Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index

Wang, Shenglei; Li, Junsheng; Zhang, Fangfang; Spyrakos, Evangelos; Tyler, Andrew N.; Shen, Qian; Zhang, Fang-Fang; Kuster, Tiit; Lehmann, Moritz K.; Wu, Yanhong; Peng, Dailiang

doi:10.1016/j.rse.2018.08.026

Cited by 265 publications

(137 citation statements)

References 97 publications

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“…Epilimnetic Chl concentration is often used to evaluate eutrophication status of lakes (Wang et al, 2018;Westberry et al, 2016), while the SCM layer can contribute more than 60% of total annual primary production (Martin et al, 2013;Omand et al, 2015). Nutrients (Brentrup et al, 2018), light conditions, and mixing status (Mellard et al, 2011) affect the portions of Chl in the epilimnion and in the SCM.…”

Section: Implications For the Estimation Of T Chl From Scmmentioning

confidence: 99%

Spatial Variations of Subsurface Chlorophyll Maxima During Thermal Stratification in a Large, Deep Subtropical Reservoir

et al. 2020

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Subsurface chlorophyll maxima (SCM) are found in stratified lakes, reservoirs, and oceans. Spatial variations of the SCM magnitude, depth, and thickness during stratification and related factors were examined in a large, deep subtropical reservoir, Lake Qiandaohu. Significant spatial differences in the SCM parameters were found throughout the lake as thermal stratification developed in the late spring of 2014. SCM depth and thickness were positively correlated with euphotic depth and mixed layer depth but negatively correlated with epilimnetic nutrient concentrations. SCM magnitude was negatively correlated with euphotic depth and mixed layer depth and positively correlated with epilimnetic nutrient concentrations. Seasonal variations in SCM differed among subregions of the lake, which were related to seasonal differences in environmental parameters. SCM (averaging 66.2 mg/m2 in spring and 96.4 mg/m2 in summer) accounted for about 82% of the total areal chlorophyll of the water column (Tchl), and the SCM magnitude was better correlated with Tchl than the surface chlorophyll concentration. This study contributes to understanding of factors causing spatial variations in of SCM in the deep subtropical lakes.

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Section: Implications For the Estimation Of T Chl From Scmmentioning

confidence: 99%

Spatial Variations of Subsurface Chlorophyll Maxima During Thermal Stratification in a Large, Deep Subtropical Reservoir

et al. 2020

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“…First, MYD09GA was originally produced for land observations [32] and it is not an ideal data source for aquatic research because sky scattering and water heterogeneity in atmospheric corrections are not considered and patchiness noise occurs as well [33]. Nevertheless, the surface reflectance products of MODIS and Landsat has been used to monitor inland waters parameters, such as the SPM, water clarity, and eutrophication status [23,34,35], because such products are low-cost and easily-available and have a certain accuracy at 645 nm and 555 nm. In particular, the ρ 645 /ρ 555 has shown a high accuracy in terms of daily spatial distributions, seasonality, and long-term trends [24], indicating that the SPM retrievals based on the ρ 645 /ρ 555 of MODISA were reliable.…”

Section: Accuracy and Uncertaintymentioning

confidence: 99%

“…Moreover, the 500 m resolution for MYD09GA in 645 nm and 555 nm was insufficient to investigate the medium-and small-sized lakes due to the adjacent land effects [36]. The MODIS surface reflectance of 500 m has been used to study the lake above 25 km 2 [23,34]. To minimize the adjacent land effect and assure the accuracy of the SPM retrieval, only large lakes (>50 km 2 ) were used in this study.…”

Section: Accuracy and Uncertaintymentioning

confidence: 99%

Effect of Satellite Temporal Resolution on Long-Term Suspended Particulate Matter in Inland Lakes

Cao

Duan

et al. 2019

Remote Sensing

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The temporal resolution of satellite determines how well remote sensing products represent changes in the lake environments and influences the practical applications by end-users. Here, a resampling method was used to reproduce the suspended particulate matter (SPM) dataset in 43 large lakes (>50 km2) on the eastern China plain during 2003–2017 at different temporal resolutions using MODIS Aqua (MODISA) based on Google Earth Engine platform, then to address the impact of temporal resolution on the long-term SPM dataset. Differences between the MODISA-derived and reproduced SPM dataset at longer temporal resolution were higher in the areas with large water dynamics. The spatial and temporal distributions of the differences were driven by unfavorable observation environments during satellite overpasses such as high cloud cover, and rapid changes in water quality, such as water inundation, algae blooms, and macrophytes. Furthermore, the annual mean difference in SPM ranged from 5–10% when the temporal difference was less than 10 d, and the differences in summer and autumn were higher than that of other seasons and surpassed 20% when the temporal resolution was more than 16 d. To assure that difference were less than 10% for long-term satellite-derived SPM datasets, the minimal requirement of temporal resolution should be within 5 d for most of the inland lakes and 3 d for lakes with large changes in water quality. This research can be used to not only evaluate the reliability of historically remote sensing products but also provide a reference for planning field campaigns and applying of high spatial resolution satellite missions to monitor aquatic systems in the future.

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“…The imagery of recently launched Sentinel-2 was selected because it is free of charge with a high spatial resolution of 10 m and a temporal resolution of 5 days. Three machine learning algorithms were introduced in the empirical methods [41][42][43] to investigate the opportunity to retrieve the non-optically active parameters ( Figure 1). This research may help urban water management by providing a more practical and efficient water quality monitoring strategy of non-optically active parameters.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

Huang¹,

Guo²,

Chen³

et al. 2020

Preprint

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Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R 2 of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.

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Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index

Cited by 265 publications

References 97 publications

Spatial Variations of Subsurface Chlorophyll Maxima During Thermal Stratification in a Large, Deep Subtropical Reservoir

Spatial Variations of Subsurface Chlorophyll Maxima During Thermal Stratification in a Large, Deep Subtropical Reservoir

Effect of Satellite Temporal Resolution on Long-Term Suspended Particulate Matter in Inland Lakes

Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy

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