Home > Publications database > Validation of Spaceborne and Modelled Surface Soil Moisture Products with Cosmic-Ray Neutron Probes > print

001     827138
005     20220930130115.0
024 7 _ |a 10.3390/rs9020103
|2 doi
024 7 _ |a 2128/13751
|2 Handle
024 7 _ |a WOS:000397013700005
|2 WOS
024 7 _ |a altmetric:15821831
|2 altmetric
037 _ _ |a FZJ-2017-01336
082 _ _ |a 620
100 1 _ |a Montzka, Carsten
|0 P:(DE-Juel1)129506
|b 0
|e Corresponding author
245 _ _ |a Validation of Spaceborne and Modelled Surface Soil Moisture Products with Cosmic-Ray Neutron Probes
260 _ _ |a Basel
|c 2017
|b MDPI
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1486374417_6847
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a The scale difference between point in situ soil moisture measurements and low resolution satellite products limits the quality of any validation efforts in heterogeneous regions. Cosmic Ray Neutron Probes (CRNP) could be an option to fill the scale gap between both systems, as they provide area-average soil moisture within a 150–250 m radius footprint. In this study, we evaluate differences and similarities between CRNP observations, and surface soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2), the METOP-A/B Advanced Scatterometer (ASCAT), the Soil Moisture Active and Passive (SMAP), the Soil Moisture and Ocean Salinity (SMOS), as well as simulations from the Global Land Data Assimilation System Version 2 (GLDAS2). Six CRNPs located on five continents have been selected as test sites: the Rur catchment in Germany, the COSMOS sites in Arizona and California (USA), and Kenya, one CosmOz site in New South Wales (Australia), and a site in Karnataka (India). Standard validation scores as well as the Triple Collocation (TC) method identified SMAP to provide a high accuracy soil moisture product with low noise or uncertainties as compared to CRNPs. The potential of CRNPs for satellite soil moisture validation has been proven; however, biomass correction methods should be implemented to improve its application in regions with large vegetation dynamics
536 _ _ |a 255 - Terrestrial Systems: From Observation to Prediction (POF3-255)
|0 G:(DE-HGF)POF3-255
|c POF3-255
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Bogena, Heye
|0 P:(DE-Juel1)129440
|b 1
700 1 _ |a Zreda, Marek
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Monerris, Alessandra
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Morrison, Ross
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Muddu, Sekhar
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Vereecken, Harry
|0 P:(DE-Juel1)129549
|b 6
773 _ _ |a 10.3390/rs9020103
|g Vol. 9, no. 2, p. 103 -
|0 PERI:(DE-600)2513863-7
|n 2
|p 103 -
|t Remote sensing
|v 9
|y 2017
|x 2072-4292
856 4 _ |y OpenAccess
|u https://juser.fz-juelich.de/record/827138/files/remotesensing-09-00103.pdf
856 4 _ |y OpenAccess
|x icon
|u https://juser.fz-juelich.de/record/827138/files/remotesensing-09-00103.gif?subformat=icon
856 4 _ |y OpenAccess
|x icon-1440
|u https://juser.fz-juelich.de/record/827138/files/remotesensing-09-00103.jpg?subformat=icon-1440
856 4 _ |y OpenAccess
|x icon-180
|u https://juser.fz-juelich.de/record/827138/files/remotesensing-09-00103.jpg?subformat=icon-180
856 4 _ |y OpenAccess
|x icon-640
|u https://juser.fz-juelich.de/record/827138/files/remotesensing-09-00103.jpg?subformat=icon-640
856 4 _ |y OpenAccess
|x pdfa
|u https://juser.fz-juelich.de/record/827138/files/remotesensing-09-00103.pdf?subformat=pdfa
909 C O |o oai:juser.fz-juelich.de:827138
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB:Earth_Environment
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)129506
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)129440
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 6
|6 P:(DE-Juel1)129549
913 1 _ |a DE-HGF
|l Terrestrische Umwelt
|1 G:(DE-HGF)POF3-250
|0 G:(DE-HGF)POF3-255
|2 G:(DE-HGF)POF3-200
|v Terrestrial Systems: From Observation to Prediction
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|b Erde und Umwelt
914 1 _ |y 2017
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b REMOTE SENS-BASEL : 2015
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Thomson Reuters Master Journal List
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IBG-3-20101118
|k IBG-3
|l Agrosphäre
|x 0
980 1 _ |a FullTexts
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)IBG-3-20101118
980 _ _ |a APC

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21