Desert Management

Desert Management

Comparison of the Accuracies of Different Methods for Estimating Atmospheric Water Vapor in the Retrieval of Land Surface Temperature Using Landsat 8 Images

Document Type : Original Article

Authors
Fahime Arabi Aliabad 1
Mohammad Zare 2
Hamid Reza Ghafarian Malamiri 3
1 Ph.D. Candidate, Combating Desertification, School of Natural Resources & Desert Studies, Department of Arid Land and Desert Management, Yazd University, Yazd, Iran.
2 Associate Professor, School of Natural Resources & Desert Studies, Department of Arid Land and Desert Management, Yazd University, Yazd, Iran.
3 Assistant Professor, Department of Geography, Yazd University, Yazd, Iran.
10.22034/jdmal.2021.244523
Abstract
Temperature is one of the most important physical parameters that control the transfer and exchange of energy between different layers of the earth and the atmosphere. LST estimation methods based on satellite images require surface and atmospheric parameters such as surface emissivity, average air temperature, atmospheric transfer coefficient, and water vapor as input. Uncertainty in these parameters causes errors in the retrieval of land surface temperature. This study aimed to compare the accuracy of different methods for estimating atmospheric water vapor in estimating land surface temperature using Landsat 8 images. In this study, atmospheric water vapor was estimated using FLAASH atmospheric correction methods, MODIS sensor images, and SWCVR method. Then, the impact of atmospheric water vapor on land surface temperature accuracy was investigated using the split window and single-channel methods. Validation of Land surface temperature images was performed using cross-validation and ground measurement methods. Therefore, 20 Landsat 8 images related to 2018 and 2019 were used to estimate atmospheric water vapor by the FLAASH atmospheric correction and SWCVR methods, and land surface temperature estimation. MODIS radiance images were used to estimate atmospheric water vapor and the land surface temperature product of this sensor was used for cross-validation. The surface temperature was measured using a thermometer in places with homogeneous cover, for ground-based validation. Results showed that among water vapor estimation methods, the SWCVR method is more suitable for estimating land surface temperature and the split-window method based on the SWCVR method shows the lowest RMSE and MADE at 3.47 and 3.18. Results of RMSE image classification of split-window algorithm based on the SWCVR showed that 1.67% of the area has an error of more than 4 °C and 98% of the study area has less than 4 °C error.
Keywords
Split-window
Land Surface Temperature
Thermal remote sensing
MODIS
Yazd
  1. Albert, P., Bennartz, R., Preusker, R., Leinweber, R., & Fischer, J. (2005). Remote sensing of atmospheric water vapor using the moderate resolution imaging spectroradiometer. Journal of Atmospheric and Oceanic Technology, 22 (3), 309-314.
  2. Allan, R. P., Lavers, D. A., & Champion, A. J., (2016). Diagnosing links between atmospheric moisture and extreme daily precipitation over the UK, International Journal of Climatology, 36 (9), 3191–3206.
  3. Alshawaf, F., Balidakis, K., Dick, G., Heise, S., & Wickert, J. (2017). Estimating trends in atmospheric water vapor and temperature time series over Germany. Atmospheric Measurement Techniques, 10, 3117-3132.
  4. Arabi Aliabad, F., Zare, M., Ghafarian Malamiri, H. (2020). A comparative assessment of the accuracies of split-window algorithms for retrieving of land surface temperature using Landsat 8 data. Modeling Earth Systems and Environment, 2, 260-272.
  5. Berk, A. L.S., Bernstein, D.C., Robertson, P.K., Acharya, G.P., & Chetwynd, J.H. (1996).  MODTRAN Cloud and Multiple Scattering Upgrades with Application to AVIRIS, Summaries of the Sixth Annual JPL Airborne Earth Science Workshop, JPL Publication, Pasadena, California, 1-7.
  6. Bhatia, N., Stein, A., Reusen, I., & Tolpekin, V. A. (2018). An optimization approach to estimate and calibrate column water vapour for hyperspectral airborne data. International journal of remote sensing, 39 (8), 2480-2505.
  7. Buntoung, S., Janjai, S., Nunez, M., Pattarapanitchai, S., Nimnuan, P., & Pariyothon, J. (2020). Spatial and temporal changes of precipitable water vapour in Thailand. Physical Geography, 41 (5), 467-488.
  8. Carlson T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index, Remote Sensing of Environment, 62, 241-252.
  9. Carlson, T. N., Perry, EM., & Schmugge, T. J., (1990). Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agricultural and Forest Meteorology, 52, 45–69.
  10. Chander, G., Markham, B.L., & Helder, D.L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote sensing of environment, 113 (5), 893-903.
