BG9.1 | Remote Sensing applications for the biosphere
EDI
Remote Sensing applications for the biosphere
Convener: Willem Verstraeten | Co-conveners: Manuela Balzarolo, Shari Van Wittenberghe, Frank Veroustraete, Benjamin DechantECSECS
Orals
| Wed, 26 Apr, 16:15–18:00 (CEST)
 
Room 2.17
Posters on site
| Attendance Wed, 26 Apr, 10:45–12:30 (CEST)
 
Hall A
Posters virtual
| Attendance Wed, 26 Apr, 10:45–12:30 (CEST)
 
vHall BG
Orals |
Wed, 16:15
Wed, 10:45
Wed, 10:45
A very tiny layer holds most of earth’s life in a complex mix of biotic and abiotic factors that interact in a subtle unique and ever changing play. In this scene, remotely-sensed (RS) signals result from the interaction of incoming, reflected and emitted electromagnetic radiation (EM) with atmospheric constituents, vegetation layers, soil surfaces, oceans or water bodies. Vegetation, soil and water bodies are functional interfaces between terrestrial ecosystems and the atmosphere. These signals can be measured by optical, thermal and microwave remote sensing including parts of the EM spectrum where fluorescence can be measured.

This session solicits for contributions on strategies, methodologies or approaches leading to the development and assimilation in models, of remote sensing products originating from different EM regions, angular constellations, fluorescence as well as data measured in situ for validation purposes.
We welcome presentations on topics advancing our understanding of terrestrial ecosystem dynamics under climate change such as ecosystem resilience, fire damage, plant drought stress, food production, food security, nature preservation, biodiversity, epidemiology, anthropogenic and biogenic air pollution. Insights on the assimilation of remote sensing and in-situ measurements in bio-geophysical and atmospheric models, as well as RS extraction techniques themselves, are also welcome.

Finally, this session aim, is to bring together scientists developing remote sensing techniques, products and models leading to strategies with a higher bio-geophysical impact on the stability and sustainability of this very thin layer of the earth we live in.

Orals: Wed, 26 Apr | Room 2.17

Chairpersons: Manuela Balzarolo, Shari Van Wittenberghe, Willem Verstraeten
16:15–16:20
Remotely-sensed vegetation productivity
16:20–16:30
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EGU23-5659
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On-site presentation
Catherine Morfopoulos and Colin Prentice

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.

16:30–16:40
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EGU23-3866
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ECS
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On-site presentation
Subhrasita Behera and Debsunder Dutta

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.

16:40–16:50
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EGU23-5146
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ECS
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On-site presentation
Lucas Leverne, Camille Abadie, Cédric Bacour, Yhoan Zhang, Nina Raoult, Vladislav Bastrikov, Philippe Peylin, Anja Krieger-Liszkay, and Fabienne Maignan

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.

16:50–17:00
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EGU23-120
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On-site presentation
Maria Pilar Cendrero-Mateo*, Shari Van Wittenberghe, Valero Laparra, Uwe Rascher, Shirley A. Papuga, Guillermo Ponce-Campos, and Jose F. Moreno

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 (Anet), 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 Anet 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.

17:00–17:10
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EGU23-9722
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On-site presentation
Gregory Duveiller, Jacob Nelson, Zayd Hamdi, Johannes Gensheimer, Jadu Dash, Harry Morris, Booker Ogutu, Subhajit Bandopadhyay, Luis Guanter, Sophia Walther, Martin Jung, and Stephen Plummer

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.

17:10–17:20
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EGU23-3145
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ECS
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On-site presentation
Anne Hoek van Dijke, René Orth, Adriaan Teuling, Martin Herold, Martin Schlerf, Mirco Migliavacca, Miriam Machwitz, Tessa van Hateren, Xin Yu, and Kaniska Mallick

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.

Remote sensing methods and applications
17:20–17:30
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EGU23-16447
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Virtual presentation
Aleixandre Verger, Marie Weiss, Adrià Descals, Fernando Camacho, Jorge Sánchez-Zapero, Roselyne Lacaze, and Frédéric Baret

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.

17:30–17:40
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EGU23-14886
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ECS
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On-site presentation
Georg Kodl, Richard Streeter, and Tobias Bolch

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.

