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Optical Remote Sensing for Surface Water Parameters Retrieval

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 8254

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Dr. Mathew Grant Allan

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Guest Editor

Water, Science and Strategy, Waikato Regional Council, Hamilton, New Zealand
Interests: water quality; spatial analysis; environment; satellite image analysis

Special Issue Information

Dear Colleagues,

The eutrophication of inland water bodies is of increasing concern globally. Remote sensing is potentially able to synoptically quantify water quality at a high temporal frequency, therefore presenting an innovative opportunity to provide a cost-effective monitoring solution. The remote sensing of inland water quality is challenging, and often hampered by optically complex Case II waters. However, recent publications have addressed these challenges and demonstrated that the operational remote sensing of inland waters is feasible provided that limitations are discussed. Therefore, we would like to encourage submissions for the upcoming Special Issue entitled “Optical Remote Sensing for Surface Water Parameters Retrieval”. Papers are encouraged that address challenges in innovative ways, or that demonstrate the application of previously published methodology in the operational remote sensing of optically active water quality constituents in inland waters. Potential topics could include:

Influence of atmospheric correction on the retrieval accuracy of optically active constituents in inland water
Addressing adjacency effects in remote sensing of inland waters
Detecting harmful algal blooms – distinguishing cyanobacteria from algae
Remote sensing of water quality in extremely eutrophic or turbid inland waters
Remote sensing of water quality in oligotrophic inland waters
Application of innovative tools or software (e.g., Google Earth Engine, cloud computing).

Dr. Mathew Grant Allan
Guest Editor

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Published Papers (3 papers)

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Research

16 pages, 5889 KiB

Open AccessArticle

An Improved QAA-Based Method for Monitoring Water Clarity of Honghu Lake Using Landsat TM, ETM+ and OLI Data

by Miaomiao Chen, Fei Xiao, Zhou Wang, Qi Feng, Xuan Ban, Yadong Zhou and Zhengzheng Hu

Remote Sens. 2022, 14(15), 3798; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153798 - 06 Aug 2022

Cited by 3 | Viewed by 1674

Abstract

Secchi disk depth (Z_SD) is used to quantify water clarity as an important water-quality parameter, and one of the most used mechanistic models for Z_SD is the quasi-analytical algorithm (QAA), of which the latest version is QAA_v6. There are two models in QAA for clear and turbid waters (referred to as QAA_clear and QAA_turbid). QAA_v6 switches between the two models by setting a threshold value for the remote sensing reflectance (R_rs, sr⁻¹) at the selected reference band of 656 nm. However, some researchers found that this reference band or the threshold value does not apply to many turbid inland lakes. In Honghu Lake, the R_rs (656) (R_rs at 656 nm) in the whole lake is less than 0.0015 sr⁻¹; therefore, only QAA_turbid can be applied. Moreover, we found that QAA_clear resulted in overestimation while QAA_turbid resulted in significant underestimations. The waters of inland lakes usually continuously vary between clear and turbid water. We proposed a hypothesis that QAA_turbid and QAA_clear transition evenly, rather than being distinguished by one threshold value, and we developed a model that combined QAA_clear and QAA_turbid according to our assumption. This model simulated the process of continuous change in water clarity. The results showed that our model had a better performance with an RMSE that reduced from 0.5 to 0.28, an MAE that reduced from 0.43 to 0.21, and bias that reduced from −0.4 to −0.05 m compared with QAA_v6. We applied QAA_Honghu to Landsat TM, ETM+, and OLI data and obtained 205 Z_SD maps with high spatial resolution in Honghu Lake. The results were consistent with the existing in situ measurements. From 1987–2020, the Z_SD results of Honghu Lake showed an overall downward trend and a distinct seasonal pattern. Full article

(This article belongs to the Special Issue Optical Remote Sensing for Surface Water Parameters Retrieval)

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20 pages, 2986 KiB

Open AccessArticle

Evaluating Landsat-8 and Sentinel-2 Data Consistency for High Spatiotemporal Inland and Coastal Water Quality Monitoring

by Sidrah Hafeez, Man Sing Wong, Sawaid Abbas and Muhammad Asim

Remote Sens. 2022, 14(13), 3155; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133155 - 30 Jun 2022

