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This review provides an overview of the use of remote sensing data, the development of spectral reflectance indices for detecting plant water stress, and the usefulness of field measurements for ground-truthing purposes. Reliable measurements of plant water stress over large areas are often required for management applications in the fields of agriculture, forestry, conservation and land rehabilitation. The use of remote sensing technologies and spectral reflectance data for determining spatial patterns of plant water stress is widely described in the scientific literature. Airborne, space-borne and hand-held remote sensing technologies are commonly used to investigate the spectral responses of vegetation to plant stress. Earlier studies utilised multispectral sensors which commonly collect four to seven spectral bands in the visible and near-infrared region of the electromagnetic spectrum. Advances in sensor and image processor technology over the past 3 decades now allow for the simultaneous collection of several hundred narrow spectral bands resulting in more detailed hyperspectral data. The availability of hyperspectral data has led to the identification of several spectral indices that have been shown to be useful in identifying plant stress. Such studies have revealed strong linear relationships between plant pigment concentration and the visible (VIS) and near-infrared (NIR) reflectance, while plant water content has been linked to specific bands in the shortwave infrared (SWIR) region of the spectrum. Ground-truthing is essential to identifying useful reflectance information for detecting plant water stress, and four commonly used ground-based methods viz. predawn leaf water potential, leaf chlorophyll fluorescence, leaf pigment concentrations and leaf water content are reviewed for their, usefulness and practical application.

In thermal remote sensing the invisible radiation patterns of objects are converted into visible images and these images are called thermograms or thermal images. Thermal images can be acquired using portable, hand-held or thermal sensors that are coupled with optical systems mounted on an airplane or satellite. This technology is a non-invasive, non-contact and non-destructive technique used to determine thermal properties and features of any object of interest and therefore it can be used in many fields, where heat is generated or lost in space and time. Potential use of thermal remote sensing in agriculture includes nursery and greenhouse monitoring, irrigation scheduling, plants disease detection, estimating fruit yield, evaluating maturity of fruits and bruise detection in fruits and vegetables. This paper reviews the application of thermal imaging in agriculture and its potential use in various agricultural practices.

The use of disparate data sources within a pixel level image fusion procedure has been well documented for pan-sharpening studies. The present paper explores various image fusion procedures for the fusion of multi-spectral ASTER data and a RadarSAT-1 SAR scene. The research sought to determine which fusion procedure merged the largest amount of SAR texture into the ASTER scenes, while also preserving the spectral content. An additional application based maximum likelihood classification assessment was also undertaken. Three SAR scenes were tested namely, one backscatter scene and two textural measures calculated using grey level co-occurrence matrices (GLCM). Each of these were fused to the ASTER data using the following established approaches; Brovey transformation, Intensity Hue and Saturation, Principal Component Substitution, Discrete wavelet transformation, and a modified discrete wavelet transformation using the IHS approach. Resulting data sets were assessed using qualitative and quantitative (entropy, universal image quality index, maximum likelihood classification) approaches. Results from the study indicated that while all post fusion data sets contained more information (entropy analysis), only the frequency-based fusion approaches managed to preserve the spectral quality of the original imagery. Furthermore results also indicated that the textural (mean, contrast) SAR scenes did not add any significant amount of information to the post-fusion imagery. Classification accuracy was not improved when comparing ASTER optical data and pseudo optical bands generated from the fusion analysis. Accuracies range from 68.4% for the ASTER data to well below 50% for the component substitution methods. Frequency based approaches also returned lower accuracies when compared to the unfused optical data. The present study essentially replicated (pan-sharpening) studies using the high resolution SAR scene as a pseudo panchromatic band.

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