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Land cover, which is an essential climate variable and a remote sensing-enabled essential biodiversity variable is important for understanding terrestrial ecosystems functioning. Many studies have investigated forest land cover classification using remote sensing data from the visible, near, and short-wave infrared (VNIR-SWIR, 0.4- 2.5 μm) regions. However, to our knowledge, no study has addressed forest land cover classification using thermal infrared (TIR, 8-14 μm) hyperspectral data. In this study, for the first time, we present the preliminary assessment of vegetation classification using TIR hyperspectral data. TIR hyperspectral images (7.5 – 12.5 μm) were acquired by EUFAR aircraft using the AISA Owl sensor in July 2017 in Bavaria Forest National Park, Germany. In addition, fieldwork was conducted in 2017, concurrent to the flight campaign as well as in 2020 and 2021, and vegetation types were recorded in 92 plots. Canopy emissivity spectra were extracted for three vegetation classes namely, coniferous, broadleaves, and mixed classes. The extracted emissivity spectra were further used to classify three vegetation classes by means of a supervised Random Forest classifier. The results confirmed the expected capabilities of hyperspectral TIR data to produce an acceptable land cover map with an overall accuracy of 66%. The study showed that for coniferous class the most important spectral bands for classification were wavelengths 8.9 μm, between 9.7 – 9.9 μm and 10.3 μm. While for broadleaves there were,10.2 μm, 10.8 μm, and between 11.0 – 11.4 μm bands. The findings of this study show the possibility of using airborne hyperspectral TIR data for forest land cover classification. However, further investigation should be done applying other machine learning and deep learning techniques to examine the potential of TIR hyperspectral data for land cover classification.

The leaf area index (LAI)- as one of the most important vegetation biophysical variables, has been retrieved in vegetation canopies using data from different remote sensing platforms. LAI was recently proposed as a remote sensing-enabled essential biodiversity variable. To our knowledge, however, the retrieval of the LAI using hyperspectral thermal infrared (i.e., TIR 8-14 m) data has been addressed only under controlled laboratory conditions and has not yet been accomplished using thermal infrared hyperspectral data acquired from an airborne platform. Therefore, the primary goal of this study is to determine the accuracy of LAI prediction using thermal infrared hyperspectral data acquired from an airborne platform. The field campaign was conducted in July 2017 in the Bavarian Forest National Park in southeast Germany, and biophysical parameters, including LAI, were measured for 36 plots. Concurrently, thermal hyperspectral data were obtained using the Twin Otter aircraft operated by NERC-ARF (i.e., the U.K. Natural Environment Research Council- Airborne Research Facility) and the AISA Owl sensor. LAI was retrieved using an artificial neural network Levenberg-Marquardt algorithm. The results indicated that thermal infrared hyperspectral data could estimate LAI with relatively high accuracy (R= 0.734, RMSE=0.554). The study showed the significance of using an artificial neural network. It proved the possibility of using hyperspectral thermal infrared data to estimate vegetation biophysical properties at the canopy level and over a large forest area.

Machine learning algorithms, and specifically kernel-based methods such as Gaussian processes regression (GPR), have been shown to outperform traditional empirical methods for retrieving vegetation traits. GPR is attractive for its property of automatically generating uncertainty estimates for predicted traits. GPR has been increasingly used for the estimation of canopy traits from hyperspectral remote sensing data in agricultural fields and grassland ecosystems. However, to our knowledge, the application of GPR using full-spectrum airborne hyperspectral data in forest ecosystems remains under-explored. Therefore, in this study, we evaluated the performance of GPR as a representative of kernel-based machine learning algorithms in estimating two essential forest canopy traits (i.e., LAI and canopy chlorophyll content) using airborne hyperspectral data. The performance of GPR was compared with partial least square regression (PLSR) which is widely used for retrieving vegetation traits in spectroscopic studies. Field measurements of LAI and leaf chlorophyll content were collected in the Bavarian Forest National Park (BFNP) in Germany, concurrent with the acquisition of the Fenix airborne hyperspectral data (400−2500 nm) in July 2017 in the framework of the EUFAR summer school RS4forestEBV. The cross-validated coefficient of determination (R2) and normalised root mean square error (nRMSE) between the field-measured and retrieved traits were used to examine the accuracy of the respective methods. The results indicated that GPR somewhat outperformed PLSR in producing accurate estimations for LAI (GRP nRMSE = 16.7%; PLSR nRMSE = 23.0%) and canopy chlorophyll content (GPR nRMSE = 16.2%; PLSR nRMSE = 22.5%). The uncertainty maps generated by GPR showed that the retrieval uncertainties were generally low across the map, whereas higher uncertainties mainly corresponded with regions with low vegetation cover or under-represented in our field sampling. The capability to generate accurate predictions and associated uncertainty estimates suggest the GPR may be a promising candidate for the retrieval of vegetation traits.

