Gaussian Process Sensitivity Analysis for Oceanic Chlorophyll Estimation

Blix, Katalin; Camps‐Valls, Gustau; Jenssen, Robert

doi:10.1109/jstars.2016.2641583

Cited by 24 publications

(21 citation statements)

References 36 publications

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“…An alternative powerful regression approach, the Gaussian Process Regression (GPR) model, has lately been investigated for biophysical parameter retrieval from remotely sensed data. The GPR model has been shown to outperform some other parameteric and non-parameteric machine learning methods, such as NNs, in the estimation of these biophysical parameters [20][21][22][23][24]. Hence, the GPR model can be an alternative candidate for estimating water quality parameters from data acquired by S3 OLCI in Lake Balaton.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI

Blix

Pálffy

Tóth

et al. 2018

Water

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The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology.

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Section: Introductionmentioning

confidence: 99%

Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI

Blix

Pálffy

Tóth

et al. 2018

Water

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“…Although both the SVR and GPR are non-linear kernel machines, their underlying principles differ. The SA of the GPR model was introduced by [31,61], while the SA of the Support Vector Machine (SVM) for classification purposes was described in [45]. In this work, we extend the SA of the SVM to regression.…”

Section: Feature Ranking Methodsmentioning

confidence: 99%

“…Artificial Neural Networks (ANN) models have been lately successfully applied for Chl-a estimation [23][24][25], and to various other applications, such as for predicting the amount of generated electricity [26], suspendid sediment load in rivers [27] and rainfall and runoff predictions ahead in time [28]. For OC applications, satellite derived Chl-a in optically complex waters is also often estimated by using other ML algorithms [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

“…These regression models are the Gaussian Process Regression (GPR), Support Vector Regression (SVR) and Partial Least Square Regression (PLSR) models. GPR has been shown to outperform empirical [31,40] and ML regression models [41] for biophysical parameter retrieval from remotely sensed data. GPR has several advantageous properties besides its excellent regression performance, for instance the certainty level of the estimates and the possibility to access feature relevance.…”

Section: Introductionmentioning

confidence: 99%

“…GPR has several advantageous properties besides its excellent regression performance, for instance the certainty level of the estimates and the possibility to access feature relevance. Feature relevance for the GPR model can be accessed by the Sensitivity Analysis (SA) [31,32] and the Automatic Relevance Determination (ARD) [40,42] feature ranking methods.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval

Blix

Eltoft

2018

Remote Sensing

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Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality parameters of the world's oceans and inland waters. This is done by using various regression algorithms to retrieve water quality parameters from remotely sensed multi-spectral data for the given sensor and environment. There is a great number of such algorithms for estimating water quality parameters with different performances. Hence, choosing the most suitable model for a given purpose can be challenging. This is especially the fact for optically complex aquatic environments. In this paper, we present a concept to an Automatic Model Selection Algorithm (AMSA) aiming at determining the best model for a given matchup dataset. AMSA automatically chooses between regression models to estimate the parameter in interest. AMSA also determines the number and combination of features to use in order to obtain the best model. We show how AMSA can be built for a certain application. The example AMSA we present here is designed to estimate oceanic Chlorophyll-a for global and optically complex waters by using four Machine Learning (ML) feature ranking methods and three ML regression models. We use a synthetic and two real matchup datasets to find the best models. Finally, we use two images from optically complex waters to illustrate the predictive power of the best models. Our results indicate that AMSA has a great potential to be used for operational purposes. It can be a useful objective tool for finding the most suitable model for a given sensor, water quality parameter and environment.

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Comparing the performance of machine learning algorithms for remote and in situ estimations of chlorophyll‐a content: A case study in the Tri An Reservoir, Vietnam

Nguyen

Nguyen-Ngoc

et al. 2021

Water Environment Research

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Chlorophyll‐a (Chl‐a) is one of the most important indicators of the trophic status of inland waters, and its continued monitoring is essential. Recently, the operated Sentinel‐2 MSI satellite offers high spatial resolution images for remote water quality monitoring. In this study, we tested the performance of the three well‐known machine learning (ML) (random forest [RF], support vector machine [SVM], and Gaussian process [GP]) and the two novel ML (extreme gradient boost (XGB) and CatBoost [CB]) models for estimation a wide range of Chl‐a concentration (10.1–798.7 μg/L) using the Sentinel‐2 MSI data and in situ water quality measurement in the Tri An Reservoir (TAR), Vietnam. GP indicated the most reliable model for predicting Chl‐a from water quality parameters (R2 = 0.85, root‐mean‐square error [RMSE] = 56.65 μg/L, Akaike's information criterion [AIC] = 575.10, and Bayesian information criterion [BIC] = 595.24). Regarding input model as water surface reflectance, CB was the superior model for Chl‐a retrieval (R2 = 0.84, RMSE = 46.28 μg/L, AIC = 229.18, and BIC = 238.50). Our results indicated that GP and CB are the two best models for the prediction of Chl‐a in TAR. Overall, the Sentinel‐2 MSI coupled with ML algorithms is a reliable, inexpensive, and accurate instrument for monitoring Chl‐a in inland waters. Practitioner points Machine learning algorithms were used for both remote sensing data and in situ water quality measurements. The performance of five well‐known machine learning models was tested Gaussian process was the most reliable model for predicting Chl‐a from water quality parameters CatBoost was the best model for Chl‐a retrieval from water surface reflectance

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Gaussian Process Sensitivity Analysis for Oceanic Chlorophyll Estimation

Cited by 24 publications

References 36 publications

Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI

Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI

Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval

Comparing the performance of machine learning algorithms for remote and in situ estimations of chlorophyll‐a content: A case study in the Tri An Reservoir, Vietnam

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