Quality Assessment of MODIS Water Vapor Products in IR and Near-IR Bands over Iran

آب و خاک

Article 13, Volume 34, Issue 6 - Serial Number 74, January 2021, Pages 1367-1381   

Document Type: Research Article

DOI: 10.22067/jsw.v34i6.86574

Authors

A. Sam Khaniani^{* 1}; X. Nikraftar²

¹Babol Noshirvani University of Technology

²University of Tehran

Abstract

Introduction: Water vapor, as one of the most important greenhouse gases in the atmosphere, plays a key role in hydrological cycles, climate change, and the global climate. Many parameters for the expression of water vapor in the atmosphere have been proposed by meteorologists, one of which is Precipitable Water Vapor (PWV). There are many ground-based and space-based methods to measure PWV. Meanwhile, radiosonde is considered as one of the most common and traditional tools for measuring this parameter. However, low temporal resolution, high cost, and lack of uniform coverage across the globe are some of the limitations of this technique. In the last two decades, GPS Meteorology due to unique features such as usability in any weather conditions, long-term stability, continuous observations with very high resolution, low cost, and PWV estimation with an accuracy level of about 2 millimeters has received a lot of attention. Although radiosonde and GPS are precise methods for estimating water vapor in the atmosphere, their observations are limited to the land. While satellite remote sensing methods can provide continuous observations of the distribution of water vapor on a regional and global scale. MODIS is one of the sensors capable of measuring atmospheric water vapor measurements, which is onboard the Terra and Aqua satellites. However, PWV products obtained from remote sensing data should be evaluated with respect to the reliable in situ data before application. The main purpose of this study was to use PWV estimates obtained from ground-based GPS receivers in order to statistically evaluate the accuracy of MODIS water vapor products in IR and Near-IR bands and different times of the day over Iran.
Materials and Methods: The MODIS sensor, which is on board of the Terra and Aqua satellites, is able to provide water vapor products in the IR (both night and day) and Near-IR (day-only) bands. In order to evaluate MODIS PWV products over Iran, one year data of high temporal resolution GPS PWV values in 38 different stations in the country were considered as reliable values. For statistical analysis, water vapor values were extracted from the pixels with cloud-free conditions. Also, among the cloud-free pixels, that with the closest distance to the GPS station was selected. Moreover, the corresponding PWV values of GPS and MODIS with a maximum time difference of 10 minutes were selected for comparison.
Results and Discussion: Initially, Near-IR PWV products were assessed separately for Terra and Aqua satellite data. The results showed a good agreement between the two sets of PWV measurements. The correlation values between the GPS PWV and the corresponding values of the MODIS Near-IR products varied in the range of 0.90 to 0.98. Average bias values indicated that MODIS Near-IR overestimated PWV in comparison with GPS over Iran. In addition, a comparison of Near-IR water vapor values extracted from Terra and Aqua datasets separately showed that the data quality of both satellites in this band is almost at the same level in terms of the correlation coefficient, average bias, and RMSE. In the next step, the MODIS IR PWV products were evaluated separately during the day and night with respect to the corresponding values obtained at the GPS stations. The maximum correlation between GPS and IR PWV products during the day and night was 0.7 and 0.64, respectively. Furthermore, the average bias of MODIS IR PWV data in the study area for day and night was found to be -0.38 and 3.11 mm, respectively. In other words, MODIS IR PWV products in the study area had, on average, a positive bias with a small amount during the day and a significant negative bias during the night. On the other hand, a comparison of daytime MODIS IR and Near-IR water vapor products revealed that the quality of IR PWV data was significantly lower than the Near-IR band and requires a suitable calibration method.
Conclusion: The results of this study indicate that the MODIS Near-IR water vapor products had a high agreement with GPS PWV values with an average correlation coefficient of 0.95 in the study region. The mean bias and RMSE error of (GPS-MODIS Near-IR) PWV differences were -2.2 and 3.3 mm, respectively. A similar analysis of MODIS Near-IR PWV data from the Terra and Aqua satellites showed that almost both sets of water vapor data had the same accuracy. The average bias values of the MODIS IR PWV data compared to the GPS PWV for day and night were also investigated. Results showed that in the study area, MODIS IR products had a small positive bias during the day and significant negative bias at night. Examining the efficiency of the daytime MODIS water vapor products during the day, we found that the accuracy and precision of these data in the Near-IR band are much better than the IR band. Therefore, proper calibration should be made before employing the IR band.
 

Keywords

GPS; IR band; MODIS; Near-IR band; PWV

References

1- Adavi Z., and Mashhadi-Hossainali M. 2014. 4D tomographic reconstruction of the tropospheric wet refractivity using the concept of virtual reference station, case study: northwest of Iran. Meteorology and Atmospheric Physics, 126(3-4), 193-205.‏

2- Alexandrov M.D., Schmid B., Turner D.D., Cairns B., Oinas V., Lacis A.A., and Eilers J. 2009. Columnar water vapor retrievals from multifilter rotating shadowband radiometer data. Journal of Geophysical Research: Atmospheres, 114(D2).

