An algorithm to retrieve absorption coefficient of chromophoric dissolved organic matter from ocean color

2013 ◽  
Vol 128 ◽  
pp. 259-267 ◽  
Author(s):  
Qiang Dong ◽  
Shaoling Shang ◽  
Zhongping Lee
Ocean Science ◽  
2016 ◽  
Vol 12 (4) ◽  
pp. 1013-1032 ◽  
Author(s):  
Justyna Meler ◽  
Piotr Kowalczuk ◽  
Mirosława Ostrowska ◽  
Dariusz Ficek ◽  
Monika Zabłocka ◽  
...  

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.


2013 ◽  
Vol 51 (6) ◽  
pp. 3286-3298 ◽  
Author(s):  
Weining Zhu ◽  
Qian Yu

The significant implication of chromophoric dissolved organic matter (CDOM) for water quality and biogeochemical cycle leads to an increasing need of CDOM monitoring in coastal regions. Current ocean-color algorithms are mostly limited to open-sea water and have high uncertainty when directly applied to turbid coastal waters. This paper presents a semianalytical algorithm, quasi-analytical CDOM algorithm (QAA-CDOM), to invert CDOM absorption from Earth Observing-1 (EO-1) Hyperion satellite images. This algorithm was developed from a widely used ocean-color algorithm QAA and our earlier extension of QAA. The main goal is to improve the algorithm performance for a wide range of water conditions, particularly turbid waters in estuarine and coastal regions. The algorithm development, calibration, and validation were based on our intensive high-resolution underwater measurements, International Ocean Color Coordinating Group synthetic data, and global National Aeronautics and Space Administration Bio-Optical Marine Algorithm Data Set data. The result shows that retrieved CDOM absorption achieved accuracy (root mean square error (RMSE) = 0.115 m-1andR2= 0.73) in the Atchafalaya River plume area. QAA-CDOM is also evaluated for scenarios in three additional study sites, namely, the Mississippi River, Amazon River, and Moreton Bay, whereag(440) was in the wide range of 0.01-15 m-1. It resulted in expected CDOM distribution patterns along the river salinity gradient. This study improves the high-resolution observation of CDOM dynamics in river-dominated coastal margins and other coastal environments for the study of land-ocean interactive processes.


2021 ◽  
Vol 13 (18) ◽  
pp. 3560
Author(s):  
Xiao Sun ◽  
Yunlin Zhang ◽  
Yibo Zhang ◽  
Kun Shi ◽  
Yongqiang Zhou ◽  
...  

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.


2017 ◽  
Vol 25 (24) ◽  
pp. A1079 ◽  
Author(s):  
Giorgio Dall’Olmo ◽  
Robert J. W. Brewin ◽  
Francesco Nencioli ◽  
Emanuele Organelli ◽  
Ina Lefering ◽  
...  

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