Canopy biophysical properties play an important role in understanding forest health and productivity. Among these parameters, forest leaf area index (LAI), canopy cover fraction, and canopy chlorophyll content describe the vegetation abundance, photosynthetic capacity and primary productivity of forest stands. The new generation of remote sensing satellites such as Sentinel-2 with high spatial and temporal resolutions has provided vast opportunities for monitoring these parameters and assessing their interrelationships over vast forest landscapes. In this research, temporal Sentinel-2 data between 2017-2019 in the temperate mixed forest ecosystem of the Bavarian Forest National Park, Germany, was used to retrieve forest canopy biophysical variables. INFORM radiative transfer model was used to retrieve LAI and canopy chlorophyll content while the fraction of vegetation functional types were calculated using phenological parameters and empirical approaches. A recent landcover map of the Bavarian Forest National Park was applied to retrieve considered variables pursuant to the different land cover classes. The retrieved variables were validated using in situ measurements of LAI and canopy chlorophyll content. Primary productivity was then calculated using (i) vegetation index universal pattern decomposition approach and (ii) the process-based dynamic vegetation-terrestrial ecosystem model LPJ-GUESS model. The relationships between calculated productivities and estimated biophysical variables were then studied. Our results showed that there is a good agreement between primary productivities calculated from LPG GUESS and the decomposition approach. Among studied parameters, canopy chlorophyll content, which represents pigments and vegetation abundance within the canopy, showed a strong direct relationship with both calculated primary productivities and hence may be used to explain plant functioning. Our results also revealed that remotely sensed vegetation biophysical parameters- that are becoming more and more readily available due to the availability of Earth observation data- can be used as proxies for estimation of the primary productivity calculated using either approach. Calculation of primary productivity usually needs information about canopy life-cycle and geometry, which are often not available at large scales. The results of our study support our findings in the myVARIABLE pilot of the EuroGEOSS Showcases initiative (e-shape) on developing primary productivity as a remotely sensed- essential biodiversity variable describing ‘Ecosystem function.’

Crop lodging – the bending of crop stems from the vertical – is a major yield-reducing factor in cereal crops and causes deterioration in grain quality. Accurate assessment of crop lodging is important for improving estimates of crop yield losses, informing insurance loss adjusters and influencing management decisions for subsequent seasons. The role of remote sensing data, particularly synthetic aperture radar (SAR) data has been emphasized in the recent literature for crop lodging assessment. However, the effect of lodging on SAR scattering mechanisms is still unknown. Therefore, this research aims to understand the possible change in scattering mechanisms due to lodging by investigating SAR image pairs before and after lodging. We conducted the study in 26 wheat fields in the Bonifiche Ferraresi farm, located in Jolanda di Savoia, Ferrara, Italy. We measured temporal crop biophysical (e.g. crop angle) parameters and acquired multi-incidence angle RADARSAT-2 (R-2 FQ8-27° and R-2 FQ21-41°) and Sentinel-1 (S-1 40°) images corresponding to the time of field observations. We extracted metrics of SAR scattering mechanisms from RADARSAT-2 and Sentinel-1 image pairs in different zones using the unsupervised H/α decomposition algorithm and Wishart classifier. Contrasting results were obtained at different incidence angles. Bragg surface scattering increased in the case of S-1 (6.8%), R-2 FQ8 (1.8%) while at R-2 FQ21, it decreased (8%) after lodging. The change in double bounce scattering was more prominent at low incidence angle. These observations can guide future use of SAR-based information for operational crop lodging assessment in particular, and sustainable agriculture in general.

