AOD Data Fusion Method Based on Quadratic Polynomial

doi:10.13474/j.cnki.11-2246.2018.0012

Abstract

Abstract:

Aiming at the problem that Aqua and Terra MODIS AOD data are not accurate enough by linear regression algorithm,this paper proposes a quadratic polynomial regression algorithm to fit it,the quadratic polynomial means that the polynomial has more than 1 number and the highest square number is 2.The quadratic polynomial regression and linear regression algorithm were used to fit the randomly selected day and the AOD data from April to June respectively,and the results of the two methods are compared and analyzed.The results show that the RMSE,MAE,R values obtained by the quadratic polynomial regression method are much better than those obtained by the linear regression fitting method for the fitting of the same group of Aqua and Terra MODIS AOD data.It is shown that the quadratic polynomial regression fitting method is better than the fitting of the linear regression method in the fitting of Aqua and Terra MODIS AOD data,and it is proved that the quadratic polynomial regression fitting method is suitable for this research and can improve the accuracy of Aqua and Terra MODIS AOD data fitting results.

Key words: MODIS, AOD, Aqua, Terra, linear regression, quadratic polynomial regression

CLC Number:

P237

LI Guangchao, LI Ruren, ZHAO Yangyang, YANG Zhen, LU Yueming. AOD Data Fusion Method Based on Quadratic Polynomial[J]. 测绘通报, 2018, 0(1): 67-71.

References

[1] 刘浩,高小明,谢志英,等.京津冀晋鲁区域气溶胶光学厚度的时空特征[J].环境科学学报,2015,35(5):1506-1511.
[2] 赵胡笳.中国东北城市地区大气气溶胶光学特性及其直接辐射效应研究[D].北京:中国气象科学研究院,2014.
[3] 蒋维东.AOD卫星观测资料三维变分同化试验研究[D].南京:南京信息工程大学,2015.
[4] 李晓静,高玲,张兴赢,等.卫星遥感监测全球大气气溶胶光学厚度变化[J].科技导报,2015,33(17):30-40.
[5] 罗云峰,吕达仁,周秀骥,等.30年来我国大气气溶胶光学厚度平均分布特征分析[J].大气科学,2002,26(6):721-730.
[6] LIANG S,ZHONG B,FANG H.Improved Estimation of Aerosol Optical Depth from MODIS Imagery over Land Surfaces[J].Remote Sensing of Environment,2006,104(4):416-425.
[7] LARY D J,REMER L A,MACNEILL D,et al.Machine Learning and Bias Correction of MODIS Aerosol Optical Depth[J]. IEEE Geoscience & Remote Sensing Letters,2009,6(4):694-698.
[8] 王伟齐,张成网,臧增亮,等.Terra和Aqua卫星MODIS 3 km AOD与北京PM_(2.5)对比分析[J].气象科学,2017,37(1):93-100.
[9] 李龙.基于泛克里金方法的气溶胶光学厚度融合[D].上海:华东师范大学,2015.
[10] QI Y L,GE J M,HUANG J P.Spatial and Temporal Distribution of MODIS and MISR Aerosol Optical Depth over Northern China and Comparison with AERONET[J].Chinese Science Bulletin,2013,58(20):2497-2506.
[11] 闫婧华.MISR和MODIS气溶胶光学厚度产品的数据融合及其在大气细颗粒物监测中的应用研究[D].青岛:中国海洋大学,2014.
[12] 朱绪超,袁国富,易小波,等.基于Landsat 8 OLI影像的塔里木河下游河岸林叶面积指数反演[J].干旱区地理(汉文版),2014,37(6):1248-1256.
[13] 刘明亮,康倩.二次多项式拟合外方位元素卫星三线阵CCD影像平差[J].航天返回与遥感,2011,32(3):77-82.
[14] 马宗伟.基于卫星遥感的我国PM2.5时空分布研究[D].南京:南京大学,2015.
[15] 陈韵竹.气溶胶光学厚度的数据融合研究[D].上海:华东师范大学,2013.
[16] 黄征凯.确定区域似大地水准面方法的比较分析[D].桂林:桂林理工大学,2013.
[17] 程跃辉,王茂和.用二次多项式拟合曲线推算最终沉降量[J].华东公路,1999(6):46-49.