Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential

Accurate and timely estimation of gross domestic product (GDP) is essential for evaluating economic development. Nighttime light (NTL) data effectively estimate subindustry GDP, yet previous studies relied on single panchromatic bands. Whether multispectral nighttime remote sensing data, detecting s...

Full description

Saved in:
Bibliographic Details
Published inIEEE journal of selected topics in applied earth observations and remote sensing Vol. 18; pp. 20279 - 20293
Main Authors Zhang, Lingxian, Chen, Zuoqi, Gong, Wenkang, Wang, Congxiao, Xiong, Jing, Dong, Linxin, Ni, Jingwen, Huang, Yan, Yu, Bailang
Format Journal Article
LanguageEnglish
Published IEEE 2025
Subjects
Accuracy
Economic indicators
Estimation
Feature extraction
Industries
Land surface
Nighttime light (NL) remote sensing
nighttime thermal infrared
Remote sensing
Satellite broadcasting
SDGSAT-1 imagery
Socioeconomics
subindustry gross domestic product (GDP) estimation
Urban areas
Online AccessGet full text

Cover

Abstract Accurate and timely estimation of gross domestic product (GDP) is essential for evaluating economic development. Nighttime light (NTL) data effectively estimate subindustry GDP, yet previous studies relied on single panchromatic bands. Whether multispectral nighttime remote sensing data, detecting spectral differences from economic activities, improves subindustry GDP estimates remains unverified. This article leverages multispectral NTL and thermal infrared data from the SDGSAT-1 satellite, combined with land cover data, to estimate subindustry GDP using machine learning models. We compare support vector machines, neural networks, and random forest (RF), identifying RF as the optimal model due to its lowest RMSE values (9.16, 171.06, and 180.51 for primary, secondary, and tertiary industries, respectively). Empirical results demonstrate that multispectral SDGSAT-1 data significantly outperforms its single panchromatic band counterpart, improving R 2 values for secondary and tertiary industries from 0.58 to 0.88 and 0.68 to 0.90, respectively. Compared to VIIRS NTL data, SDGSAT-1 further reduces spatial misdistribution over farmland and industrial zones, achieving a 7.7% R 2 improvement at smaller scale (industrial parks level). Key factors driving GDP estimation vary across industries: cropland area dominates for the primary industry; thermal infrared and red light intensity for the secondary industry; and blue light intensity for the tertiary industry. These findings validate the superiority of multispectral NTL data in subindustry GDP estimation and offer actionable insights for enhancing urban economic monitoring and policy formulation.
AbstractList Accurate and timely estimation of gross domestic product (GDP) is essential for evaluating economic development. Nighttime light (NTL) data effectively estimate subindustry GDP, yet previous studies relied on single panchromatic bands. Whether multispectral nighttime remote sensing data, detecting spectral differences from economic activities, improves subindustry GDP estimates remains unverified. This article leverages multispectral NTL and thermal infrared data from the SDGSAT-1 satellite, combined with land cover data, to estimate subindustry GDP using machine learning models. We compare support vector machines, neural networks, and random forest (RF), identifying RF as the optimal model due to its lowest RMSE values (9.16, 171.06, and 180.51 for primary, secondary, and tertiary industries, respectively). Empirical results demonstrate that multispectral SDGSAT-1 data significantly outperforms its single panchromatic band counterpart, improving R 2 values for secondary and tertiary industries from 0.58 to 0.88 and 0.68 to 0.90, respectively. Compared to VIIRS NTL data, SDGSAT-1 further reduces spatial misdistribution over farmland and industrial zones, achieving a 7.7% R 2 improvement at smaller scale (industrial parks level). Key factors driving GDP estimation vary across industries: cropland area dominates for the primary industry; thermal infrared and red light intensity for the secondary industry; and blue light intensity for the tertiary industry. These findings validate the superiority of multispectral NTL data in subindustry GDP estimation and offer actionable insights for enhancing urban economic monitoring and policy formulation.