17:40–17:50
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EGU23-17060
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Highlight
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On-site presentation
Anne Reichmuth, Ingolf Kühn, Oldrich Rakovec, Friedrich Boeing, Sebastian Müller, Luis Samaniego, and Daniel Doktor

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.

17:50–18:00
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EGU23-1918
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ECS
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On-site presentation
Shuo Zong, Jeanine Brantschen, Xiaowei Zhang, Camille Albouy, Alice Valentini, Heng Zhang, Florian Altermatt, and Loïc Pellissier

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.

Wrap-up

Posters on site: Wed, 26 Apr, 10:45–12:30 | Hall A

Chairpersons: Shari Van Wittenberghe, Manuela Balzarolo, Benjamin Dechant
A.279
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EGU23-14
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ECS
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Roma Varghese, Swadhin K. Behera, and Mukunda Dev Behera

Sea surface temperature (SST) is a key physical attribute of upper ocean thermal conditions that provide crucial information on the earth’s climate system by playing vital role in air-sea interactions. Some regional-scale SST variations are linked to large-scale climate variability which has catastrophic consequences in the social-economic sectors of many countries. Such anomalous SST conditions in the tropical oceans causes severe impacts on the functioning of terrestrial ecosystems by altering the fluxes of heat and moisture on land, and thus threatens terrestrial carbon dynamics as well as global food security. Thus, monitoring the vegetation response to SST anomalies is fundamental to understand, quantify, and predict the effects of oceanic variability on terrestrial vegetation activity. Solar-induced chlorophyll fluorescence (SIF) is a promising plant biophysical variable that has been used for the continuous observation of global vegetation activity, especially the photosynthetic characteristics. Our study comprehensively evaluates the relationship between tropical Pacific SST variability and SIF anomalies across India to assess the spatial and temporal variability in the ocean-vegetation interactions. Overall, SIF anomaly over the Indian mainland shows negative association with SST variability in the eastern equatorial Pacific. The persistence of warm anomalies in this oceanic region forces the reduction of average SIF in all the Indian agro-climatic zones notably during the summer monsoon. While during the years of cold anomalies in the eastern equatorial Pacific, SIF appears to be enhanced. Similarly, the composite of SIF demonstrated negative (positive) anomalies during the years of positive (negative) SST anomalies. However, the implications of SST variability on the SIF anomalies are not uniform all over India even during the summer monsoon. There exist a high spatial and temporal variability in the observed SST-SIF interactions. Within the monsoon months, the influence of both positive and negative SST anomalies was predominant only during July and August across much of the Indian mainland. In addition, this oceanic influence was also significantly notable in March, particularly in the Deccan plateau. Overall, the impact of warm anomalies is comparatively stronger on the functioning of the terrestrial ecosystem in India than the cold anomalies with a limited influence mainly over the southern peninsular region. This difference in the implications of positive and negative SST anomalies is evident in all the months except during March, July, and August. Annually, SST variability in the eastern equatorial Pacific significantly contributes to the interannual variability of SIF anomalies in Gujarat plains and hills, Western plateau and hills, Southern plateau and hills, Central plateau and hills, Eastern plateau and hills, and Western dry region. The observed significant SST-SIF linkage between the eastern equatorial Pacific and the Indian vegetation was feasible through the atmospheric teleconnections. The present study provides the fundamental information that aids the early detection of possible vegetation growth anomalies to various climate extremes associated with the tropical Pacific region. This can be useful for planning long-term strategies and policies to improve precision agriculture and forest management practices in India. 

How to cite: Varghese, R., K. Behera, S., and Behera, M. D.: Tropical Pacific Ocean SST teleconnections for the vegetation photosynthetic activity in India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14, https://doi.org/10.5194/egusphere-egu23-14, 2023.

