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Remote Sensing applications in the Biogeosciences

Remotely-sensed signals result from the interaction of incoming and emitted electromagnetic radiation with atmospheric constituents, vegetation, soil surfaces 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 the fluorescence parts of the remotely-sensed signal spectrum.
This session solicits for papers presenting strategies, methodologies or approaches leading to the assimilation of remote sensing products from different EM regions, angular constellations, fluorescence as well as data measured in situ for validation purposes.
We welcome contributions on topics related to climate change, food production & security, nature preservation, biodiversity, epidemiology, atmospheric chemistry & pollution (tropospheric ozone, anthropogenic and biogenic aerosols, nitrogen oxides, VOC’s, etc). We also welcome papers focusing on the assimilation of remote sensing and in-situ measurements in bio-geophysical and atmospheric models, as well as the RS extraction techniques themselves.

This session aims 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 the Earth’s ecosystems, for the benefit of humanity and its next generations.

Co-organized by AS4
Convener: Willem Verstraeten | Co-convener: Frank Veroustraete
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Tue, 27 Apr, 11:00–12:30

Chairperson: Frank Veroustraete

5-minute convener introduction

Martin Van Damme et al.

The Infrared Atmospheric Sounding Interferometer (IASI) mission consists of a suite of three infrared sounders providing today over 13 years of consistent global measurements (from end of 2007 up to now). In this work we use the recently developed version 3 of the IASI NH3 dataset to derive global, regional and national trends from 2008 to 2018. Reported national trends are analysed in the light of changing anthropogenic and pyrogenic NH3 emissions, meteorological conditions and the impact of sulphur and nitrogen oxides emissions. A case study is dedicated to the Netherlands. Temporal variation on shorter timescales will also be investigated.

How to cite: Van Damme, M., Clarisse, L., Franco, B., Sutton, M. A., Erisman, J. W., Wichink Kruit, R., van Zanten, M., Hadji-Lazaro, J., Hurtmans, D., Clerbaux, C., and Coheur, P.-F.: Temporal variations of atmospheric ammonia (NH3) derived from over a decade of IASI satellite measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9691, https://doi.org/10.5194/egusphere-egu21-9691, 2021.

Adrián García Bruzón et al.

The climate change has turned out to be a determining factor in the development of forest in Spain. Production systems have emitted polluting gases and other particles into the atmosphere, for which some plants have not yet developed adaptation systems. Among the most harmful pollutants for the environment are gases such as nitrous oxides, ozone, particulate matter.

However, this condition is not the same in Peninsular Spain, and the Balearic Islands since the plant compositions differ in the territory and the bioclimatic, topographic, and anthropic characteristics. Monitoring the vegetation with sufficient spatial and temporal resolution, studying variables conditioning plant health is a challenge from the nature of the variables and the amount of data to be handled. 

The Mediterranean forest is one of the most ecosystem affected by climate change because of usually experimented long periods of drought that, in combination with increased temperatures, can drastically reduce the photosynthetic activity of trees and therefore the biomass of forests.

That is why the application of environmental technologies based on Remote Sensing (which provide plant health indices from passive sensors on satellite platforms and other variables of interest), Geographic Information Systems (to integrate, process, analyze spatial and temporal data) and machine learning models (which facilitate the extraction of relationships between variables, conditioning factors and predict patterns). 

In this regard, this work's objective is to evaluate the possible effect that different pollutants have on the health of the vegetation, measured from the annual values of the Normalized Difference Vegetation Index (NDVI), in the Mediterranean forests of Peninsular Spain. To achieve this, we are used machine learning techniques using the Random Forest algorithm. The study has also been done with various climatic, topographic, and anthropic variables that characterize the forest to carry it out. 

The results showed that certain variables such as the aridity index had generated the NDVI values and therefore plant development, while others are limiting factors such as the concentration of certain pollutants and the direct relationship between them particulates and NOx. This study can verify how the Random Forest algorithm offers reliable results, even when working with heterogeneous variables. 

How to cite: García Bruzón, A., Arrogante Funes, P., and Muñoz Moral, L.: Effects of air quality on the health of Mediterranean forests, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16171, https://doi.org/10.5194/egusphere-egu21-16171, 2021.

Temuulen Sankey et al.