  11. Coll, C., Caselles, V., Valor, E., & Niclòs, R. (2012). Comparison between different sources of atmospheric profiles for land surface temperature retrieval from single channel thermal infrared data. Remote Sensing of Environment, 117, 199–210.
  12. Cristóbal, J., Jiménez-Muñoz, J., Prakash, A., Mattar, C., Skoković, D., & Sobrino, J. (2018). An improved single-channel method to retrieve land surface temperature from the Landsat-8 Thermal Band. Remote Sensing, 10 (3), 431.
  13. Ding, H. and Shi, W. (2013). Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City, International Journal of Remote Sensing, 34 (15), 5503-5517.
  14. Dymond, J.R., Stephens, P.R., Newsome, P.F., & Wilde, R.H.  (1992). Percentage vegetation cover of a degrading rangeland from SPOT. International Journal of Remote Sensing. 13, 1999–2007.
  15. Felde, G. W., G. P. Anderson, S. M. Adler-Golden, M. W. Matthew & A. Berk, (2003). Analysis of Hyperion Data with the FLAASH Atmospheric Correction Algorithm. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX. SPIE Aerosense Conference, Orlando. 21-25 April 2003.
  16. Frey, R.A., Ackerman, S.A., Liu, Y., Strabala, K.I., Zhang, H., Key, J.R., & Wang, X. (2008). Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for collection 5. Journal of Atmospheric and Oceanic Technology, 25 (7), 1057-1072.
  17. Frouin, R., Deschamps, PY., Lecomte, P., (1989) Determination from space of atmospheric total water vapour amounts by differential absorption near 940 nm: theory and airborne verification. Journal of Applied Meteorology and Climatology, 29, 441-460.
  18. Gao, B. C., & Kaufman, Y. J. (2003). Water vapor retrievals using Moderate Resolution Imaging Spectroradiometer (MODIS) near‐infrared channels. Journal of Geophysical Research: Atmospheres, 108 (D13), 4389.
  19. He, J., & Liu, Z. (2019). Comparison of Satellite-Derived Precipitable Water Vapor Through Near-Infrared Remote Sensing Channels. IEEE Transactions on Geoscience and Remote Sensing, 57 (12), 10252-10262.
  20. Jedlovec, G. J. (1990). Precipitable Water Estimation from High-Resolution Split Window Radiance Measurements. Journal of Applied Meteorology, 29 (9), 863–877.
  21. Jiménez-Muñoz, J. C.  J. Cristóbal, J. A. Sobrino, G. Sòria, M. Ninyerola, & X. Pons. (2009). Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data‖, IEEE Transaction of Geosciences in Remote Sensing, 47 (1), 339–349.
  22. Jiménez-Muñoz, J.C., Sobrino, J.A., Jiménez, D., Mattar, C., & Cristóbal, J. (2014). Land Surface Temperature Retrieval Methods from Landsat-8 Thermal Infrared Sensor Data. IEEE Geoscience and Remote Sensing Letters, 11, 1840–1843.
  23. Kaufman, Y. J., & G, A.O. (1992). Remote sensing of water vapor in the near IR from EOS/MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30, 871–884.
  24. Kishore, P., Ratnam, M. V., Namboothiri, S. P., Velicogna, I., Basha,G., Jiang, J. H., Igarashi, K., Rao, S. V. B., & Sivakumar, V. (2011). Global (50 ◦ S–50 ◦ N) distribution of water vapor observed by COSMIC GPS RO: Comparison with GPS radiosonde, NCEP, ERA-Interim, and JRA-25, Journal of Atmospheric and Solar-Terrestrial Physics, 73, 1849–1860.
  25. Kleespies, T. J., & L. M. McMillin. (1984). Physical Retrieval of Precipitable Water Using the Split Window Technique, Conference on Satellite Meteorology/Remote Sensing and Applications, AMS, Boston.
  26. Li, Z. L., B. H. Tang, H., Wu, H. Z., Ren, G. J., Yan, Z. M., Wan, I. F., Trigo, & J. A. Sobrino. (2013). Satellite-Derived Land Surface Temperature: Current Status and Perspectives. Remote Sensing of Environment, 131, 14–37.
  27. Li, Z. L., Jia, L., Su, Z., Wan, Z., & Zhang, R. (2003). A new approach for retrieving precipitable water from ATSR2 split-window channel data over land area. International Journal of Remote Sensing, 24 (24), 5095-5117.
  28. Liu, C., Li, Y., Gao, W., Shi, R., & Bai, K. (2011). Retrieval of columnar water vapor using multispectral radiometer measurements over northern China. Journal of Applied Remote Sensing, 5 (1), 053558.
  29. Lu, L., Zhang, T., Wang, T., & Zhou, X. (2018).  Evaluation of Collection-6 MODIS Land Surface Temperature Product Using Multi-Year Ground Measurements in an Arid Area of Northwest China. Remote Sensing, 10, 1852.