A.280
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EGU23-639
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ECS
Islam Gomaa, Ghada Sultan, Yannis Markonis, Miltiadis Athanasiou, and Christoforos Pappas

Ongoing environmental changes challenge climate-sensitive regions worldwide with Mediterranean ecosystems being also particularly affected. Satellite remote sensing techniques allow for quantitative insights into these patterns by capturing land cover characteristics across a wide range of spatiotemporal scales. Here, focusing on natural protected areas of the European network Natura2000, we quantify the trajectories of their land cover dynamics during the last decades. To do so, we explored satellite imagery from the publicly available Landsat archive, together with advanced cloud computing services, and land cover change detection algorithms. We analysed the spatiotemporal variability in land cover, as quantified with optical remote sensing (Normalized Difference Vegetation Index, NDVI), for more than 50 Natura2000 sites distributed across Greece. The selected sites cover a wide range of environmental conditions. land cover compositions and Mediterranean vegetation patterns (i.e., tall forests, evergreen shrublands, phryganic areas and/or grasslands). Three main land cover change trajectories are examined: (1) abrupt shifts, e.g., due to natural disturbances, such as wildfires, and gradual alterations, namely (2) increase, i.e., ‘greening’, triggered, for example, by more favorable environmental conditions, and (3) decrease, i.e., ‘browning’, following, for example, drought and heat stress. Across the examined sites, the NDVI showed substantial variability, reflecting different land cover characteristics and site-specific demographics and environmental conditions. Site-level long-term mean NDVI ranged from 0.3 to 0.8 with the overall temporal trends being weakly positive. When these lumped spatiotemporal dynamics were disentangled, sites heavily affected by wildfires were identified (showing >50 % losses of their total vegetation cover) as well as sites with chronic decrease or increase in vegetation cover. Given the high significance and numerous services provided by such protected areas, a comprehensive quantification of their land cover dynamics not only enhances our process understanding, but also offers valuable insights to policy makers for the development of mitigation strategies.

How to cite: Gomaa, I., Sultan, G., Markonis, Y., Athanasiou, M., and Pappas, C.: Spatiotemporal land cover change trajectories across protected areas in Greece during the last decades, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-639, https://doi.org/10.5194/egusphere-egu23-639, 2023.

A.281
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EGU23-3599
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Guido J.M. Verstraeten and Willem Verstraeten

It is straightforward to analyse Earth´s fitness in terms of controlling and governing global warming due to human emissions of greenhouse gasses. We make room, however, for Earth´s entropy production as criterion for ecosystems. Indeed, it is a remarkable claim of Nobel Prize winner Roger Penrose to explain life as the decelerating force of earth´s production of entropy. The amount of earth´s entropy production is included in the quantity of emitted energy in the form of long wave or thermal radiation governed by the Stefan-Boltzmann law about the radiance of black bodies. Here we want to analyse how biodiversity is a substantial parameter to explain the decline of the Earth´s entropy production.

In the field of biodiversity Stephan Hubbel formulated the Unified Neutral Theory of Individual Migration of Life as an alternative to the widely accepted niche competition of species theory. According to Hubbel, species abundance is lognormally distributed within an ecosystem after dynamical equilibrium is reached. We examine the drift shift of species within neighbouring ecosystems by analysing the day (DLSTG) and night land surface temperature (NLSTG) gradient.  By restricting the examined area to a honeycomb with cells of 1 km² the assumption of constant atmospheric pressure can be assumed and in consequence the enthalpy is reduced to the entropy variation. The latter can be derived from remotely-sensed mean day and night land surface temperatures (LST).

By interpreting the entropy variation in terms of the statistical Shannon entropy formula wherein we import the lognormal distribution of species abundance, the entropy variation in the studied time interval is proportional to the difference of the natural logarithm of the respective standard deviations of the former and the latter species distribution function. Increased (decreased) entropy corresponds to a negative (positive) rate of biodiversity of the study area.

Hubbel´s area meta-community dynamics and the entropy production of the area under consideration and its surroundings provide a diversity number within the area. By integrating the mosaic of ecosystems over an extended almost isolated area (peninsula, insula, subcontinent) the decline or increase of entropy production gives a substantial support for Earth´s fitness for biological life. Preliminary, we aim at applying MODIS 1 km² day and night LST data on the area of South-western Finland to explore the idea of entropy variations.

How to cite: Verstraeten, G. J. M. and Verstraeten, W.: Can remotely-sensed Earth’s entropy production reveal its ecological fitness?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3599, https://doi.org/10.5194/egusphere-egu23-3599, 2023.

