Hyperspectral reconstruction method for optically complex inland waters based on bio-optical model and sparse representing

Abstract

For better use of well-performed water quality parameter estimation models and the comprehensive use of multi-source remote sensing data, hyperspectral reconstruction is urgently needed in the remote sensing of optically complex inland waters. In this study, we proposed a bio-optical–based hyperspectral reconstruction (BBHR) algorithm to generate hyperspectral above-surface remote-sensing reflectance (Rrs) data ranging in wavelength from 400 to 800 nm. One core advantage of the BBHR method is its in situ data independency, which theoretically renders the algorithm universal. The other advantage is its ability to reconstruct hyperspectral Rrs for the 400–800 nm spectral range, which facilitates the construction of more high accuracy chlorophyll-a concentration (Cchla) estimation models for optically complex waters. The reconstruction was tested by employing six widely used multispectral sensors: the Medium Resolution Imaging Spectrometer, (MERIS), Sentinel-3 Ocean and Land Color Instrument (S3 OLCI), Sentinel-2 Multispectral Instrument (S2 MSI), Geostationary Ocean Color Imager (GOCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and Moderate Resolution Imaging Spectroradiometer (MODIS). The model performance was validated by using a ASD FieldSpec spectroradiometer-measured hyperspectral dataset containing 1396 samples and a satellite-in-situ match-up dataset with 66 samples. The results show that the proposed BBHR method exhibits satisfactory performance. The average mean absolute percentage error (MAPE), root mean square error (RMSE), R2 and bias indices of the BBHR-reconstructed Rrs over all spectral bands of the six multispectral sensors were 3.27%, 8.86 × 10−4 sr−1, 0.98, and − 6.53 × 10−5 sr−1, respectively. In the field Cchla estimation experiment that contained 391 samples (mean Cchla is 25.42 ± 16.37 μg/L), the BBHR algorithm improved the MAPE and RMSE indices of multispectral data from 0.47 and 12.80 μg/L to 0.42 and 10.16 μg/L, respectively. For the satellite image match-up dataset (66 samples), the BBHR method decreased the MAPE and RMSE indices of multispectral images from 0.51 and 12.94 μg/L to 0.32 and 8.01 μg/L, respectively. The proposed algorithm outperformed the other two high-accuracy models in terms of spectral fidelity and Cchla estimation. In addition, the BBHR method shows great potential for the multi-source monitoring of inland water bodies. This could improve the accuracy and robustness of the reconstruction when semi-synchronized multi-source data are input and increase the consistency of multi-source data when non-synchronized multi-source data are provided. Our results revealed that BBHR is a trustworthy algorithm that offers hyperspectral Rrs data and facilitates the remote monitoring of turbid inland waterbodies.