基于Savitzky-Golay滤波算法的FY-2F地表温度产品时间序列重建

Reconstruction of land surface temperature time-series datasets of FY-2F based on Savitzky-Golay filter

卫星遥感技术可获取大面积、空间连续的地表温度(land surface temperature,LST),为全球变化、生态环境和农业生产等领域提供了宝贵的数据源,但受到云、气溶胶、观测角度和太阳光照角度等影响,遥感反演的LST在时间和空间上均存在不同程度的缺失,限制了LST遥感产品的应用。以长江三角洲地区为研究区,以风云2号F星(FY-2F) LST日均值产品为数据源,利用LST时间序列特征,基于Savitzky-Golay(S-G)滤波算法进行了LST长时间序列的重建研究。结果表明,研究区重建前FY-2F LST产品的平均时相缺失率为19.43%,经滤波后缺失率降低为1.69%,并能够保证LST空间一致性。通过模拟验证,S-G滤波重建LST的拟合精度为 0.95,平均绝对误差为1.35 K,具有较高的精度,可以用于进一步热环境时空分布规律的研究。

Regional and spatial continuous land surface temperature (LST) can be retrieved from satellite remote sensing data,and has an important significance in such fields as global change,ecology,environment,and agricultural production.However,the LST retrieved by remote sensing usually has missing data in time and space due to the influence of clouds,aerosols,satellite viewing angle and solar illumination angle,which limits the application of LST products.In this paper,the authors reconstructed FY-2F daily LST data of 2013 in the Yangtze River delta region using Savitzky-Golay (S-G) filter based on the characteristics of long time-series LST.The results show that S-G filter can fill the missing values effectively and ensure the spatial distribution consistency of the LST after reconstruction.The average time-series loss rate of the original FY-2F LST product is 19.43%,and then decreases to 1.69% after S-G filtering.In order to verify the reconstruction accuracy of S-G filter,the authors randomly selected some regions that are not deficient,and then made comparison with the results after S-G filtering.It is proved that S-G filter reconstructing method has obtained high accuracy,with the mean absolute error 1.35 K and the fitting accuracy 0.95.Higher quality and long time-series FY-2F LST which is reconstructed based on S-G filter offers a good foundation to the study of temporal and spatial distribution of further thermal environment.