Rangelands cover 70% of the world’s land surface, and provide critical ecosystem services of primary production, soil carbon storage, and nutrient cycling. These ecosystem services are governed by very fine-scale spatial patterning of soil carbon, nutrients, and plant species at the centimeter-to-meter scales, a phenomenon known as “islands of fertility”. Such fine-scale dynamics are challenging to detect with most satellite and manned airborne platforms. Remote sensing from unmanned aerial vehicles (UAVs) provides an alternative option for detecting fine-scale soil nutrient and plant species changes in rangelands over smaller extents than typically imaged with satellite and manned airborne platforms. We demonstrate that a model incorporating the fusion of UAV multispectral and structure-from-motion photogrammetry classifies plant functional types and bare soil cover with an overall accuracy of 95% in rangelands degraded by shrub encroachment and disturbed by fire. We further demonstrate that employing UAV hyperspectral and LiDAR (light detection and ranging) fusion greatly improves upon these results by classifying 9 different plant species and soil fertility microsite types (SFMT) with an overall accuracy of 87%. Creosote bush (Larrea tridentata) and black grama (Bouteloua eriopoda) are the most important native species in the rangeland and have the highest producer’s accuracies at 98% and 94%, respectively. The integration of UAV LiDAR-derived plant height differences was critical in these improvements. Finally, we use synthesis of the UAV datasets with ground-based LiDAR surveys and lab characterization of soils to estimate that the burned rangeland potentially lost 1,474 kg/ha of C and 113 kg/ha of N owing to soil erosion processes during the first year after a prescribed fire. However, during the second-year post-fire, grass and plant-interspace SFMT functioned as net sinks for sediment and nutrients and gained approximately 175 kg/ha C and 14 kg/ha N, combined. These results provide important site-specific insight that is relevant to the 423 Mha of grasslands and shrublands that are burned globally each year. While fire, and specifically post-fire erosion, can degrade some rangelands, post-fire plant-soil-nutrient dynamics might provide a competitive advantage to grasses in rangelands degraded by shrub encroachment. These novel UAV and ground-based LiDAR remote sensing approaches thus provide important details towards more accurate accounting of the carbon and nutrients in the soil surface of rangelands.

How to cite: Sankey, T., Sankey, J., Li, J., Ravi, S., Wang, G., Caster, J., and Kasprak, A.: Quantifying plant-soil-nutrient dynamics in rangelands: Fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-749, https://doi.org/10.5194/egusphere-egu21-749, 2021.

Jordan Bates et al.


Metrics such as Leaf Area Index (LAI) are key factors in agricultural monitoring to understand the health and predictive yield of crops. Knowing the spatial distribution and variability in more detail increases the precision of fertilizer and irrigation practices. Unmanned Aircraft Systems (UAS) provide a means to carry sensors at a low altitude below the clouds providing a much higher spatial and temporal resolution than previously seen with satellite remote sensing while also providing more spatially complete data as compared to ground methods. Being that LiDAR is an active sensor and does not depend on solar reflectance and its corresponding zenith angle like commonly used passive optical sensors, it can further improve upon these UAS characteristics. It can also sense further into the canopy as the laser signals can pass through small gaps and are not affected by the shadowing of plant features created by the canopy itself. Evaluating the penetration of these signals and investigating the gap fraction (GF) that relates to canopy density, we are able to retrieve LAI. However, as LiDAR is sensing all above-ground plant elements it may present the ability to estimate Plant Area Index (PAI) rather than LAI when monitoring an entire growing season for a cereal crop like winter wheat that begins browning during senescence. This study investigates the feasibility of using LiDAR to estimate LAI or similar crop canopy density metrics. As LiDAR sensors for UAS are just becoming more accessible, studies related to this topic are scarcely seen.

In this study, a winter wheat field in Selhausen, Germany (~10 ha in size) was monitored throughout the growing season using the following methods: [1] air campaigns with a DJI Matrice 600 UAS with a YellowScan Surveyor LiDAR system, [2] a DJI Matrice 600 UAS with a Micasense RedEdge-M (five band) multispectral sensor, and [3] ground measurements using a SS1 SunScan ceptometer. The resulting LAI type metrics of the UAS LiDAR methods used were compared to methods commonly used with multispectral (MS) and ground instruments to assess the proposed method’s potential. Additionally, because both products are spatially complete unlike the ground measurements, the LiDAR and multispectral methods were compared for similarities in spatial patterns.

The results showed promise in using UAS LiDAR to estimate metrics that relate to LAI. Pearson correlation coefficient between the LiDAR and multispectral methods were moderate to high (R= 0.39 – 0.66) over the growing season. The comparison of UAS LiDAR towards the ground reference was within a 3% difference at times before senescence. Later in the growing season, the discrepancy increased between LiDAR and MS sensor retrievals mainly because of plant browning related to changes in plant chlorophyll content. This study covers the benefits of using UAS mounted LiDAR for LAI related measurements and its potential for improving crop health monitoring for precision farming.

