Location: Crop Production Systems Research Unit
Title: Determining the effect of storage of cotton and soybean leaf samples for hyperspectral analysis Authors
|Haibo, Yao -|
|Bruce, Lori -|
Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 26, 2014
Publication Date: August 1, 2014
Citation: Lee, M.A., Huang, Y., Haibo, Y., Thomson, S.J., Bruce, L.M. 2014. Determining the effect of storage of cotton and soybean leaf samples for hyperspectral analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(6):2562-2570. Interpretive Summary: It is a common practice for researchers to collect leaf samples in field, transport to laboratories, and measure spectral reflectance of the leaves using a hyperspectral sensor. It is assumed that the reflectance measured on sampled leaves characterizes the plant canopies when viewed from above with airborne or space borne hyperspectral sensor. The leaf samples begin to wilt when they are removed from the plant, however there have been very few studies that examined the effect of leaves wilting on the spectral reflectance or how to preserve the leaf samples of common agricultural plants. The scientists in USDA-ARS Crop Production Systems Research Unit, Stoneville, MS and Geosystems Research Institute of Mississippi State University have collaboratively conducted a study on cotton and soybean leaves to model the quantitative effects of the elapsed time between leaf sample collection and spectral reflectance measurement, and to determine if it is useful to refrigerate leaf samples in a cooler during elapsed time. The study compared the accuracy of approximating the effects of the leaf wilting using a linear model to an exponential model, and determined that the exponential model was superior to the linear one. The exponential model was then used to determine how much time can elapse (after the leaves are collected) before the spectral reflectance of the leaves no longer characterizes the plant canopy. This study shows that the wilting effects are more pronounced than commonly believed and largely unaffected by refrigeration in a cooler. Scientists should consider the effects of the elapsed time when designing their experimental procedures; otherwise the results of their experiment may not be reliable.
Technical Abstract: This paper studies the effect of storage techniques for transporting collected plant leaves from the field to the laboratory for hyperspectral analysis. The strategy of collecting leaf samples in field for laboratory analysis is often typically used when ground truthing is needed in remote sensing studies. Results indicate that the accuracy of hyperspectral measurements depends on a combination of storage technique (in a cooler or outside a cooler), time elapsed between collecting leaf samples in the field and measuring in the laboratory, and the plant species. A nonlinear model fitting method is proposed to estimate the spectrum of decaying plant leaves. This revealed that the reflectance of soybean leaves remained within the normal range for 45 minutes when the leaves were stored in a cooler, while soybean leaves stored outside a cooler remained within the normal range for 30 minutes. However, cotton leaves stored in a cooler decayed faster initially. Regardless of storage technique, results indicate that up to a maximum of 30 minutes can elapse between plant leaf sampling in the field and hyperspectral measurements in the laboratory. Nevertheless, the limit was exceeded in cases where plant species or treatments were easily distinguishable in hyperspectral feature space. This study focused on cotton and soybean leaves, but the implication that time elapsing between sampling leaves and measuring their spectrum should be limited as much as possible can be applied to any study on other crop leaves. Results of the study could provide a guideline for crop storage limits when analyzing by laboratory hyperspectral sensing setting to improve the quality and reliability of data for precision agriculture.