The interactions between landscape structure, land use intensity (LUI), climate change, and ecological processes significantly impact hydrological processes, affecting water quality. Monitoring these factors is crucial for understanding their influence on water quality. Remote sensing (RS) provides a continuous, standardized approach to capture landscape structures, LUI, and landscape changes over long-term time series. In this study, RS-based indicators from Landsat data (2018–2021) were used to assess landscape structure, LUI, and land use change for a study area in northern Germany, applying the ESIS/Imalys tool. These indicators were then used to model and predict water quality (Chla) in 119 standing waters. Various machine learning methods, including Generalised Linear Models, Support Vector Machines, Deep Learning, Decision Trees, Random Forest, and Gradient Boosted Trees, were tested. The Random Forest model performed best, with a correlation of 0.744 ± 0.11. Indicators related to landscape structure, such as diversity_mean (0.376) and relation_mean (0.292), had the highest global correlation weights, while LUI and land use change indicators like NirV2_mean (0.369) and NirV_regme (0.284) were also significant. All indicators and their effects on water quality (Chla) are discussed in detail. The study highlights the potential of the ESIS/Imalys tool for quantifying landscape structure, LUI, and land use change with RS to model and predict water quality and suggests directions for future model improvements by incorporating additional influencing factors.

This study reports the outcomes of the third Atmospheric Correction Intercomparison Exercise (ACIX-III Aqua), which evaluated the performance of atmospheric correction (AC) methods for hyperspectral PRISMA satellite data over inland and coastal waters. The exercise included five AC processors (ACOLITE, hGRS, iCOR, MIP, and POLYMER), the standard PRISMA Level 2C product, and an adjacency correction tool (T-Mart) tested with ACOLITE. A total of 239 cloud-free PRISMA scenes from 2019 to 2024 were compared with in situ data of remote sensing reflectance, gathered from both hyperspectral and multispectral radiometers across eight distinct optical water types (OWTs). The accuracy of each AC method varied with spectral band, but all showed largest and lowest discrepancies with in situ data at 443 nm and 560 nm, respectively. All AC methods showed the best agreement with in situ data in greenish waters (OWT 4b) and highest uncertainties were yielded in humic-rich waters (OWT 7). Consistently with the previous ACIX-Aqua study focused on multispectral data, no single AC method outperformed the others across all OWTs. The study confirmed the ongoing challenges of AC over optically complex waters, yet the exercise allowed the community to advance in developing AC methods for hyperspectral satellite images and supporting the development of future operational hyperspectral missions, such as PRISMA Second Generation (PRISMA 2G) and CHIME.

Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies.

Achieving good ambient water quality for rivers, lakes and groundwater is anchored in the Sustainable Development Goals (SDGs). Poor water quality has considerable impacts on ecosystem integrity, human health, and food security. Information on the state of water quality is the basis for decision-making on pollution reduction measures.To date, water quality information has mostly relied on data from on-site sampling and, increasingly, sensor-based monitoring stations. Despite the increasing amount of in-situ data and growing efforts to make these data easily accessible, spatial coverage and temporal consistency are not sufficient to provide comprehensive water quality information worldwide. In-situ data are particularly missing in low-income countries and regions known for their lack of data sharing policy . Therefore, it is necessary to tap into additional methods to obtain water quality information worldwide.Data from satellites can provide information on optical water quality parameters such as turbidity and chlorophyll. Water quality models integrate observational data and build on the relationships between the state of water quality and its drivers such as agricultural practices and/or the discharge of untreated municipal wastewater. Models provide spatially and temporally consistent information and are the only tool that allows forecasts and projection of possible future water quality scenarios.Combining information from these three sources (in situ data, satellite data, modeled data) helps to overcome specific limitations of each data source; and provides complementary information on the state of water quality parameters. We present the outcome of the GlobeWQ project (www.globewq.info) that has developed a prototype of a web-based platform that provides access to global and regional water quality information. The platform combines data from in-situ observations, satellite-based remote sensing, and water quality modeling to provide robust and timely water quality information. GlobeWQ provides global water quality information based on the WorldQual model, data-driven approaches and by incorporating in-situ data from the GEMStat water quality database (https://gemstat.org). At European scale the long-term nitrogen surplus has been reconstructed for more than a century (1850–2019) to assist modeling of nitrogen exports in European river catchments. Regional case studies have been established in a co-design process so that the data products are tailored to the needs of the regional users.We demonstrate the capability of the “ triangulation” approach that combines the best available information from in-situ data , remote sensing and water quality modeling to improve the availability of water quality for the regional case studies (e.g.: Lake Victoria, Lake Sevan, Elbe River Basin). At the global scale, water quality modeling results are used to provide spatially and temporally resolved and consistent water quality information.

