Long-term monitoring particulate composition change in the Great Lakes using MODIS data

Xu, Jiafeng; Liu, Huaiqing; Lin, Jie; Lyu, Heng; Dong, Xinhua; Li, Yunmei; Guo, Honglei; Wang, Huaijing

doi:10.1016/j.watres.2022.118932

Cited by 13 publications

(6 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to serving the need for generating data to improve remote sensing efforts (see Sayers et al, 2016Sayers et al, , 2019Stumpf et al, 2016;Avouris and Ortiz, 2019;Bosse et al, 2019;Vander Woude et al, 2019;Pirasteh et al, 2020;Xu et al, 2022), this monitoring program will continue to serve stakeholders and communities in the Laurentian Great Lakes. As this program has grown, so too has the scope of application of its dataset.…”

Section: Discussionmentioning

confidence: 99%

“…Long-term monitoring of WLE is fundamental to the continual assessment of water quality changes in response to both stressors and water quality management efforts (Hartig et al, 2009(Hartig et al, , 2021. The GLERL/CIGLR monitoring data has been used by numerous researchers to develop and assess models (Rowe et al, 2016;Weiskerger et al, 2018;Fang et al, 2019;Liu et al, 2020;Qian et al, 2021;Wang and Boegman, 2021;Hellweger et al, 2022;Maguire et al, 2022), to calibrate remote sensing algorithms (Sayers et al, 2016(Sayers et al, , 2019Avouris and Ortiz, 2019;Bosse et al, 2019;Vander Woude et al, 2019;Pirasteh et al, 2020;Xu et al, 2022), and to elucidate ecological mechanisms and complement experimental data (Cory et al, 2016;Reavie et al, 2016;Berry et al, 2017;Steffen et al, 2017;Kharbush et al, 2019Kharbush et al, , 2023Newell et al, 2019;Den Uyl et al, 2021;Smith et al, 2021Smith et al, , 2022Hoffman et al, 2022;Marino et al, 2022;Yancey et al, 2022a, b).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Routine monitoring of western Lake Erie to track water quality changes associated with cyanobacterial harmful algal blooms

Boegehold,

Burtner,

Camilleri

et al. 2023

Earth Syst. Sci. Data

View full text Add to dashboard Cite

Abstract. The western basin of Lake Erie has a history of recurrent cyanobacterial harmful algal blooms (HABs) despite decades of efforts by the United States and Canada to limit phosphorus loading, a major driver of the blooms. In response, the National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs in western Lake Erie (WLE). Here we describe the data collected from this monitoring program from 2012 to 2021. This dataset includes observations on physicochemical properties, major nutrient fractions, phytoplankton pigments, microcystins, and optical properties for western Lake Erie. This dataset is particularly relevant for creating models, verifying and calibrating remote sensing algorithms, and informing experimental research to further understand the water quality dynamics that influence HABs in this internationally significant body of freshwater. The dataset can be freely accessed from NOAA National Centers for Environmental Information (NCEI) at https://doi.org/10.25921/11da-3x54 (Cooperative Institute for Great Lakes Research, University of Michigan and NOAA Great Lakes Environmental Research Laboratory, 2019).

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Routine monitoring of western Lake Erie to track water quality changes associated with cyanobacterial harmful algal blooms

Boegehold,

Burtner,

Camilleri

et al. 2023

Earth Syst. Sci. Data

View full text Add to dashboard Cite

show abstract

“…The AFAI has been proven to be effective in identifying algal blooms with GOCI data [19], with a threshold of 0.01 to eliminate algal bloom pixels. Highly turbid water can also lead to high Chla inversion results [29]; thus, combined with the characteristics of the AFAI and adjusted floating algae height (AFAH) [30], when AFAI > −0.01 and AFAH < −0.006 were simultaneously met, the pixel was removed. In addition, considering the influence of the adjacency effect in the nearshore, a one-pixel range along the boundary of the water was masked.…”

Section: Mask Determination and Match-up Proceduresmentioning

confidence: 99%

Retrievals of Chlorophyll-a from GOCI and GOCI-II Data in Optically Complex Lakes