In the version of this Perspective initially published, there was an error in units reported in the main text. Specifically, in the first sentence of the sixth paragraph under the heading “A critical review of EBVs retrieved by remote sensing,” in the text now reading “Finally, when harmonizing the terminology used by ecological and remote sensing communities, it is important to emphasize that utilizing broadband optical wavelengths (for example, for PlanetScope, approximately 400-700 nm) at very high spatial resolution,” 400-700 nm originally appeared as “60-90 nm.” The error has been corrected in the online version of the article.

The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies.

The ESA funded GlobDiversity project was the first large-scale project explicitly designed to develop and engineer Remote Sensing enabled Essential Biodiversity Variables (RS-enabled EBVs) and ended in June 2020. The project also aimed to contribute with the documents and procedures generated to the development of a workflow starting from user requirements to final products that can be used for policy relevant global biodiversity monitoring and assessments. As a final step, the project developed a road map discussing the project’s outputs, e.g., strategic documents, processing chain and data products derived when focusing on particular RS-enabled EBVs, in the context of the overall EBV framework with and in context of relevant players such as the Group on Earth Observations Biodiversity Observation Network (GEO BON), CBD, IPBES, CEOS, the EBV user community and decision makers, Copernicus Services and the space agencies. We will thus present this RS-enabled EBV road map strategic document with the aim to put in place the project’s output into the EBV frame work. The proposed workflow includes discussions about the involvement of different users from the very beginning, to the development of any EBV data set, as well as to the implementation and use in the framework of the indictors. In addition, we will discuss the project’s experiences gained while developing biodiversity products based on remote sensing. In particular, we will present knowledge gaps and recommendations when evaluating the proposed road map. Thus, we will present this strategic document, so that the biodiversity community can most benefit from GlobDiversity’s outcome.

Primary productivity is recognized as an Essential Biodiversity Variables (EBVs) under EBVs ‘Ecosystem Function’ class by GEOBON. According to existing literature, primary productivity has been one of the most important applications attempted by satellite remote sensing. The wide array of canopy geometry and life-cycle dynamics at large scales makes the estimation of primary production from remote sensing data very challenging. Primary productivity is either directly or indirectly linked to a number of other remote sensing- enabled biodiversity products including canopy chlorophyll content, leaf fraction exposed to light, absorbed photosynthetic active radiation, leaf area index and land use/cover change which are critical to understanding plant functioning. In the myVARIABLE pilot of the EuroGEOSS Showcases initiative (e-shape), we aim to develop primary productivity as an RS-EBV describing ‘Ecosystem Physiology’ and ‘Species Physiology’, being calibrated and validated by European observation networks including eLTER and other in situ data to support delivery at European level. Estimation of primary productivity involves using process-based models, semi-empirical light use efficiency (LUE) models or statistical models. The complexity and uncertainty of parameterization of process-based models, underlying assumptions in LUE models and dependency of statistical models to altering environmental conditions will be evaluated and assessed in order to propose and select the best approach for estimation of primary productivity at the European level using Sentinel-2 data.

Earth observation data is an ideal platform for capturing change in biodiversity at various resolutions, both spatially and temporally, while GEOBON creates an explicit structure for monitoring biodiversity by proposing EBV candidates. It is not only important to generate remote sensing (RS)-enabled biodiversity products using high-resolution data, but more than ever, it is necessary to address the biodiversity loss at the global scale from satellite acquisitions. In this regard, under the biodiversity pilot of the NextGEOSS initiative, the ITC biodiversity community portal (http://nextgeoss.itc.utwente.nl/ebv/) provides a self-service framework to generate RS-enabled biodiversity products for better understanding of biodiversity loss and ecosystem changes for the remote sensing and biodiversity communities. In addition, the WENR Biodiversity community portal (https://www.synbiosys.alterra.nl/nextgeoss) applies the RS-enabled biodiversity products as predictors, as well as in situ vegetation plot data for EUNIS habitat suitability modelling. Different users, including research and development institutions, public and private stakeholders, and decision-makers, are also making use of these resources. Currently, users can access the ITC Biodiversity community portal to generate Leaf Area Index as an RS-enabled biodiversity product in GEOBON EBV class ‘Ecosystem Function’, and also as one of the most important vegetation biophysical variable on a global scale using high-resolution satellite data (Sentinel-2, 20m) processed online using Cloud services (Terradue Cloud service). Also, under the EuroGEOSS project, additional remote sensing biodiversity products (Net primary productivity, chlorophyll content, habitat type, and fragmentation) are being moved and mirrored from the ITC biodiversity community portal to the GEOBON biodiversity portal, where they will be permanently available for use by the biodiversity and remote sensing communities.

