Leaf fluorescence is a natural process by which chlorophyll pigments relax part of the electromagnetic energy they absorb in the form of electromagnetic energy of lower intensity. Chlorophyll fluorescence happens in the photosynthetic apparatus and is tightly linked to processes generating reduction power and energy to drive carbon assimilation. For more than 40 years, scientists measured plant photosynthesis using Pulse-Amplitude-Modulation (PAM) chlorophyll fluorometer, an apparatus where fluorescence is measured upon actinic illumination and saturating pulse of light to retrieve quantum yield for photosynthesis.

A little more than a decade ago, a breakthrough in spatial earth observation occurred: using narrow band observations in the oxygen A-band, the first global Solar Induced Chlorophyll Fluorescence (SIF) measurements were obtained. For the first time, the scientific community had access to observations directedly linked to photosynthetic processes arousing high expectations to constrain carbon uptake by terrestrial vegetation.

In this study we assess to what extend these expectations have been met though an extended literature review. In addition, as SIF measurements are also linked to the vegetation structure and how an emitted photon escape the canopy, we will discuss the influence of the canopy structure in SIF measurements. We will also compare SIF products from different platforms in term of fluorescence yield, which is the first step to evaluate photochemical yield.  Finally, we will discuss difficulties arising when comparing vegetation models simulations and SIF measurements.

How to cite: Morfopoulos, C. and Prentice, C.: A status report after a decade of remotely sensed Solar Induced Fluorescence (SIF), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5659, https://doi.org/10.5194/egusphere-egu23-5659, 2023.

Food security is dependent on agriculture as such prediction and monitoring of crop yield is of essential importance. Crop productivity primarily depends upon its potential to transmute light energy to sugar through photosynthesis and the total amount of carbon fixed by this process is coined as Gross Primary Production (GPP). Therefore, a reliable estimation of GPP is a vital step toward crop monitoring. Typically, accurate quantification of GPP at various spatial and temporal scales through the light use efficiency models remains challenging. Novel remote sensing methods such as Solar Induced chlorophyll Fluorescence (SIF) is a direct probe into the photosynthetic machinery and has been demonstrated to be a much better indicator of primary production compared to traditional vegetation indices. SIF has also been demonstrated to be a proxy for GPP as well as estimating crop productivity. However, most studies focus on homogeneous crop areas across the globe by using satellite or ground-based SIF concurrently with flux tower GPP. In this study, we examine how well satellite-based SIF products can monitor the GPP and crop productivity across the heterogeneous cropping system of India. Linear correlation analysis is carried out to analyse the relationship between FLUXCOM GPP with Global Ozone Monitoring Experiment-2 (GOME-2) SIF and TROPOspheric Monitoring Instrument (TROPOMI) SIF at different spatio-temporal scales. The results indicate a significant pixel-wise correlation at 8 daily and monthly scales across the crop area of India. However, a weak linear correlation is found between GPP and SIF at yearly scale. From the analysis of TROPOMI SIF and GOME-2 SIF, we find that TROPOMI SIF has a higher potential to predict GPP across the crop area of India. To explore the spatial and temporal variability in GPP and SIF relationship, we used GPP/SIF ratio as an indicative parameter. The maximum GPP/SIF values occurred in September and October. We found the seasonal pattern of GPP/SIF ratio following the seasonal dynamics of Leaf area index (LAI, canopy structural metric). During peak growing season GPP/SIF was positively corelated to short wave radiation and moisture availability, but during the early growing season it mostly dependent on soil moisture. Our results will enhance the understanding of the mechanisms of the link between GPP and SIF. 

How to cite: Behera, S. and Dutta, D.: The spatio-temporal dynamics of the relationship between gross primary productivity and solar induced chlorophyll fluorescence across the agricultural ecosystem of India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3866, https://doi.org/10.5194/egusphere-egu23-3866, 2023.

