Chinese Geographical Science

, Volume 29, Issue 5, pp 741–755 | Cite as

Remote Detection of Varying Water Storage in Relation to Surfacial Temperature of Aral Sea

  • Guangyi Mu
  • Li Chen
  • Liangjun HuEmail author
  • Kaishan SongEmail author


Lake monitoring by remote sensing is of significant importance to understanding the lake and ambient ecological and environmental processes. In particular, whether lake water storage variation could predict lake surfacial temperature or vice versa has long fascinated the research community, in that it would greatly benefit the monitoring missions and scientific interpretation of the lake change processes. This study attempted to remotely detect the dynamics of the Aral Sea and pursue the relationships between varying lake water storage attributes and surface water temperature by using MODIS LST (Moderate-resolution Imaging Spectroradiometer Land Surface Temperature) 8-day composite products, satellite altimeter data, and actual meteorological measurements. Their associations with lake Surface Water Temperatures (SWT) were then analyzed. Results showed the lake water surface areas and elevations of the North Aral Sea tended to increasing trend from 2001 (2793.0 km2, 13.6 m) to 2015 (6997.8 km2, 15.9 m), while those of the South Aral Sea showed a decreasing trend during 2001 (20 434.6 km2, 3.9 m) and 2015 (3256.1 km2, 0.9 m). In addition, the annual daytime and nighttime lake SWT both decreased in the North Aral Sea, while only the daytime SWT in the South Aral Sea exhibited an increase, indicating a rising deviation of diurnal temperatures in the South Aral Sea during the past 15 yr. Moreover, a lower correlation was found between variations in the daytime SWT and storage capacity in the South Aral Sea (R2 = 0.33; P < 0.05), no fair correlations were tested between lake water storage and daytime SWT in the North Aral Sea nor between lake water storage and nighttime SWT in either part of the sea. These results implied that climate change, if any at least during the research period, has no significant effects on lake dynamics over the two sectors of the Aral Sea with anthropogenic disturbances. However, climate change and human activities may overlap to explain complex consequences in the lake storage variations. Our results may provide a reference for monitoring the spatiotemporal variations of lakes, increasing understanding of the lake water storage changes in relation to the lake SWT, which may benefit the ecological management of the Aral Sea region, in the effort to face the likely threats from climate change and human activities to the region.