How to cite: Bates, J., Montzka, C., Schmidt, M., and Jonard, F.: UAS mounted LiDAR for Estimating LAI Type Metrics for Winter Wheat, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5982, https://doi.org/10.5194/egusphere-egu21-5982, 2021.

Jaime Caballer Revenga et al.

Current efforts to enhance the understanding of global carbon (C) cycle rely on novel monitoring campaigns of C sequestration in terrestrial ecosystems.The successful outcome of such efforts will be relevant to sectors ranging from climate change and land use studies (global scale) to precision agriculture and land management consultancy (local scale).To that end, current investigations apply recently developed scientific instrumentation - e.g.  Light detection and Ranging (LiDAR) -  and computational methods - e.g. Machine Learning (ML). Near-field remote sensing - i.e.  Unmanned Aerial Vehicle (UAV)-LiDAR -, can provide high resolution LiDAR data, increasing the monitoring accuracy of C stocks estimates and biophysical variables at the ecosystem scale. In contrast to previous approaches (e.g. image-derived vegetation indices), UAV-LiDAR provides a true 3D description of the canopy vertical structure. In order to evaluate the potential of new approaches towards precise C stock quantification in an agricultural field of Denmark (13 ha.), using near-field remote sensed data, we compare the results based on using 3D canopy metrics - derived from UAV-LiDAR - against the well-established multispectral image based metrics. Then, the performance of six different machine learning (ML) models  - two Random Forest variations, KNN, AdaBoost, ElasticNet, Support Vector, and Linear regression - designed to predict above ground biomass (AGB) based on a set of features derived from (i) UAV-LiDAR point cloud data (PCD), and (ii) multispectral imagery is evaluated. Their prediction quality are tested against unseen data from the same species, and sampling campaigns. Also, the sources of uncertainty are assessed as well as the importance of each predicting feature. The field work was conducted within the footprint of an Integrated Carbon Observation System (ICOS) class 1 station site, facilitating ecosystem traits monitoring in real time. The aerial and biomass sampling campaigns have been operated at 15-days frequency during the crops' growing period, in which, simultaneously, UAV-LiDAR and multispectral image data as well as ground truth biomass data were collected. By means of laboratory analysis, C and nutrient content in the crops' biomass was also determined. Based on arithmetic and morphological methods, the PCD were pre-processed to remove noise and classify them to ground and vegetation points. By means of the methods described, we demonstrate that UAV-LiDAR combined with multispectral data and ML methods can be used to accurately estimate AGB, 3D ecosystem structure as well as C-stocks in agricultural ecosystems. 

How to cite: Caballer Revenga, J., Trepekli, K., Oehmcke, S., Gieseke, F., Igel, C., Jensen, R., and Friborg, T.: Prediction of above ground biomass and C-stocks based on UAV-LiDAR,multispectral imagery and machine learning methods., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15708, https://doi.org/10.5194/egusphere-egu21-15708, 2021.

Paulina Grigusova et al.

Bioturbation is assumed to be coupled with vegetation, soil properties and topography. The soil properties influence the amount of nutrients needed for plant growth and determine the resistance of the soil to the burrowing itself and to the burrow stability. Vegetation provides food and shelter for the animals. At the same time, the burrowing destroys the plant roots while the animal presence and changed vegetation distribution affect soil properties. Additionally, the soil properties and vegetation also depend on topographic features as height, aspect or curvature.

This relation between the bioturbation, soil properties and topography are to date understudied, in particular how and if the co-dependencies differ between various climate zones. High resolution remote sensing data provide here a sufficient method to study these dependencies as the soil characteristics change rapidly on microscale. However, the application of fused high resolution WorldView-2 data and LiDAR data for the prediction of bioturbation and soil properties are completely missing.

In our study we used WorldView-2 and LiDAR data with a resolution of 0.5m for a machine learning based prediction of visible indicators of bioturbation activity (number of holes and mounds) and related soil properties. We obtained a land cover classification from the WorldView-2 data and topographic features from the LiDAR data. We then analyzed the relationship between bioturbation, soil properties, land cover and topography in arid, semi-arid and Mediterranean climate zone in Chile.

