基于ICESat-2卫星测高数据的呼伦湖水位变化监测

doi:10.13866/j.azr.2023.09.07

Abstract

Abstract:

The water level changes of grassland lakes indicate the changes of grassland ecological environment, which is an important indicator of grassland ecological changes. We use the ATL13 global inland water data of ICESat-2 satellite from November 2018 to January 2022 to construct a high-precision water level change sequence of Hulun Lake. The results are verified using DAHITI, Hydroweb, and G-REALM water level data. Based on the relationship between lake area and water level change, a water level area relationship model is constructed to analyze the seasonal characteristics of lake water level changes and the influence of external factors on lake water level. The research results showed that: from November 2018 to January 2022, the water level of Hulun Lake showed an overall upward trend, with an average annual water level rise rate of 0.49 m·a^-1. The water level of the lake decreases from March to June each year, rises from July to October, and stabilizes from November to February of the following year. It is known from the comparison and verification with DAHITI, Hydroweb, and G-REALM water level data that the ICESat-2 satellite monitoring water level data is stable, the water level sequence precision is high, and the root mean square error is 9.7 cm, which is reliable. The Combination of the ICESat-2 satellite water level monitoring results and the lake water level area model can achieve multi-time and high-precision lake water level monitoring, and calculate the time series of water level seasonal changes, which showed that the seasonal characteristics of Hulun Lake water level are obvious, and the inter annual trend is basically stable. The water level decreases from spring to summer and rises from summer to winter. The changes of lake water level is greatly affected by external factors. The increase of evaporation caused by the rise of temperature is the main factor leading to the decrease of lake water level. The temperature, evaporation, and water level change showed a strong negative correlation. The recharge of the lake water volume by artificial water injection has increased the lake level and the trend of increasing water level is obvious during the injection period in 2021.

Key words: ICESat-2, satellite altimetry, water level monitoring, influencing factors, Hulun Lake

LIU Junyan,WANG Shijie. Monitoring of Hulun Lake water level changes based on ICESat-2 satellite altimetry data[J].Arid Zone Research, 2023, 40(9): 1438-1445.