A.282
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EGU23-7475
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ECS
|
Highlight
Classification of flying insects in NEXRAD polarimetric weather radar using machine learning and aphid trap data
(withdrawn)
Samuel Kwakye, Heike Kalesse-Los, Maximilian Maahn, Patric Seifert, Roel van Klink, Christian Wirth, and Johannes Quaas
A.283
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EGU23-11534
|
ECS
Samuel Scherrer, Gabrielle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El Madany, and Wouter Dorigo

Vegetation is a major control on land-atmosphere fluxes of carbon and water. An improved representation of vegetation in land surface and dynamic vegetation models can therefore improve both short-term weather predictions as well as long-term climate projections. 

State update data assimilation (DA) of remotely sensed leaf area index (LAI) is one way to obtain vegetation state estimates consistent with physical constraints from a land surface model and observational data. Most LAI DA studies so far used bias-blind DA systems, i.e. they did not explicitly take bias between observations and model into account. However, if the observations are biased against the land surface model, this might hamper  the performance of the DA system, because it can induce instabilities in the model. We therefore examined the effect of bias on an LAI DA system, and compared a bias-blind LAI DA system with bias-aware approaches.  

For this purpose, we assimilated the Copernicus Global Land Service (CGLS) LAI into the Noah-MP land surface model over Europe in the 2002-2019 period. 

We find that in areas with large LAI bias, the bias-blind LAI DA by design leads to a reduced bias between observed and modelled LAI and GPP, but it also has strong impacts on soil moisture, leading to a worse agreement with independent, satellite-derived ESA CCI soil moisture. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between update steps. This drift also propagates to short-term estimates of GPP and ET. Furthermore, internal DA diagnostics indicate suboptimal DA system performance. 

The bias-aware approaches avoid the negative effects of the bias-blind assimilation, and still improve anomaly estimates of LAI. Therefore, bias-aware LAI DA might be a useful method to consider in LAI DA, especially when anomalies of LAI or GPP are of interest. 

Our results furthermore show that LAI of CGLS and Noah-MP show strong disagreement especially in dry climates. Model calibration or DA methods that include parameter updating could be an alternative to bias-aware DA to reduce these discrepancies. Our results can guide such efforts, and highlight the need for multiple constraints. 

How to cite: Scherrer, S., De Lannoy, G., Heyvaert, Z., Bechtold, M., Albergel, C., El Madany, T. S., and Dorigo, W.: Effects of bias in an  LAI data assimilation system on carbon uptake and hydrological variables and  over Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11534, https://doi.org/10.5194/egusphere-egu23-11534, 2023.

A.284
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EGU23-15103
Kristin Boettcher, Tea Thum, Kimmo Rautiainen, Mika Aurela, Jouni Pulliainen, Stephen Plummer, Bruce Johnson, Sampsa Koponen, Fabrice Lacroix, and Sönke Zaehle

Climate change induced increases in surface temperatures in the northern high-latitudes have consequences for cryosphere conditions in the boreal zone (snow cover, soil freeze-thaw and permafrost). Cryosphere changes will in turn influence the biosphere e. g. through changes in the carbon uptake and release by vegetation. However, the current knowledge about these interactions is insufficient for assessing the carbon balance accurately and uncertainties remain in model predictions of how the carbon cycle will respond to the changing climate.

In this work, we assessed the suitability of satellite-based and in situ soil freeze and thaw observations to inform on the start and end of the carbon uptake period in boreal forest and modelling to investigate the relationship between freeze-thaw dynamics and the carbon uptake and release by boreal forest ecosystems. Eddy covariance measurements from six coniferous forest sites in Finland and Canada were used to determine the start and end dates of the carbon uptake period. Satellite-based soil freeze and thaw dates, determined from the ESA SMOS Level 3 Soil Freeze and Thaw product (Rautiainen et al. 2016) for the period 2010 to 2020, agreed well in timing with site level observations and significant relationships with start and end dates of the carbon uptake period were found. This suggests that SMOS soil thaw and freeze dates could be used in the estimation of the length of the carbon uptake period in boreal coniferous forests although the relationship weakens for the warmer southern boreal site (Hyytiälä, Finland).

