Prof. Xiaohua Xu Profile

Prof. Xiaohua Xu

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Proceedings Article | 13 June 2024 Paper

Study on multi-annual and simultaneous satellite-ground remote sensing retrieval of water transparency (Secchi disk depth) in Poyang lake

Pu Zheng, Xin-fa Xu, Xiao-hua Xu, Ping Huang, Xin-wen Zhou

Proceedings Volume 13180, 1318028 (2024) https://doi.org/10.1117/12.3033764

KEYWORDS: Transparency, Data modeling, Remote sensing, Landsat, Earth observing sensors, Modeling, Satellites, Reflectivity, Chlorophyll, Error analysis

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Poyang Lake is the largest freshwater lake in China. It is located in the middle and lower reaches of the Yangtze River in the north-central Jiangxi Province. As the largest Lake which is connecting the Yangtze River, the water ecological security of Poyang Lake affects the entire Yangtze River basin. Water transparency(SDD) is an important indicator of water quality, so it is of great significance to carry out routine monitoring of SDD using remote sensing methods. This study takes Poyang Lake as the experimental area, and uses the SDD as the inversion index to characterize water quality. Based on observation data from 2015 to 2020 and Landsat series images, the inversion of SDD model suitable for summer Poyang Lake was constructed by analyzing and comparing the correlation coefficients between different wave bands and the SDD under the condition of standardized modeling phase and "star-ground" collaborative matching. After verification by four sets of independent data, the mean R2 is 0.8621, the mean RSME is 15.37cm, and the mean MAPE is 23.6%. It is believed that the method used in this study can be well applied to the inversion of SDD in the Poyang Lake area, and also provides a new reference method for monitoring the transparency of inland water bodies under long-term observation conditions.

Proceedings Article | 9 October 2023 Paper

Analysis and prediction of urban drought characteristics: Nanchang City as an example

Weifeng Yang, Xiaohua Xu, Delong Li, Yewei Liu, Xindong Xie

Proceedings Volume 12791, 127911Z (2023) https://doi.org/10.1117/12.3004692

KEYWORDS: Education and training, Data modeling, Neural networks, Analytical research, Artificial neural networks, Neurons, Temperature metrology, Performance modeling, Excel, Positron emission tomography

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This study was undertaken in Nanchang City with the aim of enhancing resilience against droughts, which have become a growing concern and have inflicted significant harm to both the economy and the quality of life of the city's inhabitants. The research efforts utilized the monthly data of precipitation and average temperature in Nanchang from January 1960 to December 2018 to accomplish the following objectives: assess the trend and abrupt variations of precipitation and temperature via a combination of linear regression, M-K test, and sliding t-test; analyze the drought intensity and frequency through two drought indices, using different time scales; and employ NAR Artificial Neural Network to predict the SPI-1 and SPEI-1 indices, and evaluate their accuracy. The findings indicated that there was a significant increase in annual precipitation and average temperature in Nanchang over a period of 59 years. At the annual scale, droughts were more frequent and light to medium droughts were the most prevalent, whereas heavy and exceptional droughts were less frequent. At the seasonal scale, summer and autumn were more susceptible to droughts than spring and winter. On the whole, the SPEI index was found to be more effective in identifying droughts in Nanchang, being more sensitive to the variations of drought and capable of offering varying assessments of drought intensity at different time scales. The overall performance of the prediction model was deemed satisfactory, with the prediction accuracy of SPI-1 and SPEI-1 indices being 69.4% and 81.5% respectively in the short-term prediction period. This resulted in the conclusion that the model had a superior prediction effect on the SPEI-1 index. The findings of the research could serve as a reference for future predictions of drought trends in Nanchang in the short term.

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