内陆淡水湖博斯腾湖水质遥感反演及时空演变特征

doi:10.12118/j.issn.1000-6060.2023.386

摘要/Abstract

摘要：

湖泊作为干旱区重要的水源载体，旱区湖泊的水质变化对维持区域生态平衡和水分循环发挥着重要作用。因此，以我国内陆淡水湖泊博斯腾湖为研究区，对2001—2020年Landsat系列遥感影像进行大气校正、辐射定标等预处理；利用改进的归一化指数法提取水体分析其面积变化趋势，并结合实测矿化度浓度及叶绿素a浓度数据，利用经验方法对比分析波段及波段组合反射率与矿化度浓度以及叶绿素a浓度的相关性，并筛选出相关性最好的波段建立模型。以B5、B3/B4、B3+B4的波段组合构建矿化度浓度反演模型，以B2、B3、B2×B3的波段组合构建叶绿素a浓度反演模型。最后利用决定系数和均方根误差对遴选出的2个反演模型进行精度验证。结果表明：（1）湖区面积变化呈2001—2013年逐渐缩小、2013年后逐渐回升两阶段趋势。（2）博斯腾湖矿化度浓度空间分布具有湖中心浓度高，湖周边浓度低的特点；叶绿素a浓度空间分布与矿化度浓度分布相反。（3）长时间序列下的博斯腾湖区矿化度浓度与叶绿素a浓度在年尺度上均呈现先增加后减小的趋势。其中矿化度浓度最大值为2013年的1023.8 mg·L^-1，叶绿素a浓度最大值为2015的5.04 μg·L^-1。在过去的几十年里，中国在博斯腾湖的水质监控及其治理成果方面取得了显著进展，但在时空覆盖、指标列表、人与自然相互作用的结合、反演精度和模型泛化等方面还需进一步改进。

关键词: 叶绿素a, 矿化度, 遥感反演, 博斯腾湖

Abstract:

Lakes serve as important water sources in arid regions, and changes in water quality of lakes in these areas play a crucial role in maintaining regional ecological balance and water cycle. Therefore, focusing on China’s inland freshwater lake, Bosten Lake, Xinjiang, this study conducted pre-processing on Landsat series remote sensing images from 2001 to 2020, including atmospheric correction and radiometric calibration. An improved normalized index method was used to extract water bodies and analyze their area change trend. Combined with measured mineralization and chlorophyll-a concentration data, an empirical method was employed to compare and analyze the correlation between band and band combination reflectance values with mineralization and chlorophyll-a, selecting the bands with the best correlation to establish models. Mineralization inversion models were constructed using bands B5, B3/B4, and B3+B4, while chlorophyll-a concentration inversion models were developed using bands B2, B3, and B2×B3. Subsequently, the accuracy of the selected two inversion models was validated using determination coefficients and root mean square errors. The results indicate: (1) The lake area gradually decreased from 2001 to 2013 and then began to increase in two phases with 2013 as the turning point. (2) Bosten Lake exhibits a spatial distribution of mineralization concentration higher in the lake center and lower in the lake periphery, while chlorophyll-a concentration distribution is opposite to mineralization distribution. (3) Over a long time series, mineralization concentration and chlorophyll-a concentration in Bosten Lake show an increasing followed by decreasing trend at an annual scale. The maximum mineralization concentration was 1023.8 mg·L^-1 in 2013 and the maximum chlorophyll-a concentration was 5.04 µg·L^-1 in 2015. In the past few decades, significant progress has been made in China’s water quality monitoring and management in Bosten Lake. However, further improvements are needed in terms of spatiotemporal coverage, index lists, the integration of human-nature interactions, inversion accuracy, and model generalization.

Key words: chlorophyll-a, salinity, remote sensing inversion, Bosten Lake

吕娜, 郭梦京, 赵馨, 刘可乐, 黄宇佳. 内陆淡水湖博斯腾湖水质遥感反演及时空演变特征[J]. 干旱区地理, 2024, 47(6): 953-966.

LYU Na, GUO Mengjing, ZHAO Xin, LIU Kele, HUANG Yujia. Remote sensing inversion of water quality and spatiotemporal evolution characteristics of the Bosten Inland Freshwater Lake[J]. Arid Land Geography, 2024, 47(6): 953-966.