Accurate and timely estimation of gross domestic product (GDP) is essential for evaluating economic development. Nighttime light (NTL) data effectively estimate subindustry GDP, yet previous studies relied on single panchromatic bands. Whether multispectral nighttime remote sensing data, detecting spectral differences from economic activities, improves subindustry GDP estimates remains unverified. This article leverages multispectral NTL and thermal infrared data from the SDGSAT-1 satellite, combined with land cover data, to estimate subindustry GDP using machine learning models. We compare support vector machines, neural networks, and random forest (RF), identifying RF as the optimal model due to its lowest RMSE values (9.16, 171.06, and 180.51 for primary, secondary, and tertiary industries, respectively). Empirical results demonstrate that multispectral SDGSAT-1 data significantly outperforms its single panchromatic band counterpart, improving R2 values for secondary and tertiary industries from 0.58 to 0.88 and 0.68 to 0.90, respectively. Compared to VIIRS NTL data, SDGSAT-1 further reduces spatial misdistribution over farmland and industrial zones, achieving a 7.7% R2 improvement at smaller scale (industrial parks level). Key factors driving GDP estimation vary across industries: cropland area dominates for the primary industry; thermal infrared and red light intensity for the secondary industry; and blue light intensity for the tertiary industry. These findings validate the superiority of multispectral NTL data in subindustry GDP estimation and offer actionable insights for enhancing urban economic monitoring and policy formulation.
Author Chen, Zuoqi
Dong, Linxin
Xiong, Jing
Gong, Wenkang
Ni, Jingwen
Huang, Yan
Zhang, Lingxian
Wang, Congxiao
Yu, Bailang
Author_xml – sequence: 1
  givenname: Lingxian
  surname: Zhang
  fullname: Zhang, Lingxian
  email: 51253901050@stu.ecnu.edu.cn
  organization: Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, China
– sequence: 2
  givenname: Zuoqi
  orcidid: 0000-0002-3654-9658
  surname: Chen
  fullname: Chen, Zuoqi
  email: zqchen@fzu.edu.cn
  organization: Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National and Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Academy of Digital China, Fuzhou University, Fuzhou, China
– sequence: 3
  givenname: Wenkang
  surname: Gong
  fullname: Gong, Wenkang
  email: 52213901020@stu.ecnu.edu.cn
  organization: Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, China
– sequence: 4
  givenname: Congxiao
  orcidid: 0009-0000-6882-1380
  surname: Wang
  fullname: Wang, Congxiao
  email: cxwang@geo.ecnu.edu.cn
  organization: Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, China
– sequence: 5
  givenname: Jing
  surname: Xiong
  fullname: Xiong, Jing
  email: bearnear@sjtu.edu.cn
  organization: China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, China
– sequence: 6
  givenname: Linxin
  orcidid: 0000-0002-4962-2420
  surname: Dong
  fullname: Dong, Linxin
