Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review
Abstract
:1. Introduction
2. Methodological Approach
2.1. The Neccessity to Improve the Monitoring in the Framework of the WFD
2.2. Developing a thorough Picture of the Current State of Surface Water Monitoring EO Capacities
3. Exploring the Current Situation to Find the Key for the WFD Efficient Implementation
3.1. WFD Needs and Current Limitations
3.1.1. WFD Overview in the EU
3.1.2. Deeper Understanding of the WFD Requirements
Water Body Type | ||||||||
---|---|---|---|---|---|---|---|---|
Rivers | Lakes | Transitional | Coastal | |||||
WFD Key QE | SF | USL | SF | USL | SF | USL | SF | USL |
Chl-a (phyto.) | monthly/quarterly | 97% | monthly/quarterly | 54% | seasonal | 57% | 15 days | 27% |
Depth var. | annual | 50% | every 15 y. | 24% | every 5 y. | 68% | every 5/6 | 64% |
Oxygenation | fortnightly/monthly | 62% | daily/monthly | 85% | monthly | 47% | 15–30 d | 51% |
Acidification | fortnightly/monthly | 62% | monthly/quarterly | 72% | - | - | - | - |
Salinity | fortnightly/monthly | 86% | monthly/quarterly | 88% | monthly | 89% | 15–30 d | 95% |
Nutrients | fortnightly/monthly | 51% | monthly/quarterly | 36% | monthly | 42% | 15–30 d | 34% |
Transparency | - | monthly/quarterly | 83% | monthly | 83% | 15–30 d | 62% | |
Thermal | fortnightly/monthly | 74% | monthly/quarterly | 97% | monthly | 90% | 15–30 d | 93% |
3.1.3. WFD Current Gaps and Limitations
- The drafting of RBMPs during the COVID-19 pandemic has been challenging, and several EU member states are facing issues submitting their drafts on time.
- In some cases, the draft RBMPs anticipate that objectives will not be achieved before 2050 [22].
- According to the 2019 Fitness Check, slow implementation, a lack of funding, and a failure to integrate environmental objectives into sectoral policies are the main obstacles to preserving and restoring water bodies.
- Lack of budget allocation for RBMPs is a main constraint due to the failure to recover environmental and resource costs from strong economic sectors.
- According to Zingraff-Hamed et al. (2020) [28], the implementation problems are caused by a lack of horizontal cooperation and communication, not by a lack of adequate policy integration as suggested by earlier studies.
- Conflicts between the water policy and other key policies such as those in the agricultural domain, CAP, have arisen [24]. The ambitious WFD PoMs could not be completed unless the gap between these policies is bridged.
3.2. Overview of EO Capacities for Water Quality Monitoring and Limitations
3.2.1. Quality Elements (QEs) and Water Body Types
Biological QEs
Physico-Chemical QEs
Hydromorphological QEs
3.2.2. The Spaceborne Domain
Platform | Sensor/Equipment | Parameter (s) | WB | Ref. | |
---|---|---|---|---|---|
UAV | Quadcopter md4-1000 | Sony Alpha ILCE-5100 camera with a 24.3 MPix res. | Aquatic vegetation cover | Lake | [76] |
Multirotor G4 SkyCrane UAS | Headwall Photonics Nano-Hyperspec sensor, 270 spectral bands across a spectral range 400–1000 nm | Chl-a, Turbidity, Phycocyanin | Pond | [34] | |
DJI P4 Multispectral | Six 1/2.9” CMOS, including 1 RGB sensor for visible light imaging and 5 monochrome sensors for mult. imaging | Chl-a, TN, TP, COD | River | [39] | |
DJI Matrice 600 PRO® | Nano-Hyperspec®, 400–1000 nm with 272 spectral bands | TN | Lake | [41] | |
