Hyperspectral imaging is a recent technology that has been gaining popularity in the geosciences since the 1990s, both in remote sensing and in the field or laboratory. Indeed, it allows the rapid acquisition of a large amount of data that are spatialized on the studied object with a low-cost, compact, and automatable sensor. This practical article aims to present the current state of knowledge on the use of hyperspectral imaging for sediment core analysis (core logging). To use the full potential of this type of sensor, many points must be considered and will be discussed to obtain reliable and quality data to extract many environmental properties of sediment cores. Hyperspectral imaging is used in many fields (e.g., remote sensing, geosciences and artificial intelligence) and offers many possibilities. The applications of the literature will be reviewed under five themes: lake and water body trophic status, source-to-sink approaches, organic matter and mineralogy studies, and sedimentary deposit characterization. Afterward, discussions will be focused on a multisensor core logger, data management, integrated use of these data for the selection of sample areas, and other opportunities. Through this practical article, we emphasize that hyperspectral imaging applied to sediment cores is still an emerging tool and shows many possibilities for refining the understanding of environmental processes.

Past primary productivity is tracked in lake sediments to reconstruct environmental changes. However, the resolution of the routinely used destructive techniques is not suitable for the analysis of a large number of samples due to cost. Non-destructive analysis of chlorophyll-a performed by hyperspectral imagery enables the quick determination of indices of past primary productivity. Eighteen indices used in paleo-environmental reconstruction were inventoried to define the best index capable of tracking chlorophyll-a by this technique. All the indices were applied to hyperspectral data measured on the sediment of Lake Bresson, in which detrital organic matter input is likely to skew chlorophyll-a identification, and the results were compared with concentrations measured by a routinely used destructive analysis. The 18 indices all produced a different result and only three indices were positively correlated with chlorophyll-a concentrations (n = 28, p < 0.0001). The detrital organic matter impacts chlorophyll-a characterization and shows the bias produced by the sediment matrix variations. Moreover, artificial modification of the sediment matrix revealed an impact of the mineral phase. To tackle this issue, the indices are normalized by two proxies of the sediment components. This new approach reduces the impact of detrital organic matter, hence the sediment matrix variations also reduce the normalization of the chlorophyll-a indices by a specific proxy of the mineral phase. These results identify the impact of local geochemical features that limit past primary productivity assessment and show the necessity of characterizing the sediment composition prior to tracking the chlorophyll-a by hyperspectral imaging.