Chlorophyll-a (Chl-a) concentration estimation by remote sensing is an important means for monitoring offshore water quality and eutrophication. In-situ hyperspectral data can achieve accurate analyses of Chl-a, but it is not suitable for regional inversion. Satellite remote sensing provides the possibility for regional inversion, but the precision is lower limited to atmospheric correction result. Therefore, this work uses machine learning to fuse in-situ hyperspectral data and Sentinel-2 multispectral instrument images to combine their complementary advantages, so as to improve the precision of regional Chl-a concentration inversion. First, the in-situ spectra were resampled based on the satellite spectral response function to obtain equivalent reflectance. Second, the spectral feature bands of Chl-a were determined by correlation analysis. Then three machine learning models, support vector regression, random forest, and back propagation neural network, were used to establish mapping relationships of feature bands between equivalent reflectance and satellite image reflectance so as to correct the satellite feature bands. Finally, Chl-a inversion models were constructed based on the satellite feature bands before and after correction. The results demonstrate that the corrected inversion model shows an increase in R2 by 0.25 and a decrease in mean relative error by 7.6%. This fusion method effectively improves the accuracy of large-scale Chl-a concentration estimation.

Understanding the impact of climate change on Earth presents a significant scientific challenge. Monitoring changes in terrestrial ecosystems, including leaf water content, is essential for assessing plant transpiration, water use efficiency, and physiological processes. Optical remote sensing, utilizing multi-angular reflectance measurements in the near infrared and shortwave infrared wavelengths, offers a precise method for estimating leaf water content. We propose and evaluate a new index based on multi-angular reflection, using 256 leaf samples from 10 plant species for calibration and 683 samples for validation. Hyperspectral indices derived from multi-angular spectra were assessed, facilitating efficient leaf water content analysis with minimal time and specific bands required. We investigate the relationship of leaf water content using spectral indices and apply linear and nonlinear regression models to calibration data, resulting in two indices for each indicator. The newly proposed indices, (R1−R2)/(R1−R3) for linear and (R1905−R1840)/(R1905−R1875) for nonlinear, demonstrate high coefficients of determination for leaf water content (>0.94) using multi-angular reflectance measurements. Published spectral indices exhibit weak relationships with our calibration dataset. The proposed leaf water content indices perform well, with an overall root mean square error of 0.0024 (g/cm2) and 0.0026 (g/cm2) for linear and nonlinear indices, respectively, validated by Leaf Optical Properties Experiment, ANGERS, and multi-angular datasets. The (R1−R2)/(R1−R3) bands show promise for leaf water content estimation. Future studies should encompass more plant species and field data.

Classification of complex and large hyperspectral images (HSIs) with machine learning (ML) algorithms is an important research area. Recently, explainable artificial intelligence (XAI), which helps to explain and interpret black-box ML algorithms, has become popular. Our study aims to present extensive research on the use of XAI methods in explaining the band effect in HSI classification and the impact of reducing the high band number of HSIs by feature selection on the performance of the classifiers. The importance levels of the spectral bands that are effective in the decisions of the different ML classifiers were examined with the deep reinforcement learning and XAI methods, such as Shapley additive explanations (SHAP) and permutation feature importance (PFI). Our work selects representative bands using SHAP and PFI as XAI analysis techniques. We evaluated the XAI-based band selection performance on three publicly available HSI datasets using random forest, light gradient-boosting machine, and extreme gradient boosting classifier algorithms. The results obtained by applying XAI and deep learning methods were used to select spectral bands. Additionally, principal component analysis, a common dimension reduction technique, was performed on the dataset used in our study. Comparable performance evaluation shows that XAI-based methods choose informative bands and outperform other methods in the subsequent tasks. Thus the global and class-based effects of spectral bands can be explained, and the performance of classifiers can be improved by eliminating features that have a negative impact on classification. In HSI classification, studies examining the decisions of ML classifiers using XAI techniques are limited. Our study is one of the pioneer studies in the usage of XAI in HSI classification.