Lei Fangni, Crow Wade T, Shen Huanfeng, Su Chun-Hsu, Holmes Thomas R H, Parinussa Robert M, Wang Guojie
USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA.
School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei 430072, China.
Remote Sens Environ. 2018 Feb;205:85-99. doi: 10.1016/j.rse.2017.11.002. Epub 2017 Nov 24.
An accurate temporal and spatial characterization of errors is required for the efficient processing, evaluation, and assimilation of remotely-sensed surface soil moisture retrievals. However, empirical evidence exists that passive microwave soil moisture retrievals are prone to periodic artifacts which may complicate their application in data assimilation systems (which commonly treat observational errors as being temporally white). In this paper, the link between such temporally-periodic errors and spatial land surface heterogeneity is examined. Both the synthetic experiment and site-specified cases reveal that, when combined with strong spatial heterogeneity, temporal periodicity in satellite sampling patterns (associated with exact repeat intervals of the polar-orbiting satellites) can lead to spurious high frequency spectral peaks in soil moisture retrievals. In addition, the global distribution of the most prominent and consistent 8-day spectral peak in the Advanced Microwave Scanning Radiometer - Earth Observing System soil moisture retrievals is revealed via a peak detection method. Three spatial heterogeneity indicators - based on microwave brightness temperature, land cover types, and long-term averaged vegetation index - are proposed to characterize the degree to which the variability of land surface is capable of inducing periodic error into satellite-based soil moisture retrievals. Regions demonstrating 8-day periodic errors are generally consistent with those exhibiting relatively higher heterogeneity indicators. This implies a causal relationship between spatial land surface heterogeneity and temporal periodic error in remotely-sensed surface soil moisture retrievals.
为了有效地处理、评估和同化遥感地表土壤湿度反演数据,需要对误差进行准确的时空特征描述。然而,有经验证据表明,被动微波土壤湿度反演容易出现周期性伪像,这可能会使其在数据同化系统中的应用变得复杂(数据同化系统通常将观测误差视为时间上的白噪声)。本文研究了这种时间周期性误差与空间地表异质性之间的联系。综合实验和特定地点的案例均表明,当与强烈的空间异质性相结合时,卫星采样模式中的时间周期性(与极轨卫星的精确重复周期相关)会导致土壤湿度反演中出现虚假的高频光谱峰值。此外,通过峰值检测方法揭示了先进微波扫描辐射计-地球观测系统土壤湿度反演中最显著且一致的8天光谱峰值的全球分布。提出了基于微波亮温、土地覆盖类型和长期平均植被指数的三个空间异质性指标,以表征地表变异性能够在基于卫星的土壤湿度反演中引入周期性误差的程度。表现出8天周期性误差的区域通常与那些具有相对较高异质性指标的区域一致。这意味着在遥感地表土壤湿度反演中,空间地表异质性与时间周期性误差之间存在因果关系。