Evaluations of surface temperature variation in the Itapeva lake through satelite images
DOI:
https://doi.org/10.5902/2179460X39436Keywords:
Surface temperature, Shallow lakes, MOD11A1, LandsatAbstract
The role of temperature in water is fundamental for the community aquatic dynamics once it regulates several processes on different scales. The spatial and temporal variability of water temperature can be assessed by satellite images, which allows a better understanding of ecosystems. In this work, we evaluated the surface temperature variation of Itapeva Lake, located in Rio Grande do Sul, Brazil, between 1985 and 2017, using MOD11A1 product and images Landsat 5, 7 and 8. An homogeneous seasonal variation pattern was identify between the two sensors used. The information provided by MODIS and Landsat has a coefficient R2 = 0.91 and RMSE = 2.32 ° C. The analysis between the Landsat series adjusted data and the original data allowed the smoothing of maximum and minimum temperatures of water, reducing biased records. Water temperature for the summer and autumn months increases, while for the winter season the regime decrease. However, the surface temperature response may be better understood by involving climatic variables in the study.
Downloads
References
BARNES, W. L. et al. Prelaunch characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-AM1. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1088–1100, 1998.
CARDOSO LM, SILVEIRA ALL, MARQUES DMLM. Rate of change of the phytoplankton community in Itapeva Lake (North Coast of Rio Grande do Sul, Brazil), based on the wind driven hydrodynamic regime. Hydrobiologia. 2003;497:1–12.
CASTRO D, ROCHA CM. Qualidade das águas da bacia hidrográfica do Rio Tramandaí. 1st ed. Porto Alegre: Sapiens; 2016.
CHANDER, Gyanesh; MARKHAM, Brian L.; HELDER, Dennis L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment, 2009, vol. 113, no 5, p. 893-903.
CURTARELLI M, ALCANTARA E, RENNO C, STECH J. Effects of cold fronts on MODIS-derived sensible and latent heat fluxes in Itumbiara reservoir (Central Brazil). Advances in Space Research. 2013; 52: 1668-1677.
DENG, Chengbin; WU, Changshan. Examining the impacts of urban biophysical compositions on surface urban heat island: A spectral unmixing and thermal mixing approach. Remote Sensing of Environment, 2013, vol. 131, p. 262-274.
EDINGER JE. The response of water temperatures to meteorological conditions. Water Resour. Res., 1968; 4(5):1137–1143.
FANG, X. S. . Simulations of climate effects on water temperature, dissolved oxygen, and ice and snow covers in lakes of the contiguous United States under past and future climate scenarios. Limnol Oceanogr. 2009 54, 2359 –2370.
HORNE, A. J., E GOLDMAN, C. Limnology McGraw-Hill. 1994 .New York.
HULLEY, G. C. et al. Optimized split-window coefficients for deriving surface temperatures from inland water bodies. Remote Sensing of Environment, 115(12), 3758–3769, 2011.
IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 p.
JIMÉNEZ E SOBRINO JA. A generalized single-channel method for retrieving land Surface temperature from remote sensing data. Journal of Geophysical Research. 2003; 108(D22): 4688.
JIMÉNEZ-MUÑOZ, J. C., Cristóbal, J., Sobrino, J. A., Sòria, G., Ninyerola, M., & Pons, X. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data. IEEE Transactions on geoscience and remote sensing, 2009; 47(1), 339-349.
KENAWY AM et al. Daily temperature extremes over Egypt: Spatial patterns, temporal trends, and driving forces. Atmospheric Research. 2019, 226: 219-239.
KILPATRICK KA, PODESTA G, WALSH S, WILLIAMS E, HALLIWELL V, SZCZODRAK M, BROWN OB, MINNETT PJ, EVANS R. A decade of sea surface temperature from MODIS. Remote Sensing of Environment. 2015; 165:27-41.
MUNAR, Andrés Mauricio, et al. Coupling large-scale hydrological and hydrodynamic modeling: Toward a better comprehension of watershed-shallow lake processes. Journal of hydrology, 2018, vol. 564, p. 424-441.
MUNAR, Andrés Mauricio, et al. Assessing the large-scale variation of heat budget in poorly gauged watershed-shallow lake system using a novel integrated modeling approach. Journal of Hydrology, 2019, vol. 575, p. 244-256.
LAMARO AA, MARINELARENA A, TORRUSIO SE, SALA SE. Water surface temperature estimation from Landsat 7 ETM+ thermal infrared data using the generalized single-channel method: Case study of Embalse del Río Tercero (Córdoba, Argentina). Advances in Space Research. 2013; 51:492-500.
LISSNER, Juliane Beatriz; GUASSELLI, Laurindo Antonio. Variação do Índice de Vegetação por Diferença Normalizada na lagoa Itapeva, litoral norte do Rio Grande do Sul, Brasil, a partir de análise de séries temporais. Sociedade & Natureza, 2013, vol. 25, no 2, p. 427-440.
LUZ GA, GUASSELLI LA, ROCHA D. Temperature surface of Guaíba Lake, RS, from time series of MODIS images. Brazilian Journal of Water Resources. 2017;22:1-12.
PIRES, A., PF; SRIVASTAVA, D.S.; FARJALLA, V. F. Is biodiversity able to buffer ecosystems from climate change? What we know and what we don’t. BioScience, 2018, vol. 68, no 4, p. 273-280.
O’REILLY, CS et al. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett. 2015; 42:773-781.
R CORE TEAM . (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. Obtenido de https://www.R-project.org/.
PAREETH S, SALMASO N, ADRIAN R, NETELER M. Homogenized daily lake surface water temperature data generated from multiple satellite sensors: A long-term case study of a large sub-Alpine lake. Scientific Reports. 2016; 6(31252):1-14.
