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One- and Two-Band Sensors and Algorithms to Derive a(CDOM)(440) from Global Above- and In-Water Optical Observations
The colored (or chromophoric, depending on the literature) dissolved organic matter (CDOM) spectral absorption coefficient, [Formula: see text] , is a variable of global interest that has broad application in the study of biogeochemical processes. Within the funding for scientific research, there is...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401297/ https://www.ncbi.nlm.nih.gov/pubmed/34450822 http://dx.doi.org/10.3390/s21165384 |
Sumario: | The colored (or chromophoric, depending on the literature) dissolved organic matter (CDOM) spectral absorption coefficient, [Formula: see text] , is a variable of global interest that has broad application in the study of biogeochemical processes. Within the funding for scientific research, there is an overarching trend towards increasing the scale of observations both temporally and spatially, while simultaneously reducing the cost per sample, driving a systemic shift towards autonomous sensors and observations. Legacy [Formula: see text] measurement techniques can be cost-prohibitive and do not lend themselves toward autonomous systems. Spectrally rich datasets carefully collected with advanced optical systems in diverse locations that span a global range of water bodies, in conjunction with appropriate quality assurance and processing, allow for the analysis of methods and algorithms to estimate [Formula: see text] from spectrally constrained one- and two-band subsets of the data. The resulting algorithms were evaluated with respect to established fit-for-purpose criteria as well as quality assured archival data. Existing and proposed optical sensors capable of exploiting the algorithms and intended for autonomous platforms are identified and discussed. One-band in-water algorithms and two-band above-water algorithms showed the most promise for practical use (accuracy of 3.0% and 6.5%, respectively), with the latter demonstrated for an airborne dataset. |
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