For this, we measured the number of holes and mounds created by burrowing animals within 60 plots of 10mx10m randomly dispersed on six hillsides in the three climate zones. On each hillside, 20 soil samples were taken in regular distances from the crest to the bottom of each hillside. The soil samples were analyzed for soil skeleton fraction, above ground skeletal fraction, nine soil texture classes, bulk density, water content, organic carbon, porosity, erodibility and skin factor. We carried out an orographic and topographic correction of the WorldView-2 images and classified the land cover into soil, rocks, cacti, shrub, trees and palms. We calculated several topographic features from the LiDAR data as height, slope, aspect, curvature, surface roughness and flow direction. We then used the WorldView-2 bands, vegetation indices and topographic features to upscale the bioturbation activity and soil properties into the area of 5x5 km at each site using the random forest machine learning algorithm.

Our results show that the bioturbation activity is best predicted by WorldView-2 data and vegetation indices while the soil properties can be best predicted by topography. The bioturbation activity strongly depends on land cover and vegetation distribution in the Mediterranean climate zone while there is a stronger link of bioturbation activity to topography and soil properties in the arid and semi-arid climate zone.

How to cite: Grigusova, P., Larsen, A., Klug, A., Kraus, D., Chifflard, P., Farwig, N., and Bendix, J.: Identification of the preferred areas for animal burrowing activity with regard to the land cover, topography and soil properties using very high resolution WorldView-2 and LiDAR data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8848, https://doi.org/10.5194/egusphere-egu21-8848, 2021.

Matteo G. Ziliani et al.

Assimilating biophysical metrics from remote sensing platforms into crop-yield forecasting models can increase overall model performance. Recent advances in remote sensing technologies provide an unprecedented resource for Earth observation that has both, spatial and temporal resolutions appropriate for precision agriculture applications. Furthermore, computationally efficient assimilation techniques can integrate these new satellite-derived products into modeling frameworks. To date, such modeling approaches work at the regional scale, with comparatively few studies examining the integration of remote sensing and crop-yield modeling at intra-field resolutions. In this study, we investigate the potential of assimilating daily, 3 m satellite-derived leaf area index (LAI) into the Agricultural Production Systems sIMulator (APSIM) for crop yield estimation in a rainfed corn field located in Nebraska. The impact of the number of satellite images and the definition of homogeneous spatial units required to re-initialize input parameters was also evaluated. Results show that the observed spatial variability of LAI within the maize field can effectively drive the crop simulation model and enhance yield forecasting that takes into account intra-field variability. The detection of intra-field biophysical metrics is particularly valuable since it may be employed to infer inefficiency problems at different stages of the season, and hence drive specific and localized management decisions for improving the final crop yield.

How to cite: Ziliani, M. G., Aragon, B., Franz, T., Hoteit, I., Sheffield, J., and McCabe, M. F.: Target food security: assimilating ultra-high resolution satellite images into a crop-yield forecasting model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12357, https://doi.org/10.5194/egusphere-egu21-12357, 2021.

Richard Fernandes et al.

The Sentinel 2 (S2) constellation mission was designed to facilitate the systematic mapping canopy biophysical variables at medium resolution on a global basis and in a free and open manner.  The mission concept requires the development of downstream services to map variables such as the fraction of absorbed photosynthetically active radiation (fAPAR), fraction of canopy cover (fCOVER) and leaf area index (LAI) using Level 2A surface reflectance inputs from the S2 ground segment.  Currently, free and open products generation can be performed using the Simplified Level 2 Prototype Processor (SL2P) applied on a product granule basis.  Considering that the processor is a prototype this study addresses three questions: 1) Can the SL2P algorithm, or subsequent versions, be engineered to facilitate systematic product generation over large extents in a free and open manner? 2) What is the uncertainty of SL2P products over North America during the growing season? 3) Can the uncertainty be reduced by changing the calibration database used within SL2P?  

To facilitate validation and product generation, SL2P was ported to a Google Earth Engine application (the Landscape Evolution and Forecasting Toolbox).  This now allows mapping of up to one million square kilometers in near real time using either the original SL2P algorithm or updated versions.  SL2P uncertainty was quantified over North America using direct comparison to 20 in-situ sites within the National Environmental Observing Network in the continental United States of America and within a Canada wide field campaign over forests and shrublands conducted by Canada Centre for Remote Sensing. SL2P outputs were also compared to MODIS and Copernicus Global Land Service products over the Belmanip II regional sites and 30 additional forested regions in North America.  Results from NEON validation indicate SL2P is generally within uncertainty requirements except for forests; where it underestimates fAPAR, fCOVER and LAI.  Results for other sites will also be presented.  To address the forest bias, SL2P was recalibrated using simulations from the FLIGHT 3D radiative transfer model representative of North American forests.  The uncertainty of the recalibrated SL2P algorithm will be compared to baseline SL2P estimates to determine if increased model complexity is warranted.

