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Συμβολή της τηλεπισκόπησης στην εκτίμηση της εδαφικής υγρασίας

Mexis Filippos-Dimitrios

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Summary

Soil moisture is a key variable to environments natural cycle, farming, soil quality and, as well, it is related to climate change and meteorological phenomena. Though, the majority of the datasets acquired and available to the scientific community depend on ground measurements which are expensive and time consuming. As a result, soil moisture time series datasets have a low spatial and temporal resolution making it difficult to actualize extensive environmental researches and studies. Remote sensing has the potential to overcome the limits of ground measurement and to contribute to the almost real time and accurate soil moisture content satellite measurements. There is a plethora of studies on passive remote sensing that accomplishes accurate soil moisture content measurements at global scale, but when it comes to microwave active remote sensing there is a need for further research and optimization.In this pre-graduate thesis it is held an effort to relate ground measurements of soil moisture content in the area of Chania to satellite backscatter measurement of the active remote sensing Sentinel 1 mission. This was accomplished using classical single and multiple linear regression methods and nonlinear function fitting neural networks. Moreover in the methods described above it was integrated an vegetation index (NDVI) produced by the images of the passive remote sensing Landsat 8 satellite, as it is known that vegetation is one of the major factors that interpolate the microwave signal, when it comes to soil moisture content measurements.Once the regression was completed and the neural network was trained a validation followed, utilizing measurements that where not integrated in the methods in the first place. Last, the conclusions were discussed and future outlook was given.

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