Quantifying and modelling the effect of external and internal vegetation water dynamics on radar data

Agriculture plays a critical role in the economy and environment worldwide, and the provision of real-time, reliable information on large-scale agricultural activity is essential for precision agriculture and global economic prosperity. In this context, remote sensing, especially through Synthetic Aperture Radar (SAR), can play an important role by offering accurate estimation of crop biophysical parameters such as Leaf Area Index (LAI), crop height, dry biomass, and Vegetation Water Content (VWC). Unlike traditional high-resolution optical imagery, which is often undermined by cloud cover, SAR microwave remote sensing overcomes these limitations by generating and transmitting longer wavelengths (300MHz – 10 GHz) that penetrate clouds and aerosols and allowing data acquisition both day and night. However, SAR data is influenced by various factors such as sensor characteristics such as frequency, polarization, and incidence angle, as well as target characteristics like the size and shape distribution of crop constituents, and more importantly, the water content of crop constituents. In the path towards precision farming and more sustainable and efficient farming using SAR data, understanding the role of these factors, particularly the dynamics of external and internal vegetation water content on radar backscatter, is vital.

To date, however, the potentially confounding effects of both internal and, particularly, external water dynamics in vegetation on radar backscatter have not been adequately addressed. Existing studies have indeed illustrated the effects of SCW on radar backscatter, but the degree to which it influences different frequencies and polarizations, and the subsequent impact on crop bio-geophysical parameters remains unclear. Therefore, the main goal of this thesis is to expand our knowledge of the relationship between radar backscatter, vegetation dynamics, and surface canopy water (SCW) in agricultural monitoring. In this thesis we utilized statistical analysis and radiative transfer modeling in combination with fully polarimetric L-band data from a truck-mounted scatterometer and C-band data from Sentinel-1, along with extensive field data…