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Remote Sens. 2016, 8(11), 927; doi:10.3390/rs8110927

Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

Institute of Computer Science, Osnabrück University, Wachsbleiche 27, D-49090 Osnabrück, Germany
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi, Clement Atzberger and Prasad S. Thenkabail
Received: 13 July 2016 / Revised: 30 October 2016 / Accepted: 3 November 2016 / Published: 8 November 2016
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Abstract

The determination of soil texture and organic carbon across agricultural areas provides important information to derive soil condition. Precise digital soil maps can help to till agricultural fields with more accuracy, greater cost-efficiency and better environmental protection. In the present study, the laboratory analysis of sand, silt, clay and soil organic carbon (SOC) content was combined with hyperspectral image data to estimate the distribution of soil texture and SOC across an agricultural area. The aim was to identify regions with similar soil properties and derive uniform soil regions based on this information. Soil parameter data and corresponding laboratory spectra were used to calibrate cross-validated (leave-one-out) partial least squares regression (PLSR) models, resulting in robust models for sand (R2 = 0.77, root-mean-square error (RMSE) = 5.37) and SOC (R2 = 0.89, RMSE = 0.27), as well as moderate models for silt (R2 = 0.62, RMSE = 5.46) and clay (R2 = 0.53, RMSE = 2.39). The regression models were applied to Airborne Imaging Spectrometer for Applications DUAL (aisaDUAL) hyperspectral image data to spatially estimate the concentration of these parameters. Afterwards, a decision tree, based on the Food and Agriculture Organization (FAO) soil texture classification scheme, was developed to determine the soil texture for each pixel of the hyperspectral airborne data. These soil texture regions were further refined with the spatial SOC estimations. The developed method is useful to identify spatial regions with similar soil properties, which can provide a vital information source for an adapted treatment of agricultural fields in terms of the necessary amount of fertilizers or water. The approach can also be adapted to wider regions with a larger sample size to create detailed digital soil maps (DSMs). Further, the presented method should be applied to future hyperspectral satellite missions like Environmental Mapping and Analysis Program (EnMap) and Hyperspectral Infrared Imager (HyspIRI) to cover larger areas in shorter time intervals. Updated DSMs on a regular basis could particularly support precision farming aspects. View Full-Text
Keywords: hyperspectral; stratification; sand; silt; clay; soil organic carbon (SOC); partial least squares regression (PLSR); decision-tree; digital soil maps (DSMs) hyperspectral; stratification; sand; silt; clay; soil organic carbon (SOC); partial least squares regression (PLSR); decision-tree; digital soil maps (DSMs)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Kanning, M.; Siegmann, B.; Jarmer, T. Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations. Remote Sens. 2016, 8, 927.

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