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An empirical algorithm to seamlessly retrieve the concentration of suspended particulate matter from water color across ocean to turbid river mouths
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2019
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Source: Remote Sensing of Environment 235 (2019) 111491
[PDF-1.76 MB]
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Journal Title:Remote Sensing of Environment
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Description:We propose a globally applicable algorithm (GAASPM) to seamlessly retrieve the concentration of suspended particulate matter (SPM) (CSPM) from remote sensing reflectance (Rrs(λ)) across ocean to turbid river mouths without any hard-switching in its application. GAASPM is based on a calibrated relationship between CSPM and a generalized index for SPM (GISPM) from water color. The GISPM is mainly composed of three Rrs(λ) ratios (671, 745, and 862 nm over 551 nm, respectively), along with weighting factors assigned to each ratio. The weighting factors are introduced to ensure the progressive application of Rrs(λ) in the longer wavelengths for increasing CSPM. Calibration of GAASPM employed data collected from multiple estuarine and coastal regions of Europe, China, Argentina, and the USA with the measured CSPM spanning from 0.2 to 2068.8 mg/L. Inter-comparison with several recalibrated well-known CSPM retrieval algorithms demonstrates that GAASPM has the best retrieval accuracy over the entire CSPM range with a relative mean absolute difference (rMAD) of 41.3% (N = 437). This averaged uncertainty in GAASPM-derived CSPM is mostly attributed to the retrievals from less turbid waters where CSPM < 50 mg/L (rMAD = 50%, N = 214). GAASPM was further applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) measurements over prominent coastal areas and produced reliable CSPM maps along with realistic spatial patterns. In contrast, applications of other CSPM algorithms resulted in less reliable CSPM maps with either unjustified numerical discontinuities in the CSPM spatial distribution or unsatisfactory retrieval accuracy. Therefore, we propose GAASPM as a preferred algorithm to retrieve CSPM over regions with a wide range of CSPM, such as river plume areas.
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Source:Remote Sensing of Environment 235 (2019) 111491
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Rights Information:Accepted Manuscript
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Compliance:Submitted
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