Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery
<p>On the left, a true color composition image as acquired by DESIS on 4 August 2019 showing the position of the WISPStation (43.122, 12.134—red box); on the right, a picture of the platform with the WISPStation.</p> "> Figure 2
<p>Time series of the environmental data: air temperature (°C) shown in orange, precipitation (mm) shown in light blue and lake level (m) shown in green. The four summer periods in which satellite images were acquired are highlighted in grey with dashed lines.</p> "> Figure 3
<p>Flowchart of the methodology applied in the study. The oval with a black outline represents the input products where the study started from. Grey boxes indicate the methodology applied. Green diamond shapes stand for decision-making steps in the process. Blue parallelograms represent products generated, and the violet oval indicates the end point of the process.</p> "> Figure 4
<p>Comparison of DESIS and in situ Rrs data, before (“DESIS”, dark blue) and after (“DESIS deglint”, light blue) sun glint removal. In situ data are displayed in orange. Statistical results are displayed in the boxes.</p> "> Figure 5
<p>Comparisons of the average Rrs values gathered from the spaceborne data and corresponding in situ Rrs data. The variability in the mean spectra of PRISMA (6 images) and DESIS (6 images) is displayed as blue curves, with the shaded blue area representing the standard deviation. The mean and standard deviation of the in situ data are equivalently shown in orange. In the case of the EnMAP data, the comparison is limited to a single image, and it is shown with the same color configuration. In this case, the standard deviation refers to the variability present in the ROI and in the set of three measurements of the in situ data. The statistical results are displayed in the boxes.</p> "> Figure 6
<p>Water quality maps and bottom characterization for the 13 images of the available dataset. From <b>left</b> to <b>right</b>: Chl-a, TSM and PC maps; bottom characterization products, in terms of emergent macrophytes and the three cover classes: b0 (semi-emergent macrophytes), b1 (permanently submerged macrophytes), b2 (sand).</p> "> Figure 6 Cont.
<p>Water quality maps and bottom characterization for the 13 images of the available dataset. From <b>left</b> to <b>right</b>: Chl-a, TSM and PC maps; bottom characterization products, in terms of emergent macrophytes and the three cover classes: b0 (semi-emergent macrophytes), b1 (permanently submerged macrophytes), b2 (sand).</p> "> Figure 7
<p>Distributions of sand and submerged macrophyte cover classes (sparse, moderate and dense) in the four-year study period. Un-colonized (sand) pixel percentage is represented in yellow; sparse, moderate and dense submerged macrophytes are shown with a gradient of green from lightest to darkest.</p> "> Figure 8
<p>From <b>left</b> to <b>right</b>, standard deviation maps of Chl-a, PC, TSM, submerged macrophytes’ fractional cover and emergent macrophytes’ density (WAVI).</p> ">
Abstract
:1. Introduction
2. Data and Methods
2.1. Study Area
2.2. Data Collection
2.2.1. In Situ Data
2.2.2. Spaceborne Data
PRISMA | DESIS | EnMAP | |
---|---|---|---|
Launch | 22 March 2019 | 29 June 2018 | 1 April 2022 |
Coverage | 70°N to 70°S | 55°N to 52°S | Global in near-nadir mode |
Ground sampling distance | HYP: 30 m; PAN: 5 m | 30 m | 30 m |
Number of bands | HYP: 240 [400–2500 nm] PAN: 1 [400–700 nm] | 235 (no binning) 60 (binning) [400–1000 nm] | 246 [420–2450 nm] |
