Evaluation of Simulated AVIRIS-NG Imagery Using a Spectral Reconstruction Method for the Retrieval of Leaf Chlorophyll Content

Abstract

The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas hyperspectral images which have high spectral resolution are very limited in availability. In this work, hyperspectral imagery (AVIRIS-NG) is simulated using a multispectral image (Sentinel-2) and a spectral reconstruction method, namely, the universal pattern decomposition method (UPDM). UPDM is a linear unmixing technique, which assumes that every pixel of an image can be decomposed as a linear composition of different classes present in that pixel. The simulated AVIRIS-NG was very similar to the original image, and its applicability in estimating LCC was further verified by using the ground based measurements, which showed a good correlation value (R = 0.65). The simulated image was further classified using a spectral angle mapper (SAM), and an accuracy of 87.4% was obtained, moreover a receiver operating characteristic (ROC) curve for the classifier was also plotted, and the area under the curve (AUC) was calculated with values greater than 0.9. The obtained results suggest that simulated AVIRIS-NG is quite useful and could be used for vegetation parameter retrieval.

Code and data availability

The paper's core phenotyping inputs are the AVIRIS-NG level-2 reflectance scene (Anand district, Gujarat, March 2018) and ground-based LCC measurements used to validate simulated imagery. The authors state the AVIRIS-NG data are available only upon request; no author analysis code, trained models, or supplementary phen

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