For the modelling, the terrestrial biosphere model QUINCY (QUantifying Interactions between Nutrient Cycles and the climate) (Thum et al. 2019) will be applied at three coniferous forest sites, stretching from the southern to the northern boreal zone. QUINCY has a multi-layer snow scheme (Lacroix et al. 2022) and fully coupled carbon, water, energy, and nitrogen cycles. First simulations were carried out for a Scots pine forest at Sodankylä (Finland). At the Sodankylä site, gross primary production (GPP) started when soil thaw was detected from in situ and satellite observations. The increase of total ecosystem respiration (TER) lagged behind GPP in spring and occurred when snow had melted. QUINCY captured the seasonal cycle of GPP well, however, simulated TER showed biases in spring that were related to snow melt dynamics. Simulations showed snow depth was too low and melting was too early which in turn led to increase in simulated TER too early in the year. The QUINCY modelling will be extended to sites Hyytiälä (Finland) and the Saskatchewan, Old Jack Pine forest (Canada). In further work, we plan to combine satellite information on snow melt with soil thaw and freeze to provide proxy indicators on the carbon uptake and release period that could be utilized in model evaluation.

 

 

References

Thum, T., et al., 2019. Geosci. Model Dev. 12, 4781-4802.

Rautiainen, K., et al., 2016. Remote Sensing of Environment, SMOS special issue 180, 346-360.

 

How to cite: Boettcher, K., Thum, T., Rautiainen, K., Aurela, M., Pulliainen, J., Plummer, S., Johnson, B., Koponen, S., Lacroix, F., and Zaehle, S.: Assessing cryosphere-biosphere linkages in boreal forests with Earth Observation and modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15103, https://doi.org/10.5194/egusphere-egu23-15103, 2023.

A.285
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EGU23-14270
Embedding the Spatial Heterogeneity of the Agricultural Landscape into Crop Model Parameters to Refine Sub-Regional Yield Predictions 
(withdrawn)
Sofia Bajocco, Fabrizio Ginaldi, Elisabetta Raparelli, Gianni Fila, and Simone Bregaglio
A.286
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EGU23-6097
Wenzhi Zeng, Shenzhou Liu, and Jiesheng Huang

Meteorological disasters such as windstorm, waterlogging, drought and so on, are crucial factors affecting crop production and farmers’ income. Agricultural insurance is one of the important strategies to protect the interests of farmers, especially in developing countries such as China. However, the accurate identification and quantification of meteorological disasters in large scale are still difficult issues for the popularization and development of agricultural insurance. One possible solution is to combine the high-resolution remote sensing satellite images with machine learning algorithms. In this study, we conducted the measurements for the yield of soybean and maize and determined the damage degrees of about 2000 fields in 2021. The Sentinel-2 satellite images were also collected in the same or adjacent date as the field measurements. The clustering algorithm was applied to amplify the field measurements. After that, three machine learning algorithms named LightGBM, XGboost and RandomForest were used to relate the surface reflectance, crop types, disaster damage degrees, and crop yields of soybean and maize. The results indicated that the accuracy of the XGBoost algorithm is better than the LightGBM and RandomForest. In addition, the present method obtained higher accuracy for the maize than the soybean, which indicates that meteorological and image data during crop growth periods should also be added in the yield estimation process, and the differences between crop loss mechanisms of different crops should be studied in the future.

How to cite: Zeng, W., Liu, S., and Huang, J.: Determining the meteorological disaster of maize and soybean by machine learning algorithms with Sentinel-2 satellite images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6097, https://doi.org/10.5194/egusphere-egu23-6097, 2023.

A.287
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EGU23-8175
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ECS
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Sophia Walther, Jacob A. Nelson, Mirco Migliavacca, Wouter A. Dorigo, Sofia L. Ermida, Gregory Duveiller, Fabian Gans, Darren Ghent, Basil Kraft, Karen L. Veal, Ulrich Weber, Ruxandra-Maria Zotta, and Martin Jung

