Analysis of the regional spectral properties in northwestern South China Sea based on an empirical orthogonal function

This study presents an analysis of the spectral characteristics of remote sensing reflectance(Rrs)in northwestern South China Sea based on the in situ optical and water quality data for August 2018.Rrswas initially divided into four classes,classes A to D,using the max-classification algorithm,and the spectral properties of whole Rrs were characterized using the empirical orthogonal function(EOF)analysis.Subsequently,the dominant factors in each EOF mode were determined.The results indicated that more than 95%of the variances of Rrs are partly driven by the back-scattering characteristics of the suspended matter.The initial two EOF modes were well correlated with the total suspended matter and back-scattering coefficient.Furthermore,the first EOF modes of the four classes of Rrs(A-D Rrs-EOF1)significantly contributed to the total variances of each Rrs class.In addition,the correlation coefficients between the amplitude factors of class A-D Rrs-EOF1 and the variances of the relevant water quality and optical parameters were better than those of the unclassified ones.The spectral shape of class ARrs-EOF1 was governed by the absorption characteristic of chlorophyll a and colored dissolved organic matter(CDOM).The spectral shape of class B Rrs-EOF1 was governed by the absorption characteristic of CDOM since it exhibited a high correlation with the absorption coefficient of CDOM(ag(λ)),whereas the spectral shape of class C Rrs-EOF1 was governed by the back-scattering characteristics but not affected by the suspended matter.The spectral shape of class D Rrs-EOF1 exhibited a relatively good correlation with all the water quality parameters,which played a significant role in deciding its spectral shape.

Performance evaluation of operational atmospheric correction algorithms over the East China Seas

To acquire high-quality operational data products for Chinese in-orbit and scheduled ocean color sensors, the performances of two operational atmospheric correction(AC) algorithms(ESA MEGS 7.4.1 and NASA Sea DAS 6.1) were evaluated over the East China Seas(ECS) using MERIS data. The spectral remote sensing reflectance R_(rs)(λ), aerosol optical thickness(AOT), and ?ngstr?m exponent(α) retrieved using the two algorithms were validated using in situ measurements obtained between May 2002 and October 2009. Match-ups of R_(rs), AOT, and α between the in situ and MERIS data were obtained through strict exclusion criteria. Statistical analysis of R_(rs)(λ) showed a mean percentage difference(MPD) of 9%–13% in the 490–560 nm spectral range, and significant overestimation was observed at 413 nm(MPD>72%). The AOTs were overestimated(MPD>32%), and although the ESA algorithm outperformed the NASA algorithm in the blue-green bands, the situation was reversed in the red-near-infrared bands. The value of α was obviously underestimated by the ESA algorithm(MPD=41%) but not by the NASA algorithm(MPD=35%). To clarify why the NASA algorithm performed better in the retrieval of α, scatter plots of the α single scattering albedo(SSA) density were prepared. These α-SSA density scatter plots showed that the applicability of the aerosol models used by the NASA algorithm over the ECS is better than that used by the ESA algorithm, although neither aerosol model is suitable for the ECS region. The results of this study provide a reference to both data users and data agencies regarding the use of operational data products and the investigation into the improvement of current AC schemes over the ECS.

Assessing Landsat-8 atmospheric correction schemes in low to moderate turbidity waters from a global perspective

Atmospheric correction is one of the major challenges in ocean color remote sensing,thus threatening comprehensive evaluation of water quality within aquatic environments.In this study,five state-of-the-art atmospheric correction(AC)processors(i.e.Acolite,C2RCC,iCOR,L2gen,and Polymer)were applied to Operational Land Imager(OLI)Landsat-8 scenes and evaluated against in situ measurements across various types of waters worldwide.A total of 262 matchups between in situ measured and satellite-derived remote sensing reflectance(R_(rs))at 20 sites were obtained between August 2013 and August 2021.Classification of optical water types(OWTs)was carried out using in situ measurements with matched satellite observations.OWT-specific analysis demonstrated that L2gen produced the most accurate Rrs with R^(2)≥0.74 and root mean squared error(RMSE)≤0.0018 sr^(–1) for the four visible bands of OLI,followed by Polymer,C2RCC,iCOR,and Acolite.In terms of R_(rs) spectral similarity,C2RCC yielded the lowest spectral angle(SA)of 8.55°,followed by L2gen(SA=9.20°).The advantage and disadvantage of each AC scheme were discussed.Recommendations to improve the accuracy for atmospheric correction were made,such as polarization observations and concurrent aerosol and ocean color measurements.

Developing water quality retrieval models with in situ hyperspectral data in Poyang Lake,China

Applying remote sensing techniques to develop the retrieval models and further to obtain the spatiotemporal information of water quality parameters is necessary for understanding,managing,and protecting lake ecosystems.This study aimed to calibrate and validate the retrieval models for estimating the concentrations of chlorophyll a(C_(CHL)),suspended particulate matter(C_(SPM)),and dissolved organic carbon(C_(DOC))with the in situ hyperspectral measurements in Poyang Lake,China in 2010 and 2011.The model calibration and validation results indicated that:(1)for C_(CHL)retrieval,significantly strong and moderate correlations existed between the measured and estimated values(with the correlation coefficient r=0.92 and r=0.76)using the exponential model and the three-band model,respectively,with biased estimation observed for the exponential model;(2)for retrieving C_(SPM),there was a strong correlation between the measured and estimated values(r=0.95)using the exponential model;and(3)no significant correlation between measured and estimated C_(DOC)values was found with our developed models.More work is needed to allow the water quality of Poyang Lake to be accurately and steadily estimated,especially for C_(CHL)and C_(DOC).