3- Bevis M., Businger S., Herring T.A., Rocken C., Anthes R.A., and Ware R.H. 1992. GPS meteorology: Remote sensing of atmospheric water vapor using the Global Positioning System. Journal of Geophysical Research: Atmospheres, 97(D14), 15787-15801.‏

4- Bevis M., Businger S., Chiswell S., Herring T.A., Anthes R.A., Rocken C., and Ware R. H. 1994. GPS meteorology: Mapping zenith wet delays onto precipitable water. Journal of applied meteorology, 33(3), 379-386.‏

5- Boccolari M., Fazlagic S., Frontero P., Lombroso L., Pugnaghi S., Santangelo R., and Teggi S. 2002. GPS Zenith Total Delays and Precipitable Water in comparison with special meteorological observations in Verona (Italy) during MAP-SOP. Annals of Geophysics.‏

6- Bokoye A.I., Royer A., O'Neill N.T., Cliche P., McArthur L.J.B., Teillet P.M., and Theriault J.M. 2003. Multisensor analysis of integrated atmospheric water vapor over Canada and Alaska. Journal of Geophysical Research: Atmospheres, 108(D15).‏

7- Davis J.L., Herring T.A., Shapiro I.I., Rogers A.E.E., and Elgered G. 1985. Geodesy by radio interferometry: Effects of atmospheric modeling errors on estimates of baseline length. Radio science, 20(6), 1593-1607.‏

8- Deeter M.N. 2007. A new satellite retrieval method for precipitable water vapor over land and ocean. Geophysical research letters, 34(2).‏

9- Dietrich S.V.R., Johnsen K.P., Miao J., Heygster G. 2004. Comparison of tropospheric water vapour over Antarctica derived from AMSUB data, ground-based GPS data and the NCEP/NCAR reanalysis. Journal of the Meteorological Society of Japan. Ser. II, 82(1B), 259-267.

10- Divakarla M. G., Barnet C. D., Goldberg M. D., McMillin L. M., Maddy E., Wolf, W., and Liu X. 2006. Validation of Atmospheric Infrared Sounder temperature and water vapor retrievals with matched radiosonde measurements and forecasts. Journal of Geophysical Research: Atmospheres, 111(D9).‏

11- Durre I., Vose R.S., and Wuertz D.B. 2006. Overview of the integrated global radiosonde archive. Journal of Climate, 19(1), 53-68.‏

12- Elgered G., Johansson J.M., Rönnäng B. O., and Davis J. L. 1997. Measuring regional atmospheric water vapor using the Swedish permanent GPS network. Geophysical Research Letters, 24(21), 2663-2666.‏

13- Ferrare R., Brasseur L., Clayton M., Turner D., Remer L., and Gao B.C. 2002. Evaluation of TERRA aerosol and water vapor measurements using ARM SGP data. In American Meteorological Society 11th Conference on Atmospheric Radiation, Ogden, Utah (pp. 3-7).‏

14- Gao B., and Kaufman Y. J. 1998. The MODIS Near-IR water vapor algorithm: product ID: MOD05-total precipitable water, algorithm technical background document. Remote Sensing Division, Code, 7212.‏

15- Gao B.C., Yang P., Guo G., Park S.K., Wiscombe W.J., and Chen B. 2003. Measurements of water vapor and high clouds over the Tibetan Plateau with the Terra MODIS instrument. IEEE Transactions on geoscience and remote sensing, 41(4), 895-900.‏

16- Gui K., Che H., Chen Q., Zeng Z., Liu H., Wang Y., and Zhang X. 2017. Evaluation of radiosonde, MODIS-NIR-Clear, and AERONET precipitable water vapor using IGS ground-based GPS measurements over China. Atmospheric Research, 197, 461-473.‏

17- Gurbuz G., and Jin S. 2017. Long‐time variations of precipitable water vapour estimated from GPS, MODIS and radiosonde observations in Turkey. International Journal of Climatology, 37(15), 5170-5180.‏

18- Iwabuchi T., Naito I., and Mannoji N. 2000. A comparison of Global Positioning System retrieved precipitable water vapor with the numerical weather prediction analysis data over the Japanese Islands. Journal of Geophysical Research: Atmospheres, 105(D4), 4573-4585.‏

19- Khaniani A.S., Nikraftar Z., and Zakeri S. 2020. Evaluation of MODIS Near-IR water vapor product over Iran using ground-based GPS measurements. Atmospheric Research, 231, 104657.‏

20- Li Z., Muller J. P., and Cross P. 2003. Comparison of precipitable water vapor derived from radiosonde, GPS, and Moderate‐Resolution Imaging Spectroradiometer measurements. Journal of Geophysical Research: Atmospheres, 108(D20).‏

21- Liu H., Tang S., Zhang S., and Hu J. 2015. Evaluation of MODIS water vapour products over China using radiosonde data. International Journal of Remote Sensing, 36(2), 680-690.‏

22- Lu N., Qin J., Yang K., Gao Y., Xu X., and Koike T. 2011. On the use of GPS measurements for Moderate Resolution Imaging Spectrometer precipitable water vapor evaluation over southern Tibet. Journal of Geophysical Research: Atmospheres, 116(D23).‏

23- Mazany R.A., Businger S., Gutman S.I., Roeder, W., 2002. A Lightning Prediction Index that Utilizes GPS Integrated Precipitable Water Vapor. Weather Forecast. 17, 1034-1047.