Climate change is caused by the ever-increasing anthropogenic CO₂ emissions and the resulting accumulation of carbon dioxide in the atmosphere, whose effects are so far mitigated by the oceanic and continental CO₂ uptakes. The terrestrial sink is the most uncertain component of the global carbon cycle mainly because the gross primary production (GPP), which is the quantity of atmospheric carbon absorbed by plants through photosynthesis, is highly variable in time and space, and because the involved processes are complex. As photosynthesis is not directly measurable at a scale larger than the leaf, land surface modelers have been looking for large-scale proxies of GPP. Solar-induced chlorophyll fluorescence (SIF) estimates from satellite instruments have emerged as a promising resource to inform on the space-time distribution of GPP, and have been increasingly used over the last decade. However, numerous challenges remain to be addressed to acutely understand the SIF signal, and its relationship with plant photosynthesis, to be able to correctly exploit space-borne SIF retrievals to constrain GPP simulated by land surface models (LSMs). Notably, we still lack knowledge on the non-photochemical quenching (NPQ), which represents the third deactivation pathway of the light energy absorbed by chlorophyll pigments, alongside photochemistry and fluorescence. In this study, we focused on boreal evergreen needleleaf forests (BorENF), which cover 29% of the world's total forest area, and whose GPP budget is still debated. We took advantage of both passive and active fluorescence measurements to improve the representation of NPQ, SIF and GPP in the ORCHIDEE LSM. We first used active measurements taken at the Hyytiälä BorENF site on Pinus sylvestris trees, to separately model the sustained and reversible NPQ components. Indeed, it was previously documented that during winter such evergreen trees suppress photosynthesis and sustain molecular modifications of the photosynthetic chain allowing the dissipation of the excess energy absorbed as heat (sustained NPQ). The reversible NPQ occurs during growth season in response to environmental stress (e.g., excessive light or droughts). In a second step, we optimised several ORCHIDEE parameters related to NPQ, SIF and GPP representations, using data assimilation techniques. We performed a multi-variables and multi-sites approach, simultaneously assimilating in situ GPP estimates at nine BorENF FLUXNET sites and collocated SIF estimates from the TROPOMI satellite instrument. The improvements brought to SIF and GPP were evaluated at those sites over independent years (i.e. not used in assimilation) with positive results, and at the regional scale against the FLUXCOM and FLUXSAT GPP products, as well as against TROPOMI SIF data.

How to cite: Leverne, L., Abadie, C., Bacour, C., Zhang, Y., Raoult, N., Bastrikov, V., Peylin, P., Krieger-Liszkay, A., and Maignan, F.: Improving non-photochemical quenching, fluorescence emission and gross primary production of boreal evergreen needleleaf forests in a land surface model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5146, https://doi.org/10.5194/egusphere-egu23-5146, 2023.

In this study, we address two relevant gaps when monitoring plant photosynthesis using remote sensing techniques; these are i) assess the seasonal trends and relationships observed between photosynthesis, optical vegetation indices, and chlorophyll fluorescence in crop systems and ii) evaluate the contribution of Sun-induced chlorophyll fluorescence (SIF) on linear and non-linear light-use efficiency-based (LUE) models for the remote estimation of plant photosynthesis. Coincident measurements of net plant photosynthesis (A_net), optical vegetation indices (i.e., Red edge index and photochemical reflectance index (PRI) among others), PSII operating efficiency (ΦPSII), and SIF were made at leaf level once a week in a wheat field under different nitrogen treatments. In LUE models, three key variables explain the seasonal variability of photosynthesis; these are the fraction of absorbed photosynthetically active radiation (fAPAR), LUE, and a correction factor related to meteorological conditions that limit LUE. In this study, the Red edge index was highly correlated with fAPAR (R²>0.70, p-value<0.05); however, neither PRI nor SIF were able to explain the seasonal changes of LUE (R²<0.10).  ΦPSII seasonal values (0.10 – 0.40) measured during the experiment indicated strong downregulation of the photosynthetic machinery. This explained why, in this study, SIF did not present a linear relationship with LUE. Our results confirmed that under stress conditions the non-photochemical quenching mechanisms (NPQ) control the energy dissipation pathway, breaking the linear relationship between photochemistry and fluorescence. Additionally, our study proved that changes in A_net could be better explained when optical vegetation indices, chlorophyll fluorescence, and meteorological conditions are combined in non-linear LUE-based models (R² increased from 0.10 for the linear model to 0.60 for the non-linear model). These results confirmed the need to build non-linear models for the remote quantification of photosynthesis.

