh s.taLgaensd osbattabinin athrye
wanodrsftr arcetpiorensaelnmtaatpiosnp, raess eenxtpgelcotebdal, wRMhaStEevvearl uaelgsoorfit0h.1m2 ims 2semle−c2teadn; dho0w.09evme2r, mth−e2 b,irnesapryec atpivperlyo,adchu rcianng
pthreodstuucdey lapregrei oodv.eTraebstliem2asthioonws sotfh tehea bssnoolwut ecoavnedr,r ewlahtiicvhe iesr nroort vfoauluneds wfoirthe atchhe Lfraancdtisoantaml mapetahnoddd. ate.

FFiigguurree 66.. EEvvoolluuttiioonn ooff SSCCFF oovveerr tthhee ccoonnttrrooll aarreeaa tthhrroouugghhoouutt tthhee vvaalliiddaattiioonn ppeerriioodd ((22000099––22001133)) ((rriigghhtt))..
DDiissppeerrssiioonn ggrraapphh ccoommppaarriinngg tthhee eevvaalluuaatteedd SSCCFF oobbttaaiinneedd ffrroomm LLaannddssaatt wwiitthh tthhee vvaalliiddaattiioonn ddaattaasseett
oobbttaaiinneedd ccaallccuullaatteedd uussiinngg TTPP ((lleefftt))..
4.3. Snow Cover Evolution from SMA Snow Maps
Generally, the fractional method results have a lower error than the binary, being this more
relevAancctofordrisnngo two cthoev eprreavreioaulso wreesrutlhtsa, nth8e5 %SMoAf tmheetchoondtr oplroavreida.esT ah ebelattteerm spelattiinagl rsetapgreesseonbtatatiionnt hoef
twhoe rssntorewp rthesreonutgahtioount, tahsee sxtpuedcyte adr,ewa.h Matoerveeorvaelrg, otrhieth qmuiicsks cehleacntegdes; hino wtheev senr,otwhepabcinka ursyuaapllpyr fooaucnhdc ainn
tphreosdeu lcaetiltaurdgeeso avreer aedsteiqmuaattieolnys roefptrheesesnntoewd bcyo vtehre, wrehsuiclhtinisgn motixfeodu npdixwelist,h wthiteh far arcetailoinstaicl meveothluotdio.n of
the snow captured by the sequence of fractional maps.
4.3. SnowCoverEvolutionfromSMASnowMaps
Figure 7 represents the evolution of the average SCF for different elevation bands over the Sierra
NevaAdcac omrdouinngtationtohuesp rraenvgioeu ussriensgu lftrsa,ctthieonSaMl Amampest.h Tohdep rreolvaitdioenssahbipet tbeertwspeaetnia lSrCeFp raensedn etaletivoantioofnt haet
tshneo wstutdhryo aurgehao cuatnt bhee astsusedsyseadre, ab.oMtho lroecoavlleyr, atnhde qguloibckalclyh,a fnrgoems itnhetshee rsensouwltsp.a Bceklouwsu 1a7ll5y0f mou an.ds.li.n, stnhoeswe
vlaetriytu rdaereslayr aepapdreoqauchaetesl aynr eapvreersaegnet SeCdFb vyatlhuee roefs 0u.5lt;i nbegtwmeixeend 17p5ix0–el2s2,5w0 imth aa.sr.le.a, SliCstFic veavluoelus tairoen hoigfhtheer
bsnuot wnecvaeprt ureraecdhb tyheth ceosmeqpuleetnec ceoovfefrr aocft tiohne aalrmeaa. p2s2.50 m a.s.l. is estimated as the threshold elevation
for wFhiigcuhr eSC7Fre epqruesael notrs vtheerye vcloolsuet itoon 1o afrteh efoauvnedra egveeSrCy Fyfeoarr,d wiffiethre snntoewle vpaetrisoinstbeannced sinocvreerasthinegS wieritrha
aNlteivtuaddea; mthoeu inntfaliuneonucse roafn gheiguhs isnlogpferas ctthioant aclomndaiptiso.nTsh tehree lsantoiown sahcicpubmetuwlaeteionnS CstFabainlidtye lienv tahteio hniagthtehset
bstaunddys,a ersepaeccaianllbye wahsseens cseodm,pbloetthe cloocvaelrl yisa rnedacghleodb,a clalyn, afrlosom bteh oebseserrevseudl tfsr.oBme ltohwe r1e7s5u0ltsm, par.os.vl.i,nsgn tohwe
SvMeryAr aapreplryoaapchp rtooa bche ecsaapnabalvee oraf gceapStCuFrivnagl utheeosfe 0lo.5c;abl ectownedeintio17n5s0. – 2250ma.s.l.,SCFvaluesarehigher
butneverreachthecompletecoverofthearea. 2250ma.s.l. isestimatedasthethresholdelevation
forwhichSCFequalorverycloseto1arefoundeveryyear,withsnowpersistenceincreasingwith
altitude;theinfluenceofhighslopesthatconditionsthesnowaccumulationstabilityinthehighest
bands,especiallywhencompletecoverisreached,canalsobeobservedfromtheresults,provingthe
SMAapproachtobecapableofcapturingtheselocalconditions.