An Integrated Model of Summer and Winter for Chlorophyll-a Retrieval in the Pearl River Estuary Based on Hyperspectral Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Field Data Collection
2.3. Data Pre-Processing and Models
2.3.1. Normalization Processing
2.3.2. First-Derivate Processing
2.3.3. Equivalent Reflectance Simulation
2.3.4. The Slope Difference Index
2.3.5. Model Validation and Evaluation
3. Results
3.1. In-Situ Spectral Characteristics
3.2. Model of Normalized Reflectance
3.2.1. Correlation between Normalized Results and Chla
3.2.2. Established Single Band Model for Comparison
3.2.3. Established Ratio Model for Comparison
3.3. First-Derivate Model Exploration
3.4. Discrete Spectral Model Based on Sentinel-2 Images
3.4.1. Slope Difference Index Model
3.4.2. Derivate Method of Discrete Spectral
3.5. Model Summary and Validation
3.6. Applicability of the Model to the Satellite Images
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter | Time | Range | Mean Value | Median Value |
---|---|---|---|---|
Chla (mg/m3) | 2020.07 | 3.995~27.312 | 12.84 | 10.73 |
2020.12 | 1.527~4.168 | 2.27 | 2.08 | |
Suspended solids (mg/L) | 2020.07 | 0.33~54 | 24.28 | 24.33 |
2020.12 | 25.2~278 | 112.31 | 84.60 |
Bands | Central Wavelength (nm) | Resolution (m) | Bands | Central Wavelength (nm) | Resolution (m) |
---|---|---|---|---|---|
B1 | 443 | 60 | B8 | 842 | 10 |
B2 | 490 | 10 | B8a | 865 | 20 |
B3 | 560 | 10 | B9 | 945 | 60 |
B4 | 665 | 10 | B10 | 1375 | 60 |
B5 | 705 | 20 | B11 | 1610 | 20 |
B6 | 740 | 20 | B12 | 2190 | 20 |
B7 | 783 | 20 | B8 | 842 | 10 |
Data | Pre-Processing | Time | Method | Bands | Model | R2 | RMSE | MAPE | MAE |
---|---|---|---|---|---|---|---|---|---|
Hyperspectral data from in-situ investigation | Normalization | July | Single band | 673 | y = 0.462x2 − 0.8083x + 0.4323 | 0.61 | 7.08 | 0.31 | 4.88 |
Ratio | 705/665 | y = 53.531x − 31.291 | 0.49 | 4.71 | 0.30 | 4.08 | |||
December | Single band | 489 | y = 18.069x2 − 43.719x + 28.307 | 0.78 | 0.46 | 0.19 | 0.40 | ||
First-derivate | Both | Single band | 680 | y = 4.7956e18.716x | 0.80 | 3.21 | 0.26 | 2.48 | |
Discrete spectral (simulated reflectance) | Slope | Both | Slope difference | 560~665~705 | y = 5.6949e14.543x | 0.78 | 5.18 | 0.35 | 3.26 |
First-derivate | Subtraction | 665–705 | y = 10.45e−9.671x | 0.68 | 6.99 | 0.47 | 4.26 | ||
Multiplication | 705 × 783 | y = 13.282e−398.4x | 0.70 | 6.50 | 0.55 | 4.20 |
Data Used First | Area | Image Source | Method | Bands Involved | Model Validation (RMSE) and Chla Range (mg/m3) | Validation of Image Retrieval Result |
---|---|---|---|---|---|---|
In-situ hyperspectral | Bangpakong River estuary (Thailand) [25] | Landsat-5 | Empirical model | 435, 488, 692 | 1.13 (0.13~7.25) Figure 16e | 1.6 (0.13~7.25) Figure 18f |
Lake Huron (USA) [31] | Sentinel2-L2A | Red–NIR Ratio Multiple regression | 705, 665 443, 490, 560, 665, 705, 740, 783 | 9.972 (1.62~51.68) Figure 15b and Figure 16b 3.127 (1.62~51.68) Figure 15e and Figure 16e | Did not show the validation, only discusses that it is similar to the real distribution | |
The Bohai Sea (China) [52] | MODIS | OC4 OC3Mv6 Empirical model | 443, 490, 510, 555 443, 490, 555 443, 412, 555, 490 | 2.36 (0~13) Figure 16e | No specific figure was given, but compared with MODIS standard Chla product, it was found that the retrieval error is large, ranging from 3~5 mg/m3 | |
Mundaú-Manguaba Estuarine-Lagoon System(Brazil) [53] | MODIS MERIS Sentinel-MSI/OLCI | Blue–Green Ratio NIR–Red Ratio Three-Band Four-Band | 645, 555 681, 709, 665 681, 709, 674 674, 709, 681,665 | 14.55 (5.99~117.54) 10.44 (0.97~117.54) 5.02 (average 27.6) 7.69 (average 27.6) | Discussed the performance of different algorithms before and after classification; no images application. | |
Spectral from images directly | Lake Erie (USA) [4] | Sentinel2-L1C/L2A | MCI model and processor from SNAP | 665, 705, 740 | 9.6 (0.13~88) Figure 16a,d | Compared the modeling results, not applied to images |
Some inland lakes (Brazil) [54] | Sentinel2-L1C/ C2RCC | C2RCC-C2X processor and its product from SNAP | Unclear, using the C2RCC NN model, possibly all bands | Did not build models by themselves, use the retrieval result calculated by C2RCC processor of SNAP | For different water: 1.84 (5~8) Figure 18e,g 2.26 (0~20) 2.91 (0~20) | |
Three subalpine lakes and a turbid lake (Italy) [55] | Sentinel2- L1C/L2A | C2RCC MASI OC3 | No details for C2RCC and MASI; 443, 490 for OC3 | Compared the agreement (R2) between different algorithms | For different lakes: Subalpine (0~6); Turbid (1~25): C2RCC: 0.57; 3.2 MASI: 0.6; 2.6 OC3: 1.39; 4.1 |
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Li, H.; Xie, X.; Yang, X.; Cao, B.; Xia, X. An Integrated Model of Summer and Winter for Chlorophyll-a Retrieval in the Pearl River Estuary Based on Hyperspectral Data. Remote Sens. 2022, 14, 2270. https://doi.org/10.3390/rs14092270
Li H, Xie X, Yang X, Cao B, Xia X. An Integrated Model of Summer and Winter for Chlorophyll-a Retrieval in the Pearl River Estuary Based on Hyperspectral Data. Remote Sensing. 2022; 14(9):2270. https://doi.org/10.3390/rs14092270
Chicago/Turabian StyleLi, Haitao, Xuetong Xie, Xiankun Yang, Bowen Cao, and Xuening Xia. 2022. "An Integrated Model of Summer and Winter for Chlorophyll-a Retrieval in the Pearl River Estuary Based on Hyperspectral Data" Remote Sensing 14, no. 9: 2270. https://doi.org/10.3390/rs14092270
APA StyleLi, H., Xie, X., Yang, X., Cao, B., & Xia, X. (2022). An Integrated Model of Summer and Winter for Chlorophyll-a Retrieval in the Pearl River Estuary Based on Hyperspectral Data. Remote Sensing, 14(9), 2270. https://doi.org/10.3390/rs14092270