On the Potential of a Novel Satellite-Based Time-Series of Normalized Far-Red Solar-Induced Fluorescence to Track Short-Term Changes in Subsurface Water Storage

Effectively tracking drought effects using satellite data can be conducted by combining atmospheric data with additional information of vegetation indices (VIs) from optical data. While VIs detect drought when plant damage is often irreversible, information about the plant physiological status can help detect drought effects much earlier. Remotely-sensed solar-induced chlorophyll fluorescence (SIF), emitted directly from the photosynthetic apparatus (Drusch et al., 2017), provides such information.  When abiotic stress occurs due to an increased dissipation of thermal energy through the process of non-photochemical quenching (NPQ), the fluorescence yield is decreased, which can be measured as SIF (Berger et al., 2022, Damm et al., 2018).

Top of canopy (TOC) SIF is available from Sentinel-5P’s TROPOMI sensor since 2018 (Guanter et al., 2021, Köhler et al., 2018). This data, however, is affected by incoming radiation and canopy structure. These effects need to be removed In order to calculate the fluorescence yield in form of the quantum efficiency at leaf level (ΦF), which provides the pure information on the actual physiological status of the plant. Equation (1) uses the vegetation index NIRv (NDVI*NIR (Badgley et al., 2017)) to serve as a combined proxy of the fraction of absorbed photosynthetically active radiation (fAPAR) and the fluorescence escape probability (fesc) (Dechant et al. 2020, Liu et al. 2023). Both SIF data at 743 nm and the reflectance used to calculate the NIRv come from TROPOMI, while the photosynthetically active radiation (PAR) is provided by MODIS.

ΦF = π*SIF₇₄₃canopy/(NIRv*PAR) (1)

This study presents a new multi-year (2018-2023) ΦF dataset at 0.05° resolution covering Germany with daily temporal resolution. To assess ΦF’s potential as an early drought stress indicator for agricultural and forest ecosystems, it is compared to the anomaly of subsurface water storage (sss), which serves a reference parameter for plant water availability generated by combining the hydrological model PARFLOW and common land model (CLM) (Belleflamme et al., 2023). ΦF and sss anomaly data were split into periods of prolonged negative sss anomaly indicating drought events (cross-referenced as watch/warning periods using the Combined Drought Indicator (European Commission)). Cross-correlation coefficients for different time lags were calculated to compare the datasets. The data was spatially aggregated daily and temporally averaged using a two-day rolling average.

Results show that cross-correlation coefficients for ΦF and sss anomaly are highest at a 2-day lag, dropping again after 3 days, indicating that ΦF follows the negative sss anomaly trend with a 2-day delay in both agricultural and forest ecosystems. Non-normalized canopy SIF and vegetation indices (NIRv, NDVI) showed no pattern and low cross-correlation coefficients during the observed periods. Our findings prove that ΦF has the ability to detect insufficient plant water availability and thus can be used for early drought stress detection in agricultural and forest ecosystems. The comparison of the capabilities of ΦF and TOC SIF to track short-term changes in subsurface water storage illustrates that a proper downscaling and normalization of canopy SIF is essential to use SIF satellite measurements for the early detection of drought events.