Muesgen-von den Driesch, M.; Bendix, J. & Thies, B. (2026): <b>Novel SAR vegetation indices improve cloud-independent monitoring of forest vegetation dynamics through NIRv index reconstruction under post-drought conditions</b>. <i>Science of Remote Sensing</i> <b>14</b>, 100501.
Resource Description
Title:
Novel SAR vegetation indices improve cloud-independent monitoring of forest vegetation dynamics through NIRv index reconstruction under post-drought conditions
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.
