modis/viirs snow and ice · npp_viae_l1.a2016024.1810.p1_03110.*.hdf bands i1,i2, i3, showing snow...
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![Page 1: MODIS/VIIRS Snow and Ice · NPP_VIAE_L1.A2016024.1810.P1_03110.*.hdf bands I1,I2, I3, showing snow in hues of yellow. NASA VNP10_L2 Example 20 40 50 60 70 80 90 100 NDSI SNOW COVER](https://reader033.vdocuments.net/reader033/viewer/2022050607/5fae4cbeebad5c6ded123e7e/html5/thumbnails/1.jpg)
MODIS/VIIRSSnowandIce
GeorgeRiggsNASA/GSFCCode615/SSAI
MarkTschudiUniversityofColorado
MiguelRomanNASA/GSFCCode619
DorothyK.HallUnderContracttotheTerrestrialInformaLon
SystemsLaboratory,Code619
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MODISC6SnowAlgorithmDetec6onofsnowcoverextentforhydrologicandclimatologyapplica6onsorresearchSnowcoverdetec6onusestheNormalizedDifferenceSnowIndex(NDSI)techniqueSnowcoveralwayshasNDSI>0.0AsurfacewithNDSI>0.0isnotalwayssnowcoverDatascreensareappliedtoalleviatesnowcommissionerrorsandflaguncertainsnowcoverdetec6onsFrac6onalsnowcover(FSC)isnotcalculatedinC6
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman
2
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MODISC6SnowAlgorithmNDSIsnowdetec6onappliedtoalllandandinlandwaterpixels.MOD02HKMTOAreflectanceinput.MODISland/watermaskused.CloudsmaskedwithMODIScloudmaskproductMOD35_L2.Datascreensappliedtoalleviatesnowcommissionerrorsandflaguncertainsnowcoverdetec6onsitua6ons.• Inlandwaterflag• Lowvisiblereflectancescreen• LowNDSIscreen• Surfacetemperatureandheightscreen/flag• HighSWIRscreen/flag• Solarzenithflag• QAbitflagsaresetforsnowcoverdetec6onsthatarechangedtonosnowandaresetforhigheruncertaintyinsnowcoverdetec6on,andforhighsolarzenithangles.TheNDSIvaluesareoutputforalllandandinlandwaterpixels--cloudmaskisnotapplied.
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 3
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MODISC5toC6SnowProductsDataContentComparison
MOD10_L2SDSs
C5 C6Snow_Cover,binarysnowcover,withmaskedfeatures
Frac6onal_Snow_Cover,0-100%withmaskedfeatures
NDSI_Snow_Cover,--SnowcovermapbyNDSIin0-100range,withmaskedfeatures.
NDSI_Snow_Cover_Algorithm_Flags_QA–bitflagsfordatascreensappliedinthealgorithm.
Snow_Cover_Pixel_QA,basicqualityvalue
NDSI_Snow_Cover_Basic_QA,--basicqualityvalue
NDSI–NDSIvalueforalllandandinlandwaterpixelsinaswath
La6tude(5kmresolu6on) La6tude(5kmresolu6on)
Longitude(5kmresolu6on) Longitude(5kmresolu6on)
MOD10A1SDSs
C5 C6Snow_Cover,binarysnowcover,withmaskedfeatures
Frac6onal_Snow_Cover,0-100%withmaskedfeatures
NDSI_Snow_Cover,--SnowcovermapbyNDSIin0-100range,withmaskedfeatures.
NDSI_Snow_Cover_Algorithm_Flags_QA–bitflagsfordatascreensappliedinthealgorithm.
Snow_Cover_Pixel_QA,basicqualityvalue
NDSI_Snow_Cover_Basic_QA,--basicqualityvalue
NDSI–NDSIvalueforalllandandinlandwaterpixelsinaswath
Snow_Albedo_Daily_Tile Snow_Albedo_Daily_Tile
orbit_pnt(pointer)
granule_pnt(pointer)
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 4
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Spring–summermountainsnowcoveromissionerrorprevalentinC5iscorrectedinC6
C5 C6
MOD10_L2.A2010170.1900(19June)SierraNevadaregion.C6hasgreatlyimprovedaccuracycomparedtoC5.C6snowcoverextentis~2303km2greaterthaninC5whichis~74%improvement.ThesurfacetemperaturescreenisnotappliedonmountainsinC6.
