SYNTHETIC IMAGERY OF COTTON CROPS: SCALING FROM LEAF TO FULL CANOPY

Authors: Sassenrath, Gretchen; ALARCON-CALDERON, V - MISS. STATE UNIV; PRINGLE, H - DELTA RES. & EXT. CTR.

Submitted to: Digital Imaging and Spectral Techniques: Application to Precision Agriculture
Publication Type: Monograph
Publication Acceptance Date: 5/1/2003
Publication Date: 10/1/2003
Citation: Sassenrath Cole, G.F., Alarcon-Calderon, V.J., Pringle, H.C. 2003. Synthetic imagery of cotton crops: scaling from leaf to full canopy. Digital Imaging and Spectral Techniques: Application to Precision Agriculture. Chapter 10, pg. 111-133

Interpretive Summary: Remote imagery offers the potential for rapid, timely, and continuous monitoring of crop status. These systems have the potential to offer significant benefit to crop production and management. However, errors are introduced in moving from the well-characterized measurements of individual leaves to interpretation of factors operating in an entire production canopy. While many physiological functions can be examined in detail at the individual leaf level, extrapolating from an individual leaf to an entire plant canopy is erratic. Changes in canopy structure and leaf angle alter the reflectance from the individual leaves, and introduce deviations in the recorded spectra due to increases in soil reflectance and reflectance from lower canopy layers. These factors, together with atmospheric distortions, alter the reflectance spectra of a crop canopy from that recorded for individual leaves within that canopy. This study develops a methodology of building synthetic images from reflectance spectra of individual leaves. The individual factors impacting canopy reflectance can then be examined separately. The manuscript details a method of approximation of leaf spectral characteristics through the use of Fourier transformations that adequately describe the spectra and reduces the information required from 512 bands to 20 pairs of harmonics. These spectra from soil and vegetation are then randomly mixed and visualized using an imaging software package. The resultant synthetic spectra are remarkably similar to actual remote images of crop fields obtained from aerial sources. This methodology offers a powerful analytical technique to explore crop reflectance spectra, and build spectral libraries of reflectance patterns with physiological and morphological changes.

Technical Abstract: Advances in sensing technology are creating the rapid development of remote imaging systems for crop monitoring. Sensors are able to distinguish crop stress, such as that associated with low nutrient status, herbicide-induced stress, insect pressure and poor stand establishment. Most of these systems rely on the reflectance properties of natural pigments present in leaf tissue (carotenoids and chlorophylls), and detect the production of alternate compounds under stress conditions (e.g. xanthophylls) to indicate plant stress. Reflectance properties of these plant compounds in the visible region have led to the development of remote imagery systems in the visible and near-infrared regions. While substantial progress has been made in developing spectral libraries of known reflectance properties for specific plant physiological properties at the leaf and whole plant level, limitations in the application of remote imagery to production settings arise through the need for adequate ground-truthing of imagery to field conditions in the absence of atmospheric distortion. Field spectroradiometric measurements of individual cotton (Gossypium hirsutum, L.) leaves, dry soil and wet soil were used to create hyperspectral images with known radiometric, geometric and spatial properties. Fourier series were used to interpolate the spectrum of individual leaves and soil for subsequent generation of synthetic mixed spectra. The mixtures of hyperspectral profiles were built from the randomized linear summation of soil and vegetation spectra. The resulting spectra were assigned to pixels that simulate a resolution of 1m x 1m size. The dimensions of the generated plots were 18 m x 20 m, similar to the size of the field plots from which the spectral measurements were taken. From these synthetic images, we will explore the impact of leaf angle, soil reflectance and atmospheric distortion on canopy reflectance from aerial imagery.