How to cite: Fernandes, R., Baret, F., Brown, L., Canisius, F., Dash, J., Djamai, N., Hong, G., MacDougall, C., Shah, H., Weiss, M., and Zhong, D.: Improvements to the Simplified Level 2 Prototype Processor for Retrieving Canopy Biophysical Variables from Sentinel 2 Multispectral Instrument Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-510, https://doi.org/10.5194/egusphere-egu21-510, 2021.

Melissa Latella et al.

Coastal sand dunes provide a large variety of ecosystem services, among which the inland protection from marine floods. Nowadays, this protection is fundamental, and its importance will further increase in the future due to the rise of the sea level and storm violence induced by climate change. Despite the crucial role of coastal dunes and their potential application in mitigation strategies, the phenomenon of the coastal squeeze, which is mainly caused by the urban sprawl, is progressively reducing the extents of the areas where dune can freely undergo their dynamics, thus dramatically impairing their capability of providing ecosystem services.

Aiming to embed the use of satellite images in the study of coastal foredune and beach dynamics, we developed a classification algorithm that uses the satellite images and server-side functions of Google Earth Engine (GEE). The algorithm runs on the GEE Python API and allows the user to retrieve all the available images for the study site and the chosen time period from the selected sensor collection. The algorithm also filters the cloudy and saturated pixels and creates a percentile-composite image over which it applies a random forest classification algorithm. The classification is finally refined by defining a mask for land pixels only. 

According to the provided training data and sensor selection, the algorithm can give different outcomes, ranging from sand and vegetation maps, beach width measurements, and shoreline time evolution visualization. This very versatile tool that can be used in a great variety of applications within the monitoring and understanding of the dune-beach systems and associated coastal ecosystem services. For instance, we show how this algorithm, combined with machine learning techniques and the assimilation of real data, can support the calibration of a coastal model that gives the natural extent of the beach width and that can be, therefore, used to plan restoration activities. 

How to cite: Latella, M., Luijendijk, A., and Camporeale, C.: Regional-scale analysis of dune-beach systems using Google Earth Engine, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12923, https://doi.org/10.5194/egusphere-egu21-12923, 2021.

Gillian Simpson et al.

Peatland ecosystems are historical carbon sinks of global importance, whose management and restoration are becoming an increasingly popular approach to reach climate change targets via natural capital. However, the Net Ecosystem Exchange (NEE) of carbon dioxide (CO2) can exhibit substantial variability on seasonal and inter-annual timescales, with some peatlands shifting from being a sink to a source of CObetween years. This variability is due to the complex interaction between factors such as meteorology and phenology, which are both known to control a peatland’s net carbon sink strength. An improved understanding of these two drivers of peatland carbon cycling is needed to allow for better prediction of the impact of climate change on these ecosystems. This task requires us to study these environmental controls at multiple spatial and temporal scales. The role of vegetation in regulating NEE however, can be difficult to determine over shorter timescales (e.g. seasonal) and especially in peatland landscapes, which typically display strong spatial heterogeneity at the microsite scale (< 0.5 m). Digital phenology cameras (PhenoCams) and Unmanned Aerial Vehicles (UAVs), offer novel opportunities to improve the temporal resolution and spatial coverage of traditional vegetation survey approaches. UAVs in particular are a more flexible, often cheaper alternative to satellite products, and can be used to collect data at the sub-centimetre scale. We employ PhenoCam imagery and UAV surveys with a Parrot Sequoia multispectral camera to map vegetation and track its phenology using vegetation indices such as the Normalised Difference Vegetation Index (NDVI) over the course of two growing seasons at Auchencorth Moss, a Scottish temperate peatland. By combining this digital camera imagery with in-situ NEE measurements (closed chambers and eddy-covariance) and meteorological data, we seek to quantify the impact of weather and phenology on carbon balance at the site.

How to cite: Simpson, G., Helfter, C., Nichol, C., and Wade, T.: A fusion of UAVs, digital cameras and micrometeorology to quantify the link between phenology and the carbon balance of a temperate peatland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14748, https://doi.org/10.5194/egusphere-egu21-14748, 2021.

Yury Davidovich

Studying of the optical properties of agricultural vegetation is one of the methods for plants condition estimation, prediction of their development and changes influenced by natural and anthropogenic factors.

