Remote sensing of inland waters: challenges, progress and future directions

Monitoring and understanding the physical, chemical and biological status of global inland waters are immensely important to scientists and policy makers alike. Whereas conventional monitoring approaches tend to be limited in terms of spatial coverage and temporal frequency, remote sensing has the potential to provide an invaluable complementary source of data at local to global scales. Furthermore, as sensors, methodologies, data availability and the network of researchers and engaged stakeholders in this field develop, increasingly widespread use of remote sensing for operational monitoring of inland waters can be envisaged. This special issue on Remote Sensing of Inland Waters comprises 16 articles on freshwater ecosystems around the world ranging from lakes and reservoirs to river systems using optical data from a range of in situ instruments as well as airborne and satellite platforms. The papers variably focus on the retrieval of in-water optical and biogeochemical parameters as well as information on the biophysical properties of shoreline and benthic vegetation. Methodological advances include refined approaches to adjacency correction, inversion-based retrieval models and in situ inherent optical property measurements in highly turbid waters. Remote sensing data are used to evaluate models and theories of environmental drivers of change in a number of different aquatic ecosystems. The range of contributions to the special issue highlights not only the sophistication of methods and the diversity of applications currently being developed, but also the growing international community active in this field. In this introductory paper we briefly highlight the progress that the community has made over recent decades as well as the challenges that remain. It is argued that the operational use of remote sensing for inland water monitoring is a realistic ambition if we can continue to build on these recent achievements.Output Type: Editoria

Report on the checklist for maintaining contact with individuals taking family breaks

The report compiles a checklist that will help to maintain contacts with the individuals taking family breaks and for employees returning to regular working conditions afterwards. Binding procedures will be recommended to guarantee a successful career continuation with continuous institutional support

Remote sensing of inland waters: Challenges, progress and future directions

Monitoring and understanding the physical, chemical and biological status of global inland waters are immensely important to scientists and policy makers alike.Whereas conventional monitoring approaches tend to be limited in terms of spatial coverage and temporal frequency, remote sensing has the potential to provide an invaluable complementary source of data at local to global scales. Furthermore, as sensors,methodologies, data availability and the network of researchers and engaged stakeholders in this field develop, increasingly widespread use of remote sensing for operational monitoring of inland waters can be envisaged. This special issue on Remote Sensing of Inland Waters comprises 16 articles on freshwater ecosystems around the world ranging from lakes and reservoirs to river systems using optical data from a range of in situ instruments as well as airborne and satellite platforms. The papers variably focus on the retrieval of in-water optical and biogeochemical parameters as well as information on the biophysical properties of shoreline and benthic vegetation.Methodological advances include refined approaches to adjacency correction, inversion-based retrieval models and in situ inherent optical property measurements in highly turbid waters. Remote sensing data are used to evaluate models and theories of environmental drivers of change in a number of different aquatic ecosystems. The range of contributions to the special issue highlights not only the sophistication of methods and the diversity of applications currently being developed, but also the growing international community active in this field. In this introductory paper we briefly highlight the progress that the community has made over recent decades as well as the challenges that remain. It is argued that the operational use of remote sensing for inland water monitoring is a realistic ambition if we can continue to build on these recent achievements

Spatial and temporal changes of primary production in a deep peri-alpine lake

Lake productivity is fundamental to biogeochemical budgets as well as estimating ecological state and predicting future development. Combining modelling with Earth Observation data facilitates a new perspective for studying lake primary production. In this study, primary production was modelled in the large Lake Geneva using the MEdium Resolution Imaging Spectrometer (MERIS) image archive for 2002–2012. We used a semi-empirical model that estimates primary production as a function of photosynthetically absorbed radiation and quantum yield of carbon fixation. The necessary input parameters of the model—concentration of chlorophyll a, downwelling irradiance, and the diffuse attenuation coefficient—were obtained from MERIS products. The primary production maps allow us to study decennial temporal (with daily frequency) and spatial changes in this lake that a single sample point cannot provide. Modelled estimates agreed with in situ results (R2 = 0.68) and showed a decreasing trend (∼27%) in production in Lake Geneva for the selected decade. Yet, in situ monitoring measurements missed the general increase of productivity near the incoming Rhône River. We show that the temporal and spatial resolution provided by satellite observations allows estimates of primary production at the basin-scale. The phytoplankton annual primary production was estimated as ∼302 (SD 20) g C m−2 yr−1 for Lake Geneva for 2003 to 2011. This study demonstrates that maps of primary production can be obtained even with reduced resolution (1200  m) MERIS data and relatively simple methods, and thereby calls for deeper integration of remote sensing products into conventional in situ observation approaches

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years

GALENE - Understanding coastal and inland ecosystem properties, processes and dynamics

Coastal and inland aquatic ecosystems are of fundamental interest for societal and economical purposes due to a significant part of the population living there. They both highly contribute to carbon cycling and biodiversity. Those ecosystems are continuously impacted by natural processes and human activities. Many of these impacts become more frequent and severe, particularly with increasing population and climate change. Hence, there is a need (i) to generate reliable, robust and timely evidence of how these environments are changing, (ii) to understand processes causing these changes and their societal, health, and economic consequences, and (iii) to identify steps towards conservation, restoration and sustainable use of water and dependent ecosystems, and resources. Systematic, high-quality and global observations, such as those provided by satellite remote sensing techniques, are key to understand complex aquatic systems. While multitudes of remote sensing missions have been specifically designed for studying ocean biology and biogeochemistry as well as for evaluating terrestrial environments, remote sensing missions dedicated to studying critical coastal and inland aquatic ecosystems at global scale are non-existent. Thus, these ecosystems remain among the most understudied habitats on the Earth’s surface. Specific reasons for such an observational gap lie in the dynamic and optical complexity of water ecosystems, in combination with technological challenges to optimize the relevant spatial, spectral, radiometric, and temporal characteristics. Current and forthcoming missions are either not suited to provide a global coverage (e.g., PRISMA, EnMAP) or to obtain reliable data over dark waters (e.g., carbon-rich lakes) due to inadequate radiometric sensitivity (e.g., Sentinel-2/MSI). They are also not adapted for characterizing the biodiversity patchiness of submerged habitats and water column compositions such as phytoplankton assemblages due to their inadequate spectral resolution (e.g., Sentinel-2/MSI, Sentinel-3/OLCI). Wetland ecosystems are insufficiently described as current sensors do not adequately capture their diversity, which compromises their management and protection. A future satellite mission, so-called Global Assessment of Limnological, Estuarine and Neritic Ecosystems (GALENE), has been proposed to the Earth Explorer 11 call (ESA) to respond to the future challenges linked to coastal and inland ecosystems. GALENE will provide optimized measurements of these aquatic ecosystems, and enable an adaptive sampling of dynamic properties and processes in water columns, benthic habitats and associated wetlands. GALENE will thus fill a major gap by comprehensively quantifying the state of Earth’s water bodies and aquatic ecosystems. It will substantially contribute addressing global water challenges, including water pollution and ensuring clean drinking water supply for all and protecting coastal environments and populations. GALENE mission concept consists of a synergy of three innovative instruments, namely a hyperspectral sensor, a panchromatic camera and a polarimeter. The GALENE science objectives and main technological features will be presented