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A Seasonally Robust Empirical Algorithm to Retrieve Suspended Sediment Concentrations in the Scheldt River
Flemish Institute for Technological Research (VITO), Center for Remote Sensing and Earth Observation Processes, Boeretang 200, B-2400 Mol, Belgium
* Author to whom correspondence should be addressed.
Received: 6 July 2010; in revised form: 5 August 2010 / Accepted: 19 August 2010 / Published: 27 August 2010
Abstract: A seasonally robust algorithm for the retrieval of Suspended Particulate Matter (SPM) in the Scheldt River from hyperspectral images is presented. This algorithm can be applied without the need to simultaneously acquire samples (from vessels and pontoons). Especially in dynamic environments such as estuaries, this leads to a large reduction of costs, both in equipment and personnel. The algorithm was established empirically using in situ data of the water-leaving reflectance obtained over the tidal cycle during different seasons and different years. Different bands and band combinations were tested. Strong correlations were obtained for exponential relationships between band ratios and SPM concentration. The best performing relationships are validated using airborne hyperspectral data acquired in June 2005 and October 2007 at different moments in the tidal cycle. A band ratio algorithm (710 nm/596 nm) was successfully applied to a hyperspectral AHS image of the Scheldt River to obtain an SPM concentration map.
Keywords: remote sensing; suspended particulate matter; empirical; estuaries
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MDPI and ACS Style
Knaeps, E.; Sterckx, S.; Raymaekers, D. A Seasonally Robust Empirical Algorithm to Retrieve Suspended Sediment Concentrations in the Scheldt River. Remote Sens. 2010, 2, 2040-2059.
Knaeps E, Sterckx S, Raymaekers D. A Seasonally Robust Empirical Algorithm to Retrieve Suspended Sediment Concentrations in the Scheldt River. Remote Sensing. 2010; 2(9):2040-2059.
Knaeps, Els; Sterckx, Sindy; Raymaekers, Dries. 2010. "A Seasonally Robust Empirical Algorithm to Retrieve Suspended Sediment Concentrations in the Scheldt River." Remote Sens. 2, no. 9: 2040-2059.