Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm
Abstract
:1. Introduction
2. Data and Methods
2.1. Assessment of the Quasi-Analytical Algorithm (QAA)
2.2. In Situ Data
2.2.1. V19 Dataset
2.2.2. BOU Dataset
2.2.3. CNR Dataset
2.3. Satellite ESA OC-CCI Rrs Data
3. Results and Discussion
3.1. QAA Performance for bbp Retrievals from In Situ Data
3.2. Estimation of the bbp Spectral Slope from Rrs Data
3.3. Validation of CCI Rrs
3.4. QAA Performance for bbp Retrievals from CCI Data
4. Conclusions
- (1)
- Raman scattering compensation of Rrs prior to the application of the QAA significantly reduces errors in the retrieval of bbp with respect to in situ bbp. Inclusion of this processing step in operational schemes is recommended.
- (2)
- The QAA-derived bbp from in situ radiometry has negligible biases with respect to in situ bbp.
- (3)
- CCI Rrs shows low biases but higher RMS differences with respect to in situ data, that could be excessive for the monitoring of natural change over short periods. Here, the standardization of in situ radiometry protocols is highly encouraged [52], in order to reduce the errors when in situ datasets formed by multiple contributors are merged and used for Rrs matchup analysis.
- (4)
- In part as a consequence of the findings above, QAA-derived bbp from CCI Rrs displays negligible biases respect to in situ bbp, with moderately low RMS errors.
- (5)
- The in situ radiometry-derived spectral backscattering slope (η) has low predictive value as compared to η derived from bbp matchups. In this context, the impact of using the best fitted curve instead of the widely used expression [22] is negligible, thus validating the application of the latter without its retuning.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
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Band (nm) | Bias (%) | RMS (%) | r2 | N | |
---|---|---|---|---|---|
V19 | 412 | 40.3 | 128.4 | 0.35 | 319 |
443 | 42.7 | 129.4 | 0.37 | 319 | |
490 | 44.5 | 127.8 | 0.41 | 319 | |
510 | 45.0 | 127.1 | 0.42 | 319 | |
555 | 45.2 | 124.2 | 0.44 | 319 | |
670 | 43.1 | 114.2 | 0.47 | 319 | |
All | 43.4 | 125.3 | 0.43 | 1914 | |
BOU | 442 | 44.5 | 50.7 | 0.73 | 172 |
488 | 71.3 | 79.2 | 0.73 | 172 | |
550 | 29.0 | 36.5 | 0.78 | 172 | |
620 | 52.0 | 60.2 | 0.73 | 172 | |
All | 49.2 | 58.7 | 0.75 | 688 | |
CNR | 470 | 11.8 | 25.1 | 0.88 | 93 |
530 | 7.7 | 22.8 | 0.89 | 93 | |
660 | −9.6 | 20.7 | 0.93 | 93 | |
All | 3.3 | 22.9 | 0.88 | 279 |
Band (nm) | Bias (%) | RMS (%) | r2 | N | |
---|---|---|---|---|---|
V19 | 412 | 28.5 | 94.6 | 0.45 | 319 |
443 | 30.7 | 95.0 | 0.47 | 319 | |
490 | 32.2 | 93.4 | 0.50 | 319 | |
510 | 32.6 | 92.8 | 0.51 | 319 | |
