EAGLE 2006 – Multi-purpose, multi-angle and multi-sensor in-situ and airborne campaigns over grassland and forest

EAGLE2006 - an intensive field campaign - was carried out in the Netherlands from the 8th until the 18th of June 2006. Several airborne sensors - an optical imaging sensor, an imaging microwave radiometer, and a flux airplane – were used and extensive ground measurements were conducted over one grassland (Cabauw) site and two forest sites (Loobos & Speulderbos) in the central part of the Netherlands, in addition to the acquisition of multi-angle and multi-sensor satellite data. The data set is both unique and urgently needed for the development and validation of models and inversion algorithms for quantitative surface parameter estimation and process studies. EAGLE2006 was led by the Department of Water Resources of the International Institute for Geo-Information Science and Earth Observation and originated from the combination of a number of initiatives coming under different funding. The objectives of the EAGLE2006 campaign were closely related to the objectives of other ESA Campaigns (SPARC2004, Sen2Flex2005 and especially AGRISAR2006). However, one important objective of the campaign is to build up a data base for the investigation and validation of the retrieval of bio-geophysical parameters, obtained at different radar frequencies (X-, C- and L-Band) and at hyperspectral optical and thermal bands acquired over vegetated fields (forest and grassland). As such, all activities were related to algorithm development for future satellite missions such as Sentinels and for satellite validations for MERIS, MODIS as well as AATSR and ASTER thermal data validation, with activities also related to the ASAR sensor on board ESA’s Envisat platform and those on EPS/MetOp and SMOS. Most of the activities in the campaign are highly relevant for the EU GEMS EAGLE project, but also issues related to retrieval of biophysical parameters from MERIS and MODIS as well as AATSR and ASTER data were of particular relevance to the NWO-SRON EcoRTM project, while scaling issues and complementary between these (covering only local sites) and global sensors such as MERIS/SEVIRI, EPS/MetOP and SMOS were also key elements for the SMOS cal/val project and the ESA-MOST DRAGON programme. This contribution describes the mission objectives and provides an overview of the airborne and field campaigns

Remote Sensing of Water Quality, Demonstrating the Capabilities of Sentinel-2 for the Nile Delta

This paper describes a hydro-optical model for deriving water quality variables from satellite images, hereafter HydroSat. HydroSat corrects images for atmospheric interferences and simultaneously retrieves water quality variables. An application of HydroSat to Landsat Enhanced Thematic Mapper (ETM) observations over the Rosetta Branch of the Nile River demonstrates that reliable estimates of water quality are obtained. For example, the impact of the many water inlets along the Rosetta on the water quality can be very well identified. Quantitatively, the accuracy of the derived products is assessed via comparison with the output of a validated water quality process model for the Rosetta Branch. This matchup between the HydroSat and process model's output results in determination coefficients, R2 s, larger than 0.6 for all derived water quality variables. It should be noted that derivation of water quality variables using Hydro-Sat does not rely on any tuning parameters. Hence, the successful application of HydroSat to Landsat-ETM data could also be seen as a demonstration of the future Sentinel-2 capabilities for mapping water quality over rivers and inland lakes

BRDFs acquired by directional radiative measurements during EAGLE and AGRISAR

Radiation is the driving force for all processes and interactions between earth surface and atmosphere. The amount of measured radiation reflected by vegetation depends on its structure, the viewing angle and the solar angle. This angular dependence is usually expressed in the Bi-directional Reflectance Distribution Function (BRDF). This BRDF is not only different for different types of vegetation, but also different for different stages of the growth. The BRDF therefore has to be measured at ground level before any satellite imagery can be used the calculate surface-atmosphere interaction. The objective of this research is to acquire the BRDFs for agricultural crop types. A goniometric system is used to acquire the BRDFs. This is a mechanical device capable of a complete hemispherical rotation. The radiative directional measurements are performed with different sensors that can be attached to this system. The BRDFs are calculated from the measured radiation. In the periods 10 June - 18 June 2006 and 2 July - 10 July 2006 directional radiative measurements were performed at three sites: Speulderbos site, in the Netherlands, the Cabauw site, in the Netherlands, and an agricultural test site in Goermin, Germany. The measurements were performed over eight different crops: forest, grass, pine tree, corn, wheat, sugar beat and barley. The sensors covered the spectrum from the optical to the thermal domain. The measured radiance is used to calculate the BRDFs or directional thermal signature. This contribution describes the measurements and calculation of the BRDFs of forest, grassland, young corn, mature corn, wheat, sugar beat and barley during the EAGLE2006 and AGRISAR 2006 fieldcampaigns. Optical BRDF have been acquired for all crops except barley. Thermal angular signatures are acquired for all the crop

