Evaluating the performance of artificial neural network techniques for pigment retrieval from ocean color in Case I waters

Zhang, Tinglu; Fell, Frank; Liu, Zhishen; Preusker, R.; Fischer, Jürgen; He, Maoxian

doi:10.1029/2002jc001638

Cited by 37 publications

(23 citation statements)

References 19 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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“…It has been proven in the last years that NNs produce reasonable approximations of ocean color products from optically complex (Case-2) waters. NNs have been applied to different satellite sensors in order to derive concentrations of water constituents, inherent and apparent optical properties (IOPs and AOPs), and photosynthetically available radiation (PAR), or to discriminate algae species (Gross , 1999;Schiller and Doerffer, 1999;D'Alimonte and Zibordi, 2003;Zhang et al, 2003;Tanaka et al, 2004;Schiller, 2006;Bricaud et al, 2007;Schroeder et al, 2007;Ioannou et al, 2011;Jamet et al, 2012;Chen et al, 2014;Hieronymi et al, 2015;D'Alimonte et al, 2016). Due to their speed, NN-based ocean color algorithms are deployed for operational and near-real time satellite observations, e.g., the MERIS Case-2 water algorithm (Doerffer and Schiller, 2007) and C2RCC .…”

Section: Introductionmentioning

confidence: 99%

The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters

2017

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The processing scheme of a novel in-water algorithm for the retrieval of ocean color products from Sentinel-3 OLCI is introduced. The algorithm consists of several blended neural networks that are specialized for 13 different optical water classes. These comprise clearest natural waters but also waters reaching the frontiers of marine optical remote sensing, namely extreme absorbing, or scattering waters. Considered chlorophyll concentrations reach up to 200 mg m −3 , non-algae particle concentrations up to 1,500 g m −3 , and the absorption coefficient of colored dissolved organic matter at 440 nm is up to 20 m −1 . The algorithm generates different concentrations of water constituents, inherent and apparent optical properties, and a color index. In addition, all products are delivered with an uncertainty estimate. A baseline validation of the products is provided for various water types. We conclude that the algorithm is suitable for the remote sensing estimation of water properties and constituents of most natural waters.

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“…In this study, PCA was used to reduce the variable dimensions and eliminate the multi-collinearity among input variables of ANN model (Zhang et al 2003). The geometric interpretation of PCA is to use a rotated coordinate system to express the new spatial distribution of original samples (Barbieri et al 1999).…”

Section: Principle Components Analysis (Pca)mentioning

confidence: 99%

Developing a PCA–ANN Model for Predicting Chlorophyll a Concentration from Field Hyperspectral Measurements in Dianshan Lake, China

Zhou

Zhang

et al. 2015

Water Qual Expo Health

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This paper aims at combining principle component analysis (PCA) and artificial neural network (ANN) algorithm to predict chlorophyll a concentration in Dianshan Lake, Shanghai, eastern China. Firstly, based on field hyperspectral measurements, the sensitive wavelengths were selected as the input variables to build the basic ANN model, and the estimate accuracy (R 2 ) reached 0.85. In order to improve the accuracy and stability of the ANN model, the total nitrogen, total phosphorus, chemical oxygen demand, dissolve oxygen, and dissolved potential of hydrogen were selected as additional input variables. Consequently, the model accuracy increased to 0.9091. Further, aiming at eliminating the effect of inter-correlation of input variables, the PCA method was utilized to reduce the dimension of input variables. The result shows that the combined PCA-ANN model can reach an estimated accuracy with R 2 = 0.9184 and RMSE \ 5.6 mg m -3 . Moreover, the stability and performance of the enhanced model was further evaluated by cross-validation of PCA-ANN model output and in situ measured datasets. The model sensitivity test through adding 10 % Gauss white noise to the input variables also proved that the enhanced PCA-ANN model has better noise tolerance ability.

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Evaluating the performance of artificial neural network techniques for pigment retrieval from ocean color in Case I waters

Cited by 37 publications

References 19 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

The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters

Developing a PCA–ANN Model for Predicting Chlorophyll a Concentration from Field Hyperspectral Measurements in Dianshan Lake, China

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