1-20%21-40%41-50%51-60%61-70%71-80%81-90%91-100%
FRACTIONALSNOWCOVER
CloudNight
0%
Nodata
20
405060708090100
NDSISNOWCOVER
CloudWater
30
0
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 5
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MOD02HKM.A2003003.0440.006.*.hdfRGBofbands1,4,6
MOD10_L2.A2003003.0440.006.*.hdfNDSI_Snow_Cover
MOD10_L2.A2003003.0440.006.*.hdfMaskofbit3oftheQAalgorithmflags
Thecombinedsurfacetemperatureandheightscreeneffec6velyblockserroneoussnowcoverdetec6onsatloweleva6onsthatmaynotbeblockedbyotherdatascreens,doesnotaffecthigheleva6onsnowcoverdetec6on.Wherethatscreenblockssnowcommissionerrorshowninredonrightimage.Himalayasinnorthhalfofswath,IndiaandBayofBengaltothesouth.
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 6
Loweleva6on&surfacetemperaturescreenon–notsnow
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InC6theQIRtechniqueisusedtorestoreAquaMODISband6dataforuseinthesnowalgorithmMoreaccuratemappingofsnowcoverinC6comparedtoC5notablyindifficulttodetectsitua6onsalongsnowextentboundaryasflaggedbythealgorithmQAbitflags.
MYD10A1.A2016024.h11v05.005fracLonalsnowcover–band7
MYD10A1.A2016024.h11v05.006NDSI_Snow_Cover
MYD10A1.A2016024.h11v05.006AlgorithmQAbitflagsforchangedsnowdetecLonoruncertainsnowdetecLonTypicallyalongboundaryofsnowcover.
MOD09GA.A2016024.h11v05.006RGBbands1,4,6
AquaMYD10usesQIRofBand6
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 7
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MOD10A1.A2016112.h09v05.006
20
405060708090100
NDSISNOWCOVER
CloudWater
30
0
1.00.0
NDSIrange
Granule_ptrPointertoinputMOD10_L2,useasindextogetswathstart6mefromlistofproductinputsinthemetadata.
1740UTC
1745UTC
1920UTC
NDSI
TimeofobservaLonincludedinC6M*D10A1products
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 8
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NSIDCreleasedC6productsinAprilandMay2016MODISC6userguideisavailableC6dataproductcontentisdifferentfromC5UsersfamiliarwiththeC5binarysnowcoverandFSCdatawillhavetoadjusttousingthenewNDSI_Snow_Coverdata.ProvidinguserswiththeNDSIdataenablesthemtohaveflexibilityinderivingsnowcoverareaSCAmapsfortheirpurposes.UserscanusetheAlgorithm_flags_QAdatatoevaluatethesnowcoverdatafortheirpar6cularresearchorapplica6on.Alsopossibletoadjustsnowcoverextentbycombiningthealgorithmflags,snowcoverandNDSIdata.
MODISC6ProductsReleased
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 9
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OurprocessistodevelopandtestthescienceofthealgorithmonourcomputerusingIDLthentocodethescienceintothePGEusingtheLSIPS
compu6ngenvironment.GainaccesstoLSIPScompu6ngenvironment.Developcodei.e.theopera6onalPGEintheLSIPScompu6ngenvironmentfollowingtheguidelinesfromLSIPSandusingtheircodingenvironmentsetup.Ini6alstepwastoadapttheMODISC6algorithmPGE07torunwithVIIRSdatainputs.NextwerevisedthatalgorithmintotheNASAVIIRSsnowcoveralgorithmPGE507.NextwecodedtooutputtheproductinHDF5usingtheLSIPSHDF5libraries.WehavecodedandtestedVNP10_L2algorithmandoutputasLSIPSPGE507.DevelopandtestcodeusinginputdatafromanLSIPSArchiveSet(AS).LSIPSrunsunitandglobaltests
NASAVIIRSVNP10SnowCoverAlgorithmI
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 10
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CurrentversionofPGE507runswithLSIPSIDPSversionsofVIIRSdataproducts.
VNP10_L2productisinHDF5WehaveaversionofPGE507thatrunswithLSIPSNASAVNP*L1Binputproducts.CoderevisedtoreadHDF5.WorkingintheLSIPScompu6ngenvironmentallowsfor:Ø efficientdeliveryofalgorithmcodeanddownloadofCMbaselinedcode
Ø comparisonofcodesandtestrunsbetweenthePIcodinginLSIPStounitorglobaltestrunsmadebyLSIPS/LDOPEfocusonscienceanddatacontentconsistenciesandarenotsidetrackedbydifferencesaoributabletodifferentopera6ngsystemsorPGEversionsbetweenthePI’scompu6ngenvironmentandLSIPS.
