遥感定量反演地表参数时间序列产品已被广泛应用于植被动态变化、全球气候变化、防灾减灾及环境保护等领域。由于卫星观测往往受到大气条件（如云、气溶胶、水汽等）以及传感器自身稳定性的影响等，许多由卫星观测反演得到的陆表产品，如归一化差值植被指数（NDVI）、叶面积指数（LAI）、地表温度（LST）、微波极化亮温（PDBT）等存在严重的时空不连续问题。为了获取时间序列上连续、空间上完整的地表参数遥感产品以满足长时序的陆面过程分析与建模的需求，目前已发展多种遥感时间序列重建模型。本文介绍了基于傅里叶变换的时间序列谐波分析（HANTS）方法，能够识别并去除受到云和大气影响的像元（噪声），对原始时序数据进行时间插值来重建连续时间序列的数据，并针对其面向多种不同时空尺度的遥感反演地表参数以及在非洲、南美洲、欧洲、中国及印度等全球不同地区的应用研究进行了综述，包括植被动态变化对于气候变化及流域水循环过程的响应、干旱监测、基于土壤含水量饱和度时间序列分析的洪涝灾害易发区监测、遥感估算地表蒸散发时间尺度扩展等方面的研究，充分阐释了遥感时间序列产品在地气相互作用的各类研究领域的应用。

Satellite observations of the terrestrial biosphere cover a period of time sufficiently extended to allow the calculation of a reliable climatology. The latter is particularly relevant for studies of vegetation response to climate variability. Observations from space of the land surface are hampered by clouds at shorter wavelength and affected by water in the atmosphere in the microwave range. Both polar orbiting and geostationary satellites have a revisit frequency high enough to allow for some redundancy relative to the processes being observed, so that time series where a fraction of observations are removed and the resulting gaps filled are still very useful to monitor land surface processes. We applied the Harmonic ANalysis of Time Series (HANTS) to identify and remove anomalous observations (outliers) and to fill the resulting gaps. The HANTS algorithm has been widely used to reconstruct time series of Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST) as well as the Polarization Difference Brightness Temperature (PDBT) during the past 30 years to remove random noise or eliminate cloud/snow contamination. Several studies in North and Southern Africa, South America, Europe, China and India captured the response of the land surface to climate forcing, modulated by water availability across a range of temporal scales from hourly to decennial. These studies are reviewed to illustrate how the analysis of time series of different land surface properties reveal processes and interactions.