Tholeg THO-R-PX8 | Senop Oy ’Rikola’ Hyperspectral (HS) Camera, VNIR spectral range between 504 and 900 nm | pH | River | [77] | |
Multi-rotor UAV | Rededge-MX multi-spectral camera | Chl-a, TN, TP, NH3-N, Turbidity | River | [38] | |
Remo-M | Parrot Sequoia camera | Chl-a | River | [74] | |
SenseFly, Swinglet CAM model | Canon ELPH 110HS | TSS | Lake | [78] | |
S800 EVO Hexacopter | Canon EOS 5DS R and Headwall Nano-Hyperspec® 274 bands and 400–1000 nm | Reef monitoring | Sea | [79] | |
Custom hexacopter | Custom off-the-marker sensors | Temperature, EC, DO, pH | Pond | [80] | |
USV | Custom USV | Van Veen grab sampler | Sediment sampling | Lake | [75] |
Custom USV | AlgaeChek Ultra fluorometer | Chl-a | River | [81] | |
Data provided by Satria MGA, 2019 | Not specified-underwater camera | Seagrass | Coast | [32] | |
Custom USV | Vernier pH, oxidation, salinity, DO, flow rate sensor | DO, salinity, water flow, pH | River | [49] | |
Airborne | Vulcanair P68 TC Observer plane | Parrot Sequoia camera and Riegl VQ-1560i-DW LiDAR system | Bathymetry | River | [6] |
ASIA Aero Survey | ASIA Eagle-SPECIM, 400–970 nm | Chl-a, Phycocyanin | River | [33] |
3.2.3. Modeling, Processing, and Data Handling Digital Infrastructures
- The most used algorithms for this analysis come from the group of AI Neural Networks (NN), with 54% of studies utilizing them.
- The Random Forest (RF) algorithm follows with 46%, and Boosting algorithms and Support Vector Machines (SVM) are also high in the ranking with 25% and 21.4%, respectively.
- PLSR and DT are used less, due to the increasing interest in AI methods, such as LSTM which can be more effective at capturing the linear and non-linear correlations for water quality parameters estimation.
- The majority of studies utilizing AI algorithms focused on Chl-a (around 46%) and TN (14.28%).
Year | EO Resource | Parameter | Model | Water Body | Ref. |
---|---|---|---|---|---|
2022 | S-2 | TSM, Secchi depth | ANN | Re | [85] |
2022 | MODIS | Temperature | RF | S | [66] |
2022 | L-8 | MNDWI | CNN | L | [87] |
2022 | S-2, L-8/5, GAOFEN-2 | NDWI | RF | R, L | [57] |
2022 | L-7/8 | Chl-a, Transparency, TP | MLR | L | [40] |
2022 | UAV | Aquatic vegetation cover | ANN | L | [76] |
2022 | Spectra meas. fixed platform | Chl-a | BP, SVR, RFR | L, Re, R | [50] |
2022 | MODIS | DIN, DIP | DBN, MPNN, GRNN | C | [64] |
2022 | UAV | Chl-a, TN, TP, COD | LASSO, BP, RF, XGBOOST | R | [39] |
2021 | S-2 | Chl-a | MDN | L | [88] |
2021 | UAV | TN | RF, Bagging alg., XGBOOST | L | [41] |
2021 | S-1/2, L-5/8 | Macroalgal | ANN | E | [56] |
2021 | S-2 | Chl-a | OC3, SLR, MLR, GAMs | C | [89] |
2021 | UAV | pH | RF, SVM, SAM | R | [77] |
2021 | UAV | Chl-a, TP, TN, NH3-N, Turbidity | GA-BOOST, DNN, RF, GA-RF, AdaBOOST, GA_ADABOOST | R | [38] |
2021 | S-2 | Chl-a | RF, SVM, ANN, DNN | La | [86] |
2021 | S-2 | NDWI, NWI, EWI, AWE-nsh | RF, SVM, PLSR, PLSR-SVM | L | [90] |
2021 | S-3 | Chl-a | NN | S | [91] |
2020 | GAOFEN-1 | Surface water extent | RF, ADABoost, DT | R | [45] |
2020 | MODIS, S-2 | Chl-a | SVR, RFR, LSTM | C | [63] |
2020 | S-2, 3 | Chl-a | MDN | C, L | [92] |
2020 | S-2 | Chl-a, TSS | Cubist | R | [93] |
2020 | L-5/7/8, S-2 | Chl-a | SVM | L | [94] |
2019 | L-8, S-2 | Turbidity, TSS, TP, TN | NN, SVM | R | [7] |
2019 | UAV | TSS | ANN | L | [78] |
2019 | Airborne flight | Phycocyanin, Chl-a | CNN | R | [33] |
