Novel Spectra-Derived Features for Empirical Retrieval of Water Quality Parameters: Demonstrations for OLI, MSI, and OLCI Sensors

Niroumand-Jadidi, Milad; Bovolo, Francesca; Bruzzone, Lorenzo

doi:10.1109/tgrs.2019.2933251

Cited by 59 publications

(43 citation statements)

References 65 publications

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“…After comparing the correlations among these band forms, we determined X7, i.e., (R rs (555) + R rs (660))/(R rs (555)/R rs (660)), with the highest R of 0.847 among those band forms for establishing the TSM model. This method to find the optimal band form and optimal band combination was similar to that used in previous studies [33,34]. Several mathematical methods, including linear, power, exponential, and logarithmic functions, were used to model TSM, and their accuracies were then intercompared to obtain a good retrieval model.…”

Section: Development Of N P Estimation Modelmentioning

confidence: 99%

A Remote-Sensing Method to Estimate Bulk Refractive Index of Suspended Particles from GOCI Satellite Measurements over Bohai Sea and Yellow Sea

et al. 2019

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The bulk refractive index (np) of suspended particles, an apparent measure of particulate refraction capability and yet an essential element of particulate compositions and optical properties, is a critical indicator that helps understand many biogeochemical processes and ecosystems in marine waters. Remote estimation of np remains a very challenging task. Here, a multiple-step hybrid model is developed to estimate the np in the Bohai Sea (BS) and Yellow Sea (YS) through obtaining two key intermediate parameters (i.e., particulate backscattering ratio, Bp, and particle size distribution (PSD) slope, j) from remote-sensing reflectance, Rrs(λ). The in situ observed datasets available to us were collected from four cruise surveys during a period from 2014 to 2017 in the BS and YS, covering beam attenuation (cp), scattering (bp), and backscattering (bbp) coefficients, total suspended matter (TSM) concentrations, and Rrs(λ). Based on those in situ observation data, two retrieval algorithms for TSM and bbp were firstly established from Rrs(λ), and then close empirical relationships between cp and bp with TSM could be constructed to determine the Bp and j parameters. The series of steps for the np estimation model proposed in this study can be summarized as follows: Rrs (λ) → TSM and bbp, TSM → bp → cp → j, bbp and bp → Bp, and j and Bp → np. This method shows a high degree of fit (R2 = 0.85) between the measured and modeled np by validation, with low predictive errors (such as a mean relative error, MRE, of 2.55%), while satellite-derived results also reveal good performance (R2 = 0.95, MRE = 2.32%). A spatial distribution pattern of np in January 2017 derived from GOCI (Geostationary Ocean Color Imager) data agrees well with those in situ observations. This also verifies the satisfactory performance of our developed np estimation model. Applying this model to GOCI data for one year (from December 2014 to November 2015), we document the np spatial distribution patterns at different time scales (such as monthly, seasonal, and annual scales) for the first time in the study areas. While the applicability of our developed method to other water areas is unknown, our findings in the current study demonstrate that the method presented here can serve as a proof-of-concept template to remotely estimate np in other coastal optically complex water bodies.

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Section: Development Of N P Estimation Modelmentioning

confidence: 99%

A Remote-Sensing Method to Estimate Bulk Refractive Index of Suspended Particles from GOCI Satellite Measurements over Bohai Sea and Yellow Sea

et al. 2019

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“…and the given parameter through a supervised regression analysis [9][10][11][12][13]. These methods are mainly employed for retrieval of individual biophysical parameters such as bathymetry [14][15][16] and in-water constituents like chlorophyll-a (Chl-a), total suspended matter (TSM), and colored dissolved organic matter (CDOM) [17,18]. In the context of exploiting PlanetScope imagery, the bathymetry of a coastal area in Greece is mapped using these images, by calibrating a regression model using in-situ measurements of water depths [1].…”

Section: Introductionmentioning

confidence: 99%

“…Likewise, the bottom-reflected radiance and its variability can induce challenges into the estimation of in-water constituents. In general, the characteristic spectral feature of a given biophysical parameter is subject to degradation or interference in the presence of other radiance components, particularly if the desired component is not dominant [17,[30][31][32]. The atmospheric effects also can contaminate the water-leaving signal to a large extent as water bodies may reflect only up to 10 % of the downwelling irradiance, due to the high attenuation of the light in pure water [10,17].…”

