Physical model simulations have been widely utilized to simulate the reflectance of vegetation canopies. Such simulations can be used to estimate key biochemical and physical vegetation parameters, such as leaf chlorophyll content (LCC), leaf area index (LAI), and leaf inclination angle (LIA) from remotely sensed data via model inversion. In simulations, field crops are typically regarded as one-dimensional (1D) vegetation canopies with constant leaf properties in the vertical direction and across the growing season. We investigated the seasonal effects of these two simplifications, 1D canopy structure, and vertically constant leaf properties, on canopy reflectance simulations in a rice field using measurements and the 3D discrete anisotropic radiative transfer model (DART). We also developed a new methodology for reconstructing 3D crop canopy architecture, which was validated using measurements of gap fraction and canopy reflectance. Our results revealed that the 1D canopy assumption only holds during the early stage of the growing season, then leaf clumping affects canopy reflectance from the jointing stage onwards. Consideration of the 3D canopy structure and its seasonal variation significantly reduced the deviation between simulated and measured canopy reflectance in the green and near-infrared wavelengths when compared to the typical 1D canopy assumption and produced the closest multi-angular distribution pattern to the measurements. The vertical heterogeneity of leaf spectra affected canopy reflectance weakly during the maturation stage when senescence started from the bottom of the canopy. Consideration of seasonal and vertical variation in LIAs significantly improved the results of 1D canopy reflectance simulations, including the multi-angular distribution patterns. In contrast, the directionally-averaged clumping index (CI) only slightly improved the 1D canopy reflectance simulation. To summarize, these findings can be used to reduce the simulation bias of canopy reflectance and improve the retrieval accuracy of key vegetation parameters in crop canopies at the seasonal scale.

中文翻译：

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冠层结构和叶片光谱特性的季节性和垂直变化决定了稻田的冠层反射率


物理模型模拟已被广泛用于模拟植被冠层的反射率。这种模拟可用于通过模型反演从遥感数据中估计关键的生化和物理植被参数，例如叶片叶绿素含量（LCC）、叶面积指数（LAI）和叶片倾角（LIA）。在模拟中，大田作物通常被视为一维 (1D) 植被冠层，在垂直方向和整个生长季节具有恒定的叶子特性。我们使用测量和 3D 离散各向异性辐射传输模型 (DART) 研究了这两种简化（1D 冠层结构和垂直恒定叶片属性）对稻田冠层反射率模拟的季节性影响。我们还开发了一种重建 3D 作物冠层结构的新方法，该方法通过间隙分数和冠层反射率的测量进行了验证。我们的结果表明，一维冠层假设仅在生长季节的早期阶段成立，然后叶子丛集从拔节阶段开始影响冠层反射率。与典型的 1D 冠层假设相比，考虑 3D 冠层结构及其季节变化显着减少了绿色和近红外波长中模拟和测量的冠层反射率之间的偏差，并产生了与测量结果最接近的多角度分布模式。在成熟阶段，当衰老从冠层底部开始时，叶片光谱的垂直异质性对冠层反射率的影响微弱。 考虑 LIA 的季节和垂直变化显着改善了一维冠层反射率模拟的结果，包括多角度分布模式。相比之下，方向平均聚集指数 (CI) 仅略微改善了一维树冠反射率模拟。综上所述，这些发现可用于减少冠层反射率的模拟偏差，提高季节性尺度上作物冠层关键植被参数的反演精度。