Solar-induced chlorophyll fluorescence extraction based on heterogeneous light distribution for improving in-situ chlorophyll content estimation

Abstract

Accurate estimation of leaf chlorophyll content (LCC) in field for wheat crops is important to provide guidance for precision management. Solar-induced chlorophyll fluorescence (SIF) extraction based on hyperspectral imaging is an effective tool for LCC estimation, which is because SIF is a signal directly and intrinsically related to photosynthetic activity of crops. However, soil background and structural difference always lead to mixing of soil, shadowed and sunlit canopy components, and heterogeneous light distribution among canopy, which increase the complexity of SIF extraction and reduce the LCC estimation accuracy. The purpose of this study is to propose an accurate SIF extraction method by eliminating above challenges to improve the LCC estimation accuracy. Hyperspectral image and LCC measurements were conducted at the jointing and heading stages of wheat crops. Firstly, to eliminate the effect of soil background, two image segmentation methods, excess green index (ExG) and optimized soil-adjusted vegetation index (OSAVI), are compared to extract the crop region. Secondly, to reduce the canopy structure effect, light distribution condition is evaluated by the brightness, which is extracted from intensity component of hue-saturation-intensity color space. Then K-means clustering based on the brightness distribution is utilized to divide the segmented crop region further into shadowed and sunlit regions. Finally, SIF was extracted based on the Fraunhofer line discriminator method at 687 and 761 nm, and was modified by normalization of brightness to reduce the influence of heterogeneous light distribution. The performance of random forest regression models, which are built by extracting SIF based on raw image, segmented crop by ExG segmentation, segmented crop by OSAVI segmentation, and shadowed and sunlit regions, respectively, is compared. Results show the estimation accuracy of the models increases when image segmentation is performed (determination coefficient of prediction set (RP2) = 0.37 for ExG segmentation; RP2 = 0.47 for OSAVI segmentation) compared with raw image (RP2 = 0.30). Moreover, shadowed region clustering based on brightness was optimal region for SIF extraction (RP2 = 0.54) compared with the sunlit region (RP2 = 0.27). The modified SIF at shadowed regions, normalized by the brightness of crops to reduce the effect of heterogeneous light distribution, yields the highest relationship with LCC (RP2 = 0.79), which outperforms SIF normalized by the absorbed photosynthetically active radiation (APAR). The reason is that brightness is more related to LAI, a parameter representing canopy structure, than APAR. It suggests that OSAVI segmentation for soil background effect elimination and SIF extraction considering canopy structure and heterogeneous light distribution facilitate the improvement of LCC estimation accuracy for high-throughput phenotyping in field.