The integration of global land surface remote sensing and in situ measured ecosystem carbon fluxes through machine learning approaches offers a unique data-driven perspective to diagnose the carbon cycle. Earth Observation (EO) data sets from different parts of the electromagnetic spectrum contain specific information on the land surface status, but also on the structural and physiological vegetation conditions. Each EO-derived land surface variable alone has a limited scope, addresses only individual aspects of the complex system, and can be confounded by other factors.  Here we use the new generation statistical flux upscaling framework Fluxcom-X to analyse the individual and synergistic contributions of different EO data sets to site-level terrestrial carbon fluxes in tailored cross-validation experiments. Several distinct data streams are explored as predictor variables:  land surface temperature (LST) from both polar orbiters and geostationary satellites (MODIS and SEVIRI), far-red SIF from GOME2, multi-spectral vegetation optical depth (VOD) from different sources (ku-band climate archive from Moesinger et al.(2020) and L-band from SMOS), and soil moisture (SM) from ESA CCI.  Each predictor variable undergoes a dedicated pre-processing in terms of quality checks and gap-filling. Beyond their overall added value in prediction skill, we are interested in the impacts of the EO predictors on different scales of carbon flux variability (e.g. diurnal, seasonal, seasonal anomalies, inter-annual, and between sites), specifically during situations of unusual water scarcity and surplus. We also compute SHAP values to understand how the machine learning model uses the EO information. Additionally, a second line of analysis addresses the role of acquisition properties for the accuracy of the estimates.

The first results for the predictor variable MODIS LST show that the inclusion of MODIS LST improves GPP estimates on all time scales.  The model strongly profits from LST as surrogate for moisture availability during dry anomalies, and for light availability during wet anomalies. Regarding the impact of acquisition properties of MODIS, we find that the variability in viewing geometry and overpass time does not affect the accuracy of simulated site-level GPP. However, failing to account for the clear-sky bias in availability of MODIS LST will result in a substantial decrease in accuracy, especially for overcast days.

Further experiments will include SEVIRI LST, SIF, VOD, as well as soil moisture, and we will analyse their role in the data-driven simulations of carbon fluxes. The lessons learned from the site-level cross-validation experiments will guide the production of gridded estimates of gross and net carbon fluxes for Europe and the globe.

How to cite: Walther, S., Nelson, J. A., Migliavacca, M., Dorigo, W. A., Ermida, S. L., Duveiller, G., Gans, F., Ghent, D., Kraft, B., Veal, K. L., Weber, U., Zotta, R.-M., and Jung, M.: Improved data-driven ecosystem carbon fluxes under moisture stress through synergistic Earth observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8175, https://doi.org/10.5194/egusphere-egu23-8175, 2023.

A.288
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EGU23-8223
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ECS
Trina Merrick, Andrei Abelev, Robert Liang, Michael Vermillion, Rong-Rong Li, Willibroad Buma, Christine Swanson, and Marcos Montes

There is a need to quantify relationships among vegetation structure and soil properties in coastal areas to better understand resilience, erosion, land use impacts, eutrophication, and mobility in this terrain. Remote sensing observations have been shown to effectively capture both vegetation and soil properties. However, identifying linked vegetation-soil properties and inferring soil properties from remote sensing when vegetation obscures the pixels remains challenging. Leveraging multiscale and multisensor remote sensing data and fusion techniques, we investigated the capability to identify linked vegetation-soil properties and infer the strength and stability of a barrier island along a swath where the soil surface is bare to partially to fully obscured by vegetation. To this end, we asked (1) Which remote sensing and field measured vegetation properties relate most strongly to soil properties, especially strength and moisture? (2) Do observations of soil conditions, geotechnical descriptors, vegetation species and health, and sensible hyperspectral signatures (HSI) allow accurate characterization of the combined soil-vegetation complex? We used multilevel and multi-sensor ground-, uncrewed aerial system (UAS)-, and satellite-based observations, namely HSI, lidar, ground optical, and geotechnical measurements, to test the variability and relationships among remote sensing-based measurements and geotechnical measurements, such as soil bearing strength. Firstly, we found high capability of HSI data to discriminate soil and soil moisture when any soil was exposed (beach, foredune, back dune, and marsh) and discrimination of vegetation at UAS and satellite scale. We found strong relationships among relative vegetation structure and soil properties, namely biomass estimates and soil strength, when using combined ground-based observations and UAS-based HSI observations and relative high accuracy upscaling to high-resolution satellite level maps of soil and vegetation properties. In addition, we found that adding slope and aspect data moderately enhanced the assessment of soil strength parameters in vegetated areas, although improvement of lidar data collection protocols in subsequent data collections promise further improvements in upcoming studies. In areas with tallest vegetation or soils that were highly saturated (inundation), results were mixed, likely due to poorer inference of soil background from remote sensing and soil strength from field measurements approaching zero, respectively. However, using a combination of shortwave infrared data, full spectra for analyses (spectral unmixing, dimensionality analyses, and supervised classification techniques), water-specific indices, and vegetation type information, wetland soil delineation was improved. Differences in soil and vegetation properties detected using field optical measurements were used to test upscaling techniques, i.e. training, for UAS-based HSI. With the help from ground-based data, a framework of mapping vegetation and soil specific properties was developed which enabled finer spatial analyses to be carried out with respect to the interdependence of vegetation and soil properties from remote sensing observations on a coastal barrier island.

