The leaf chlorophyll content (LCC) is an important plant physiological trait and is critical for accurate modeling of vegetation photosynthesis over time and space. To date, there is still a lack of a global long time-series dataset of LCC. In this study, we developed an algorithm to retrieve global LCC from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data from 2000 to 2020. An essential requirement for generating LCC time series is to capture its seasonal dynamics. This issue was addressed by using a matrix system with two pairs of vegetation indices to minimize the impacts of leaf area index and canopy nonphotosynthetic material on LCC estimation in different seasons. The matrix system algorithm was applied to Landsat data and MODIS data, respectively. The validation based on Landsat data and ground measurements reveals that the algorithm has the ability to catch the seasonal variations of LCC in different plant functional types, and the MODIS-derived LCC shows good agreement with Landsat-upscaled LCC ( R2=0.77R^{2}=0.77 and RMSE = 6.9μg6.9 \,\mu \text{g} /cm2). The global eight-day LCC data at 500-m resolution in 2000–2020 were generated using the matrix system from MODIS and presented distinct temporal and spatial variations, which provides a new opportunity for analyzing vegetation physiological dynamics in climate change studies.

中文翻译：

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来自 MODIS 图像的全球叶子叶绿素含量地图的 21 年时间序列


叶子叶绿素含量（LCC）是重要的植物生理性状，对于植被光合作用随时间和空间的精确建模至关重要。迄今为止，仍然缺乏全球 LCC 长时间序列数据集。在本研究中，我们开发了一种算法，用于从 2000 年至 2020 年的中分辨率成像光谱辐射计 (MODIS) 表面反射率数据中检索全球 LCC。生成 LCC 时间序列的基本要求是捕获其季节性动态。通过使用具有两对植被指数的矩阵系统来解决这个问题，以尽量减少叶面积指数和冠层非光合材料对不同季节 LCC 估计的影响。矩阵系统算法分别应用于Landsat数据和MODIS数据。基于Landsat数据和地面测量的验证表明，该算法能够捕获不同植物功能类型中LCC的季节变化，并且MODIS衍生的LCC与Landsat放大的LCC表现出良好的一致性（R2=0.77R^{ 2}=0.77 且 RMSE = 6.9μg6.9 \,\mu \text{g} /cm2)。利用MODIS矩阵系统生成2000-2020年全球8天500米分辨率LCC数据，呈现出明显的时空变化，为气候变化研究中植被生理动态分析提供了新的契机。