Guo,

Wei,

Huang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The chlorophyll-a (Chla) concentration is a key parameter to evaluate the eutrophication conditions of water, which is very important for monitoring algal blooms. Although Geostationary Ocean Color Imager (GOCI) has been widely used in Chla inversion, the consistency of the Rayleigh-corrected reflectance (Rrc) of GOCI and GOCI-II sensors still needs to be further evaluated, and a model suitable for lakes with complex optical properties needs to be constructed. The results show that (1) the derived Chla values of the GOCI and GOCI-II synchronous data were relatively consistent and continuous in three lakes in China. (2) The accuracy of the random forest (RF) model (R2 = 0.84, root mean square error (RMSE) =11.77 μg/L) was higher than that of the empirical model (R2 = 0.79, RMSE = 12.63 μg/L) based on the alternative floating algae index (AFAI). (3) The interannual variation trend fluctuated, with high Chla levels in Lake Chaohu in 2015 and 2019, while those in Lake Hongze were high in 2013, 2015, and 2022, and those in Lake Taihu reached their peak in 2017 and 2019. There were three types of diurnal variation patterns, namely, near-continuous increase (Class 1), near-continuous decrease (Class 2), and first an increase and then a decrease (Class 3), among which Lake Chaohu and Lake Taihu occupied the highest proportion in Class 3. The results analyzed the temporal and spatial variations of Chla in three lakes for 12 years and provided support for the use of GOCI and GOCI-II data and monitoring of Chla in optical complex inland waters.

show abstract

“…Compared with traditional water quality monitoring methods, remote sensing monitoring methods have the advantages of a large range and high efficiency, and information can be obtained in real time. Multisource remote sensing data, such as the MODIS [5], TM [6], ETM+ [7], Qui Bird [8], OLI [9], SeaWiFS [10], and MERIS [11] products, have been used in the monitoring of lake water quality. For example, Liu et al [12] used Landsat data to model and invert the water quality parameters of Erlong Lake and explored the seasonal changes in different water quality indicators, and Zhu et al [13] inverted the water quality parameters of a complex river network based on multispectral data.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Parameter Retrieval with GF5-AHSI Imagery for Dianchi Lake (China)

Zhang,

Hu,

Jiao

2024

Water

View full text Add to dashboard Cite

In response to the rapid changes in the chlorophyll-a concentration and eutrophication issues in lakes, with Dianchi Lake as an example, a remote sensing estimation model for chlorophyll-a, total phosphorus, and total nitrogen in Dianchi Lake was constructed using the three band method and ratio band method based on the visible-light shortwave infrared (AHSI) hyperspectral satellite data from Gaofen 5 (GF-5) and the water quality data collected at Dianchi Lake. The model results were compared with the multispectral data from the Gaofen 1 (GF-1) wide field-of-view (WFV) camera. The accuracy evaluation results indicate that the overall mean absolute percentage error of the remote sensing estimation models for chlorophyll a, total phosphorus, and total nitrogen are 7.658%, 4.511%, and 4.577%, respectively, which can meet the needs of lake water quality monitoring and evaluation. According to the remote sensing simulation results, chlorophyll a is mainly distributed in the northern part of Dianchi Lake, with phosphorus and nitrogen pollution throughout Dianchi Lake and relatively more abundant in the central and southern regions. The pollution is mainly concentrated in the northern and southern regions of Dianchi Lake, which is consistent with the actual situation. Further confirming the feasibility of using GF-5 satellite AHSI data for water quality parameter retrieval can provide new technical means for relevant departments to quickly and efficiently monitor the inland lake water environment.

show abstract

Long-term monitoring particulate composition change in the Great Lakes using MODIS data

Cited by 13 publications

References 53 publications

Routine monitoring of western Lake Erie to track water quality changes associated with cyanobacterial harmful algal blooms

Routine monitoring of western Lake Erie to track water quality changes associated with cyanobacterial harmful algal blooms

Retrievals of Chlorophyll-a from GOCI and GOCI-II Data in Optically Complex Lakes

Water Quality Parameter Retrieval with GF5-AHSI Imagery for Dianchi Lake (China)

Contact Info

Product

Resources

About