EBVs have been proposed as a layer between biodiversity observation and biodiversity indicators, used in policy. More specifically, EBV classes – such as species traits, species populations, ecosystem functions as well as ecosystem structure – are being implemented by ecologists to identify global monitoring priorities. To support this there is an urgent need for remote sensing enabled EBVs to fill the spatial and temporal gaps between in situ observation data of biodiversity. In other words, without remotely sensed synoptic, systematic and continuous observations, a global framework for monitoring biodiversity cannot exist. Several RS-EBVs are anticipated to be derived from satellite remote sensing, because satellite remote sensing is the only methodology able to provide a global coverage and continuous measures across space at relatively high spatial and temporal resolutions. Habitats are very significant as an indicator for biodiversity and habitats have a strong links to species of which many are not being monitored at all. The NextGEOSS habitat mapping suitability interactive web facility (https://www.synbiosys.alterra.nl) uses more than 1 million European in-situ vegetation plot data in combination with climate, topographic, soil data, next to RS-enabled EBVs to produce European habitat suitability maps for each EUNIS habitat type (at level 3) using the MAXENT habitat distribution model (HDM). In situ plot observation data (derived from the EVA database; http://euroveg.org/eva-database) are available for 160 EUNIS terrestrial habitats . The model can be executed by end-users by making a aselection of currently 30 predictors, comprising 7 climate parameters, 7 soil parameters, and 13 RS-EBVs (LULC, vegetation height, Inundation, Phenology, LAI). For the modelling Maxent version 3.4.1 is used. The habitat suitability model is running in the cloud on Terradue servers. Model raster output can be downloaded by the client for further processing. End-users are invited not only to use the NextGeoss community portal for finetuning European habitat suitability maps but also to give their feedback.

Assessment of canopy chlorophyll content (CCC) is an essential variable in developing indicators for biodiversity monitoring and climate change studies. The Sentinel-2 Multi-Spectral Imager (MSI) is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving CCC from Sentinel-2 in Bavarian forest national park, Germany. Fourteen statistical-based methods, including 13 different vegetation indices (VIs) and a non-parametric statistical approach, and two physical-based methods such as INFORM and PROSAIL radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A field data collected in July 2017, and cloud-free Sentinel-2 image acquired on 13 July 2017 were used for evaluating the performance of the methods. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. The modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m2 (R2 = 0.75). The PLSR resulted in the highest R2 of 0.78, and slightly higher RMSE = 0.22 g/m2 than mSR3. Further, the physical-based approach-INFORM inversion using look-up table resulted in an RMSE = 0.31 g/m2, and R2 = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for large scale mapping.

Quantifying Canopy chlorophyll content (CCC) is fundamental for the understanding of terrestrial ecosystems through monitoring and evaluating terrestrial ecosystem properties such as carbon and water fluxes, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects of biodiversity. However, robust and rigorous methods for regional and global mapping of CCC from remote sensing data is not well defined. This study aimed at evaluating the spatiotemporal consistency and scalability retrieval methods for large scale mapping of CCC. Four methods (i.e., Radiative transfer models (RTMs) inversion using look-up table (LUT), the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR), were investigated for their performance across biomes. Statistical measures were computed and spatiotemporal consistency pairwise comparison applied to evaluate the similarities and differences among CCC products generated by the four methods in four biomes (Temperate forest, Tropical forest, wetland, and Arctic Tundra). All the tested methods, except PLSR showed similar patterns and no significant difference in the spatial distribution in temperate forests. The CCC products obtained using the SRVI and the SNAP toolbox approach result in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent predictions of CCC with range closer to expectations. Therefore, the RTM inversion using LUT approaches, particularly INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems are recommended for CCC mapping from Sentinel-2 data for regional and global mapping of CCC. Further validation of the two RTMs using in situ CCC data in different terrestrial biomes is required in the future.

