Understanding and monitoring pressures on ecosystems and their consequences for ecosystem services (ES) is essential for management decisions and verification of progress towards national and international policies (e.g. Aichi Biodiversity Targets, Sustainable Development Goals). Remote sensing (RS) offers a unique capability to assess ES systematically and regularly across spatial and temporal scales. We aim to evaluate the benefits of RS to monitor spatio-temporal variations of ES by assessing several ES in Switzerland between 2004 and 2014. We coupled mechanistic ES models and RS data to estimate time series of three regulating (i.e. carbon dioxide regulation (CO₂R), soil erosion prevention (SEP), and air quality regulation (AQR)) and one cultural ES (recreational hiking (RH)). The resulting ES were used to assess spatial and temporal changes, trade-offs and synergies of ES potential supply and flow in Switzerland between 2004 and 2014. Resulting ES trends showed diverse spatial patterns across Switzerland with largest changes in CO₂R and AQR. ES interactions revealed a scale and elevation dependency. We identified weak to strong synergies between all ES combinations except for trade-offs between CO₂R-AQR and AQR-RH at Swiss scale. Spatially, all ES interactions revealed a heterogeneous mix of synergies and trade-offs within Switzerland.
Our results demonstrate the strength of RS for systematic and regular spatio-temporal ES monitoring and contribute insights to the large potential of RS, which will be extended with future Earth observation missions. Derived spatially explicit ES information will facilitate decision-making in landscape planning and conservation and will allow examining progress towards environmental policies.

Leaf fluorescence can be used to track plant development and stress, and is considered the most direct measurement of photosynthetic activity available from remote sensing techniques. Red and far-red sun-induced chlorophyll fluorescence (SIF) maps were generated from high spatial resolution images collected with the HyPlant airborne spectrometer over even-aged loblolly pine plantations in North Carolina (United States). Canopy fluorescence yield (i.e., the fluorescence flux normalized by the light absorbed) in the red and far-red peaks was computed. This quantifies the fluorescence emission efficiencies that are more directly linked to canopy function compared to SIF radiances. Fluorescence fluxes and yields were investigated in relation to tree age to infer new insights on the potential of those measurements in better describing ecosystem processes. The results showed that red fluorescence yield varies with stand age. Young stands exhibited a nearly twofold higher red fluorescence yield than mature forest plantations, while the far-red fluorescence yield remained constant. We interpreted this finding in a context of photosynthetic stomatal limitation in aging loblolly pine stands. Current and future satellite missions provide global datasets of SIF at coarse spatial resolution, resulting in intrapixel mixture effects, which could be a confounding factor for fluorescence signal interpretation. To mitigate this effect, we propose a surrogate of the fluorescence yield, namely the Canopy Cover Fluorescence Index (CCFI) that accounts for the spatial variability in canopy structure by exploiting the vegetation fractional cover. It was found that spatial aggregation tended to mask the effective relationships, while the CCFI was still able to maintain this link. This study is a first attempt in interpreting the fluorescence variability in aging forest stands and it may open new perspectives in understanding long-term forest dynamics in response to future climatic conditions from remote sensing of SIF.

Vegetation is a highly dynamic component of the Earth surface and substantially alters the water cycle. Particularly the process of oxygenic plant photosynthesis determines vegetation connecting the water and carbon cycle and causing various interactions and feedbacks across Earth spheres. While vegetation impacts the water cycle, it reacts to changing water availability via functional, biochemical and structural responses. Unravelling the resulting complex feedbacks and interactions between the plant-water system and environmental change is essential for any modelling approaches and predictions, but still insufficiently understood due to currently missing observations. We hypothesize that an appropriate cross-scale monitoring of plant-water relations can be achieved by combined observational and modelling approaches. This paper reviews suitable remote sensing approaches to assess plant-water relations ranging from pure observational to combined observational-modelling approaches. We use a combined energy balance and radiative transfer model to assess the explanatory power of pure observational approaches focussing on plant parameters to estimate plant-water relations, followed by an outline for a more effective use of remote sensing by their integration into soil-plant-atmosphere continuum (SPAC) models. We apply a mechanistic model simulating water movement in the SPAC to reveal insight into the complexity of relations between soil, plant and atmospheric parameters, and thus plant-water relations. We conclude that future research should focus on strategies combining observations and mechanistic modelling to advance our knowledge on the interplay between the plant-water system and environmental change, e.g. through plant transpiration.

Reliable information about plant transpiration (T) is essential to advance understanding of various interactions and feedbacks across Earth spheres and eventually facilitate their predictions. Estimating T at larger scales is particularly challenged by the difficulty to constrain the biological control on T (i.e. stomatal conductance), while existing approaches based on potential rather than actual photosynthesis are less reliable than required. New Earth observation approaches such as measurements of suninduced chlorophyll fluorescence (SIF), the most direct observable of ecosystem photosynthesis, opens new perspectives to advance estimates of T. This contribution aims to sensitize for variability of SIF-T relationships across species and in dependency on environmental conditions. We use the combined energy balance and radiative transfer model SCOPE to assess relationships between SIF and T in dependency on relevant environmental and plant specific variables. Our results provide important insight to further define SIF based approaches for advanced estimates of T.

