Sensitivity of Snow NDSI to Simulated Snow Grain Shape Characteristics

The normalized difference snow index (NDSI) is a fundamental spectral indicator of snow/ice in visible and shortwave-infrared imagery. The complex grain shapes in nature have well-known significant effects on the single-scattering properties (SSPs) and subsequently the bidirectional reflectance of s...

Full description

Saved in:
Bibliographic Details
Published inIEEE geoscience and remote sensing letters Vol. 20; pp. 1 - 5
Main Authors Wang, Gongxue, Jiang, Lingmei, Pan, Fangbo, Weng, Haiteng, Zhang, Yongsheng
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Online AccessGet full text
ISSN1545-598X
1558-0571
DOI10.1109/LGRS.2022.3233379

Cover

Abstract The normalized difference snow index (NDSI) is a fundamental spectral indicator of snow/ice in visible and shortwave-infrared imagery. The complex grain shapes in nature have well-known significant effects on the single-scattering properties (SSPs) and subsequently the bidirectional reflectance of snow. The shape effects on snow NDSI need to be further characterized as NDSI is a nonlinear combination of two reflectance bands. Considering the common snow grain shapes represented by sphere, spheroid, hexagonal plate, and Koch snowflake, we use the ray-tracing approach to simulate the SSPs of ice particles and the discrete ordinate algorithm to solve the bidirectional reflectance function and calculate NDSI of snow. According to simulating results, the angular pattern of snow NDSI is subject to snow grain shape, whereas the shape effects can be significantly weakened by the increasing surface roughness of ice particles. The shape of Koch snowflake causes an NDSI habit different from other three shapes for large snow grain size. Moreover, snow NDSI also has complex responses to aspect ratio (AR) for spheroid and hexagonal prism. The theoretical characterization of the snow NDSI responses to various grain shapes would enrich the knowledge of NDSI variation mechanism in snow-covered area mapping applications.
AbstractList The normalized difference snow index (NDSI) is a fundamental spectral indicator of snow/ice in visible and shortwave-infrared imagery. The complex grain shapes in nature have well-known significant effects on the single-scattering properties (SSPs) and subsequently the bidirectional reflectance of snow. The shape effects on snow NDSI need to be further characterized as NDSI is a nonlinear combination of two reflectance bands. Considering the common snow grain shapes represented by sphere, spheroid, hexagonal plate, and Koch snowflake, we use the ray-tracing approach to simulate the SSPs of ice particles and the discrete ordinate algorithm to solve the bidirectional reflectance function and calculate NDSI of snow. According to simulating results, the angular pattern of snow NDSI is subject to snow grain shape, whereas the shape effects can be significantly weakened by the increasing surface roughness of ice particles. The shape of Koch snowflake causes an NDSI habit different from other three shapes for large snow grain size. Moreover, snow NDSI also has complex responses to aspect ratio (AR) for spheroid and hexagonal prism. The theoretical characterization of the snow NDSI responses to various grain shapes would enrich the knowledge of NDSI variation mechanism in snow-covered area mapping applications.
Author Zhang, Yongsheng
Wang, Gongxue
Weng, Haiteng
Jiang, Lingmei
Pan, Fangbo
Author_xml – sequence: 1
  givenname: Gongxue
  orcidid: 0000-0002-1563-1388
  surname: Wang
  fullname: Wang, Gongxue
  email: wanggx@mail.bnu.edu.cn
  organization: Institute of Geospatial Information, PLA Information Engineering University, Zhengzhou, China
– sequence: 2
  givenname: Lingmei
  orcidid: 0000-0002-9847-9034
  surname: Jiang
  fullname: Jiang, Lingmei
  email: jiang@bnu.edu.cn
  organization: State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing, China
– sequence: 3
  givenname: Fangbo
  surname: Pan
  fullname: Pan, Fangbo
  email: panfb@mail.bnu.edu.cn
  organization: State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing, China
– sequence: 4
  givenname: Haiteng
  surname: Weng
  fullname: Weng, Haiteng
