Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-959-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-959-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2023
Research article |
| 01 Mar 2023
| 01 Mar 2023
Fusion of Landsat 8 Operational Land Imager and Geostationary Ocean Color Imager for hourly monitoring surface morphology of lake ice with high resolution in Chagan Lake of Northeast China
Qian Yang
School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Remote Sensing and Geographic Information Research Center, Changchun 130102, China
Xiaoguang Shi
School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Remote Sensing and Geographic Information Research Center, Changchun 130102, China
Weibang Li
School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China
Kaishan Song
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Remote Sensing and Geographic Information Research Center, Changchun 130102, China
Zhijun Li
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
Xiaohua Hao
Northwest Institute of Eco-Environment and Resources, Chinese
Academy of Sciences, Lanzhou 730000, China
Fei Xie
State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
Nan Lin
School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China
Zhidan Wen
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Remote Sensing and Geographic Information Research Center, Changchun 130102, China
Chong Fang
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Remote Sensing and Geographic Information Research Center, Changchun 130102, China
Ge Liu
CORRESPONDING AUTHOR
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Remote Sensing and Geographic Information Research Center, Changchun 130102, China
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Cited articles
Arp, C. D., Cherry, J. E., Brown, D. R. N., Bondurant, A. C., and Endres, K. L.: Observation-derived ice growth curves show patterns and trends in maximum ice thickness and safe travel duration of Alaskan lakes and rivers, The Cryosphere, 14, 3595–3609, https://doi.org/10.5194/tc-14-3595-2020, 2020.
Bai, L., Cai, J., Liu, Y., Chen, H., Zhang, B., and Huang, L.: Responses of
field evapotranspiration to the changes of cropping pattern and groundwater
depth in large irrigation district of Yellow River basin, Agr. Water.
Manage., 188, 1–11, https://doi.org/10.1016/j.agwat.2017.03.028, 2017.
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and
Zemp, M.: The concept of essential climate variables in support of climate
research, applications, and policy, B. Am. Meteorol. Soc., 95, 1431–1443,
https://doi.org/10.1175/bams-d-13-00047.1, 2014.
Brown, L. C. and Duguay, C. R.: The response and role of ice cover in
lake-climate interactions, Prog. Phys. Geog., 34, 671–704,
https://doi.org/10.1177/0309133310375653, 2010.
Cai, Y., Ke, C. Q., and Duan, Z.: Monitoring ice variations in Qinghai Lake
from 1979 to 2016 using passive microwave remote sensing data, Sci. Total
Environ., 607, 120–131, https://doi.org/10.1016/j.scitotenv.2017.07.027, 2017.
Cai, Y., Ke, C. Q., Li, X., Zhang, G., Duan, Z., and Lee, H.: Variations of
lake ice phenology on the Tibetan Plateau from 2001 to 2017 based on MODIS
data, J. Geophys. Res.-Atmos., 124, 825–843, https://doi.org/10.1029/2018jd028993, 2019.
Canny, J.: A computational approach to edge detection, IEEE T. Pattern
Anal., 8, 679–698, 1986.
Dierking, W.: Mapping of different sea ice regimes using images from
Sentinel-1 and ALOS synthetic aperture radar, IEEE T. Geosci. Remote., 48,
1045–1058, https://doi.org/10.1109/TGRS.2009.2031806, 2010.
Doernhoefer, K. and Oppelt, N.: Remote sensing for lake research and
monitoring – Recent advances, Ecol. Indic., 64, 105–122,
https://doi.org/10.1016/j.ecolind.2015.12.009, 2016.
Du, J., Kimball, J. S., Duguay, C., Kim, Y., and Watts, J. D.: Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015, The Cryosphere, 11, 47–63, https://doi.org/10.5194/tc-11-47-2017, 2017.
Du, J., Watts, J. D., Jiang, L., Lu, H., Cheng, X., Duguay, C., Farina, M.,
Qiu, Y., Kim, Y., Kimball, J. S., and Tarolli, P.: Remote sensing of
environmental changes in cold regions: Methods, achievements and challenges,
Remote. Sens., 11, 1592, https://doi.org/10.3390/rs11161952, 2019.
