Understanding Fields by Remote Sensing: Soil Zoning and Property Mapping
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Date
2020-04
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
Precision agriculture aims to optimize field management to increase agronomic yield, reduce environmental impact, and potentially foster soil carbon sequestration. In 2015, the Copernicus mission, with Sentinel-1 and -2, opened a new era by providing freely available high spatial and temporal resolution satellite data. Since then, many studies have been conducted to understand, monitor and improve agricultural systems. This paper presents results from the SolumScire project, focusing on the prediction of the spatial distribution of soil zones and topsoil properties, such as pH, soil organic matter (SOM) and clay content in agricultural fields through random forest algorithms. For this purpose, samples from 120 fields were investigated. The zoning and soil property prediction has an accuracy greater than 90%. This is supported by a high agreement of the derived zones with farmer’s observations. The trained models revealed a prediction accuracy of 94%, 89% and 96% for pH, SOM and clay content, respectively. The obtained models for soil properties can support precision field management, the improvement of soil sampling and fertilization strategies, and eventually the management of soil properties such as SOM.
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Publication status
published
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Book title
Journal / series
Volume
12 (7)
Pages / Article No.
1116
Publisher
MDPI
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Edition / version
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Software
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Date collected
Date created
Subject
Soil property prediction; pH; Soil organic matter; Soil clay content; Precision agriculture; Copernicus mission; Sentinel; Multi-spectral imagery; Synthetic aperture radar imagery; Machine learning; Random forest
Organisational unit
03894 - Walter, Achim / Walter, Achim