Integration of multi-sensor data and ground observations in order to improve accuracy and spatial resolution in near-surface water vapor retrieval

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. Therefore, retrieving the near surface water vapor content with accuracy and appropriate spatial resolution is very important, and the purpose of this research is to provide four innovative and accurate methods to estimate the mass mixing ratio of near surface water vapor in Isfahan province in 1 km resolution. Different sensors measure water vapor with different resolution and sensitivities to this parameter. Thus, providing methods based on the integration of different sensor's and ground observations data is essential to simultaneously improve the spatial resolution and accuracy of water vapor retrievals. In this research, the combination of MODIS and AIRS data and ground station observations have been used. Also, the band ratio method, IDW interpolation and scaling have been used along with the proposed methods. Correcting the bias of AIRS-derieved water vapor during the scaling stage and interpolation error is on the agenda. Validation results of proposed methods show that the method based on the generalization of accurate ground-basedwater vapor observations and removing interpolation error, through integration with MODIS-derieved water vapor values, has the best performance (R2=0.55, RMSE=1.05 Gr/Kr).