Key words

Surface Water Temperature (SWT) lake water surface dynamics lake water storage Moderate-resolution Imaging Spectroradiometer (MODIS) the Aral Sea 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bai J, Chen X, Li J et al., 2011. Changes in the area of inland lakes in arid regions of central Asia during the past 30 years. Environmental Monitoring and Assessment, 178(1–4): 247–256. doi: 10.1007/s10661-010-1686-yCrossRefGoogle Scholar
  2. Boomer I, Wünnemann B, Mackay A W et al., 2009. Advances in understanding the late Holocene history of the Aral Sea region. Quaternary International, 194(1–2): 79–90. doi: 10.1016/j.quaint.2008.03.007CrossRefGoogle Scholar
  3. Cai X, Feng L, Hou X et al., 2016. Remote sensing of the water storage dynamics of large lakes and reservoirs in the Yangtze River Basin from 2000 to 2014. Scientific Reports, 6: 36405. doi: 10.1038/srep36405CrossRefGoogle Scholar
  4. Chen J L, Wilson C R, Tapley B D et al., 2017. Long-term and seasonal Caspian Sea level change from satellite gravity and altimeter measurements. Journal of Geophysical Research: Solid Earth, 122(3): 2274–2290. doi: 10.1002/2016JB013595Google Scholar
  5. Cretaux J F, Letolle R, Bergé-Nguyen M, 2013. History of Aral Sea level variability and current scientific debates. Global and Planetary Change, 110: 99–113. doi: 10.1016/j.gloplacha.2013.05.006CrossRefGoogle Scholar
  6. Crosman E T, Horel J D, 2009. MODIS-derived surface tempera¬ture of the Great Salt Lake. Remote Sensing of Environment, 113(1): 73–81. doi: 10.1016/j.rse.2008.08.013CrossRefGoogle Scholar
  7. Dessler A E, Palm S P, Spinhirne J D, 2006. Tropical cloud-top height distributions revealed by the ice, cloud, and land eleva¬tion satellite (ICESat)/Geoscience laser altimeter system (GLAS). Journal of Geophysical Research: Atmospheres, 111(D12). doi: 10.1029/2005JD006705Google Scholar
  8. Gafurov A, 2010. Water Balance Modeling Using Remote Sensing Information: Focus on Central Asia. Stuttgart: University of Stuttgart.Google Scholar
  9. Gong P, 2012. Remote sensing of environmental changes over China: a review. Chinese Science Bulletin, 57: 2793–2801. doi: 10.1007/s11434-012-5268-yCrossRefGoogle Scholar
  10. Gorham E, 1964. Morphometric control of annual heat budgets in temperate lakes. Limnology and Oceanography, 9(4): 529–533. doi: 10.4319/lo.1964.9.4.0525CrossRefGoogle Scholar
  11. Hu Liangjun, Yang Haijun, Yang Qinke et al., 2010. A GIS-based modeling approach for fast assessment of soil erosion by water at regional scale, Loess Plateau. Chinese Geographical Sci¬ence, 20(5): 423–433. doi: 10.1007/s11769-010-0416-2CrossRefGoogle Scholar
  12. Huang X, Xie H, Liang T et al., 2011. Estimating vertical error of SRTM and map-based DEMs using ICESat altimetry data in the eastern Tibetan Plateau. International Journal of Remote Sensing, 32(18): 5177–5196. doi: 10.1080/01431161.2010.495092CrossRefGoogle Scholar
  13. Ke L H, Song C Q, 2014. Remotely sensed surface temperature variation of an inland saline lake over the central Qinghai–Tibet Plateau. ISPRS Journal of Photogrammetry and Remote Sensing, 98: 157–167. doi: 10.1016/j.isprsjprs.2014.09.007CrossRefGoogle Scholar
  14. Livingstone D M, Dokulil M T, 2001. Eighty years of spatially coherent Austrian lake surface temperatures and their rela¬tionship to regional air temperature and the North Atlantic Os¬cillation. Limnology and Oceanography, 46(5): 1220–1227. doi: 10.4319/lo.2001.46.5.1220CrossRefGoogle Scholar
  15. Luyssaert S, Jammet M, Stoy P C et al., 2014. Land management and land-cover change have impacts of similar magnitude on surface temperature. Nature Climate Change, 4: 389–393. doi: 10.1038/nclimate2196CrossRefGoogle Scholar
  16. McFeeters S K, 1996. The use of the Normalized Difference Wa¬ter Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425–1432. doi: 10.1080/01431169608948714CrossRefGoogle Scholar
  17. Micklin P, 2010. The past, present, and future Aral Sea. Lakes & Reservoirs: Research & Management, 15(3): 193–213. doi: 10.1111/j.1440-1770.2010.00437.xCrossRefGoogle Scholar