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年份	影像日期（月-日）			数量
年份	TM	ETM+	OLI	数量
2001		05-15、06-16、08-03、10-22		4
2002		05-02、06-03、07-21、08-06、10-25		5
2003	07-16	05-21、08-09、10-28		4
2004	07-02、10-22	04-05、05-23、08-11、09-12		6
2005		04-08、05-10、06-11、06-27、07-29、08-14、08-30、09-15		8
2006	05-21、06-06、06-22、09-10、10-12	05-13、06-30、08-01、10-20		9
2007	05-24、07-27、09-13	04-14、06-01、07-19、08-20、10-23		8
2008	05-10、07-13	03-31、06-19、08-06、09-07、10-09		7
2009	05-29、07-16、08-01、10-20	04-03、04-19、06-22、08-25、09-26		9
2010	04-14、08-04、08-20、09-05、10-07	05-08、06-09		7
2011	05-03、06-04、07-06、08-07、 08-23、09-24	04-09、06-28、10-18		9
2012		04-27、05-13、05-29、07-16、08-17、09-02、10-20		7
2013			04-22、05-24、06-25、07-27、08-28、09-29、10-15	7
2014		07-22	04-25、05-27、06-12、07-14、08-15、08-31、10-18	8
2015		08-26	04-12、05-30、06-15、07-17、08-18、09-19、10-21	8
2016		06-25、07-27	04-14、05-16、06-01、08-04、09-05、10-07	8
2017			04-17、05-03、06-20、07-22、08-07、09-08、10-26	7
2018			05-22、06-23、07-09、08-10、08-26、09-27、 10-13、10-29	8
2019			05-09、06-10、07-12、07-28、08-13、09-14、10-16	7
2020			04-09、05-27、06-12、07-30、08-15、08-31、 09-16、10-02	8

名称	年份	平均值/mg·L^-1	最大值/mg·L^-1	最小值/mg·L^-1	名称	年份	平均值/μg·L^-1	最大值/μg·L^-1	最小值/μg·L^-1
矿化度	2016	1133.88	1197.91	1106.56	叶绿素a	2016	4.89	5.02	4.71
	2017	983.46	1078.13	971.74		2017	4.53	4.86	4.23
	2018	899.63	970.28	879.53		2018	3.01	3.26	2.87
	2019	786.39	868.64	771.21		2019	3.44	3.89	3.14

波段	相关系数	波段	相关系数
B2	-0.539	B2+B3	-0.480
B3	-0.399	B2+B4	-0.446
B4	-0.473	B3+B4	-0.516
B2×B3	-0.485	B2+B3+B4	-0.484
B2×B4	-0.447	B3+B4+B5	-0.443
B3×B4	-0.504	B2+B3+B4+B5	-0.434
B2/B4	0.352	B3+B4+B5+B6	-0.394
B4/B6	-0.436	B2+B3+B4+B5+B6	-0.398

自变量	函数表达式	R²	RMSE/mg·L^-1
B2、B3	Y₁=1169.92+1.65B2-1.66B3	0.547	131.91
B2、B3+B4	Y₂=1130.003+1.65B2+0.0004(B3+B4)	0.459	144.92
B2、B3+B4	Y₃=1012.88+1.13B2-0.65(B3+B4)	0.406	151.02
B2、B3、B4	Y₄=1159.14+1.67B2-1.62B3-0.05B4	0.548	132.48
B2、B3/B4、B3+B4	Y₅=1133.69+1.09B2-35.11B3/B4- 0.67(B3+B4)	0.421	149.96
B2、B3、B4、B5、B6、B7	Y₆=3203+1.52B2-1.43B3-0.24B4+0.68B5-3.62B6+3.78B7	0.561	132.63

模型	最小相对误差/%	最大相对误差/%	平均相对误差/%	R²	RMSE/mg·L^-1
Y₁	0.68	134.15	16.03	0.496	141.43
Y₂	10.74	566.03	141.10	0.166	633.84
Y₃	0.23	150.38	18.68	0.546	143.11
Y₄	1.19	132.21	16.22	0.538	141.89
Y₅	0.12	140.30	16.22	0.761	111.25
Y₆	0.42	104.07	15.99	0.473	169.42