Abstract Long-term ground LAI measurements from the global networks of sites (e.g. FLUXNET) have emerged as a promising data source to validate remotely sensed global LAI product time-series. However, the spatial scale-mismatch issue between site and satellite observations hampers the use of such invaluable ground measurements in validation practice. Here, we propose an approach (Grading and Upscaling of Ground Measurements, GUGM) that integrates a spatial representativeness grading criterion and a spatial upscaling strategy to resolve this scale-mismatch issue and maximize the utility of time-series of site-based LAI measurements. The performance of GUGM was carefully evaluated by comparing this method to both benchmark LAI and other widely used conventional approaches. The uncertainty of three global LAI products (i.e. MODIS, GLASS and GEOV1) was also assessed based on the LAI time-series validation dataset derived from GUGM. Considering all the evaluation results together, this study suggests that the proposed GUGM approach can significantly reduce the uncertainty from spatial scale mismatch and increase the size of the available validation dataset. In particular, the proposed approach outperformed other widely used approaches in these two respects. Furthermore, GUGM was successfully implemented to validate global LAI products in various ways with advantaging frequent time-series validation dataset. The validation results of the global LAI products show that GLASS has the lowest uncertainty, followed by GEOV1 and MODIS for the overall biome types. However, MODIS provides more consistent uncertainties across different years than GLASS and GEOV1. We believe that GUGM enables us to better understand the structure of LAI product uncertainties and their evolution across seasonal or annual contexts. In turn, this method can provide fundamental information for further LAI algorithm improvements and the broad application of LAI product time-series.

中文翻译：

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使用基于站点的测量的全球网络验证长期叶面积指数产品的综合方法

摘要 来自全球站点网络（例如 FLUXNET）的长期地面 LAI 测量已成为验证遥感全球 LAI 产品时间序列的有前途的数据源。然而，站点和卫星观测之间的空间尺度不匹配问题阻碍了在验证实践中使用这种宝贵的地面测量。在这里，我们提出了一种方法（地面测量的分级和升级，GUGM），该方法将空间代表性分级标准和空间升级策略相结合，以解决此尺度不匹配问题并最大化基于站点的 LAI 测量时间序列的效用。通过将此方法与基准 LAI 和其他广泛使用的传统方法进行比较，仔细评估了 GUGM 的性能。三种全球 LAI 产品（即 MODIS、GLASS 和 GEOV1) 也基于源自 GUGM 的 LAI 时间序列验证数据集进行了评估。综合考虑所有评估结果，本研究表明，所提出的 GUGM 方法可以显着降低空间尺度不匹配带来的不确定性，并增加可用验证数据集的大小。特别是，所提出的方法在这两个方面都优于其他广泛使用的方法。此外，利用频繁的时间序列验证数据集，成功实施了 GUGM，以各种方式验证全球 LAI 产品。全球 LAI 产品的验证结果表明，GLASS 具有最低的不确定性，其次是 GEOV1 和 MODIS 的整体生物群落类型。然而，与 GLASS 和 GEOV1 相比，MODIS 在不同年份提供了更一致的不确定性。我们相信 GUGM 使我们能够更好地了解 LAI 产品不确定性的结构及其在季节性或年度背景下的演变。反过来，该方法可以为进一步的 LAI 算法改进和 LAI 产品时间序列的广泛应用提供基础信息。