Mapping of water constituents in Lake Constance using multispectral airborne scanner data and a physically based processing scheme

Heege, Thomas; Fischer, Jürgen

doi:10.5589/m03-056

Cited by 49 publications

(42 citation statements)

References 14 publications

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“…The satellite images were processed with and without correction for stray light from adjacent land pixels, using the Improve Contrast over Ocean and Land (ICOL) processor [25]. The images were atmospherically corrected using several processors: Case 2 Regional (C2R, [26]), CoastColour with C2R ( CC2R, [27]) and the Modular Inversion and Processing scheme (MIP) [28][29][30]. This is a subset from the atmospheric correction (AC) methods tested in the GLaSS project [31], because not all AC output was suitable as input for the OWT tool.…”

Section: Characteristics Of Remote Sensing Datamentioning

confidence: 99%

An Optical Classification Tool for Global Lake Waters

et al. 2017

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Shallow and deep lakes receive and recycle organic and inorganic substances from within the confines of these lakes, their watershed and beyond. Hence, a large range in absorption and scattering and extreme differences in optical variability can be found between and within global lakes. This poses a challenge for atmospheric correction and bio-optical algorithms applied to optical remote sensing for water quality monitoring applications. To optimize these applications for the wide variety of lake optical conditions, we adapted a spectral classification scheme based on the concept of optical water types. The optical water types were defined through a cluster analysis of in situ hyperspectral remote sensing reflectance spectra collected by partners and advisors of the European Union 7th Framework Programme (FP7) Global Lakes Sentinel Services (GLaSS) project. The method has been integrated in the Envisat-BEAM software and the Sentinel Application Platform (SNAP) and generates maps of water types from image data. Two variations of water type classification are provided: one based on area-normalized spectral reflectance focusing on spectral shape (6CN, six-class normalized) and one that retains magnitude with no modification to the reflectance signal (6C). This resulted in a protocol, or processing scheme, that can also be applied or adapted for Sentinel-3 Ocean and Land Colour Imager (OLCI) datasets. We apply both treatments to MERIS imagery of a variety of European lakes to demonstrate its applicability. The studied target lakes cover a range of biophysical types, from shallow turbid to deep and clear, as well as eutrophic and dark absorbing waters, rich in colored dissolved organic matter (CDOM). In shallow, high-reflecting Dutch and Estonian lakes with high sediment load, 6C performed better, while in deep, low-reflecting clear Italian and Swedish lakes, 6CN performed better. The 6CN classification of in situ data is promising for very dark, high CDOM, absorbing lakes, but we show that our atmospheric correction of the imagery was insufficient to corroborate this. We anticipate that the application of the protocol to other lakes with unknown in-water characterization, but with comparable biophysical properties will suggest similar atmospheric correction (AC) and in-water retrieval algorithms for global lakes.

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Section: Characteristics Of Remote Sensing Datamentioning

confidence: 99%

An Optical Classification Tool for Global Lake Waters

et al. 2017

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“…MIP is a physics-based, sensor-independent, atmospheric correction software developed for coastal and inland waters [61][62][63]. Atmospheric scattering and absorption is calculated based on radiative transfer modelling considering bidirectional properties.…”

Section: Preprocessing Of S2-a Datamentioning

confidence: 99%

“…We conducted atmospheric correction procedures on reprocessed L1C data (processing baseline: 02.01) using three different algorithms, i.e., Sen2Cor (Version 2.2.1, [58]), ACOLITE (Version 20160520.1, [59,60]) and MIP (Modular Inversion and Processing System, [61][62][63]). …”

Section: Preprocessing Of S2-a Datamentioning

confidence: 99%

Water Constituents and Water Depth Retrieval from Sentinel-2A—A First Evaluation in an Oligotrophic Lake

et al. 2016

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Satellite remote sensing may assist in meeting the needs of lake monitoring. In this study, we aim to evaluate the potential of Sentinel-2 to assess and monitor water constituents and bottom characteristics of lakes at spatio-temporal synoptic scales. In a field campaign at Lake Starnberg, Germany, we collected validation data concurrently to a Sentinel-2A (S2-A) overpass. We compared the results of three different atmospheric corrections, i.e., Sen2Cor, ACOLITE and MIP, with in situ reflectance measurements, whereof MIP performed best (r = 0.987, RMSE = 0.002 sr −1 ). Using the bio-optical modelling tool WASI-2D, we retrieved absorption by coloured dissolved organic matter (a CDOM (440)), backscattering and concentration of suspended particulate matter (SPM) in optically deep water; water depths, bottom substrates and a CDOM (440) were modelled in optically shallow water. In deep water, SPM and a CDOM (440) showed reasonable spatial patterns. Comparisons with in situ data (mean: 0.43 m −1 ) showed an underestimation of S2-A derived a CDOM (440) (mean: 0.14 m −1 ); S2-A backscattering of SPM was slightly higher than backscattering from in situ data (mean: 0.027 m −1 vs. 0.019 m −1 ). Chlorophyll-a concentrations (~1 mg·m −3 ) of the lake were too low for a retrieval. In shallow water, retrieved water depths exhibited a high correlation with echo sounding data (r = 0.95, residual standard deviation = 0.12 m) up to 2.5 m (Secchi disk depth: 4.2 m), though water depths were slightly underestimated (RMSE = 0.56 m). In deeper water, Sentinel-2A bands were incapable of allowing a WASI-2D based separation of macrophytes and sediment which led to erroneous water depths. Overall, the results encourage further research on lakes with varying optical properties and trophic states with Sentinel-2A.

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“…Thus, the developed algorithm is suitable for implementation in automated processing chains. The algorithm was tested on different sensor data (AISA Eagle and HyMap), works for different types of landscapes (tested: urban, rural and coastal) and is not influenced by different atmospheric correction methods (tested: ATCOR-4 (Richter, 2011), MIP (Heege & Fischer, 2004), ACUM-R (unpublished in-house development by K. Segl), the method of L. Guanter et al (Guanter et al, 2009), and empirical line correction). Future issues will be to improve the detection of small and narrow water bodies, the detection of white water and of water under shadow.…”

Section: Resultsmentioning

confidence: 99%