Duan, H., Zhang, Y., Zhang, B., Song, K., and Wang, Z.: Assessment of
chlorophyll-a concentration and trophic state for Lake Chagan using Landsat
TM and field spectral data, Environ. Monit. Assess., 129, 295–308,
https://doi.org/10.1007/s10661-006-9362-y, 2007.
Feng, G., Masek, J., Schwaller, M., and Hall, F.: On the blending of the
Landsat and MODIS surface reflectance: predicting daily Landsat surface
reflectance, IEEE T. Geosci. Remote., 44, 2207–2218,
https://doi.org/10.1109/tgrs.2006.872081, 2006.
Geldsetzer, T., Sanden, J. V. D., and Brisco, B.: Monitoring lake ice during
spring melt using RADARSAT-2 SAR, Can. J. Remote Sens., 36, S391–S400, 2010.
Gogineni, P. and Yan, J.-B.: Remote sensing of ice thickness and surface
velocity, in: Remote Sensing of the Cryosphere, edited by: Tedesco, M., John
Wiley & Sons, Ltd., https://doi.org/10.1002/9781118368909.ch9, 2015.
Gusmeroli, A. and Grosse, G.: Ground penetrating radar detection of subsnow slush on ice-covered lakes in interior Alaska, The Cryosphere, 6, 1435–1443, https://doi.org/10.5194/tc-6-1435-2012, 2012.
Hao, X., Yang, Q., Shi, X., Liu, X., Huang, W., Chen, L., and Ma, Y.:
Fractal-based retrieval and potential driving factors of lake ice fractures
of Chagan Lake, Northeast China using Landsat remote sensing images, Remote
Sens., 13, 4233, https://doi.org/10.3390/rs13214233, 2021.
Hilker, T., Wulder, M. A., Coops, N. C., Linke, J., McDermid, G., Masek, J.
G., Gao, F., and White, J. C.: A new data fusion model for high spatial- and
temporal-resolution mapping of forest disturbance based on Landsat and
MODIS, Remote Sens. Environ., 113, 1613–1627, https://doi.org/10.1016/j.rse.2009.03.007,
2009.
Hoekstra, M., Jiang, M., Clausi, D. A., and Duguay, C.: Lake ice-water
classification of RADARSAT-2 images by integrating IRGS Segmentation with
pixel-based random forest labeling, Remote Sens., 12, 1425, https://doi.org/10.3390/rs12091425,
2020.
Howell, S. E. L., Brown, L. C., Kang, K.-K., and Duguay, C. R.: Variability
in ice phenology on Great Bear Lake and Great Slave Lake, Northwest
Territories, Canada, from SeaWinds/QuikSCAT: 2000–2006, Remote Sens.
Environ., 113, 816–834, https://doi.org/10.1016/j.rse.2008.12.007, 2009.
IPCC: Climate change 2021: The physical science basis., Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental
Panel on Climate Change, https://iupac.org/climate-change-2021-the-physical-science-basis/ (last access: 16 February 2022), 2021.
Jarihani, A., McVicar, T., Van Niel, T., Emelyanova, I., Callow, J., and
Johansen, K.: Blending Landsat and MODIS data to generate multispectral
indices: A comparison of “Index-then-Blend” and “Blend-then-Index”
approaches, Remote Sens., 6, 9213–9238, https://doi.org/10.3390/rs6109213, 2014.
Jeffries, M. O., Morris, K., and Kozlenko, N.: Ice characteristics and
processes, and remote sensing of frozen rivers and lakes, in: Remote Sensing
in Northern Hydrology: Measuring Environmental Change, edited by:
Pietroniro, C. R. D. A., https://doi.org/10.1029/GM163, 2013.
Jones, B. M., Gusmeroli, A., Arp, C. D., Strozzi, T., Grosse, G., Gaglioti, B. V., and Whitman, M. S.: Classification of freshwater ice conditions on the Alaskan Arctic Coastal Plain using ground penetrating radar and TerraSAR-X satellite data, Int. J. Remote Sens., 34, 8267–8279, https://doi.org/10.1080/2150704X.2013.834392, 2013.
