Crop Monitoring by Satellite Polarimetric SAR Interferometry

Abstract

The agricultural sector is the backbone which supports the livelihoods and the economic development of nations across the globe. In consequence, the need for robust and continuous monitoring of agricultural crops is primordial to face the interlinked challenges of growth rate population, food security and climate change. Synthetic Aperture Radar (SAR) sensors have the powerful imaging capability of operating at almost all weather conditions, independent of day and night illumination. By penetrating through clouds and into the vegetation canopy, the incident radar signal interacts with the structural and dielectric properties of the vegetation and soil, thus providing critical information of the crop state, such as height, biomass, crop yield or leaf structure, which can help devise sustainable agricultural management practices. This is achieved by means of the Polarimetric SAR Interferometry (PolInSAR) technique, which by coherently combining interferometric SAR acquisitions at different polarization states allows for the retrieval of biophysical parameters of the vegetation. In this framework, this thesis focuses on the development of crop monitoring techniques that properly exploit satellite-based PolInSAR data. All the known InSAR and PolInSAR methodologies for this purpose have been analysed. The sensitivity of these data provided by the TanDEM-X bistatic system to both the physical parameters of the scene (height and structure of the plants, moisture and roughness of the soil) and the sensor configuration (polarization modes and observation geometry) is evaluated. The effect of different simplifications made in the physical model of the scene on the crop estimates is assessed. The interferometric sensitivity requirements to monitor a crop scenario are more demanding than others, such as forests. Steep incidences associated with the largest spatial baselines provided by the available data set lead to the most accurate estimates under all the different model assumptions. Shallower incidences, on the other hand, generally yield important errors due to their characteristic shorter spatial baselines. Through the methodologies proposed in this thesis, PolInSAR data have shown potential to refine current methods for the quantitative estimation of crop parameters. Results encourage to continue further research towards the objective of achieving operational crop monitoring applications.