2018 | UAV | Reef monitoring | SVM | S | [79] |
2018 | S-1, L-8 | Surface water extent | RF | C | [44] |
3.2.4. Project Initiatives—Lessons Learned and Impact
3.2.5. Current EO Dynamics and Limitations
Spaceborne Dynamics and Limitations
- Bottom influence
- Shoreline effect
- Dark waters
- Spatial and spectral resolution
- Atmospheric correction
In Situ Moving Platforms Dynamics and Limitations
The General Outlook—Synergistic Use of EO Driven Techniques
4. What Is the Key for the Efficient Implementation of WFD?
4.1. The Upcoming EO Contribution
4.2. The Citizen Science and IoT Contribution
5. Conclusions
- (i).
- better monitoring frequencies of surface water bodies,
- (ii).
- a significant increase of the number of monitored water QEs per water body type,
- (iii).
- a minimization of the percentage of the water bodies with unknown ecological status,
- (iv).
- a decrease in the overall monitoring cost supporting indirectly the PoMs implementation, and
- (v).
- the RBMPS from all the Members states being delivered on time.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
ANN | Artificial Neural Networks |
API | Application Programming Interfaces |
BP | Back Propagation |
CAP | Common Agricultural Policy |
CAVIS | Computer Aided Verification of Information Systems |
CDOM | Colored Dissolved Organic Matter |
CEOS | Committee on Earth Observation Satellites |
CGLS | Copernicus Global Land Service |
CHIME | Copernicus Hyperspectral Imaging Mission |
Chl-a | Chlorophyll-a |
CMEMS | Copernicus Marine Environment Monitoring Service |
CNN | Convolutional Neural Network |
COD | Chemical Oxygen Demand |
DC | Data Cube |
DE | Digital Earth |
DIN | Dissolved Inorganic Nitrogen |
DIP | Dissolved Inorganic Phosphate |
DNN | Deep Neural Network |
DO | Dissolved Oxygen |
DT | Decision Tree |
EC | Electrical Conductivity |
EnMAP | Environmental Mapping and Analysis Program |
EO | Earth Observation |
ESA | European Space Agency |
EU | European Union |
EWI | Enhanced Water Index |
GA | Genetic Algorithm |
GAMs | General Algebraic Modeling |
GEE | Google Earth Engine |
GEO | Group on Earth Observation |
GRNN | Generalized Regression Neural Network |
ICT | Information and Communication Technology |
IoT | Internet of Things |
LASSO | Least Absolute Shrinkage and Selection Operator |
LSTM | Long Short-Term Memory |
MDN | Mixture Density Network |
MNDWI | Modified Normalized Difference Water Index |
NASA | National Aeronautics and Space Administration |
NDWI | Normalized Difference Water Index |
NN | Neural Network |
ODC | Open Data Cube |
OOAO | One Out All Out |
PAR | Photosynthetically Active Radiation |
PoMs | Program of Measures |
PRISMA | Precursore Iperspettrale della Missione Applicativa |
QE | Quality Element |
RBD | River Basin District |
RBMP | River Basin Management Plan |
RF | Random Forest |
RFR | Random Forest Regression |
SAM | Spectral Angle Mapper |
SAR | Synthetic Aperture Radar |
SAV | Submerged Aquatic Vegetation |
SDG | Sustainable Development Goal |
SPM | Suspended Particular Matter |
SPOM | Suspended Particulate Organic Matter |
SVM | Support Vector Machine |
TN | Total Nitrogen |
TP | Total Phosphorous |
TSS | Total Suspended Solids |
UAV | Unmanned Aerial Vehicle |
USV | Unmanned Surface Vehicle |
WFD | Water Framework Directive |
Wofs | Water Observations from Space |
Appendix A
