Quantifying arsenic-induced morphological changes in spinach leaves: implications for remote sensing

• Published in: International journal of remote sensing 2010-08, Vol.31 (15), p.4163-4177
• Main Authors: Bandaru, Varaprasad, Hansen, David J., Codling, Eton E., Daughtry, Craig S., White-Hansen, Susan, Green, Carrie E.
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Arsenic; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Leaves; Remote sensing; Spinach; Spinacia oleracea; Teledetection and vegetation maps
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2010.498453

Arsenic (As) is a common soil contaminant that can be accumulated into plant parts. The ability to detect As in contaminated plants is an important tool to minimize As-induced health risks in humans. Near-infrared (NIR) spectra are strongly affected by leaf structural characteristics. Therefore, quantitative analyses of structural changes in the arrangement of mesophyll cells caused by As will help to explain spectral responses to As. The objectives of this study were to use stereological methods to quantify internal structural changes in leaves with As treatment in spinach plants, and to relate these changes to leaf spectral properties in NIR spectra. Hydroponically grown spinach was treated with 0, 5, 10 and 20 μmol l −1 for four weeks in a growth chamber. Spectral properties of leaves were obtained for visible and infrared frequencies. Leaf structural properties, such as mesophyll thickness and mesophyll surface area, were measured using stereological methods. Quantitative analysis of leaf structure showed that total leaf thickness and intercellular spaces in spongy mesophyll cells decreased with increasing As treatment. Changes in leaf reflectance in NIR wavelengths were strongly correlated with leaf As concentration and leaf structural changes. Multiple linear regression of leaf reflectance values at the highest correlated wavelengths (1048, 1098 and 1080 nm) generated an R 2 value of 0.69. Results from this study support the hypothesis that relationships between leaf structure and reflectance may be useful in the interpretation of spectral data to detect plant leaf As concentration.