Marine sediments enriched with rare earth elements (REEs) serve as a significant reservoir, particularly for heavy REEs. Conventional lab-based REE exploration restricts rapid and large-scale assessment, whereas hyperspectral imaging provides a promising approach for quantitative evaluation. This study evaluates the capacity of hyperspectral data for the quantitative determination of REEs in marine sediments. A total of 53 samples from various locations were analyzed to determine their chemical composition and spectral characteristics within the 380–1000 nm range under natural light. The influence of surface conditions on spectral integrity was evaluated, and multiple preprocessing and spectral feature extraction methods were applied to enhance data reliability. This study proposes a novel approach, termed Feature Importance within Pearson Correlation Coefficient-Based High-Correlation Spectral Range (PCCR-FI), designed for the identification of characteristic spectral bands associated with REEs. Machine learning models were subsequently constructed to estimate REE concentrations, and the following key findings were observed: (a) technical adjustments effectively addressed variations in sediment surface conditions, ensuring data reliability. (b) The PCCR-FI technique identified characteristic REEs spectral bands, enhancing processing efficiency and prediction accuracy. (c) The integration of the reciprocal logarithmic first derivative (TLOG-FD) technique with a multilayer perceptron (MLP) model, termed TLOG-FD-MLP, efficiently captured critical spectral features, resulting in improved prediction accuracy. For light REEs, the model achieved coefficient of determination (R²) values exceeding 0.60 and relative performance deviation (RPD) values exceeding 1.60, with some elements demonstrating R² values as high as 0.81 with RPD values surpassing 2.00. Furthermore, several heavy REEs exhibited moderate prediction performance, with R² values consistently exceeding 0.60. When considering the total REE content, an R² of 0.73 and an RPD of 1.97 were achieved. These findings demonstrate the use of hyperspectral imaging as a viable tool for quantitative evaluation of REE concentrations in marine sediments, providing valuable guidance for resource mapping and the exploration of seafloor polymetallic deposits.

Cobalt-rich ferromanganese crusts are an important deep-sea mineral resource, and the ore grade is a key indicator for evaluating their resource potential. Conventional ore-grade assessment relies on representative samples and extensive laboratory analyses, posing significant challenges due to limited sampling opportunities, high operational costs, and the pronounced structural heterogeneity of the crusts. To enable rapid estimation, the study combined uncalibrated hyperspectral radiance data acquired under natural daylight (without conversion to reflectance) with high-resolution electron probe micro-analysis (EPMA) measurements from the MED69A sample collected from the Magellan Seamounts to construct a sample-scale spectral–chemical dataset. This dataset was used to systematically compare spectral preprocessing methods, feature band selection strategies, and machine learning models. Based on the overall evaluation, the CR-CARS-MLP framework was selected as the optimal approach for spectral feature extraction, informative band selection, and metal concentration estimation under the current acquisition conditions. For cobalt (Co), the proposed framework achieved a coefficient of determination (R²) of 0.9646, a root mean square error (RMSE) of 0.0404, a residual predictive deviation (RPD) of 5.3720, and a mean absolute error (MAE) of 0.0216, demonstrating satisfactory internal test performance on the available EPMA–hyperspectral dataset. The results indicate that radiance data acquired under natural illumination at a close range (15.5 cm) retain statistically informative value for estimating element concentrations in cobalt-rich ferromanganese crusts (the data were not converted to reflectance values). The proposed framework therefore provides an effective approach for rapid, non-destructive estimation of metal elements in cobalt-rich ferromanganese crusts. Further, it facilitates investigation of metal enrichment patterns and grade variations associated with crust growth layers, providing a valuable reference for ore-grade estimation and resource assessment. The real-time application potential and robustness of the proposed framework across different instrument platforms and illumination conditions require further validation.