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Towards Mapping of Soil Crust Using Multispectral Imaging

Soil crusts and surface roughness are properties which are highly dynamic in both space and time that change in response to biotic processes, meteorological conditions and farming operations. These factors, however, are difficult to quantify and are usually described using simplified expert-based cl...

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Autores principales: Crucil, Giacomo, Van Oost, Kristof
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961633/
https://www.ncbi.nlm.nih.gov/pubmed/33800859
http://dx.doi.org/10.3390/s21051850
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author Crucil, Giacomo
Van Oost, Kristof
author_facet Crucil, Giacomo
Van Oost, Kristof
author_sort Crucil, Giacomo
collection PubMed
description Soil crusts and surface roughness are properties which are highly dynamic in both space and time that change in response to biotic processes, meteorological conditions and farming operations. These factors, however, are difficult to quantify and are usually described using simplified expert-based classes. This hampers a clear identification of the controlling factors and their relation to soil erosion and sediment generation processes. The availability of new small portable multispectral cameras offers the potential to study soil surface dynamics at a high spatial and temporal resolution. The objective of this study was to analyse the relationship between soil crusting, represented by cumulative rainfall kinetic energy, and soil surface reflectance, as derived from vis-NIR multispectral imaging. We designed a series of rainfall-soil surface experiments to disentangle the effects of soil crusting on spectral reflectance factors from those related to surface micro-scale roughness. Partial least squared regression (PLSR) models were developed to predict both kinetic energy and roughness from multispectral images. We evaluated different roughness removal methods which were based on the transformation of reflectance through standard normal variate (SNV) and roughness thresholding using high resolution digital elevation models. Furthermore, we assigned the light scattering effect related to roughness in the multispectral spatial domain by calculating the inter-quantile range of the reflectance values in a kernel. Our experiments and workflow demonstrate that it is possible to model crust development, using rainfall kinetic energy as a proxy, from vis-NIR based multispectral imaging.
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spelling pubmed-79616332021-03-17 Towards Mapping of Soil Crust Using Multispectral Imaging Crucil, Giacomo Van Oost, Kristof Sensors (Basel) Article Soil crusts and surface roughness are properties which are highly dynamic in both space and time that change in response to biotic processes, meteorological conditions and farming operations. These factors, however, are difficult to quantify and are usually described using simplified expert-based classes. This hampers a clear identification of the controlling factors and their relation to soil erosion and sediment generation processes. The availability of new small portable multispectral cameras offers the potential to study soil surface dynamics at a high spatial and temporal resolution. The objective of this study was to analyse the relationship between soil crusting, represented by cumulative rainfall kinetic energy, and soil surface reflectance, as derived from vis-NIR multispectral imaging. We designed a series of rainfall-soil surface experiments to disentangle the effects of soil crusting on spectral reflectance factors from those related to surface micro-scale roughness. Partial least squared regression (PLSR) models were developed to predict both kinetic energy and roughness from multispectral images. We evaluated different roughness removal methods which were based on the transformation of reflectance through standard normal variate (SNV) and roughness thresholding using high resolution digital elevation models. Furthermore, we assigned the light scattering effect related to roughness in the multispectral spatial domain by calculating the inter-quantile range of the reflectance values in a kernel. Our experiments and workflow demonstrate that it is possible to model crust development, using rainfall kinetic energy as a proxy, from vis-NIR based multispectral imaging. MDPI 2021-03-06 /pmc/articles/PMC7961633/ /pubmed/33800859 http://dx.doi.org/10.3390/s21051850 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Crucil, Giacomo
Van Oost, Kristof
Towards Mapping of Soil Crust Using Multispectral Imaging
title Towards Mapping of Soil Crust Using Multispectral Imaging
title_full Towards Mapping of Soil Crust Using Multispectral Imaging
title_fullStr Towards Mapping of Soil Crust Using Multispectral Imaging
title_full_unstemmed Towards Mapping of Soil Crust Using Multispectral Imaging
title_short Towards Mapping of Soil Crust Using Multispectral Imaging
title_sort towards mapping of soil crust using multispectral imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7961633/
https://www.ncbi.nlm.nih.gov/pubmed/33800859
http://dx.doi.org/10.3390/s21051850
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