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Using Visual Ozone Damage Scores and Spectroscopy to Quantify Soybean Responses to Background Ozone

1
Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, USA
2
Geospatial Institute, Saint Louis University, 3694 West Pine Mall, St. Louis, MO 63108, USA
3
Department of Mathematics and Natural Sciences, Harris-Stowe State University, St. Louis, MO 63103, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(1), 93; https://doi.org/10.3390/rs12010093
Received: 1 November 2019 / Revised: 23 December 2019 / Accepted: 23 December 2019 / Published: 26 December 2019
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Remotely-sensed identification of ozone stress in crops can allow for selection of ozone resistant genotypes, improving yields. This is critical as population, food demand, and background tropospheric ozone are projected to increase over the next several decades. Visual scores of common ozone damage have been used to identify ozone-stress in bio-indicator plants. This paper evaluates the use of a visual scoring metric of ozone damage applied to soybeans. The scoring of the leaves is then combined with hyperspectral data to identify spectral indices specific to ozone damage. Two genotypes of soybean, Dwight and Pana, that have shown different sensitivities to ozone, were grown and visually scored for ozone-specific damage on multiple dates throughout the growing season. Leaf reflectance, foliar biophysical properties, and yield data were collected. Additionally, ozone bio-indicator plants, snap beans, and common milkweed, were investigated with visual scores and hyperspectral leaf data for comparison. The normalized difference spectral index (NDSI) was used to identify the significant bands in the visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) that best correlated with visual damage score when used in the index. Results were then compared to multiple well-established indices. Indices were also evaluated for correlation with seed and pod weight. The ozone damage scoring metric for soybeans evaluated in August had a coefficient of determination of 0.60 with end-of-season pod weight and a Pearson correlation coefficient greater than 0.6 for photosynthetic rate, stomatal conductance, and transpiration. NDSI [R558, R563] correlated best with visual scores of ozone damage in soybeans when evaluating data from all observation dates. These wavelengths were similar to those identified as most sensitive to visual damage in August when used in NDSI (560 nm, 563 nm). NDSI [R560, R563] in August had the highest coefficient of determination for individual pod weight (R2 = 0.64) and seed weight (R2 = 0.54) when compared against 21 well-established indices used for identification of pigment or photosynthetic stress in plants. When evaluating use of spectral bands in NDSI, longer wavelengths in SWIR were identified as more sensitive to ozone visual damage. Trends in the bands and biophysical properties of the soybeans combined with evaluation of ozone data indicate likely timing of significant ozone damage as after late-July for this season. This work has implications for better spectral detection of ozone stress in crops and could help with efforts to identify ozone tolerant varieties to increase future yield. View Full-Text
Keywords: soybean; ambient ozone; hyperspectral data; visual damage scoring soybean; ambient ozone; hyperspectral data; visual damage scoring
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MDPI and ACS Style

Gosselin, N.; Sagan, V.; Maimaitiyiming, M.; Fishman, J.; Belina, K.; Podleski, A.; Maimaitijiang, M.; Bashir, A.; Balakrishna, J.; Dixon, A. Using Visual Ozone Damage Scores and Spectroscopy to Quantify Soybean Responses to Background Ozone. Remote Sens. 2020, 12, 93.

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