DOI: https://doi.org/10.14710/geoplanning.6.1.1-12

Evaluation of MODIS-Derived LST Products with Air Temperature Measurements in Cyprus

*Andreas Marios Georgiou  -  Enalia Physis Environmental Research Center (ENALIA), Cyprus
Stefani Theofanis Varnava  -  Department of Geography, University of the Aegean, Greece

Citation Format:
Abstract

Air temperature data is usually obtained from measurements made in meteorological stations, providing only limited information about spatial patterns over wide areas. The use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of air surface temperature at both regional and global scales. Land Surface (skin) Temperatures (LST) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms provide spatial estimates of near-surface temperature values. In this study, LST values from MODIS are compared to ground-based near surface air (Tair) measurements obtained from 4 observational stations during 2011 to 2015, covering coastal, mountainous and urban areas over Cyprus. Combining Terra and Aqua LST-8 Day and Night acquisitions into a mean 8-day value, provide a large number of LST observations and a better overall agreement with Tair. Comparison between mean monthly LSTs and mean monthly Tair for all sites and all seasons pooled together yields a very high correlations (r > 0.96) and biases ranging from 1.9oC to 4.1oC. MODIS capture overall variability with a slightly systematic overestimation of seasonal fluctuations of surface temperature. For the evaluation of intra-seasonal temperature variability, MODIS showed biases up to 6.7oC in summer with a tendency to overestimate the variability while in cold seasons, limited biases were presented (0.10oC ± 0.50oC) with a tendency to underestimate the variability. Finally, there was no indication of tendency for MODIS to systematically under- or overestimate the amplitude of the inter-annual variability analysis. The presented high standard deviation can be explained by the influence of surface heterogeneity within MODIS 1km2 grid cells, the presence of undetected clouds and the inherent difference between LST and Tair. Overall, MODIS LST data proved to be a reliable proxy for surface temperature and mostly for studies requiring temperature reconstruction in areas with lack of observational stations.

Fulltext View|Download
Keywords: MODIS; Land Surface Temperature; Air Temperature; Regression analysis; Cyprus

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
Assessing Landscape Pattern Relationship with Dengue Incidence in Peninsular Malaysia Geometric Accuracy Assessment for Shoreline Derived from NDWI, MNDWI, and AWEI Transformation on Various Coastal Physical Typology in Jepara Regency using Landsat 8 OLI Imagery in 2018 Monte Carlo Simulation for Outlier Identification Studies in Geodetic Network: An Example in A Levelling Network Using Iterative Data Snooping More recent articles
Most cited articles
REFLEKSI 5 TAHUN PASKA ERUPSI GUNUNG MERAPI 2010: MENAKSIR KERUGIAN EKOLOGIS DI KAWASAN TAMAN NASIONAL GUNUNG MERAPI ASSESSMENT OF MANGROVE FOREST DEGRADATION THROUGH CANOPY FRACTIONAL COVER IN KARIMUNJAWA ISLAND, CENTRAL JAVA, INDONESIA PEMANFAATAN CITRA SATELIT UNTUK PENENTUAN LAHAN KRITIS MANGROVE DI KECAMATAN TUGU, KOTA SEMARANG QUALITY ANALYSIS OF SINGLE TREE OBJECT WITH OBIA AND VEGETATION INDEX FROM LAPAN SURVEILLANCE AIRCRAFT MULTISPECTRAL DATA IN URBAN AREA THE PERFORMANCE OF LAND USE CHANGE CAUSATIVE FACTOR ON LANDSLIDE SUSCEPTIBILITY MAP IN UPPER UJUNG-LOE WATERSHEDS SOUTH SULAWESI, INDONESIA More cited articles

  1. Anderson, M., Norman, J., Kustas, W., Houborg, R., Starks, P., & Agam, N. (2008). A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sensing of Environment, 112(12), 4227–4241. https://doi.org/10.1016/j.rse.2008.07.009">[Crossref]

  2. Anderson, S. (2002). An evaluation of spatial interpolation methods on air temperature in Phoenix, AZ. Department of Geography, Arizona State University Tempe, 85287, 104.

  3. Benali, A., Carvalho, A. C., Nunes, J. P., Carvalhais, N., & Santos, A. (2012). Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment, 124, 108–121. https://doi.org/10.1016/j.rse.2012.04.024">[Crossref]

  4. Bernardello, R., Serrano, E., Coma, R., Ribes, M., & Bahamon, N. (2016). A comparison of remote-sensing SST and in situ seawater temperature in near-shore habitats in the western Mediterranean Sea. Marine Ecology Progress Series, 559, 21–34. https://doi.org/10.3354/meps11896">[Crossref]

  5. Caparrini, F., Castelli, F., & Entekhabi, D. (2004). Variational estimation of soil and vegetation turbulent transfer and heat flux parameters from sequences of multisensor imagery. Water Resources Research, 40(12). https://doi.org/10.1029/2004wr003358">[Crossref]

  6. Crosson, W. L., Al-Hamdan, M. Z., Hemmings, S. N. J., & Wade, G. M. (2012). A daily merged MODIS Aqua-Terra land surface temperature data set for the conterminous United States. Remote Sensing of Environment, 119, 315–324. https://doi.org/10.1016/j.rse.2011.12.019">[Crossref]

  7. Ermida, S. L., Trigo, I. F., DaCamara, C. C., Göttsche, F. M., Olesen, F. S., & Hulley, G. (2014). Validation of remotely sensed surface temperature over an oak woodland landscapetextemdash The problem of viewing and illumination geometries. Remote Sensing of Environment, 148, 16–27. https://doi.org/10.1016/j.rse.2014.03.016">[Crossref]

