Mapping the trophic state index of eastern lakes in China using an empirical model and Sentinel-2 imagery data

Lake eutrophication has become a critical environmental issue due to the global effects of anthropogenic activities and climate change, and has been comprehensively studied for many years. A series of models and indicators have been proposed to assess the trophic state of lakes. The trophic state index (TSI) is a synthetic index that integrates chlorophyll-a, water clarity, and total phosphorus and is widely used to evaluate the trophic state of aquatic environments. In this study, we collected in situ lake samples (N = 431) from typical lakes to match Sentinel-2 MultiSpectral Instrument (MSI) imagery data using the Case 2 Regional Coast Color processor. Then we developed a new empirical model, TSI = -34.04 x (band 4/band 5) - 1.114 x (band 1/band 4) + 97.376. This model is valid for all of China, with good performance and few errors (RMSE = 7.36;MAE = 6.25) for the validation dataset. Recognizing that over 94% of the Chinese population located along eastern watersheds and large lakes have competing water uses, and given the TSI model on the seasonal scales, we further estimated the mean TSI and trophic state in eastern Chinese lakes (> 100 km2) from 2019 to 2020. The results revealed that more lakes were eutrophic in autumn (94.28%) than in spring (> 77.14%), indicating a serious eutrophication of eastern lakes. Although the eastern lakes have been studied in more detail, this study found that eutrophication still has markedly negative impacts on lake ecosystems. In addition, no significant improvement was observed in spring, most likely due to the months of curfew/lockdown from January 2020 onwards due to COVID-19. This may be due to the enrichment of nutrients deposited in sediment or watershed soil, which can be characterized as "autochthonous sources" of lake eutrophication, over decades with high rates of economic development. This study demonstrates the applicability of Sentinel-2 MSI data to monitor lake eutrophication as well as the feasibility of blue/red and red/red edge combinations. The framework and TSI model used bands available on MSI sensors to develop a novel approach for generating historical eutrophication data for large-scale evaluation of and decision-making related aquatic environmental changes, even in poorly studied areas.

Remote sensing of fluorescent humification levels and its potential environmental linkages in lakes across China.

The pollution or eutrophication affected by dissolved organic matter (DOM) composition and sources of inland waters had attracted concerns from the public and government in China. Combined with remote sensing techniques, the fluorescent DOM (FDOM) parameters accounted for the important part of optical constituent as chromophoric dissolved organic matter (CDOM) was a useful tool to trace relative DOM sources and assess the trophic states for large-scale regions comprehensively and timely. Here, the objective of this research is to calibrate and validate a general model based on Landsat 8 OLI product embedded in Google Earth Engine (GEE) for deriving humification index (HIX) based on EEMs in lakes across China. The Landsat surface reflectance was matched with 1150 pairs fieldtrip samples and the nine sensitive spectral variables with good correlation with HIX were selected as the inputs in machine learning methods. The calibration of XGBoost model (R2 = 0.86, RMSE = 0.29) outperformed other models. Our results indicated that the entire dataset of HIX has a strong association with Landsat reflectance, yielding low root mean square error between measured and predicted HIX (R2 = 0.81, RMSE = 0.42) for lakes in China. Finally, the optimal XGBoost model was used to calculate the spatial distribution of HIX of 2015 and 2020 in typical lakes selected from the Report on the State of the Ecology and Environment in China. The significant decreasing of HIX from 2015 to 2020 with trophic states showed positive control of humification level of lakes based on the published document of Action plan for prevention and control of water pollution in 2015 of China. The calibrated model would greatly facilitate FDOM monitoring in lakes, and provide indicators for relative DOM sources to evaluate the impact of water protection measures or human disturbance effect from Covid-19 lockdown, and offer the government supervision to improve the water quality management for lake ecosystems.

Mapping the Trophic State Index of Eastern Lakes in China using an Empirical Model and Sentinel-2 Imagery Data

Lake eutrophication has become a critical environmental issue due to the global effects of anthropogenic activities and climate change, and has been comprehensively studied for many years. A series of models and indicators have been proposed to assess the trophic state of lakes. The trophic state index (TSI) is a synthetic index that integrates chlorophyll-a, water clarity, and total phosphorus and is widely used to evaluate the trophic state of aquatic environments. In this study, we collected in situ lake samples (N=431) from typical lakes to match Sentinel-2 MultiSpectral Instrument (MSI) imagery data using the Case 2 Regional Coast Color processor. Then we developed a new empirical model, TSI = –34.04 × (band 4/band 5) – 1.114 × (band 1/band 4) + 97.376). This model is valid for all of China, with good performance and few errors (RMSE=7.36;MAE=6.25) for the validation dataset. Recognizing that over 94% of the Chinese population located along eastern watersheds and large lakes have competing water uses, and given the TSI model on the seasonal scales, we further estimated the mean TSI and trophic state in eastern Chinese lakes (> 100 km2) from 2019 to 2020. The results revealed that more lakes were eutrophic in autumn (94.28%) than in spring (> 77.14%), indicating a serious eutrophication of eastern lakes. Although the eastern lakes have been studied in more detail, this study found that eutrophication still has markedly negative impacts on lake ecosystems. In addition, no significant improvement was observed in spring, most likely due to the months of curfew/lockdown from January 2020 onwards due to COVID-19. This may be due to the enrichment of nutrients deposited in sediment or watershed soil, which can be characterized as “autochthonous sources” of lake eutrophication, over decades with high rates of economic development. This study demonstrates the applicability of Sentinel-2 MSI data to monitor lake eutrophication as well as the feasibility of blue/red and red/red edge combinations. The framework and TSI model used bands available on MSI sensors to develop a novel approach for generating historical eutrophication data for large-scale evaluation of and decision-making related aquatic environmental changes, even in poorly studied areas.