  18. Peneva E L, Stanev E V, Stanychni S V et al., 2004. The recent evolution of the Aral Sea level and water properties: analysis of satellite, gauge and hydrometeorological data. Journal of Marine Systems, 47(1–4): 11–24. doi: 10.1016/j.jmarsys.2003.12.005CrossRefGoogle Scholar
  19. Riordan B, Verbyla D, McGuire A D, 2006. Shrinking ponds in subarctic Alaska based on 1950–2002 remotely sensed im¬ages. Journal of Geophysical Research: Biogeosciences, 111(G4). doi: 10.1029/2005JG000150Google Scholar
  20. Shi K, Zhang Y, Zhu G et al., 2015. Long-term remote monitoring of total suspended matter concentration in Lake Taihu using 250 m MODIS-Aqua data. Remote Sensing of Environment, 164: 43–56. doi: 10.1016/j.rse.2015.02.029CrossRefGoogle Scholar
  21. Shi K, Zhang Y, Zhang Y et al., 2019. Phenology of Phytoplankton Blooms in a Trophic Lake Observed from Long-term MODIS Data. Environmental Science & Technology, 53: 2324–2331. doi: 10.1021/acs.est.8b06887CrossRefGoogle Scholar
  22. Sima S, Ahmadalipour A, Tajrishy M, 2013. Mapping surface temperature in a hyper-saline lake and investigating the effect of temperature distribution on the lake evaporation. Remote Sensing of Environment, 136: 374–385. doi: 10.1016/j.rse.2013.05.014CrossRefGoogle Scholar
  23. Song K, Wang M, Du J et al., 2016. Spatiotemporal variations of lake surface temperature across the Tibetan Plateau using MODIS LST product. Remote Sensing, 8(10): 854. doi: 10.3390/rs8100854CrossRefGoogle Scholar
  24. Sun F, Sun W, Chen J et al., 2012. Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. International journal of Remote Sensing, 33(21): 6854–6875. doi: 10.1080/01431161.2012.692829CrossRefGoogle Scholar
  25. Wan Z, Zhang Y, Zhang Q et al., 2002. Validation of the land surface temperature products retrieved from terra moderate resolution imaging spectroradiometer data. Remote Sensing of Environment, 83(1–2): 163–180. doi: 10.1016/S0034-4257(02)00093-7CrossRefGoogle Scholar
  26. Wang J, Song C, Reager J T et al., 2018. Recent global decline in endorheic basin water storages. Nature geoscience, 11(12): 926. doi: 10.1038/NGEO2999CrossRefGoogle Scholar
  27. Wang X, Gong P, Zhao Y et al., 2013. Water-level changes in China’s large lakes determined from ICESat/GLAS data. Remote Sensing of Environment, 132: 131–144. doi: 10.1016/j.rse.2013.01.005CrossRefGoogle Scholar
  28. Wang X W, Cheng X, Li Z et al., 2012. Lake water footprints identification from time-series ICESat/GLAS data. IEEE Geoscience and Remote Sensing Letters, 9: 333–337. doi: 10.1109/LGRS.2011.2167495CrossRefGoogle Scholar
  29. Wang Z M, Song K S, Zhang B et al., 2009. Shrinkage and frag¬mentation of grasslands in the West Songnen Plain, China. Ag¬riculture, Ecosystems and Environment, 129: 315–324. doi: 10.1016/j.agee.2008.10.009CrossRefGoogle Scholar
  30. Xiao F, Ling F, Du Y et al., 2013. Evaluation of spatial-temporal dynamics in surface water temperature of Qinghai Lake from 2001 to 2010 by using MODIS data. Journal of Arid Land, 5(4): 452 464. doi: 10.1007/s40333-013-0188-5CrossRefGoogle Scholar
  31. Zhang G, Xie H, Kang S et al, 2011. Monitoring lake level changes on the Tibetan Plateau using ICESat altimetry data (2003–2009). Remote Sensing of Environment, 115(7): 1733–1742. doi: 10.1016/j.rse.2011.03.005CrossRefGoogle Scholar
  32. Zhang S, Gao H, Naz B S, 2014. Monitoring reservoir storage in South Asia from multisatellite remote sensing. Water Resources Research, 50(11): 8927 8943. doi: 10.1002/2014WR015829CrossRefGoogle Scholar

Copyright information

© Science Press, Northeast Institute of Geography and Agroecology, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory for Vegetation Ecology Science, Ministry of EducationNortheast Normal UniversityChangchunChina
  2. 2.Jilin Provincial Museum of NatureNortheast Normal UniversityChangchunChina
  3. 3.Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchunChina

Personalised recommendations