Satellite-Derived Bathymetry (SDB) methods have found their way into the hydrographers’ toolbox and are part of integrated survey concepts, nautical charts and support global and European programs such as Seabed2030 or EMODnet Bathymetry. The concept of the ‘physics-based’ SDB describes the calculation of bathymetry by modelling the sunlight path from the sun to the seafloor to the satellite sensor. It is a highly sophisticated model which enables the calculation of shallow water depth in the absence of any other survey or ground-truth data. Thus, bathymetric data can also be retrieved for remote and inaccessible areas - in contrast to empirical SDB approaches. Key questions which arise for SDB results are vertical accuracy, potential and feasibility for different sites and the methods to upscale SDB solutions. These questions are addressed in the current European innovation project 4S. Within the project SDB-Online was developed, a fully physics-based SDB concept which is installed in a cloud and accessible via a web user interface. The backend is parallelised and can be accessed via application programming interface (API) which allows a fully scalable and automatic SDB processing. In this study SDB-Online results are validated at ten sites, ranging from the higher latitudes of Canada to turbid UK waters to the Caribbean. Furthermore, a relationship between the Secchi Disc Depth and the cutoff depth of the SDB results is established and a global map of water-clarity potential of the SDB solution is presented.

Satellite remote sensing provides valuable data for understanding spatial and temporal variability of water quality parameters. In particular, PRISMA and DESIS are showing increasing capabilities in water quality mapping even if further studies might be needed to fully exploit these relatively new data. In this context, high-resolution airborne hyperspectral sensors might support preliminary tests to evaluate satellite-based systems, while represent enhanced mapping tools. In the context of the H2020 PrimeWater project, this study presents the use of HySpex hyperspectral airborne data of the freshwater reservoir Mulargia (Italy) to: i) calibrate and validate algorithms to retrieve optically active parameters; ii) assess the feasibility of retrieve water quality parameters; iii) export of this knowledge on PRISMA and DESIS data. The Hyspex images were acquired on 24 September 2020 in coincidence with in situ data, both radiometric and water quality. HySpex data were geocoded with PARGE and atmospherically corrected with ATCOR code and the results showed a good consistency between water reflectance with in situ data. The retrieval of the optical properties from imagery was achieved with a spectral inversion of bio-optical modelling (namely BOMBER), parametrized with absorption and backscattering data of the site. The estimates matched well field data (r(2)> 0.8) and depicted the mesotrophic conditions of the reservoir with higher Total Suspended Matter in shallow coastal waters.

