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Article

Clustering Tools for Integration of Satellite Remote Sensing Imagery and Proximal Soil Sensing Data

1
Department of Bioresource Engineering, McGill University, Montreal, QC H9X 3V9, Canada
2
School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
3
Ontario Ministry of Agriculture, Food and Rural Affairs, Guelph, ON N1G 4Y2, Canada
4
Woodrill Farms Ltd., Guelph, ON N1H 6H8, Canada
5
Department of Soil and Environment, Precision Agriculture and Pedometrics, Swedish University of Agricultural Sciences, SE-532 23 Skara, Sweden
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1036; https://doi.org/10.3390/rs11091036
Received: 28 February 2019 / Revised: 9 April 2019 / Accepted: 25 April 2019 / Published: 1 May 2019
(This article belongs to the Special Issue Selected Papers from Agro-Geoinformatics 2018)
Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected by these sensors may provide essential information for precision or site-specific management in a production field. Data clustering techniques are crucial for data mining, and high-density data analysis is important for field management. A new clustering technique was introduced and compared with existing clustering tools to determine the relatively homogeneous parts of agricultural fields. A DUALEM-21S sensor, along with high-accuracy topography data, was used to characterize soil variability in three agricultural fields situated in Ontario, Canada. Sentinel-2 data assisted in quantifying bare soil and vegetation indices (VIs). The custom Neighborhood Search Analyst (NSA) data clustering tool was implemented using Python scripts. In this algorithm, part of the variance of each data layer is accounted for by subdividing the field into smaller, relatively homogeneous, areas. The algorithm’s attributes were illustrated using field elevation, shallow and deep apparent electrical conductivity (ECa), and several VIs. The unique feature of this proposed protocol was the successful development of user-friendly and open source options for defining the spatial continuity of each group and for use in the zone delineation process. View Full-Text
Keywords: remote sensing; proximal soil sensing; clustering techniques; spatial homogeneity; management zones remote sensing; proximal soil sensing; clustering techniques; spatial homogeneity; management zones
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MDPI and ACS Style

Saifuzzaman, M.; Adamchuk, V.; Buelvas, R.; Biswas, A.; Prasher, S.; Rabe, N.; Aspinall, D.; Ji, W. Clustering Tools for Integration of Satellite Remote Sensing Imagery and Proximal Soil Sensing Data. Remote Sens. 2019, 11, 1036. https://doi.org/10.3390/rs11091036

AMA Style

Saifuzzaman M, Adamchuk V, Buelvas R, Biswas A, Prasher S, Rabe N, Aspinall D, Ji W. Clustering Tools for Integration of Satellite Remote Sensing Imagery and Proximal Soil Sensing Data. Remote Sensing. 2019; 11(9):1036. https://doi.org/10.3390/rs11091036

Chicago/Turabian Style

Saifuzzaman, Md; Adamchuk, Viacheslav; Buelvas, Roberto; Biswas, Asim; Prasher, Shiv; Rabe, Nicole; Aspinall, Doug; Ji, Wenjun. 2019. "Clustering Tools for Integration of Satellite Remote Sensing Imagery and Proximal Soil Sensing Data" Remote Sens. 11, no. 9: 1036. https://doi.org/10.3390/rs11091036

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