Solar-induced chlorophyll fluorescence extraction based on heterogeneous light distribution for improving in-situ chlorophyll content estimation
Solar-induced chlorophyll fluorescence extraction based on heterogeneous light distribution for improving in-situ chlorophyll content estimation
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DOI：
10.1016/j.compag.2023.108405
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Ruomei Zhao;Weijie Tang;Lulu An;Lang Qiao;Nan Wang;Hong Sun;Minzan Li;Guo-hui Liu;Yang Liu
中科院分区：
文献类型：
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作者：
Ruomei Zhao;Weijie Tang;Lulu An;Lang Qiao;Nan Wang;Hong Sun;Minzan Li;Guo-hui Liu;Yang Liu
小麦田间叶片叶绿素含量的准确测定对指导作物精准管理具有重要意义。基于高光谱成像的太阳诱导叶绿素荧光（Solar-induced Chlorophylla Fluorescence，SIF）提取是估算LCC的有效工具，因为SIF是与作物光合活性直接相关的信号。然而，由于土壤背景和结构的差异，导致土壤、阴影和日照成分的混合，以及冠层间光照分布的不均匀，增加了SIF提取的复杂性，降低了LCC估计的精度。本研究的目的是提出一种精确的SIF提取方法，通过消除上述挑战，以提高LCC估计精度。在小麦拔节期和抽穗期进行了高光谱图像和LCC测量。首先，为了消除土壤背景的影响，比较了过量绿色指数（ExG）和优化土壤调节植被指数（OSAVI）两种图像分割方法对作物区域的提取效果。其次，为了减少冠层结构效应，利用亮度来评价光分布状况，亮度是从色调-饱和度-强度颜色空间的强度分量中提取的。然后利用基于亮度分布的K均值聚类将分割后的作物区域进一步划分为阴影区域和日照区域。最后，基于夫琅和费线鉴别器方法提取687和761 nm处的SIF，并通过亮度归一化进行修改，以减少非均匀光分布的影响。比较了基于原始图像提取SIF、ExG分割作物、OSAVI分割作物以及阴影和日照区域分别建立的随机森林回归模型的性能。结果表明，与原始图像（RP 2 = 0.30）相比，在进行图像分割时，模型的估计精度有所提高（ExG分割的预测集决定系数RP 2 = 0.37; OSAVI分割的预测集决定系数RP 2 = 0.47）。此外，基于亮度的阴影区域聚类是SIF提取的最佳区域（RP 2 = 0.54）相比，阳光照射区域（RP 2 = 0.27）。修改后的SIF在阴影区，归一化作物的亮度，以减少不均匀的光分布的影响，产生最高的LCC（RP 2 = 0.79），这优于SIF归一化的吸收光合有效辐射（APAR）的关系。其原因是亮度与代表冠层结构的叶面积指数（LAI）的相关性大于与APAR的相关性。研究表明，基于土壤背景效应的OSAVI分割和考虑冠层结构和非均匀光分布的SIF提取，有助于提高高通量表型分析的LCC估算精度。
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.