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dc.contributor.authorDorji, Passang
dc.contributor.authorFearns, Peter
dc.date.accessioned2018-02-06T06:15:49Z
dc.date.available2018-02-06T06:15:49Z
dc.date.created2018-02-06T05:49:51Z
dc.date.issued2016
dc.identifier.citationDorji, P. and Fearns, P. 2016. A quantitative comparison of total suspended sediment algorithms: A case study of the last decade for MODIS and landsat-based sensors. Remote Sensing. 8 (10): 810.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/63195
dc.identifier.doi10.3390/rs8100810
dc.description.abstract

© 2016 by the authors. A quantitative comparative study was performed to assess the relative applicability of Total Suspended Solids (TSS) models published in the last decade for the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-based sensors. The quantitative comparison was performed using a suite of statistical tests and HydroLight simulated data for waters ranging from clear open ocean case-1 to turbid coastal case-2 waters. The quantitative comparison shows that there are clearly some high performing TSS models that can potentially be applied in mapping TSS concentration for regions of uncertain water type. The highest performing TSS models tested were robust enough to retrieve TSS from different water types with Mean Absolute Relative Errors (MARE) of 69.96%-481.82% for HydroLight simulated data. The models were also compared in regional waters of northernWestern Australia where the highest performing TSS models yielded a MARE in the range of 43.11%-102.59%. The range of Smallest Relative Error (SRE) and Largest Relative Error (LRE) between the highest and the lowest performing TSS models spanned three orders of magnitude, suggesting users must be cautious in selecting appropriate models for unknown water types.

dc.publisherMDPI AG
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleA quantitative comparison of total suspended sediment algorithms: A case study of the last decade for MODIS and landsat-based sensors
dc.typeJournal Article
dcterms.source.volume8
dcterms.source.number10
dcterms.source.issn2072-4292
dcterms.source.titleRemote Sensing
curtin.departmentDepartment of Physics and Astronomy
curtin.accessStatusOpen access


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