Kang, K.-K. K., Duguay, C. R., Lemmetyinen, J., and Gel, Y.: Estimation of
ice thickness on large northern lakes from AMSR-E brightness temperature
measurements, Remote Sens. Environ., 150, 1–19, 2014.
Ke, C.-Q., Tao, A.-Q., and Jin, X.: Variability in the ice phenology of Nam
Co Lake in central Tibet from scanning multichannel microwave radiometer and
special sensor microwave/imager: 1978 to 2013, J. Appl. Remote. Sens., 7, 073477,
https://doi.org/10.1117/1.Jrs.7.073477, 2013.
Knauer, K., Gessner, U., Fensholt, R., and Kuenzer, C.: An ESTARFM fusion
framework for the generation of large-scale time series in cloud-prone and
heterogeneous landscapes, Remote. Sens., 8, 425, https://doi.org/10.3390/rs8050425, 2016.
Lang, J., Lyu, S., Li, Z., Ma, Y., and Su, D.: An investigation of ice
surface albedo and its influence on the high-altitude lakes of the Tibetan
Plateau, Remote. Sens., 10, 218, https://doi.org/10.3390/rs10020218, 2018.
Leppäranta, M.: Modelling the formation and decay of lake ice, in: The
Impact of Climate Change on European Lakes, edited by: George, G., Springer
Netherlands, Dordrecht, 63–83, https://doi.org/10.1007/978-90-481-2945-4_5,
2010.
Leppäranta, M.: Freezing of lakes and the evolution of their ice cover,
Springer Science & Business Media, https://doi.org/10.1007/978-3-642-29081-7, 2015.
Li, W., Lu, P., Li, Z., Zhuang, F., Lu, Z., and Li, G.: Analysis of ice
cracks morphology on lake surface of Lake Wuliangsuhai in the winter of
2017–2018, J. Glaciol. Geocry., 42, 919–926,
https://doi.org/10.7522/j.issn.1000-0240.2020.0066, 2020 (in Chinese).
Li, Z., Ao, Y., Lyu, S., Lang, J., Wen, L., Stepanenko, V., Meng, X., and
Zhao, L. I. N.: Investigation of the ice surface albedo in the Tibetan
Plateau lakes based on the field observation and MODIS products, J.
Glaciol., 64, 506–516, https://doi.org/10.1017/jog.2018.35, 2018.
Liu, C., Duan, P., Zhang, F., Jim, C.-Y., Tan, M. L., and Chan, N. W.:
Feasibility of the spatiotemporal fusion model in monitoring Ebinur Lake's
suspended particulate matter under the missing-data scenario, Remote Sens.,
13, 3952, https://doi.org/10.3390/rs13193952, 2021.
Liu, M., Liu, X., Wu, L., Zou, X., Jiang, T., and Zhao, B.: A modified
spatiotemporal fusion algorithm using phenological information for
predicting reflectance of paddy Rice in Southern China, Remote Sens., 10, 772,
https://doi.org/10.3390/rs10050772, 2018.
Liu, X., Li, B., Li, Z., and Shen, W.: A new fracture model for reservoir
ice layers in the northeast cold region of China, Constr. Build. Mater.,
191, 795–811, https://doi.org/10.1016/j.conbuildmat.2018.10.050, 2018.
Liu, X., Zhang, G., Sun, G., Wu, Y., and Chen, Y.: Assessment of Lake water
quality and eutrophication risk in an agricultural irrigation area: A case
study of the Chagan Lake in Northeast China, Water, 11, 2380, https://doi.org/10.3390/w11112380,
2019.
Liu, X., Zhang, G., Zhang, J., Xu, Y. J., Wu, Y., Wu, Y., Sun, G., Chen, Y.,
and Ma, H.: Effects of irrigation discharge on salinity of a large
freshwater lake: A case study in Chagan Lake, Northeast China, Water, 12, 2112,
https://doi.org/10.3390/w12082112, 2020.
