Eutrophication and lakes dynamic conditions control the endogenous and terrestrial POC observed by remote sensing: Modeling and application

Zhao, Zhihui; Huang, Changchun; Meng, Lize; Lu, Lingfeng; Wu, Yusheng; Fan, Ruihua; Li, Shuaidong; Sui, Zhengwei; Huang, Tao; Huang, Chu-Long; Zhang, Limin

doi:10.1016/j.ecolind.2021.107907

Cited by 12 publications

(10 citation statements)

References 65 publications

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“…The total dissolved carbon (TDC), dissolved organic carbon (DOC) in water were measured using a carbon elemental analyzer (Shimadzu Corporation., Japan) (Huang et al., 2018). Total suspended particulate matter (TSM) was measured by filtration with suspended particles dried at 55°C for 8 hr (Zhao et al., 2021). The IC‐free TSS and filtrate were also used to measure particulate organic carbon (POC) using a carbon elemental analyzer (Huang et al., 2022).…”

Section: Methodsmentioning

confidence: 99%

Changes in Dissolved Inorganic Carbon Across Yangtze River Regulated by Dam and River‐Lake Exchange

Zhao,

Wang,

et al. 2023

Global Biogeochemical Cycles

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The boom in dam construction and continuous river‐lake exchange has had a profound impact on the transmission and transformation of riverine dissolved inorganic carbon (DIC). An in‐depth understanding of the change mechanisms of DIC concentrations and sources driven by dam operation and lake recharge is crucial for regulating greenhouse gas emissions and evaluating the impact of DIC on the global carbon cycle. This study investigated dam‐ and lakes‐driven DIC via the concentration and δ13C of DIC, combined with anions, cations, δD and δ18O in the main stream of the Yangtze River. DIC showed a decreasing trend from upper reach (2,262.31 ± 113.69 μmol kg−1) to lower reach (1,771.61 ± 89.36 μmol kg−1). Carbonate dissolution proportion (from 36.45% to 28.44%) and atmospheric CO2 proportion (from 37.51% to 22.94%) of DIC decreased from upper reach to lower reach, whereas soil CO2 proportion of DIC (from 26.01% to 48.63%) increased. The control of dam operation on DIC biogeochemical process was revealed from different time scales. From the perspective of short‐term seasonal changes (from 2020 to 2021), the mineralization of organic matter in the dry season strengthened CO2 degassing and calcite precipitation, reducing the DIC and increasing the proportion of soil CO2. Meanwhile, longer periods of runoff retention provided sufficient time for water–rock reactions in the wet season and increased the DIC and carbonate dissolution source in the reservoir area. On a long‐term scale (from 2009 to 2021), a decrease in pH driven by sediment mineralization contributed to an annual increase in DIC in the reservoir. The flow of lakes mixed into the mainstream was revealed by the enrichment of δ18O, and river‐communicating recharge decreased the DIC and carbonate dissolution source. We show that dam operation and lake inflow change DIC concentrations and sources and therefore need to be considered in the transmission and transformation processes of DIC in the river‐ocean continuum.

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Section: Methodsmentioning

confidence: 99%

Changes in Dissolved Inorganic Carbon Across Yangtze River Regulated by Dam and River‐Lake Exchange

Zhao,

Wang,

et al. 2023

Global Biogeochemical Cycles

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“…Local and off-site sources of POC can be discerned by the magnitude of δ 13 C values. Xu et al [16] and Zhao et al [23] were able to estimate the percentage of terrestrial and endogenous organic C in Lake Taihu in a reliable manner using the δ 13 C end-member mixing model. The ratios and concentrations of marine and terrestrial POC can also be precisely measured using the δ 13 C end-member mixing model.…”

Section: Introductionmentioning

confidence: 99%

“…Although these developments have improved our understanding of POC dynamics, determining the sources and composition of POC remains unresolved. Two recent studies that combined C isotope and remote sensing technology to develop a POC end-member ratio model have successfully identified POC sources in inland lakes [16,23].…”

Section: Introductionmentioning

confidence: 99%

“…However, marine environments are substantially different from inland lakes. Firstly, the concentrations of chlorophyll-a and POC in inland lakes are generally higher than those in oceans [16,23,33], which causes differences in the bio-optical properties between marine waters and inland lake waters. Secondly, the δ 13 C POC values of end-member in inland lakes are significantly different from those in oceans [16,23,34,35], indicating that the sources of POC in oceans and inland lakes are different.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes