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Initiative | Description | Sources | Water Related Services | Powered by | Access Link (Accessed on 15 June 2022) | Ref. |
---|---|---|---|---|---|---|
DE Australia | Embed satellite imagery and data into decisions that support a sustainable Australian environment. | S-2, L-8 | Water availability (WOfS), | ODC | https://www.dea.ga.gov.au/ | [103] |
DE Africa | Apply EO data to address local and national needs as well the objectives of the GEO and the 2030 SDGs Agenda. | L-5/7/8/9, S-1/2 | Coastline erosion, WOfS, | ODC | https://www.digitalearthafrica.org/ | [104] |
Swiss DC | Support the Swiss government in environmental monitoring and reporting. | L-5/7/8, S-1/2 | Snow cover evolution | ODC | https://www.swissdatacube.org/ | [105] |
Virginia’s ODC | Helping to solve Virginian’s environmental and social challenges through the use of satellite data. | Landsat’s | Water quality & supply, coastal resiliency | ODC | https://www.data4va.org/ | - |
Vietnam DC | Address the needs of satellite data users, giving them a better picture of their land resources and land change. | S-1/2, L, SPOT, VNREDSat 1, ALOS | Water management | ODC | https://vnsc.org.vn/en/ | [106] |
Armenia DC | A game-changing technology for remote sensing EO and national-level data visualization. | L-5/7/8, S-1 | Water detection, water quality, coastal change. | ODC | http://datacube.sci.am/ | [107] |
Catalonia DC | A DC solution useful for small natural regions such as the Catalonia sub-national region. | S-2 | NDWI, water vapor, | ODC | http://datacube.uab.cat/ | [108] |
Mexican Geospatial DC | Combination of in situ with EO data to produce an ever-expanding range of derived, decision -ready products. | L-4/5/6/7/8 | MNDW, WOfS | ODC | http://en.www.inegi.org.mx/default.html | [109] |
EODataBee | Generation of high-quality information for value-adding industry in the coastal and inland water market. | S-2 | Chl-a, PAR, Turbidity, SPM, Temperature | xCube | https://eodatabee.eu/ | - |
Project | Objective | Study Area | Parameters | Sources | |
---|---|---|---|---|---|
Spaceborne | In-Situ | ||||
MONOCLE (H2020) | Improve sensors on autonomous platforms and incorporate EO to fill the gaps of the gathered in-situ information | C, L, and E | Chl-a, Turbidity, SPM, Temperature, Transparency | S-1/2/3, Suomi-NPP VIIRS, MODIS | Buoys, ships, drones, hyperspectral radiometer, WISPstation, mini-Secchi disk, smartphones |
EOMORES (H2020) | Integrate cutting-edge, optical in situ instrument with free and open satellite data and sophisticated numerical models. | R, L, and C | Chl-a, TSS, Turbidity, Transparency, CDOM, Cyanobacteria biomass, Temperature | S-2/3, L-8, MODIS, MERIS, VHR | In situ point measurements (WISPstation) |
CoastObs (H2020) | Develop a service platform for coastal water monitoring with validated products derived from EO | C | Algal blooms, chl-a, SPM, TSS, Temperature, SPM, turbidity, seagrass, phytoplankton- macrophytes, | S-2, L-8 | - |
INTCATCH (H2020) | Bring the lab on the field through the development and application of Novel, integrated Tools for monitoring and management Catchments | L, R, E, and Re | Microbiological analyses, Metagenomics analysis, E. coli, Heavy metals | - | Eco-innovative autonomous and radio-controlled boats, sensors, DNA test kits and run-off treatment technologies |