Lu, Y., Wu, P., Ma, X., and Li, X.: Detection and prediction of land
use/land cover change using spatiotemporal data fusion and the Cellular
Automata–Markov model, Environ. Monit. Assess., 191, 68,
https://doi.org/10.1007/s10661-019-7200-2, 2019.
Magnuson, J. J., Robertson, D. M., Benson, B. J., Wynne, R. H., Livingstone,
D. M., Arai, T., Assel, R. A., Barry, R. G., Card, V., Kuusisto, E., Granin,
N. G., Prowse, T. D., Stewart, K. M., and Vuglinski, V. S.: Historical
trends in lake and river ice cover in the Northern Hemisphere, Science, 289,
1743–1746, https://doi.org/10.1126/science.289.5485.1743, 2000.
Murfitt, J. and Duguay, C. R.: Assessing the performance of methods for
monitoring ice phenology of the world's largest high Arctic lake using
high-density time series analysis of Sentinel-1 data, Remote Sens., 12, 382,
https://doi.org/10.3390/rs12030382, 2020.
Murfitt, J. and Duguay, C. R.: 50 years of lake ice research from active
microwave remote sensing: Progress and prospects, Remote Sens. Environ.,
264, 112616, https://doi.org/10.1016/j.rse.2021.112616, 2021.
Murfitt, J., Brown, L. C., and Howell, S. E. L.: Evaluating RADARSAT-2 for
the monitoring of lake ice phenology events in mid-latitudes, Remote Sens.,
10, 1641, https://doi.org/10.3390/rs10101641, 2018a.
Murfitt, J. C., Brown, L. C., and Howell, S. E.: Estimating lake ice
thickness in Central Ontario, Plos one, 13, e0208519, https://doi.org/10.1371/journal.pone.0208519, 2018b.
Qi, M., Liu, S., Yao, X., Xie, F., and Gao, Y.: Monitoring the ice phenology
of Qinghai Lake from 1980 to 2018 using multisource remote sensing data and
Google Earth Engine, Remote Sens, 12, 2217, https://doi.org/10.3390/rs12142217, 2020.
Qiu, Y., Wang, X., Ruan, Y., Xie, P., Zhong, Y., and Yang, S.: Passive
microwave remote sensing of lake freeze-thawing over Qinghai-Tibet Plateau,
J. Lake. Sci., 30, 1438–1449, 2018 (in Chinese).
Ryu, J. H. and Ishizaka, J.: GOCI data processing and ocean applications,
Ocean. Sci. J., 47, 221–221, https://doi.org/10.1007/s12601-012-0023-5, 2012.
Ryu, J. H., Han, H. J., Cho, S., Park, Y. J., and Ahn, Y. H.: Overview of
geostationary ocean color imager (GOCI) and GOCI data processing system
(GDPS), Ocean. Sci. J., 47, 223–233, https://doi.org/10.1007/s12601-012-0024-4, 2012.
Sisheber, B., Marshall, M., Mengistu, D., and Nelson, A.: Tracking crop
phenology in a highly dynamic landscape with knowledge-based Landsat–MODIS
data fusion, Int. J. Appl. Earth. Obs., 106, 102670, https://doi.org/10.1016/j.jag.2021.102670,
2022.
Song, K., Wang, Z., Blackwell, J., Zhang, B., Li, F., Zhang, Y., and Jiang,
G.: Water quality monitoring using Landsat Themate Mapper data with
empirical algorithms in Chagan Lake, China, J. Appl. Remote. Sens., 5, 3506,
https://doi.org/10.1117/1.3559497, 2011.
Song, K., Wang, M., Du, J., Yuan, Y., Ma, J., Wang, M., and Mu, G.:
Spatiotemporal variations of lake surface temperature across the Tibetan
Plateau using MODIS LST product, Remote. Sens., 8, 854, https://doi.org/10.3390/rs8100854, 2016.
SROCC: IPCC special report on the ocean and cryosphere in a changing climate Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009157964, 2019.
