How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies?
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Study Area
2.2. Materials
3. Data Compilation and Pre-Processing
3.1. Calibration Database
3.1.1. Collection of Data from the Lake
3.1.2. Hyperspectral Image Acquisition
3.1.3. Sentinel-2 Bands Simulation
3.1.4. Simulated Bands Reflectance
- ➢
- Its values are usually comprised between 0 and 1;
- ➢
- It is easy to compare the reflectance from one spectral band to another;
- ➢
- It is possible to compare the reflectance directly at different times of the year or of the day, since it is already corrected for variations of earth–sun distance and solar zenith angle (https://labo.obs-mip.fr/multitemp/les-grandeurs-radiometriques-eclairement-luminance-reflectance/, accessed on 4 February 2021).
3.2. Validation Database
3.2.1. In Situ Measurements
3.2.2. Remote Sensing Data
3.2.3. Atmospheric Correction of Sentinel-2 Images
4. Methodology and Statistical Evaluation Indices
4.1. Methodological Approach
- ➢
- Geometric and radiometric (Equation (1)) corrections;
- ➢
- Upscaling to 20 m spatial resolution (equal to that of Sentinel-2);
- ➢
- Sentinel-2 spectral bands simulation (Equation (2));
- ➢
- Simulated bands reflectance computation (Equation (3)).
4.2. Ensemble-Based System
4.3. Statistical Indices for the Ensemble-Based System Assessment
5. Results and Discussion
5.1. The Ensemble-Based Classifier Development
5.2. The Ensemble-Based Estimator Development
5.3. The Hybrid Ensemble-Based System for Chlorophyll-a Modeling
5.4. Calibration of the Experts
5.5. Cross-Validation: Local Evaluation
5.6. Validation with the Blind Dataset: Regional Evaluation
5.7. Spatial Distribution Assessment of the Ensemble-Based System
6. Challenges, Advantages, and Limitations of Chlorophyll-a Monitoring Using UAVs and Satellites Data
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Lakes | chl_a (µg L−1) Avrg ± Std (Min − Max) | Pheo_a (µg L−1) Avrg ± Std (Min − Max) | SDD (m) Avrg ± Std (Min − Max) | DOC (µg L−1) Avrg ± Std (Min − Max) | TP (µg P/L) | TN (mg N/L) | |
---|---|---|---|---|---|---|---|
FC 1 | CL | 34.2 ± 4.7 (26.0 − 44.8) | 7.7 ± 0.8 (5.9 − 8.9) | 1.2 ± 0.1 (1.1 − 1.4) | 6.9 ± 1.7 (5.6 − 9.5) | 46 | 0.806 |
BL | 3.4 ± 0.3 (2.8 − 4.3) | 1.0 ± 0.1 (0.9 − 1.2) | 4.2 ± 1.7 (1.9 − 2.2) | 3.7 ± 0.9 (3 − 4.9) | 23 | 0.409 | |
ML | 3.4 ± 0.4 (2.8 − 4.2) | 0.7 ± 0.1 (0.6 − 0.9) | 3.6 ± 0.2 (3.2 − 4.3) | 4.4 ± 1.5 (3.4 − 7.1) | 13 | 0.330 | |
FC 2 | CL | 61.8 ± 8.5 (38.6 − 77.0) | 19.7 ± 5.4 (11.0 − 27.5) | 0.8 ± 0.1 (0.7 − 0.9) | 4.8 ± 0.2 (4.5 − 5.2) | 66 | 0.957 |
BL | 17.1 ± 1.3 (14.6 − 20.6) | 1.1 ± 0.5 (0.1 − 1.8) | 1.5 ± 0.1 (1.0 − 1.7) | 3.2 ± 0.1 (3.1 − 3.3) | 18 | 0.425 | |
ML | 2.5 ± 0.2 (2.2 − 2.9) | 0.5 ± 0.2 (0.3 − 0.9) | 3.1 ± 0.2 (2.7 − 3.2) | 3.3 ± 0.1 (3.3 − 3.5) | 10 | 0.279 |
Years | chl_a (µg L−1) Avrg ± Std (Min − Max) | Number of Samples | Number of Images | |
---|---|---|---|---|
VLMN | 2015 | 0 ± 0 (0 − 0) | 0 | 0 |
2016 | 3.4 ± 2.3 (0.9 − 10.8) | 32 | 16 | |
2017 | 4.4 ± 5.9 (0.7 − 41.8) | 62 | 9 | |
Total | 4.1 ± 5.1 (0.7 − 41.8) | 94 | 25 |
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El-Alem, A.; Chokmani, K.; Venkatesan, A.; Rachid, L.; Agili, H.; Dedieu, J.-P. How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies? Remote Sens. 2021, 13, 1134. https://doi.org/10.3390/rs13061134
El-Alem A, Chokmani K, Venkatesan A, Rachid L, Agili H, Dedieu J-P. How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies? Remote Sensing. 2021; 13(6):1134. https://doi.org/10.3390/rs13061134
Chicago/Turabian StyleEl-Alem, Anas, Karem Chokmani, Aarthi Venkatesan, Lhissou Rachid, Hachem Agili, and Jean-Pierre Dedieu. 2021. "How Accurate Is an Unmanned Aerial Vehicle Data-Based Model Applied on Satellite Imagery for Chlorophyll-a Estimation in Freshwater Bodies?" Remote Sensing 13, no. 6: 1134. https://doi.org/10.3390/rs13061134