Understanding forest decline under drought pressure is receiving research attention due to the increasing frequency of large-scale heat waves and massive tree mortality events. However, since assessing mortality on the ground is challenging and costly, this study explores the capability of satellite-borne Copernicus Sentinel-1 (S-1) C-band radar data for monitoring drought-induced tree canopy damage. As droughts cause water deficits in trees and eventually lead to early foliage loss, the S-1 radiometric signal and polarimetric indices are tested regarding their sensitivities to these effects, exemplified in a deciduous broadleaf forest. Due to the scattered nature of mortality in the study site, we employed a temporal-only time series filtering scheme that provides very high spatial resolution (10 m $\times$10 m) for measuring at the scale of single trees. Finally, the anomaly between heavily damaged and non-damaged tree canopy samples (n = 146 per class) was used to quantify the level of damage. With a maximum anomaly of −0.50 dB ± 1.38 for S-1 Span (VV+VH), a significant decline in hydrostructural scattering (moisture and geometry of scatterers as seen by SAR) was found in the second year after drought onset. By contrast, S-1 polarimetric indices (cross-ratio, RVI, $H\alpha$) showed limited capability in detecting drought effects. From our time series evaluation, we infer that damaged canopies exhibit both lower leaf-on and leaf-off backscatters compared to unaffected canopies. We further introduce an NDVI/Span hysteresis showing a lagged signal anomaly of Span behind NDVI (by ca. one year). This time-lagged correlation implies that SAR is able to add complementary information to optical remote sensing data for detecting drought damage due to its sensitivity to physiological and hydraulic tree canopy damage. Our study lays out the promising potential of SAR remote sensing information for drought impact assessment in deciduous broadleaf forests.

Microwave scattering from forests generates pixel geolocation shifts in Synthetic aperture radar (SAR) data that require an adequate representation within digital elevation models (DEM) for preprocessing. We analyze the impact of DEM properties on the radiometry and geolocation of radiometric terrain corrected (RTC) Copernicus Sentinel-1 imagery of forests to improve consistency in backscatter intensities for time series analyses. To account for the penetration depth of the C-Band sensor, we approximate the structure of stands in a temperate deciduous forest using height percentiles from Aerial Laser Scanning (ALS) point clouds in the Hainich National Park, Germany. Comparing the RTC results obtained using DEMs of SRTM, Copernicus, and ALS DEMs, the latter reduces topographically induced errors, resulting in visibly smaller effects from topography and spatially shifted information. Based on the P50 ALS vegetation elevation, results show homogeneous intensities within the same orbit and reduce variance from 2.4 dB ² to 1.2 dB ² in the difference of mid-range data from ascending and descending azimuth directions. Over forest, we observe lower intensities on sensor-facing and increased intensities on away-facing slopes and correlations with the illuminated pixel area (IPA) and local incidence angle. We reduce this bias with linear regressions of intensity on IPA. ALS DEMs in RTC and the proposed regression correction increase the consistency of images across orbits, measured by the inter-orbit range, throughout the selected year at our study site. We suggest the proposed method applies to other areas, requiring further testing under different forest types and topography.

Detection of geomorphological changes based on structure from motion (SfM) photogrammetry is highly dependent on the quality of the 3D reconstruction from high-quality images and the correspondingly derived point precision estimates. For long-term monitoring, it is interesting to know if the resulting 3D point clouds and derived detectable changes over the years are comparable, even though different sensors and data collection methods were applied. Analyzing this, we took images of a sinkhole terrestrially with a Nikon D3000 and aerially with a DJI drone camera in 2017, 2018, and 2019 and computed 3D point clouds and precision maps using Agisoft PhotoScan and the SfM_Georef software. Applying the “multiscale model to model cloud comparison using precision maps” plugin (M3C2-PM) in CloudCompare, we analyzed the differences between the point clouds arising from the different sensors and data collection methods per year. Additionally, we were interested if the patterns of detectable change over the years were comparable between the data collection methods. Overall, we found that the spatial pattern of detectable changes of the sinkhole walls were generally similar between the aerial and terrestrial surveys, which were performed using different sensors and camera locations. Although the terrestrial data collection was easier to perform, there were often challenges due to terrain and vegetation around the sinkhole to safely acquire adequate viewing angles to cover the entire sinkhole, which the aerial survey was able to overcome. The local levels of detection were also considerably lower for point clouds resulting from aerial surveys, likely due to the ability to obtain closer-range imagery within the sinkhole.

Water status, water dynamics, and ecohydrological resilience of a protected German beech forest during the 2018–2020 multi‐year drought are assessed using over six years of multi‐frequency remote sensing data, integrating active, passive, and optical sensors with varying canopy penetration depths, highlighting the importance of monitoring forests under extreme conditions. In this study, we investigate Sentinel‐1 C‐band backscatter (S‐1 γ ⁰ ) and the relative water content estimated from vegetation optical depth (RWC _VOD ) of AMSR2 (X‐ and C‐bands) and SMAP (L‐band) within the soil‐plant‐atmosphere system (SPAS). In addition, time series are intercompared and examined through correlation and sensitivity analyses applying tailored environmental and newly developed hydrological selection strategies. Our results show that S‐1 γ _VH is most influenced by leaf area index and thus leaf biomass when sensed during dense vegetation (leaf‐on), no‐frost, and very wet conditions ( r = −0.94). In contrast, during sparse vegetation (leaf‐off), no‐frost, stable dry‐down, and extremely dry conditions, S‐1 γ _VH is very sensitive to topsoil moisture ( r = 0.91). Due to increased microwave attenuation, resulting in reduced S‐1 γ _VH backscatter, an anti‐cyclical behavior (negative correlations) is observed between almost all SPAS‐based variables/proxies and S‐1 γ _VH during leaf‐on conditions. Conversely, this reverses to a cyclical behavior (positive correlations) during leaf‐off conditions. Our results reveal that X‐ and C‐band RWC _VOD effectively detect drought onset by capturing fast water content changes in leaves and twigs of the top canopy due to shallow sensing depth, while L‐band RWC _VOD captures legacy effects after repetitive droughts through slower water content changes in branches and trunks of lower tree compartments.