  30. Malakar, N.K., Hulley, G.C., Hook, S.J., Laraby, K., Cook, M., & Schott, J.R. (2018). An operational land surface temperature product for Landsat thermal data: Methodology and validation. IEEE Transactions on Geoscience and Remote Sensing. 56, 5717–5735.
  31. Moradizadeh, M., Momeni, M. & Saradjian, M.R. (2014).  Estimation and validation of atmospheric water vapor content using a MODIS NIR band ratio technique based on AIRS water vapor products. Arabian Journal of Geosciences, 7, 1891–1897.
  32. Omidvar, J., Davari, K., Arshad, S., Mousavi bayegi, M., Akbari, M., Farid hosseini, A. (2012). Estimation of Evapotranspiration Actual Using Sensor Aster and Model Metric. Irrigation and Water Engineering, 3 (1), 38-49. (in Farsi)
  33. Padmanabhan, S., Reising, S. C., Vivekanandan, J., & Iturbide-Sanchez, F. (2009). Retrieval of atmospheric water vapor density with fine spatial resolution using three-dimensional tomographic inversion of microwave brightness temperatures measured by a network of scanning compact radiometers. IEEE Transactions on Geoscience and Remote Sensing, 47 (11), 3708-3721.
  34. Papandrea, E., Casadio, S., Castelli, E., Dinelli, B. M., De Grandis, E., & Bojkov, B. (2018). Validation of the Advanced Infra-Red Water Vapour Estimator (AIRWAVE) Total Column Water Vapour using Satellite and Radiosonde products. Annals of Geophysics, 61 (2018), 7524.
  35. Prince, S.D., Goetz, S.J., Dubayah, R., Czajkowski, K., & Thawley, M. (1998). Inference of surface and air temperature, atmospheric precipitable water and vapor pressure deficit using AVHRR satellite observations: validation of algorithms. Journal of Hydrology, 4, 230–24.
  36. Pouyan, S., Zare, M., Ekhtesasi, M., Mokhtari, M. (2019). Estimating of Surface Albedo in Geomorphological Facies of Desert Regions of the Yazd-Ardakan Plain Using Landsat 8 Data. Desert Management, 6 (12), 33-48. (in Farsi)
  37. Qian, Y. G., Li, Z.-L., & Nerry, F. (2013). Evaluation of land surface temperature and emissivities retrieved from MSG-SEVIRI data with MODIS land surface temperature and emissivity products. International Journal of Remote Sensing, 34 (9-10), 3140-3152.
  38. Rajeshwari, A., Mani, N. (2014). Estimationof Land Surface Temperature ofDindigul District Using Landsat 8 Data, International Journal of Research in Engineering and Technology, 3, 1250-1269.
  39. Ren, H., Du, C., Liu, R., Qin, Q., Yan, G., Li, Z. L., & Meng, J. (2015). Atmospheric water vapor retrieval from Landsat 8 thermal infrared images. Journal of Geophysical Research: Atmospheres, 120 (5), 1723-1738.
  40. Ren, H., Du, C., Qin, Q., Liu, R., Meng, J., & Li, J. (2014). Atmospheric water vapor retrieval from Landsat 8 and its validation. IEEE Geoscience and Remote Sensing Symposium, 3045-3048.
  41. Román, R., Antón, M., Cachorro, V., Loyola, D., Ortiz de Galisteo, J., de Frutos, A., Romero-Campos, P., 2015. Comparison of total water vapor column from GOME-2 on MetOp-A against ground-based GPS measurements at the Iberian Peninsula. Science of the Total Environment. 533, 317–328.
  42. Rouse, J.W, Haas, R.H., Scheel, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS. Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium, 1, 48-62.
  43. Sabol, D. E., Gillespie, A. R., Abbott, E., & Yamada, G. (2009). Field validation of the ASTER Temperature-Emissivity Separation algorithm. Remote Sensing of Environment, 113, 2328–2344.
  44. Sánchez, J. M., Galve, J. M., González-Piqueras, J., López-Urrea, R., Niclòs, R., & Calera, A. (2020). Monitoring 10-m LST from the Combination MODIS/Sentinel-2, Validation in a High Contrast Semi-Arid Agroecosystem, Remote Sensing, 12, 1453.
  45. Sanders, L.C., Schott, J.R., & Raquen, R., (2001). AVNIR/SWIR atmospheric correction algorithm for hyperspectral imagery with adjacency effect. Remote Sensing of Environment, 78, 252–263.
  46. Sobrino, J.A., Kharraz, J., & Li, Z.L., (2003). Surface temperature and water vapor retrieval from MODIS data. Remote Sensing, 24, 5161– 5182.