Selected Examples of Potential Early Cartographic Data Sources for the Carpathian Basin

In our present study, we would like to draw attention to some early graphic data sources that could provide thematic information for GIS applications focusing on the Carpathian Basin. There is one map in particular that stands out from the works listen in this study. The map Tabula Hungariae by Script Lázár, listed in the UNESCO Memory of the World Registry, is considered to mark the beginning of Hungarian geography. Other early maps were also undeservedly neglected in recent thematic applications. It is our hope that future geo-graphic studies will start with Lázár’s map and that historical maps will become a more signifi cant part of GIS applications

Technique for validating remote sensing products of water quality

Remote sensing of water quality is initiated as an additional part of the on going activities of the EAGLE2006 project. Within this context intensive in-situ and airborne measurements campaigns were carried out over the Wolderwijd and Veluwemeer natural waters. However, in-situ measurements and image acquisitions were not simultaneous. This poses some constraints on validating air/space-borne remote sensing products of water quality. Nevertheless, the detailed insitu measurements and hydro-optical model simulations provide a bench mark for validating remote sensing products. That is realized through developing a stochastic technique to quantify the uncertainties on the retrieved aquatic inherent optical properties (IOP). The output of the proposed technique is applied to validate remote sensing products of water quality. In this processing phase, simulations of the radiative transfer in the coupled atmosphere-water system are performed to generate spectra at-sensor-level. The upper and the lower boundaries of perturbations, around each recorded spectrum, are then modelled as function of residuals between simulated and measured spectra. The perturbations are parameterized as a function of model approximations/inversion, sensor-noise and atmospheric residual signal. All error sources are treated as being of stochastic nature. Three scenarios are considered: spectrally correlated (i.e. wavelength dependent) perturbations, spectrally uncorrelated perturbations and a mixed scenario of the previous two with equal probability of occurrence. Uncertainties on the retrieved IOP are quantified with the relative contribution of each perturbation component to the total error budget of the IOP. This technique can be used to validate earth observation products of water quality in remote areas where few or no in– situ measurements are available

Satellite-based analysis of recent trends in the ecohydrology of a semi-arid region

We present a regional framework for an integrated and spatiotemporally distributed assessment of human-induced trends in the hydrology and the associated ecological health of a semi-arid basin where both human activities (i.e. agriculture) and natural ecosystems are highly groundwater dependent. To achieve this, we analysed the recent trends (from year 2000 to 2010) in precipitation, evapotranspiration (actual and potential) and vegetation greenness (i.e. NDVI) using a combination of satellite and ground-based observations. The trend assessment was applied for the semi-arid Konya Basin (Turkey), one of the largest endorheic basins in the world. The results revealed a consistent increasing trend of both yearly evapotranspiration (totally 63 MCM yr−1 from croplands) and mean NDVI (about 0.004 NDVI yr−1 in irrigated croplands), especially concentrating in the plain part of the basin, while no significant trends were observed for the precipitation and potential evapotranspiration variables. On the contrary, a consistent decreasing trend of both yearly evapotranspiration (totally −2.1 MCM yr−1) and mean NDVI (−0.001 NDVI yr−1) was observed in the wetlands, which also cannot be explained by trends in precipitation and potential evapotranspiration. The emerging picture suggest that the greening trend of the vegetation and increasing of evapotranspiration in the plain are related to land cover changes (i.e. conversion into irrigated croplands) and to the intensification of the supplementary irrigation for agriculture, which in turn caused drying out of some wetlands and the natural vegetation which mostly depend on the groundwater, the main source of irrigation water as well. Our study presented an example of the utility of spatially and temporally continuous RS data in assessing the regional trends in hydrological and ecological variables and their interactions in a spatially distributed manner in a semi-arid region, which can also be adapted to other regions. Such spatiotemporally distributed analysis at the basin level is particularly important considering that most of the water management interventions also take place at this scale

A versatile Cloud Computing environment to facilitate African-European partnership in research: EO AFRICA R&D Innovation Lab

