Artificial neural networks for modeling the transfer function between marine reflectance and phytoplankton pigment concentration

Gross, Lidwine; Thiria, Sylvie; Frouin, Robert; Mitchell, B. Greg

doi:10.1029/1999jc900278

Cited by 81 publications

(40 citation statements)

References 26 publications

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“…Comparisons with other NN retrieval techniques are not so straightforward. Several of the previously reported NN techniques have used in situ measurements of IOPs, including sea surface temperatures and other physical parameters as inputs to NNs for bloom prediction, apparently with some success [57][58][59]. The more directly comparable NN technique reported, is the NN algorithm product for [Chla] retrievals in Case 2 waters [60][61][62][63] from the MERIS satellite, which is no longer operational.…”

Section: Discussionmentioning

confidence: 99%

Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques

et al. 2016

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Abstract:We describe the application of a Neural Network (NN) previously developed by us, to the detection and tracking, of Karenia brevis Harmful Algal Blooms (KB HABs) that plague the coasts of the West Florida Shelf (WFS) using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previous approaches for the detection of KB HABs in the WFS primarily used observations from the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite. They depended on the remote sensing reflectance signal at the 678 nm chlorophyll fluorescence band (Rrs678) needed for both the normalized fluorescence height (nFLH) and Red Band Difference algorithms (RBD) currently used. VIIRS which has replaced MODIS-A, unfortunately does not have a 678 nm fluorescence channel so we customized the NN approach to retrieve phytoplankton absorption at 443 nm (a ph443 ) using only Rrs measurements from existing VIIRS channels at 486, 551 and 671 nm. The a ph443 values in these retrieved VIIRS images, can in turn be correlated to chlorophyll-a concentrations [Chla] and KB cell counts. To retrieve KB values, the VIIRS NN retrieved a ph443 images are filtered by applying limiting constraints, defined by (i) low backscatter at Rrs 551 nm and (ii) a minimum a ph443 value known to be associated with KB HABs in the WFS. The resulting filtered residual images, are then used to delineate and quantify the existing KB HABs. Comparisons with KB HABs satellite retrievals obtained using other techniques, including nFLH, as well as with in situ measurements reported over a four year period, confirm the viability of the NN technique, when combined with the filtering constraints devised, for effective detection of KB HABs.

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Section: Discussionmentioning

confidence: 99%

Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques

et al. 2016

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“…23 The mean total phosphorus, total nitrogen, and Secchi depth of Lake Qiandaohu are 0.025 mg∕L, 1.042 mg∕L, and 4.7 m, respectively, and the magnitude of absorption by particulates and CDOM is low (as detailed in Table 1). The lake has a surface water area of 580 km 2 and a mean depth of 37 m at its normal water level. It is a long and narrow reservoir with more than 80 bays.…”

Section: Study Areamentioning

confidence: 99%

Retrieval of absorption coefficients for a drinking water source using a green–red band quasianalytical algorithm

Shi

Tao

Mao

et al. 2018

J. Appl. Rem. Sens.

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"Retrieval of absorption coefficients for a drinking water source using a green-red band quasianalytical algorithm," J. Appl. Remote Sens. 12(4), 042802 (2018), doi: 10.1117/1.JRS.12.042802. Abstract. This study proposed a green-red band quasianalytical algorithm, QAA-GRI, and evaluates its performance using an in situ dataset from Lake Qiandaohu, a drinking water source, in China. First, by shifting the reference wavelength from 555 to 510 nm, a green-red index (GRI) can be calculated from remote sensing reflectance at 510, 560, and 620 nm, and the index was then used to retrieve the total absorption coefficients at the reference band, að510Þ. Second, a semianalytical model based on að510Þ and the GRI was deduced to establish the QAA-GRI, replacing the empirical model in quasianalytical algorithm version 5 (QAA-v5). The QAA-GRI was applied to retrieve absorption coefficients from an in situ dataset of Lake Qiandaohu, and the QAA-GRI's performance was compared with that of QAA-v5 and another red-green QAAbased approach (QAA-RGR). The results demonstrate that, for this dataset, the QAA-GRI exhibited better performance (R 2 ¼ 0.81, mean absolute percentage error, MAPE ¼ 15.7%), which indicated a clear improvement in the accuracy of absorption coefficient retrieval, compared with QAA-v5 (R 2 ¼ 0.56, MAPE ¼ 21.2%) and QAA-RGR (R 2 ¼ 0.67, MAPE ¼ 22.6%). In addition, to illustrate the QAA-GRI's assumptions and its applicability, the QAA-GRI was also applied to an in situ dataset from Taihu Lake, a highly turbid and eutrophic water source. As predicted, when applied to Taihu Lake, the QAA-GRI did not perform as well as it did when applied to Lake Qiandaohu because of the former's extremely high turbidity, which can cause greater uncertainty with regard to backscattering coefficients. This study suggests that the QAA-GRI is better suited to the retrieval of absorption coefficients from drinking water resources. Our algorithm will also have potential applications to satellite data from the Medium Resolution Imaging Spectrometer or Ocean and Land Color Instrument. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…However, according to the inverse problem to deal with, it may be difficult to gather enough measurements to cover the data space with sufficient density. It is common to use simulated data for the training step and to add some geophysical noise in the data set to account for measurement errors [7][4]. Therefore, in this paper, synthetic data obtained with a radiative transfer model were used to perform the learning step.…”

Section: Inverse Problemmentioning

confidence: 99%

Teams of Genetic Predictors for Inverse Problem Solving

Platel¹,

Chami

Clergue³

et al. 2005

Lecture Notes in Computer Science

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Abstract. Genetic Programming (GP) has been shown to be a good method of predicting functions that solve inverse problems. In this context, a solution given by GP generally consists of a sole predictor. In contrast, Stack-based GP systems manipulate structures containing several predictors, which can be considered as teams of predictors. Work in Machine Learning reports that combining predictors gives good results in terms of both quality and robustness. In this paper, we use Stack-based GP to study different cooperations between predictors. First, preliminary tests and parameter tuning are performed on two GP benchmarks. Then, the system is applied to a real-world inverse problem. A comparative study with standard methods has shown limits and advantages of teams prediction, leading to encourage the use of combinations taking into account the response quality of each team member.

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Artificial neural networks for modeling the transfer function between marine reflectance and phytoplankton pigment concentration

Cited by 81 publications

References 26 publications

Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques

Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques

Retrieval of absorption coefficients for a drinking water source using a green–red band quasianalytical algorithm

Teams of Genetic Predictors for Inverse Problem Solving

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