How to cite: Cendrero-Mateo*, M. P., Van Wittenberghe, S., Laparra, V., Rascher, U., Papuga, S. A., Ponce-Campos, G., and Moreno, J. F.: Use of Sun-induced chlorophyll fluorescence in linear and non-linear light use efficiency models for remote estimation of plant photosynthesis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-120, https://doi.org/10.5194/egusphere-egu23-120, 2023.

Photosynthesis is a major driver of terrestrial ecosystem dynamics. Unfortunately, gross primary productivity (GPP), or the rate at which solar energy is captured and stored into sugar molecules during photosynthesis, cannot be directly measured from remote sensing (RS) signals. Several RS signals related to vegetation pigments and to canopy structure can, however, serve as proxies for GPP. They can further be combined with different types and degrees of modelling to derive spatio-temporal estimations of GPP. Different strategies exist to do so, which often vary with respect to how much they depend on an in-situ reference for GPP, the gold standard being those derived from eddy covariance (EC) measurements at flux tower sites.

Here we investigate several such strategies with a specific goal: to explore the potential contribution of Sentinel satellites to improve GPP estimation. The Sentinel fleet is maintained by the European Union’s Copernicus programme, thereby guaranteeing a certain longevity and enabling the establishment of operational services that do not depend on single satellite missions. The main RS signals we consider are: the OLCI global vegetation index (OGVI) and OLCI terrestrial chlorophyll index (OTCI) from the Sentinel-3 OLCI instrument; daytime and night-time land surface temperature (LST) from Sentinel-3 SLSTR; and sun-induced chlorophyll fluorescence (SIF) from TROPOMI on-board of Sentinel-5-P. We further use time series of Sentinel-2 data to quantify the spatial homogeneity within the observational footprints of these coarser spatial resolution products in order to ensure a proper comparison to flux-tower data. The whole exercise is part of the Sen4GPP project funded by the European Space Agency (ESA).

The three strategies we explore to derive GPP are: (1) empirical SIF-based estimation of GPP, including a version involving spatial downscaling to reach a finer resolution of SIF; (2) deterministic modelling based on a quantum yield light use efficiency (LUE) model calibrated on EC flux towers; and (3) purely data-driven machine learning (ML) based on EC measurements at flux towers using dedicated 10-fold cross-validation using the FLUXCOM-X framework. The cross-comparison is done for independent flux tower sites over Europe based on the Warm Winter 2020 database, covering the recent past (2018-2020) when TROPOMI SIF observations are available.

The results indicate that the ML approach clearly outperforms the process-based LUE approach, which itself performs better than SIF. However, this order also reflects a decreasing reliance in flux tower data and possibly increasing capacity to extrapolate to situations not present in the learning dataset. The results further indicate that the ML approach using Sentinel data can perform better than a baseline using MODIS data alone, probably due to the inclusion of SIF information. Results also illustrate how ensuring the spatial consistency between grid and tower does improve performance, strengthening the rational for spatially downscaling coarse RS signals such as SIF. Overall, these encouraging results bode well for the potential use of Sentinel data to improve our current capacity to monitor biogeochemical process at global scale.

How to cite: Duveiller, G., Nelson, J., Hamdi, Z., Gensheimer, J., Dash, J., Morris, H., Ogutu, B., Bandopadhyay, S., Guanter, L., Walther, S., Jung, M., and Plummer, S.: Cross-comparing different avenues for improving our estimation of GPP by exploiting Sentinel remote sensing data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9722, https://doi.org/10.5194/egusphere-egu23-9722, 2023.

