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Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and they play a significant role in the global cycles of carbon, nitrogen and water. Remote sensing data from satellites can be used to estimate leaf area index (LAI), leaf chlorophyll (CHLl) and leaf nitrogen density (Nl). However, methods are often developed using plot scale data and not verified over extended regions that represent a variety of soil spectral properties and canopy structures. In this paper, field measurements and high spatial resolution (10-20m) remote sensing images acquired from the HRG and HRVIR sensors aboard the SPOT satellites were used to assess the predictability of LAI, CHLl and Nl. Five spectral vegetation indices (SVIs) were used (the Normalized Difference Vegetation index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green Chlorophyll Index) together with the image-based inverse canopy radiative transfer modelling system, REGFLEC (REGularized canopy reFLECtance). While the SVIs require field data for empirical model building, REGFLEC can be applied without calibration. Field data measured in 93 fields within crop- and grasslands of five Euro- pean landscapes showed strong vertical CHLl gradient profiles in 20% of fields. This affected the predictability of SVIs and REGFLEC. However, selecting only homogeneous canopies with uniform CHLl distributions as reference data for statistical evaluation, significant (p < 0.05) predictions were achieved for all landscapes, by all methods. The best performance was achieved by REGFLEC for LAI (r² = 0.7; rmse=0.73), canopy chlorophyll content (r² = 0.51; rmse=439mg m-2 ) and canopy nitrogen con- tent (r² = 0.53; rmse=2.21 gm-2 ). Predictabilities of SVIs and REGFLEC simulations generally improved when constrained to single land use categories (wheat, maize, bar- ley, grass) across the European landscapes, reflecting sensitivity to canopy structures. Predictability further improved when constrained to local (10x10 km² ) landscapes, thereby reflecting sensitivity to local environmental conditions. All methods showed different predictabilities for land use categories and landscapes. Combining the best methods, LAI, canopy chlorophyll content (CHLc) and canopy nitrogen content (Nc) for the five landscapes could be predicted with improved accuracy (LAI rmse=0.59; CHLc rmse=346 gm-2; Nc rmse=1.49 gm-2 ). Remote sensing-based results showed that the vegetation nitrogen pools of the five agricultural landscapes varied from 0.6 to 4.0 tkm-2. Differences in nitrogen pools were attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. Information on Nl and total Nc pools within the landscapes is important for the spatial evaluation of nitrogen and carbon cycling processes. The upcoming Sentinel-2 satellite mission will provide new multiple narrow-band data opportunities at high spatioemporal resolution which are expected to further improve remote sensing predictabilities of LAI, CHLl and Nl. [ABSTRACT FROM AUTHOR]

Resolving the spatial and temporal dynamics of gross primary productivity (GPP) of terrestrial ecosystems across different scales remains a challenge. Remote sensing is regarded as the solution to upscale point observations conducted at the ecosystem level, using the eddy covariance (EC) technique, to the landscape and global levels. In addition to traditional vegetation indices, the photochemical reflectance index (PRI) and the emission of solar-induced chlorophyll fluorescence (SIF), now measurable from space, provide a new range of opportunities to monitor the global carbon cycle using remote sensing. However, the scale mismatch between EC observations and the much coarser satellite-derived data complicates the integration of the two sources of data. The solution is to establish a network of in situ spectral measurements that can act as bridge between EC measurements and remote sensing data. In situ spectral measurements have been already conducted for many years at EC sites, but using variable instrumentation, setups, and measurement standards. In Europe in particular, in situ spectral measurements remain highly heterogeneous. The goal of EUROSPEC Cost Action ES0930 was to promote the development of common measuring protocols and new instruments towards establishing best practices and standardization of in situ spectral measurements. In this review we describe the background and main tradeoffs of in situ spectral measurements, review the main results of EUROSPEC Cost Action, and discuss the future challenges and opportunities of in situ spectral measurements for improved estimation of local and global carbon cycle. [ABSTRACT FROM AUTHOR]

