基于MODIS的青藏高原湖泊透明度遥感反演

doi:10.18306/dlkxjz.2017.05.007

地理科学进展 ›› 2017, Vol. 36 ›› Issue (5): 597-609.doi: 10.18306/dlkxjz.2017.05.007

基于MODIS的青藏高原湖泊透明度遥感反演

刘翀^1,³(), 朱立平^1,^2,^3,^*(), 王君波^1,², 乔宝晋^1,³, 鞠建廷¹, 黄磊^1,³

1. 中国科学院青藏高原研究所青藏高原地表过程与环境变化实验室,北京 100101
2. 中国科学院青藏高原地球科学卓越创新中心,北京 100101
3. 中国科学院大学,北京100049

收稿日期:2017-04-01 出版日期:2017-05-20 发布日期:2017-05-20
通讯作者: 朱立平
作者简介:
作者简介：刘翀(1992-),男,江西南昌人,硕士研究生,研究方向为湖泊环境遥感应用,E-mail:liuchong@itpcas.ac.cn。
基金资助:
科技部基础性科技工作重点项目(2012FY111400);中国科学院战略性科技先导专项(XDB03030000);中国科学院国际合作重点项目(131C11KYSB20160061)

Remote sensing-based estimation of lake water clarity on the Tibetan Plateau

Chong LIU^1,³(), Liping ZHU^1,^2,^3,^*(), Junbo WANG^1,², Baojin QIAO^1,³, Jianting JU¹, Lei HUANG^1,³

1. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, CAS, Beijing 100101, China
2. CAS Center for Excellence in Tibetan Plateau Earth System, Beijing 100101, China
3. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2017-04-01 Online:2017-05-20 Published:2017-05-20
Contact: Liping ZHU
Supported by:
中国科学院国际合作重点项目(131C11KYSB20160061) [Foundation：National Basic Research Program of Ministry of Science and Technology (MOST),China, No.2012FY111400;Strategic Priority Research Program of CAS, No.XDB03030000;Key Projects of International Cooperation of CAS, No.131C11KYSB20160061

摘要/Abstract

摘要：

湖泊透明度是湖泊水体性质的一个重要参数,是湖泊浮游生物和进入湖泊的有机和无机颗粒溶解程度的综合反映,对湖泊生态环境研究具有重要的科学及实践意义。遥感影像是获取面积广、时间长的湖泊透明度的重要手段,但由于实测数据缺乏,目前对青藏高原地区湖泊透明度的遥感反演研究相对不足。本文基于青藏高原地区24个湖泊实测透明度SD(Secchi Depth)值和相应的MODIS遥感影像,建立了该地区湖泊水体透明度SD值MODIS遥感反演模型。结果表明：基于MODIS绿色波段B4的单波段幂函数模型在该地区反演效果最好,精度较高(R2=0.91, N=24),并具有较好的稳定性。以当惹雍错为例,选用该模型反演得到湖泊透明度的时间变化序列,发现该湖存在明显的季节波动和较为明显的年际变化。初步分析得出,降水/融水季节的湖泊透明度与湖泊所在流域的降水率具有密切的关系。本文结果表明,利用遥感手段能够有效地开展青藏高原地区湖泊透明度的反演,可为进一步深入研究该地区湖泊透明度及其影响要素奠定基础。

关键词: 青藏高原, 湖泊, 遥感, MODIS, 透明度, SD, 当惹雍错

Abstract:

：Lake water clarity is an important parameter of lake water property, which is an integrated response of lake plankton and organic and inorganic solutions, and has significant scientific and practical implications for lake ecological condition research. Remote sensing is a key method for obtaining lake clarity in wide areas and within long time spans. On the Tibetan Plateau, there are more than 389 lakes with area greater than 10 km², making the Plateau an ideal region for environmental and climate change research. However, study on the estimation of lake water clarity on the Tibetan Plateau by satellite data is insufficient at present due to the paucity of in situ lake water clarity measurement data. In this study, retrieval models of lake water clarity were established based on the in situ water clarity measurements of 24 lakes distributed in different areas on the Tibetan Plateau and the corresponding MODIS imageries. Statistical methods including linear, exponential, power function, and logarithm regressions were used to build relationships between lake water clarity and the reflectance of MODIS bands on the Tibetan Plateau. The results show that power function model with MODIS green band B4 as single independent variable is the best model for estimating lake water clarity (SD value) on the Plateau (R2=0.91, N=24). The stability of the model was also tested based on 10 in situ SD data at different times in a single lake. Based on this model, we analyzed the temporal variation of lake water clarity of a typical lake Tangra Yumco as an example. The result demonstrates clear seasonal and inter-annual variations of lake water clarity for this lake. Preliminary analysis indicates that the variation of water clarity in precipitation-meltwater rich season is correlated with precipitation intensity of the basin. Our work proved that the reflectance of remote sensing imageries is valid for estimating lake water clarity on the Tibetan Plateau. This may promote further investigation of lake water clarity and its influencing factors on the Tibetan Plateau.