PRATS J, REYNAUD N, REBIERE D, PEROUX T, TORMOS T, DANIS PA. LakeSST: Lake Skin Surface Temperature in French inland water bodies for 1999-2016 from Landsat archives. Earth System Science Data. 2018; 10: 727-743.
SCHNEIDER, K.; MAUSER, W. Processing and accuracy of Landsat Thematic Mapper data for lake surface temperature measurement. International Journal of Remote Sensing, 1996, vol. 17, no 11, p. 2027-2041.
SEKERTEKIN, A.; BONAFONI, S. Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation. Remote Sensing, 2020, vol. 12, no 2, p. 294.
SIMA S, AHMADALIPOUR A, TAJRISHY M. Mapping surface temperature in a hyper-saline lake and investigating the effect of temperature distribution on the lake evaporation. Remote Sensing of Environment. 2013; 136:374-386.
SCHMID MH, HUNZIKER S, WUEST A. Lake surface temperatures in a changing climate: a global sensitivity analysis. Climatic Change. 2014; 124:301–315.
TAVARES et al. Comparison of methods to estimate lake-surface-water temperature using Landsat 7 ETM+ and MODIS Imagery: case study of a large shallow tropical lake in Southern Brazil. Water. 2019a, 11(168): 1-21.
TAVARES M., MARQUES D., FRAGOSO C.R. Combinando modelo de temperatura da água e sensoriamento remoto para estimar o efeito da mudança climática sobre a temperatura da lagoa Mangueira-RS. XXIII Simpósio Brasileiro de Recursos Hídricos. 2019b. ISSN 2318-0358.
TOFFOLON M, PICCOLROAZ S, MAJONE B, SOJA AM, PEETERS F, SCHMID M, WUEST A. Prediction of surface temperature in lakes with different morphology using air temperature. Limnol. Oceanogr. 2014; 59(6):2185-2202.
TORBICK NB, ZINITI B, WU S, LINDER E. Spatiotemporal Lake Skin Summer Temperature Trends in the Northeast United States. Earth Interactions. 2016; 20:1-25.
WAN, Z. New refinements and validation of the MODIS land-surface temperature emissivity products. Remote Sens. Environ. 112, 59–74, 2008. https://doi.org/10.1016/j.rse.2006.06.026.
WAN, Z.; DOZIER, J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens. 34, 892-905, 1996.
WENG Q, FU P, GAO F. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sensing of Environment. 2014, 145:55-67.
WLOCZYK, Carolin, et al. Sea and lake surface temperature retrieval from Landsat thermal data in Northern Germany. International Journal of Remote Sensing, 2006, vol. 27, no 12, p. 2489-2502.
WOOLWAY, R. I. et al. Diel surface temperature range scales with lake size. PLoS One, 11(3), e0152466), 2016. doi:10.1371/journal. pone.0152466.
YANG K, YU Z, LUO Y, YANG Y, ZHAO L, ZHOU X. Spatial and temporal variations in the relationship between lake water surface temperatures and water quality – A case study of Dianchi Lake. Science of the Total Environment. 2018;624:859-871.
YANG, Kun, et al. Spatial‐Temporal Variation of Lake Surface Water Temperature and Its Driving Factors in Yunnan‐Guizhou Plateau. Water Resources Research, 2019, vol. 55, no 6, p. 4688-4703.
ZHANG, Qi, et al. An investigation of enhanced recessions in Poyang Lake: comparison of Yangtze River and local catchment impacts. Journal of Hydrology, 2014, vol. 517, p. 425-434.
Downloads
Published
How to Cite
Issue
Section
License
To access the DECLARATION AND TRANSFER OF COPYRIGHT AUTHOR’S DECLARATION AND COPYRIGHT LICENSE click here.
Ethical Guidelines for Journal Publication
The Ciência e Natura journal is committed to ensuring ethics in publication and quality of articles.
Conformance to standards of ethical behavior is therefore expected of all parties involved: Authors, Editors, Reviewers, and the Publisher.
In particular,
Authors: Authors should present an objective discussion of the significance of research work as well as sufficient detail and references to permit others to replicate the experiments. Fraudulent or knowingly inaccurate statements constitute unethical behavior and are unacceptable. Review Articles should also be objective, comprehensive, and accurate accounts of the state of the art. The Authors should ensure that their work is entirely original works, and if the work and/or words of others have been used, this has been appropriately acknowledged. Plagiarism in all its forms constitutes unethical publishing behavior and is unacceptable. Submitting the same manuscript to more than one journal concurrently constitutes unethical publishing behavior and is unacceptable. Authors should not submit articles describing essentially the same research to more than one journal. The corresponding Author should ensure that there is a full consensus of all Co-authors in approving the final version of the paper and its submission for publication.
Editors: Editors should evaluate manuscripts exclusively on the basis of their academic merit. An Editor must not use unpublished information in the editor's own research without the express written consent of the Author. Editors should take reasonable responsive measures when ethical complaints have been presented concerning a submitted manuscript or published paper.
Reviewers: Any manuscripts received for review must be treated as confidential documents. Privileged information or ideas obtained through peer review must be kept confidential and not used for personal advantage. Reviewers should be conducted objectively, and observations should be formulated clearly with supporting arguments, so that Authors can use them for improving the paper. Any selected Reviewer who feels unqualified to review the research reported in a manuscript or knows that its prompt review will be impossible should notify the Editor and excuse himself from the review process. Reviewers should not consider manuscripts in which they have conflicts of interest resulting from competitive, collaborative, or other relationships or connections with any of the authors, companies, or institutions connected to the papers.