Figures/Tables 9

Fig. 1

Tab. 1

Fig. 2

Fig. 3

Fig. 4

Tab. 2

Fig. 5

Fig. 6

Fig. 7

References 22

[1]	Pekel J F, Cottam A, Gorelick N, et al. High-resolution mapping of global surface water and its long-term changes[J]. Nature, 2016, 540(7633): 418-422. doi: 10.1038/nature20584
[2]	赵云, 廖静娟, 沈国状, 等. 卫星测高数据监测青海湖水位变化[J]. 遥感学报, 2017, 21(4): 633-644.
	[Zhao Yun, Liao Jingjuan, Shen Guozhuang, et al. Monitoring the water level changes in Qinghai Lake with satellite altimetry data[J]. Journal of Remote Sensing, 2017, 21(4): 633-644.]
[3]	葛莉, 习晓环, 王成, 等. ICESat-1/GLAS数据湖泊水位监测研究进展[J]. 遥感技术与应用, 2017, 32(1): 14-19.
	[Ge Li, Xi Xiaohuan, Wang Cheng, et al. Research Progress of ICESat-1/GLAS in Lake Level Monitoring[J]. Remote Sensing Technology and Application, 2017, 32(1): 14-19.]
[4]	Song C, Huang B, Ke L, et al. Seasonal and abrupt changes in the water level of closed lakes on the Tibetan Plateau and implications for climate impacts[J]. Journal of Hydrology, 2014, 514(6): 131-144. doi: 10.1016/j.jhydrol.2014.04.018
[5]	Yuan C, Gong P, Liu C, et al. Water-volume variations of Lake Hulun estimated from serial Jason altimeters and Landsat TM/ETM+ images from 2002 to 2017[J]. International Journal of Remote Sensing, 2019, 40(2): 670-692. doi: 10.1080/01431161.2018.1516316
[6]	廖静娟, 沈国状, 赵云. 多源雷达高度计全球典型湖泊水位变化数据集(2002—2016)[J]. 全球变化数据学报, 2018, 2(3): 295-302, 184-191.
	[Liao Jingjuan, Shen Guozhuang, Zhao Yun. Dataset of global lake level changes using multialtimeter data (2002-2016)[J]. Journal of Global Change Data & Discovery, 2018, 2(3): 295-302, 184-191.]
[7]	Yan T L, Yi C L, Jen Y H. Automatic water-level detection using single-camera images with varied poses[J]. Measurement, 2018, 127(139): 167-174. doi: 10.1016/j.measurement.2018.05.100
[8]	Narin O G, Abdikan S. Multi-temporal analysis of inland water level change using ICESat-2 ATL-13 data in lakes and dams[J]. Environmental Science and Pollution Research, 2022, 30(6): 15364-15376. doi: 10.1007/s11356-022-23172-9
[9]	Guo X, Jin S, Zhang Z.Evaluation of water level estimation in the upper Yangtze River from ICESat-2 data[C]// Hangzhou: Photonics & Electromagnetics Research Symposium (PIERS), 2021, 2260-2264.
[10]	Ryan J C, Smith L C, Cooley S W, et al. Global characterization of inland water reservoirs using ICESat-2 altimetry and climate reanalysis[J]. Geophysical Research Letters, 2020, 47(17): e2020GL-088543.
[11]	马山木, 甘甫平, 吴怀春, 等.ICESat-2数据监测青藏高原湖泊2018—2021年水位变化[J]. 自然资源遥感, 2022, 34(3): 164-172.
	[Ma Shanmu, Gan Fuping, Wu Huaichun, et al. ICESat-2 data-based monitoring of 2018-2021 variations in the water levels of lakes in the Qinghai-Tibet Plateau[J]. Remote Sensing of Natural Resources, 2022, 34(3): 164-172.]
[12]	吴红波, 王宁练, 郭忠明. ICESat-2/ATLAS测高数据在青海湖湖泊水位估计中的应用[J]. 水资源与水工程学报, 2021, 32(5): 11-18, 26.
	[Wu Hongbo, Wang Ninglian, Guo Zhongming. Application of ICESat-2/ATLAS altimetry data to the estimation of the Qinghai Lake level[J]. Journal of Water Resources and Water Engineering, 2021, 32(5): 11-18, 26.]
[13]	孙伟, 金建文, 李国元, 等. 激光测高卫星ICESat-2监测太湖水位精度评价[J]. 测绘科学, 2021, 46(11): 6-11.
	[Sun Wei, Jin Jianwen, Li Guoyuan, et al. Accuracy evaluation of laser altimetry satellite ICESat-2 in monitoring water level of Taihu Lake[J]. Geomatics Science, 2021, 46(11): 6-11.]
[14]	Liu C, Hu R, Wang Y, et al. Monitoring water level and volume changes of lakes and reservoirs in the Yellow River Basin using ICESat-2 laser altimetry and Google Earth Engine[J]. Journal of Hydro-environment Research, 2022, 44(12): 53-64. doi: 10.1016/j.jher.2022.07.005
[15]	Xu N, Ma Y, Zhang W H, et al. Monitoring annual changes of lake water levels and volumes over 1984-2018 using landsat imagery and ICESat-2 data[J]. Remote Sensing, 2020, 12(23): 4004. doi: 10.3390/rs12234004
[16]	郭孝祖, 金双根. 利用ICESat-2激光测高监测长江三峡水位变化[J]. 测绘科学, 2022, 47(7): 21-26.
	[Guo Xiaozu, Jin Shuanggen. Water level changes in the Three Gorges of the Yangtze River from ICESat-2 laser altimetry[J]. Geomatics Science, 2022, 47(7): 21-26.]
[17]	李国元. 对地观测卫星激光测高数据处理方法与工程实践[D]. 武汉: 武汉大学, 2017.
	[Li Guoyuan. Earth Observation Satellite Laser Altimetry Data Processing Methods and Engineering Practices[D]. Wuhan: Wuhan University, 2017.]
[18]	Crétaux J F, Calmant S, Romanovski V, et al. An absolute calibration site for radar altimeters in the continental do-main: Lake Issykkul in Central Asia[J]. Journal of Geodesy, 2019, 83(8): 723-735. doi: 10.1007/s00190-008-0289-7
[19]	Schwatke C, Dettmering D, Bosch W et al. DAHITI-an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry[J]. Hydrology and Earth System Sciences, 2015, 19(10): 4345-4364. doi: 10.5194/hess-19-4345-2015
[20]	Leys C, Ley C, Klein O, et al. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median[J]. Journal of Experimental Social Psychology, 2013, 49(4): 764-766. doi: 10.1016/j.jesp.2013.03.013
[21]	孙明智, 刘新, 汪海洪, 等. 多源卫星测高数据监测拉昂错1992—2020年水位变化[J]. 遥感学报, 2022, 26(1): 126-137.
	[Sun Mingzhi, Liu Xin, Wang Haihong, et al. Monitoring water level changes in La-ang Co from 1992-2020 using multi-source data[J]. Journal of Remote Sensing, 2022, 26(1): 126-137.]
[22]	王文种, 黄对, 刘九夫, 等.基于Landsat与Sentinel-3A卫星数据的当惹雍错1988—2018年湖泊水位—水量变化及归因[J]. 湖泊科学, 2020, 32(5): 1552-1563.
	[Wang Wenzhong, Huang Dui, Liu Jiufu, et al. Patterns and causes of changes in water level and volume in Tangra Yumco from 1988 to 2018 based on Landsat images and Sentinel-3A synthetic aperture radar[J]. Lake Sciences, 2020, 32(5): 1552-1563.]

