Satellite retrieval of the absorption coefficient of chromophoric dissolved organic matter in continental margins

1995 ◽  
Vol 100 (C12) ◽  
pp. 24847 ◽  
Author(s):  
Frank E. Hoge ◽  
Mark E. Williams ◽  
Robert N. Swift ◽  
James K. Yungel ◽  
Anthony Vodacek
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Absorption Coefficient ◽  
Chromophoric Dissolved Organic Matter ◽  
Continental Margins ◽  
Satellite Retrieval

scholarly journals Parameterization of the light absorption properties of chromophoric dissolved organic matter in the Baltic Sea and Pomeranian lakes

Ocean Science ◽  
2016 ◽  
Vol 12 (4) ◽  
pp. 1013-1032 ◽  
Author(s):  
Justyna Meler ◽  
Piotr Kowalczuk ◽  
Mirosława Ostrowska ◽  
Dariusz Ficek ◽  
Monika Zabłocka ◽  
...  
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Absorption Coefficient ◽  
Baltic Sea ◽  
Chlorophyll A ◽  
Chromophoric Dissolved Organic Matter ◽  
The Baltic Sea ◽  
Absorption Properties ◽  
Chlorophyll A Concentration ◽  
The Baltic

Abstract. This study presents three alternative models for estimating the absorption properties of chromophoric dissolved organic matter aCDOM(λ). For this analysis we used a database containing 556 absorption spectra measured in 2006–2009 in different regions of the Baltic Sea (open and coastal waters, the Gulf of Gdańsk and the Pomeranian Bay), at river mouths, in the Szczecin Lagoon and also in three lakes in Pomerania (Poland) – Obłęskie, Łebsko and Chotkowskie. The variability range of the chromophoric dissolved organic matter (CDOM) absorption coefficient at 400 nm, aCDOM(400), lay within 0.15–8.85 m−1. The variability in aCDOM(λ) was parameterized with respect to the variability over 3 orders of magnitude in the chlorophyll a concentration Chl a (0.7–119 mg m−3). The chlorophyll a concentration and aCDOM(400) were correlated, and a statistically significant, nonlinear empirical relationship between these parameters was derived (R2 =  0.83). On the basis of the covariance between these parameters, we derived two empirical mathematical models that enabled us to design the CDOM absorption coefficient dynamics in natural waters and reconstruct the complete CDOM absorption spectrum in the UV and visible spectral domains. The input variable in the first model was the chlorophyll a concentration, and in the second one it was aCDOM(400). Both models were fitted to a power function, and a second-order polynomial function was used as the exponent. Regression coefficients for these formulas were determined for wavelengths from 240 to 700 nm at 5 nm intervals. Both approximations reflected the real shape of the absorption spectra with a low level of uncertainty. Comparison of these approximations with other models of light absorption by CDOM demonstrated that our parameterizations were superior (bias from −1.45 to 62 %, RSME from 22 to 220 %) for estimating CDOM absorption in the optically complex waters of the Baltic Sea and Pomeranian lakes.

scholarly journals Machine Learning Algorithms for Chromophoric Dissolved Organic Matter (CDOM) Estimation Based on Landsat 8 Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3560
Author(s):  
Xiao Sun ◽  
Yunlin Zhang ◽  
Yibo Zhang ◽  
Kun Shi ◽  
Yongqiang Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Absorption Coefficient ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Chromophoric Dissolved Organic Matter ◽  
Estimation Accuracy ◽  
Inland Waters ◽  
Landsat 8

Chromophoric dissolved organic matter (CDOM) is crucial in the biogeochemical cycle and carbon cycle of aquatic environments. However, in inland waters, remotely sensed estimates of CDOM remain challenging due to the low optical signal of CDOM and complex optical conditions. Therefore, developing efficient, practical and robust models to estimate CDOM absorption coefficient in inland waters is essential for successful water environment monitoring and management. We examined and improved different machine learning algorithms using extensive CDOM measurements and Landsat 8 images covering different trophic states to develop the robust CDOM estimation model. The algorithms were evaluated via 111 Landsat 8 images and 1708 field measurements covering CDOM light absorption coefficient a(254) from 2.64 to 34.04 m−1. Overall, the four machine learning algorithms achieved more than 70% accuracy for CDOM absorption coefficient estimation. Based on model training, validation and the application on Landsat 8 OLI images, we found that the Gaussian process regression (GPR) had higher stability and estimation accuracy (R2 = 0.74, mean relative error (MRE) = 22.2%) than the other models. The estimation accuracy and MRE were R2 = 0.75 and MRE = 22.5% for backpropagation (BP) neural network, R2 = 0.71 and MRE = 24.4% for random forest regression (RFR) and R2 = 0.71 and MRE = 24.4% for support vector regression (SVR). In contrast, the best three empirical models had estimation accuracies of R2 less than 0.56. The model accuracies applied to Landsat images of Lake Qiandaohu (oligo-mesotrophic state) were better than those of Lake Taihu (eutrophic state) because of the more complex optical conditions in eutrophic lakes. Therefore, machine learning algorithms have great potential for CDOM monitoring in inland waters based on large datasets. Our study demonstrates that machine learning algorithms are available to map CDOM spatial-temporal patterns in inland waters.

scholarly journals Determination of the absorption coefficient of chromophoric dissolved organic matter from underway spectrophotometry

2017 ◽  
Vol 25 (24) ◽  
pp. A1079 ◽  
Author(s):  
Giorgio Dall’Olmo ◽  
Robert J. W. Brewin ◽  
Francesco Nencioli ◽  
Emanuele Organelli ◽  
Ina Lefering ◽  
...  
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Absorption Coefficient ◽  
Chromophoric Dissolved Organic Matter

scholarly journals Effects of land use and nutrients on the characteristics of dissolved organic matter in the Nanchong Section of Jialing River, China in December 2019

2021 ◽  
Author(s):  
Liuqing Zhang ◽  
Xiaohua Zhu ◽  
Xing Huang ◽  
Chaorong Liu ◽  
Yan Yang
Keyword(s):  
Land Use ◽  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Absorption Coefficient ◽  
Aquatic Ecosystems ◽  
Direct Evidence ◽  
Chromophoric Dissolved Organic Matter ◽  
Jialing River ◽  
Parallel Factor ◽  
Effects Of Land Use

Abstract Chromophoric dissolved organic matter (CDOM) in aquatic ecosystems can reflect the impacts of human activities on the carbon-cycling process. However, direct evidence of the combined effect of land use and anthropogenic nutrients on CDOM characteristics in river ecosystems is limited. Herein, we collected water samples from 18 sites in the Nanchong section of Jialing River in December 2019 to elucidate how the land use and nutrients affect the source and composition of CDOM through parallel factor (PARAFAC) analysis of excitation–emission matrices (EEMs). First, the absorption coefficient a254 (r2=0.29, p<0.01) and three fluorescence components (humic-like C1 and C2 and protein-like C3) (r2=0.31–0.37, p<0.01) significantly increased with increased urban area, and the four parameters were higher in the urban than in the suburb (p<0.05). The correlation between small CDOM molecule and cropland land was positive (p<0.01). Second, the increase in nutrient levels increased the a254 (r2=0.84 and 0.33, p<0.01) and three fluorescence components (r2=0.30–0.84, p<0.01 or p<0.05). Third, allochthonous CDOM were prevalent in the Nanchong Section of Jialing River, and the proportions of C1 and C2 were 42 and 41%, respectively. Our findings indicated that the variability of source and composition of CDOM significantly depended on urbanization and increased nutrients in the Nanchong Section of Jialing River.

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