{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T21:20:33Z","timestamp":1774646433675,"version":"3.50.1"},"reference-count":106,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2016,9,30]],"date-time":"2016-09-30T00:00:00Z","timestamp":1475193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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%\u2013481.82% for HydroLight simulated data. The models were also compared in regional waters of northern Western Australia where the highest performing TSS models yielded a MARE in the range of 43.11%\u2013102.59%. 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.<\/jats:p>","DOI":"10.3390\/rs8100810","type":"journal-article","created":{"date-parts":[[2016,9,30]],"date-time":"2016-09-30T10:06:56Z","timestamp":1475230016000},"page":"810","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Quantitative Comparison of Total Suspended Sediment Algorithms: A Case Study of the Last Decade for MODIS and Landsat-Based Sensors"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1130-0581","authenticated-orcid":false,"given":"Passang","family":"Dorji","sequence":"first","affiliation":[{"name":"Remote Sensing and Satellite Research Group, Curtin University, GPO Box U1987, Perth 6845, Australia"}]},{"given":"Peter","family":"Fearns","sequence":"additional","affiliation":[{"name":"Remote Sensing and Satellite Research Group, Curtin University, GPO Box U1987, Perth 6845, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bukata, R.P. 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