Tiantian Shao
Chinese Academy of Sciences
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Featured researches published by Tiantian Shao.
IEEE Transactions on Geoscience and Remote Sensing | 2014
Kaishan Song; Lin Li; Shuai Li; Lenore Tedesco; Hongtao Duan; Zuchuan Li; Kun Shi; Jia Du; Ying Zhao; Tiantian Shao
Accurate remote estimation of chlorophyll-a (CHL) concentration for turbid inland waters is a challenging task due to their optical complexity. In situ spectra (n=666) measured with ASD and Ocean Optics spectrometers from three drinking water sources in Indiana, USA, were used to calibrate the partial least squares model (PLS), artificial neural network model (ANN), and the three-band model (TBM) for CHL estimates; model performances are validated with three independent datasets (n=360) from China. The PLS-ANN model resulted in accurate model calibration ( R2=0.94; Range=0.2-296.6 μg/l of CHL), outperforming the PLS (R2=0.87), ANN (R2=0.91), and TBM (R2=0.86). With an independent validation dataset, the PLS-ANN yielded relatively high accuracy (RMSE: 6.12 μg/l; rRMSE=42.12%; range=0.45-97.2 μg/l of CHL), while TBM yielded acceptable accuracy (RMSE: 8.85 μg/l; rRMSE=63.21%). With simulated ESA/MERIS and EO-1/Hyperion spectra, the PLS-ANN also (MERIS: R2=0.84; Hyperion: R2=0.88) outperforms the TBM (MERIS: R2=0.69; Hyperion: R2=0.76) for model calibration. For validation, the PLS-ANN achieves good performance with simulated spectra (MERIS: RMSE=7.83 μg/l, rRMSE=48.79%; Hyperion: RMSE=6.98 μg/l, rRMSE=45.57%) as compared to the TBM (MERIS: RMSE=10.39 μg/l, rRMSE=68.92%; Hyperion: RMSE=9.54 μg/l, rRMSE=65.35%). Nevertheless, considering the large and diverse datasets, the TBM is a robust semiempirical algorithm. Based on our observations, both the PLS-ANN and TBM are effective approaches for CHL estimation in turbid waters.
Journal of Geophysical Research | 2017
Ying Zhao; Kaishan Song; Yingxin Shang; Tiantian Shao; Zhidan Wen; Lili Lv
The spatial characteristics of fluorescent-DOM (FDOM) components in river waters in China were firstly examined by excitation-emission matrix (EEM) spectra and fluorescence regional integration (FRI) with the data collected during September to November between 2013 and 2015. One tyrosine-like (R1), one tryptophan-like (R2), one fulvic-like (R3), one microbial protein-like (R4) and one humic-like (R5) components have been identified by FRI method. Principle components analysis (PCA) was conducted to assess variations in the five FDOM components (FRί (ί = 1, 2, 3, 4, 5)) and the humification index (HIX) for all 194 river water samples. The average fluorescence intensities of the five fluorescent components and the total fluorescence intensities FSUM differed under spatial variation among the seven major river basins (Songhua, Liao, Hai, Yellow and Huai, Yangtze, Pearl and Inflow Rivers) in China. When all the river water samples were pooled together, the fulvic-like FR3 and the humic-like FR5 showed a strong positive linear relationship (R2 = 0.90, n = 194), indicating that the two allochthonous FDOM components R3 and R5 may originate from similar sources. There is a moderate strong positive correlation between the tryptophan-like FR2 and the microbial protein-like FR4 (R2 = 0.71, n = 194), suggesting parts of two autochthonous FDOM components R2 and R4 are likely from some common sources. However, the total allochthonous substance FR(3+5) and the total autochthonous substances FR(1+2+4) exhibited a weak correlation (R2 = 0.40, n = 194). Significant positive linear relationships between FR3 (R2 = 0.69, n = 194), FR5 (R2 = 0.79, n = 194) and CDOM absorption coefficient a(254) were observed, respectively, which demonstrated the CDOM absorption were dominated by the allochthonous FDOM components R3 and R5.
Water Science and Technology | 2016
Tiantian Shao; Kaishan Song; Pierre-André Jacinthe; Jia Du; Ying Zhao; Zhi Ding; Ying Guan; Zhang Bai
Chromophoric dissolved organic matter (CDOM) in riverine systems can be affected by environmental conditions and land-use, and thus could provide important information regarding human activities in surrounding landscapes. The optical properties of water samples collected at 42 locations across the Liaohe River (LHR, China) watershed were examined using UV-Vis and fluorescence spectroscopy to determine CDOM characteristics, composition and sources. Total nitrogen (TN) and total phosphorus (TP) concentrations at all sampling sites exceeded the GB3838-2002 (national quality standards for surface waters, China) standard for Class V waters of 2.0 mg N/L and 0.4 mg P/L respectively, while trophic state index (TSIM) indicated that all the sites investigated were mesotrophic, 64% of which were eutrophic at the same time. Redundancy analysis showed that total suspended matter (TSM), dissolved organic carbon (DOC), and turbidity had a strong correlation with CDOM, while the other parameters (Chl a, TN, TP and TSIM) exhibited weak correlations with CDOM absorption. High spectral slope values and low SUVA254 (the specific UV absorption) values indicated that CDOM in the LHR was primarily comprised of low molecular weight organic substances. Analysis of excitation-emission matrices contour plots showed that CDOM in water samples collected from upstream locations exhibited fulvic-acid-like characteristics whereas protein-like substances were most likely predominant in samples collected in estuarine areas and downstream from large cities. These patterns were interpreted as indicative of water pollution from urban and industrial activities in several downstream sections of the LHR watershed.
Hydrology and Earth System Sciences | 2013
Kaishan Song; Shuying Zang; Ying Zhao; Lin Li; Jia Du; N. N. Zhang; X. D. Wang; Tiantian Shao; Y. Guan; L. Liu
Biogeosciences | 2015
Ying Zhao; Kaishan Song; Zhidan Wen; Lin Li; Shuying Zang; Tiantian Shao; Sijia Li; Jia Du
Wetlands | 2016
Tiantian Shao; Kaishan Song; Jia Du; Ying Zhao; Zhi Ding; Ying Guan; Lei Liu; Bai Zhang
Journal of The Indian Society of Remote Sensing | 2016
Tiantian Shao; Kaishan Song; Jia Du; Ying Zhao; Zhiming Liu; Bai Zhang
Journal of Geophysical Research | 2017
Ying Zhao; Kaishan Song; Yingxin Shang; Tiantian Shao; Zhidan Wen; Lili Lv
Environmental Science and Pollution Research | 2017
Tiantian Shao; Hui Zheng; Kaishan Song; Ying Zhao; Bai Zhang
Hydrology and Earth System Sciences Discussions | 2016
Kaishan Song; Ying Zhao; Zhidan Wen; Jianhang Ma; Tiantian Shao; Chong Fang; Yingxin Shang