The EC HYPOS (HYdro-POwer-Suite) project (https://hypos-project.eu/) has the main goal of assessing the environmental impact of existing and future hydropower systems. The project will provide a suite of data analysis applications which integrates Earth Observation (EO) technologies and hydrological modelling. These include an online Decision Support Tool (DST) for investment planning and monitoring, as well as a subscription portal combining satellite data over time, current measurements and detailed estimates for present and near future assessments. A dedicated analysis on the “blue footprint” (i.e. the amount of water used to produce a service) of reservoirs is included for addressing sustainable monitoring solutions. Such analysis comprises the evaluation of the climate change effects on reservoirs management and hydropower production. For instance, extreme weather events like short-term heavy precipitations are connected with flooding and transport of large amounts of sediments in dammed reservoirs, with critical consequences for their management. Similarly, global warming can heat the surface of water bodies and induce higher evaporation rates, thus decreasing the amount of water available for energy production. In this study we present the first products from HYPOS project. These products are representative of what can be generated within the DST using elaboration techniques of EO data. Gridded products of water quality parameters (e.g. water turbidity, Chlorophyll-a concentration, suspended sediments concentration) are generated for the test sites of the project, which are small dammed reservoirs located in Switzerland, France, Albania and Georgia. These products are obtained using the Modular Inversion and Processing System (MIP), a sensor independent image processing chain based on radiative transfer models, which works in a multi-layer system, solving the light transfer in the atmosphere, at the water surface and inside the waterbody. For the assessment of the “blue footprint” of a reservoir, the water loss due to evaporation is computed by applying a consolidated mass transfer evaporation method to EO data. The resulting evaporation rates are first compared with the outputs of semi-automatic evapotranspiration EO-based models (e.g. SEBAL), and then with the estimates ​​obtained from two different numerical models: a hydrological model (E-Hype) and a 3D hydrodynamic model (Delft3D). The key parameters influencing water evaporation rates, their behavior and the issues related to each approach are analyzed. The first comparison results are made for lake Garda, where a complete set of data is available for the production of evaporation maps.

This dataset contains the surface reflectance Hyspex images derived with ATCOR code by CNR of Lake Mulargia (Sardinia, Italy). The acquisition was done by CGR Spa (Italy).

BOOK GeoHab Nodal Conference of Europe and Africa (GNCE&A)

Smart monitoring, planning and decision making for projects in the coastal and nearshore regions requires spatial and temporal understanding on the environmental parameters. Very often time and budget constraints preclude a comprehensive physical and environmental survey data collection exercise. Bathymetry, for example, is typically valid for one timestamp (during the period of data collection), one-dimensional (e.g. single beam surveys), and has sparse resolution in the shallow nearshore regions. In recent years, significant advances in satellite sensor technology and analysis have been developed to produce relevant information for coastal and nearshore monitoring applications at a fraction of both the time and cost of traditional methods. Aquatic Earth Observation techniques have been evolving since the 1970s. The recent advances on satellite sensor hardware and analytics have allowed the once crude methodology to be efficiently applied into practice-in particular very high-resolution satellite data availability and the sound understanding on the physical modelling of the light path from the surface/seafloor to the sensor. Applying over 20 years of continuous research and development, EOMAP has advanced a unique physics-based procedure which allows mapping of shallow water bathymetry, water quality parameters, seafloor characteristics and topography in dense spatial grids. Uncertainties in Earth Observation products are subject to a number of environmental factors that need to be accounted for. At the core of the technology are state-of-the-art algorithms for extracting quantitative environmental information from the aquatic remote sensing signal. Mechanisms for quantifying uncertainties and flagging relative reliabilities are embedded in the algorithms, which include: (1) allowance for coupled atmospheric and in-water parameter retrievals, which includes a correction of the (terrestrial) adjacency effect, critical for the accurate remote sensing of any coastal or inland water body, (2) a physically accurate implementation of the bi-directional effect inside the water column, at the water surface and in the atmosphere, (3) accounting for the full range of reflecting, absorbing and scattering properties of the water body and the interfaces. Those procedures are included in EOMAP’s Watcor-X physics-based Satellite-Derived Bathymetry (SDB) software. This paper provides an overview of SDB, demonstrates successful project applications, and describe tools that support coastal and nearshore monitoring projects through the use of the software in the Pacific, Caribbean Sea and Arabian waters. We showcase the capability to monitor spatial seabed changes in highly dynamic environments, and demonstrate the latest technology that jointly incorporates the passive multispectral satellite imagery with complementary active Satellite-Lidar bathymetric data technology using NASA’s ICESAT-2 Advanced Topographic Laser Altimeter System (ATLAS) sensor.

