Laboratory for
Climatology and Remote Sensing

Abstract:

Tree-ring width (TRW) is key for the identification of forest growth responses to climate, but field sampling inherently limits spatial and temporal coverage. Satellite-derived vegetation indices (VIs) can bridge this gap, yet most canopy-growth studies continue to rely on Normalized Difference Vegetation Index (NDVI), a greenness metric long known to saturate in high-biomass canopies and to decouple from stem growth under stress. Unlike previous studies relying on single vegetation indices and static seasonal metrics, we implemented an approach that integrates multiple vegetation indices across several accumulated temporal windows to capture intra-annual tree growth dynamics and stress responses. Rolling-sum correlations for the growth-year and previous-year linked canopy signals from greenness-, structural-, wetness- and pigment-related indices to annual TRW. The analysis used a 23-year (2000–2022) 8-day Landsat seamless data cube (SDC) at 30 m resolution covering 24 different forest sites in western and central Germany. Fifteen trees were investigated at each site, from mixed and pure stands consisting of the tree species Fagus sylvatica, Quercus petraea, Quercus robur, Pinus sylvestris, Pseudotsuga menziesii, and Larix decidua. Greenness indices, including the NDVI, consistently underperformed. Structure, including Near Infrared of Vegetation Index (NIRv), and wetness indices produced higher and more temporally targeted correlations, especially in pure stands and for beech during summer. Coniferous and mixed plots showed weaker, broader signals, while pigment-ratio indices proved comparatively most informative in mixed stands. High-resolution temporal analysis of the 8-day SDC Landsat time series revealed species-specific peak correlation windows, enabling the identification of critical periods driving radial tree growth. The results of this study by moving beyond greenness-only approaches and single-value, growing-season analyses reveals much better performance compared to studies averaging to growing-season means that blur these peaks and consequently report weaker or no correlations between tree growth and the satellite signal.

Abstract:

Monitoring vegetation dynamics and optical proxies of photosynthetic activity is essential for understanding ecosystem functioning and forest responses to climate change. Optical remote sensing indices such as Normalized Difference Vegetation Index (NDVI) and Near Infrared of Vegetation Index (NIRv) are widely used as proxies for vegetation health and photosynthetic capacity, but their application is limited by cloud-induced temporal gaps. While SAR data offer weather-independent observations, NIRv reconstruction from SAR has rarely been investigated, particularly in forest ecosystems. In this study, we introduce two novel SAR-based indices, the Radar Volume Vegetation Index (RVVI) and the Radar Structure Vegetation Index (RSVI), and assess their relationship with NIRv as an established optical proxy for photosynthetic capacity. Using Sentinel-1 SAR and Sentinel-2 optical data, we analyzed species-specific relationships between a collinearity-reduced set of seven SAR vegetation indices and NIRv, and subsequently evaluated the predictive performance of the complete set of 15 SAR vegetation indices using a Foreward Feature Selection based Random Forest model. RVVI and RSVI showed low collinearity with existing SAR indices and the strongest correlations with NIRv, reaching up to |r| = 0.65 for deciduous species, while correlations were lower for conifers (|r| = 0.26). The cross-species Random Forest model achieved high predictive accuracy with an overall R2 of 0.85 and MAE of 0.03. However, performance differed between forest types, with higher predictive accuracy for deciduous species (R2 = 0.86 – 0.88) than for conifers (R2 = 0.77 – 0.79). RVVI and RSVI were identified as the most important SAR predictors. These findings demonstrate that the proposed indices provide complementary structural information that can improve the reconstruction of NIRv-like variability and show the potential to support more temporally continuous, cloud-independent monitoring of this optical proxy across forest ecosystems. However, the lower performance observed for coniferous species highlights the need for further refinement to fully capture species-specific NIRv variability.