  email: dong_linxin@163.com
  organization: Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, China
– sequence: 7
  givenname: Jingwen
  orcidid: 0009-0000-0405-4346
  surname: Ni
  fullname: Ni, Jingwen
  email: jingwen_ni@foxmail.com
  organization: Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, China
– sequence: 8
  givenname: Yan
  orcidid: 0000-0001-6314-1802
  surname: Huang
  fullname: Huang, Yan
  email: yhuang@geo.ecnu.edu.cn
  organization: Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, China
– sequence: 9
  givenname: Bailang
  orcidid: 0000-0001-5628-0003
  surname: Yu
  fullname: Yu, Bailang
  email: blyu@geo.ecnu.edu.cn
  organization: Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, China
BookMark eNpFUd1u0zAYtdCQ6AZPABd-gRT_xgl31VpKpgITKeLScuwvrac0qWx30t6Ax567TvDdfH_nHOnoXKOrcRoBoY-UzCkl9ee7drv41c4ZYXLOZS1VKd6gGaOSFlRyeYVmtOZ1QQUR79B1jA-ElEzVfIb-NodjmB79uMPtqSua0Z1iCk94vbzHq5j8wSQ_jfiPT3vcLtftYltQ_P00JB-PYFMwA_7hd_uUkYA35wmb0eHtHsIh_5qxDyaAw0uTzBe86vtM8o8wQowvwPspwZi8Gd6jt70ZInx47Tfo99fV9vZbsfm5bm4Xm8IyUYnCyM7UrubCuryWlJZEUMVVB6zuhapkNq24kBKsc8Cs4k6JCkzXg7Okk_wGNRddN5kHfQzZYXjSk_H65TCFnTYheTuAZkLJruyhKisQtmcVk7bq6o5U1pSCVVmLX7RsmGIM0P_To0Sfg9GXYPQ5GP0aTGZ9urA8APxn5GJKSP4Mh7iM7A
CODEN IJSTHZ
Cites_doi 10.1016/j.resourpol.2020.101940
10.1016/j.asr.2019.09.035
10.1117/12.2674217
10.3390/rs6021705
10.1016/j.jag.2023.103313
10.1007/s11222-016-9646-1
10.1016/j.cageo.2009.01.009
10.3390/su8020108
10.1257/aer.102.2.994
10.1016/j.eneco.2019.01.004
10.1177/2399808320951580
10.1016/j.envpol.2021.118383
10.1080/15481603.2013.823732
10.3390/rs16050768
10.3390/rs15082120
10.1016/j.xinn.2023.100419
10.5194/essd-10-847-2018
10.1016/j.apenergy.2023.122355
10.1038/scientificamerican0778-86
10.1038/s41597-022-01300-x
10.1016/j.scib.2019.12.007
10.1080/01431161.2012.684076
10.1016/j.energy.2022.123576
10.1007/s12076-024-00375-x
10.1016/j.rse.2021.112557
10.1080/2150704x.2014.905728
10.1016/j.fecs.2023.100144
10.1016/j.rse.2013.03.001
10.1016/j.jag.2019.04.017
10.1016/j.gloenvcha.2015.06.004
10.1016/j.infgeo.2025.100018
10.1016/j.rse.2019.111443
10.1080/01431161.2016.1217440
10.3390/rs11171971
10.1016/j.jag.2024.103812
10.1109/jstars.2019.2916323
10.3390/ijerph19138048
10.1016/j.apenergy.2016.10.032
10.1080/09720502.2018.1498307
10.1016/j.rse.2017.01.006
10.1016/j.scs.2023.105125
10.3390/rs15030716
10.1016/j.scib.2022.12.014
10.1016/j.worlddev.2014.08.017
10.1016/j.jclepro.2022.130602
10.1016/j.rse.2024.114137
10.1639/0044-7447(2000)029[0157:ntiaat]2.0.co;2
10.3390/rs16020417
10.3390/app14062450
10.1080/014311697218485
10.1016/j.rse.2017.01.005
10.1016/j.rse.2024.114079
10.1016/j.jclepro.2024.141205
10.1016/j.ress.2020.107312
10.3390/ijgi12030123
10.1109/jstars.2022.3149028
10.1080/19475683.2019.1570336
ContentType Journal Article
DBID 97E
ESBDL
RIA
RIE
AAYXX
CITATION
DOA
DOI 10.1109/JSTARS.2025.3595764
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005-present
IEEE Open Access Journals
IEEE All-Society Periodicals Package (ASPP) 1998-Present
