Mr. Yayong Sun Profile

Yayong Sun

at China Institute of Water Resources and Hydropower

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Publications (4)

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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

Read Abstract +

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.

Proceedings Article | 9 December 2015 Paper

Assessing potential of vertical average soil moisture (0-40cm) estimation for drought monitoring using MODIS data: a case study

Jianwei Ma, Shifeng Huang, Jiren Li, Xiaotao Li, Xiaoning Song, Pei Leng, Yayong Sun

Proceedings Volume 9808, 980834 (2015) https://doi.org/10.1117/12.2207901

KEYWORDS: Soil science, MODIS, Agriculture, Vegetation, Hydrology, Meteorology, Roads, Remote sensing, Thermography, Composites

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Soil moisture is an important parameter in the research of hydrology, agriculture, and meteorology. The present study is designed to produce a near real time soil moisture estimation algorithm by linking optical/IR measurements to ground measured soil moisture, and then used to monitoring region drought. It has been found that the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) are related to surface soil moisture. Therefore, a relationship between ground measurement soil moisture and NDVI and LST can be developed. Six days’ NDVI and LST data calculated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) of Shandong province during October in 2009 to May in 2010 were combined with ground measured volumetric soil moisture in different depth (10cm, 20cm, 40cm, and mean in vertical (0-40cm)) and different soil type to determine regression relationships at a 1 km scale. Based on the regression relationships, mean volumetric soil moisture in vertical (0-40cm) at 1 km resolution can be calculated over the Shandong province, and then drought maps were obtained. The result shows that significantly relationship exists between the NDVI and LST and soil moisture at different soil depths, and regression relationships are soil type dependent. What is more, the drought monitoring results agree well with actual situation.

Proceedings Article | 9 December 2015 Paper

Dynamic monitoring of Poyang Lake water body area using MODIS images between 2000 and 2014

Yayong Sun, Shifeng Huang, Jiren Li, Xiaotao Li, Jianwei Ma, Shanyang Li, Hui Wang

Proceedings Volume 9808, 98081S (2015) https://doi.org/10.1117/12.2209293

KEYWORDS: MODIS, Meteorology, Floods, Remote sensing, Climate change, Reflectivity, Image segmentation, Near infrared, Environmental sensing, Vegetation

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Poyang Lake, the largest freshwater lake of China, is well known for its ecological and economic importance as a dynamic wetland system. But, influenced by the climate change and human activity, Poyang Lake wetland has changed a lot. The long time series of Terra/MODIS data between 2000 and 2014 were utilized to investigate the variation of Poyang Lake and to analyze Poyang lake response to variation of local precipitation with the meteorological data. The results showed: (1) Poyang Lake water body area showed a significant seasonal variation, minimum value was about 690 km² and maximum value reached 3500 km², and inter-annual fluctuation; (2)For the past 15 year , local precipitation directly affected the inundation changes. In particular, the impact of rainfall during the first half of the year is more significant (the relation coefficient with R2 of 0.61); (3) Taking into account humid activities, the impoundment of the Three Gorges dam (TGD) had a certain impact on Poyang Lake water body area, especially the persistent reduction of Poyang lake surface area in November was deteriorated by the impounding of TGD in October after 2006. Finally, the study provides a theoretical basis and data for changes in Poyang Lake wetland research and protection.

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