基于地面观测资料的MODIS云量产品订正  

Correction methods of MODIS cloud product based on ground observation data

作　　者：曹芸[1] 何永健[1] 邱新法[1] 曾燕[2] 罗庆洲[1] 高婷[1] 

机构地区：[1]南京信息工程大学遥感学院,江苏南京210044 [2]江苏省气象科学研究所,江苏南京210008

出　　处：《遥感学报》2012年第2期325-342,共18页NATIONAL REMOTE SENSING BULLETIN

基　　金：科技部公益性行业科研专项(编号:GYHY200806002);江苏省2008年度普通高校研究生科研创新计划(编号:CX08B_021Z)~~

摘　　要：通过对MODIS云量数字产品与气象站云量观测资料的对比分析,发现两者间存在较大偏差。本文以气象站观测云量资料为基准,提出了差值订正、比值订正、差值混合订正、比值混合订正和归一化混合订正5种MODIS云量数字产品的订正方法。结果表明:5种订正方法均有效,其中比值订正法最简单易行,且效果最好,是基于地面观测资料MODIS云量数字产品的最优订正方法。订正后的MODIS云量与气象站观测云量在空间分布特征和数值上都非常吻合。加密站验证结果表明:各月绝对误差平均值均小于5%。本研究为利用地面观测资料订正相关卫星数字产品提供了借鉴方法,有效发挥了卫星空间连续观测的优势,对高原、荒漠和山地等地面测站稀缺地区的相关研究具有重要意义。Comparative analysis of cloud fraction obtained from Moderate-resolution Imaging Spectroradiometer （MODIS） and ground observations shows the existence of considerable deviations. Five correction methods, based on cloud fraction ob- served from ground meteorological stations, have been proposed in this paper, including difference correction, ratio correction, difference-mixed correction, ratio-mixed correction and normalization-mixed correction of MODIS cloud product. The results show that all of the five correction methods are workable. Comparatively, the ratio correction method has the highest precision and is easy to be implemented. Hence it is the best recommended correction method for MODIS cloud product. After correction, the cloud fraction of MODIS is identical with that of ground observations, both in spatial distribution and quantitative analysis. Furthermore, intensive observations test show that monthly Mean Absolute Bias Error （MABE） of cloud fraction obtained from MODIS after ratio correction is less than 5%. This study gives a reference for relative researches using ground observation data to correct the related satellite products, which is of great significance to the related investigations of plateau, mountains and desert with sparse stations.

关 键 词：云量 MODIS 订正 空间分布 地面观测资料 相关性 差异性 

分 类 号：P416[天文地球—大气科学及气象学]