# Understanding the errors in input prescription maps based on high spatial resolution remote sensing images

Understanding the errors in input prescription maps based on high spatial resolution remote... 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 site-specific 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 with the corresponding co-ordinates for each image to determine the position error (PE) and error direction angle ( $${\Upphi_{\text{ge}}}^{^\circ }$$ ). The PE and $${\Upphi_{\text{ge}}}^{^\circ }$$ for each GCP varied slightly for any given GeoEye-1 image and the overall PE among images estimated through the root mean square error (RMSE) varied considerably. RMSE ranged from approximately 2–9 m and from 3.5 to 9 m for the panchromatic and multi-spectral images studied, respectively, and the average was approximately 6.0 m for each of the series of images. Consequently, the geo-referencing of GeoEye-1 images is recommended to increase the positioning accuracy. Conventional geo-referencing using GCPs provided an average RMSE of 2 m for the panchromatic and 3.5 m for the multi-spectral images. The AUGEO System® geo-referencing of the 4-May GeoEye-1 image provided an RMSE of 0.75 m for the panchromatic and 2.70 ± 1.30 m for the multi-spectral images. The IPM delineated from remote-sensed images takes up the image geo-referencing error and, consequently, each micro-plot does not coincide with its corresponding ground-truth micro-plot. In this report, the percentage of non-overlapping area (%NOA) has been developed as a function of the PE/RMSE, α° (the angle between Φge and the operating direction, Φop), and the micro-plot size. The %NOA consistently increased as the RMSE and α° increased, and it decreased as the micro-plot width or length increased. The decision about micro-plot size should be based on the RMSE, α°, and the maximum admissible %NOA. In the case of the GeoEye-1 images studied with an average RMSE of 6 m, a micro-plot size of 6 × 30 m would have yielded an IPM inaccuracy (%NOA) of approximately 5 %, assuming an α° = 0°. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

# Understanding the errors in input prescription maps based on high spatial resolution remote sensing images

, Volume 13 (5) – Jun 17, 2012
13 pages

/lp/springer_journal/understanding-the-errors-in-input-prescription-maps-based-on-high-GpnkY0Kf99
Publisher
Springer US
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-012-9270-9
Publisher site
See Article on Publisher Site

### 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 site-specific 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 with the corresponding co-ordinates for each image to determine the position error (PE) and error direction angle ( $${\Upphi_{\text{ge}}}^{^\circ }$$ ). The PE and $${\Upphi_{\text{ge}}}^{^\circ }$$ for each GCP varied slightly for any given GeoEye-1 image and the overall PE among images estimated through the root mean square error (RMSE) varied considerably. RMSE ranged from approximately 2–9 m and from 3.5 to 9 m for the panchromatic and multi-spectral images studied, respectively, and the average was approximately 6.0 m for each of the series of images. Consequently, the geo-referencing of GeoEye-1 images is recommended to increase the positioning accuracy. Conventional geo-referencing using GCPs provided an average RMSE of 2 m for the panchromatic and 3.5 m for the multi-spectral images. The AUGEO System® geo-referencing of the 4-May GeoEye-1 image provided an RMSE of 0.75 m for the panchromatic and 2.70 ± 1.30 m for the multi-spectral images. The IPM delineated from remote-sensed images takes up the image geo-referencing error and, consequently, each micro-plot does not coincide with its corresponding ground-truth micro-plot. In this report, the percentage of non-overlapping area (%NOA) has been developed as a function of the PE/RMSE, α° (the angle between Φge and the operating direction, Φop), and the micro-plot size. The %NOA consistently increased as the RMSE and α° increased, and it decreased as the micro-plot width or length increased. The decision about micro-plot size should be based on the RMSE, α°, and the maximum admissible %NOA. In the case of the GeoEye-1 images studied with an average RMSE of 6 m, a micro-plot size of 6 × 30 m would have yielded an IPM inaccuracy (%NOA) of approximately 5 %, assuming an α° = 0°.

### Journal

Precision AgricultureSpringer Journals

Published: Jun 17, 2012

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