Browsing by Author "Stenberg, Bo"

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    In-field soil spectroscopy in Vis–NIR range for fast and reliable soil analysis: A review
    (John Wiley & Sons, 2024) Piccini, Chiara; Metzger, Konrad; Debaene, Guillaume; Stenberg, Bo; Götzinger, Sophia; Boru˚ vka, Luboˇs; Sandén, Taru; Bragazza, Luca; Liebisch, Frank
    In-field soil spectroscopy represents a promising opportunity for fast soil analysis, allowing the prediction of several soil properties from one spectral reading representing one soil sample. This facilitates data acquisition from large amounts of samples through its rapidity and the absence of required chemical processing. This is of particular interest in agriculture, where the chance to retrieve information from soils directly in the field is very appealing. This review is focused on infield visible to near infrared (Vis–NIR) spectroscopy (350–2500 nm), aimed at analysing soils directly in the field through proximal sensing. The main scope was to explore the available knowledge to identify existing gaps limiting the reliability and robustness of in-field measurement, to foster future research and help transition towards the practical application of this technology. For this purpose, a literature review was performed, and surveyed information encompassed sensor range, carrier platforms in use, sensor type, distance to the soil sample, measurement methodology, measured soil properties and soil management, among many others. From this, we derived a list of tools in use with their spectral measurement properties, including the potential cross-calibration with soil spectral libraries from laboratory spectroscopy of soil samples and potential measured target soil properties. Different instruments and sensors used to measure at varying wavelength ranges and with different spectral qualities are available for a large range of prices. The most frequently analysed soil properties included soil carbon contents (soil organic carbon, soil organic matter, total carbon), texture (clay, silt, sand), total nitrogen, pH and cation exchange capacity. Future perspectives comprise the implementation of larger databases, including different instruments and cropping systems as well as methodologies combining existing knowledge regarding laboratory spectroscopy with in-field methods. The authors highlight the need for a broadly accepted measurement protocol for in-field soil spectroscopy, fostering harmonization and standardization and consequently a more robust application in practice.
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    Influence of Soil Texture on the Estimation of Soil Organic Carbon From Sentinel‐2 Temporal Mosaics at 34 European Sites
    (Wiley, 2025) Wetterlind, Johanna; Simmler, Michael; Castaldi, Fabio; Borůvka, Luboš; Gabriel, José L.; Gomes, Lucas Carvalho; Khosravi, Vahid; Kivrak, C.; Koparan, Muhammed Halil; Lázaro-López, Alberto; Łopatka, Artur; Liebisch, Frank; Rodriguez, Jose Antonio; Savaş, A. Ö.; Stenberg, Bo; Tunçay, T.; Vinci, I.; Volungevičius, Jonas; Žyledis, Renaldas; Vaudour, Emmanuelle
    Multispectral imaging satellites such as Sentinel‐2 are considered a possible tool to assist in the mapping of soil organic carbon (SOC) using images of bare soil. However, the reported results are variable. The measured reflectance of the soil surface is not only related to SOC but also to several other environmental and edaphic factors. Soil texture is one such factor that strongly affects soil reflectance. Depending on the spatial correlation with SOC, the influence of soil texture may improve or hinder the estimation of SOC from spectral data. This study aimed to investigate these influences using local models at 34 sites in different pedo‐climatic zones across 10 European countries. The study sites were individual agricultural fields or a few fields in close proximity. For each site, local models to predict SOC and the clay particle size fraction were developed using the Sentinel‐2 temporal mosaics of bare soil images. Overall, predicting SOC and clay was difficult, and prediction performances with a ratio of performance to deviation (RPD) > 1.5 were observed at 8 and 12 of the 34 sites for SOC and clay, respectively. A general relationship between SOC prediction performance and the correlation of SOC and clay in soil was evident but explained only a small part of the large variability we observed in SOC prediction performance across the sites. Adding information on soil texture as additional predictors improved SOC prediction on average, but the additional benefit varied strongly between the sites. The average relative importance of the different Sentinel‐2 bands in the SOC and clay models indicated that spectral information in the red and far‐red regions of the visible spectrum was more important for SOC prediction than for clay prediction. The opposite was true for the region around 2200 nm, which was more important in the clay models.