基于风云气象卫星的土壤湿度数据降尺度方法研究
投稿时间:2019-11-09  修订日期:2021-01-07  点此下载全文
引用本文:盛佳慧,饶鹏.基于风云气象卫星的土壤湿度数据降尺度方法研究[J].红外与毫米波学报,2021,40(1):74~88].SHENG Jia-Hui,RAO Peng.The research on downscaling methods based on Fengyun meteorological satellite soil moisture data[J].J.Infrared Millim.Waves,2021,40(1):74~88.]
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作者单位E-mail
盛佳慧 中国科学院上海技术物理研究所上海 200083
中国科学院智能红外感知重点实验室上海 200083
中国科学院大学北京 100049 
shengjiahui@mail.sitp.ac.cn 
饶鹏 中国科学院上海技术物理研究所上海 200083
中国科学院智能红外感知重点实验室上海 200083 
peng_rao@mail.sitp.ac.cn 
基金项目:中国科学院先导培育计划项目( 09KCE043N2)
中文摘要:针对被动微波土壤湿度数据空间分辨率较低的问题,分别基于随机森林、多项式拟合及DISPATCH等统计学和物理模型,融合可见光、热红外和地表高程参量对风云三号B星(FY3B)微波土壤湿度数据进行降尺度,使其空间分辨率从25 km提高至1 km。同时,考虑FY3B、与相关输入数据源过境时间不匹配现象,设置升降轨共计四组对照实验,对比分析FY3B降尺度的最优化数据组合。采用2015年4月1日至2016年12月31日的REMEDHUS土壤湿度原位站点及ECA&D气象站点数据验证,结果显示随机森林方法综合降尺度精度最高,模型拟合效果最好。此外,采用FY3B升轨数据降尺度效果更优。
中文关键词:土壤湿度  FY3B/MWRI  MODIS  降尺度  随机森林  DISPATCH  多项式拟合  REMEDHUS
 
The research on downscaling methods based on Fengyun meteorological satellite soil moisture data
Abstract:In view of the low spatial resolution of passive microwave soil moisture (SM) data, statistical and physical models including random forest (RF), polynomial fitting and DISPATCH are utilized to disaggregate the FY3B microwave SM product from 25 km to 1 km with the synergistic application of Optical/Thermal infrared (TIR) observations and surface elevation parameters. Meanwhile, considering different overpass times of FY3B and other relevant input data source observations, four data combinations are separately used to derive the spatially downscaled SM with above three downscaling method, and the optimized data combination of FY-3B downscaling is proposed by comparison and analysis. Validation is performed from April 1, 2015 to December 31, 2016 with the in-situ measurements of REMEDHUS network and the precipitation time series of ECA&D meteorological site. Experimental results show that RF-based method can achieve the highest comprehensive downscaling accuracy and the best model fitting effect. In addition, the effect of applying FY-3B ascending data to downscale turns out to be better.
keywords:Soil moisture  FY3B/MWRI  MODIS  downscaling  random forest  DISPATCH  polynomial fitting  REMEDHUS
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