ICESat-2卫星	参数	ICESat-2卫星	参数
高度计	ATLAS	脉冲宽度	1 ns
运行时间	2018年至今	波长	532 nm
轨道高度	500 km	波束数	6 束
重复周期	91 d	光斑间隔	0.7 m
轨道倾角	92°	光斑直径	17 m
脉冲能量	0.2~1.2 mJ	单次测距标称精度	2.3 cm
脉冲重复频率	10 kHz	相邻波束间隔	90 m

产品水位差	Max/cm	Min/cm	STD/cm	RMS/cm
DAHITI-本研究	16.7	-13.7	6.8	7.0
Hydroweb-本研究	24.1	-26.0	10.8	10.8
DAHITI-Hydroweb	21.7	-26.4	9.7	9.8
G-REALM-本研究	28.1	-21.0	10.6	11.2
G-REALM-DAHITI	27.5	-23..4	10.7	11.1
G-REALM-Hydroweb	16.9	-10.6	5.8	6.6

Monitoring of Hulun Lake water level changes based on ICESat-2 satellite altimetry data

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 22

Related Articles 15

Metrics

Comments

Recommended 0

[1]	HUANG Kunlin, WU Guozhou, XU Weixin, LI Lidong, WANG Haimei, LI Hang, LI Zixiang, SI Jingke, LIU Hongbin, WU Chengna. Dynamic snowmelt process and its influencing factors in the eastern farmland region of Hulun Buir [J]. Arid Zone Research, 2024, 41(9): 1514-1526.
[2]	ZHANG Hongwei, BIE Qiang, SHI Ying, SU Xiaojie, LI Xinzhang. Characteristics of vegetation cover changes in the upper reaches of the Yellow River Basin and the influencing factors [J]. Arid Zone Research, 2024, 41(8): 1385-1394.
[3]	HU Guanglu, LIU Peng, LI Jia’nan, TAO Hu, ZHOU Chengqian. Characteristics of soil moisture dynamics and influencing factors of three landscape types at the oasis edge in the middle reaches of the Heihe River [J]. Arid Zone Research, 2024, 41(4): 550-565.
[4]	NIE Hanlin, FAN Liangxin, GUO Jin, ZHANG Mengke, WANG Zhijun. Spatial and temporal characteristics of crop water footprint and influencing factors in Guanzhong region at the county scale [J]. Arid Zone Research, 2024, 41(2): 339-352.
[5]	MA Xiaolei,QIAO Yaqi,WANG Jie,JIAO Shixing,ZHANG Man. The spatiotemporal patterns of water ecological footprints, depth, size, and influencing factors in Shaanxi Province [J]. Arid Zone Research, 2023, 40(3): 469-480.
[6]	LI Suyun, QI Donglin, WEN Tingting, SHI Feifei, QIAO Bin, XIAO Jianshe. The variation characteristics and influencing factors of vapor pressure deficit in Qinghai Province from 1961 to 2020 [J]. Arid Zone Research, 2023, 40(2): 173-181.
[7]	HE Junqi,BAI Hanwei,XU Yiwei,NI Lili. Main nutrient characteristics and influencing factors of farmland soil in the Loess Plateau of the Shaanxi Province [J]. Arid Zone Research, 2023, 40(12): 1907-1917.
[8]	WU Wanmin, LIU Tao, CHEN Xin. Seasonal changes of NDVI in the arid and semi-arid regions of Northwest China and its influencing factors [J]. Arid Zone Research, 2023, 40(12): 1969-1981.
[9]	DANG Hui, RONG Lihua, LI Yitong, ZHAO Mingjun. Spatiotemporal evolution characteristics and influencing factors of production-living-ecological spaces in the farming-pastoral ecotone: Taking Hohhot of Inner Mongolia as an example [J]. Arid Zone Research, 2023, 40(10): 1698-1706.
[10]	ZHANG Yunxia,ZHANG Jinxi,GONG Jie. Landscape pattern vulnerability and its influencing factors on a semi-arid lake basin: A case study of Liangcheng County [J]. Arid Zone Research, 2022, 39(4): 1259-1269.
[11]	GUO Yin,LEI Jiaqiang,FAN Jinglong,WANG Haifeng,LYU Zhentao. Soil wind erosion characteristics and main influencing factors in Mongolia in recent 20 years [J]. Arid Zone Research, 2022, 39(4): 1200-1211.
[12]	CAO Yongxiang,MAO Donglei,Xue Jie,SU Songling,Kaimaierguli Abulaiti,CAI Fuyan. Dynamic changes and driving factors of vegetation cover in the oasis-desert ecotone: A case study of Cele, Xinjiang [J]. Arid Zone Research, 2022, 39(2): 510-521.
[13]	WANG Yufang,ZHAO Chengzhang,ZENG Hongxia,KANG Manping,ZHAO Tingting,TANG Yurui. Spatial-temporal evolution of wetland landscape patterns and its influencing factors in the middle reaches of the Shule River [J]. Arid Zone Research, 2022, 39(1): 282-291.
[14]	WANG Ziwei,HUANG Laiming,SHAO Ming’an,PEI Yanwu. Soil water holding capacity under different land use patterns in the Qinghai alpine region [J]. Arid Zone Research, 2021, 38(6): 1722-1730.
[15]	YU Haifeng,SHI Xiaohong,SUN Biao,ZHAO Shengnan,LIU Yu,ZHAO Meili. Analysis of water quality and eutrophication changes in Hulun Lake from 2011 to 2020 [J]. Arid Zone Research, 2021, 38(6): 1534-1545.