Inversion of Chlorophyll-a Concentration in Donghu Lake Based on Machine Learning Algorithm

Tang, Xiaodong; Huang, Meifa

doi:10.3390/w13091179

Cited by 14 publications

(4 citation statements)

References 51 publications

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“…In this paper, taking the experimental sample area of Nanyi Lake as an example, a sampling design study was carried out on the three parameters of Chl-a, TSM, and SD. Previous studies have shown that the near-infrared/red, red/green, and green bands can be used as remote sensing feature bands for Chl-a, TSM, and SD [43][44][45][46][47]. Therefore, this paper uses Band 8/Band 3, Band 4/Band 3, and Band 3 of Sentinel-2 satellites as the sensitive bands of Chl-a, TSM, and SD and generates the spatial distribution characteristics map of the water parameters.…”

Section: Spatial Distribution Of Water Parametersmentioning

confidence: 99%

Water Multi-Parameter Sampling Design Method Based on Adaptive Sample Points Fusion in Weighted Space

et al. 2022

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The spatial representativeness of the in-situ data is an important prerequisite for ensuring the reliability and accuracy of remote sensing product retrieval and verification. Limited by the collection cost and time window, it is essential to simultaneously collect multiple water parameter data in water tests. In the shipboard measurements, sampling design faces problems, such as heterogeneity of water quality multi-parameter spatial distribution and variability of sampling plan under multiple constraints. Aiming at these problems, a water multi-parameter sampling design method is proposed. This method constructs a regional multi-parameter weighted space based on the single-parameter sampling design and performs adaptive weighted fusion according to the spatial variation trend of each water parameter within it to obtain multi-parameter optimal sampling points. The in-situ datasets of three water parameters (chlorophyll a, total suspended matter, and Secchi-disk Depth) were used to test the spatial representativeness of the sampling method. The results showed that the sampling method could give the sampling points an excellent spatial representation in each water parameter. This method can provide a fast and efficient sampling design for in-situ data for water parameters, thereby reducing the uncertainty of inversion and the validation of water remote sensing products.

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Section: Spatial Distribution Of Water Parametersmentioning

confidence: 99%

Water Multi-Parameter Sampling Design Method Based on Adaptive Sample Points Fusion in Weighted Space

et al. 2022

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“…Mohammad M. Hasan et al have adjusted the hyperparameters for unmanned aerial vehicle target classification using range/micro-Doppler features combined with R-PCA-SVM, ultimately achieving a high accuracy of 98% [30]. Tang et al achieved improved accuracy in inverting chlorophyll a concentration in Donghu through multi-factor modeling using various machine learning methods, showcasing the potential for advancement in this field [31]. Xu et al have showcased enhanced accuracy when employing Bayesian methods in the crop radiative transfer model inversion process [32].…”

Section: Introductionmentioning

confidence: 99%

Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels

Wu,

Bai,

2024

Agronomy

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Chlorophyll content is highly susceptible to environmental changes, and monitoring these changes can be a crucial tool for optimizing crop management and providing a foundation for research in plant physiology and ecology. This is expected to deepen our scientific understanding of plant ecological adaptation mechanisms, offer a basis for improving agricultural production, and contribute to ecosystem management. This study involved the collection of hyperspectral data, image data, and SPAD data from jujube leaves. These data were then processed using SG smoothing and the isolated forest algorithm, following which eigenvalues were extracted using a combination of Pearson’s phase relationship method and the Partial Least Squares Regression–continuous projection method. Subsequently, seven methods were employed to analyze the results, with hyperspectral data and color channel data used as independent variables in separate experiments. The findings indicated that the integrated BPNN-RF-Ridge Regression algorithm provided the best results, with an R2 of 0.8249, MAE of 2.437, and RMSE of 2.9724. The inclusion of color channel data as an independent variable led to a 3.2% improvement in R2, with MAE and RMSE increasing by 1.6% and 3.9%, respectively. These results demonstrate the effectiveness of integrated methods for the determination of chlorophyll content in jujube leaves and underscore the potential of using multi-source data to improve the model fit with a minimal impact on errors. Further research is warranted to explore the application of these findings in precision agriculture for jujube yield optimization and income-related endeavors, as well as to provide insights for similar studies in other plant species.

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“…Cao et al established the inversion model of Chl-a concentrations with synchronous image data of Taihu Lake by using an RBF neural network model [42]. Tang et al used a variety of band combinations as the input of an SVM model to retrieve Chl-a concentrations [43,44]. Feng et al used convolutional neural networks for water quality inversion and achieved high inversion accuracy [45].…”

Section: Introductionmentioning

confidence: 99%

Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

Yang

Zhao

et al. 2022

Remote Sensing

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The combination of remote sensing technology and traditional field sampling provides a convenient way to monitor inland water. However, limited by the resolution of remote sensing images and cloud contamination, the current water quality inversion products do not provide both high temporal resolution and high spatial resolution. By using the spatio-temporal fusion (STF) method, high spatial resolution and temporal fusion images were generated with Landsat, Sentinel-2, and GaoFen-2 data. Then, a Chl-a inversion model was designed based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1. Finally, the results of the Chl-a concentrations were corrected using a pixel correction algorithm. The images generated from STF can maintain the spectral characteristics of the low-resolution images with the R2 between 0.7 and 0.9. The Chl-a inversion results based on the spatio-temporal fused images and CNN were verified with measured data (R2 = 0.803), and then the results were improved (R2 = 0.879) after further combining them with the pixel correction algorithm. The correlation R2 between the Chl-a results of GF2-like and Sentinel-2 were both greater than 0.8. The differences in the spatial distribution of Chl-a concentrations in the BYD lake gradually increased from July to August. Remote sensing water quality inversion based on STF and CNN can effectively achieve high frequency in time and fine resolution in space, which provide a stronger scientific basis for rapid diagnosis of eutrophication in inland lakes.

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Inversion of Chlorophyll-a Concentration in Donghu Lake Based on Machine Learning Algorithm

Cited by 14 publications

References 51 publications

Water Multi-Parameter Sampling Design Method Based on Adaptive Sample Points Fusion in Weighted Space

Water Multi-Parameter Sampling Design Method Based on Adaptive Sample Points Fusion in Weighted Space

Inverting Chlorophyll Content in Jujube Leaves Using a Back-Propagation Neural Network–Random Forest–Ridge Regression Algorithm with Combined Hyperspectral Data and Image Color Channels

Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

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