Application of remote sensing for the optimization of in-situ sampling for monitoring of phytoplankton abundance in a large lake

Kiefer, Isabel; Odermatt, Daniel; Anneville, Orlane; Wüest, Alfred; Bouffard, Damien

doi:10.1016/j.scitotenv.2015.05.011

Cited by 70 publications

(59 citation statements)

References 28 publications

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“…Suitable sensors must deploy bands in the visible and near-infrared (VNIR) wavelengths with high radiometric sensitivity such as ocean colour sensors (e.g., MODIS and MERIS). Owing to their spatial resolution (≥300 m), studies conducted with these sensors focus mainly on large lakes such as Lake Balaton [15], Lake Geneva [16], Lake Taihu [17][18][19], or Great Lakes [20], but rarely on small lakes [21,22]. Studies on smaller lakes refer to less sensitive Landsat data [23] or to high spatial resolutions from commercial sensors such as WorldView [24] or Quickbird [25].…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…It is composed of several processors for the ESA BEAM Toolbox (Fomferra and Brockmann, 2005), which has recently evolved into the Sentinel Application Platform (SNAP). The same input and auxiliary data and pre-and post-processing modules were also used to create 10-day aggregates for the investigation of phenological cycles in Lake Balaton (Palmer et al, 2015), and corresponding CaLimnos v1 L2 intermediate outputs were used for assessing the spatio-temporal variability of chl-a in Lake Geneva (Kiefer et al, 2015).…”

Section: Data Processing Methodsmentioning

confidence: 99%

“…It applies several arithmetic expressions, a spectral unmixing algorithm for mixed pixel identification, and two back-propagation neural networks (NNs) for cloud identification to MERIS FSG L1B and BRR input data (Kirches et al, 2013). Output is a pixel identification flag layer which is much better suited for water constituent retrieval than the original L1B product flags (Ruescas et al, 2014).…”

Section: Pre-processingmentioning

confidence: 99%

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Diversity II water quality parameters from ENVISAT (2002–2012): a new global information source for lakes

Odermatt

Danne

Philipson

et al. 2018

Earth Syst. Sci. Data

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Abstract. The use of ground sampled water quality information for global studies is limited due to practical and financial constraints. Remote sensing is a valuable means to overcome such limitations and to provide synoptic views of ambient water quality at appropriate spatio-temporal scales. In past years several large data processing efforts were initiated to provide corresponding data sources. The Diversity II water quality dataset consists of several monthly, yearly and 9-year averaged water quality parameters for 340 lakes worldwide and is based on data from the full ENVISAT MERIS operation period (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). Existing retrieval methods and datasets were selected after an extensive algorithm intercomparison exercise. Chlorophyll-a, total suspended matter, turbidity, coloured dissolved organic matter, lake surface water temperature, cyanobacteria and floating vegetation maps, as well as several auxiliary data layers, provide a generically specified database that can be used for assessing a variety of locally relevant ecosystem properties and environmental problems. For validation and accuracy assessment, we provide matchup comparisons for 24 lakes and a group of reservoirs representing a wide range of bio-optical conditions. Matchup comparisons for chlorophyll-a concentrations indicate mean absolute errors and bias in the order of median concentrations for individual lakes, while total suspended matter and turbidity retrieval achieve significantly better performance metrics across several lake-specific datasets. We demonstrate the use of the products by illustrating and discussing remotely sensed evidence of lake-specific processes and prominent regime shifts documented in the literature. The Diversity II data are available from https://doi.pangaea.de/10.1594/PANGAEA.871462, and Python scripts for their analysis and visualization are provided at https://github.com/odermatt/diversity/.

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“…Along with the rapid development of state economy construction and process of urbanization, the water use and the demand for higher water quality have increased, too, for drinking and domestic use, fisheries, agriculture, navigation, cooling of power plants, hydropower generation and recreational activities [1].…”

Section: Introductionmentioning

confidence: 99%

Remote Monitoring System Based on GPRS - A Case Study for Water Withdrawal

Wang

Bian²,

et al. 2016

Int. J. Onl. Eng.

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Abstract-In order to realize supervision of water withdrawal, it is necessary to establish a modern remote monitoring system. This study focuses on the design of a remote monitoring terminal system that is operating in a south province in China. This terminal system adopts GPRS mode to establish communication with monitoring center. Detailed hardware design is provided, as well as its software functions. This terminal device finishes the basic function of water quantity collection. At the meantime, remote upgrade function and picture collection function are proposed in order to perfect the overall functions of the system. Kinds of tests were conducted to verify its performances and functionalities. We conclude that the proposed remote monitoring terminal might become a useful tool for supervision of water withdrawal in the dispersed areas.

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Application of remote sensing for the optimization of in-situ sampling for monitoring of phytoplankton abundance in a large lake

Cited by 70 publications

References 28 publications

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

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

Diversity II water quality parameters from ENVISAT (2002–2012): a new global information source for lakes

Remote Monitoring System Based on GPRS - A Case Study for Water Withdrawal

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