Temperate forests and grasslands have different drought response strategies. Trees often control their stomata to reduce water loss in order to prevent hydraulic failure and ensure the survival of their aboveground biomass. In contrast, grasses generally have a less strong stomatal control and maintain high photosynthesis and transpiration until the soil moisture gets depleted. That is when their leaves wilt and the grasslands see a reduction in their aboveground green biomass. Both the increased stomatal control and the reduction in aboveground biomass decrease the surface conductance, i.e. decrease the exchange of water and carbon between the leaves and the atmosphere. Therefore, the drought response of vegetation has major impacts on the land-atmosphere fluxes of water, energy, and carbon, as well as the development of droughts and heat waves.

Here, we study to which extent the different drought responses of forests and grasslands are reflected in remote sensing data. We hypothesise that (i) for both forests and grasslands, there are drought-induced changes in thermal infrared based data (e.g., land surface temperature), because of the decreased surface conductance for both land cover types. Furthermore, we hypothesise that (ii) drought-induced changes in optical based indices (e.g. the normalized difference vegetation index) can be detected for grasslands but not for forests, because of the different drought response strategies of trees and grasses. In this study we jointly analyze site-scale and remote sensing data. We use eddy-covariance data for 52 forest sites and 11 grassland sites across the northern hemisphere to calculate the surface conductance, and we identify droughts from low soil moisture content and reduced surface conductance. Then we analyse how the drought response is reflected in thermal and optical indices derived from MODIS satellite data.

The results show that our hypotheses are largely confirmed. The land surface temperature increases with drought-induced reductions in surface conductance for both forests and grasslands. By contrast, the optical indices show a much stronger response for grasslands than for forests. We conclude that the different canopy-level drought response strategies of trees and grasses are reflected in remote sensing data. Our study highlights that the joint investigation of multiple remote sensing data streams enables insights beyond the analyses of individual indices, such as a better understanding of the drought response strategies across land cover types.  Further, a host of different satellite data should be used to monitor and study vegetation drought responses of forests and grasslands to ensure accurate inference on the implications on water, energy, and carbon fluxes.

How to cite: Hoek van Dijke, A., Orth, R., Teuling, A., Herold, M., Schlerf, M., Migliavacca, M., Machwitz, M., van Hateren, T., Yu, X., and Mallick, K.: Forest vs. grassland drought response inferred from eddy covariance and Earth observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3145, https://doi.org/10.5194/egusphere-egu23-3145, 2023.

Long term global terrestrial vegetation monitoring from satellite Earth Observation system is a critical issue within global climate and earth science modelling applications. A set of Essential Climate Variables was identified as being both accessible from remote sensing observations and intervening within key processes. Among those related to land surfaces, the leaf area index (LAI) and the fraction of absorbed photosynthetic active radiation (FAPAR) are derived from observations in the reflective solar domain. These vegetation biophysical variables play a key role in several processes, including photosynthesis, respiration and transpiration. LAI is defined as half the total developed area of leaf elements per unit horizontal ground area. It controls the exchanges of energy, water and greenhouse gases between the land surface and the atmosphere. FAPAR is defined as the fraction of radiation absorbed by the canopy in the 400 - 700 nm spectral domain under specified illumination conditions. It is one of the main inputs in light use efficiency models. The cover fraction (FCOVER) defined as the fraction of background covered by green vegetation as seen from nadir appears also as a very pertinent variable that can be used in surface energy balance models to separate the contribution of the soil from that of the canopy.

This paper describes the GEOVx products consisting in LAI, FAPAR and FCOVER derived every 10 days at the global scale at kilometric and hectometric resolution within THEIA and Copernicus Global Land Service (CGLS) initiatives. GEOV2/AVHRR is derived from Long Term Data Record (LTDR) AVHRR data. It provides a global coverage at 0.05° ground sampling distance (~4km) every 10 days from 1981 to 2021. The GEOV2/CGLS Collection 1km of LAI, FAPAR and FCOVER products starts in 1999 with SPOT/VEGETATION data, and continues from 2014 to June 2020 with PROBA-V. The GEOV3/CGLS Collection 300m of LAI, FAPAR and FCOVER products is available from 2014 with PROBA-V and from July 2020 to present with Sentinel-3. The products are delivered with associated uncertainties and quality indicators. The products are accessible free of charge respectively through the THEIA (https://www.theia-land.fr/product/serie-de-variables-vegetales-avhrr-fr/) and GCLS (http://land.copernicus.eu/global/) websites, along with documentation describing the physical methodologies, the technical properties of products, and the quality of variables based on the results of validation exercises.

