Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model

Xun Zhao , Jianbo Qi , Jingyi Jiang , Shangbo Liu , Haifeng Xu , Simei Lin , Zhexiu Yu , Linyuan Li , Huaguo Huang
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Abstract

Leaf chlorophyll content (LCC) retrieval from remote sensing imagery is essential for monitoring vegetation growth and stress in the agroforestry industry. Many remote sensing inversion methods for estimating LCC primarily rely on 1D radiative transfer models (RTMs) that abstract canopies into horizontal layers or simple geometric primitives. Yet, this methodology faces challenges when applied to heterogeneous canopies, particularly in fine-scale mapping where each pixel's reflectance is significantly influenced by its surroundings, e.g. crown shadows. While 3D RTMs hold promise for addressing these challenges by explicitly describing complex canopy structures, their computational demands and the complexity involved in parameterizing detailed 3D structures limit the generation of extensive training datasets, requiring simulations across numerous parameter combinations. In this study, we used a semi-empirically accelerated 3D RTM, termed Semi-LESS, with a 1D residual network to accurately retrieve leaf chlorophyll content (LCC) from UAV images and LiDAR data at a 3-m resolution. We first reconstructed structures of forest plots using UAV LiDAR point cloud, based on which, UAV images with varying leaf and soil optical properties are simulated using the Semi-LESS. Subsequently, a training dataset consisting LCC and its corresponding reflectance was generated from the simulated UAV images by focusing on sunlit pixels. A 1D residual network is trained using the training dataset for LCC estimation. For comparison, we also trained an estimation model using a dataset generated from PROSAIL. The results show that estimation model trained with Semi-LESS surpasses PROSAIL in retrieving LCC from both simulation datasets and field measurements of two forest plots. The RMSE of Semi-LESS was 5.40–6.92 µg/cm2 for simulation datasets and 8.21–9.76 µg/cm2 for field measurements, whereas PROSAIL exhibited lower accuracy with an RMSE of 7.76–9.83 µg/cm2 for simulation datasets and 12.76–13.06 µg/cm2 in field measurements. The results demonstrate that Semi-LESS coupled with deep learning is reliable and has great potential for LCC mapping using UAV images, which is particularly useful for fine-scale applications such as crop and orchard monitoring. This approach also highlights the impact of shadows on LCC retrieval.
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利用半经验加速三维辐射传递模型精细检索叶片叶绿素含量
从遥感图像中检索叶片叶绿素含量(LCC)对于监测农林业的植被生长和压力至关重要。许多估算叶绿素含量的遥感反演方法主要依赖于一维辐射传递模型(RTM),这种模型将树冠抽象为水平层或简单的几何基元。然而,这种方法在应用于异质树冠时面临挑战,特别是在精细尺度绘图中,每个像素的反射率都会受到周围环境(如树冠阴影)的显著影响。虽然三维 RTM 有望通过明确描述复杂的树冠结构来应对这些挑战,但其计算要求和详细三维结构参数化所涉及的复杂性限制了大量训练数据集的生成,需要对众多参数组合进行模拟。在这项研究中,我们利用半经验加速三维 RTM(称为 Semi-LESS)和一维残差网络,从 3 米分辨率的无人机图像和激光雷达数据中精确获取叶绿素含量(LCC)。我们首先利用无人机激光雷达点云重建了林地结构,在此基础上,利用 Semi-LESS 模拟了具有不同叶片和土壤光学特性的无人机图像。随后,通过聚焦阳光像素,从模拟的无人机图像中生成由 LCC 及其相应反射率组成的训练数据集。利用训练数据集训练一维残差网络,以估算 LCC。为了进行比较,我们还使用 PROSAIL 生成的数据集训练了一个估计模型。结果表明,使用 Semi-LESS 训练的估计模型在从模拟数据集和两个林地的实地测量数据中检索 LCC 方面超过了 PROSAIL。在模拟数据集和实地测量中,Semi-LESS 的均方根误差分别为 5.40-6.92 µg/cm2 和 8.21-9.76 µg/cm2,而 PROSAIL 的精度较低,模拟数据集和实地测量的均方根误差分别为 7.76-9.83 µg/cm2 和 12.76-13.06 µg/cm2。结果表明,Semi-LESS 与深度学习相结合的方法是可靠的,在利用无人机图像绘制 LCC 地图方面具有巨大潜力,尤其适用于作物和果园监测等精细应用。这种方法还强调了阴影对 LCC 检索的影响。
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来源期刊
International journal of applied earth observation and geoinformation : ITC journal
International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences
CiteScore
12.00
自引率
0.00%
发文量
0
审稿时长
77 days
期刊介绍: The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.
期刊最新文献
Back to geometry: Efficient indoor space segmentation from point clouds by 2D–3D geometry constrains Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model Improved early detection of wheat stripe rust through integration pigments and pigment-related spectral indices quantified from UAV hyperspectral imagery GNSS-denied geolocalization of UAVs using terrain-weighted constraint optimization Investigating overlapping deformation patterns of the Beijing Plain by independent component analysis of InSAR observations
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