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Assessment of satellite images terrestrial surface temperature and WVP using GNSS radio occultation data

  • Aya M. Megahed ORCID logo EMAIL logo , Ibrahim F. Ahmed , Heba S. Tawfik ORCID logo and Gamal S. El-Fiky
Published/Copyright: December 5, 2023
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Abstract

Land Surface Temperature (LST) and Water Vapor Pressure (WVP) contour maps can be produced using cameras aboard satellites, for instance, under the name “Remote Sensing (RS)”. Satellite image observations should be verified before using based on a reliable data. Global Navigation Satellite System Radio Occultation (GNSS-RO) method is observing accurate Earth atmosphere parameters continuously. In the present research, LST and WVP differences between Landsat 8 (LC08), Sentinel-3 (S3), and MODIS (Terra and Aqua) images and GNSS-RO are assessed in Egypt depending on the satellites operating periods and data availability during the years from 2015 to 2020. Statistically, S3 and Terra have insignificant differences with RO temperature with an average bias of 3.48 °C and 1.47 °C, respectively, but LC08 and Aqua have significant differences with it. For WVP, Aqua and LC08 have insignificant differences with an average bias of 0.02 kg/m2 and 2.31 kg/m2, respectively, but S3 and Terra have significant differences with RO observations. When comparing LC08 LST data to other satellites, it was found that there were insignificant differences between LC08 and S3 as well as Terra. However, significant differences were observed when comparing LC08 LST data to Aqua. Additionally, significant differences were noted when comparing LC08 WVP data to other satellites. In response to these differences, improvement models have been developed to enhance the estimation of terrestrial data through remote sensing, particularly for satellites that exhibited significant disparities when compared to reference observations (RO).


Corresponding author: Aya M. Megahed, Construction Department and Utilities, Faculty of Engineering, Zagazig University, Zagazig, Egypt, E-mail:

References

1. Mondejar, T1, Tongco, AF. Near infrared band of Landsat 8 as water index: a case study around Cordova and Lapu-Lapu City, Cebu, Philippines. Sustain Environ Res 2019;29:16. https://doi.org/10.1186/s42834-019-0016-5.Search in Google Scholar

2. Zhang, X, Pazner, M, Duke, N. Lithologic and mineral information extraction for gold exploration using ASTER data in the south Chocolate Mountains (California). ISPRS J Photogrammetry Remote Sens 2007;62:271–82. https://doi.org/10.1016/j.isprsjprs.2007.04.004.Search in Google Scholar

3. Duan, S, Li, Z, Wu, H, Leng, P, Gao, M, Wang, C. Radiancebased validation of land surface temperature products derived from collection6 MODIS thermal infrared data. Int J Appl Earth Geoinform 2018;70:84–92. 1569–8432. https://doi.org/10.1016/j.jag.2018.04.006.Search in Google Scholar

4. Malakar, NK, Hulley, GC, Hook, SJ, Laraby, K, Cook, M, Schott, JR. An operational land surface temperature product for landsat thermal data: methodology and validation. IEEE Trans Geosci Remote Sens 2018;56:10–5735. 5717–5735 https://doi.org/10.1109/TGRS.2018.2824828.Search in Google Scholar

5. Xu, X, Zou, X. Estimating GPS radio occultation observation error standard deviations over China using the three-cornered hat method. Q J R Meteorol Soc 2020;147:734–659. 647–659. https://doi.org/10.1002/qj.3938.Search in Google Scholar

6. Ansari, M. I and Sc-‘E’. Upper Air Observations. National Meteorological Library and Archive, Factsheet 13, Core Member-WMO-Task Team Upper Air Intercomparison 2021. (TT-UAI) Upper Air Instruments Division India Meteorological Department.Search in Google Scholar

7. Kursinski, ER, Hajj, GA, Schofield, JT, Linfield, RP, Rer, HK. Observing Earth’s atmosphere with radio occultation measurements using the Global Positioning System. Wiley Online Libr J Geophys Res Atmosp 1997;102: 23429–65, 0148–0227.10.1029/97JD01569Search in Google Scholar