Cited by 12 | Viewed by 3412

Abstract

The synergy of fine-to-moderate-resolutin (i.e., 10–60 m) satellite data of the Landsat-8 Operational Land Imager (OLI) and the Sentinel-2 Multispectral Instrument (MSI) provides a possibility to monitor the dynamics of sensitive aquatic systems. However, it is imperative to assess the spectral consistency of both sensors before developing new algorithms for their combined use. This study evaluates spectral consistency between OLI and MSI-A/B, mainly in terms of the top-of-atmosphere reflectance (

ρ_{t}),

Rayleigh-corrected reflectance (

ρ_{r c}), and

remote-sensing reflectance (

R_{r s})

. To check the spectral consistency under various atmospheric and aquatic conditions, near-simultaneous same-day overpass images of OLI and MSI-A/B were selected over diverse coastal and inland areas across Mainland China and Hong Kong. The results showed that spectral data obtained from OLI and MSI-A/B were consistent. The difference in the mean absolute percentage error (MAPE) of the OLI and MSI-A products was ~8% in

ρ_{t}

and ~10% in both

ρ_{r c}

and

R_{r s}

for all the matching bands, whereas the MAPE for OLI and MSI-B was ~3.7% in

ρ_{t}

, ~5.7% in

ρ_{r c}

, and ~7.5%

in R_{r s}

for all visible bands except the ultra-blue band. Overall, the green band was the most consistent, with the lowest MAPE of ≤ 4.6% in all the products. The linear regression model suggested that product difference decreased significantly after band adjustment with the highest reduction rate in

R_{r s}

(NIR band) and

R_{r s}

(red band) for the OLI–MSI-A and OLI–MSI-B comparison, respectively. Further, this study discussed the combined use of OLI and MSI-A/B data for (i) time series of the total suspended solid concentrations (TSS) over coastal and inland waters; (ii) floating algae area comparison; and (iii) tracking changes in coastal floating algae (FA). Time series analysis of the TSS showed that seasonal variation was well-captured by the combined use of sensors. The analysis of the floating algae bloom area revealed that the algae area was consistent, however, the difference increases as the time difference between the same-day overpasses increases. Furthermore, tracking changes in coastal FA over two months showed that thin algal slicks (width < 500 m) can be detected with an adequate spatial resolution of the OLI and the MSI. Full article

(This article belongs to the Special Issue Optical Remote Sensing for Surface Water Parameters Retrieval)

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22 pages, 7588 KiB

Open AccessArticle

Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach

by Wonjin Jang, Yongeun Park, JongCheol Pyo, Sanghyun Park, Jinuk Kim, Jin Hwi Kim, Kyung Hwa Cho, Jae-Ki Shin and Seongjoon Kim

Remote Sens. 2022, 14(7), 1754; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14071754 - 06 Apr 2022

Cited by 4 | Viewed by 2153

Abstract

Understanding the concentration and distribution of cyanobacteria blooms is an important aspect of managing water quality problems and protecting aquatic ecosystems. Airborne hyperspectral imagery (HSI)—which has high temporal, spatial, and spectral resolutions—is widely used to remotely sense cyanobacteria bloom, and it provides the distribution of the bloom over a wide area. In this study, we determined the input spectral bands that were relevant in effectively estimating the main two pigments (PC, Phycocyanin; Chl-a, Chlorophyll-a) of cyanobacteria by applying data-driven algorithms to HSI and then evaluating the change in the spatio-temporal distribution of cyanobacteria. The input variables for the algorithms consisted of reflectance band ratios associated with the optical properties of PC and Chl-a, which were calculated by the selected hyperspectral bands using a feature selection method. The selected input variable was composed of six reflectance bands (465.7–589.6, 603.6–631.8, 641.2–655.35, 664.8–679.0, 698.0–712.3, and 731.4–784.1 nm). The artificial neural network showed the best results for the estimation of the two pigments with average coefficients of determination 0.80 and 0.74. This study proposes relevant input spectral information and an algorithm that can effectively detect the occurrence of cyanobacteria in the weir pool along the Geum river, South Korea. The algorithm is expected to help establish a preemptive response to the formation of cyanobacterial blooms, and to contribute to the preparation of suitable water quality management plans for freshwater environments. Full article

(This article belongs to the Special Issue Optical Remote Sensing for Surface Water Parameters Retrieval)

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