The European bark beetle (Ips typographus, L.) is a potentially severe invasive species in the UK and North America. It is resulting in a high degree of fragmentation, forest productivity, and phenology. Understanding its biology, as well as developing early detection based on its behavior, is an important aspect of its successful management and eradication. Bark beetle infestation causes changes biochemical and biophysical characteristics such as chlorophyll water and nitrogen content. This study showcases the potential of the Canopy Chlorophyll Content (CCC) product derived from remote sensing datasets to detect early bark beetle infestation in Bavarian forest national park. We generated time series CCC maps from RapidEye and Sentinel-2 images of the study area through Radiative transfer model inversion. The CCC products were then classified into infested and healthy using CCC mean and variance collected in 2015 and 2016 from infested and healthy Norway spruce trees in the Park. Reference data obtained from processing and interpretation of high resolution (0.1m) color aerial photographs were used to validate the accuracy of the infestation maps. Our results demonstrated that CCC products as derived from remote sensing data were a rigorous proxy to early detect bark beetle infestation. Validation of the infestation maps revealed > 70% classification accuracy throughout the time-space. Hence, CCC products play a significant role to understand the dynamics of the infestation and improve the management of bark beetle outbreaks in forest ecosystem. Despite these promising results, other plant traits such as dry matter content and Nitrogen content will need to be investigated as additional predictors, which may considerably improve the accuracy of early detection of bark beetle infestation using remote sensing derived products.

In NextGEOSS Biodiversity Pilot WP 6.2.1, we focus on creating the NextGEOSS European remote sensing-enabled EBVs (RS enabled-EBVs) data-hub by identifying and populating available RS-enabled EBVs products. 123 variables were compiled as EBV candidates for five out of six EBV classes, as the genetic composition cannot be measured using remote sensing data. All EBV candidates were prioritized based on different criteria and observation requirements including relevancy to Aichi biodiversity targets, availability through remote sensing data (i.e., feasibility), and a measure of accuracy and maturity of remote sensing technologies and techniques. The 30 highest-prioritized RS-enabled EBVs were selected, and from these available RS-enabled EBVs products were identified with special consideration to their spatial resolution and scales. Metadata was created for each considered RS- enabled EBVs products with respect to the data provider and inserted in the NextGEOSS data-hub

The poster summarizes the implementation of the Innovative Pilot on Biodiversity; WP 6.2.1 developed within NextGEOSS project. The pilot focuses on generating remote sensing –enabled essential biodiversity variables (RS-enabled EBVs) by means of high- resolution satellite data using an empirical approach. From the RS-enabled EBVs, which were initially proposed to be derived from high-resolution satellite data, leaf area index (LAI) was selected as one of the most important vegetation biophysical parameters as well as the EBVs. Sentinel-2 data (Level-2A product) was used and further LAI was retrieved using the relationship between LAI and Enhanced Vegetation Index

Rice management practices which make the sustainable use of resources more efficient are important interventions towards food security. Monitoring rice crop establishment methods (transplanting or direct seeding) using remote sensing data, can provide vital information on the type of practices as well as their spread and change over time. Direct seeding is a water and labour saving practice that is being promoted across Asia, yet most existing rice crop monitoring methods assume that the rice is transplanted and hence they may not perform as well as direct seeding becomes more popular. To improve the ability of rice crop monitoring methods, it is important to understand the differences in the multi-temporal information due to different crop establishment methods and then incorporate that into improved monitoring systems. Crop establishment is a rapid event that cannot be easily seen with remote sensing. However, we can infer which establishment method is used from resulting observable differences in land surface characteristics such as field condition and crop development stage in terms of delayed or prolonged stages that occur over a longer time. In this study, temporal information from Sentinel-1 Synthetic Aperture Radar (SAR) backscatter was used to first detect alterations in field condition and rice growth, and then link those to crop establishment practices. Farmer surveys and field observations were conducted in the province of Nueva Ecija (Philippines) in four selected municipalities across the province in 2017, to obtain information on field boundaries and crop management practices for 61 fields. Multi-temporal, dual-polarised, C-band backscatter data at 20m spatial resolution was acquired from Sentinel-1A every 12 days over the study area during the dry season, from November 2016 to May 2017. Mean backscatter values were calculated for each rice field and SAR acquisition date. The SAR acquisition dates were selected based on the reported dates for land management activities and the estimated dates of the crop growth stages. We used a Mann-Whitney U test to study whether there are significant differences in backscatter between the two practices during the land management activities and crop growth stages. Significant differences were observed in the early growing season, particularly during land preparation, crop establishment, rice tillering and stem elongation in cross-polarised, co-polarised and band ratio backscatter values. Our findings demonstrated that crop establishment methods could be clearly discriminated by SAR at these stages and that there is more opportunity for their discrimination than has been presented in the earlier literature. The increased practice of dry and wet direct seeding has implications for many remote sensing-based rice monitoring methods that rely on a strong water signal during the early season to determine if a field has been (trans)planted with rice. This signal is weakened or is even non existent when direct seeding is practised. Rice monitoring systems will need to adapt so that they can still accurately monitor the cultivated rice area as new resource conserving crop management practices become more common. As well as SAR intensity information, these algorithms should also incorporate coherence, spectral reflectance and vegetation indices in a multi-sensor approach to rice crop monitoring.