The work is dedicated to the investigation of spectral reflectance function of agricultural Brassica napus taking into account the degree of soil moisture. When most of the agricultural lands in Belarus are covered with vegetation in summer, employing the optical properties of agricultural vegetation for deciphering the soil depends on the degree of soil moisture. Insufficient numbers of days in year when the soil is not covered by vegetation or is in a plowed state requires in-situ optical measurements, because there are more than 50 % cloudy conditions in the year, especially in spring and autumn time.

The study has been carried out near the Minsk 11.06.2020 (53.837004º N, 27.487597º E) in clear, cloudless day. The relief for investigated field is hilly-ridge, characterized by a predominance of elevation marks from 250 to 300 m and it is actively sown field. During the spectrometric measurements, the field has been sown with Brassica napus in the phenological phase of pod formation.

When studying the spectral reflectance of Brassica napus, in-situ spectrometric measurements and analysis of a multispectral image have been carried out. Spectrometric measurements have been carried out by FSR-02 spectrometer (spectral range 400-900 nm, spectral resolution 4.3 nm) aiming to retrieve spectral reflectance function.

The normalized vegetation index NDVI has been used for analyzing the multispectral image from Landsat 8 OLI system with a spatial resolution of 30 m. The results of a study of the correlation between the reflection coefficient of Brassica napus and the area of observed soils will be presented. In addition, the results of the analysis of quasi-synchronous values of the NDVI index and in-situ measurements of the spectral reflectance of Brassica napus will be discussed.

How to cite: Davidovich, Y.: Spectral reflectivity variations of Brassica napus depending on degree of soil moisture, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15781, https://doi.org/10.5194/egusphere-egu21-15781, 2021.

Joris Timmermans and Daniel Kissling

Biodiversity is rapidly declining and monitoring biodiversity change is thus of key importance to prevent the destabilization of ecosystems and their services. A key component of monitoring biodiversity change is the development of Essential Biodiversity Variables (EBVs) which facilitate the harmonization and standardization of raw data from disparate sources. In this context, consistent and adequate geospatial information needs to be available to ecologists and policymakers around the world, even for countries in which comprehensive in-situ biodiversity measurements cannot be taken on a regular basis. Satellite remote sensing (SRS) currently represents the only tool which allows to acquiree spatially contiguous and temporally replicated observations for monitoring biodiversity over continental or (near-)global spatial extents. Observations from SRS already provide a wealth of information on the distribution, structure and functioning of ecosystems, but user requirements of ecologists and policymakers have not been systematically quantified for allowing the development of roadmaps by SRS experts.

In response, we performed a top-down user requirement analysis combined with a bottom-up technical review to highlight (i) how currently available remote sensing products can contribute to biodiversity monitoring, and (ii) which immature SRS products could be prioritized for further development. We performed a systematic review of the Post2020 goals (for 2050) and biodiversity targets (for 2030) of the Convention on Biological Diversity (CBD) and their corresponding biodiversity indicators. Subsequently we evaluated SRS products according to relevance (to biodiversity indicators), (im)maturity, feasibility, and suitability for provisioning user-adequate spatio-temporal information. We found that currently existing CBD-relevant biodiversity indicators mainly use EBV-related information on ecosystem structure and distribution (e.g. available from remote sensing products of landcover and Leaf Area Index, LAI) or on species populations (predominantly acquired from in-situ biodiversity measurements because current SRS products are too limited in the spatio-temporal resolutions of their sensors). Moreover, only few biodiversity indicators derived from SRS currently focus on species traits or community composition EBVs, as both the identification of individual species and the quantification of species traits such as LAI and foliar nitrogen, phosphorus, kalium and chlorophyll content remain challenging. We outline how further advances in data-science techniques (e.g. merging SRS observations of high spectral and high spatial resolution) provide tremendous opportunities for advancing community composition and species-focused EBVs for global biodiversity monitoring.

How to cite: Timmermans, J. and Kissling, D.: Challenges and opportunities of remote sensing for monitoring biodiversity change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2630, https://doi.org/10.5194/egusphere-egu21-2630, 2021.

Guido J. M. Verstraeten and Willem W. Verstraeten

Decisions about preventing further climate change involve human means for obtaining an equal and fair result for all humans. In consequence we have to make room for a complete different discours balancing between natural facts obeying natural laws and social behavior according to Duhem-Quine´s principle. In order to explain complex phenomena such as global warming and the isostatic uplift one should reject any one-hypothesis claim according to Duhem-Quine´s principle.

