摘要
基于2020年2月1日FY-3C VIRR LST和FY-3D MWRI L1B亮温数据,以18°-54°N,73°-135°E的区域为样例区,利用统计回归模型和层次贝叶斯融合模型,分别进行FY-3D MWRI L1B LST反演和降尺度研究,构建了基于FY-3D单频率水平和垂直极化亮温的LST二元线性回归反演模型及基于FY-3D反演LST和FY-3C VIRR LST的层次贝叶斯融合降尺度模型,并以MYD11A1 day LST为参考数据进行了验证。结果表明:反演统计模型,对于FY-3D降轨数据,平均偏差-1.28 K,误差标准差8.85 K,均方根误差8.85 K,对于FY-3D升轨数据,平均偏差-0.81 K,误差标准差6.74 K,均方根误差6.78 K;层次贝叶斯融合降尺度模型,对于FY-3D降轨数据,平均偏差0.50 K,误差标准差5.45 K,均方根误差5.41 K,对于FY-3D升轨数据,平均偏差0.25 K,误差标准差5.54 K,均方根误差5.54 K,精度满足需求,可以为被动微波LST反演与降尺度提供思路。
Based on FY-3C VIRR LST and FY-3D/MWRI L1B brightness temperature data of February 1,2020 and taking the area with geographical coordinates of 18°-54°N,73°-135°E as an example,the LST reversion and downscaling based on the FY-3D/MWRI L1B data were studied using a statistical regression model and a hierarchical Bayesian fusion model.As a result,two models were constructed,namely a LST binary linear regression inversion model based on FY-3D single-channel horizontal and vertical polarization-corrected brightness temperature data and a hierarchical Bayesian fusion downscaling model based on FY-3D retrieved LST and FY-3C VIRR LST.They were verified with the LST on the day of MYD11A1 as reference data,obtaining the following results.As for the reversion statistical model,the mean bias,error standard deviation,and root mean square error were-1.28 K,8.85 K,and 8.85 K,respectively for the FY-3D descending data and were-0.81 K,6.74 K,and 6.78 K,respectively for the FY-3D ascending data.As for the hierarchical Bayesian fusion downscaling model,the mean bias,error standard deviation,and root mean square error were 0.50 K,5.45 K,and 5.41 K,respectively for the FY-3D descending data and were 0.25 K,5.54 K,and 5.54 K,respectively for the FY-3D ascending data.This study will provide a novel idea for the LST inversion and downscaling of passive microwaves.
作者
朱瑜馨
吴门新
鲍艳松
李鑫川
张锦宗
ZHU Yuxin;WU Menxin;BAO Yansong;LI Xinchuan;ZHANG Jinzong(School of Urban and Environmental Sciences,Huaiyin Normal University,Huaian 223300,China;National Meteorological Centre,Beijing 100081,China;School of Atmospheric Physics,Nanjing University of Information Science&Technology,Nanjing 210044,China)
出处
《自然资源遥感》
CSCD
北大核心
2021年第3期27-35,共9页
Remote Sensing for Natural Resources
基金
国家重点研发计划“全球气象卫星遥感动态监测、分析技术及定量应用方法及平台研究”(编号:2018YFC1506500)
“生态安全气象监测评估预警能力建设”子课题“基于卫星微波的地表干旱监测算法软件包”(编号:2019h564)
国家自然基金“数据驱动的时空过程建模及其约束下的多源SST遥感产品融合方法研究”(编号:41401405)。
作者简介
第一作者:朱瑜馨(1976-),女,博士,副教授,主要从事遥感时空统计与不确定研究。Email:zhuyuxin_402@163.com;通信作者:吴门新(1976-),男,博士,教授级高级工程师,主要从事生态和农业遥感研究。Email:wumx@cma.gov.cn。