8. Li, F, Hou, C, Kan, L, Fu, N, Wang, M, Wang, Z. Mountain top-based atmospheric radio occultation observations with open/closed loop tracking: experiment and validation. Experiment and validation. Rem Sens 2020;12:4078. https://doi.org/10.3390/rs12244078.Search in Google Scholar

9. Yu, K, Rizos, C, Burrage, D, Dempster, GA, Zhang, K, Markgraf, M. An overview of GNSS remote sensing. EURASIP J Appl Signal Process 2014;2014:134. https://doi.org/10.1186/1687-6180-2014-134.Search in Google Scholar

10. Ghonim, IF, Mousa, AE, El-Fiky, G. GNSS-RO LEO satellite orbit optimization for Egypt and the Middle East region. Alex Eng J 2020;59:389–97. https://doi.org/10.1016/j.aej.2020.01.006.Search in Google Scholar

11. Li, K, Guan, K, Jiang, C, Wang, S, Peng, B, Cai, Y. Evaluation of four new land surface temperature (LST) products in the U.S. Corn Belt: ECOSTRESS, GOES-R, landsat, and sentinel-3. IEEE J Sel Top Appl Earth Obs Rem Sens 2021;14:9931–45. https://doi.org/10.1109/JSTARS.2021.3114613.Search in Google Scholar

12. Digital elevation model (DEM). https://blog.arabnubia.com/2020/05/egypt-nasadem-digital-elevation-model-1-arc-second/ [Accessed 16 Apr 2022].Search in Google Scholar

13. United States Geological Survey (USGS) and remote sensing. https://www.usgs.gov/faqs/what-remote-sensing-and-what-it-used [Accessed 4 Feb 2022].Search in Google Scholar

14. Acharya, DT, Yang, I. Exploring landsat 8. Int J IT Eng Appl Sci Res 2015;4:4. 10, 2319–4413.Search in Google Scholar

15. Ren, H, Du, C, Liu, R, Qin, Q, Yan, G, Li, Z-L, et al.. Atmospheric water vapor retrieval from Landsat 8 thermal infrared images. J Geophys Res Atmos 2015;120:1723–38. https://doi.org/10.1002/2014JD022619.Search in Google Scholar

16. Avdan, U, Jovanovska, G. Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. J Sens 2016;2016:1480307–8. https://doi.org/10.1155/2016/1480307.Search in Google Scholar

17. Li, K, Guan, K, Jiang, C, Wang, S, Peng, B, Cai, Y. Evaluation of four new land surface temperature (LST) products in the U.S. Corn Belt: ECOSTRESS, GOES-R, landsat, and sentinel-3. IEEE J Sel Top Appl Earth Obs Rem Sens 2021;14:9931–45. https://doi.org/10.1109/JSTARS.2021.3114613.Search in Google Scholar

18. European space agency (ESA) and European operational satellite agency for monitoring weather, climate and the environment from space (EUMETSAT). https://sentinels.copernicus.eu/web/sentinel/-missions/sentinel-3 [Accessed 4 Jan 2022].Search in Google Scholar

19. Moderate resolution imaging Spectroradiometer (MODIS). https://modis.gsfc.nasa.gov/ [Accessed 4 Feb 2022].Search in Google Scholar

20. Varamesh, S, Hosseini, SM, Rahimzadegan, M. Estimation of atmospheric water vapor using MODIS data 1 (case study: Golestan Province of Iran). J Mater Environ Sci 2017;8:1690–5.Search in Google Scholar

21. Environmental Systems Research (ESRI). Institute. ArcGIS 10.2 for (Desktop, engine, Server) landsat 8 Patch. http://support.esri.com/\en/downloads/patches,servicepacks/view/productid/66/metaid/2012 [Accessed 16 Apr 2022].Search in Google Scholar

22. Wan, Z. MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (LST ATBD). Version 3.3. Washington DC: National Aeronautics and Space US Department of Commerce; 1999.Search in Google Scholar

Received: 2023-10-04
Accepted: 2023-11-03
Published Online: 2023-12-05
Published in Print: 2024-07-26

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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