Remote Sensing of Water Quality

Allan, Mathew Grant; McBride, Chris

doi:10.1007/978-3-319-93043-5_14

Cited by 2 publications

(2 citation statements)

References 102 publications

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“…Whenever possible, it is recommended to validate environmental data derived from satellite products at regional and local scales by comparing pixels on a satellite image with ‘real’ field observation data (Henson et al 2015, Trull et al 2018). Simple correlation analyses or more complex ground-truth processes are available to compare satellite and in situ data and to secure the interpretation of satellite-derived products (White-Newsome et al 2013, Allan 2014). This, however, constitutes a huge task and is not performed generally before implementing SDMs.…”

Section: Quality Of Datasetsmentioning

confidence: 99%

Species distribution modelling of the Southern Ocean benthos: a review on methods, cautions and solutions

2021

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Species distribution modelling studies the relationship between species occurrence records and their environmental setting, providing a valuable approach to predicting species distribution in the Southern Ocean (SO), a challenging region to investigate due to its remoteness and extreme weather and sea conditions. The specificity of SO studies, including restricted field access and sampling, the paucity of observations and difficulties in conducting biological experiments, limit the performance of species distribution models. In this review, we discuss some issues that may influence model performance when preparing datasets and calibrating models, namely the selection and quality of environmental descriptors, the spatial and temporal biases that may affect the quality of occurrence data, the choice of modelling algorithms and the spatial scale and limits of the projection area. We stress the importance of evaluating and communicating model uncertainties, and the most common evaluation metrics are reviewed and discussed accordingly. Based on a selection of case studies on SO benthic invertebrates, we highlight important cautions to take and pitfalls to avoid when modelling the distribution of SO species, and we provide some guidelines along with potential methods and original solutions that can be used for improving model performance.

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Section: Quality Of Datasetsmentioning

confidence: 99%

Species distribution modelling of the Southern Ocean benthos: a review on methods, cautions and solutions

2021

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“…Indicators of this nature are frequently evaluated through statistical correlations with other indicators [56]. In this review, the focus of research is on eight optically active water parameters (Table 2), as follows: chl-α [30,32,33,35,, transparency (SDD) [33,35,[57][58][59]67,77,87,88,90,93,95,97,[101][102][103]110,[112][113][114][115][116][117][118][119][120][121][122][123][124][125][126], CDOM [60,75,77,82,83,90,92,102,115,, EC [148][149][150]…”

Section: Introductionmentioning

confidence: 99%

A Review of Remote Sensing Applications for Determining Lake Water Quality

Batina,

Krtalić

2023

Preprint

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Remote sensing methods have the potential to improve lake water quality monitoring and deci-sion-making in water management. This reviews introduces novel findings in the field of opti-cally active water quality parameters using remote sensing. It summarizes existing retrieval methods (analytical, semi-analytical, empirical, semi-empirical, and artificial intelli-gence/machine learning (AI/ML)), examines measurement methods used to determine concen-tration of specific water quality parameters, summarizes satellite systems that enable temporal data for understanding the state of the lake with focus on water quality parameters, and pro-poses enhancements for future research of lake water quality using remote sensing. As part of this review, eight optically active biological and physical water quality parameters were ana-lyzed, including chlorophyll-α (chl-α), transparency (Secchi disk depth (SDD)), colored dis-solved organic matters (CDOM), turbidity (TUR), electrical conductivity (EC), surface salinity (SS), total suspended matter (TSM), and water temperature (WT). The research proposes a shift from point-based data representation to a more reliable raster representation and encourages optimizing grid selection for in situ measurements by combining hydrodynamic model with re-mote sensing methods. This review presents a comprehensive summary of the bands, band combinations, and band equations per sensor for eight optically active water quality parameters as listed in Tables A1-A8. The review’s findings indicate that use of remotely sensed data is an effective method for estimating water quality parameters in lakes, with a significant increase in global utilization. The review highlights potential solutions and limitations to the challenges of remote sensing water quality determination in lakes.

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Remote Sensing of Water Quality

Cited by 2 publications

References 102 publications

Species distribution modelling of the Southern Ocean benthos: a review on methods, cautions and solutions

Species distribution modelling of the Southern Ocean benthos: a review on methods, cautions and solutions

A Review of Remote Sensing Applications for Determining Lake Water Quality

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