Abstract Radiative transfer models (RTMs) of vegetation canopies can be applied for the retrieval of numerical values of vegetation properties from satellite data. For such retrieval, it is necessary first to apply atmospheric correction to translate the top-of-atmosphere (TOA) satellite data into top-of-canopy (TOC) values. This atmospheric correction typically assumes a Lambertian surface reflection, which introduces errors if the real surface is non-Lambertian. Furthermore, atmospheric correction requires atmospheric characterization as input, which is not always available. In this study, we present an RTM for soil-plant-atmosphere systems to model TOC and TOA reflectance as observed by sensors, and to retrieve vegetation properties directly from TOA reflectance skipping the atmosphere correction processes with the inversion mode of the RTM. The model uses three computationally efficient RTMs for soil (BSM), vegetation canopies (PROSAIL) and atmosphere (SMAC), respectively. The sub-models are coupled by using the four-stream theory and the adding method. The resulting ‘Soil-Plant-Atmosphere Radiative Transfer model’ (SPART) simulates directional TOA spectral observations, with all major effects included, such as sun-observer geometries and non-Lambertian reflectance of the land surface. A sensitivity anaylsis of the model shows that neglecting anisotropic reflection of the surface in coupling the surface with atmosphere causes considerable errors in TOA reflectance. The model was validated by comparing TOC and TOA reflectance simulations with those simulated with the atmosphere-included version of the DART RTM model. We show that the differences between DART and SPART are less than 7% for simulating TOC reflectance, and are less than 20% (less than 10% at most bands) for simulating TOA reflectance. The model performance in retrieving key vegetation and atmospheric properties was evaluted by using a synthetic dataset and a satellite dataset. The inversion mode allows estimating vegetation properties along with atmospheric properties and TOC reflectance with reasonable accuracy directly from TOA observations, and remarkable accuracy can be achieved if prior information is used in the model inversion. The model can be used to investigate the sensitivity of surface and atmospheric properties on TOC and TOA reflectance and for the simulation of synthetic data of existing and forthcoming satellite missions. More importantly, it facilitates a quantitative use of remote sensing data from satellites directly without the need for atmospheric correction.

中文翻译：

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SPART 模型：用于太阳光谱中卫星测量的土壤-植物-大气辐射传输模型

摘要 植被冠层的辐射传输模型 (RTM) 可用于从卫星数据中检索植被特性的数值。对于这种反演，首先需要应用大气校正将大气顶 (TOA) 卫星数据转换为冠层顶 (TOC) 值。这种大气校正通常假设朗伯表面反射，如果真实表面是非朗伯表面反射，则会引入误差。此外，大气校正需要大气特征作为输入，这并不总是可用的。在这项研究中，我们提出了一种用于土壤-植物-大气系统的 RTM，以模拟传感器观察到的 TOC 和 TOA 反射率，并直接从 TOA 反射率中检索植被特性，跳过了 RTM 反演模式的大气校正过程。该模型分别对土壤 (BSM)、植被冠层 (PROSAIL) 和大气 (SMAC) 使用三种计算效率高的 RTM。子模型采用四流理论和相加法耦合。由此产生的“土壤-植物-大气辐射传递模型”(SPART) 模拟定向 TOA 光谱观测，包括所有主要影响，例如太阳观测器几何形状和地表的非朗伯反射率。该模型的灵敏度分析表明，在将地表与大气耦合时忽略地表的各向异性反射会导致 TOA 反射率出现相当大的误差。通过将 TOC 和 TOA 反射率模拟与使用包含大气的 DART RTM 模型模拟的模拟进行比较来验证该模型。我们表明，在模拟 TOC 反射率时，DART 和 SPART 之间的差异小于 7%，在模拟 TOA 反射率时小于 20%（大多数波段小于 10%）。通过使用合成数据集和卫星数据集对模型在检索关键植被和大气特性方面的性能进行了评估。反演模式允许直接从 TOA 观测中以合理的精度估计植被特性以及大气特性和 TOC 反射率，如果在模型反演中使用先验信息，则可以获得显着的精度。该模型可用于研究地表和大气特性对 TOC 和 TOA 反射率的敏感性，以及模拟现有和即将进行的卫星任务的合成数据。更重要的是，