  email: haiteng_weng@mail.bnu.edu.cn
  organization: First Research Institute of Ministry of Public Security, Beijing, China
– sequence: 5
  givenname: Yongsheng
  surname: Zhang
  fullname: Zhang, Yongsheng
  email: yszhang2001@vip.163.com
  organization: Institute of Geospatial Information, PLA Information Engineering University, Zhengzhou, China
BookMark eNp9kE9LAzEQxYNUsK1-AMHDguet-bPZJEepthaKgqvgbcmmszSl3dQkVfrt3WV7EA-eZph5vxneG6FB4xpA6JrgCSFY3S3nr8WEYkonjDLGhDpDQ8K5TDEXZND1GU-5kh8XaBTCBmOaSSmGaFZAE2y0XzYeE1cnReO-k-eHYpFElxR2d9jqCKt-PPfaNkmx1ntIpmvttYngbYjWhEt0XuttgKtTHaP32ePb9CldvswX0_tlaqjKYlrlspKUVZLDSmKcGaYoYZyspNSMKqiMlhRknlHNKQbF6loIMLRSK5NXnLMxuu3v7r37PECI5cYdfNO-LKkQLOM5JXmrEr3KeBeCh7o0NupoXRNbC9uS4LILrexCK7vQylNoLUn-kHtvd9of_2VuesYCwC9964-36x-Vk3jv
CODEN IGRSBY
CitedBy_id crossref_primary_10_1109_TGRS_2024_3516882
crossref_primary_10_5194_essd_16_2501_2024
Cites_doi 10.1016/j.jhydrol.2021.126020
10.1016/j.rse.2013.12.022
10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2
10.1029/2006JD007290
10.3390/rs9100983
10.1109/LGRS.2020.2982053
10.5194/tc-9-1277-2015
10.1016/0034-4257(89)90101-6
10.1016/j.rse.2011.07.018
10.1016/S0034-4257(02)00095-0
10.3390/rs11202391
10.1109/JSTARS.2018.2879666
10.1029/96JD01155
10.1016/j.rse.2019.111618
10.1016/j.rse.2006.03.002
10.1016/j.jqsrt.2010.07.008
10.1175/1520-0469(1996)053<2813:SSPOAI>2.0.CO;2
10.1029/2020MS002431
10.1109/TGRS.2022.3165986
10.1175/JCLI-D-17-0300.1
10.1016/j.rse.2004.11.013
10.1364/AO.27.002502
10.1016/j.rse.2009.01.001
10.1109/TGRS.2015.2411592
10.1002/2013JD021329
10.1109/36.581987
10.1016/S0022-4073(99)00028-X
10.1029/2002JD003142
10.1016/j.rse.2003.10.016
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
DBID 97E
RIA
RIE
AAYXX
CITATION
7SC
7SP
7TG
7UA
8FD
C1K
F1W
FR3
H8D
H96
JQ2
KL.
KR7
L.G
L7M
L~C
L~D
DOI 10.1109/LGRS.2022.3233379
DatabaseName IEEE Xplore (IEEE)
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Electronic Library (IEL)
CrossRef
Computer and Information Systems Abstracts
Electronics & Communications Abstracts
Meteorological & Geoastrophysical Abstracts
Water Resources Abstracts
Technology Research Database
Environmental Sciences and Pollution Management
ASFA: Aquatic Sciences and Fisheries Abstracts
Engineering Research Database
Aerospace Database
Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources
ProQuest Computer Science Collection
Meteorological & Geoastrophysical Abstracts - Academic
Civil Engineering Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) Professional
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
DatabaseTitle CrossRef
Civil Engineering Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) Professional
Technology Research Database
Computer and Information Systems Abstracts – Academic
Electronics & Communications Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Water Resources Abstracts
Environmental Sciences and Pollution Management
Computer and Information Systems Abstracts Professional
Aerospace Database
Meteorological & Geoastrophysical Abstracts
Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources
ASFA: Aquatic Sciences and Fisheries Abstracts
Engineering Research Database
Advanced Technologies Database with Aerospace
Meteorological & Geoastrophysical Abstracts - Academic
DatabaseTitleList
Civil Engineering Abstracts
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Xplore Digital Library
  url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Geography
Geology
EISSN 1558-0571
EndPage 5
ExternalDocumentID 10_1109_LGRS_2022_3233379
10004579
Genre orig-research
GrantInformation_xml – fundername: National Natural Science Foundation of China
  grantid: 42090014; 42171317
  funderid: 10.13039/501100001809
– fundername: National Key Research and Development Program of China
  grantid: 2022YFF0801302
  funderid: 10.13039/501100012166
GroupedDBID 0R~
29I
4.4
5GY
5VS
6IK
97E
AAJGR
AARMG
AASAJ
AAWTH
ABAZT
ABQJQ
ABVLG
ACGFO
ACIWK
AENEX
AETIX
AFRAH
AGQYO
AGSQL
AHBIQ
AIBXA
AKJIK
AKQYR
ALMA_UNASSIGNED_HOLDINGS
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
EBS
EJD
HZ~
H~9
IFIPE
IPLJI
JAVBF
LAI
M43
O9-
OCL
P2P
RIA
RIE
RNS
~02
AAYXX
CITATION
RIG
7SC
7SP
7TG
7UA
8FD
C1K
F1W
FR3
H8D
H96
JQ2
KL.