Considering climatologic problems as only pure positive scientific matter for making decisions for mankind how to deal with, is a pure essentialist and substantivistic conception of decision making. While climatologic models explain changes globally, the more unpredictable weather concerns rather local scales, implying that decisions must be adapted to the local situation. The unpredictability of some processes is universal but the consequences can be very local and form the boundary conditions of living. Stated otherwise, we do not just live on the planet Earth, but we live in a specific village/town in a specific region/country. 

Contrary to the widely accepted dominant paradigm decision making should not be based on the slogan ‘think global, act local’, but from the device ‘think local, manage the local effects of global warming’. Indeed, worldwide climate change will generally cause raising oceans, but more locally it will restore the former water balance of uplifted shear costs in Sweden and Finland and it affects the small fisher and farmer societies.

Here we suggest that knowledge of climate changes does not intervene in terms of their universal value such as truth, but under the local horizon of the social practices, artefacts and hierarchical relations with which they are associated. We advocate to reverse the former dominant technical code monopolized by technocracy from dominance of nature to creation of progress of the encroaching new ecosystems that develops out of the original shear coast in south-western Finland. We show based on Landsat imagery that this coast is rapidly changing due to the uplift. Furthermore, we demonstrate that the Eco-Development paradigm may rebalance nature, environment, humans and culture and that it is a valid alternative against the past and present-day socio-economical approach that have accelerated the change of the Earth’s climate, provided the global technocracy´s codes of the dominant paradigm are converted into local adapted social, economic and political codes.

How to cite: Verstraeten, G. J. M. and Verstraeten, W. W.: Beyond the technocratic truth to establish eco-development in the uplifted area of the Finnish southwestern shear coast as observed from space, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8298, https://doi.org/10.5194/egusphere-egu21-8298, 2021.

Enrique Portalés et al.

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is in preparation to carry a unique visible to shortwave infrared spectrometer. This mission is aimed to be operational, providing routine observations to support agricultural management and biodiversity conservation. In addition to the level 1B, 1C and 2A products, the mission will provide L2B products, i.e. among others including a set of vegetation traits. In view of preparing retrieval models applicable in an operational setting, we developed a hybrid retrieval workflow based on the combination of field data and look-up tables (LUT) generated from radiative transfer models (RTM) at leaf and canopy evel. The presented workflow corresponds to the version 1.8 of the L2B vegetation models. For each variable, the LUT was optimized by an active learning (AL) technique ran against field validation data. This hybrid optimization method is aimed to achieve a good trade-off between specialization, generalism and size of the LUT, in order to perform well in a variety of scenarios and deliver fast processing. Eventually the reduced LUTs were used to train final retrieval models. We selected Gaussian process regression (GPR) and heteroscedastic Gaussian process regression (VHGPR). These are nonlinear, machine learning algorithms that lie in a solid probabilistic framework and not only provide competitive estimates, but also associated uncertainties. Based on this workflow we developed 13 vegetation models of leaf and canopy variables, which are under investigation to be implemented into CHIME’s L2B vegetation processing chain. Models performance was tested in ESA’s CHIME end-to-end (E2E) simulator. Furthermore, we applied the prototype models to images derived from current hyperspectral airborne (APEX and HyPlant) and also spaceborne imagery (PRISMA) resampled to CHIME band settings, resulting into meaningful vegetation maps over heterogeneous European landscapes. For some canopy variables such as fraction of absorbed photosynthetically active radiation (FAPAR), fractional vegetation cover (FVC) and canopy nitrogen content (CNC), we obtained relative errors (NMRSE, in %) of 3.80, 4.25 and 16.83 respectively, and high quality maps. Altogether, obtained maps demonstrate the feasibility of routinely providing vegetation products from the CHIME imaging spectroscopy mission. 


How to cite: Portalés, E., Verrelst, J., De Grave, C., Amin, E., Reyes, P., Morata, M., Berger, K., and Tagliabue, G.: Development of hybrid models for the operational retrieval of vegetation traits from the hyperspectral CHIME mission, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11084, https://doi.org/10.5194/egusphere-egu21-11084, 2021.

Lena Jänicke et al.

During the tandem phase of Sentinel-3A and B the channels of the spectrometer of the Ocean and Land Imager (OLCI) on Sentinel 3-B were reprogrammed to imitate measurements of ESA’s 8th Earth Explorer mission FLEX [1]. FLEX is designed to retrieve the complete fluorescence spectrum from the high resolutive visible spectrum and to quantify the contribution of the two photocycles in green plants [2]. Fluorescence is a valuable proxy of plant photosynthesis activity [3], [4]. By measuring the emitted light of plants on satellites, plant distribution and state can be monitored on a global scale.