  8. Florio, E. N., Lele, S. R., Chang, Y. C., Sterner, R., & Glass, G. E. (2004). Integrating AVHRR satellite data and NOAA ground observations to predict surface air temperature: a statistical approach. International Journal of Remote Sensing, 25(15), 2979–2994. https://doi.org/10.1080/01431160310001624593">[Crossref]

  9. Georgiou, A., & Akçit, N. (2016). Investigation of Sea Surface Temperature (SST) anomalies over Cyprus area. In K. Themistocleous, D. G. Hadjimitsis, S. Michaelides, & G. Papadavid (Eds.), Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy 2016). inproceedings, SPIE. https://doi.org/10.1117/12.2241893">[Crossref]

  10. Hachem, S., Duguay, C. R., & Allard, M. (2012). Comparison of MODIS-derived land surface temperatures with ground surface and air temperature measurements in continuous permafrost terrain. The Cryosphere, 6(1), 51–69.  https://doi.org/10.5194/tc-6-51-2012">[Crossref]

  11. Hengl, T., Heuvelink, G. B. M., Tadić, M. P., & Pebesma, E. J. (2011). Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Theoretical and Applied Climatology, 107(1–2), 265–277. [https://doi.org/10.1007/s00704-011-0464-2">Crossref]

  12. Kustas, W. P., & Norman, J. M. (1996). Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrological Sciences Journal, 41(4), 495–516. https://doi.org/10.1080/02626669609491522">[Crossref]

  13. Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., … Sobrino, J. A. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131, 14–37. https://doi.org/10.1016/j.rse.2012.12.008">[Crossref]

  14. Michaelides, S. C., Tymvios, F. S., & Michaelidou, T. (2009). Spatial and temporal characteristics of the annual rainfall frequency distribution in Cyprus. Atmospheric Research, 94(4), 606–615. https://doi.org/10.1016/j.atmosres.2009.04.008">[Crossref]

  15. Mostovoy, G. V, King, R. L., Reddy, K. R., Kakani, V. G., & Filippova, M. G. (2006). Statistical Estimation of Daily Maximum and Minimum Air Temperatures from MODIS LST Data over the State of Mississippi. GIScience & Remote Sensing, 43(1), 78–110. https://doi.org/10.2747/1548-1603.43.1.78">[Crossref]

  16. Nemani, R., Pierce, L., Running, S., & Goward, S. (1993). Developing Satellite-derived Estimates of Surface Moisture Status. Journal of Applied Meteorology, 32(3), 548–557. https://doi.org/10.1175/1520-0450(1993)032%3c0548:dsdeos%3e2.0.co;2">[Crossref]

  17. Vancutsem, C., Ceccato, P., Dinku, T., & Connor, S. J. (2010). Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sensing of Environment, 114(2), 449–465. https://doi.org/10.1016/j.rse.2009.10.002">[Crossref]

  18. Vogt, J. V, Viau, A. A., & Paquet, F. (1997). Mapping regional air temperature fields using satellite-derived surface skin temperatures. International Journal of Climatology, 17(14), 1559–1579. https://doi.org/10.1002/(sici)1097-0088(19971130)17:14%3c1559::aid-joc211%3e3.3.co;2-x">[Crossref]

  19. Wan, Z. (1999). MODIS land-surface temperature algorithm theoretical basis document (LST ATBD). Institute for Computational Earth System Science, Santa Barbara, 75.

  20. Wan, Z. (2008). New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products. Remote Sensing of Environment, 112(1), 59–74. https://doi.org/10.1016/j.rse.2006.06.026">[Crossref]

  21. Wan, Z., Wang, P., & Li, X. (2004). Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA. International Journal of Remote Sensing, 25(1), 61–72. https://doi.org/10.1080/0143116031000115328">[Crossref]

  22. Wang, K. (2005). Estimation of surface long wave radiation and broadband emissivity using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature/emissivity products. Journal of Geophysical Research, 110(D11). https://doi.org/10.1029/2004jd005566">[Crossref]

  23. Wang, W., Liang, S., & Meyers, T. (2008). Validating MODIS land surface temperature products using long-term nighttime ground measurements. Remote Sensing of Environment,   623–635. https://doi.org/10.1016/j.rse.2007.05.024">[Crossref]

  24. Yu, W., Ma, M., Wang, X., Geng, L., Tan, J., & Shi, J. (2014). Evaluation of MODIS LST Products Using Longwave Radiation Ground Measurements in the Northern Arid Region of China. Remote Sensing, 6(11), 11494–11517. https://doi.org/10.3390/rs61111494">[Crossref]

  25. Zhu, W., Lu, A., & Jia, S. (2013). Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products. Remote Sensing of Environment, 130, 62–73. https://doi.org/10.1016/j.rse.2012.10.034">[Crossref]


Last update:

  1. Downscaling Satellite Retrieved Soil Moisture Using Regression Tree‐Based Machine Learning Algorithms Over Southwest France

    Yangxiaoyue Liu, Xiaolin Xia, Ling Yao, Wenlong Jing, Chenghu Zhou, Wumeng Huang, Yong Li, Ji Yang. Earth and Space Science, 7 (10), 2020. doi: 10.1029/2020EA001267

  2. Evaluation of SWAT Model in Runoff Simulation Using Rainfall and Temperature Derived From Satellite Images

    Mohammd Tavosi, Mehdi Vafakhah, Vahid Moosavi. Iranian Journal of Science and Technology, Transactions of Civil Engineering, 48 (6), 2024. doi: 10.1007/s40996-024-01597-1

Last update: 2024-11-21 21:36:44

  1. Downscaling Satellite Retrieved Soil Moisture Using Regression Tree-Based Machine Learning Algorithms Over Southwest France

    Liu Y.. Earth and Space Science, 7 (10), 2020. doi: 10.1029/2020EA001267