KR7
L.G
L7M
L~C
L~D
ID FETCH-LOGICAL-c294t-b68b823b85ed8004c3921351d88a329ebca82e8642a520e93ff77ec2b9dc6b553
IEDL.DBID RIE
ISSN 1545-598X
IngestDate Mon Jun 30 08:26:28 EDT 2025
Tue Jul 01 03:45:54 EDT 2025
Thu Apr 24 23:03:35 EDT 2025
Wed Aug 27 02:47:55 EDT 2025
IsPeerReviewed true
IsScholarly true
Language English
License https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
https://doi.org/10.15223/policy-029
https://doi.org/10.15223/policy-037
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c294t-b68b823b85ed8004c3921351d88a329ebca82e8642a520e93ff77ec2b9dc6b553
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ORCID 0000-0002-1563-1388
0000-0002-9847-9034
PQID 2773456216
PQPubID 75725
PageCount 5
ParticipantIDs crossref_primary_10_1109_LGRS_2022_3233379
proquest_journals_2773456216
crossref_citationtrail_10_1109_LGRS_2022_3233379
ieee_primary_10004579
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 20230000
2023-00-00
20230101
PublicationDateYYYYMMDD 2023-01-01
PublicationDate_xml – year: 2023
  text: 20230000
PublicationDecade 2020
PublicationPlace Piscataway
PublicationPlace_xml – name: Piscataway
PublicationTitle IEEE geoscience and remote sensing letters
PublicationTitleAbbrev LGRS
PublicationYear 2023
Publisher IEEE
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml – name: IEEE
– name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References ref13
ref12
ref15
ref14
ref11
ref10
ref2
ref1
ref17
ref16
ref19
ref18
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref29
ref8
ref7
ref9
ref4
ref3
ref6
ref5
References_xml – ident: ref18
  doi: 10.1016/j.jhydrol.2021.126020
– ident: ref11
  doi: 10.1016/j.rse.2013.12.022
– ident: ref1
  doi: 10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2
– ident: ref25
  doi: 10.1029/2006JD007290
– ident: ref12
  doi: 10.3390/rs9100983
– ident: ref14
  doi: 10.1109/LGRS.2020.2982053
– ident: ref26
  doi: 10.5194/tc-9-1277-2015
– ident: ref3
  doi: 10.1016/0034-4257(89)90101-6
– ident: ref9
  doi: 10.1016/j.rse.2011.07.018
– ident: ref5
  doi: 10.1016/S0034-4257(02)00095-0
– ident: ref13
  doi: 10.3390/rs11202391
– ident: ref15
  doi: 10.1109/JSTARS.2018.2879666
– ident: ref24
  doi: 10.1029/96JD01155
– ident: ref16
  doi: 10.1016/j.rse.2019.111618
– ident: ref29
  doi: 10.1016/j.rse.2006.03.002
– ident: ref22
  doi: 10.1016/j.jqsrt.2010.07.008
– ident: ref20
  doi: 10.1175/1520-0469(1996)053<2813:SSPOAI>2.0.CO;2
– ident: ref21
  doi: 10.1029/2020MS002431
– ident: ref19
  doi: 10.1109/TGRS.2022.3165986
– ident: ref2
  doi: 10.1175/JCLI-D-17-0300.1
– ident: ref7
  doi: 10.1016/j.rse.2004.11.013
– ident: ref27
  doi: 10.1364/AO.27.002502
– ident: ref8
  doi: 10.1016/j.rse.2009.01.001
– ident: ref23
  doi: 10.1109/TGRS.2015.2411592
– ident: ref17
  doi: 10.1002/2013JD021329
– ident: ref28
  doi: 10.1109/36.581987
– ident: ref4
  doi: 10.1016/S0022-4073(99)00028-X
– ident: ref10
  doi: 10.1029/2002JD003142
– ident: ref6
  doi: 10.1016/j.rse.2003.10.016
SSID ssj0024887
Score 2.3690386
Snippet The normalized difference snow index (NDSI) is a fundamental spectral indicator of snow/ice in visible and shortwave-infrared imagery. The complex grain shapes...
