Development of a Neural Network Algorithm for Retrieving Concentrations of Chlorophyll, Suspended Matter and Yellow Substance from Radiance Data of the Ocean Color and Temperature Scanner

Tanaka, Akihiko; Kishino, Motoaki; Doerffer, R.; Schiller, Helmut; Oishi, Tomohiko; Kubota, Tadashi

doi:10.1023/b:joce.0000038345.99050.c0

Cited by 61 publications

(25 citation statements)

References 33 publications

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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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Section: Introductionmentioning

confidence: 99%

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

2017

Self Cite

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“…For the model sensitivity test through adding Gauss white noise (Tanaka et al 2004), models ANN#2 and ANN#3 both shown the good noise tolerance ability. In particular, model ANN#3 performs better than model ANN#2, which expressed that R 2 increased from 0.7586 to 0.9091.…”

Section: Discussionmentioning

confidence: 98%

“…Compared to ordinary empirical or semi-analytical models, ANN is not easily to be affected by noise (Tanaka et al 2004). Although ANN algorithm cost much time for training a dataset, yet was very quickly when applied to aiming dataset (Ranković et al 2010).…”

Section: Introductionmentioning

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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“…The earlier version (Version 1.5) of the GLI atmospheric correction algorithm has a slightly different aerosol model set with no Asian dust model included. It incorporates a bio-optical model and a neural network (Tanaka et al, 2004) to determine iteratively the magnitude of [ρ w (λ NIR )] N , which considers the effect of inorganic suspended matter as well as phytoplankton. Version 2.1 of this algorithm introduces an empirical iterative scheme for absorptive aerosol correction.…”

Section: The Octs/gli Algorithmmentioning

confidence: 99%

Interactive Online Maps Make Satellite Ocean Data Accessible

Mikelsons¹,

Wang²

2018

Eos

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Development of a Neural Network Algorithm for Retrieving Concentrations of Chlorophyll, Suspended Matter and Yellow Substance from Radiance Data of the Ocean Color and Temperature Scanner

Cited by 61 publications

References 33 publications

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

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

Interactive Online Maps Make Satellite Ocean Data Accessible

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