الفهرس 
Soil Moisture
Study Area Description and In-Situ MeasurementsOnly 14 pages are availabe for public view
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Abstract
Soil moisture is an important element in the hydrological models that contributes to defining the infiltration rate and deep percolation amount. However, measuring soil moisture at the watershed scale is difficult using traditional point measurement methods such as the gravimetric approach. These methods are not accurate for understanding the spatial and temporal behaviour of soil moisture. Therefore, precise estimates of the spatiotemporal distributions of soil moisture are now possible using different techniques of remote sensing, each with unique advantages and disadvantages. Two techniques of remote sensing are used in this thesis for detecting soil moisture. The first one is applied by using Landsat-8 based on thermal bands to compute the Land Surface Temperature (LST) and Soil Moisture Index (SMI). The second is using Sentinel-1 based on the active microwave in soil moisture detection, defining the backscattering coefficient (σo). A total of 100 soil moisture measurements were performed on five different dates by the Theta probe at twenty locations along the mainstream of Wadi Kharouba, Matrouh City on the Northwestern Coast of Egypt. Data from Landsat-8 were downloaded and analyzed for three dates only due to the existing cloud, but Sentinel-1 data were downloaded for the five dates. Regression analysis was used to develop relationships between the measured Soil Moisture Content (SMC) and both the retrieved (SMI) from Landsat-8 and the derived (σo) from Sentinel-1. Results showed that the produced index using the Landsat data as one predictor represents a relative value, not an absolute value, also, the anomalous scattering behaviour was observed due to using Sentinel-1 data as one predictor. Therefore, (HSM) is a Hydrologic Surface Moisture predictor that was developed to solve these issues based on using the Soil Conservation Service-Curve Number method with minor modifications. The antecedent rainfall and the evaporation scaler were also considered in developing this predictor. Accordingly, three soil moisture prediction models were developed with two predictors: Landsat-8 with HSM, Sentinel-1 with HSM, and Sentinel-1 with Landsat-8. These models helped in enhancing the estimation of soil moisture by overcoming the relativity of the soil moisture index and the anomalous scattering issue of Sentinel-1. The first model was used for rescaling the SMI to be an absolute value, while the second and third models solved the anomalous backscattering issue. Moreover, the third one is the most reliable approach due to its advantages of blending the powers of the two sensors and eliminating the requirement for ground observations. Finally, one predictor method using Landsat or Sentinel alone is not sufficient to estimate the soil moisture, and the two predictor methods are more applicable in arid and semi-arid regions.