Spatial downscaling of AIRS-derived column water vapor using ratio model to improve LST retrieval

Document Type : Original Article

Author

University of Isfahan

Abstract

Atmospheric column water vapor, which is the total atmospheric precipitable water vapor contained in a vertical air column, is one of the most important factors in all surface-atmosphere interactions (such as energy fluxes between the earth and the atmosphere) and plays a key role in wide variety of environmental studies, ecological and agricultural applications. However, measuring this parameter at meteorological stations requires the use of radiosonde instruments, which being pointwise and costly are limitations of these observations. Therefore, remote sensing is used as an alternative to estimate this important atmospheric parameter. Compared to other atmospheric parameters, atmospheric water vapor which attenuates remotely sensed radiance is of great importance. Although this atmospheric parameter is measured by AIRS (Atmospheric Infrared Sounder) sensor, its low resolution (about 40 km) is not acceptable for many applications. Therefore, developing an algorithm to downscale the AIRS-derived column water vapor is the main goal of this study, so that its spatial resolution can be improved. To do this, using the ratio method, the AIRS-derived column water vapor is fused with the MODIS (Moderate Resolution Imaging spectroradiometer) data. Then, due to the major influence of this parameter on Land Surface Temperature (LST) estimation, the role of improved resolution atmospheric column water vapor in the estimation of LST is investigated as a secondary goal. In order to validate the estimated parameters and evaluate their accuracy, independent datasets were used. Results of the implementation indicate that proposed downscaling method has high potential to enhance the spatial resolution of AIRS-derived atmospheric column water vapor, without significant degradation of the RMSE. It was also found that the atmospheric column water vapor when moving into higher spatial resolution can dramatically increase the accuracy of the LST estimation.

Keywords


بیات، ع.، مشهدی‌زادة ملکی، س.، 1394، اعتبارسنجی داده‌های بخار آب قابل بارش مدل  ERA-Interim  و سنجندة AIRS با اندازه‌گیری شیدسنج، سومین همایش منطقه ای تغییر اقلیم و گرمایش زمین.
مباشری، م.ر.، پورباقر کردی، س.م.، فرج‌زاده، م.، صادقی نایینی، ع.، 1389، برآورد آب قابل بارش کلی با استفاده از تصاویر ماهواره‌ای MODIS و داده‌های رادیوساوند: ناحیة تهران، فصلنامة برنامه‌ریزی و آمایش فضا، سال 14، شمارة 1 (پیاپی 65)، صص. 126-107.
Alkasim, A.,  Hayatu, A.A. & Salihu, M.K., 2018, Estimation of Land Surface Temperature of Yola, North Eastern Nigeria Using Landsat-7 ETM+ Satellite Image, Energy and Power Engineering, 10(10), PP. 449-456.
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, PP. 3117-3132.
Aumann, H.H., Chahine, M.T.,  Gautier, C., Goldberg, M.D., Kalnay, E., McMillin, L.M.  & Revercomb, H., 2003, AIRS/AMSU/HSB on the Aqua Mission: Design, Science Objectives, Data Products, and Processing Systems, IEEE Transactions on Geoscience and Remote Sensing, 41(2), PP. 253-264.
Becker, F. & Li, Z.L., 1990, Temperature Independent Spectral Indices in Thermal Infrared Bands, Remote Sensing of Environment, 32, PP. 17-33.
Cristóbal, J., Jiménez‐Muñoz, J.C., Sobrino, J.A., Ninyerola, M. & Pons, X., 2009, Improvements in Land Surface Temperature Retrieval from the Landsat Series Thermal Band Using Water Vapor and Air Temperature, Journal of Geophysical Research Atmospheres, 114, P. D8.
Gao, B.C., 2003, Water Vapor Retrievals Using Moderate Resolution Imaging Spectro-Radiometer (MODIS) Near-Infrared Channels, Journal of Geophysical Research, 108, P. D13.
Hausmann, P., Sussmann, R., Trickl, T. & Schneider, M., 2017, A Decadal Time Series of Water Vapor and D/ H Isotope Ratios above Zugspitze: Transport Patterns to Central Europe, Atmospheric Chemistry and Physics, 17, PP. 7635–7651.
Kaufman, Y.J. & Gao, B.C., 1992, Remote Sensing of Water Vapor in the Near IR from EOS/MODIS, IEEE Translation of Geoscience and Remote Sensing, 30(5), PP. 871-884.
 
Momeni, M. & Saradjian, M.R., 2007, Evaluating NDVI-Based Emissivities of MODIS Bands 31 and 32 Using Emissivities Derived by Day/Night LST Algorithm, Remote Sensing of Environment, 106(2), PP. 190-198.
Moradizadeh, M., Momeni, M. & Saradjian, M.R., 2008a, Estimation of LST Using the Radiance Values of MODIS and Comparison with Meteorological Data, Conference of Map Asia, Kuala Lumpur, Malaysia.
Moradizadeh, M., Momeni, M. & Saradjian, M.R., 2008b, Estimation of Atmospheric Column and Near Surface Water Vapor Content Using the Radiance Values of MODIS, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, China, PP. 523-528.
Moradizadeh, M., Momeni, M. & Saradjian, M.R., 2013, 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(5), PP. 1891-1897.
Moradizadeh, M. & Saradjian, M.R., 2018, Estimation of Improved Resolution Soil Moisture in Vegetated Areas Using Passive AMSR-E Data, Journal of Earth System Science, 127(2), P. 24.
Peng, W., Tongchuan, X., Jiageng, D., Jingmin, S., Yanling, W., Qingli, S., Xin, D., Hongliang, Y., Dejun, S. & Jinrong, Z., 2017, Trends and Variability in Precipitable Water Vapor throughout North China from 1979 to 2015, Advances in Meteorology, 2017, 7804823.
Price, J.C., 1983, Estimating Surface Temperature from Satellite Thermal Infrared Data, Remote Sensing of Environment, 13, PP. 112-134.
Rama Varma Raja, M.K., Gutman, S., Yoe, J.G., McMillin, L.M. & Zhao, J., 2008, The Validation of AIRS Retrievals of Integrated Precipitable Water Vapor Using Measurements from a Network of GroundBased GPS Receivers over the Contiguous United States, Journal of Atmospheric and Oceanic Technology, 25(3), PP. 416- 428.
Schroedter-Homscheidt, M., Drews, A. & Heise, S., 2008, Total Water Vapor Column Retrieval from MSG-SEVIRI Split Window Measurements Exploiting the Daily Cycle of Land Surface Temperatures, Remote Sensing of Environment, 112(1), PP. 249-258.
Sobrino, J.A., El. Kharraz, J. & Li, Z.L., 2003, Surface Temperature and Vapor Retrieval from MODIS Data, International Journal of Remote Sensing, 24(24), PP. 5161-5182.
Tang, M.C., Agarwal, S., Alsewailem, F.D., Choi, H.J. & Gupta, R.K., 2018, Journal of Applied Polymer Science, 135(30), P. 46506.
Zhang, Q., Xu, C.Y., Zhang, Z. & Chen, Y.D., 2010, Changes of Atmospheric Water Vapor Budget in the Pearl River Basin and Possible Implications for Hydrological Cycle, Theoretical and Applied Climatology,102, PP. 185-195.