An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze

Lei, Shaohua; Xu, Jie; Li, Yunmei; Du, Chunyu; Liu, Ge; Zheng, Zhuo; Xu, Yifan; Lyu, Heng; Mu, Meng; Miao, Shihai; Zeng, Shuai; Xu, Jiafeng; Li, Lingling

doi:10.1016/j.scitotenv.2019.134524

Cited by 41 publications

(14 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The result confirmed that the MDN method exhibits better performance than the empirical models [27]. Other machine learning methods have been found to be successful in the study of water quality parameter inversion, including hidden Markov models, self-organizing decision trees, and Gaussian process regression [2,15,[28][29][30][31][32][33][34]. These methods can effectively resolve the nonlinear relationship between water-color parameters and remote sensing signals in ocean color retrieval.…”

Section: Introductionsupporting

confidence: 54%

Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Chen

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new machine learning model, Light Gradient Boosting Machine (LightGBM), for estimating time-series chl-a concentration in Fujian’s coastal waters using multitemporal OLCI data and in situ data. We applied the Case 2 Regional CoastColour (C2RCC) processor to obtain OLCI band reflectance and constructed four spectral indices based on OLCI feature bands as supplementary input features. We also used root-mean-square error (RMSE), mean absolute error (MAE), median absolute percentage error (MAPE), and R2 as performance indicators. The results indicate that the addition of spectral indices can easily improve the prediction accuracy of the model, and normalized fluorescence height index (NFHI) has the best performance, with an RMSE of 0.38 µg/L, MAE of 0.22 µg/L, MAPE of 28.33%, and R2 of 0.785. Moreover, we used the well-known band ratio and three-band methods for chl-a estimation validation, and another two OLCI chl-a products were adopted for comparison (OC4Me chl-a and Inverse Modelling Technique (IMT) Neural Net chl-a). The results confirmed that the LightGBM model outperforms the traditional methods and OLCI chl-a products. This study provides an effective remote sensing technique for coastal chl-a concentration estimation and promotes the advantage of OLCI data in ocean color remote sensing.

show abstract

Section: Introductionsupporting

confidence: 54%

Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Chen

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…GOCI images, which were captured in Lake Hongze and Lake Taihu from 2014 to 2018, were downloaded from the Korea Ocean Satellite Center (KOSC). The 6S method (Second Simulation of the Satellite Signal in the Solar Spectrum, 6S) was used to correct the GOCI Level-1B data, which are the same as the latest research [19,22]. After image data pre-processing, these data were used to validate the performance of 6s model and estimate the Z SD in Lake Hongze and Lake Taihu.…”

Section: Satellite Data and Preprocessingmentioning

confidence: 99%

“…Spatially, the distribution of high ZSD is concentrated in the north and west of Lake Hongze, whereas the low values were observed in the southern region, especially in the upstream riverine region (Figure 6b). The headwater of Huai River accounts for more than 70% of the annual mean incoming runoff of Lake Hongze; the vigorous movement of water is one of main factors for the low transparency in these areas [19]. Figure 6.…”

Section: Mapping Z Sd From Goci Based On Developed Algorithmmentioning

confidence: 99%

“…The Geostationary Ocean Color Imager (GOCI), an ocean color satellite imager that has been set in geostationary orbit, provides eight images during the daytime from 8:30 to 16:30 local time. With its eight bands from visible to near-infrared spectral region (412~865 nm) and one hourly short temporal resolutions, GOCI has been confirmed as an ideal satellite sensor with high signal-to-noise ratios (SNR) for mapping suspended particulate matter, colored dissolved organic matter (CDOM), and harmful algal blooms [13,[17][18][19]. Although the application of GOCI data for Z SD estimation has seldom been reported in inland turbid waters, it could be of great significance for the observation of Z SD in inland turbid lakes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm

Zeng

Lei

et al. 2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

The accurate remote estimation of the Secchi disk depth (ZSD) in turbid waters is essential in the monitoring the ecological environment of lakes. Using the field measured ZSD and the remote sensing reflectance (Rrs(λ)) data, a new semi-analytical algorithm (denoted as ZSDZ) for retrieving ZSD was developed from Rrs(λ), and it was applied to Geostationary Ocean Color Imager (GOCI) images in extremely turbid waters. Our results are as follows: (1) the ZSDZ performs well in estimating ZSD in turbid water bodies (0.15 m < ZSD < 2.5 m). By validating with the field measured data that were collected in four turbid inland lakes, the determination coefficient (R2) is determined to be 0.89, with a mean absolute square percentage error (MAPE) of 22.39%, and root mean square error (RMSE) of 0.24 m. (2) The ZSDZ improved the retrieval accuracy of ZSD in turbid waters and outperformed the existing semi-analytical schemes. (3) The developed algorithm and GOCI data are in order to map the hourly variation of ZSD in turbid inland waters, the GOCI-derived results reveal a significant spatiotemporal variation in our study region, which are significantly driven by wind forcing. This study can provide a new approach for estimating water transparency in turbid waters, offering important support for the management of inland waters.

show abstract

“…e empirical methods search for the best band or band combination to build a mathematical model, mainly including single-band model [8,9], band ratio model [10], and multiple regression model [11]. With the continuous development and combination of computer technology and remote sensing technology, the application of machine learning in the field of remote sensing image processing has gradually become a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China

Liu

Zhang

Jiang

et al. 2021

Journal of Spectroscopy

View full text Add to dashboard Cite

Total suspended matter (TSM) is a core parameter in the quantitative retrieval of ocean color remote sensing and an important indicator for evaluating the quality of the aquatic environment. This study selects part of Nansi Lake in North China as the study area. Researchers used Hyperion remote sensing data and field-measured TSM concentration as data sources. Firstly, the characteristic variables with high correlation were selected based on spectral analysis. Then, seven methods such as linear regression, BP neural network (BP), KNN, random forest (RF), and random forest based on genetic algorithm optimization (GA_RF) are used to construct the inversion model of TSM concentration. The retrieval accuracy of each model shows that the machine learning models are much more accurate than the linear model. Among them, the GA_RF model retrieves the suspended solids concentration with the best performance and the highest prediction accuracy, with a determination coefficient R2 of 0.98, a root mean square error (RMSE) of 1.715 mg/L, and an average relative error (ARE) of 6.83%. Additionally, the spatial distribution of TSM concentration was inversed by Hyperion remote sensing image. The results showed that the concentration of TSM was lower in the northwest and higher in the southeast, and the concentration distribution was uneven, showing the characteristics of a typical shallow macrophytic lake. This study provides an effective method for monitoring TSM concentration and other water quality parameters in the shallow macrophytic lake and further proves the advantages of machine learning in ocean color inversion. All in all, this research provides some useful methods and suggestions for quantitative inversion of TSM concentration in shallow macrophytic lakes.

show abstract

An approach for retrieval of horizontal and vertical distribution of total suspended matter concentration from GOCI data over Lake Hongze

Cited by 41 publications

References 83 publications

Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Estimating Coastal Chlorophyll-A Concentration from Time-Series OLCI Data Based on Machine Learning

Retrieval of Secchi Disk Depth in Turbid Lakes from GOCI Based on a New Semi-Analytical Algorithm

Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China

Contact Info

Product

Resources

About