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Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface.

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High temporal resolution measurements of solar-induced chlorophyll fluorescence (F) and the Photochemical Reflectance Index (PRI) encode vegetation functioning. However, these signals are modulated by time-dependent processes. We tested the applicability of the Singular Spectrum Analysis (SSA) for disentangling fast components (physiology-driven) and slow components (controlled by structural and biochemical properties) from PRI, far-red F (F 760 ), and far-red apparent fluorescence yield (Fy * 760 ). The proof of concept was developed on spectral and flux time series simulated with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. This allowed the evaluation of SSA decomposition against variables that are independent of physiology or are modified by it. Slow SSA-decomposed components of PRI and Fy * 760 showed high correlations with the reference variables (R 2 = 0.97 and 0.96, respectively). Fast SSA-decomposed components of PRI and Fy * 760 were better related to the physiological reference variables than the original signals during periods when leaf area index (LAI) was above 1 m 2 m −2 . The method was also successfully applied to predict light-use efficiency (LUE) from the fast SSA-decomposed components of PRI (R 2 = 0.70) and Fy * 760 (R 2 = 0.68) when discarding data modeled with LAI < 1 m 2 m −2 and short-wave radiation R in < 250 W m −2 . The method was then tested on data acquired in a Mediterranean grassland. In this case, the fast SSA-decomposed component of apparent LUE * showed a stronger correlation with the fast SSA-decomposed component of Fy * 760 (R 2 = 0.42) than with original Fy * 760 (R 2 = 0.01). SSA-based approach is a promising tool for decoupling physiological information from measurements acquired with automated proximal sensing systems.Plain Language Summary A fraction of the solar light, which is absorbed by leaves but is not used during photosynthesis, is released through heat or as chlorophyll fluorescence (F), a small emission of energy. Recently, it became possible to indirectly estimate the heat and F by measuring the solar light incoming and reflected from leaves using high-resolution optical instruments. Heat release can be monitored with the Photochemical Reflectance Index (PRI). While both PRI and F are theoretically linked to the processes associated with photosynthesis, there is a need to remove the disturbing effects from these signals. We tested whether the Singular Spectrum Analysis (SSA) method can identify at which temporal scale (e.g., seasonal, diurnal) physiological processes (i.e., photosynthesis) and vegetation biophysical changes (e.g., phenology) drive variability in PRI and F. We applied the method on artificial time series of PRI and F simulated with a model (Soil Canopy Observation of Photochemistry and Energy fluxes [SCOPE]) and found that SSA can successfully split these signals into several components recognized as slow (seasonally changing structure and pigments) and fast (physiological response to stress) processes. The method...

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