Assessing the contribution of optical, SAR and LiDAR data for vegetation applications: from crop monitoring to forest characterization

Abstract

Vegetation monitoring across agricultural and forest ecosystems is essential for food security, sustainable forest management, and climate change mitigation. Remote sensing provides systematic, spatially consistent observations at large scales, becoming an indispensable tool for this purpose. The availability of different sensor types offers complementary perspectives on vegetation characteristics and dynamics, though their utility, either used individually or in combination, remains an active research area. This thesis evaluates the potential of optical, SAR, and LiDAR remote sensing for vegetation monitoring across agricultural and forest ecosystems, addressing four specific objectives. First, Sentinel-1 intensity and polarimetric features were evaluated for modelling Sentinel-2 NDVI time series across four crop types. Strong performance was achieved for barley, wheat, and sunflower, with temporal SAR features identified as a critical factor for accurate NDVI reconstruction. Second, the use of Sentinel-1, Sentinel-2, and LiDAR was assessed for forest type and species classification across three taxonomic levels. Sentinel-2 emerged as the most informative sensor, with LiDAR contributing complementary structural information. Overall accuracies of 0.90, 0.80, and 0.79 were achieved across the three levels. Third, the same sensors were evaluated for basal area and stand volume estimation in European beech stands. LiDAR provided the strongest predictive capacity, with Sentinel-2 contributing complementary spectral information. Sentinel-1 added negligible predictive power under dense canopy conditions. Finally, L-band interferometric coherence derived from NISAR GSLC products was investigated for aboveground biomass retrieval beyond the backscatter saturation limit. A two-stage residual correction framework was adopted, showing meaningful improvements within the data-driven framework at a high-biomass site. Overall, the results of this thesis are expected to contribute to a better understanding of the potential of different remote sensors for vegetation monitoring. The findings suggest that progress may come not only from sensor integration, but also from exploiting the full information content of existing SAR products combined with more advanced modelling strategies.