This talk will focus on the retrieval algorithms used to generate the GEOVx LAI, FAPAR and FCOVER products. The GEOVx products will be assessed based on the comparison with other existing satellite products and ground data. The consistency of the time series will be evaluated with due attention to the switchover from different sensors (from AVHRR to SPOT/VEGETATION, from SPOT/VEGETATION to PROBA-V and from PROBA-V to Sentinel-3). Finally, some applications of the GEOVx biophysical products will be presented.

How to cite: Verger, A., Weiss, M., Descals, A., Camacho, F., Sánchez-Zapero, J., Lacaze, R., and Baret, F.: Long-term time series of global vegetation products: challenges and lessons learnt from AVHRR to Sentinel-3, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16447, https://doi.org/10.5194/egusphere-egu23-16447, 2023.

The Arctic tundra biome is large and difficult to access. Satellite monitoring is essential for observing changes in these areas, but in most cases the resolution of imagery is larger than typical vegetation patch sizes. NDVI greening trends have been observed in most parts of the Arctic since the 1980s. These trends can be explained by the expansion of vegetated areas, higher biological productivity and changing vegetation composition. Low to tall stature shrubs are changing the spectral reflectance characteristics of the overall tundra vegetation. In particular, the NIR wavelengths are affected, resulting in higher NDVI values.

At the same time, soil erosion threatens parts of the tundra biome such as in Iceland. Soil erosion occurs due to biogeomorphological processes. Uncertainties remain in how these processes will adjust to a rapidly changing climate and to anthropogenic pressures such as grazing. This makes the future trajectory of these landscapes difficult to predict. Critical thresholds may exist in these landscapes, which if crossed, can lead to irreversible desertification. For these reasons, it’s important to be able to accurately assess the environmental state of these landscapes, especially the extent of soil erosion.

Satellite observation of NDVI greening in areas undergoing shrub expansion could mask other trends, such as increasing levels of soil erosion. This is because typical satellite datasets used for tundra monitoring have resolutions in the order of 10s of m, which means that each pixel tends to include multiple different land cover types. At the level of a sensed pixel, higher NDVI values of shrubs could lead to a net positive NDVI trend, despite the eroded area increasing.

To address this issue, we need to establish which spatial resolutions are appropriate for monitoring. We used a multi-scale study in a degraded tundra landscape in northern Iceland. Different satellite products from (PlanetScope, Sentinel-2, Landsat-8) were compared with 1.1 km² multispectral UAV imagery collected in 2021. The very high-resolution UAV imagery (0.05 m resolution) is used to classify land cover and allows us to explore how the composition of different land cover classes affects the overall NDVI value of a satellite pixel (3 – 30 m resolution) at the same location.

We find that for the same NDVI values in a satellite pixel, the UAV data reveals large variations in the degree of soil erosion. This can mainly be attributed to variability in the ratio of shrub cover to other vegetation cover, which alters the spectral signature of a pixel. This makes the interpretation of NDVI trends more difficult and stresses the importance of using an appropriate spatial resolution. Landsat-8 (30 m) revealed low accuracy in resolving tundra heterogeneity, while Sentinel-2 (10 m) and PlanetScope (3 m) showed significant improvements.

This study highlights the importance of using the right spatial resolution when monitoring highly fragmented environments, and the need to consider that an increase in NDVI may not reflect an improvement in environmental state.

How to cite: Kodl, G., Streeter, R., and Bolch, T.: Soil erosion trends can be obscured in remote sensing data if inappropriate spatial resolutions are used: evidence from a high-latitude tundra landscape undergoing shrub expansion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14886, https://doi.org/10.5194/egusphere-egu23-14886, 2023.

