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| Preliminary Study on Water Quality Parameter Inversion for the Yuqiao Reservoir Based on Zhuhai-1 Hyperspectral Satellite Data |
| YIN Zi-yao1, 2, LI Jun-sheng1, 2*, FAN Hai-sheng3, GAO Min1, 2, XIE Ya1 |
1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Zhuhai Orbita Aerospace Technology Co., Ltd., Zhuhai 519080, China |
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Abstract The concentration of suspended matter and water clarity are basic parameters for water quality investigations and important indexes to evaluate the quality of water environments. Remote sensing technologies can be used to realize the inversion of the main water quality parameters in a large range, quickly, and at low cost; however, those same technologies cannot be applied to inland water bodies that extend over small areas and have complex and variable water qualities. The Zhuhai-1 satellite constellation is composed of new hyperspectral satellites launched by the Zhuhai Orbita Aerospace Technology Company. Such satellites possess high spatial, temporal, and hyperspectral resolutions; moreover, they have considerable potential for the monitoring of inland water quality. The applicability of remote sensing to the monitoring of inland water quality parameters using data from the Zhuhai-1 satellites has not been studied in detail, and satellite ground synchronization experiments need to be carried out to test its effectiveness. In this paper, we considered remote sensing data collected from those hyperspectral satellites to study the Yuqiao reservoir: based on water surface synchronous data, we constructed inversion models of suspended matter and water clarity using the empirical regression method, and subsequently tested their accuracy. The test results showed that the best band combinations for the inversion models, in order to obtain the concentration of suspended matter and the water clarity in the Yuqiao reservoir, were Rrs(684)/Rrs(540) (relative error=8.6%; root mean square error=1.0 mg·L-1) and Rrs(656)/Rrs(556) (relative error=11.7%; root mean square error=18.2 cm), respectively. The distribution map of the water quality parameters in the Yuqiao Reservoir on November 22, 2018 was obtained by using the modeling formula: suspended matter was found to be more abundant in the north than in the south, while water clarity was lower in the north and higher in the south. These spatial distributions mainly depended on the configuration of the Yuqiao reservoir, in which the water depth increases from north to south: in the case of a deeper water column, sediments are less likely to be resuspended. According to the inversion modeling of the water quality parameters and to the test results of the measurements conducted on the Yuqiao Reservoir, we conclude that the hyperspectral remote sensing data of Zhuhai-1 can be used to retrieve the water quality parameters quantitatively. These results have important implications for the monitoring of inland water quality; still, more satellite-ground synchronization experiments are needed to improve further the preprocessing of data and the correspondent inversion models.
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Received: 2019-12-29
Accepted: 2020-04-06
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Corresponding Authors:
LI Jun-sheng1
E-mail: lijs@radi.ac.cn
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[1] Bukata R P. Journal of Great Lakes Research, 2013, 39: 2.
[2] Li J S, Wang S L, Wu Y H, et al. International Journal of Digital Earth, 2016, 9(8): 788.
[3] YUN Xing(云 行). Satellite Application(卫星应用), 2017, 6: 78.
[4] CHANG Chun, FENG Ping, SUN Dong-mei, et al(常 淳, 冯 平, 孙冬梅, 等). China Environmental Science(中国环境科学), 2015, 35(9): 2805.
[5] FANG Xu, DUAN Hong-tao, CAO Zhi-gang, et al(房 旭, 段洪涛, 曹志刚, 等). Journal of Lake Sciences(湖泊科学), 2018, 30(4): 967.
[6] ZHOU Ping, CHEN Dong-sheng, ZHAO Wei-guo(周 平, 陈东升, 赵卫国). Water Sciences and Engineering Technology(水科学与工程技术), 2010, 5: 18.
[7] Wang S L, Li J S, Zhang B. International Journal of Remote Sensing, 2016, 37(24): 6076.
[8] Le C F, Hu C M, Cannizzaro J, et al. Remote Sensing of Environment, 2013, 129: 75.
[9] Bailey S W, Werdell P J. Remote Sensing of Environment, 2006, 102: 12.
[10] El-Alem A, Chokmani K, Laurion I, et al. Remote Sensing, 2012, 4: 2373.
[11] Efron B. Journal of the American Statistical Association, 1983, 78: 316.
[12] Feng L, Hu C M, Han X X, et al. Remote Sensing, 2015, 7: 275.
[13] Shao Z F, Er M J. Neurocomputing, 2016, 194: 260.
[14] YUE Ang, JI Yun-feng, LIU Hong-lei, et al(岳 昂, 戢运峰, 刘红磊, 等). Journal of Safety and Environment(安全与环境学报), 2019, 19(4): 1448.
[15] Gao L, Yao H Y, Zhang M M, et al. Journal of Marine Sciences, 2017, 35(3): 79. |
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