Freshwater as one of the most relevant resources for life is facing increasing human made pressures. Suitable information about the status of the water quality in lakes and rivers is sparse, although required for environmental assessments and impact monitoring: There is a vast demand on actual data in many countries, where water policies and management decisions are based on scarce and unreliable information. Satellite data with newest data analytics technologies can already contribute to this today with regular mapping and monitoring in freshwater systems: Consistent information of valuable water quality products are derived for single applications in small lakes, covering extended river basins or the whole world, as provided by the UNESCO World Water Quality Portal. A number of examples from this first global water quality portal is discussed, addressing ecological and economic issues in Africa. At the conceptual level, UNESCO and EOMAP advocate the long-term consistency of the data of these new measurement capabilities: Both satellite sensing and data processing technologies are rapidly evolving. Hence, nowadays concepts should already ensure that the data products are globally intercomparable and in future, even if the accuracy of information products become better and better. This ensures that the sustainable development goals can be supported with meaningful, comparable indicators over time.

Providing relatively fine spatial resolution multispectral data, Landsat-8, Landsat-7 (L8 and L7, respectively) and Sentinel-2 (S2) from 2013 to 2018 have been used in this study for enabling high-frequency monitoring of water quality of two small (the smaller with an area of 1.6 km2) freshwater dammed reservoirs. Located in Sardinia (Italy) and Crete (Greek), respectively, Mulargia and Aposelemis represent vital resources to supply drinking water in downstream valleys. A total of 400 cloud-free satellite images were turned into information on water quality by using an image processing chain implementing physically based methods for retrieving chlorophyll-a concentration (Chl-a), turbidity, Secchi disk depth (SDD) and surface water temperature. These estimates have been successfully validated (the lower Pearson correlation r was 0.88 for Chl-a) with 23 match-ups of in situ and satellite data. Results of the multi-temporal analyses showed a decrease of SDD due to the increase of Chl-a in Aposelemis or an increase of turbidity in Mulargia. For both freshwater reservoirs, the satellite-derived trophic state index assigned both lakes to mesotrophic conditions. The results finally suggested the effectiveness of S2 and Landsat in increasing, for the latest investigated years, the frequency of observations.

Satellites that capture large areas with high spatial and temporal resolution allow extensive analyses of water bodies and thus represent an ideal supplement to existing in situ point measurements. In the joint project WasMon-CT (Water Monitoring of Chlorophyll and Turbidity) the usability of satellite data for official monitoring of flowing waters and lakes was examined. The subproject at the Institute for Lake Research of the LUBW focused on satellite-based monitoring of chlorophyll a, an important indicator for water quality, in lakes. Freely available data from spatially reasonable high-resolution satellites, e.g. Sentinel-2, open up new possibilities for monitoring the water quality of a larger number of small lakes. The aim of the comprehensive validation study presented here was to get information about applicability and potential limitations of remote sensing techniques for different types of lakes. EOMAP processed the satellite data used in the validation (Sentinel-2/3, Landsat 7/8 and MODIS) by applying its Modular Inversion and Processing System MIP. Results extracted from satellite data between 2000 and 2017 were compared with in situ measurement data of about 20 lakes in Baden-Wuerttemberg, including Lake Constance, for water quality parameters such as chlorophyll a and Secchi depth. First results of the validation study show that in general the statistical values, e.g. annual mean values of in situ and remote sensing retrieved chlorophyll a and Secchi depth data, agree well, but some systematic differences occur. Further validation and data interpretation steps take into account methodical differences as well as time differences between in situ and satellite measurements.