Zhong

Liu

et al. 2023

Remote Sensing

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The source information of coastal particulate organic carbon (POC) with high spatial and temporal resolution is of great significance for the study of marine carbon cycles and marine biogeochemical processes. Over the past decade, satellite ocean color remote sensing has greatly improved our understanding of the spatiotemporal dynamics of ocean particulate organic carbon concentrations. However, due to the complexity of coastal POC sources, remote sensing methods for coastal POC sources have not yet been established. With an attempt to fill the gap, this study developed an algorithm for retrieving coastal POC sources using remote sensing and geochemical isotope technology. The isotope end-member mixing model was used to calculate the proportion of POC sources, and the response relationship between POC source information and in situ remote sensing reflectance (Rrs) was established to develop a retrieval algorithm for POC sources with the following four bands: (Rrs(443)/Rrs(492)) × (Rrs(704)/Rrs(665)). The results showed that the four-band algorithm performed well with R2, mean absolute percentage error (MAPE) and root mean square error (RMSE) values of 0.78, 33.57% and 13.74%, respectively. Validation against in situ data showed that the four-band algorithm derived calculated the proportion of marine POC accurately, with an MAPE and RMSE of 27.49% and 13.58%, respectively. The accuracy of the algorithm was verified based on the Sentinel-2 data, with an MAPE and RMSE of 28.02% and 15.72%, respectively. Additionally, we found that the proportion of marine POC sources was higher outside the Zhanjiang Bay than inside it using in situ survey data, which was consistent with the retrieved results. Influencing factors of POC sources may be due to the occurrence of phytoplankton blooms outside the bay and the impact of terrestrial inputs inside the bay. Remote sensing in combination with carbon isotopes provides important technical assistance in comprehending the biogeochemical process of POC and uncovering spatiotemporal variations in POC sources and their underlying causes.

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“…By analyzing the characteristic sensitive bands of particulate organic carbon, many scholars extracted different characteristic information to invert POC content, such as the blue-green band ratio [7], Maximum Normalized Difference Carbon Index (MNDCI) [8], Three-band combination based on the bio-optical model [9], POC-source color index [10], etc. Meanwhile, they have found that POC has a close correlation with other water quality parameters, such as Chl-a [11], suspended particulate matter (SPM) [12], turbidity [13], etc. The models built using these features often have the characteristics of high accuracy, wide mobility, and specificity to the study region, and promote the application of remote sensing and spectroscopy in the quantitative analysis of POC in the ocean [14].…”

Section: Introductionmentioning

confidence: 99%

A CNN–LSTM Machine-Learning Method for Estimating Particulate Organic Carbon from Remote Sensing in Lakes

Pan,

Yu,

Cheng

et al. 2023

Sustainability

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As particulate organic carbon (POC) from lakes plays an important role in lake ecosystem sustainability and carbon cycle, the estimation of its concentration using satellite remote sensing is of great interest. However, the high complexity and variability of lake water composition pose major challenges to the estimation algorithm of POC concentration in Class II water. This study aimed to formulate a machine-learning algorithm to predict POC concentration and compare their modeling performance. A Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) algorithm based on spectral and time sequences was proposed to construct an estimation model using the Sentinel 2 satellite images and water surface sample data of Chaohu Lake in China. As a comparison, the performances of the Backpropagation Neural Network (BP), Generalized Regression Neural Network (GRNN), and Convolutional Neural Network (CNN) models were evaluated for remote sensing inversion of POC concentration. The results show that the CNN–LSTM model obtained higher prediction precision than the BP, GRNN, and CNN models, with a coefficient of determination (R2) of 0.88, a root mean square error (RMSE) of 3.66, and residual prediction deviation (RPD) of 3.03, which are 6.02%, 22.13%, and 28.4% better than the CNN model, respectively. This indicates that CNN–LSTM effectively combines spatial and temporal information, quickly captures time-series features, strengthens the learning ability of multi-scale features, is conducive to improving estimation precision of remote sensing models, and offers good support for carbon source monitoring and assessment in lakes.

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Eutrophication and lakes dynamic conditions control the endogenous and terrestrial POC observed by remote sensing: Modeling and application

Cited by 12 publications

References 65 publications

Changes in Dissolved Inorganic Carbon Across Yangtze River Regulated by Dam and River‐Lake Exchange

Changes in Dissolved Inorganic Carbon Across Yangtze River Regulated by Dam and River‐Lake Exchange

Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes

A CNN–LSTM Machine-Learning Method for Estimating Particulate Organic Carbon from Remote Sensing in Lakes

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