Journal
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
Volume 43, Issue -, Pages 32-42Publisher
ELSEVIER
DOI: 10.1016/j.jag.2015.03.017
Keywords
Remote sensing; High spatial resolution imagery; Relevance vector machine; Precision agriculture; Chlorophyll concentration
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Funding
- Utah Water Research Laboratory (UWRL)
- Provo/Utah Office of the US Bureau of Reclamation
- AggieAir Flying Circus at the UWRL
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Precision agriculture requires high-resolution information to enable greater precision in the management of inputs to production. Actionable information about crop and field status must be acquired at high spatial resolution and at a temporal frequency appropriate for timely responses. In this study, high spatial resolution imagery was obtained through the use of a small, unmanned aerial system called AggieAir (TM). Simultaneously with the AggieAir flights, intensive ground sampling for plant chlorophyll was conducted at precisely determined locations. This study reports the application of a relevance vector machine coupled with cross validation and backward elimination to a dataset composed of reflectance from high-resolution multi-spectral imagery (VIS-NIR), thermal infrared imagery, and vegetative indices, in conjunction with in situ SPAD measurements from which chlorophyll concentrations were derived, to estimate chlorophyll concentration from remotely sensed data at 15-cm resolution. The results indicate that a relevance vector machine with a thin plate spline kernel type and kernel width of 5.4, having LAI, NDVI, thermal and red bands as the selected set of inputs, can be used to spatially estimate chlorophyll concentration with a root-mean-squared-error of 5.31 mu g cm(-2), efficiency of 0.76, and 9 relevance vectors. (C) 2015 The Authors. Published by Elsevier B.V.
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