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Sensors 2008, 8(1), 314-326;

Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data

Department of Geosciences, 921 S. 8th Ave Stop 8072, Idaho State University, Pocatello, Idaho 83209-8072, USA
Land Resources and Environmental Sciences Department, PO Box 173120, Montana State University, Bozeman, MT 59717-3120, USA
Author to whom correspondence should be addressed.
Received: 15 November 2007 / Accepted: 9 January 2008 / Published: 21 January 2008
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
Full-Text   |   PDF [315 KB, uploaded 21 June 2014]


Agricultural producers require knowledge of soil water at plant rooting depths,while many remote sensing studies have focused on surface soil water or mechanisticmodels that are not easily parameterized. We developed site-specific empirical models topredict spring soil water content for two Montana ranches. Calibration data sample sizeswere based on the estimated variability of soil water and the desired level of precision forthe soil water estimates. Models used Landsat imagery, a digital elevation model, and asoil survey as predictor variables. Our objectives were to see whether soil water could bepredicted accurately with easily obtainable calibration data and predictor variables and toconsider the relative influence of the three sources of predictor variables. Independentvalidation showed that multiple regression models predicted soil water with average error(RMSD) within 0.04 mass water content. This was similar to the accuracy expected basedon a statistical power test based on our sample size (n = 41 and n = 50). Improvedprediction precision could be achieved with additional calibration samples, and rangemanagers can readily balance the desired level of precision with the amount of effort tocollect calibration data. Spring soil water prediction effectively utilized a combination ofland surface imagery, terrain data, and subsurface soil characterization data. Rancherscould use accurate spring soil water content predictions to set stocking rates. Suchmanagement can help ensure that water, soil, and vegetation resources are usedconservatively in irrigated and non-irrigated rangeland systems. View Full-Text
Keywords: empirical models; precision agriculture; rangeland; site-specific agriculture; soil survey empirical models; precision agriculture; rangeland; site-specific agriculture; soil survey
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Sankey, J.B.; Lawrence, R.L.; Wraith, J.M. Ad Hoc Modeling of Root Zone Soil Water with Landsat Imagery and Terrain and Soils Data. Sensors 2008, 8, 314-326.

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