Modeling the Chlorophyll-a from Sea Surface Reflectance in West Africa by Deep Learning Methods: A Comparison of Multiple Algorithms

Diouf, Doudou; Seck, Djibril

doi:10.5121/ijaia.2019.10603

Cited by 10 publications

(10 citation statements)

References 12 publications

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“…The Extra tree shows high accuracy (96.46%) and low mean absolute error (0.07 mg/m-3), and the model performed well with mixed or single sensor data. Also, the estimated Chl-a values by the Extra tree model were consistent with upwelling phenomena observed in this area . However, using Bayesian maximum entropy (BME) and SVR improved Chl-a estimation from satellite reflectance data by reducing the non-negligible uncertainties .…”

Section: Biological Oceanographysupporting

confidence: 64%

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Applications of Machine Learning in Chemical and Biological Oceanography

et al. 2023

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Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.

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Section: Biological Oceanographysupporting

confidence: 64%

“…However, Extra tree, a deep learning model, successfully measured the Chl-a concentration from the sea surface reflectance data over West Africa . The ESA Ocean Color Climate Change Initiative satellite sensor data was used as a training data set, whereas the MODIS sensor data set was used to validate the model.…”

Section: Biological Oceanographymentioning

confidence: 99%

Applications of Machine Learning in Chemical and Biological Oceanography

et al. 2023

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“…training data) defines a random forest. The Random Forest allows to improve the predictive accuracy and to control over-fitting [54]. PLS linearize models that have nonlinear parameters.…”

Section: Plos Onementioning

confidence: 99%

Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing

et al. 2021

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Reference evapotranspiration (ETo) is a fundamental parameter for hydrological studies and irrigation management. The Penman-Monteith method is the standard to estimate ETo and requires several meteorological elements. In developing countries, the number of weather stations is insufficient. Thus, free products of remote sensing with evapotranspiration information must be used for this purpose. In this context, the objective of this study was to estimate monthly ETo from potential evapotranspiration (PET) made available by MOD16 product. In this study, the monthly ETo estimated by Penman-Monteith method was considered as the standard. For this, data from 265 weather station of the National Institute of Meteorology (INMET), spread all over the Brazilian territory, were acquired for the period from 2000 to 2014 (15 years). For these months, monthly PET values from MOD16 product for all Brazil were also downloaded. By using machine learning algorithms and information from WorldClim as covariates, ETo was estimated through images from the MOD16 product. To perform the modeling of ETo, eight regression algorithms were tested: multiple linear regression; random forest; cubist; partial least squares; principal components regression; adaptive forward-backward greedy; generalized boosted regression and generalized linear model by likelihood-based boosting. Data from 2000 to 2012 (13 years) were used for training and data of 2013 and 2014 (2 years) were used to test the models. The PET made available by the MOD16 product showed higher values than those of ETo for different periods and climatic regions of Brazil. However, the MOD16 product showed good correlation with ETo, indicating that it can be used in ETo estimation. All models of machine learning were effective in improving the performance of the metrics evaluated. Cubist was the model that presented the best metrics for r2 (0.91), NSE (0.90) and nRMSE (8.54%) and should be preferred for ETo prediction. MOD16 product is recommended to be used to predict monthly ETo, which opens possibilities for its use in several other studies.

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“…Models such as GlobColour or Modis Aqua can be used globally or adapted to local reservoir conditions. They can take the form of polynomial dependence, as shown in the research of [19,20], dependence, as a form of the products or quotients of expressions [21,22], or a logarithmic form [23,24]. The estimation of model parameters can be carried out in different ways-using multiple linear recreation, support vector machine regression (SVR), or genetic algorithms.…”

Section: Remote Sensing Methods For a Chlorophyll Contentmentioning

confidence: 99%

“…GlobColour (Johnson et al, 2013) The models are characterized by a different precision of results. Johnson et al, 2013 [25], testing global models GlobColour or Modis Aqua, obtained low R 2 determination coefficients of 0.25-0.51; on the other hand, for local conditions, this indicator may reach up to the value 0.96, with an error for RMSE chlorophyll a content of 0.07 mg chl/m 3 , determined on the basis of a few data covering 5 days of composite images for the year 2014 [24].…”

Section: Formula Sourcementioning

confidence: 99%

Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir

Biełski

Toś

2022

Applied Sciences

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This study examines the chlorophyll a content and turbidity in the shallow dam reservoir of Lake Dobczyce. The analysis of satellite images for thirteen wavelength ranges enabled the selection of wavelengths applicable for a remote determination of chlorophyll a and turbidity. The selection was completed as the test of the significance of the coefficients in the equation, which calculates the values of the parameters on the basis of reflectance. The reflectance of the reservoir surface differs from the reflectance of individual water components, and the overlapping of spectral curves makes it difficult to isolate the significant reflectance. In the case of Lake Dobczyce, the significant reflectance was for wavelengths 665, 705, 740, and 842 nm (chlorophyll a) and for wavelengths 705, 740, and 783 nm (turbidity). In the model, the natural logarithm of chlorophyll a or turbidity was a linear combination of the natural log reflectance and the squares of those logarithms. A lake surface reflectance also includes the bottom reflectance. The reflectance obtained from the Sentinel-2 satellite was corrected with a bottom reflectance determined using the Lambert–Beer equation. The reflectance of a given surface may vary with the position of both the satellite and the sun, atmospheric pollution, and other factors. Correction of reflectance from satellite measurements was performed, as reflectance changes for the reference surface; the reference reflectance was assumed as the first reflectance of the reference surface observed during the study. The models helped to develop the maps of turbidity and chlorophyll a content in the lake.

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Modeling the Chlorophyll-a from Sea Surface Reflectance in West Africa by Deep Learning Methods: A Comparison of Multiple Algorithms

Cited by 10 publications

References 12 publications

Applications of Machine Learning in Chemical and Biological Oceanography

Applications of Machine Learning in Chemical and Biological Oceanography

Reference evapotranspiration of Brazil modeled with machine learning techniques and remote sensing

Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir

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