Determining the Effects of Storage on Cotton and Soybean Leaf Samples for Hyperspectral Analysis

Lee, M., Huang, Y., Yao, H., Thomson, S. J., & Bruce, L. (2014). Determining the Effects of Storage on Cotton and Soybean Leaf Samples for Hyperspectral Analysis. 2014 IEEE. EEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(6). DOI:10.1109/JSTARS.2014.2330521.

Abstract

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 the field for laboratory analysis is 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 min when the leaves were stored in a cooler, while soybean leaves stored outside a cooler remained within the normal range for 30 min. However, cotton leaves stored in a cooler decayed faster initially. Regardless of storage technique, results indicate that up to a maximum of 30 min can elapse between plant leaf sampling in the field and hyperspectral measurements in the laboratory. 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 also 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. Index Terms—Ground truthing, hyperspectral imaging, leaf sampling, remote sensing, spectral decay.