研究目的
This study aims to 1) identify the optical water types of the lakes in the LYHR Basin using field-measured and OLCI-derived Rrs(λ) data; 2) characterize the bio-optical properties and IOP variations in each OWT; and 3) develop class-specific models to improve the estimation of the Chla content and Chla-specific phytoplankton absorption at 443 nm (a*ph(443)).
研究成果
Optical classification improved the estimation of bio-optical parameters, particularly Chla and a*ph(443), in optically complex lakes. Four OWTs were identified with distinct bio-optical properties. Class-specific algorithms performed better than overall models, except for type 4. The study highlights the necessity of optical classification for monitoring dynamic lake waters and suggests future work on temporal variations.
研究不足
The optical variability in OWTs is restricted to the range of field data; unclassified types may occur if water types are not represented. Atmospheric correction uncertainties, especially in blue and NIR bands, affect accuracy. The use of a constant reflectance ratio (ρ) may introduce errors; spectral variability was not considered.
1:Experimental Design and Method Selection:
The study used k-means clustering on normalized remote sensing reflectance (NRrs(λ)) to classify optical water types (OWTs). Mahalanobis distance was applied for type-labeling satellite data. Atmospheric correction was performed using the 6SV model. Bio-optical algorithms (e.g., NR-2B, Mer-3B, MCI) were evaluated and tuned for each OWT.
2:Sample Selection and Data Sources:
Field-measured datasets from 535 water samples collected from 2011 to 2017 in lakes Taihu, Chaohu, Hongze, and Shijiu in the LYHR Basin. Sentinel-3A/OLCI Level-1B images from 2017 were used for satellite data.
3:List of Experimental Equipment and Materials:
FieldSpec Pro Dual VNIR spectroradiometer for Rrs(λ) measurements, Shimadzu UV-2600 spectrophotometer for Chla and CDOM absorption measurements, filters for SPM and absorption coefficient determinations, and Sentinel-3A/OLCI satellite data.
4:Experimental Procedures and Operational Workflow:
Rrs(λ) was measured using the above-water method. Water samples were analyzed for Chla, SPM, SPIM, SPOM, and absorption coefficients. OLCI images were atmospherically corrected using 6SV. Optical classification was performed using k-means on NRrs(λ). Class-specific algorithms were developed and validated.
5:Data Analysis Methods:
Statistical analysis included mean absolute percentage error (MAPE), root mean square error (RMSE), and root mean squared difference (RMSD). Clustering performance was assessed using silhouette coefficient and SSE.
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