SourceID proquest
crossref
ieee
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 1
SubjectTerms Algorithms
Aspect ratio
Aspect ratio (AR)
Bidirectional reflectance
Grain shape
Grain size
Ice
Ice particles
Infrared imagery
normalized difference snow index (NDSI)
Optical surface waves
Ray tracing
Reflectance
Reflectance functions
Reflectivity
Rough surfaces
Scattering
Shape
Shape effects
Short wave radiation
Simulation
Snow
Snow cover
snow grain shape
snow-covered area
Snowflakes
Surface roughness
Title Sensitivity of Snow NDSI to Simulated Snow Grain Shape Characteristics
URI https://ieeexplore.ieee.org/document/10004579
https://www.proquest.com/docview/2773456216
Volume 20
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LT9wwEB4BEmovvApieVQ-cKqUJbGT2D4iYBcQ7KEp0t6i2JkI1LJBkBWCX4_HyfKqinqLIluyZsbjb-yZ-QD20ggt9ZkLpEMDQWxMGuiYCnVRlYWIiiJFqne-GKUnl_HZOBl3xeq-FgYRffIZ9unTv-WXtZ3SVdl-5BGI1PMw7-ysLdZ6baynPBseQYIg0WrcPWFGod4_H_7MXCjIeV9wIQSlbb05hDyryl-u2J8vg2UYzVbWppX87k8b07dPH5o2_vfSV2CpQ5rsoDWNVZjDyRp86UjPrx7XYHHoWX0fv8EgozT2lkeC1RXLJvUDGx1lp6ypWXZ9QxRfWLa_h8QpwbKr4hbZ4ftuz-twOTj-dXgSdAQLgeU6bgKTKqO4MCrB0gHH2DqwRIx9pVKF4JrypBRH5UKUIuEhalFVUqLlRpc2NUkiNmBhUk9wE1ghYzShcWgskbEl16UklnFlnT-o0lD3IJxJPLdd93EiwfiT-ygk1DkpKScl5Z2SevDjZcpt23rjs8HrJPQ3A1t592Bnpte82533OZdSUOQXpVv_mLYNX4lXvr1r2YGF5m6Kuw59NOa7t7pnZqnS4A
linkProvider IEEE
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Rb9MwED6NITReBhtDdIzhhz0hpUtsJ7Yf0aDtoOvDskl9i2LnoqGNZoJUaPx6fE4KA7Rpb1FkS9adffedfXcfwEGWoKM-c5HyaCCS1maRkVSoi7oqRVKWGVK988ksm5zLT_N03herh1oYRAzJZzikz_CWXzVuSVdlh0lAIMo8gsfe8cu0K9f601pPBz48AgVRavS8f8RMYnM4HZ_mPhjkfCi4EIISt265ocCr8p8xDh5m9Axmq7V1iSWXw2Vrh-7nP20bH7z457DZY032vtscW7CGi23Y6GnPL2624ck48PrevIBRTonsHZMEa2qWL5ofbPYhP2Ztw_IvX4nkC6vu95hYJVh-UV4jO_q73_MOnI8-nh1Nop5iIXLcyDaymbaaC6tTrDx0lM7DJeLsq7QuBTeUKaU5ah-klCmP0Yi6Vgodt6ZymU1T8RLWF80CXwErlUQbW4_HUiUdGS-tsJK18xahzmIzgHgl8cL1_ceJBuOqCHFIbApSUkFKKnolDeDd7ynXXfON-wbvkNBvDezkPYC9lV6L_nx-L7hSgmK_JNu9Y9pb2JicnUyL6fHs82t4Sizz3c3LHqy335b4xmOR1u6HHfgLnz3WLQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Sensitivity+of+Snow+NDSI+to+Simulated+Snow+Grain+Shape+Characteristics&rft.jtitle=IEEE+geoscience+and+remote+sensing+letters&rft.au=Wang%2C+Gongxue&rft.au=Jiang%2C+Lingmei&rft.au=Pan%2C+Fangbo&rft.au=Weng%2C+Haiteng&rft.date=2023&rft.issn=1545-598X&rft.eissn=1558-0571&rft.volume=20&rft.spage=1&rft.epage=5&rft_id=info:doi/10.1109%2FLGRS.2022.3233379&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_LGRS_2022_3233379
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1545-598X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1545-598X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1545-598X&client=summon