Ms. Dexin Meng Profile

Dexin Meng

at IWHR

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Proceedings Article | 30 June 2021 Paper

Research on retrieval of chlorophyll-a concentration in Danjiangkou-Fancheng reach of Han River based on Sentinel-2 satellite data

Furong Zhang, He Zhu, Shifeng Huang, Jianwei Ma, Yayong Sun, Denxin Meng

Proceedings Volume 11878, 118781F (2021) https://doi.org/10.1117/12.2601026

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The chlorophyll-a concentration is one of the main indicators to evaluate water quality and measure the degree of eutrophication of the water body. It is of great significance to the environmental protection of rivers and lakes. At present, the commonly used empirical/semi-empirical remote sensing inversion models have low inversion accuracy in rivers with low chlorophyll-a concentration and narrow chlorophyll-a concentration ranges. At the same time, the semianalytical models require specific wavelengths, which makes it more suitable for hyperspectral remote sensing data. But it is difficult to obtain high-resolution hyperspectral satellite data. Taking the Danjiangkou-Fancheng reach of the Han River as the research area, this paper establishes a partial least squares regression model to estimate chlorophyll-a concentration using Sentinel-2 multispectral remote sensing images and synchronously measured chlorophyll-a concentration data. Considering the impact of Wangfuzhou Hydropower Station on the upstream and downstream, a segmented modeling was adopted and verified. The results show that the average relative errors of the verification samples in the Danjiangkou-Wangfuzhou reach and Wangfuzhou-Fancheng reach are 22.53% and 11.75%, respectively and the root mean square errors are 0.65ug/L and 0.17ug/L, respectively. The prediction accuracy is high. It indicates that the partial least squares method can be applied to the inversion of chlorophyll-a in rivers with low chlorophyll-a concentration and narrow chlorophyll-a ranges based on multispectral data.

Proceedings Article | 30 June 2021 Paper

Estimation of soil moisture with SAR data in large area based on support vector regression

Denxin Meng, Jianwei Ma, Jingfeng Xin, Yayong Sun, Shifeng Huang, Furong Zhang

Proceedings Volume 11878, 118782B (2021) https://doi.org/10.1117/12.2600934

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Soil moisture is an important parameter in the surface process, and it is indispensable in the field of crop growth and drought monitoring. SAR can penetrate clouds and fog to achieve high-resolution observations, so it has great advantages in remote sensing estimation of soil moisture. In this paper, Sentinel-1A radar data and MODIS were used to explore the applicability of soil moisture retrieval in a large area based on the support vector regression (SVR) method. By analyzing the characteristics closely related to soil moisture, the input of the algorithm was determined including VV, VH polarization backscattering coefficient, radar local incident angle (LIA), digital elevation model (DEM), slope (SLP), and normalized vegetation index (NDVI). Then the accuracy of the SVR model constructed with different feature combinations was discussed, and the best performing model was selected to estimate soil moisture in northern and central Anhui province. The results showed that the model with the combination of VV, LIA, NDVI, DEM, and SLP input had the highest accuracy with R² of 0.9413 and the root mean square error (RMSE) of 0.0085 cm³·cm^-3 , in which terrain factor had a greater impact. Finally, the best model was used to achieve a wide range of soil moisture retrieval, and test samples were used to verify the estimation accuracy with R² of 0.6444 and RMSE of 0.036 cm³·cm^-3 . Moreover, the temporal and spatial distribution of the retrieval result is reasonable, which can characterize the distribution difference of a large study area.

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