The reprogrammed OLCI-B measurements consist of 45 bands between 500 and 800 nm with a bandwidth of about 1.8 nm (FWHM). The spectral and radiometric calibration of the 45 bands has not the same level of maturity as the one for the nominal setting, thus their radiometric uncertainty needs to be quantified. This is done by comparing the 45 FLEX-like bands with the co-located nominal 21 bands of OLCI on Sentinel-3A. The comparison is realised using a transfer function based on radiative transfer simulations. In a first step surface and atmosphere parameters are estimated from the OLCI-B FLEX-like measurements, that explain the measurements. The second step simulates the according OLCI-A measurements at nominal band settings and compares them with the real measurements made by OLCI-A.

This study serves also as a precursor experiment for the FLEX mission, where the radiometric calibration of FLEX will be verified using co-registered OLCI-A (or B) measurements. Based on this study the strategy for the uncertainty of the FLEX intensity measurements will be developed and tested.

The uncertainty estimation is a key factor of the Fluorescence retrieval as the Fluorescence contribution to the top of atmosphere signal is very small. To distinguish between signal and noise, the uncertainty must be kept as small as possible. Additionally, the uncertainty serves as input parameter for subsequent retrieval algorithms.

[1]          M. Celesti et al., ‘In prep.: Sentinel-3B OLCI in “FLEX mode” during the tandem phase: a novel dataset towards the future synergistic FLEX/Sentinel-3 mission’, p. 20, 2020.

[2]          M. Drusch et al., ‘The FLuorescence EXplorer Mission Concept—ESA’s Earth Explorer 8’, IEEE Trans. Geosci. Remote Sensing, vol. 55, no. 3, pp. 1273–1284, Mar. 2017, doi: 10.1109/TGRS.2016.2621820.

[3]          W. Verhoef, C. van der Tol, and E. M. Middleton, ‘Hyperspectral radiative transfer modeling to explore the combined retrieval of biophysical parameters and canopy fluorescence from FLEX – Sentinel-3 tandem mission multi-sensor data’, Remote Sensing of Environment, vol. 204, pp. 942–963, Jan. 2018, doi: 10.1016/j.rse.2017.08.006.

[4]          L. Guanter, L. Alonso, L. Gómez‐Chova, J. Amorós‐López, J. Vila, and J. Moreno, ‘Estimation of solar-induced vegetation fluorescence from space measurements’, Geophysical Research Letters, vol. 34, no. 8, 2007, doi: https://doi.org/10.1029/2007GL029289.

How to cite: Jänicke, L., Preusker, R., Celesti, M., and Schüttemeyer, D.: Uncertainty estimation of Sentinel-3B tandem data for OLCI-B in the FLEX configuration, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12606, https://doi.org/10.5194/egusphere-egu21-12606, 2021.

Joel Scott and Erin Urquhart

The next great contribution from NASA to study the Earth’s ecosystems, including the open ocean and coastal and inland waters, is the PACE (Plankton, Aerosol, Cloud, ocean Ecosystem) mission. PACE will build upon the remote sensing legacy that NASA earth science has established with over four decades of satellite instruments beginning with CZCS (Coastal Zone Color Scanner) launched in 1978 and following with SeaWiFS (Sea-viewing Wide Field-of-view Sensor), MODIS (Moderate Resolution Imaging Spectroradiometer), and VIIRS (Visible Infrared Radiometer Suite). PACE is expected to launch in 2023 and will carry the hyperspectral Ocean Color Instrument (OCI), as well as two multi-angle polarimeters (SPEXone and HARP-2). OCI will provide an unprecedented view of the entire earth every two days.

This presentation will highlight the capabilities of the novel hyperspectral and multi-angular polarimetric instruments on onboard the PACE observatory, showcasing PACE’s ability to fill societal needs and enable decision-making, in support of advanced climate observations, optimized biothreat assessment, food security support and assurance, and sustainable fishery and aquaculture monitoring and prediction for the benefit of humanity and its next generations. PACE will continue heritage MODIS and VIIRS visible, near-infrared, and shortwave-infrared data products at 1 km resolution, as well as produce new hyperspectral and multi-angular polarimetric advanced data products, not possible with MODIS and VIIRS due to their design and technological limits. PACE will leverage emerging remote sensing technologies to advance aquatic and atmospheric remote sensing in ways that fulfill real-world needs.

How to cite: Scott, J. and Urquhart, E.: Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission: Advanced Hyperspectral and Multi-Angular Polarimetric Satellite Observations for Science-driven Applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5833, https://doi.org/10.5194/egusphere-egu21-5833, 2021.

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