Aqua3s (H2020) | Combined novel technologies in water safety and security, aiming to standardize existing sensor technologies complemented by state-of-the-art detection mechanisms. | R, Re | Area classification, Oil spill detection, flood delineation | S-1/2 | UAVs, social media observations from citizens |
SWOS (H2020) | User friendly wetland monitoring and information service by taking full advantage of satellite imagery data | Wetlands | - | S-1/2, L-8 | In situ databases, results from the ESA Globwetland projects. |
EUGENIUS (H2020) | Develop viable market based EO services by involving the end users in the whole life cycle of the project. | Marine env. | Chl-a, TSS, Turbidity, transparency, Temperature | L-7/8, S-2, SPOT, Pleiades, WV, QB | - |
DCS4COP (H2020) | Addresses the downstream challenges of big data integrating Copernicus services | L, R, and C | Chl-a, PAR, Turbidity, SPM, Temperature | S-1/2/3 | - |
AquaNEX (Interreg) | Develop and test integrated solutions for the most effective monitoring of the aquatic and terrestrial ecosystem | R, L | Chl-a, NO3-N, sediments, pH, transparency, bathymetry | S-2, L-8 | Autonomous Water Telemetry Sensing System (AWTSS-1), UAV |
AG_UAS (Life+) | Develop a cost-effective, spatial tool for more efficient, sustainable, water monitoring and management, to bridge the gap between traditional satellite remote sensing and airborne remote sensing | L and R | Detection of discharges into river basins, monitoring of the ecological and chemical status of surface waters, | - | UAV with thermal infrared and multispectral camera |
● Highly Suited | EARTH OBSERVATION RESOURCES | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
● Suitable | IN SITU RESOURCES | SPACEBORNE RESOURCES | |||||||||
Moving Platforms | Satellite Sensor System | ||||||||||
Quality Elements | USV | UAV | S-1 | S-2 | S-3 | L-8 | RE | PR | WV-3 | MD | ME |
Biological | |||||||||||
Chl-a | ● | ● | ● | ● | ● | ● | ● | ● | ● | ● | ● |
Seagrass | ● | ● | ● | ● | |||||||
SAV | ● | ● | ● | ● | |||||||
Physico-chemical | |||||||||||
Turbidity | ● | ● | ● | ● | ● | ● | ● | ||||
Secchi depth | ● | ● | ● | ● | ● | ● | ● | ||||
TSS | ● | ● | ● | ● | ● | ● | ● | ● | |||
TP | ● | ● | ● | ● | ● | ● | |||||
TN | ● | ● | ● | ● | ● | ● | |||||
CDOM | ● | ● | ● | ● | ● | ● | |||||
pH | ● | ● | ● | ● | |||||||
Temperature | ● | ● | ● | ● | ● | ● | ● | ||||
DO | ● | ● | ● | ● | ● | ● | |||||
Hydro/gical | |||||||||||
Surf. Water Ext. | ● | ● | ● | ● | ● | ● | ● | ||||
Bathymetry | ● | ● | ● | ● | ● | ● | ● | ● |
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Samarinas, N.; Spiliotopoulos, M.; Tziolas, N.; Loukas, A. Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review. Remote Sens. 2023, 15, 1983. https://doi.org/10.3390/rs15081983
Samarinas N, Spiliotopoulos M, Tziolas N, Loukas A. Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review. Remote Sensing. 2023; 15(8):1983. https://doi.org/10.3390/rs15081983
Chicago/Turabian StyleSamarinas, Nikiforos, Marios Spiliotopoulos, Nikolaos Tziolas, and Athanasios Loukas. 2023. "Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review" Remote Sensing 15, no. 8: 1983. https://doi.org/10.3390/rs15081983
APA StyleSamarinas, N., Spiliotopoulos, M., Tziolas, N., & Loukas, A. (2023). Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review. Remote Sensing, 15(8), 1983. https://doi.org/10.3390/rs15081983