A new era in the retrieval of plant traits has started by emerging the new satellites such as the Copernicus Sentinel families. Among these traits, leaf area index (LAI) is a key indicator of vegetation growth and an essential variable in biodiversity studies. Numerous literature has shown that radiative transfer approach has been a successful method to retrieve LAI from remote sensing data. However, suitability and adaptability of this approach largely depend on the type of remote sensing data, and the ecosystem studied. In this regard, the retrieval of leaf area index in a saltmarsh ecosystem is examined in this study using Sentinel-2 and RapidEye data through inversion of PROSAILH radiative transfer model. Field measurements of LAI and a number of other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 in the saltmarsh of Schiermonnikoog, the Netherlands. Sentinel-2 (2016) and RapidEye (2015) data were acquired concurrent to the time of field campaigns. The broadly employed PROSAILH model was inverted using a look-up table (LUT) which contained 500, 000 records. Different scenarios of band combinations, as well as different solutions, were considered to obtain the LAI estimates. The R2 and RMSE between measured and estimated LAI were used then to evaluate the retrieval accuracy. The removal of dead materials from the measured LAI improved the estimation accuracies. Our results showed that generally the LAI retrieved using the Sentinel-2 data had higher accuracy compared to RapidEye data. In particular, the SWIR bands of Sentinel were modeled best using the PROSAILH. Leaf area index was best retrieved using the NIR and SWIR bands of Sentinel-2 (R2=0.56, RMSE=1.7). Our results highlight the importance of proper parametrization of radiative transfer models and capacity of Sentinel-2 data, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale.

Although the retrieval of leaf area index (LAI) as one of the essential biodiversity variable from remote sensing data has shown to be successful over visible/near-infrared (VNIR, 0.3-1.0 μm), shortwave infrared (SWIR, 1.0-2.5 μm), and TIR (8-14 μm) domains, integration of VNIR/SWIR with the TIR data for LAI estimation has not been addressed yet. Despite the importance, maturity, and availability of the remotely sensed data over VNIR and SWIR regions, TIR remote sensing data (i.e., emissivity spectra) has a number of advantages for LAI estimation. As such, it is known that the emissivity spectra over the TIR domain do not saturate even at relatively high values of LAI. In this respect, the utility of Landsat-8 TIR data together with its optical spectral data was examined to quantify LAI over Bavarian Forest National Park (Mixed temperate forest) in Germany. A field campaign was conducted in August 2015 in the National Park concurrent with the time of the Landsat-8 overpass. LAI was measured in the field for 37 plots. In this study, a number of vegetation indices, which have been widely applied in the literature were used to estimate LAI using VNIR/SWIR data. Furthermore, land surface emissivity (i.e., LSE) was derived from the band 10 of TIRS sensor using the normalized difference vegetation index threshold method. LSE was integrated with the reflectance data as the input layers to examine the LAI retrieval accuracy using the artificial neural network as a machine learning approach. The levenberg-marquardt algorithm was used for network training. LAI was predicted with modest accuracy using vegetation indices (R2CV=0.63, RMSECV=1.56 m2m-2, and R2CV=0.65, RMSECV=1.56 m2m-2 for NDI, and SR respectively). However, when the VNIR/SWIR bands and TIR data (LSE) were integrated, the prediction accuracy of LAI increased significantly (R2CV=0.79, RMSECV=0.75, m2m-2). Our results demonstrate that the combination of LSE and VNIR/SWIR satellite data can lead to higher retrieval accuracy for LAI. This finding has implication for retrieval of other vegetation parameters through the integration of TIR and optical satellite remote sensing data as well as regional mapping of LAI when coupled with a canopy radiative transfer model. 3

