Feng Fei, Li Xianglan, Yao Yunjun, Liang Shunlin, Chen Jiquan, Zhao Xiang, Jia Kun, Pintér Krisztina, McCaughey J Harry
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China.
State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, China.
PLoS One. 2016 Jul 29;11(7):e0160150. doi: 10.1371/journal.pone.0160150. eCollection 2016.
Accurate estimation of latent heat flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types.
基于遥感数据准确估算潜热通量（LE）对于表征陆地生态系统和模拟陆面过程至关重要。在过去几十年中发布了许多潜热通量产品，但就数据一致性和估算精度而言，它们的质量可能无法满足要求。合并多种算法可能是提高现有潜热通量产品质量的有效方法。在本文中，我们提出了一种基于改进经验正交函数（EOF）分析的数据集成方法，以整合中分辨率成像光谱仪（MODIS）潜热通量产品（MOD16）和喷气推进实验室的普里斯特利 - 泰勒潜热通量算法（PT - JPL）估算值。选择了22个具有潜热通量观测的涡度相关（EC）站点来评估我们的算法，结果表明所提出的EOF融合方法能够整合这两个卫星数据集，具有更高的一致性并减少了不确定性。需要进一步努力在更大的空间尺度和时间段以及不同的土地覆盖类型上评估和改进所提出的算法。