NASAVIIRSVNP10SnowCoverAlgorithmII
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 11
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VNP10_L2productsareinHDF5.RequiredredesignoftheproductsfromMODISheritageHDF4-EOS.Specifica6ons/requirementsfortheproductsareemergingfromseveralsourcesandfordifferingreasons.PIdevelopsthesciencedatacontentfordatasetsandaoributes.WeinteractwithNSIDCfordevelopingdatacontentregardingsnowcoverdatasets,geoloca6ondata,andforaoributes,primarilytosupporttheirdataservicesandtoolsforusers.ConformtoNetCDF4ClimateForecas6ngVersion1.6conven6onsforrelevantmetadata(HDF5aoributes).LSIPS–providesaoributesrelevanttotrackingproduc6on,PGEandCollec6onversions,provenanceofdataproduct,DOIs…DrasofVNP10_L2dataproductuserguidehasbeencompleted.
NASAVIIRSVNP10SnowCoverHDF5ProductDesign
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 12
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HDF5“VNP10_L2*.h5"{FILE_CONTENTS{group/group/Geoloca6onDatadataset/Geoloca6onData/La6tudedataset/Geoloca6onData/Longitudegroup/SnowDatadataset/SnowData/Algorithm_bit_flags_QAdataset/SnowData/Basic_QAdataset/SnowData/NDSIdataset/SnowData/NDSI_Snow_Cover}}Alldatasetsincludeaoributes.LSIPSgeneratesfilelevel(global)aoributes.
NASAVIIRSVNP10HDF5ProductDescripLon
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 13
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VNP10_L2.A2016024.1810.*.h5
NDSI_Snow_Cover NDSI Algorithm_bit_flags_QA
FalsecolorimageofNPP_VIAE_L1.A2016024.1810.P1_03110.*.hdfbandsI1,I2,I3,showingsnowinhuesofyellow.
NASAVNP10_L2Example
20
405060708090100
NDSISNOWCOVER
CloudWater
30
0
1.00.0
NDSIrange
LowNDSIscreen
InlandwaterUnspecified
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman
14LowVISscreen
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MOD10_L2C6 VNP10_L2NDSI_Snow_Cover,--SnowcovermapbyNDSIin0-100range,withmaskedfeatures.
NDSI_Snow_Cover,--SnowcovermapbyNDSIin0-100range,withmaskedfeatures.
NDSI_Snow_Cover_Algorithm_Flags_QA–bitflagsfordatascreensappliedinthealgorithm.
NDSI_Snow_Cover_Algorithm_Flags_QA–bitflagsfordatascreensappliedinthealgorithm.
NDSISnow_Cover_Basic_QA,basicqualityvalue
NDSI_Snow_Cover_Basic_QA,--basicqualityvalue
NDSI–NDSIvalueforalllandandinlandwaterpixelsinaswath
NDSI–NDSIvalueforalllandandinlandwaterpixelsinaswath
La6tude(5kmresolu6on) La6tude(375mresolu6on)
Longitude(5kmresolu6on) Longitude(375mresolu6on)
MODISandVIIRSSnowCoverConLnuity
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 15
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MODISandVIIRSSnowCoverConLnuity
Snowcoveralgorithmsareverysimilar:MODISC6andVIIRSVNP10Spa6alresolu6ondifferent:MODIS500m,VIIRS375mDataproductcontentissamebutthefileformatisdifferent:MODISC6SDSsHDF4-EOSandVIIRSVNP10datasets,HDF5Challengesofimprovingaccuracy,primarilyallevia6ngproblemsofcloud/snowconfusionaresimilarinbothMODISandVIIRSassociatedwith:
• Subpixelcloudcontamina6on• Cloudmasksexngofsnow/icebackgroundflag
• Spectraldiscrimina6onofsnowfreesurfacesfromsnowcoveredsurfaces
MODIS/VIIRSScienceTeamMee6ng,6-10June2016G.Riggs,M.Tschudi,D.Hall,M.Roman 16
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SUOMI-NPP VIIRS ICE SURFACE TEMPERATURE (IST)
MODIS/VIIRS Science Team Meeting, 6-10 June 2016 G. Riggs, M. Tschudi, D.Hall, M. Roman
17
• The VIIRS Ice Surface Temperature (IST) product • provides surface temperatures retrieved at VIIRS moderate resolution
(750m) • for Arctic and Antarctic sea ice • for both day and night
• The baseline split window algorithm, shown below, is a statistical regression method that is based on the AVHRR heritage IST algorithm (Key and Haefliger., 1992)
IST= ao + a1TM15 + a2(TM15-TM16) + a3(TM15-TM16)(sec(z)-1)
TM15 and TM16 : VIIRS TOA TB’s for the VIIRS M15 and M16 bands z: the satellite zenith angle
ao, a1, a2, a3 : regression coefficients.
