Abstract
A detailed introduction to variogram analysis of reflectance data is provided, and variogram parameters (nugget, sill, and range values) were examined as possible indicators of abiotic (irrigation regime) and biotic (spider mite infestation) stressors. Reflectance data was acquired from 2 maize hybrids (Zea mays L.) at multiple time points in 2 data sets (229 hyperspectral images), and data from 160 individual spectral bands in the spectrum from 405 to 907 nm were analyzed. Based on 480 analyses of variance (160 spectral bands × 3 variogram parameters), it was seen that most of the combinations of spectral bands and variogram parameters were unsuitable as stress indicators mainly because of significant difference between the 2 data sets. However, several combinations of spectral bands and variogram parameters (especially nugget values) could be considered unique indicators of either abiotic or biotic stress. Furthermore, nugget values at 683 and 775 nm responded significantly to abiotic stress, and nugget values at 731 nm and range values at 715 nm responded significantly to biotic stress. Based on qualitative characterization of actual hyperspectral images, it was seen that even subtle changes in spatial patterns of reflectance values can elicit several-fold changes in variogram parameters despite non-significant changes in average and median reflectance values and in width of 95% confidence limits. Such scattered stress expression is in accordance with documented within-leaf variation in both mineral content and chlorophyll concentration and therefore supports the need for reflectance-based stress detection at a high spatial resolution (many hyperspectral reflectance profiles acquired from a single leaf) and may be used to explain or characterize within-leaf foraging patterns of herbivorous arthropods.
Highlights
There are numerous studies of stress detection in crop leaves based on reflectance data acquired with either single sensor devices or imaging devices, including: biotic stress [1,2,3,4], salinity stress [5], nutrient deficiency [6], and drought stress [3,7]
A possible explanation for such increase in reflectance is that stressors partially compromise the photosynthetic efficiency of the plant, so comparatively more radiometric energy is reflected back to the atmosphere than from a non-stressed plant. If such increase in leaf reflectance occurs in response to a given stressor, a follow-up question is whether it is due to a general reflectance increase across a given leaf or whether it is associated with proportionally higher reflectance in scattered points within each leaf? Several studies have demonstrated within-leaf variation in distribution of minerals [9] and chlorophyll [10], so it seems reasonable to assume that there is spatial variability within a leaf in terms of expression of stress response
It is widely known that crops become more susceptible to spider mite (Acari: Tetranychidae) infestations, when crops are grown under drought stressed conditions (cotton, Gossypium spp. [11], sorghum, Sorghum bicolor (L.) Moench [12], and maize [13,14,15])
Summary
There are numerous studies of stress detection in crop leaves based on reflectance data acquired with either single sensor devices or imaging devices, including: biotic stress [1,2,3,4], salinity stress [5], nutrient deficiency [6], and drought stress [3,7]. A possible explanation for such increase in reflectance is that stressors partially compromise the photosynthetic efficiency of the plant, so comparatively more radiometric energy is reflected back to the atmosphere than from a non-stressed plant If such increase in leaf reflectance occurs in response to a given stressor, a follow-up question is whether it is due to a general reflectance increase across a given leaf (in all pixels) or whether it is associated with proportionally higher reflectance in scattered points within each leaf? It is obviously quite interesting that plants tend to have a quase-universal stress reflectance response near 700 nm, but it means that practical applications of reflectance based stress detection systems may be limited, unless more unique reflectance features can be associated with different abiotic and biotic stressors. It is important to emphasize that reflectance based detection of crop stress is mainly of interest if it can be used to detect emerging adverse effects of abiotic and/or biotoc stressors, before symptoms become obvious to the Human eye
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