This article presents and analyses the modular architecture and capabilities of CODE-DE (Copernicus Data and Exploitation Platform – Deutschland, www.code-de.org), the integrated German operational environment for accessing and processing Copernicus data and products, as well as the methodology to establish and operate the system. Since March 2017, CODE-DE has been online with access to Sentinel-1 and Sentinel-2 data, to Sentinel-3 data shortly after this time, and since March 2019 with access to Sentinel-5P data. These products are available and accessed by 1,682 registered users as of March 2019. During this period 654,895 products were downloaded and a global catalogue was continuously updated, featuring a data volume of 814 TByte based on a rolling archive concept supported by a reload mechanism from a long-term archive. Since November 2017, the element for big data processing has been operational, where registered users can process and analyse data themselves specifically assisted by methods for value-added product generation. Utilizing 195,467 core and 696,406 memory hours, 982,948 products of different applications were fully automatically generated in the cloud environment and made available as of March 2019. Special features include an improved visualization of available Sentinel-2 products, which are presented within the catalogue client at full 10 m resolution.

Phytoplankton indicated by its photosynthetic pigment chlorophyll-a is an important pointer on lake ecology and a regularly monitored parameter within the European Water Framework Directive. Along with eutrophication and global warming cyanobacteria gain increasing importance concerning human health aspects. Optical remote sensing may support both the monitoring of horizontal distribution of phytoplankton and cyanobacteria at the lake surface and the reduction of spatial uncertainties associated with limited water sample analyses. Temporal and spatial resolution of using only one satellite sensor, however, may constrain its information value. To discuss the advantages of a multi-sensor approach the sensor-independent, physically based model MIP (Modular Inversion and Processing System) was applied at Lake Kummerow, Germany, and lake surface chlorophyll-a was derived from 33 images of five different sensors (MODIS-Terra, MODIS-Aqua, Landsat 8, Landsat 7 and Sentinel-2A). Remotely sensed lake average chlorophyll-a concentration showed a reasonable development and varied between 2.3±0.4 and 35.8±2.0mg·m−3 from July to October 2015. Match-ups between in situ and satellite chlorophyll-a revealed varying performances of Landsat 8 (RMSE: 3.6 and 19.7mg·m−3), Landsat 7 (RMSE: 6.2mg·m−3), Sentinel-2A (RMSE: 5.1mg·m−3) and MODIS (RMSE: 12.8mg·m−3), whereas an in situ data uncertainty of 48% needs to be respected. The temporal development of an index on harmful algal blooms corresponded well with the cyanobacteria biomass development during summer months. Satellite chlorophyll-a maps allowed to follow spatial patterns of chlorophyll-a distribution during a phytoplankton bloom event. Wind conditions mainly explained spatial patterns. Integrating satellite chlorophyll-a into trophic state assessment resulted in different trophic classes. Our study endorsed a combined use of satellite and in situ chlorophyll-a data to alleviate weaknesses of both approaches and to better characterise and understand phytoplankton development in lakes.

The objective of tins article is to provide an overview of the Satellite Derived Bathymetry methods, how data can be integrated into survey campaigns and finally to showcase thine use cases. Bathymetric data in the shallow water zone is of increasing importance to support various applications such as safety of navigation, reconnaissance surveys, coastal zone management or hydrodynamic: modelling. A gap was identified between data demand, costs and the ability to map with ship and airborne sensors, This has led to the rise of a new tool to map shallow water bathymetry using multispectral satellite image data, widen! known as Satellite Derived Bathymetry (SUB).

The presented adjacency correction algorithm is based on the use of the point spread function (PSF) which allows calculating the contribution of reflections from the nearby pixels to the apparent radiance of the target. The analytical expression of the PSF for an arbitrary stratified atmosphere is obtained in the approximation of primary scattering, whereas the full equation of radiative transfer is used for the estimation of the radiance reflected from the surface. The algorithm is sensor independent and can be applied for processing images of water basins with arbitrary shape of the shore line and under different geometries of observation. The program using this algorithm is included in Modular Inversion Program — MIP (Heege et al., 2014) for processing of satellite images on a routine basis. Examples of processing results are presented in the paper.