IEEE Electronic Library (IEL)
CrossRef
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
DatabaseTitleList

Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ: Directory of Open Access Journal (DOAJ)
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Geology
EISSN 2151-1535
EndPage 20293
ExternalDocumentID oai_doaj_org_article_2475b6fe868e4cf2825c8b9b08ca6428
10_1109_JSTARS_2025_3595764
11112745
Genre orig-research
GrantInformation_xml – fundername: National Natural Science Foundation of China
  grantid: 42371332
  funderid: 10.13039/501100001809
GroupedDBID 0R~
29I
4.4
5GY
5VS
6IK
97E
AAFWJ
AAJGR
AASAJ
AAWTH
ABAZT
ABVLG
ACIWK
AENEX
AETIX
AFPKN
AFRAH
AGSQL
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
DU5
EBS
EJD
ESBDL
GROUPED_DOAJ
HZ~
IFIPE
IPLJI
JAVBF
M43
O9-
OCL
OK1
RIA
RIE
RNS
AAYXX
CITATION
ID FETCH-LOGICAL-c2484-a5ba9d934cd4846116041737be29f478521573455ecdde2c73d748eabfedc0b53
IEDL.DBID RIE
ISSN 1939-1404
IngestDate Wed Aug 27 01:28:20 EDT 2025
Wed Aug 27 16:30:25 EDT 2025
Wed Aug 27 07:36:31 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Language English
License https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c2484-a5ba9d934cd4846116041737be29f478521573455ecdde2c73d748eabfedc0b53
ORCID 0000-0002-3654-9658
0000-0002-4962-2420
0009-0000-6882-1380
0000-0001-5628-0003
0009-0000-0405-4346
0000-0001-6314-1802
OpenAccessLink https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/document/11112745
PageCount 15
ParticipantIDs ieee_primary_11112745
doaj_primary_oai_doaj_org_article_2475b6fe868e4cf2825c8b9b08ca6428
crossref_primary_10_1109_JSTARS_2025_3595764
PublicationCentury 2000
PublicationDate 20250000
2025-00-00
2025-01-01
PublicationDateYYYYMMDD 2025-01-01
PublicationDate_xml – year: 2025
  text: 20250000
PublicationDecade 2020
PublicationTitle IEEE journal of selected topics in applied earth observations and remote sensing
PublicationTitleAbbrev JSTARS
PublicationYear 2025
Publisher IEEE
Publisher_xml – name: IEEE
References ref13
ref57
ref12
ref56
ref15
ref59
ref14
ref58
ref53
ref52
ref11
ref55
ref10
ref54
ref17
ref16
ref19
ref18
Zanaga (ref46) 2022
ref51
ref50
ref45
ref48
ref47
ref42
ref41
ref44
ref43
ref49
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref40
ref35
ref34
ref37
ref36
ref31
ref30
ref33
ref32
ref2
ref1
ref39
ref38
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref29
References_xml – ident: ref57
  doi: 10.1016/j.resourpol.2020.101940
– ident: ref36
  doi: 10.1016/j.asr.2019.09.035
– ident: ref48
  doi: 10.1117/12.2674217
– ident: ref53
  doi: 10.3390/rs6021705
– ident: ref30
  doi: 10.1016/j.jag.2023.103313
– ident: ref50
  doi: 10.1007/s11222-016-9646-1
– ident: ref15
  doi: 10.1016/j.cageo.2009.01.009
– ident: ref34
  doi: 10.3390/su8020108
– ident: ref3
  doi: 10.1257/aer.102.2.994
– ident: ref6
  doi: 10.1016/j.eneco.2019.01.004
– ident: ref52
  doi: 10.1177/2399808320951580
– ident: ref54
  doi: 10.1016/j.envpol.2021.118383
– volume-title: ESA WorldCover 10 m 2021 v200
  year: 2022
