کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5755537 | 1621795 | 2017 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Tracing high time-resolution fluctuations in dissolved organic carbon using satellite and buoy observations: Case study in Lake Taihu, China
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
کامپیوتر در علوم زمین
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چکیده انگلیسی
Field measurements of dissolved organic carbon (DOC) concentration and remote-sensing reflectance were conducted to develop a regional, empirical red-blue algorithm to retrieve surface DOC from Geostationary Ocean Color Imager (GOCI) data for Lake Taihu, China. The auxiliary data (in-situ observations of the optical properties and water quality, buoy measurements of hydrodynamic data and water chemical parameters) were used to investigate the spatial and temporal variations in DOC. GOCI was shown to be capable of successfully obtaining hourly variations in DOC, with a root mean square error percentage (RMSP) of 17.29% (RMSEÂ =Â 0.69Â mg/L) for the match-up data. The GOCI-derived DOC in Lake Taihu confirms that the highest DOC concentration is in northwest Lake Taihu, followed by Meiliang Bay, Gonghu Bay and northeast Lake Taihu. Hourly DOC variation is significant and presents a different trend for each lake segment due to the variety of influencing factors. Discharge of DOC from surrounding rivers is an important factor to the variation of DOC in northeast Lake Taihu. However, organic products of algae will be the primary contributor to DOC when algal bloom occurred. During the period of algal bloom, high DOC levels in Lake Taihu can lead to hypoxia when coupled with high temperatures and low disturbance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Applied Earth Observation and Geoinformation - Volume 62, October 2017, Pages 174-182
Journal: International Journal of Applied Earth Observation and Geoinformation - Volume 62, October 2017, Pages 174-182
نویسندگان
Changchun Huang, Li Yunmei, Ge Liu, Yulong Guo, Hao Yang, A-xing Zhu, Ting Song, Tao Huang, Mingli Zhang, Kun Shi,