How to cite: Merrick, T., Abelev, A., Liang, R., Vermillion, M., Li, R.-R., Buma, W., Swanson, C., and Montes, M.: Coupled soil and vegetation properties toward remotely sensed coastal terrain characterization, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8223, https://doi.org/10.5194/egusphere-egu23-8223, 2023.

A.289
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EGU23-14652
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ECS
Manel Khlif, Maria José Escorihuela, Aicha Chahbi Bellakanji, Giovanni Paolini, Zeineb Kassouk, and Zohra Lili Chabaane

Cereals represent an essential factor in the economy and food security, especially for countries that are not self-sufficient and depend on imports like Tunisia. Obtaining an early cereal map without the need to collect field data and without waiting for the end of the agriculture season helps the government to make early decisions. 

Hence, the first objective of our study is the development of an automatic classification model first calibrated for one agricultural year, 2020/2021 (2021), and then validated over the years 2011 through 2022 in the Kairouan governorate. The second objective is the development of a forecasting model in order to have early cereal maps several months before the harvest which occurs in June. 

Using Sentinel 2 and Landsat 5-7-8 data, different vegetation indices percentiles have been calculated. In order to select the best indices for cereal classification, a feature importance study over all the indexes was performed using the random forest classification algorithm reference year classification. A land cover classification model was validated for the reference year 2021, with an overall accuracy of 89.3%. This classifier has been used to elaborate land cover classification maps since 2011, focusing mainly on cereal crops. Using Sentinel 2 data, a good precision (P) for cereal crops was found, between 85,8% and 95,1%. Good to moderate accuracies were obtained when using Landsat data, between 41% and 91,8%. Then, a land cover forecasting model was validated for 11 years for different forecasting periods where we found excellent results four months before harvest (in February). We were able to obtain the cereal crop maps with a P between 85,1% and 95,1% using Sentinel 2 data and between 42,6% and 95,4% using Landsat data from four months before harvest. However, confusion between cereals and cereals grown with arboriculture was found which is due to the similarity between these two classes.

With this automatic land cover model, we have been able to produce the cereal maps of the last 12 agricultural years. This approach could be also used in the future to obtain a cereal map as early as February.

How to cite: Khlif, M., Escorihuela, M. J., Chahbi Bellakanji, A., Paolini, G., Kassouk, Z., and Lili Chabaane, Z.: Automatization of an early cereal maps classification model from 2010/2011 to 2021/2022 in a semi-arid region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14652, https://doi.org/10.5194/egusphere-egu23-14652, 2023.

Posters virtual: Wed, 26 Apr, 10:45–12:30 | vHall BG

Chairpersons: Benjamin Dechant, Frank Veroustraete, Shari Van Wittenberghe
vBG.12
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EGU23-8305
Christina Eisfelder, Patrick Sogno, Genanaw Alemu, Rahel Hailu, Christian Mesmer, and Juliane Huth

Ethiopia is known to be currently food insecure and suffering from considerable food deficits. The Government of Ethiopia strives to increase the agricultural production and its efficiency. Therefore, Ethiopia has been promoting large-scale agricultural investment (LSAI) to transform the agricultural sector. However, the progress by agricultural development has been limited. Investors only developed a small fraction of the transferred land. Therefore, there is a great need for monitoring of the implementation and actual state of land use of every LSAI project. The use of remote sensing can substantially support agricultural monitoring. In this study, Earth observation time series are analyzed to examine the land used for agricultural production and to differentiate crop types grown within the three study areas. Current land use/land cover (LULC) is analyzed using Sentinel-2 time series to identify cropland areas. In a second step, remote-sensing time-series of Sentinel-1 and Sentinel-2 are used to differentiate among 20 different crop types grown in the region. The developed classification methods have been applied to derive information products for three study regions in Ethiopia including the LSAI areas within the provinces of Amhara, Benishangul, and Gambella. The methods and derived information products on LULC and crop types will be made available to GIZ and regional experts to support agricultural monitoring of developed land in Ethiopia.

