基于MODIS的青藏高原湖泊透明度遥感反演

doi:10.18306/dlkxjz.2017.05.007

Abstract

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

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.

Figures/Tables 13

Fig.1

Tab.1

In situ measured Secchi Depth (SD) data and information of corresponding satellite imagery"

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

Tab.1

Tab.2

Tab.3

Fitting formulas between lake SD and remote sensing parameters in other studies"

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

Tab.3

Fig.2

Fig.3

Tab.4

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.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

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

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