Section: Introductionmentioning

confidence: 99%

Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon

et al. 2020

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The recent PlanetScope constellation (130+ satellites currently in orbit) has shifted the high spatial resolution imaging into a new era by capturing the Earth’s landmass including inland waters on a daily basis. However, studies on the aquatic-oriented applications of PlanetScope imagery are very sparse, and extensive research is still required to unlock the potentials of this new source of data. As a first fully physics-based investigation, we aim to assess the feasibility of retrieving bathymetric and water quality information from the PlanetScope imagery. The analyses are performed based on Water Color Simulator (WASI) processor in the context of a multitemporal analysis. The WASI-based radiative transfer inversion is adapted to process the PlanetScope imagery dealing with the low spectral resolution and atmospheric artifacts. The bathymetry and total suspended matter (TSM) are mapped in the relatively complex environment of Venice lagoon during two benchmark events: The coronavirus disease 2019 (COVID-19) lockdown and an extreme flood occurred in November 2019. The retrievals of TSM imply a remarkable reduction of the turbidity during the lockdown, due to the COVID-19 pandemic and capture the high values of TSM during the flood condition. The results suggest that sizable atmospheric and sun-glint artifacts should be mitigated through the physics-based inversion using the surface reflectance products of PlanetScope imagery. The physics-based inversion demonstrated high potentials in retrieving both bathymetry and TSM using the PlanetScope imagery.

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“…Previously published empirical algorithms based on regression models with single or multiple spectral bands do not appear to adequately characterize water clarity in our large dataset across a range of lake types and Secchi depths. Various methods for optimizing the selection of band ratios or spectral features, such as optimal band ratio analysis (OBRA, [118]) and related methods [119] or a transformed feature space approach [120], could help maximize the effectiveness of these empirical spectral models. Additionally, our work focuses predominantly on Landsat-specific algorithm testing and does not address methods using other satellite platforms (e.g., MODIS, Sentinel-2); additional information captured in data containing a wider range of bands may yield more accurate models and should be explored.…”

Section: Algorithm Comparisonmentioning

confidence: 99%

Remote Sensing of Lake Water Clarity: Performance and Transferability of Both Historical Algorithms and Machine Learning

et al. 2021

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There has been little rigorous investigation of the transferability of existing empirical water clarity models developed at one location or time to other lakes and dates of imagery with differing conditions. Machine learning methods have not been widely adopted for analysis of lake optical properties such as water clarity, despite their successful use in many other applications of environmental remote sensing. This study compares model performance for a random forest (RF) machine learning algorithm and a simple 4-band linear model with 13 previously published empirical non-machine learning algorithms. We use Landsat surface reflectance product data aligned with spatially and temporally co-located in situ Secchi depth observations from northeastern USA lakes over a 34-year period in this analysis. To evaluate the transferability of models across space and time, we compare model fit using the complete dataset (all images and samples) to a single-date approach, in which separate models are developed for each date of Landsat imagery with more than 75 field samples. On average, the single-date models for all algorithms had lower mean absolute errors (MAE) and root mean squared errors (RMSE) than the models fit to the complete dataset. The RF model had the highest pseudo-R2 for the single-date approach as well as the complete dataset, suggesting that an RF approach outperforms traditional linear regression-based algorithms when modeling lake water clarity using satellite imagery.

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Novel Spectra-Derived Features for Empirical Retrieval of Water Quality Parameters: Demonstrations for OLI, MSI, and OLCI Sensors

Cited by 59 publications

References 65 publications

A Remote-Sensing Method to Estimate Bulk Refractive Index of Suspended Particles from GOCI Satellite Measurements over Bohai Sea and Yellow Sea

A Remote-Sensing Method to Estimate Bulk Refractive Index of Suspended Particles from GOCI Satellite Measurements over Bohai Sea and Yellow Sea

Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon

Remote Sensing of Lake Water Clarity: Performance and Transferability of Both Historical Algorithms and Machine Learning

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