Quantifying the interannual variability (IAV) of the terrestrial productivity and its sensitivity to climate is crucial for improving carbon budget predictions. However, the influence of climate and other mechanisms underlying the spatiotemporal patterns of IAV of productivity are not well understood. In this study we investigated the spatiotemporal patterns of IAV of historical observations of crop yields, tree ring width, remote sensing retrievals of FAPAR and NDVI, and other variables relevant to the terrestrial productivity in Europe in tandem with a set of climate variables. Our results reveal distinct spatial patterns in the IAV of most variables linked to terrestrial productivity. In particular, we find higher IAV in water-limited regions of Europe (Mediterranean and temperate continental Europe) compared to other regions. Our results further indicate that variations in the water balance during active growing season exert a more pronounced and direct effect than variations of temperature on explaining the spatial patterns in IAV of productivity related variables in temperate Europe. We also observe a temporally increasing trend in the IAV of terrestrial productivity and an increasing sensitivity of productivity to water availability in dry regions of Europe, which is likely attributable to the recently increased IAV of water availability in these regions. These findings suggest nonlinear responses of carbon fluxes to climate variability in Europe and that the IAV of terrestrial productivity has become more sensitive and more vulnerable to changes in water availability in the dry regions in Europe. The changing climate sensitivity of terrestrial productivity accompanied by the changing IAV of climate could impact carbon stocks and the net carbon balance of European ecosystems. [ABSTRACT FROM AUTHOR]

A series of wildfires broke out in western Russia starting in late July of 2010. Harmful particulates and gases released into the local Russian atmosphere have been reported, as have possible negative consequences for the global atmosphere. In this study, an extremely hazy area and its transport trajectory on Russian wildfires were analysed using aerosol optical depth (AOD) images retrieved via the synergy method from Moderate Resolution Imaging Spectroradiometer (MODIS) data. In addition, we used trace gases (NO2 and SO2) and CO2 products measured using Ozone Monitoring Instrument (OMI) data, vertical distribution of AOD data retrieved from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the mass trajectory analyses, synoptic maps from a HYSPLIT model simulation and ground-based data, including AERONET (both AOD and Ångström exponent) data and PM2.5. First, an Optimal Smoothing (OS) scheme was used to develop more precise and reliable AOD data based on multiple competing predictions made using several AOD retrieval models; then, integrated AOD and PM2.5 data were related using a chemical transport model (GEOS-Chem), and the integrated AOD and visibility data were related using a 6S model. The results show that the PM2.5 concentration is 3-5 times the normal amount based on both satellite data and in situ values with peak daily mean concentrations of approximately 500 µgm-3. Also, the visibility of many parts of Russia, even Moscow, was less than 100 m; in some areas, the visibility was less than 50 m. Additionally, the possible impact on neighbouring countries due to the long-transport effect was also analysed during 31 July and 15 August 2010. A comparison of the satellite aerosol products and ground observations from the neighbouring countries suggests that wildfires in western Russian have had little impact on most European and Asian countries, the exceptions being Finland, Estonia, Ukraine and Kyrgyzstan. However, a possible impact on the Arctic region was also identified; such an effect would have a serious influence on the polar atmospheric environment and on animals such as polar bears. [ABSTRACT FROM AUTHOR]

Technological advancements in the fields of Photogrammetry, Remote Sensing, and Geospatial Sciences continuously alter the way we collect, process, and interpret datasets. Accreditation bodies and institutions often need to respond to those changes and make timely modifications to stay current with technology changes and industry needs. However, it can be generally stated that the industry can adapt and adopt technological changes faster than accreditation organizations and institutions, because the process of modernizing curricula can be time consuming and slow based on organization / university / college policies. In addition to industry demands, institutions must adhere to and follow accreditation requirements. This paper reviews several accreditation requirements related to Photogrammetry, Remote Sensing, and Geospatial Sciences. The accreditation bodies that are reviewed focus on organizations in Northern America and Europe due to ease of access to information. The review provides insights about their curricula criteria, their level of detail, if they can be considered current based on industry needs, and if they provide enough flexibility for modernizing curricula without violation of accreditation policies and criteria. [ABSTRACT FROM AUTHOR]

The Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument, launched in October 2017, provides unique observations of atmospheric trace gases at a high resolution of about 5 km, with near-daily global coverage, resolving individual sources like thermal powerplants, industrial complexes, fires, medium-scale towns, roads, and shipping routes. Even though Sentinel-5P (S5P) is a global mission, these datasets are especially well suited to test high-resolution regional-scale air quality (AQ) models and provide valuable input for emission inversion systems. In Europe, the Copernicus Atmosphere Monitoring Service (CAMS) has implemented an operational regional AQ forecasting capability based on an ensemble of several European models, available at a resolution of 0.1 ∘ × 0.1 ∘. In this paper, we present comparisons between TROPOMI observations of nitrogen dioxide (NO2) and the CAMS AQ forecasts and analyses of NO2. We discuss the different ways of making these comparisons and present quantitative results in the form of maps for individual days, summer and winter months, and a time series for European subregions and cities between May 2018 and March 2021. The CAMS regional products generally capture the fine-scale daily and averaged features observed by TROPOMI in much detail. In summer, the comparison shows a close agreement between TROPOMI and the CAMS ensemble NO2 tropospheric columns with a relative difference of up to 15 % for most European cities. In winter, however, we find a significant discrepancy in the column amounts over much of Europe, with relative differences up to 50 %. The possible causes for these differences are discussed, focusing on the possible impact of retrieval and modeling errors. Apart from comparisons with the CAMS ensemble, we also present results for comparisons with the individual CAMS models for selected months. Furthermore, we demonstrate the importance of the free tropospheric contribution to the estimation of the tropospheric column and thus include profile information from the CAMS configuration of the ECMWF's (European Centre for Medium-Range Weather Forecasts) global integrated model above 3 km altitude in the comparisons. We also show that replacing the global 1 ∘ × 1 ∘ a priori information in the retrieval by the regional 0.1 ∘ × 0.1 ∘ resolution profiles of CAMS leads to significant changes in the TROPOMI-retrieved tropospheric column, with typical increases at the emission hotspots up to 30 % and smaller increases or decreases elsewhere. As a spinoff, we present a new TROPOMI NO2 level 2 (L2) data product for Europe, based on the replacement of the original TM5-MP generated global a priori profile by the regional CAMS ensemble profile. This European NO2 product is compared with ground-based remote sensing measurements of six Pandora instruments of the Pandonia Global Network and nine Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments. As compared to the standard S5P tropospheric NO2 column data, the overall bias of the new product for all except two stations is 5 % to 12 % smaller, owing to a reduction in the multiplicative bias. Compared to the CAMS tropospheric NO2 columns, dispersion and correlation parameters with respect to the standard data are, however, superior. [ABSTRACT FROM AUTHOR]

Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled "Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors", is shared via Forschungszentrum Jülich: 10.34731/x9s3-kr48 (Bogena and Ney, 2021). [ABSTRACT FROM AUTHOR]

Atmospheric inversions are used to derive constraints on the net sources and sinks of CO 2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied. In recent years, there has been an increasing demand from stakeholders for inversions at higher spatial resolution (country scale), in particular in the framework of the Paris agreement. This step up in resolution is in theory enabled by the growing availability of observations from surface in situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the bottom-up modeling community (vegetation models, fossil fuel emission inventories, etc.). However, it calls for new developments in the inverse models: diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency. In this context, we developed LUMIA, the Lund University Modular Inversion Algorithm. LUMIA is a Python library for inverse modeling built around the central idea of modularity: it aims to be a platform that enables users to construct and experiment with new inverse modeling setups while remaining easy to use and maintain. It is in particular designed to be transport-model-agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. We have constructed a first regional inversion setup using the LUMIA framework to conduct regional CO 2 inversions in Europe using in situ data from surface and tall-tower observation sites. The inversions rely on a new offline coupling between the regional high-resolution FLEXPART Lagrangian particle dispersion model and the global coarse-resolution TM5 transport model. This test setup is intended both as a demonstration and as a reference for comparison with future LUMIA developments. The aims of this paper are to present the LUMIA framework (motivations for building it, development principles and future prospects) and to describe and test this first implementation of regional CO 2 inversions in LUMIA. [ABSTRACT FROM AUTHOR]

