Modelling of Greek Lakes Water Quality Using Earth Observation in the Framework of the Water Framework Directive (WFD)

Abstract

Given the great importance of lakes in Earth’s environment and human life, continuous water quality (WQ) monitoring within the frame of the Water Framework Directive (WFD) is the most crucial aspect for lake management. In this study, Earth Observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) sensors have been combined with co-orbital in situ measurements from 50 lakes located in Greece with the main objective of delivering robust WQ assessment models. Correlation analysis among in situ co-orbital WQ data (Chlorophylla, Secchi depths, Total phosphorus-TP-) contributed to distinguishing their inter-relationships and improving the WQ models’ accuracy. Subsequently, stepwise multiple regression analysis (MLR) of the available TP and Secchi depth datasets was implemented to explore the potential to establish optimal quantitative models regardless of lake characteristics. Then, further MLR analysis concerning whether the lakes are natural or artificial was conducted with the basic aim of generating different remote sensing derived models for different types of lakes, while their combination was further utilized to assess their trophic status. Correlation matrix results showed a high and positive relationship between TP and Chlorophyll-a (0.85), whereas high negative relationships were found between Secchi depth with TP (−0.84) and Chlorophyll-a (−0.83). MLRs among Landsat data and Secchi depths resulted in 3 optimal models concerning the assessment of Secchi depth of all lakes (Secchigeneral; R = 0.78; RMSE = 0.24 m), natural (Secchinatural; R = 0.95; RMSE = 0.14 m) and artificial (Secchiartificial; R = 0.62; RMSE = 0.1 m), with reliable accuracy. Study findings showed that TP-related MLR analyses failed to deliver a statistically acceptable model for the reservoirs; nevertheless, they delivered a robust TPgeneral (R = 0.71; RMSE = 1.41 mg/L) and TPnatural model (R = 0.93; RMSE = 1.43 mg/L). Subsequently, trophic status classification was conducted herein, calculating Carlson’s Trophic State Index (TSI) initially throughout all lakes and then oriented toward natural-only and artificial-only lakes. Those three types of TSI (general, natural, artificial) were calculated based on previously published satellite-derived Chlorophyll-a (Chl-a) assessment models and the hereby specially designed WQ models (Secchi depth, TP). The higher deviation of satellite-derived TSI values in relation to in situ ones was detected in reservoirs and shallower lakes (mean depth < 5 m), indicating noticeable divergences among natural and artificial lakes. All in all, the study findings provide important support toward the perpetual WQ monitoring and trophic status prediction of Greek lakes and, by extension, their sustainable management, particularly in cases when ground truth data is limited.

Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review

“…Interesting findings resulted for the TP (Total Phosphorous) and TN (Total Nitrogen) where five studies [7,[37][38][39][40] from the total number of 71 examined the TP while four studies [7,38,39,41] the TN. The impressive thing is that for both QEs, the UAVs' contribution was noteworthy reporting 40% for TP [38,39] and 60% for TN [38,39,41].…”

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

“…These compounds are referred to as non-optically-active compounds (nOACs). To address this, researchers have focused on modeling nOACs, such as phosphorous pollutants (PP) [39,51,158,170,173,178,207], nitrogen pollutants (NP) [170,172,178,207,225], COD [172,205], pH [175,184], and DO [22,29,65,182,205,218], using machine learning models based on satellite data. In our study, we compared the median R 2 values of optically active compounds (OACs), such as Chl-a, turbidity, SS, and SDD, with those of nOACs, including DO, PP, NP, BOD [205], and COD.…”

“…Therefore, it is important to note that the distribution of water quality classes varies across different geo-locations. Researchers have investigated the water quality in inland water study areas dominated by freshwater bodies, such as rivers, lakes, and reservoirs [22,23,[25][26][27][29][30][31]34,36,39,44,45,51,65,75,157,[163][164][165][166][169][170][171][172][173][174][175][177][178][179][180][181][182][183][184]186,188,191,[193][194][195][196][197][198][204][205][206]…”

Data‐driven predictions of summertime visits to lakes across 17 US states

“…Of all the lake water quality measures or proxies for water quality included in LAGOS‐NE‐LIMNO, Secchi depth is the most prevalently recorded measure across the 51,107 lakes (Figure 4). The greater a lake's Secchi depth (a lake clarity measure), the greater the lake water's actual and perceived quality (Angradi et al, 2018; Markogianni et al, 2022; Parsons & Kealy, 1992). In this study, we used the mean of Secchi measurements taken from June 15 to September 15 in the years 1995–2013 as a representative measure of a lake's water quality during the summers of 2005 through 2014 (we also used the average of summer Secchi measurements from the years 2005 to 2013 as a representative measure of a lake's quality during the summers of 2005 through 2014 in an alternative estimate of Y = f ( Z )).…”