Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture

Abstract

Please cite this article in press as: Elarab, M., et al., Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture. Int. J. Appl. Earth Observ. Geoinf. (2015), http://dx. a b s t r a c tPrecision 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 g cm −2 , efficiency of 0.76, and 9 relevance vectors.

Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines

“…The RVM is a nonlinear model based on a Bayesian probabilistic treatment to determine a descriptive function based on existing information. The RVM has been extensively used in hydrological, water resources, and Earth image processing [6,15,18,19,54,55], with excellent results.…”

Estimation of Energy Balance Components over a Drip-Irrigated Olive Orchard Using Thermal and Multispectral Cameras Placed on a Helicopter-Based Unmanned Aerial Vehicle (UAV)

“…RSEB algorithms have been found to be useful to account for the spatial and seasonal variability of ETa at regional and field scales when using satellite platforms like Landsat (8, 7 and 5) and ASTER [18,24,25]. Main limitations of current satellite platforms for practical application of RDI and SSIM, especially in heterogeneous canopies such as orchards and vineyards, are the lack of fine spatial resolution and real-time data at the field and sub-field scales [26,27]. Berni et al [28,29] suggested that the two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery having optimum spatial and spectral resolution and unfavorable revisit times for many crop stress-detection applications.…”

Estimating potato leaf chlorophyll content using ratio vegetation indices

“…Chlorophyll plays a crucial role in photosynthesis and in plant functioning. Remote sensing can provide farmers with location-specific information of chlorophyll content as a proxy of crop response to nitrogen application during different growing phases of the season (van Evert et al 2012;Elarab et al 2015). For precision agriculture, the spatial resolution should be at least 20 m and preferably in the order of 10 m in order to make site-specific management possible (Mulla 2013).…”