How to cite: Eisfelder, C., Sogno, P., Alemu, G., Hailu, R., Mesmer, C., and Huth, J.: Remote Sensing for large-scale agricultural investment areas in Ethiopia – agricultural monitoring based on Earth observation time-series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8305, https://doi.org/10.5194/egusphere-egu23-8305, 2023.

vBG.13
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EGU23-2738
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ECS
Emna Ayari, Zeineb Kassouk, Zohra Lili-Chabaane, Nadia Ouaadi, Nicolas Baghdadi, and Mehrez Zribi

The Normalized Vegetation Different Index (NDVI) has proved its relevance to describe crop dynamics with a high spatial resolution. Therefore, it’s widely used to monitor and describe vegetation in different agronomic applications such as land use classification, yield forecasting, and biophysical variables’ estimation. As an optical index, the NDVI is limited by weather conditions where the cloud presence impacts the pixel information. In the present study, we retrieve the NDVI values according to wheat growth stages using the normalized polarization ratio (IN) and the estimated coherence in Vertical-Vertical polarization from C-band Sentinel-1 radar data. To estimate the NDVI values, we used empirical equations and the machine learning algorithms such as the support vector regressor (SVR) and random forest (RF). During the two years from 2018 to 2020, we divided the wheat cycle into two periods. The first period is extended between the sowing and heading event. The second one covers physiological maturity which is subdivided into two sub-periods where the NDVI values are lower or higher than 0.4. For the first period, the NDVI estimation is characterized by root mean square of error (RMSE) values varying between 0.07 and 0.1. When the NDVI values are lower than 0.4 through the senescence phase, the RMSE values are lower than 0.06. Throughout the grain maturity (NDVI ≥ 0.4), the RMSE values exceed 0.19 using the calibrated empirical equation as a function of IN against a moderate performance characterizing the use of machine algorithms with the IN and as features. The developed approach to estimate the NDVI according to the wheat development stage was tested on several fields. The overall RMSE values vary between 0.12 and 0.19 with a correlation coefficient fluctuating between 0.64 and 0.87 and a bias value ranging between -0.06 and -0.02. The combination of the radar variables improved the NDVI estimations during the wheat cycle. The developed approach can be tested on other crops and climatic contexts.

How to cite: Ayari, E., Kassouk, Z., Lili-Chabaane, Z., Ouaadi, N., Baghdadi, N., and Zribi, M.: NDVI time series filling over wheat fields using the Sentinel-1 data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2738, https://doi.org/10.5194/egusphere-egu23-2738, 2023.

vBG.14
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EGU23-14359
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ECS
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Highlight
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Malte Bührs and Thomas Schmitt

In the last couple of years, the Citizen Science (CS) approach became more reliable, generating a huge amount of environmental data (‘Big Data’) considering the help of volunteers and amateur researchers. In the same time, Remote Sensing capabilities and resolutions improved significantly. Especially in urban environments such as the Ruhr Metropolis, where private lands and high diverse landscapes are predominant and large concentrations of people as potential volunteers are available, combining CS data and Remote Sensing techniques with the predictive power of Species Distribution Models can play an important role to comprehensively investigate and evaluate avian biodiversity representing keystone species in urban ecosystems. However, spatially modeled habitat suitability for multiple avian species in dense and fragmented urban environments are still lacking.

An ensemble of different machine learning algorithms, CS datasets of multiple avian species expected to react differently to urban conditions and environmental predictors consisting of bioclimatic variables, digital surface models and land use derived information were applied to forecast avian biodiversity patterns and distributions. These comprehensive predictions of habitat suitability enable policymakers to make sophisticated decisions in landscape planning and conservation taking into consideration present and future land use changes due to urban densification and urban sprawl, especially within the context of an ongoing historical unique transformation process in the Metropolis Ruhr.

How to cite: Bührs, M. and Schmitt, T.: The Power of Many: Utilizing Citizen Science Data in Species Distribution Models to Forecast Urban Avian Biodiversity in the Metropolis Ruhr, Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14359, https://doi.org/10.5194/egusphere-egu23-14359, 2023.