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Soil Moisture Content Estimation Based on Satellite Imagery

Rabiei, Saman | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52832 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Tajrishy, Masoud; Jalilvand, Ehsan
  7. Abstract:
  8. The aim of this study is to provide an algorithm for estimating the Soil Moisture Content (SMC) using Sentinel satellite images for the Varamin and Karaj provinces. Soil moisture varies greatly due to various factors such as land surface changes (roughness, soil type, and vegetation cover), topography and weather conditions. Due to the high cost of traditional methods of measuring soil moisture and also the large spatial changes of soil moisture, the use of these methods to prepare high-precision maps and for long periods of time or in a global scale is not possible. This study examines the relationship between soil spectral reflection diagrams using optical images in the range of 400 to 2500 nm and using radar images in band C with a frequency of 6.8 GHz. The process of this research is done in two separate steps. In the first part; a model based on satellite image data and soil moisture values is presented which is based on the relationship between soil moisture values and satellite image data, and then the reverse model validation is performed. This is done by comparing the amount of moisture measured at the site with the estimated values. In estimation of the surface soil moisture with optical images, 110 points were sampled in the agricultural fields of Varamin and Karaj simultaneously the satellite passed. Soil moisture model was created using the machine learning algorithm. To estimate the surface moisture of the soil using radar images, another 60 points were sampled in the agricultural fields of Varamin and Karaj during the satellite crossing. The results indicate acceptance accuracy for both methods. In optical images, the root mean square error was 3.4% by weight and in RADAR images, the RMSE was 2.9% by volume
  9. Keywords:
  10. Remote Sensing ; Karaj Sity ; Satellite Soil Moisture Data ; Soil Moisture Monitoring ; Sentinel Satellite ; Soil Moisture Content ; Varamin

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