555 | 32.7 | 90.4 | 0.52 | 319 | |
670 | 30.7 | 83.1 | 0.54 | 319 | |
All | 31.2 | 91.6 | 0.52 | 1914 | |
BOU | 442 | 33.0 | 40.1 | 0.73 | 172 |
488 | 57.2 | 64.8 | 0.73 | 172 | |
550 | 18.2 | 27.1 | 0.78 | 172 | |
620 | 39.0 | 47.8 | 0.73 | 172 | |
All | 37.0 | 47.0 | 0.75 | 688 | |
CNR | 470 | 6.5 | 22.6 | 0.88 | 93 |
530 | 2.5 | 21.3 | 0.89 | 93 | |
660 | −14.2 | 23.0 | 0.93 | 93 | |
All | −1.73 | 22.3 | 0.89 | 279 |
Band (nm) | Bias (%) | RMS (%) | r2 | N | |
---|---|---|---|---|---|
V19 | 412 | −19.6 | 42.7 | 0.37 | 147 |
443 | −16.9 | 30.6 | 0.53 | 147 | |
490 | −5.0 | 19.3 | 0.66 | 147 | |
510 | −0.4 | 15.3 | 0.73 | 147 | |
555 | −4.6 | 18.7 | 0.78 | 147 | |
670 | 28.4 | 117.9 | 0.47 | 147 | |
All | −3.0 | 54.2 | 0.73 | 882 | |
BOU | 412 | −4.0 | 22.5 | 0.50 | 96 |
443 | −3.7 | 23.9 | 0.63 | 97 | |
490 | −1.9 | 11.1 | 0.66 | 97 | |
510 | −6.4 | 11.9 | 0.47 | 97 | |
555 | 9.5 | 16.0 | 0.64 | 97 | |
670 | 24.2 | 49.5 | 0.31 | 97 | |
All | 3.0 | 26.0 | 0.89 | 581 | |
CNR | 412 | −10.7 | 24.8 | 0.42 | 42 |
443 | 2.6 | 18.2 | 0.53 | 42 | |
490 | −0.4 | 13.2 | 0.75 | 42 | |
510 | −3.7 | 14.9 | 0.81 | 42 | |
555 | 0.9 | 19.9 | 0.88 | 42 | |
670 | −4.9 | 83.1 | 0.90 | 42 | |
All | −2.7 | 21.9 | 0.87 | 252 |
Band (nm) | Bias (%) | RMS (%) | r2 | N | |
---|---|---|---|---|---|
V19 | 412 | 24.2 | 51.8 | 0.66 | 147 |
443 | 26.8 | 53.9 | 0.67 | 147 | |
490 | 29.1 | 56.0 | 0.68 | 147 | |
510 | 29.9 | 56.8 | 0.67 | 147 | |
555 | 31.0 | 58.1 | 0.67 | 147 | |
670 | 31.6 | 60.7 | 0.62 | 147 | |
All | 28.8 | 56.3 | 0.68 | 882 | |
BOU | 442 | 56.6 | 62.7 | 0.67 | 97 |
488 | 86.9 | 96.2 | 0.64 | 97 | |
550 | 41.9 | 50.2 | 0.70 | 97 | |
620 | 66.8 | 75.3 | 0.69 | 97 | |
All | 63.1 | 73.1 | 0.69 | 388 | |
CNR | 470 | 10.1 | 52.9 | 0.48 | 42 |
530 | 7.8 | 54.9 | 0.46 | 42 | |
660 | −9.6 | 33.5 | 0.63 | 42 | |
All | 2.7 | 48.1 | 0.50 | 126 |
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Pitarch, J.; Bellacicco, M.; Organelli, E.; Volpe, G.; Colella, S.; Vellucci, V.; Marullo, S. Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm. Remote Sens. 2020, 12, 77. https://doi.org/10.3390/rs12010077
Pitarch J, Bellacicco M, Organelli E, Volpe G, Colella S, Vellucci V, Marullo S. Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm. Remote Sensing. 2020; 12(1):77. https://doi.org/10.3390/rs12010077
Chicago/Turabian StylePitarch, Jaime, Marco Bellacicco, Emanuele Organelli, Gianluca Volpe, Simone Colella, Vincenzo Vellucci, and Salvatore Marullo. 2020. "Retrieval of Particulate Backscattering Using Field and Satellite Radiometry: Assessment of the QAA Algorithm" Remote Sensing 12, no. 1: 77. https://doi.org/10.3390/rs12010077