Radiometric resolution | 12 bits | 13 bits + 1 bit gain | ≥14 bits |
Atmospheric correction | MODTRAN v 6.0 (land based) | PACO (land) | PACO (land) MIP (water) |
2.3. Methodology Process Flowchart
2.4. Image Pre-Processing
2.5. Algorithms for Aquatic Ecosystem Mapping
2.5.1. BOMBER
2.5.2. Mixture Density Network
2.6. Product Validation
2.7. Spatio-Temporal Analysis
3. Results
3.1. Radiometric Validation
3.2. Water Quality Product Generation and Validation
3.3. Spatio-Temporal Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sensor | Date | UTC Time |
---|---|---|
PRISMA | 4 June 2019 | 10:15 |
PRISMA | 26 July 2019 | 10:13 |
DESIS | 4 August 2019 | 13:33 |
DESIS | 5 September 2019 | 06:49 |
PRISMA | 3 June 2020 | 10:10 |
PRISMA | 25 July 2020 | 10:07 |
DESIS | 4 June 2021 | 12:20 |
DESIS | 15 October 2021 | 13:47 |
DESIS | 19 June 2022 | 16:10 |
PRISMA | 20 July 2022 | 10:08 |
DESIS | 7 August 2022 | 10:50 |
PRISMA | 12 August 2022 | 10:04 |
EnMAP | 5 October 2022 | 10:40 |
Chl-a | TSM | PC | |||||||
---|---|---|---|---|---|---|---|---|---|
Product | N | RMSD (mg/m3) | MAPD | N | RMSD (g/m3) | MAPD | N | RMSD (mg/m3) | MAPD |
PRISMA | 6 | 3.30 | 29.8% | 6 | 3.10 | 19.9% | 4 | 3.85 | 27.3% |
DESIS | 6 | 3.92 | 25.2% | 6 | 1.85 | 9.6% | 4 | 2.70 | 22.4% |
EnMAP | 1 | 1.42 | 6.5% | 1 | 3.38 | 20.2% | 1 | 2.50 | 25.5% |
* | 13 | 3.32 | 23.8% | 13 | 2.71 | 15.6% | 9 | 3.31 | 25.3% |
Spaceborne Images | ||||||
---|---|---|---|---|---|---|
b2 | b0 + b1 | EM | Deep Water | Total | ||
b2 | 7 | 2 | 9 | |||
b0 + b1 | 2 | 13 | 15 | |||
In situ | EM | 6 | 6 | |||
Deep Water | 1 | 15 | 16 | |||
Total | 9 | 16 | 6 | 15 | ||
Overall Accuracy | 89.1% |
Product | Submerged Macrophytes | Emergent Macrophytes |
---|---|---|
PRISMA 4 June 2019 | 135 ha (1.1%) | 0 ha |
PRISMA 26 July 2019 | 287 ha (2.4%) | 0 ha |
DESIS 4 August 2019 | 1140 ha (10.1%) | 52 ha (0.5%) |
DESIS 5 September 2019 | 1190 ha (10.3%) | 25 ha (0.2%) |
PRISMA 3 June 2020 | 300 ha (3.4%) | 19 ha (0.2%) |
PRISMA 25 July 2020 | 878 ha (7.9%) | 37 ha (0.3%) |
DESIS 4 June 2021 | 1523 ha (13.6%) | 33 ha (0.3%) |
DESIS 15 October 2021 | 601 ha (5.1%) | 3 ha (<0.1%) |
DESIS 19 June 2022 | 1790 ha (16.7%) | 149 ha (1.4%) |
PRISMA 20 July 2022 | 2672 ha (23.0%) | 336 ha (2.9%) |
DESIS 7 August 2022 | 1350 ha (12.3%) | 324 ha (3.0%) |
PRISMA 12 August 2022 | 1223 ha (11.7%) | 343 ha (3.3%) |
EnMAP 5 October 2022 | 344 ha (3.0%) | 199 ha (1.7%) |
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Share and Cite
Fabbretto, A.; Bresciani, M.; Pellegrino, A.; Alikas, K.; Pinardi, M.; Mangano, S.; Padula, R.; Giardino, C. Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery. Remote Sens. 2024, 16, 1704. https://doi.org/10.3390/rs16101704
Fabbretto A, Bresciani M, Pellegrino A, Alikas K, Pinardi M, Mangano S, Padula R, Giardino C. Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery. Remote Sensing. 2024; 16(10):1704. https://doi.org/10.3390/rs16101704
Chicago/Turabian StyleFabbretto, Alice, Mariano Bresciani, Andrea Pellegrino, Krista Alikas, Monica Pinardi, Salvatore Mangano, Rosalba Padula, and Claudia Giardino. 2024. "Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery" Remote Sensing 16, no. 10: 1704. https://doi.org/10.3390/rs16101704
APA StyleFabbretto, A., Bresciani, M., Pellegrino, A., Alikas, K., Pinardi, M., Mangano, S., Padula, R., & Giardino, C. (2024). Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery. Remote Sensing, 16(10), 1704. https://doi.org/10.3390/rs16101704