摘要
考虑到土壤水分会影响地表温度随太阳辐射变化的幅度,本文提出利用多时相热红外波段和可见光波段数据反演地表水分的算法,并利用第一代静止气象卫星——风云二号数据估算了2010年9月30日和10月20日西北地区范围的土壤表面水分,并与先进机械辐射扫描计(advanced mechanically scanned radiometer:AMSR)土壤水分产品进行比较验证。结果表明:该算法估算的土壤表面水分与AMSR土壤水分产品相关性为0.52,两者的均方根误差在0.025 g.cm 3以内,最大误差不超过0.07 g.cm 3。同时,利用该算法生成西北地区土壤表面水分空间分布图,与同期降水资料相比较,两者空间分布较为一致。此外,该算法可获得5 km×5 km空间尺度的土壤表面水分,提高了土壤水分遥感估算的空间分辨率。
Surface soil moisture（SSM） is a critical element of the hydrologic processes that influences exchange of water and en-ergy fluxes at the land/atmospheric interface.Current remote sensing applications in SSM studies are largely limited to polar orbiting satellites.With the development of new generation geostationary satellites such as MSG and GOES-O＆P,land surface visible and thermal infrared data can be acquired with high spatial and temporal resolutions.Consequently,great opportunities exist to analyze land surface soil moisture with retrieval methods of satellite-observed data.Fengyun-2D is a Chinese operated geostationary satellite with one visible and four infrared channels of optical imaging radiometer with temporal image acquisition frequency of 30 min.This allows mapping diurnal variations in land surface shortwave radiation（SSR） and land surface temperature（LST）.The objective of this study was to estimate SSM from diurnal evolutions of SSR and LST.FY-2D data（with a spatial resolution of 5 km） were download along with advanced mechanically scanned radiometer（AMSR） soil moisture products（with spatial resolution of 25 km）.The sets of data were geographically corrected via geo-referencing using geo-locational tools.The thermal and visible infrared soil moisture data products from FY-2D and AMSR were matched using a linear resampling method.Next,an algorithm for estimating SSM via two thermal infrared channels（IR1： 10.3~11.3 ？m and IR2： 11.5~12.5 ？m） and one visible channel（0.55~0.9 ？m） of the geostationary satellite data was proposed based on linear relationship between SSR and LST diurnal evolutions.Finally,the method was validated using FY-2D and AMSR SSM data products for September 30,2010 and SSM estimated using FY-2D data for October 20,2010.The results showed that the method was applicable in calculating SSM.SSM correlation based on analysis of FY-2D and AMSR was 0.52,root mean square error（RMSE） of 0.025 g？cm？3 and maximum errors 〈 0.07 g？cm？3.The proposed method was easy use and output mid-scale SSM that improved the spatial resolution of AMSR SSM products from 25 km to 5 km.However,it was noted that the method must be used with care as it was prone to severe error under vegetation or cloud cover.
出处
《中国生态农业学报》
CAS
CSCD
北大核心
2012年第7期882-887,共6页
Chinese Journal of Eco-Agriculture
基金
国家自然科学基金项目(40971199)资助
