Iranian Journal of Remote Sensing and GIS (25886185)17(1)pp. 61-78
Atmospheric water vapor is a key parameter in modeling the energy balance on the earth's surface and plays a major role in keeping the temperature of the earth's atmosphere balanced. Retrieving of this parameter, as the most influential atmospheric parameter on the sensors received radiance, is of great importance. Since the atmospheric water vapor content in the near of surface is more and its temporal and spatial changes are more intense, the measurements of ground meteorological stations, despite their high accuracy, are not generalizable due to temporal and spatial limitations and point measurements. Therefore, it seems necessary to provide practical satellite-based methods to accurate and continuous retrieval of this parameter with appropriate spatial distribution. The aim of this research is to present four innovative and accurate methods to estimate the near surface atmospheric water vapor of Isfahan province in 2020 with a resolution of 1 km, through the integration of meteorological station data, sensor data and finally validating and comparing their performance. For this purpose, correcting the bias error of water vapor sensor data during the co-scaling stage and correcting the interpolation error of ground station observations was put on the agenda. Material and Methods: Different sensors measure water vapor with different sensitivities and spatial resolution. Therefore, it is necessary to provide methods based on the simultaneous use of diffferent sensor data and their integration to ground station observations, in order to simultaneously improve the accuracy and spatial resolution (1 km) of retrieved near surface water vapor. In the first method used in this research, the near surface water vapor is retrieved using the water vapor absorbing and non-absorbing bands of the MODIS, through the band ratio method and using ground observations. In the second method, first, observations of near surface water vapor of ground stations are converted to 1 km grid using the inverse distance interpolation (IDW) method. Then, during the steps of the proposed method and using the water vapor values estimated by the first method, the interpolation error in each pixel is removed. In the third method, the resolution of AIRS-derieved water vapor product is reduced to 1 km by combining MODIS data during an operation similar to the steps of the second method, with the difference that the AIRS sensor product is used instead of ground station observations. It is necessary to eliminate the bias error of near surface water vapor product of the AIRS during the co-scaling stage by first. Estimation of near surface water vapor using MODIS column water vapor product is the fourth method. Of course, due to the difference in content, it is necessary to unite the two sets and equate them with an approprite method. Results and Discussion: In order to model and validate the estimation of atmospheric near surface water vapor at a spatial resolution of 1 km using the different mentioned methods, 66.6% of the data were randomly used for training and the remaining 33.3% were used to evaluate the accuracy and validation. Finally, the implementation results of the methods have been compared with each other. The validation results of proposed methods show that the second method, which is based on the generalization of accurate observations of ground stations and removing their interpolation error, during integration with the water vapor values retrieved from first method, has the best performance (R2=0.55, RMSE=1.05 Gr/Kr). Conclusion: Considering the better performance of the second method in retrieving the mixing ratio of near surface water vapor with high accuracy and resolution of 1 km, and with the aim of using the capabilities of satellite-based products and data, it is recommended to combine them with each other and also with ground observations. © 2025, Shahid Beheshti University. All rights reserved.
