عنوان مقاله [English]
Soil moisture is a critical parameter in many land surface processes, and microwave remote sensing is effective in estimating land surface moisture due to some advantages it has over optical methods. However, the soil moisture obtained from microwave remote sensing has a spatial resolution of several tens of kilometers, which is not suitable for many hydrological applications such as agricultural monitoring and drought and climate change prediction. In the current research, the aim is to present a method based on data integration in order to increase the spatial resolution of soil moisture data produced by the climate change department of the European Space Agency, Esa. First, the data with higher resolution using NOAA images and three indices ndvi, lst and albedo are placed in a linear regression relationship with soil moisture ground data ismn. Then, by comparing the output of this data and ESA data, the spatial resolution is increased. Due to some limitations, the desired model was implemented in three study areas. The validation results of each region were evaluated using ground data, so that the average coefficient of determination was 0.77 in the study area of Australia and 0.58 and 0.34 in the two study areas of Iran. According to the results, it can be said that the presented method, in addition to scalability and simplicity, has a higher efficiency in uniform and unmixed areas such as Kyeamba Creek catchment than in agricultural lands such as Pars Abad.