  ident: ref46
– ident: ref22
  doi: 10.1080/15481603.2013.823732
– ident: ref4
  doi: 10.1016/j.resourpol.2020.101940
– ident: ref37
  doi: 10.3390/rs16050768
– ident: ref40
  doi: 10.3390/rs15082120
– ident: ref41
  doi: 10.1016/j.xinn.2023.100419
– ident: ref2
  doi: 10.5194/essd-10-847-2018
– ident: ref39
  doi: 10.1016/j.apenergy.2023.122355
– ident: ref13
  doi: 10.1038/scientificamerican0778-86
– ident: ref11
  doi: 10.1038/s41597-022-01300-x
– ident: ref47
  doi: 10.1016/j.scib.2019.12.007
– ident: ref5
  doi: 10.1080/01431161.2012.684076
– ident: ref56
  doi: 10.1016/j.energy.2022.123576
– ident: ref26
  doi: 10.1007/s12076-024-00375-x
– ident: ref59
  doi: 10.1016/j.rse.2021.112557
– ident: ref25
  doi: 10.1080/2150704x.2014.905728
– ident: ref42
  doi: 10.1016/j.fecs.2023.100144
– ident: ref19
  doi: 10.1016/j.rse.2013.03.001
– ident: ref23
  doi: 10.1016/j.jag.2019.04.017
– ident: ref1
  doi: 10.1016/j.gloenvcha.2015.06.004
– ident: ref45
  doi: 10.1016/j.infgeo.2025.100018
– ident: ref32
  doi: 10.1016/j.rse.2019.111443
– ident: ref35
  doi: 10.1080/01431161.2016.1217440
– ident: ref14
  doi: 10.3390/rs11171971
– ident: ref28
  doi: 10.1016/j.jag.2024.103812
– ident: ref58
  doi: 10.1109/jstars.2019.2916323
– ident: ref55
  doi: 10.3390/ijerph19138048
– ident: ref17
  doi: 10.1016/j.apenergy.2016.10.032
– ident: ref7
  doi: 10.1080/09720502.2018.1498307
– ident: ref16
  doi: 10.1016/j.rse.2017.01.006
– ident: ref51
  doi: 10.1016/j.scs.2023.105125
– ident: ref12
  doi: 10.3390/rs15030716
– ident: ref38
  doi: 10.1016/j.scib.2022.12.014
– ident: ref33
  doi: 10.1016/j.worlddev.2014.08.017
– ident: ref8
  doi: 10.1016/j.jclepro.2022.130602
– ident: ref31
  doi: 10.1016/j.rse.2024.114137
– ident: ref20
  doi: 10.1639/0044-7447(2000)029[0157:ntiaat]2.0.co;2
– ident: ref27
  doi: 10.3390/rs16020417
– ident: ref44
  doi: 10.3390/app14062450
– ident: ref21
  doi: 10.1080/014311697218485
– ident: ref10
  doi: 10.1016/j.rse.2017.01.005
– ident: ref43
  doi: 10.1016/j.rse.2024.114079
– ident: ref9
  doi: 10.1016/j.jclepro.2024.141205
– ident: ref49
  doi: 10.1016/j.ress.2020.107312
– ident: ref29
  doi: 10.3390/ijgi12030123
– ident: ref18
  doi: 10.1109/jstars.2022.3149028
– ident: ref24
  doi: 10.1080/19475683.2019.1570336
SSID ssj0062793
Score 2.3744678
Snippet Accurate and timely estimation of gross domestic product (GDP) is essential for evaluating economic development. Nighttime light (NTL) data effectively...
SourceID doaj
crossref
ieee
SourceType Open Website
Index Database
Publisher
StartPage 20279
SubjectTerms Accuracy
Economic indicators
Estimation
Feature extraction
Industries
Land surface
Nighttime light (NL) remote sensing
nighttime thermal infrared
Remote sensing
Satellite broadcasting
SDGSAT-1 imagery
Socioeconomics
subindustry gross domestic product (GDP) estimation
Urban areas
SummonAdditionalLinks – databaseName: DOAJ Directory of Open Access Journals
  dbid: DOA
