基于时间序列MODIS LST产品的重构研究

利用遥感数据可以对地球资源环境进行大面积连续监测，得到更为精确的研究结果，MODIS LST数据因其优化的时空分辨率成为较理想和常用的数据源。同时，遥感数据由于受到云、气溶胶以及传感器角度等影响均存在不同程度的噪声污染、数据缺失等问题。针对该现象，以河南省为研究区域，以MODIS LST数据为研究对象，利用谐波分析方法对河南省2011年全年每天四个时刻的MODIS LST 时间序列数据进行重构。结果表明，利用该方法重构的数据可对MODIS缺值70%以上的影像进行弥补，并且60%以上影像误差可控制在3℃以内，能得到较好的重构结果；同时重构LST数据与相应气温数据相关性大部分在0.8左右，能够较好拟合LST的变化趋势。

Studies on Reconstructing MODIS LST Products Based on Time Series

Remote sensing data can be used to continuousely supervise the globle resources and environment by large area, and obtain more precise research results. NASAs Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST) data is an ideal and common data source due to its optimal temporal and spatial resolution. Meanwhile, the LST value is generally missing or abnormal for the impact from mixed pixels and cloud noises. To counter this phenomenon, taking Henan Province as studying eara and MODIS LST data as research object, the MODIS LST Time Series LST datum were reconstructed, which were the daily products of Henan Province in 2011 by harmonic analysis of time series method. The result indicated that the reconstructed data could remedy over 70% missing images, and the RMSE of more than 60% images were controlled within 3℃. Thus, a better reconstruction result was obtained. In addition, there was a great correlation between the reconstructed LST time series data and the actual meteorological data. And the correlation coefficient was about 0.8, which indicated that the reconstructed LST time series data could reflect the trend of temperature variation.