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(23) Cao, Z., Ma, R., Duan, H., Pahlevan, N., Melack, J., Shen, M., & Xue, K. 2020. A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes. Remote Sensing of Environment, 248, 111974
(24) Hou, X., Feng, L., Duan, H., Chen, X., Sun, D., & Shi, K. 2017. Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China. Remote Sensing of Environment, 190, 107-121
(25) Jiang, G., Loiselle, S.A., Yang, D., Gao, C., Ma, R., Su, W., & Duan, H. 2019. An absorption-specific approach to examining dynamics of particulate organic carbon from VIIRS observations in inland and coastal waters. Remote Sensing of Environment, 224, 29-43
(26) Kwon, Y.S., Pyo, J., Kwon, Y.-H., Duan, H., Cho, K.H., & Park, Y. 2020. Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir. Remote Sensing of Environment, 236, 111517
(27) Pyo, J., Duan, H., Baek, S., Kim, M.S., Jeon, T., Kwon, Y.S., Lee, H., & Cho, K.H. 2019. A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery. Remote Sensing of Environment, 233, 111350
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(29) Song, K., Li, L., Tedesco, L., Li, S., Duan, H., Liu, D., Hall, B., Du, J., Li, Z., & Shi, K. 2013. Remote estimation of chlorophyll-a in turbid inland waters: Three-band model versus GA-PLS model. Remote Sensing of Environment, 136, 342-357
(30) Xue, K., Ma, R., Duan, H., Shen, M., Boss, E., & Cao, Z. 2019. Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China's three largest freshwater lakes. Remote Sensing of Environment, 225, 328-346
英文論文清單：
https://publons.com/researcher/1175123/hongtao-duan/
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