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQpUpcEC2LWCiVDxwxjWM7jrltu-0uCK1WbCt6i_wUSFWKVuHQf8DPZsZOX6deyCmJHMeaGc_DmvmGkA_aYfli5KyWNjBpLGdWBMFgczjjeJuCywmyq2Z5Ib9eqssHrb4wJ6zAAxfCHdVSK9ek2DZtlD5hqaVvYZqq9RZ9Z9S-YPNug6mig5taZ7hd8E4MQwCZEW-IV-YIBH72fQORYa0-YVWqbuQjm5Sh-x_1Wsmm5uwleTH6iHRW1rZHnsV-n-wucg_em1fk791BAIVtz8bmGzd0MV_TU9ixpRiR_vg1_KSb-WIzO2ec5kLbXFa5halXGJJjW3n6De-o7QMFgQElfUW_9GmLWel0bgf7mRZ441En5oHr6wFTjOzVhFycnZ6fLNnYUIH5WraSWeWsCUZIH-Cx4bypJNdCu1ibJHULplxpIZWKHrRe7bUIWrbRuhSDr5wSr8lOf93HN4QmLqOUxlcJIQ2DsTEkGwR8WAUrmjQlH29J2v0uuBldjjcq0xUOdMiBbuTAlBwj2e-GIuh1fgGi0I2i0D0lClMyQabd_w8uCLrV2_8x-TvyHBdcTl8OyM6w_RPfgz8yuMMsev8AawfaRQ
  priority: 102
  providerName: Directory of Open Access Journals
Title Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential
URI https://ieeexplore.ieee.org/document/11112745
https://doaj.org/article/2475b6fe868e4cf2825c8b9b08ca6428
Volume 18
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwELZoJSQuPItYHpUPHPE2ju045raw7RaEVhXbit4iPwWiyqJV9lB-AT-bGcdbVCQkbk7kJJbm4Rlnvm8Iea0dwhcjZ7W0gUljObMiCAbG4YzjbQouF8gum9ML-fFSXRawesbCxBhz8Vmc4jD_yw9rv8WjsiM0b8ii1B7Zg8xtBGvt3G5T68ywCwGJYcgZUyiGeGWOQMdnn1eQDNZqikBU3chb21Bm67_VXiXvLicPyHK3rrGo5Pt0O7ip__kXZeN_L_whuV_iTDobFeMRuRP7x-TuIvfxvX5Cft0cJlBwHaw08Limi_kZPQarHwGN9Mu34StdzRer2TnjNIN1MzRzA69eYlqPrenpJxxR2wcKSgeO_op-6NMGK9vp3A72LR0pkotfzRPP1gOWKdmrA3Jxcnz-_pSVpgzM17KVzCpnTTBC-gCXDedNJbkW2sXaJKlbCAeUFlKp6MFz1l6LoGUbrUsx-Mop8ZTs9-s-PiM0cRmlNL5KSIsYjI0h2SDgwSpY0aQJebOTUfdj5N7ocs5SmW4UaYci7YpIJ-QdyvFmKhJn5xsgiq7YYVdLrVyTYtu0UfqEyF3fglZWrbeYik3IAYrvz_eK5J7_4_4Lcg_XMB7KvCT7w2YbX0GYMrjDnN4fZiX9DTqX5U4
linkProvider IEEE
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwELagCMGFZyuW8vCBI97GsRPH3LbddrewrCp2K3qL_BSIKotW2UP5BfxsZpxsqyIhcXMiJ7H0jcceZ75vCHmnLNIXA2e5NJ5JbTgzwgsGk8Nqy6vobUqQnZfTc_nxorjoyeqJCxNCSMlnYYjN9C_fr9wGj8oOcHpDFFXcJfdg4S94R9faOt4yV0ljF7YkmqFqTC8yxDN9AFY--rKAcDAvhkhFVaW8tRAlvf5bBVbS-nLymMy3I-vSSn4MN60dul9_iTb-99CfkEf9TpOOOtN4Su6E5hm5P0mVfK-ek9_XxwkUnAfrS3hc0cn4jB7DvO8ojfTr9_YbXYwni9GScZrouomcuYZXzzGwx-L0dIYtahpPwezA1V_S0yauMbedjk1rPtBOJLn3rKnj2arFRCVzuUvOT46XR1PWl2VgLpeVZKawRnstpPNwWXJeZpIroWzIdZSqgg1BoQTgExz4ztwp4ZWsgrExeJfZQuyRnWbVhBeERi6DlNplEYURvTbBR-MFPJh5I8o4IO-3GNU_O_WNOkUtma47SGuEtO4hHZBDxPG6K0pnpxsARd3PxDqXqrBlDFVZBekicnddBXaZVc5gMDYguwjfzfd65F7-4_5b8mC6_DyrZ6fzT_vkIY6nO6J5RXba9Sa8hk1La98kU_0D7afnog
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=Improving+Sub-Industry+GDP+Estimation+With+SDGSAT-1+Multispectral+Nighttime+Light+and+Thermal+Infrared+Data%3A+Effectiveness+and+Potential&rft.jtitle=IEEE+journal+of+selected+topics+in+applied+earth+observations+and+remote+sensing&rft.au=Zhang%2C+Lingxian&rft.au=Chen%2C+Zuoqi&rft.au=Gong%2C+Wenkang&rft.au=Wang%2C+Congxiao&rft.date=2025&rft.pub=IEEE&rft.issn=1939-1404&rft.volume=18&rft.spage=20279&rft.epage=20293&rft_id=info:doi/10.1109%2FJSTARS.2025.3595764&rft.externalDocID=11112745
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1939-1404&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1939-1404&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1939-1404&client=summon