Chromophoric dissolved organic matter in inland waters: Present knowledge and future challenges

2021 ◽  
Vol 759 ◽  
pp. 143550
Author(s):  
Yunlin Zhang ◽  
Lei Zhou ◽  
Yongqiang Zhou ◽  
Liuqing Zhang ◽  
Xiaolong Yao ◽  
...  
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Present Knowledge ◽  
Chromophoric Dissolved Organic Matter ◽  
Inland Waters ◽  
Future Challenges

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 Comparison of bacterial growth in response to photodegraded terrestrial chromophoric dissolved organic matter in two lakes

2017 ◽  
Vol 579 ◽  
pp. 1203-1214 ◽  
Author(s):  
Yaling Su ◽  
En Hu ◽  
Muhua Feng ◽  
Yongdong Zhang ◽  
Feizhou Chen ◽  
...  
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Bacterial Growth ◽  
Chromophoric Dissolved Organic Matter

scholarly journals Posidonia oceanica as a Source of Chromophoric Dissolved Organic Matter for the Oligotrophic NW Mediterranean Coast

2020 ◽  
Vol 8 (11) ◽  
pp. 911
Author(s):  
Francesca Iuculano ◽  
Carlos M. Duarte ◽  
Jaime Otero ◽  
Xosé Antón Álvarez-Salgado ◽  
Susana Agustí
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Rocky Shore ◽  
Posidonia Oceanica ◽  
Mediterranean Coast ◽  
Balearic Islands ◽  
Chromophoric Dissolved Organic Matter ◽  
Absorption Increase ◽  
Seagrass Meadows ◽  
Nw Mediterranean

Posidonia oceanica is a well-recognized source of dissolved organic matter (DOM) derived from exudation and leaching of seagrass leaves, but little is known about its impact on the chromophoric fraction of DOM (CDOM). In this study, we monitored for two years the optical properties of CDOM in two contrasting sites in the Mallorca Coast (Balearic Islands). One site was a rocky shore free of seagrass meadows, and the second site was characterized by the accumulation of non-living seagrass material in the form of banquettes. On average, the integrated color over the 250–600 nm range was almost 6-fold higher in the beach compared with the rocky shore. Furthermore, the shapes of the CDOM spectra in the two sites were also different. A short incubation experiment suggested that the spectral differences were due to leaching from P. oceanica leaf decomposition. Furthermore, occasionally the spectra of P. oceanica was distorted by a marked absorption increase at wavelength < 265 nm, presumably related to the release of hydrogen sulfide (HS−) associated with the anaerobic decomposition of seagrass leaves within the banquettes. Our results provide the first evidence that P. oceanica is a source of CDOM to the surrounding waters.

scholarly journals Erratum: Production of chromophoric dissolved organic matter by marine phytoplankton

2010 ◽  
Vol 55 (3) ◽  
pp. 1466-1466 ◽  
Author(s):  
Cristina Romera-Castillo ◽  
Hugo Sarmento ◽  
Xosé Antón Álvarez-Salgado ◽  
Josep M. Gasol ◽  
Celia Marrasé
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Marine Phytoplankton ◽  
Chromophoric Dissolved Organic Matter
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