2012
DOI: 10.1007/s11119-012-9270-9
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Understanding the errors in input prescription maps based on high spatial resolution remote sensing images

Abstract: The aim of this study was to determine the positional accuracy of GeoEye-1 images and how it affects the delineation of the input prescription map (IPM) for sitespecific strategies. Seven panchromatic and multi-spectral GeoEye-1 satellite images were taken over the LaVentilla village area (Andalusia, Spain), from April to October 2010, at an interval of approximately 3-4 weeks. Sixteen hard-edge ground control points (GCPs) were geo-referenced using a sub-decimetre DGPS. Each DGPS-GCP position was compared wit… Show more

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Cited by 10 publications
(8 citation statements)
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“…Currently, only a few researchers have considered positional accuracy requirements as they relate to plot extraction (e.g., Gomez-Candon et al, 2012). Therefore, discussion focuses on interpreting the meaning of these results in the broader context of plot extraction and analysis.…”
Section: Discussionmentioning
confidence: 99%
“…Currently, only a few researchers have considered positional accuracy requirements as they relate to plot extraction (e.g., Gomez-Candon et al, 2012). Therefore, discussion focuses on interpreting the meaning of these results in the broader context of plot extraction and analysis.…”
Section: Discussionmentioning
confidence: 99%
“…Traditional change-detection methods were originally either pixel-based [7,8] or objectbased [9][10][11]. Traditional methods of change detection in remote-sensing images are designed on the basis of handcrafted features and supervised classification algorithms.…”
Section: Change Detection Methodsmentioning
confidence: 99%
“…Many change-detection methods have been proposed over the years. Traditional methods tended to be either pixel-based [7,8] or object-based [9][10][11]. Pixel-based changedetection methods involve pixel by pixel analysis of spectral or textural information of input image pairs followed by threshold-based segmentation to obtain the detection results [7,8].…”
Section: Introductionmentioning
confidence: 99%
“…Bu et al [52] compared RapidEye satellite imagery and ground-based active optical sensors as yield predictors in sugar beet, spring wheat, corn, and sunflower. Gomez-Candon et al [53] assessed the errors in input prescription maps based on high spatial resolution GeoEye-1 images. Caturegli et al [54] compared GeoEye-1 satellite and ground-based multispectral data for estimating nitrogen status of turfgrasses.…”
Section: Applications Of High Resolution Satellite Sensors In Precisimentioning
confidence: 99%