23 December 2017 Comparison of remote sensing algorithms for retrieval of suspended particulate matter concentration from reflectance in coastal waters
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Abstract
Suspended particulate matter (SPM) is a key environmental indicator for rivers, estuaries, and coastal waters, which can be calculated from remote sensing reflectance obtained by an airborne or satellite imager. Here, algorithms from prior studies are applied to a dataset of in-situ at surface hyperspectral remote sensing reflectance, collected in three geographic regions representing different water types. These data show the optically inherent exponential nature of the relationship between reflectance and sediment concentration. However, linear models are also shown to provide a reasonable estimate of sediment concentration when utilized with care in similar conditions to those under which the algorithms were developed, particularly at lower SPM values (0 to 20  mg/L). Fifteen published SPM algorithms are tested, returning strong correlations of R2<0.7, and in most cases, R2<0.8. Very low SPM values show weaker correlation with algorithm calculated SPM that is not wavelength dependent. None of the tested algorithms performs well for high SPM values (<30  mg/L), with most algorithms underestimating SPM. A shift toward a smaller number of simple exponential or linear models relating satellite remote sensing reflectance to suspended sediment concentration with regional consideration will greatly aid larger spatiotemporal studies of suspended sediment trends.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Lauren A. Freeman, Lauren A. Freeman, Steven G. Ackleson, Steven G. Ackleson, William J. Rhea, William J. Rhea, } "Comparison of remote sensing algorithms for retrieval of suspended particulate matter concentration from reflectance in coastal waters," Journal of Applied Remote Sensing 11(4), 046028 (23 December 2017). https://doi.org/10.1117/1.JRS.11.046028 . Submission: Received: 15 August 2017; Accepted: 14 November 2017
Received: 15 August 2017; Accepted: 14 November 2017; Published: 23 December 2017
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