Human-caused climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns and reduced snow occurrence. For example, Europe has suffered from drought events in the last decade like never since the beginning of weather recording. Here we present soil moisture data from 65 Cosmic-ray neutron sensors (CRNS) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 23 research institutions and processed using state-of-the-art methods. The harmonised processing included correction of the raw neutron counts, and a harmonised methodology for the conversion into soil moisture based on available in-situ information. In addition, information on the data uncertainty is provided with the dataset, information that is particularly useful for remote sensing and modelling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products as well as first results on how the recent drought events have been captured by the CRNS network. This harmonised European soil moisture dataset will help both hydrologists and climate scientists to study individual drought events, to understand their causes, to evaluate and improve their modelling, and to estimate the extremity of current events. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021). [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) are key precursors of particulate matter and tropospheric ozone. Although the terrestrial biosphere is by far the largest source of VOCs into the atmosphere, the emissions of biogenic VOCs remain poorly constrained at the regional scale. In this work, we derive top-down biogenic emissions over Europe using weekly averaged TROPOMI formaldehyde (HCHO) data from 2018 to 2021. The systematic bias of the TROPOMI HCHO columns is characterized and corrected for based on comparisons with FTIR data at seven European stations. The top-down fluxes of biogenic, pyrogenic, and anthropogenic VOC sources are optimized using an inversion framework based on the MAGRITTEv1.1 chemistry transport model and its adjoint. The inversion leads to strongly increased isoprene emissions with respect to the MEGAN–MOHYCAN inventory over the model domain (from 8.1 to 18.5 Tgyr-1), which is driven by the high observed TROPOMI HCHO columns in southern Europe. The impact of the inversion on biomass burning VOCs (+ 13 %) and anthropogenic VOCs (- 17 %) is moderate. An evaluation of the optimized HCHO distribution against ground-based remote sensing (FTIR and MAX-DOAS) and in situ data provides generally improved agreement at stations below about 50 ∘ N but indicates overestimated emissions in northern Scandinavia. Sensitivity inversions show that the top-down emissions are robust with respect to changes in the inversion settings and in the model chemical mechanism, leading to differences of up to 10 % in the total emissions. However, the top-down emissions are very sensitive to the bias correction of the observed columns, as the biogenic emissions are 3 times lower when the correction is not applied. Furthermore, the use of different a priori biogenic emissions has a significant impact on the inversion results due to large differences among bottom-up inventories. The sensitivity run using CAMS-GLOB-BIOv3.1 as a priori emissions in the inversion results in 30 % lower emissions with respect to the optimization using MEGAN–MOHYCAN. In regions with large temperature and cloud cover variations, there is strong week-to-week variability in the observed HCHO columns. The top-down emissions, which are optimized at weekly increments, have a much improved capability of representing these large fluctuations than an inversion using monthly increments. [ABSTRACT FROM AUTHOR]

Ammonia (NH 3), a significant precursor of particulate matter, affects not only biodiversity, ecosystems, and soil acidification but also climate and human health. In addition, its concentrations are constantly rising due to increasing feeding needs and the large use of fertilization and animal farming. Despite the significance of ammonia, its emissions are associated with large uncertainties, while its atmospheric abundance is difficult to measure. Nowadays, satellite products can effectively measure ammonia with low uncertainty and a global coverage. Here, we use satellite observations of column ammonia in combination with an inversion algorithm to derive ammonia emissions with a high resolution over Europe for the period 2013–2020. Ammonia emissions peak in northern Europe due to agricultural application and livestock management, in western Europe (industrial activity), and over Spain (pig farming). Emissions have decreased by - 26 % since 2013 (from 5431 Gg in 2013 to 3994 Gg in 2020), showing that the abatement strategies adopted by the European Union have been very efficient. The slight increase (+ 4.4 %) in 2015 is also reproduced here and is attributed to some European countries exceeding annual emission targets. Ammonia emissions are low in winter (286 Gg) and peak in summer (563 Gg) and are dominated by the temperature-dependent volatilization of ammonia from the soil. The largest emission decreases were observed in central and eastern Europe (- 38 %) and in western Europe (- 37 %), while smaller decreases were recorded in northern (- 17 %) and southern Europe (- 7.6 %). When complemented with ground observations, modelled concentrations using the posterior emissions showed improved statistics, also following the observed seasonal trends. The posterior emissions presented here also agree well with respective estimates reported in the literature and inferred from bottom-up and top-down methodologies. These results indicate that satellite measurements combined with inverse algorithms constitute a robust tool for emission estimates and can infer the evolution of ammonia emissions over large timescales. [ABSTRACT FROM AUTHOR]