Key words: Tibetan Plateau, lake, remote sensing, MODIS, clarity, Secchi Depth (SD), Tangra Yumco

刘翀, 朱立平, 王君波, 乔宝晋, 鞠建廷, 黄磊. 基于MODIS的青藏高原湖泊透明度遥感反演[J]. 地理科学进展, 2017, 36(5): 597-609.

Chong LIU, Liping ZHU, Junbo WANG, Baojin QIAO, Jianting JU, Lei HUANG. Remote sensing-based estimation of lake water clarity on the Tibetan Plateau[J]. PROGRESS IN GEOGRAPHY, 2017, 36(5): 597-609.

图/表 13

图1

表1

文中获取的湖泊透明度SD值及对应遥感影像信息"

序号	湖泊名称	采样坐标		MODIS 影像文件名	SD/m	采样日期	影像日期	日期偏差/d
1	纳木错	90.7456°E	30.7338°N	MOD021KM.A2016323.0500.006.2016333195247	8.0	2016.11.19	2016.11.19	0
2	塔若错	84.1366°E	31.1403°N	MOD021KM.A2014249.0525.006.2014249130649	6.2	2014.9.06	2014.9.6	0
3	班公错	79.8094°E	33.5509°N	MOD021KM.A2012209.0455.006.2014224155957	14.0	2012.7.28	2012.7.29	1
4	结则茶卡	80.8814°E	33.9417°N	MOD021KM.A2012212.0525.006.2014224201213	3.0	2012.7.31	2012.7.30	1
5	阿翁错	81.7586°E	32.7531°N	MOD021KM.A2012216.0500.006.2014225101108	3.0	2012.8.5	2012.8.3	2
6	别若则错	82.9585°E	32.4378°N	MOD021KM.A2012216.0500.006.2014225101108	0.5	2012.8.6	2012.8.3	3
7	达热布错	83.2328°E	32.4829°N	MOD021KM.A2012216.0500.006.2014225101108	3.2	2012.8.6	2012.8.3	3
8	拉果错	84.1647°E	32.0269°N	MOD021KM.A2012223.0510.006.2014224150200	5.0	2012.8.8	2012.8.10	2
9	洞错	84.7651°E	32.1623°N	MOD021KM.A2012223.0510.006.2014224150200	0.8	2012.8.8	2012.8.10	2
10	达瓦错	84.9422°E	31.2233°N	MOD021KM.A2012223.0510.006.2014224150200	1.0	2012.8.11	2012.8.10	1
11	攸布错	84.8241°E	30.7900°N	MOD021KM.A2012223.0510.006.2014224150200	12.5	2012.8.12	2012.8.10	1
12	昂古错	85.4441°E	31.2053°N	MOD021KM.A2012223.0510.006.2014224150200	3.5	2012.8.13	2012.8.10	3
13	张乃错	87.4118°E	31.5508°N	MOD021KM.A2012229.0430.006.2014220065835	3.0	2012.8.17	2012.8.16	1
14	恰规错	88.2445°E	31.8489°N	MYD021KM.A2012232.0640.006.2012232192302	5.0	2012.8.19	2012.8.19	0
15	错鄂	88.7061°E	31.6598°N	MYD021KM.A2012232.0640.006.2012232192302	8.0	2012.8.20	2012.8.19	1
16	巴木错	90.6184°E	31.2639°N	MOD021KM.A2012238.0425.006.2014220060635	3.2	2012.8.21	2012.8.25	4
17	达则错	87.5503°E	31.8538°N	MOD021KM.A2012228.0525.006.2014220103327	6.0	2012.8.18	2012.8.15	3
18	色林错	88.8889°E	31.8190°N	MYD021KM.A2014218.0745.006.2014220180109	3.2	2014.8.7	2014.8.6	1
19	龙木错	80.4734°E	34.6240°N	MOD021KM.A2015253.0610.006.2015253195641	2.5	2015.9.10	2015.9.10	0
20	邦达错	81.5300°E	34.9575°N	MOD021KM.A2015248.0550.006.2015248134032	1.3	2015.9.5	2015.9.5	0
21	阿克赛钦湖	79.7935°E	35.2456°N	MOD021KM.A2015259.0535.006.2015259134615	1.0	2015.9.17	2015.9.17	0
22	多尔索洞措	89.8341°E	33.3490°N	MOD021KM.A2016298.0510.006.2016298133407	7.5	2016.10.24	2016.10.24	0
23	赤布张错	90.2322°E	33.3813°N	MOD021KM.A2016305.0515.006.2016305133931	5.0	2016.10.31	2016.10.31	0
24	多格错仁	88.9916°E	34.5537°N	MOD021KM.A2016311.0435.006.2016311134330	2.0	2016.11.6	2016.11.6	0

表1

表2

表3

其他地区湖泊SD值遥感反演拟合形式总结"

