Downscaling of Soil Moisture Map using Sentinel Radar Satellite Images andDistribution Analysis in the West of Iran

Soil surface moisture is a key variable to describe drought, water and energy exchanges between the earth and the air. Due to the instability of spatial and temporal conditions, the environmental parameters affecting it are highly variable. The current study aims to downscale and extract the soil moisture distribution map with high resolution and its spatial analysis in the west of Iran. An educational layer was created by using the post-scattering bands of VV and VH polarizations as well as the angle of collision band (𝜃) extracted from Sentinel 1 radar images and land use extracted from MODIS sensor. Long-term average moisture per pixel of GLDAS data was also used. The micro-scale backup machine vector algorithm and the high volume resolution soil moisture dispersion map were estimated between 0.18 and 0.46 b. Field data collected from 38 sample farms of Kurdistan Agricultural Research Center were used to verify the output map, which was calculated to be R2 = 0.5012. The results were obtained for the elliptical direction of three times the standard spatial deviation of the northwest to the southeast, which shows that more than 99% of the moisture distribution is expanded in accordance with the spatial arrangement of the heights in this direction. Statistics of 0.3978 Moran index and P_Value value of 0.0000 showed spatial autocorrelation of soil moisture. The hot spot map also showed that the soil surface moisture is nuclear in the northwest and southeast directions and more at altitudes above 2000 meters. Hot spot analysis also reveal that the moisture has strongly clustered to the east and inside the country. Using the obtained spatial analysis results, low or high soil moisture areas can be identified in order to identify environmental potentials and improve the decision-making process, allocation and spatial distribution of services. 

  Soil moisture is a fundamental variable in water and climate cycles that plays an important role in our understanding of the interaction of the atmosphere and the earth's surface. In contrast to linear and nonlinear algorithms extracted from different bands of satellite images, machine support learning techniques have recently been introduced to improve low-resolution soil moisture data from various satellites. Extraction of soil moisture anomalies GLDAS data were scaled microscopically using Sentinel radar images, the results of which showed a correlation of 0.7 with ground data. The purpose of this study is to extract soil surface moisture with high spatial resolution by microscaling the soil moisture layer of the global system of data integration and sentinel radar images as a practical method in environmental studies and spatial analysis of moisture dispersion in western Iran during the study period.

The study area is between latitudes "36 '51 ° 31 to" 45 '49 ° 36 north to "18 '27 ° 45 to" 26 '04 ° 50 east with an area of ​​466.121 square kilometers. Western Iran generally has a mountainous climate. Among climatic variables, rain is considered as the most important climatic variable affecting soil moisture. Therefore, the water-rich year of 1997-98 was selected as the statistical period and western Iran for the study. In this research, first, the soil moisture layer with a spatial resolution of 0.25 degrees was extracted from the global land data integration system. In the next step, the middle of the radar images of Scintil 1 was extracted in a period of time to use the desired bands. The other two parameters, namely surface cover and vegetation, which were extracted from the images of Madis surveyed lands in effective microscaling. By combining the mentioned layers, an educational layer was obtained. Using the soil moisture layer backup (SVM) vector machine classification method, a small scale was obtained and a map with high resolution power was obtained. In this study, soil moisture microscaling based on the work of Pasoli (2015) and Jennifer (2016) with the functions available in the Google Earth Engine system was obtained. Field data collected from the Kurdistan Agricultural Research Center were used to validate the output. Spatial criterion deviation and Moran statistic were calculated to investigate the direction of scattering and spatial autocorrelation. Then Gates statistic was obtained to investigate severe and low clustering.

The soil moisture map of the global system showed the study range between 0.22 to 0.45 cubic meters per cubic meter. After executing the algorithm implemented in Google Earth system, the soil moisture layer engine with high spatial resolution between 0.22 to 0.45 cubic meters per cubic meter showed that by comparing the pixels of the high resolution layer with the real data, square root error and correlation coefficient 0.1641 and 0.5012 were obtained, respectively. Spatial standard deviation demonstrated the spatial distance of the moisture volume of each pixel from the mass moisture center in a northwest to southeast direction. The Gates statistic was calculated to show that hot spots were located in the same elliptical direction, ie northwest to southeast, and cold spots (low humidity clustering) were mostly studied in the southwest of the area. The intersection of severe clustering with the elevation layer indicates that the most intense clustering is located at an altitude of 2000 m and above and there is little clustering at low altitudes in the study area. The intersection of hotspots with the soil layer also indicated that the highest percentage of hotspots is in soils with Vertsul category. Simulated global system moisture data have been available since 1954, but the spatial resolution of this data is low. In this study, using high-resolution Sentinel 1 radar satellite imagery and the valuable algorithms available in Google Earth Engine, soil surface moisture was scaled and a high-resolution soil moisture map was extracted and then spatially analyzed. According to the correlation coefficient of R2 = 0.5012 with field data, it can be concluded that this method can be used to estimate soil moisture. The standard deviation ellipse for soil moisture is northwest to southeast, indicating that moisture expands in this direction due to the spatial arrangement of unevenness that causes rainfall to be diverted. The value of Moran and P. Valio index of 0.3978 showed the existence of spatial autocorrelation of soil moisture in the west of the country. The hot spot map also revealed that the surface moisture of the soil is nuclear in the northwest direction and to the southeast and most of the altitudes are above 2000 meters. The maximum nuclei are located in the form of three separate centers in the north of Marivan city, around Tuyserkan and south of Dorud city. Moisture clustering layer also intersected with soil layer, which was the highest percentage with 23% soil clustering in soils with Vertsols. These types of soils have expandable clay. The predominant type of clay is montmorilliant, which has increased water absorption in them.

The results of this study with respect to the correlation coefficient of 0.5012 with real data and high spatial resolution of the output map showed the efficiency of using different bands of radar images in estimating surface moisture. The spatial distribution of moisture with the ellipse indicated the standard deviation of the direction of northwest to southeast in accordance with the direction of roughness and spatial arrangement of the Zagros, which can explain the role of roughness in high rainfall in the west of the country. The spatial pattern also revealed the surface moisture of the soil with the Moran index of cluster moisture distribution and non-randomness. In future works, to increase the accuracy of the moisture map extracted from the algorithm implemented in this paper, infrared bands can be used to extract vegetation indices and increase the information of educational data to the algorithm. The results of this study also confirm that the algorithm used in this research can effectively lead to the extraction of the soil surface moisture layer with a higher resolution.