The African Framework for Research, Innovation, Communities and Applications (EO AFRICA) is an ESA initiative in collaboration with the African Union Commission that aims to foster an African-European R&D partnership facilitating the sustainable adoption of Earth Observation and related space technologies in Africa. EO AFRICA R&D Facility is the flagship of EO AFRICA with the overarching goals of enabling an active research community and promoting creative and collaborative innovation processes by providing funding, advanced training, and computing resources. The Innovation Lab is a state-of-the-art Cloud Computing infrastructure provided by the Facility to 30 research projects of African-European research tandems and participants of the capacity development activities of the Space Academy. The Innovation Lab creates new opportunities for innovative research to develop EO algorithms and applications adapted to African challenges and needs, through interactive Virtual Research Environments (VREs) with ready-to-use research and EO analysis software, and facilitated access to a wide range of analysis-ready EO datasets by leveraging the host DIAS infrastructure. The Innovation Lab is a cloud-based, user-friendly, and versatile Platform as a service (PaaS) that allows the users to develop, test, run, and optimize their research code making full use of the Copernicus DIAS infrastructure and a tailor-made interactive computing environment for geospatial analysis. Co-located data and computing services enable fast data exploitation and analysis, which in turn facilitates the utilization of multi-spectral spatiotemporal big data and machine learning methods. Each user has direct access to all online EO data available on the host DIAS (CreoDIAS), especially for Africa, and if required, can also request archived data, which is automatically retrieved and made available within a short delay. The Innovation Lab also supports user-provided in-situ data and allows access to EO data on the Cloud (e.g., other DIASes, CNES PEPS, Copernicus Hub, etc.) through a unified and easy-to-use and open-source data access API (EODAG). Because all data access and analysis are performed on the server-side, the platform does not require a fast Internet connection, and it is adapted for low bandwidth access to enable active collaboration of African – European research tandems. As a minimum configuration, each user has access to computing units with four virtual CPUs, 32 GB RAM, 100 GB local SSD storage, and 1 TB network storage. To a limited extent and for specific needs (e.g., AI applications like Deep Learning), GPU-enabled computing units are also provided. The user interface of the Innovation Lab allows the use of interactive Jupyter notebooks through the JupyterLab environment, which is served by a JupyterHub deployment with improved security and scalability features. For advanced research code development purposes, the Innovation Lab features a web-based VS Code integrated development environment, which provides specialized tools for programming in different languages, such as Python and R. Code analytics tools are also available for benchmarking, code profiling, and memory/performance monitoring. For specific EO workflows that require exploiting desktop applications (e.g., ESA SNAP, QGIS) for pre-processing, analysis, or visualization purposes, the Innovation Lab provides a web-based remote desktop with ready-to-use EO desktop applications. The users can also customize their working environment by using standard package managers. As endorsed by the European Commission Open Science approach, data and code sharing and versioning are crucial to allow reuse and reproduction of the algorithms, workflows, and results. In this context, the Innovation Lab has tools integrated into its interactive development environment that provide direct access to code repositories and allow easy version control. Although public code repositories (e.g., Github) are advised for better visibility, the Innovation Lab also includes a dedicated code repository to support the users' particular needs (e.g., storage of sensitive information). The assets (e.g., files, folders) stored on the platform can be easily accessed and shared externally through the FileBrowser tool. Besides providing a state-of-the-art computing infrastructure, the Innovation Lab also includes other necessary services to ensure a comfortable virtual research experience. All research projects granted by the EO AFRICA R&D Facility receive dedicated technical support for the Innovation Lab facilities. Scientific support and advice from senior researchers and experts for developing geospatial computing workflows are also provided. Users are able to request support contacting a helpdesk via a dedicated ticketing and chat system. After a 6-month development and testing period, the Innovation Lab became operational in September 2021. The first field testing of the platform took place in November 2021 during a 3-day hackathon jointly organized by EO AFRICA R&D, GMES & Africa, and CURAT as part of the AfricaGIS 2021 conference. Forty participants utilized the platform to develop innovative solutions to food security and water resources challenges, such as the impact of the COVID-19 pandemic on agricultural production or linking the decrease in agricultural production to armed conflicts. The activity was successful and similar ones are expected to be organized during major GIS and EO conferences in Africa during the lifetime of the project. Thirty research projects of African-European research tandems granted by the Facility will utilize the Innovation Lab to develop innovative and open-source EO algorithms and applications, preferably as interactive notebooks, adapted to African solutions to African challenges in food security and water scarcity by leveraging cutting-edge cloud-based data access and computing infrastructure. The call for the first 15 research projects was published in November 2021, and the projects are expected to start using the Innovation Lab in February 2022. In parallel, the Innovation Lab provides the computing environment for the capacity development activities of the EO AFRICA R&D Facility, which are organized under the umbrella of EO AFRICA Space Academy. These capacity development activities include several MOOCs, webinars, online and face-to-face courses designed and tailored to improve the knowledge and skills of African researchers in the utilization of Cloud Computing technology to work with EO data. Selected participants of the capacity development activities will use the Innovation Lab during their training. Moreover, the instructors in the Facility use the Innovation Lab to develop the training materials for the Space Academy. Access to the Innovation Lab will also be granted to individual researchers and EO experts depending on the use case and resource availability. Application for access can be made easily through the EO AFRICA R&D web portal after becoming a member of the EO AFRICA Community.This study is funded by ESA Contract No. 4000133905/21/I-EF