The climate crisis leads to a change in forest tree species distributions, favouring most likely heat and drought tolerant species. As a consequence, many forest sites across Europe will become unsuitable for drought sensitive species. The combination of climate change and conservation goals of Natura2000 forest habitat types will lead to severe conflicts in conservation and forestry. The concept of “no deterioration” in article 6 of the Habitats Directive supports a static conservation of the prevalent flora and fauna. In those areas forestry is oriented towards conservation of natural forest habitat types. Especially areas with reduced silvicultural activities or strict silvicultural requirements, such as Natura 2000 sites, are prone to a long forest conversion process towards more suitable tree species. As forestry is based on long-term life cycles, this development will impact forest condition, forest cover, silviculture and conservation negatively. The Natura2000 legislation is under pressure.

This study aims at analysing (1) the changes of future tree species ranges in Europe, (2) how severe changes will impact current natural forest habitat types of Natura 2000 sites and (3) which new tree species might be present in future climate scenarios. We selected a combination of generalised additive models, generalised linear models and boosted regression trees for the modelling process. As model input serve four preselected bio-climatic variables from a total of 26 bio-climatic variables, derived from EURO-CORDEX CMIP5 climate simulations for 1971-2098 for IPCC’s representative concentration pathways 2.6, 4.5 and 8.5. JRC soil characteristics and JRC European tree species data serve as additional input variables. Potential tree species ranges with 1km spatial resolution as model outcome is compared to current definition of natural forest habitat types of Natura 2000 sites. This allows conclusions about their potential future occurrence and endangered static protection state. Most tree species reveals a severe decline of suitable ranges in all RCP scenarios and range shift towards polwards regions and higher elevations. As a consequence protection goals of forest Natura 2000 areas are at stake.

How to cite: Reichmuth, A., Kühn, I., Rakovec, O., Boeing, F., Müller, S., Samaniego, L., and Doktor, D.: Natura 2000 areas under climate change: Effects of tree species distribution shifts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17060, https://doi.org/10.5194/egusphere-egu23-17060, 2023.

Biodiversity loss in freshwater river ecosystems is much faster and more severe than in terrestrial systems, and spatial conservation and restoration plans are needed to halt this erosion. Reliable and highly resolved data on the state of and change in biodiversity are critical for effective measures. However, high-resolution biodiversity maps still need to be improved, especially for large riverine systems. Coupling data from the latest global satellite sensors with broad-scale environmental DNA (eDNA) and machine learning could enable fast and precise mapping of the distribution of river organisms. Here, we investigated the potential for combining these methods using a unique fish eDNA data set sampled along the entire length of the Rhone river in Switzerland and France. Using Sentinel 2 and Landsat 8 images, we generated a set of ecological variables describing both the aquatic part (blue) and the surrounding terrestrial landscape of the river (green). We combined these variables with eDNA-based presence and absence data on 29 fish species and used three models to assess environmental suitability for these species. Most models showed good performance, indicating that ecological variables derived from remote sensing can provide valuable information on the ecological determinants of fish species distributions. Variable importance analyses showed that the blue variables (water temperature, water quality, water clarity) had stronger associations than the green variables surrounding the river. The species range mapping indicated a significant transition in the species occupancy along the Rhone, from its source in the Swiss Alps to its outlet into the Mediterranean Sea in southern France. Our study demonstrates the feasibility of combining remote sensing and eDNA to map species distributions in large rivers; this method can be up-scaled to any large river worldwide. Hence, in the future, the approach presented here could be used to predict precise biodiversity distributions in rivers to help design conservation schemes.

How to cite: Zong, S., Brantschen, J., Zhang, X., Albouy, C., Valentini, A., Zhang, H., Altermatt, F., and Pellissier, L.: Mapping fish species distributions in River Rhone using environmental DNA and remote sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1918, https://doi.org/10.5194/egusphere-egu23-1918, 2023.