Forest management requires the use of comprehensive remote sensing data which enable monitoring of biodiversity changes. Biophysical and biochemical vegetation parameters can characterize changes in biodiversity through changes in ecosystem structure and function. To address this need the University of Twente, Faculty ITC (the Netherlands) in collaboration with the Bavarian Forest National Park and the German Aerospace Center, (DLR) in Oberpfaffenhofen coordinated a summer school in July 2017. The two weeks of summer school was funded by EUFAR and was hosted by the Bavarian Forest National Park and DLR. The 19 participants of the summer school were PhD students and post-docs from 10 EU member states. The summer school offered the field expertise as well as the technical skills to understand and measure a number of essential biodiversity variables (EBVs) in forest ecosystems. Further, the students learned how to process the hyperspectral, thermal , and LiDAR data for the estimation of EBVs. The course contained two days of fieldwork in the Bavarian Forest National Park, and the participants of different themes (Hyperspectral, Thermal, LiDAR) were trained how to perform field measurements of various EBVs in a forest ecosystem. Further, the course participants were taught how to conduct field spectroscopy, thermal spectrometry and terrestrial LiDAR measurements. Concurrent to the time of field measurements an airborne campaign with the NERC Airborne Research Facility (NERC-ARF) was organized that simultaneously acquired hyperspectral as well as thermal-infrared imaging data using the Specim AISA Fenix and Owl systems, respectively. The second half of the summer school was parallel with the ICARE 2017 conference, and the course participants visited the aircraft exhibition and were welcomed by the airborne research and operator community.

In NextGEOSS Biodiversity Pilot WP 6.2.1, we focus on creating the NextGEOSS European remote sensing-enabled EBVs (RS enabled-EBVs) data-hub by identifying and populating available RS-enabled EBVs products. 123 variables were compiled as EBV candidates for five out of six EBV classes, as the genetic composition cannot be measured using remote sensing data. All EBV candidates were prioritized based on different criteria and observation requirements including relevancy to Aichi biodiversity targets, availability through remote sensing data (i.e., feasibility), and a measure of accuracy and maturity of remote sensing technologies and techniques. The 30 highest-prioritized RS-enabled EBVs were selected, and from these available RS-enabled EBVs products were identified with special consideration to their spatial resolution and scales. Metadata was created for each considered RS-enabled EBVs products with respect to the data provider and inserted in the NextGEOSS data-hub.

Seasonal changes in leaf chlorophyll across the canopy vertical profile provide information on ecosystem structure and functioning. However, studies on the retrieval of leaf chlorophyll content (Cab) using radiative transfer models such as PROSPECT across the canopy vertical profile throughout the growing season are lacking. In this regard, we sought to evaluate the performance of the PROSPECT in modeling leaf optical properties and retrieving Cab across the canopy position throughout the growing season. We collected 588 leaf samples from the upper and lower canopies of deciduous stands over three seasons in Bavaria Forest National Park, Germany. PROSPECT input parameters were measured for all the samples, and their respective reflectance spectra were obtained using an ASD FieldSpec-3 Pro FR spectroradiometer coupled with an Integrating Sphere. To retrieve Cab, we inverted the PROSPECT using a look-up-table (LUT) approach. Our results consistently revealed a strong agreement between the measured and PROSPECT simulated reflectance spectra for the lower canopy compared to the upper canopy, especially in the NIR. This observation concurred with the pattern of Cab retrieval accuracies across the canopy i.e. the Cab retrieval accuracy for the lower canopy was consistently higher (NRMSE = 0.1-0.2) when compared to the upper canopy (NRMSE = 0.122 - 0.269) across all seasons. Results of this study demonstrate that although the PROSPECT model provides acceptable inversion of Cab, subtle seasonal variations in leaf biochemistry and morphology across the canopy potentially affect the performance of the model.