Predicting soil properties for sustainable agriculture using vis-NIR spectroscopy: a case study in northern Greece

Tsakiridis, Nikolaos L.; Tziolas, Nikolaos; Dimitrakos, Agathoklis; Galanis, George; Ntonou, Eleftheria; Tsirika, Anastasia; Terzopoulou, Evangelia; Kalopesa, Eleni; Zalidis, George C.

doi:10.1117/12.2277905

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…Tillage-induced soil erosion brings about the progressive removal of soil horizons and the corresponding accumulation of soil materials at the slope (Previtali 2014). Furthermore, soil erosion may reduce crop production potential, lower surface water quality and damage tile drainage systems (Toy et al 2002). Erosion is associated with about 85% of land degradation in the world, causing up to a 17% reduction in crop productivity (Oldeman et al 1990).…”

Section: Soil Erosion and Degradationmentioning

confidence: 99%

Imaging Spectroscopy for Soil Mapping and Monitoring

et al. 2019

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There is a renewed awareness of the finite nature of the world's soil resources, growing concern about soil security and significant uncertainties about the carrying capacity of the planet. Regular assessments of soil conditions from local through to global scales are requested, and there is a clear demand for accurate, up-to-date and spatially referenced soil information by the modelling scientific community, farmers and land users, and policy-and decision-makers. Soil and imaging spectroscopy, based on visible-near-infrared and shortwave infrared (400-2500 nm) spectral reflectance, has been shown to be a proven method for the quantitative prediction of key soil surface properties. With the upcoming launch of the next generation of hyperspectral satellite sensors in the next years, a high potential to meet the demand for global soil mapping and monitoring is appearing. In this paper, we briefly review the basic concepts of soil spectroscopy with a special attention to the effects of soil roughness on reflectance and then provide a review of state of the art, achievements and perspectives in soil mapping and monitoring based on imaging spectroscopy from airand spaceborne sensors. Selected application cases are presented for the modelling of soil organic carbon, mineralogical composition, topsoil water content and characterization of soil crust, soil erosion and soil degradation stages based on airborne and simulated spaceborne imaging spectroscopy data. Further, current challenges, gaps and new directions toward enhanced soil properties modelling are presented. Overall, this paper highlights the potential and limitations of multiscale imaging spectroscopy nowadays for soil mapping and monitoring, and capabilities and requirements of upcoming spaceborne sensors as support for a more informed and sustainable use of our world's soil resources.

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Section: Soil Erosion and Degradationmentioning

confidence: 99%

Imaging Spectroscopy for Soil Mapping and Monitoring

et al. 2019

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“…However, concerning the possibility to predict the amount of different N forms by vis–NIR spectroscopy, the results are sometimes contradictory (Fystro, 2002; Soriano‐Disla et al, 2014; Stenberg et al, 2010). Recently, Chen et al (2022) conducted a small‐scale study where they mixed different types of fertilizers with two soil types and were able to predict the levels of N fertilizer based on vis–NIR imaging spectroscopy, whereas Tsakiridis et al (2017) successfully predicted the amount of NO 3 − from diffuse reflectance spectra. These studies showed promising results on dried soils and raise the question about whether good correlations can be found with vis–NIR spectra recorded in situ.…”

Section: Introductionmentioning

confidence: 99%

Prediction of nitrogen, active carbon, and organic carbon‐to‐clay ratio in agricultural soils by in‐situ spectroscopy

Metzger,

Bragazza

2024

European J Soil Science

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Visible and near‐infrared (vis–NIR) spectroscopy is a promising technology for the analysis of different soil quality parameters. In this study, we used in‐situ vis–NIR spectroscopy in association with partial least squares regression to predict the total and the mineral (nitrate + ammonium) nitrogen content, the permanganate oxidizable carbon (POXC), as well as the ratio of soil organic carbon‐to‐clay content in different agricultural soils in Switzerland. These parameters can indeed be used as indicators of soil quality in response to agronomic practices. To this goal, a total number of 134 soil samples were used for carbon‐, total nitrogen‐ and clay‐related parameters, whereas 69 soil samples were used for the mineral nitrogen‐related parameters. We found that the partial least squares regression model can successfully predict the total nitrogen and the POXC content as well as the ratio of soil organic carbon‐to‐clay content (ratio of performance to interquartile range, RPIQ > 2.62, R2 > 0.73, Lin's concordance correlation coefficient > 0.83). As concerns the mineral nitrogen, it was not possible to successfully predict this parameter by vis–NIR spectroscopy. By demonstrating the possibility to reliably predict POXC content and the soil organic carbon‐to‐clay ratio, we show that vis–NIR can be also used to analyse soil parameters associated with both the quality of organic carbon and the structural quality of agricultural soils.

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