24- Merrikhpour M.H., and Rahimzadegan M.(2017. Improving the algorithm of extracting regional total precipitable water vapor over land from MODIS images. IEEE Transactions on Geoscience and Remote Sensing, 55(10), 5889-5898.‏

25- Mobasheri M.R., Purbagher Kordi S.M., Farajzadeh M., and Sadeghi Naeini A. 2008. Improvement of remote sensing techniques in TPW assessment using radiosonde data. Journal of Applied Sciences, 8(3), 480-488.‏

26- Mockler S. 1995. Water Vapor in the Climate System: Special Report.

27- Morisette J. T., Privette J. L., and Justice C. O. 2002. A framework for the validation of MODIS land products. Remote sensing of environment, 83(1-2), 77-96.‏

28- Niell A E., Coster A.J., Solheim F.S., Mendes V B., Toor P.C., Langley R.B., and Upham C.A. 2001. Comparison of measurements of atmospheric wet delay by radiosonde, water vapor radiometer, GPS, and VLBI. Journal of Atmospheric and Oceanic Technology, 18(6), 830-850.‏

29- Pottiaux E., and Warnant R. 2002. First comparisons of precipitable water vapor estimation using GPS and water vapor radiometers at the Royal Observatory of Belgium. GPS Solutions, 6(1-2), 11-17.‏

30- Pramualsakdikul S., Haas R., Elgered G., and Scherneck H.G. 2007. Sensing of diurnal and semi‐diurnal variability in the water vapour content in the tropics using GPS measurements. Meteorological Applications: A journal of forecasting, practical applications, training techniques and modelling, 14(4), 403-412.‏

31- Prasad A.K., and Singh R.P. 2009. Validation of MODIS Terra, AIRS, NCEP/DOE AMIP‐II Reanalysis‐2, and AERONET Sun photometer derived integrated precipitable water vapor using ground‐based GPS receivers over India. Journal of Geophysical Research: Atmospheres, 114(D5).‏

32- Ross R.J., and Rosenfeld S. 1997. Estimating mean weighted temperature of the atmosphere for Global Positioning System applications. Journal of Geophysical Research: Atmospheres, 102(D18), 21719-21730.‏

33- Sadeghi E., Mashhadi-Hossainali M., and Etemadfard H. 2014. Determining precipitable water in the atmosphere of Iran based on GPS zenith tropospheric delays. Annals of Geophysics, 57(4), 0430.

34- Sharifi M.A., Azadi M., and Khaniani A.S. (2016). Numerical simulation of rainfall with assimilation of conventional and GPS observations over north of Iran. Annals of Geophysics, 59(3), 0322.‏

35- Sobrino J.A., Jimenez-Muñoz J.C., Mattar C., and Sòria G. 2015. Evaluation of Terra/MODIS atmospheric profiles product (MOD07) over the Iberian Peninsula: A comparison with radiosonde stations. International Journal of Digital Earth, 8(10), 771-783.‏

36- Van Baelen J., Aubagnac J.P., and Dabas A. 2005. Comparison of near–real time estimates of integrated water vapor derived with GPS, radiosondes, and microwave radiometer. Journal of Atmospheric and Oceanic Technology, 22(2), 201-210.‏

37- Vaquero-Martinez J., Anton M., de Galisteo J.P.O., Cachorro V.E., Wang H., Abad G.G., and Costa M. J. 2017. Validation of integrated water vapor from OMI satellite instrument against reference GPS data at the Iberian Peninsula. Science of the Total Environment, 580, 857-864.‏

38- Wang Y., Yang K., Pan Z., Qin J., Chen D., Lin C., and Lu N. 2017. Evaluation of precipitable water vapor from four satellite products and four reanalysis datasets against GPS measurements on the Southern Tibetan Plateau. Journal of Climate, 30(15), 5699-5713.‏

39- Wong M.S., Jin X., Liu Z., Nichol J., and Chan P.W. 2015. Multi‐sensors study of precipitable water vapour over mainland China. International Journal of Climatology, 35(10), 3146-3159.‏

40- Zhai P., and Eskridge R.E. 1997. Atmospheric water vapor over China. Journal of Climate, 10(10), 2643-2652.‏