Imaging spectroscopy based methods offer unique capabilities for retrieving narrow-band vegetation indices which can be empirically related to functional traits of plants. However, in areas with complex topography, illumination effects affect the retrieval of such indices from high spatial resolution airborne or satellite data. Irradiance components at the pixel level are determined by atmospheric composition, as well as instantaneous illumination-surface-sensor geometries. An accurate pixel-wise description of direct and diffuse irradiance components is necessary to perform atmospheric corrections, finally resulting in improved surface reflectances and hence products. We assess three atmospheric correction strategies, differing in their approaches to simulate instantaneous as well as pixel-wise abundances of diffuse and direct irradiance. We use physically-based approaches in combination with either digital elevation models (DEM), fine resolution digital object models (DOM), or 3D modelling output from the Discrete Anisotropic Radiative Transfer (DART) model. The such obtained top-of-canopy reflectances at the Laegern test-site in Switzerland, are used to assess retrieval improvement for a set of indices (Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), as well as chlorophyll and carotenoid indices). We demonstrate that both, the DOM and the DART based approach, improve the retrievals for flat cast-shadows by ≤71% compared to using a DEM. In dense forest areas, improvements are less significant. Remaining key issues are related to overestimating surface reflectance under extreme illumination conditions.

Solar-induced chlorophyll fluorescence (SIF), an electromagnetic signal that can potentially indicate vegetation photosynthetic activity, can be retrieved from ground-based, airborne and satellite measurements. However, due to the scattering and re-absorption effects inside the leaves and canopy, SIF measured at the canopy level is only a small part of the total SIF emission at the photosystem level. Therefore, a downscaling mechanism of SIF from the canopy level to the photosystem level is important for better understanding the relationship between SIF and the vegetation gross primary production (GPP). In this study, firstly, we analyzed the canopy scattering effects using a simple parameterization model based on the spectral invariant theory. The probability for SIF photons to escape from the canopy was found to be related to the anisotropic spectral reflectance, canopy interception of the upward solar radiation, and leaf absorption. An empirical approach based on a Random Forest (RF) regression algorithm was applied to downscale SIF constrained by the red, red-edge and far-red anisotropic reflectance. The RF was trained using simulations conducted with the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. The performance of the SIF downscaling method was evaluated with SCOPE and Discrete Anisotropic Radiative Transfer (DART) model simulations, ground measurements and airborne data. Results show that estimated SIF at the photosystem level matches well with simulated reference data, and the relationship between SIF and photosynthetically active radiation absorbed by chlorophyll is improved by SIF downscaling. This finding in combination with other evaluation criteria suggests the downscaling of canopy SIF as an efficient strategy to normalize species dependent effects of canopy structure and varying solar-view geometries. Based on our results for the SIF-APAR relationship, we expect that such normalization approaches can be helpful to improve estimates of photosynthesis using remote sensing measurements of SIF.

Due to the large carbon dioxide (CO₂) fluxes between terrestrial ecosystems and the atmosphere, dynamics of photosynthesis can have significant effects on atmospheric CO₂ concentrations and lead to large uncertainties in ecosystem C budgets. Remote sensing approaches using sun-induced chlorophyll fluorescence (SIF) hold the potential to directly assess ecosystem photosynthesis. However, many challenges remain linked to using the SIF emission signal to estimate gross primary production (GPP). The goal of this study was to gain a better understanding of the relationships between GPP and SIF over different time scales (minutes to years) and under varying environmental conditions. Two different ecosystems were investigated, a cropland and a mixed forest, with continuous eddy covariance flux measurements. Continuous tower-based SIF retrievals were performed in 2015 and 2016 at both ecosystems.
In both ecosystems, SIF was found to be more affected by environmental conditions than GPP. Annual cycles for GPP and SIF differed at the mixed forest due in part to the influence of the different footprint size of the two independent measurements. Diurnal cycles in GPP and SIF corresponded well under unstressed conditions and followed the incoming photosynthetic photon flux density (PPFD). However, depressions in SIF were found at both sites either at midday or in the afternoon during the growing season. At the cropland site, reductions in SIF occurred at high PPFD (PPFD > 1470 μmolm^-2s^-1, R² = 0.62) and high VPD (VPD > 1590 Pa, R² = 0.35). Whereas at the forest site, reductions in SIF were linked to high VPD (VPD > 1250 Pa, R² = 0.25), but not to high PPFD (R² = 0.84). The depression in SIF was also associated with an increase in non-photochemical quenching, as indicated by the photochemical reflectance index (R² = 0.78), thus showing the complementarity between SIF and non-photochemical quenching as different energy pathways. Our results show the importance of characterizing the influence of different environmental conditions on SIF-GPP relationships for specific ecosystems to reliably estimate GPP from remote sensing measurements.

The growing availability of global measurements of sun-induced chlorophyll fluorescence (SIF) can help in improving crop monitoring, especially the monitoring of photosynthetic activity. However, variations in top-of-canopy (TOC) SIF cannot be directly interpreted as physiological changes because of the confounding effects of vegetation biochemistry (i.e. pigments, dry matter and water) and structure. In this study, we propose an approach of using radiative transfer models (RTMs) and TOC reflectance to estimate the biochemical and structural effects on TOC SIF, as a necessary step in retrieving physiological information from TOC SIF. The approach was assessed by using airborne (HyPlant) reflectance and SIF data acquired over an agricultural experimental farm in Germany on two days, before and during a heat event in summer 2015 with maximum temperatures of 27°C and 34°C, respectively. The results show that over 76% variation among different crops in SIF observations was explained by variation in vegetation biochemistry and structure. In addition, the changes of vegetation biochemistry and structure explained as much as 73% variation between the two days in far-red SIF, and 40% variation in red SIF. The remaining unexplained variation was mostly attributed to the variability in physiological status. We conclude that reflectance provides valuable information to account for biochemical and structural effects on SIF and to advance analysis of SIF observations. The combination of RTMs, reflectance and SIF opens new pathways to detect vegetation biochemical, structural and physiological changes.