Retrieval of leaf chlorophyll content using drone imagery and fusion with Sentinel-2 data

Abstract

Leaf chlorophyll is an important dynamic biochemical parameter to assess crop phenology, health stress, and yield. Optical remote sensing data is a widely used technique for the estimation of vegetation leaf chlorophyll. Drone technology is a promising solution for high-resolution monitoring of the leaf chlorophyll content and employed in this study. Image fusion was also accomplished to provide high-resolution multi-spectral images by combining Sentinel-2A and drone imagery. Support vector machine regression was adopted to determine the best vegetation indices for the retrieval of leaf chlorophyll content. Random forest regression and support vector machine regression algorithms were adopted to develop the best models for chlorophyll retrieval. The 34 models derived from drone data and the 46 models derived from fusion data were evaluated for chlorophyll retrieval. It was found that the best support vector machine model, based on anthocyanin content index and enhanced vegetation index (M31), had the best correlation coefficient (r = 0.732) and root mean square error (1.93). The random forest regression models M32 (based on chlorophyll vegetation index and visible atmospherically resistant index) and M16 (based on canopy chlorophyll content index and visible atmospherically resistant index) for drone data were found to be the best, which performed equally well in terms of correlation coefficients (r = 0.77) and root mean square error (1.51). The multiple vegetation indices of drone-based leaf chlorophyll content random forest regression models (M16 and M32) provided higher performance than single vegetation indices-based linear (simple linear fit) and nonlinear leaf chlorophyll content models. Out of 46 models of fusion products, the random forest regression M14 (green normalized difference vegetation index with narrow near infrared band and visible atmospherically resistant index) offered the best performance for leaf chlorophyll content retrieval, as can be observed through a comparison of correlation (r = 0.77) and RMSE (=1.71) values. It was also investigated that inversion performances (r ∼ 0.8 and RMSD ∼ 1.4 with standard deviation ∼ 2.6) of fusion data-based models in cross-applicability and transferability were found to be suitable for large-scale inversion of Sentinel-2A data products.