This paper presents a new data set of vertical column densities of the water vapour isotopologues H2O and HDO retrieved from short-wave infrared (2.3 μm) reflectance measurements by the Tropospheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite. TROPOMI features daily global coverage with a spatial resolution of up to 7 km × 7 km. The retrieval utilises a profile-scaling approach. The forward model neglects scattering, thus strict cloud filtering is necessary. For validation, recent ground-based water vapour isotopologue measurements by the Total Carbon Column Observing Network (TCCON) are employed. A comparison of TCCON δD with measurements by the project Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) for data prior to 2014 (where MUSICA data is available) shows a bias in TCCON δD estimates. As TCCON HDO is currently not validated, an overall correction of recent TCCON HDO data is derived based on this finding. The agreement between the corrected TCCON measurements and collocated TROPOMI observations is good with an average bias of (0.02 ± 2) · 1021 molec cm−2 in H2O and (−0.3 ± 7) · 1017 molec cm−2 in HDO, which corresponds to a bias of (−12 ± 17) ‰ in a posteriori δD. The use of the data set is demonstrated with a case study of a blocking anticyclone in northwestern Europe in July 2018 using single overpass data. [ABSTRACT FROM AUTHOR]

The atmospheric boundary layer (ABL) defines the volume of air adjacent to the Earth's surface for the dilution of heat, moisture, and trace substances. Quantitative knowledge on the temporal and spatial variations in the heights of the ABL and its sub-layers is still scarce, despite their importance for a series of applications (including, for example, air quality, numerical weather prediction, greenhouse gas assessment, and renewable energy production). Thanks to recent advances in ground-based remote-sensing measurement technology and algorithm development, continuous profiling of the entire ABL vertical extent at high temporal and vertical resolution is increasingly possible. Dense measurement networks of autonomous ground-based remote-sensing instruments, such as microwave radiometers, radar wind profilers, Doppler wind lidars or automatic lidars and ceilometers are hence emerging across Europe and other parts of the world. This review summarises the capabilities and limitations of various instrument types for ABL monitoring and provides an overview on the vast number of retrieval methods developed for the detection of ABL sub-layer heights from different atmospheric quantities (temperature, humidity, wind, turbulence, aerosol). It is outlined how the diurnal evolution of the ABL can be monitored effectively with a combination of methods, pointing out where instrumental or methodological synergy are considered particularly promising. The review highlights the fact that harmonised data acquisition across carefully designed sensor networks as well as tailored data processing are key to obtaining high-quality products that are again essential to capture the spatial and temporal complexity of the lowest part of the atmosphere in which we live and breathe. [ABSTRACT FROM AUTHOR]

Coastal lagoons are important sites for nitrogen (N) removal via sediment burial and denitrification. Blooms of heterocystous cyanobacteria may diminish N retention as dinitrogen (N 2) fixation offsets atmospheric losses via denitrification. We measured N 2 fixation in the Curonian Lagoon, Europe's largest coastal lagoon, to better understand the factors controlling N 2 fixation in the context of seasonal changes in phytoplankton community composition and external N inputs. Temporal patterns in N 2 fixation were primarily determined by the abundance of heterocystous cyanobacteria, mainly Aphanizomenon flos-aquae , which became abundant after the decline in riverine nitrate inputs associated with snowmelt. Heterocystous cyanobacteria dominated the summer phytoplankton community resulting in strong correlations between chlorophyll a (Chl a) and N 2 fixation. We used regression models relating N 2 fixation to Chl a , along with remote-sensing-based estimates of Chl a to derive lagoon-scale estimates of N 2 fixation. N 2 fixation by pelagic cyanobacteria was found to be a significant component of the lagoon's N budget based on comparisons to previously derived fluxes associated with riverine inputs, sediment–water exchange, and losses via denitrification. To our knowledge, this is the first study to derive ecosystem-scale estimates of N 2 fixation by combining remote sensing of Chl a with empirical models relating N 2 fixation rates to Chl a. [ABSTRACT FROM AUTHOR]

Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and play a significant role in the global cycles of carbon, nitrogen and water. The purpose of this study is to use field-based and satellite remote-sensing-based methods to assess leaf nitrogen pools in five diverse European agricultural landscapes located in Denmark, Scotland (United Kingdom), Poland, the Netherlands and Italy. REGFLEC (REGularized canopy reFLECtance) is an advanced image-based inverse canopy radiative transfer modelling system which has shown proficiency for regional mapping of leaf area index (LAI) and leaf chlorophyll (CHLl) using remote sensing data. In this study, high spatial resolution (10-20 m) remote sensing images acquired from the multispectral sensors aboard the SPOT (Satellite For Observation of Earth) satellites were used to assess the capability of REGFLEC for mapping spatial variations in LAI, CHL1 and the relation to leaf nitrogen (N1) data in five diverse European agricultural landscapes. REGFLEC is based on physical laws and includes an automatic model parameterization scheme which makes the tool independent of field data for model calibration. In this study, REGFLEC performance was evaluated using LAI measurements and non-destructive measurements (using a SPAD meter) of leaf-scale CHL1 and N1 concentrations in 93 fields representing crop- and grasslands of the five landscapes. Furthermore, empirical relationships between field measurements (LAI, CHL1 and N1) and five spectral vegetation indices (the Normalized Difference Vegetation Index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green chlorophyll index) were used to assess field data coherence and to serve as a comparison basis for assessing REGFLEC model performance. The field measurements showed strong vertical CHL1 gradient profiles in 26% of fields which affected REGFLEC performance as well as the relationships between spectral vegetation indices (SVIs) and field measurements. When the range of surface types increased, the REGFLEC results were in better agreement with field data than the empirical SVI regression models. Selecting only homogeneous canopies with uniform CHL1 distributions as reference data for evaluation, REGFLEC was able to explain 69% of LAI observations (rmse=0.76), 46% of measured canopy chlorophyll contents (rmse=719 mgm-2) and 51% of measured canopy nitrogen contents (rmse=2.7 gm-2). Better results were obtained for individual landscapes, except for Italy, where REGFLEC performed poorly due to a lack of dense vegetation canopies at the time of satellite recording. Presence of vegetation is needed to parameterize the REGFLEC model. Combining REGFLEC- and SVI-based model results to minimize errors for a "snap-shot" assessment of total leaf nitrogen pools in the five landscapes, results varied from 0.6 to 4.0 t km-2. Differences in leaf nitrogen pools between landscapes are attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. In order to facilitate a substantial assessment of variations in N1 pools and their relation to landscape based nitrogen and carbon cycling processes, time series of satellite data are needed. The upcoming Sentinel-2 satellite mission will provide new multiple narrowband data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing capabilities for mapping LAI, CHL1 and N1. [ABSTRACT FROM AUTHOR]

Within the framework of FP7, an EU-funded SafeLand project, a questionnaire was prepared to collect information about the use of remote sensing for landslide study and to evaluate its actual application in landslide detection, mapping and monitoring. The questionnaire was designed using a Google form and was disseminated among endusers and researchers involved in landslide studies in Europe. In total, 49 answers from 17 different European countries were collected. The outcomes showed that landslide detection and mapping is mainly performed with aerial photos, often associated with optical and radar imagery. Concerning landslide monitoring, satellite radars prevail over the other types of data. Remote sensing is mainly used for detection/mapping and monitoring of slides, flows and lateral spreads with a preferably large scale of analysis (1 : 5000-1 : 25 000). All the compilers integrate remote sensing data with other thematic data, mainly geological maps, landslide inventory maps and DTMs and derived maps. According to the research and working experience of the compilers, remote sensing is generally considered to have a medium effectiveness/reliability for landslide studies. The results of the questionnaire can contribute to an overall sketch of the use of remote sensing in current landslide studies and show that remote sensing can be considered a powerful and well-established instrument for landslide mapping, monitoring and hazard analysis. [ABSTRACT FROM AUTHOR]