Accurate satellite ocean color retrieval in complex atmosphere-ocean systems faces significant challenges. Traditional algorithms relying on remote sensing reflectance (Rrs(λ)) are hindered by considerable uncertainty or invalid values due to imperfections in or the failure of atmospheric correction. This study addresses these limitations by leveraging Machine Learning (ML) to explore Rayleigh-corrected reflectance (Rrc(λ)) data towards accurate Forel-Ule Index (FUI) retrieval in the complex atmosphere-ocean environment of the China adjacent seas. First, satellite-ground matchups were constructed with 1,005 in-situ FUI data and quasi-synchronized Moderate-resolution Imaging Spectroradiometer (MODIS) Rrc(λ) images (0rc(λ) images. The new product shows a 10% (7%) increase in percentage of valid data for daily (monthly) time series, with notable improvements of 17% (24%) in coastal waters (within 20 km from coastline), when compared to Rrs(λ)-derived FUI products. Both the high accuracy and complete spatio-temporal coverage help revealing short-term FUI variability after a heavy rainfall and long-term FUI changes missed by Rrs(λ)-derived products. The proposed approach may provide some insights for retrieving other ocean color parameters and contribute to high-accuracy remote sensing retrievals in complex atmosphere-ocean systems.

Remote sensing of leaf chlorophyll content (LCC) is critical in precision agriculture, forest monitoring, and pest management. Chlorophyll absorbs light in the visible and red-edge spectrum areas. However, many multispectral sensors lack red-edge bands. Subsequently, the development of effective visible chlorophyll-related indicators is important. The hue angle defined in the CIE xy chromaticity diagram, which reflects the human eye’s color discrimination ability relying on visible light (400–700 nm), is introduced in this study for LCC estimation. This study proposes a new multispectral hue angle calculation method employing D65 standard illuminant data, investigates the effect of illuminant factors and band settings on hue angle calculation, and presents tristimulus weight coefficients for calculating MERIS, MSI, and ETM+ hue angles. Furthermore, the sensitivity of the link between LCC and different hue angles, as well as vegetative indices (VIs), is assessed in dense and sparse canopies. The results show that using wavelength-independent illuminant data or discrete bands overcalculates the reflectance-based hue angle. Moreover, our proposed hue angle calculated using D65 standard illuminant data is more sensitive to LCC than the existing hue angle using wavelength-independent illuminant data. In dense canopies, the multispectral hue angle has a stronger relationship with LCC than in sparse canopies. The multispectral hue angle generally appears weaker or similar to the red-edge VIs (MTCI and CIred-edge), but stronger than the non-red-edge VIs (CIgreen, NDVI, NGRDI, and TGI). These findings imply that the multispectral hue angle calculated from visible bands provides the potential to monitor LCC of large-scale plants.

Water clarity (Zsd) is a widely used quality indicator that can be estimated from remote sensing imagery. China’s newest generation Advanced HyperSpectral Imager (AHSI) onboard the ZY1-02D satellite is expected to enable accurate water clarity retrieval for inland waters, since AHSI can provide abundant band choices while its 30-m spatial resolution is advantageous for monitoring small inland water bodies. In this study, to retrieve Zsd from the ZY1-02D imagery for inland waters with varying turbidities, we propose a classification-based, semi-analytical method in which the red/blue band ratio is employed to distinguish clear to moderately turbid water and highly turbid waters. Two Quasi Analytical Approaches (QAAs), QAAv5 and QAAm14, are used to estimate the total absorption coefficient (a(λ)) and the backscattering coefficient (bb(λ)) for clear to moderately turbid water and highly turbid waters, respectively. The estimated a(λ) and bb (λ) are utilized to obtain the diffuse attenuation coefficient Kd, followed by the Zsd calculations. Compared with 70 matchups of in situ measured Zsd values (0–6.5 m), the ZY1-02D image-derived Zsd achieved an R2 of 0.98, with an average unbiased relative error and root mean square error of 29.1% and 0.52 m, respectively. In addition, the proposed method can yield Zsd with higher accuracies than that of optimized empirical models. Therefore, the ZY1-02D AHSI imagery can retrieve reliable Zsd for both clear (> 3 m) and turbid waters (0–3.0 m), thereby serving as a useful satellite data source for monitoring the water clarity of large-scale inland water bodies.

Urban black and odorous water (BOW) is a serious global environmental problem. Since these waters are often narrow rivers or small ponds, the detection of BOW waters using traditional satellite data and algorithms is limited both by a lack of spatial resolution and by imperfect retrieval algorithms. In this paper, we used the Chinese high-resolution remote sensing satellite Gaofen-2 (GF-2, 0.8 m). The atmospheric correction showed that the mean absolute percentage error of the derived remote sensing reflectance (Rrs) in visible bands is 25.19%. We first measured Rrsspectra of two classes of BOW [BOW with high concentrations of iron (II) sulfide, i.e., BOW1 and BOW with high concentrations of total suspended matter, i.e., BOW2] and ordinary water in Shenyang. Then, in situ Rrsdata were converted into Rrs corresponding to the wide GF-2 bands using the spectral response functions. We used the converted Rrsdata to calculate several band combinations, including the baseline height, [Rrs(green) - Rrs(red))/(Rrs(green) + Rrs(red)], and the color purity on a Commission Internationale de L'Eclairage (CIE) chromaticity diagram. The color purity was found to be the best index to extract BOW from ordinary water. Then, Rrs(645) was applied to categorize BOW into BOW1 and BOW2. We applied the algorithm to two synchronous GF-2 images. The recognition accuracy of BOW2 and ordinary water are both 100%. The extracted river water type near Weishanhu Road was BOW1, which agreed well with ground truth. The algorithm was further applied to other GF-2 data for Shenyang and Beijing.