研究地区	反演方法	最优拟合形式	R2	N	传感器	参考文献
美国明尼苏达州、威斯康星州	单波段/波段组合、逐步回归、多元线性回归	ln(SD) = b₀ + b₁(B₂/B₄) + b₂B₁	0.82	311	OLI	Olmanson et al, 2016
中国吉林省中西部	先取相关系数较高的单波段、波段倒数、波段比值,再作多元线性回归	SD=a(B1/B2)+b(B1/B3)+c(B2/B3)+d	0.639	70	HJ-1 CCD	马建行等, 2016
中国吉林省中西部	先取相关系数较高的单波段、波段倒数、波段比值,再作多元线性回归	SD=aB1+bB2+c(B2/B3)+d	0.894	63	MODIS	马建行等, 2016
美国五大湖	单波段多项式	1/SD=aRrs₍₅₅₀₎³+ bRrs₍₅₅₀₎²+cRrs₍₅₅₀₎ +d	0.74	1328	CZCS, SeaWiFS, MODIS	Binding et al, 2015
美国缅因州	单波段、波段比值	SD= a(B1/B3)+bB1+cB7+d	0.70~0.89	24~71	TM	Courville et al, 2014
美国缅因州	单波段、波段比值	SD= aB1+bB3+ c	0.63~0.83	31~117	TM	McCullough et al, 2012
美国明尼苏达州	根据前人经验直接利用蓝、红波段	ln(SD)= aB1+bB3+ c	0.32~0.71	748	MODIS	Knight et al, 2012
中国太湖	直接利用前人经验公式	ln(SD)=a(TM1/TM3)+bTM1+c	0.77	32	ETM+	Zhao et al, 2011
鄱阳湖	取相关性较好的单波段、波段组合,做线性、对数、指数、倒数、开方、多项式回归取最优取相关性较好的单波段、波段组合,做线性、对数、指数、倒数、开方、多项式回归取最优	ln(SD)= aB_blue+bB_red+ c	0.88	71	MODIS	Wu et al, 2008
鄱阳湖		ln(SD)= aB_blue+bB_red+ c	0.83	25	TM	Wu et al, 2008
中国东北部	取单波段、波段比值、平均作一元回归	SD= aB3/B2+b 或SD= aB3/B1+b 或SD= a (B1+B4)/2+b 或SD= a (B3+B2)/2+b 及上述方式变量取对数	0.69~0.98	7~20	TM	Duan et al, 2009
美国明尼苏达州	直接利用前人经验公式	ln(SD)=a(TM1/TM3)+bTM1+c	0.71~0.96	13~278	MSS, TM, ETM+	Olmanson et al, 2008
美国明尼阿波利斯	单波段、波段组合、逐步回归选取关系较强参数,作多元回归	ln(SD)=a(TM1/TM3)+bTM1+c	0.70~0.80	21~53	MSS,TM	Kloiber et al, 2002

表3

图2

图3

表4

图4

图5

图6

图7

图8

图9

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波段名称	波段宽度/nm	信噪比/NE△t	空间分辨率
B8	405~420	880	1 km
B9	438~448	838	1 km
B3	459~479	243	500 m合成为1 km
B10	483~493	802	1 km
B11	526~536	754	1 km
B12	546~556	750	1 km
B4	545~565	228	500 m合成为1 km
B1	620~670	128	500 m合成为1 km
B13	662~672	910	1 km
B14	673~683	1087	1 km
B15	743~753	586	1 km
B2	841~876	201	250 m合成为1 km
B16	862~877	516	1 km

自变量类型	因变量	最优拟合自变量	最优拟合方法	最优拟合结果	R2	N
波段比值	SD	x=B8/B11	线性	SD= 8.8254x - 4.9669	0.65	24
			指数	SD= 0.3635e^2.0575^x	0.59	24
			对数	SD= 10.027ln(x) + 4.1792	0.63	24
			幂	SD = 3.0486x^2.5065	0.65	24
			多项式	SD= 0.4808x² + 7.6165x - 4.2702	0.65	24
波段比值	ln(SD)	x=B8/B4	线性	ln(SD) = 1.5093x - 0.5485	0.60	24
			多项式	y = -0.9299x² + 4.0536x - 2.0735	0.68	24
			对数	ln(SD) = 1.9501ln(x) + 1.04	0.68	24
			幂	变量含负值,无法拟合
			指数	变量含负值,无法拟合
主成分	ln(SD)	主成分PC₁、PC₂、PC₃、PC₄ (PC₁-PC₄累计贡献率>99%)	主成分回归	ln(SD)=-1.5PC₁+0.95PC₂+10.79PC₃-21.55PC₄	0.88	24
红绿蓝比值与单波段	ln(SD)	x₁=B1/B3、x₂=B3	二元回归	ln(SD)=1.211767x₁-13.1984x₂+1.149798	0.90	24
红绿蓝加减	SD	x=B12+B4	幂	SD=0.2187x^-1.536	0.91	24

基于MODIS的青藏高原湖泊透明度遥感反演

Remote sensing-based estimation of lake water clarity on the Tibetan Plateau

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