Mapping within-field soil drainage using remote sensing, DEM and apparent soil electrical conductivity Jiangui Liu a , Elizabeth Pattey a, ⁎ , Michel C. Nolin b , John R. Miller c , Oumar Ka b a Agriculture and Agri-Food Canada, Eastern Cereal and Oilseed Research Centre, 960 Carling Avenue, Ottawa, Ontario, Canada K1A 0C6 b Agriculture and Agri-Food Canada, Soils and Crops Research and Development Centre, 979 de Bourgogne Avenue, Quebec, Quebec, Canada G1W 2L4 c Department of Earth and Space Science and Engineering, York University, Petrie Science Building, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3 Received 1 March 2007; received in revised form 23 October 2007; accepted 6 November 2007 Abstract In this study, we evaluated the capability of different datasets for soil drainage mapping within agricultural fields. The evaluated datasets include apparent soil electrical conductivity (ECa), remotely sensed high-resolution airborne hyperspectral reflectance (HR) and C-band synthetic aperture radar (SAR) backscattering coefficients, and a high precision digital elevation model (DEM) generated from GPS measurements. The study site was located in an experimental farm in Ottawa, Ontario, Canada. Three drainage classes representing moderately well drained, imperfectly drained, and poorly drained soils were identified during field surveys according to soil surveyor expert knowledge. Variables that significantly contributed to soil drainage classification were selected from the evaluated datasets with a stepwise discriminant analysis procedure. The selected variables were then used to classify soil drainage with a maximum likelihood classifier. A substantial agreement between the observed and classified drainage classes was achieved using the HR dataset, with a kappa coefficient (κ) of 0.68. Moderate agreement was achieved using the SAR and the ECa datasets, with κ = 0.52 and 0.55, respectively. The result obtained using the DEM-derived topographic variables showed only a fair agreement (κ = 0.31). Canonical analysis was also conducted to investigate the association between these datasets and field-observed soil water regime descriptors. This potentially provides an alternative way of drainage mapping using canonical variate. The canonical correlation between the water regime descriptors and the evaluated datasets was 0.81, 0.75 and 0.83 for the HR, SAR and soil ECa datasets, respectively. In this study, the topographic variables were not as efficient, but when combined with the SAR and soil ECa datasets, they improved soil drainage mapping. Crown Copyright © 2007 Published by Elsevier B.V. All rights reserved. Keywords: Soil drainage; Within-field mapping; Remote sensing; Apparent soil electrical conductivity; DEM; Discriminant analysis; Canonical analysis 1. Introduction Precision agriculture is envisioned as a key approach to in- creasing the sustainability of crop production; however, traditional soil maps are often not accurate and reliable enough to fulfill the requirements of site-specific crop management. Therefore these soil maps need to be upgraded to finer scales using accurate and objective soil information. Among the var- ious soil properties, soil drainage is important as it directly affects plant growth, water flow and solute transport in soils (Kravchenko et al., 2002). Here, drainage refers to the natural ability of soil to allow water to infiltrate and percolate. Drainage mapping is of interest because soil map users usually need information about soil properties or soil behaviour rather than taxonomic classes for land use and management decision (Bartelli, 1979). Conventionally, soil mapping is made by a trained soil taxonomist to delineate predetermined classes using soil survey along controlled land transects. It is challenging and often problematic to classify a soil concept (e.g., soil drainage class) consistently this way, since the soil classes are frequently overlapping and usually defined by multiple soil properties, and the identification of a soil drainage class relies on the expertise of an individual soil surveyor (Webster and Burrough, 1974). It is Available online at www.sciencedirect.com Geoderma 143 (2008) 261 – 272 www.elsevier.com/locate/geoderma ⁎ Corresponding author. Tel.: +1 613 7591523; fax: +1 613 7591724. E-mail addresses: liu_jiangui@yahoo.com (J. Liu), patteye@agr.gc.ca (E. Pattey). 0016-7061/$ - see front matter. Crown Copyright © 2007 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2007.11.011