• Threshold Measurement Uncertainty = 1K over a measurement range of 213–280 K.
Key, J., and M. Haefliger (1992), Arctic ice surface temperature retrieval from AVHRR thermal channels, J. Geophys. Res., 97(D5), 5885–5893.
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IST NEEDED BY NASA’S OPERATION ICEBRIDGE (OIB)
MODIS/VIIRS Science Team Meeting, 6-10 June 2016 G. Riggs, M. Tschudi, D.Hall, M. Roman 18
• IST: April 24, 2016 • Produced by http://landweb.nascom.nasa.gov
(NASA Goddard) • Utilized by NASA OIB Science Team during OIB
Spring 2016 P-3 deployment • Needed to assess melt onset in Beaufort Sea
• Performance of OIB RADARs affected when surface layer begins to melt
• Will also be compared to OIB onboard IST imagers
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NASA VIIRS IST
MODIS/VIIRS Science Team Meeting, 6-10 June 2016 G. Riggs, M. Tschudi, D.Hall, M. Roman
19
• Initial code generated from MODIS code by NASA’s Land Science Investigator-led Processing System (LSIPS)
• Code being updated for VIIRS (calibration coefficients, etc.)
• New Quality Flags to be added • Inter-comparison: MODIS, NCEP • Validation: IceBridge, buoys • First draft of ATBD delivered Jan. 2016
Left: VIIRS IST (K) from the NASA VIIRS IST product uses new calibration coefficients from J. Key Sept 12, 2014, 21:10 UTC Beaufort Sea, AK
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NASA IST VS. IDPS IST
MODIS/VIIRS Science Team Meeting, 6-10 June 2016 G. Riggs, M. Tschudi, D.Hall, M. Roman 20
IST for previous scene: _____ NASA VIIRS IST algorithm with MODIS calibration coefficients - - - - NASA VIIRS algorithm with updated calibration coefficients _ . _ . NOAA IDPS IST - NASA IST looks comparable to the
IDPS IST
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NASA VIIRS SEA ICE COVER
MODIS/VIIRS Science Team Meeting, 6-10 June 2016 G. Riggs, M. Tschudi, D.Hall, M. Roman 21
• Sea ice cover can aid in the estimation of sea ice extent using pmw data
• Sea ice extent = areal coverage of sea ice over the Arctic (km2)
• Sea ice extent is important for trends in ice extent, sea ice modeling, navigation, operations, …
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NASA VIIRS SEA ICE COVER ALGORITHM
MODIS/VIIRS Science Team Meeting, 6-10 June 2016 G. Riggs, M. Tschudi, D.Hall, M. Roman
22
• Utilizes the Normalized Difference Snow Index (NDSI):
NDSI = [VIIRS M4 (0.555µm) – VIIRS M10 (1.61µm)] / [VIIRS M4 + VIIRS M10] • If NDSI ≥ 0.4 and VIIRS M4 > threshold, then pixel contains snow
covered sea ice • Relatively thin sea ice (< 10 cm, with no snow cover) which has a lower
albedo may not be detected using the NDSI. Methods to detect thin sea ice are being investigated.
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NASA VIIRS SEA ICE COVER TEST RUN
MODIS/VIIRS Science Team Meeting, 6-10 June 2016 G. Riggs, M. Tschudi, D.Hall, M. Roman 23
• Sea ice extent code using VIIRS channels • April 7, 2015 • Beaufort Sea – multiyear ice floes • Can envision a follow-on sea ice
concentration product
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NASA LSIPS ACCESS SUMMARY
MODIS/VIIRS Science Team Meeting, 6-10 June 2016 G. Riggs, M. Tschudi, D.Hall, M. Roman 24
• Have received / used NASA token • Have NASA Launchpad access • Took necessary training • Have been activated on cluster • At “press time,” working an access
issue to the LSIPS
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THANK YOU
MODIS/VIIRS Science Team Meeting, 6-10 June 2016 G. Riggs, M. Tschudi, D.Hall, M. Roman 25