The interferometric coherence parameter γ estimates the degree of correlation between two Synthetic Aperture Radar (SAR) images and can be influenced by vegetation structure. Here, we investigate the use of repeat-pass interferometric coherence γ to map stand age, an important parameter for the study of carbon stocks and forest regeneration. In August 2009 NASA’s L-band airborne sensor UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) acquired zero-baseline data over Quebec with temporal separation ranging between 45 min and 9 days. Our analysis focuses on a 66 km² managed boreal forest and addresses three questions: (i) Can coherence from L-band systems be used to model forest age? (ii) Are models sensitive to weather events and temporal baseline? and (iii) How is model accuracy impacted by the spatial scale of analysis? Linear regression models with 2-day baseline showed the best results and indicated an inverse relationship between γ and stand age. Model accuracy improved at 5 ha scale (R² = 0.75, RMSE = 5.3) as compared to 1 ha (R² = 0.67, RMSE = 5.8). Our results indicate that coherence measurements from L-band repeat-pass systems can estimate forest age accurately and with no saturation. However, empirical model relationships and their accuracy are sensitive to weather events, temporal baseline, and spatial scale of analysis.

Perennial crops are a key component of sustainable agriculture due to their higher carbon storage compared to annual systems. Improving their mapping is essential to support effective public policies. This study proposes an integrated approach combining field observations, waveform LiDAR data from Global Ecosystem Dynamics Investigation (GEDI), and hyperspectral imagery from EnMAP to enhance perennial crop classification.Field data were used to develop an interpretation key for the analyzed classes. GEDI Level 1B waveforms were normalized to extract metrics such as the number of peaks, while structural attributes, including canopy height (RH95), were derived from GEDI Level 2A. From EnMAP imagery, spectral reflectance was obtained and processed using mean values, first- and second-order derivatives, and continuum removal. Differences among classes were evaluated using Tukey’s test and principal component analysis (PCA). Results show that waveform structure and canopy height significantly distinguish secondary vegetation from perennial crops. Additionally, spectral regions in the visible and shortwave infrared present significant differences between these classes. These findings demonstrate that integrating structural and spectral variables improves the discrimination of perennial crops and helps overcome challenges in separating them from secondary vegetation.

Perennial crops are a key component of sustainable agriculture due to their higher carbon storage compared to annual systems. Improving their mapping is essential to support effective public policies. This study investigates the use of field observations, waveform Light Detection and Ranging (LiDAR) data from the Global Ecosystem Dynamics Investigation (GEDI), and hyperspectral imagery from EnMAP to identify spectral and structural features and assess their separability. Field data were used to develop an interpretation key for the analyzed classes. GEDI Level 1B waveforms were normalized to extract metrics such as the number of peaks, while structural attributes, including canopy height (RH95), were derived from GEDI Level 2A. From EnMAP imagery, spectral reflectance was obtained and processed using mean values, first- and second-order derivatives, and continuum removal. Differences among classes were evaluated using Tukey’s test and principal component analysis (PCA). Results show that waveform structure and canopy height significantly distinguish secondary vegetation from perennial crops. Additionally, spectral regions in the visible and shortwave infrared present significant differences between these classes. These findings demonstrate that the structural and spectral variables could improve the discrimination of perennial crops and help overcome challenges in separating them from secondary vegetation.