  47. Sobrino, J. A., Z.-L. Li, M. P. Stoll, & F, Becker. (1994). Improvements in the Split-Window Technique for Land Surface Temperature Determination. IEEE Transactions on Geoscience and Remote Sensing, 32, 243–253.
  48. Sobrino, J., & Raissouni, N. (2000). Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco. International Journal of Remote Sensing, 21, 353-366.
  49. Sobrino, J., Li, Z., Stoll, M., & Becker, F. (1996). Multi-channel and multi-angle algorithms for estimating sea and land surface temperature with ATSR data. International Journal of Remote Sensing, 17, 2089-2114.
  50. Stemn, E., & Kumi-Boateng, B., (2020). Modelling of land surface temperature changes as determinant of urban heat island and risk of heat-related conditions in the Wassa West Mining Area of Ghana. Modeling Earth Systems and Environment, 6, 1727–1740.
  51. Vaquero-Martínez, J., Antón, M., de Galisteo, J. P. O., Román, R., & Cachorro, V. E. (2018). Water vapor radiative effects on short-wave radiation in Spain. Atmospheric Research, 205, 18-25.
  52. Varade, D., & Dikshit, O. (2019). Improved Assessment of Atmospheric Water Vapor Content in the Himalayan Regions Around the Kullu Valley in India Using Landsat‐8 Data. Water Resources Research, 55, 462-475.
  53. Varamesh, S., Hosseini, S. M., & Rahimzadegan, M. (2017). Estimation of atmospheric water vapor using MODIS data (case study: Golestan province of Iran). Journal of Materials and Environmental Science, 8, 1690-1695
  54. Vlassova, L., Perez-Cabello, F., Nieto, H., Martín, P., Riaño, D., & De La Riva, J. (2014). Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sensing, 6, 4345-4368.
  55. Wan, Z., & Dozier, J., (1996). A generalized split-window algorithm for retrieving land-surface temperature measurement from space, IEEE Transactions on Geoscience and Remote Sensing, 34, 892–905.
  56. Wan, Z. & Li, Z.-L. (2008). Radiance-based validation of the V5 MODIS land-surface temperature product. International Journal of Remote Sensing, 29, 5373–5395.
  57. Wan. Z., Zhang, Y., Zhang, Q., and Li, Z. L., (2002). Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data, Remote Sensing of Environment, 83, 163-180.
  58. Wang, F., Qin, Z., Song, C., Tu, L., Karnieli, A., & Zhao, S. (2015). An improved mono-window algorithm for land surface temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sensing, 7, 4268-4289.
  59. Wang, L., Lu, Y., & Yao, Y. (2019). Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images. Sensors, 19 (22), 5049.
  60. Wang, M., G. He, & Z. Zhang. (2015). NDVI-Based Split-Window Algorithm for Precipitable Water Vapour Retrieval from Landsat-8 TIRS Data over Land Area. Remote Sensing Letters, 6, 904– 913.
  61. Wu, H., Ni, L., Qian, Y., Tang, B.-H., & Li, Z.-L. (2013). Estimation of atmospheric profiles from hyperspectral infrared IASI sensor. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6, 1485-1494.
  62. Yang, F., Guo, J., Shi, J., Zhao, Y., Zhou, L., & Song, S. (2019). A new method of GPS water vapor tomography for maximizing the use of signal rays. Applied Sciences, 9 (7), 1446.
  63. Yang, J., Duan, S.-B., Zhang, X., Wu, P., Huang, C., Leng, P., & Gao, M. (2020). Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval. Remote Sensing, 12, 780-791.
  64. Yu, X., Guo, X., & Wu, Z., (2014). Land surface temperature retrieval from Landsat 8 TIRS-comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing, 6, 9829–9852.
  65. Zare Ernani, M. (2009). Biophysical assessment of desertification in the Yazd-Ardakan basin, Iran. Ghent University. Faculty of Bioscience Engineering, Ghent, Belgium.
  66. Zhang, T., Wen, J., Van der Velde, R., Meng, X., Li, Z., Liu, Y., & Liu, R. (2008). Estimation of the total atmospheric water vapor content and land surface temperature based on AATSR thermal data. Sensors, 8 (3), 1832-1845.
  67. Zhang, Z., He, G., Wang, M., Long, T., Wang, G., Zhang, X., & Jiao, W. (2016). Towards an operational method for land surface temperature retrieval from Landsat 8 data. Remote sensing letters, 7, 279-288.
Desert Management
Volume 9, Issue 1 - Serial Number 17
6 Articles
Spring 2021
Pages 15-34

  • Receive Date 22 November 2020
  • Revise Date 08 January 2021
  • Accept Date 17 January 2021