Tan, B., Li, Z.-j., Lu, P., Haas, C., and Nicolaus, M.: Morphology of sea
ice pressure ridges in the northwestern Weddell Sea in winter, J. Geophys.
Res-Oceans., 117, C06024, https://doi.org/10.1029/2011jc007800, 2012.
Wang, K., Leppäranta, M., and Reinart, A.: Modeling ice dynamics in Lake
Peipsi, Journal Verhandlungen der Internationalen Vereinigung für
theoretische und angewandte Limnologie, 29, 1443–1446, 2006.
Wang, X., Feng, L., Gibson, L., Qi, W., Liu, J., Zheng, Y., Tang, J., Zeng,
Z., and Zheng, C.: High-resolution mapping of ice cover changes in over
33,000 lakes across the North Temperate Zone, Geophys. Res. Lett., 48,
e2021GL095614, https://doi.org/10.1029/2021GL095614, 2021.
Wang, Y., Xie, D., Zhan, Y., Li, H., Yan, G., and Chen, Y.: Assessing the
accuracy of Landsat-MODIS NDVI fusion with limited input data: A strategy
for base data selection, Remote. Sens., 13, 266, https://doi.org/10.3390/rs13020266, 2021.
Weber, H., Riffler, M., Noges, T., and Wunderle, S.: Lake ice phenology from
AVHRR data for European lakes: An automated two-step extraction method,
Remote Sens. Environ., 174, 329–340, https://doi.org/10.1016/j.rse.2015.12.014, 2016.
Wen, Z., Song, K., Shang, Y., Lyu, L., Yang, Q., Fang, C., Du, J., Li, S.,
Liu, G., Zhang, B., and Cheng, S.: Variability of chlorophyll and the
influence factors during winter in seasonally ice-covered lakes, J. Environ.
Manage., 276, 111338, https://doi.org/10.1016/j.jenvman.2020.111338, 2020.
Xie, P., Qiu, Y., Wang, X., Shi, L., and Liang, W.: Lake ice phenology
extraction using machine learning methodology, IOP Conf. Ser.: Earth Environ. Sci., 502, 012034, https://doi.org/10.1088/1755-1315/502/1/012034, 2020.
Yang, Q., Song, K. S., Wen, Z. D., Hao, X. H., and Fang, C.: Recent trends
of ice phenology for eight large lakes using MODIS products in Northeast
China, Int. J. Remote. Sens., 40, 5388–5410, https://doi.org/10.1080/01431161.2019.1579939,
2019.
Yang, X., Pavelsky, T. M., and Allen, G. H.: The past and future of global
river ice, Nature, 577, 69–73, https://doi.org/10.1038/s41586-019-1848-1, 2020.
Zhang, X., Wang, K., and Kirillin, G.: An automatic method to detect lake
ice phenology using MODIS daily temperature imagery, Remote. Sens., 13, 2711,
https://doi.org/10.3390/rs13142711, 2021.
Zhu, X., Chen, J., Gao, F., Chen, X., and Masek, J. G.: An enhanced spatial
and temporal adaptive reflectance fusion model for complex heterogeneous
regions, Remote. Sens. Environ., 114, 2610–2623, https://doi.org/10.1016/j.rse.2010.05.032,
2010.
Zhu, X., Helmer, E. H., Gao, F., Liu, D., Chen, J., and Lefsky, M. A.: A
flexible spatiotemporal method for fusing satellite images with different
resolutions, Remote. Sens. Environ., 172, 165–177,
https://doi.org/10.1016/j.rse.2015.11.016, 2016.
Short summary
A large-scale linear structure has repeatedly appeared on satellite images of Chagan Lake in winter, which was further verified as being ice ridges in the field investigation. We extracted the length and the angle of the ice ridges from multi-source remote sensing images. The average length was 21 141.57 ± 68.36 m. The average azimuth angle was 335.48° 141.57 ± 0.23°. The evolution of surface morphology is closely associated with air temperature, wind, and shoreline geometry.
A large-scale linear structure has repeatedly appeared on satellite images of Chagan Lake in...
