Wang Caiqun, He Tao, Song Dan-Xia, Zhang Lei, Zhu Peng, Man Yuanbin
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.
Sci Total Environ. 2024 Jun 1;927:172014. doi: 10.1016/j.scitotenv.2024.172014. Epub 2024 Mar 26.
Fine-resolution land surface phenology (LSP) is urgently required for applications on agriculture management and vegetation-climate interaction, especially over heterogeneous areas, such as agricultural lands and fragmented forests. The critical challenge of fine-resolution LSP monitoring is how to reconstruct the spatiotemporal continuous vegetation index time series. To solve this problem, various data fusion methods have been devised; however, the comprehensive inter-comparison is lacking across different spatial heterogeneity, data quality, and vegetation types. We divide these methods into two main categories: the change-based methods fusing satellite observations with different spatiotemporal resolutions, and the shape-based methods fusing prior knowledge of shape models and satellite observations. We selected four methods to rebuilt two-band enhanced vegetation index (EVI2) series based on the harmonized Landsat and Sentinel-2 (HLS) data, including two change-based methods, namely the Spatial and temporal Adaptive Reflectance Fusion Model (STARFM), the Flexible Spatiotemporal DAta Fusion (FSDAF), and two shape-based methods, namely the Multiple-year Weighting Shape-Matching (MWSM), and the Spatiotemporal Shape-Matching Model (SSMM). Four phenological transition dates were extracted, evaluated with PhenoCam observations and the 500 m Visible Infrared Imaging Radiometer Suite (VIIRS) phenology product. The 30 m transition dates show more spatial details and reveal more apparent intra-class and inter-class phenology variation compared with 500 m product. The four transition dates of SSMM and FSDAF (R>0.74, MAD<15 days) show better agreement with PhenoCam-derived dates. The performance difference between fusion methods over various application scenarios are then analyzed. Fusion results are more robust when temporal frequency is higher than 15 observations per year. The shape-based methods are less sensitive to temporal sampling irregularity than change-based methods. Both change-based methods and shape-based methods cannot perform well when the region is heterogeneous. Among different vegetation types, SSMM-like methods have the highest overall accuracy. The findings in this paper can provide references for regional and global fine-resolution phenology monitoring.
农业管理和植被-气候相互作用应用，尤其是在农业用地和破碎森林等异质区域，迫切需要高分辨率的地表物候（LSP）。高分辨率LSP监测的关键挑战在于如何重建时空连续的植被指数时间序列。为解决这一问题，人们设计了各种数据融合方法；然而，目前缺乏针对不同空间异质性、数据质量和植被类型的全面相互比较。我们将这些方法分为两大类：基于变化的方法，即将不同时空分辨率的卫星观测数据进行融合；基于形状的方法，即将形状模型的先验知识与卫星观测数据进行融合。我们选择了四种方法，基于协调后的陆地卫星和哨兵-2（HLS）数据重建双波段增强植被指数（EVI2）序列，其中包括两种基于变化的方法，即时空自适应反射率融合模型（STARFM）和灵活时空数据融合（FSDAF），以及两种基于形状的方法，即多年加权形状匹配（MWSM）和时空形状匹配模型（SSMM）。提取了四个物候转变日期，并与PhenoCam观测数据和500米可见红外成像辐射仪套件（VIIRS）物候产品进行了评估。与500米的产品相比，30米的转变日期显示出更多的空间细节，并揭示了更明显的类内和类间物候变化。SSMM和FSDAF的四个转变日期（R>0.74，MAD<15天）与PhenoCam得出的日期显示出更好的一致性。然后分析了融合方法在各种应用场景下的性能差异。当年时间频率高于15次观测时，融合结果更稳健。基于形状的方法对时间采样不规则性的敏感度低于基于变化的方法。当区域异质性较大时，基于变化的方法和基于形状的方法都不能很好地发挥作用。在不同植被类型中，类似SSMM的方法总体准确率最高。本文的研究结果可为区域和全球高分辨率物候监测提供参考。
