利用FY-3D卫星上搭载的微波成像仪(microwave radiation imager,MWRI)的一级亮温数据,结合二级降雨率沿轨产品,基于极化订正温度及散射指数(polarization corrected temperature and scattering index,PCT-SI)综合法,建立了升轨洋面和...利用FY-3D卫星上搭载的微波成像仪(microwave radiation imager,MWRI)的一级亮温数据,结合二级降雨率沿轨产品,基于极化订正温度及散射指数(polarization corrected temperature and scattering index,PCT-SI)综合法,建立了升轨洋面和降轨洋面两种降雨率反演模型,并通过多个台风个例对本研究建立的两种模型进行验证。结果表明,升轨数据与降轨数据反演降雨的效果差异不大,反演的降雨分布区域比二级产品降雨区域略大;两种模型均倾向于高估降雨低值、低估降雨高值;升轨反演模型的相关系数、平均绝对误差和均方根误差分别为0.72632、2.3055mm·h^(−1)和2.7254mm·h^(−1),降轨反演模型的相关系数、平均绝对误差和均方根误差分别为0.73363、1.9079mm·h^(−1)和2.3651mm·h^(−1)。展开更多
Sea ice concentration(SIC)is one of the most important indicators when monitoring climate changes in the polar region.With the development of the Chinese satellite technology,the Feng Yun(FY)series has been applied to...Sea ice concentration(SIC)is one of the most important indicators when monitoring climate changes in the polar region.With the development of the Chinese satellite technology,the Feng Yun(FY)series has been applied to retrieve the sea ice parameters in the polar region.In this paper,to improve the SIC retrieval accuracy from the passive microwave(PM)data of the Microwave Radiation Imager(MWRI)aboard on the Feng Yun-3 B(FY-3 B)Satellite,the dynamic tie-point(DT)Arctic Radiation and Turbulence Interaction Study(ARTIST)Sea Ice(ASI)(DT-ASI)SIC retrieval algorithm is applied and obtained Arctic SIC data for nearly 10 a(from November 18,2010 to August 19,2019).Also,by applying a land spillover correction scheme,the erroneous sea ice along coastlines in melt season is removed.The results of FY-3 B/DT-ASI are obviously improved compared to that of FY-3 B/NT2(NASA-Team2)in both SIC and sea ice extent(SIE),and are highly consistent with the results of similar products of AMSR2(Advanced Microwave Scanning Radiometer 2)/ASI and AMSR2/DT-ASI.Compared with the annual average SIC of FY-3 B/NT2,our result is reduced by 2.31%.The annual average SIE difference between the two FY-3 Bs is 1.65×10^(6) km^(2),of which the DT-ASI algorithm contributes 87.9%and the land spillover method contributes12.1%.We further select 58 MODIS(Moderate-resolution Imaging Spectroradiometer)cloud-free samples in the Arctic region and use the tie-point method to retrieve SIC to verify the accuracy of these SIC products.The root mean square difference(RMSD)and mean absolute difference(MAD)of the FY-3 B/DT-ASI and MODIS results are 17.2%and 12.7%,which is close to those of two AMSR2 products with 6.25 km resolution and decreased 8%and 7.2%compared with FY-3 B/NT2.Further,FY-3 B/DT-ASI has the most significant improvement where the SIC is lower than 60%.A high-quality SIC product can be obtained by using the DT-ASI algorithm and our work will be beneficial to promote the application of Feng Yun Satellite.展开更多
近年来,由于“北极放大”的气候效应,使得北极海冰变化受到了越来越多的关注。而作为海冰被动微波遥感的主要参数,海冰密集度SIC(Sea Ice Concentration)能够表征海冰的主要状态,可用于指导极区走航以及进行不同尺度的海冰变化研究。通...近年来,由于“北极放大”的气候效应,使得北极海冰变化受到了越来越多的关注。而作为海冰被动微波遥感的主要参数,海冰密集度SIC(Sea Ice Concentration)能够表征海冰的主要状态,可用于指导极区走航以及进行不同尺度的海冰变化研究。通过该参数还可以计算出海冰面积、海冰范围等信息,对极区冰情预测以及气候变化研究具有重要意义。本研究探讨了如何利用FY-3B/MWRI(FY-3B/MicroWave Radiometer Imager)较高分辨率通道数据来反演北极地区海冰密集度。基于ASI(ARTIST(Arctic Radiation and Turbulence Interaction STudy)Sea Ice)算法,本研究通过改进算法系点值的方法反演了北极地区海冰密集度,并将反演结果与MWRI海冰密集度产品进行了对比。首先利用Aqua/MODIS(Moderate Resolution Imaging Spectroradiometer)反射率数据获得的海冰密集度对二者进行了验证。结果表明,本研究选用的新系点值ASI算法在全部数据集范围内的平均偏差与MWRI海冰密集度产品相当,但标准偏差和均方根误差均较之明显降低,且在海冰密集度低于95%时精度远高于MWRI产品;然后将二者与不莱梅大学的SIC_UB(Sea Ice Concentration from University of Bremen)海冰密集度产品进行了对比,其中本研究反演海冰密集度与SIC_UB产品的平均偏差和标准偏差分别为3.3%和10.6%,低于MWRI产品与SIC_UB产品之间的5.9%和16.4%;最后,对本研究反演结果、MWRI产品、NSIDC/AMSR-E(National Snow and Ice Data Center/Advanced Microwave Scanning Radiometer-EOS)产品以及SIC_UB产品的日均海冰密集度和海冰面积、海冰范围进行了时间序列对比,结果表明本研究反演海冰密集度的数值在3种统计方式下均显著低于MWRI产品,且较之更接近NSIDC/AMSR-E和SIC_UB产品。本研究利用国产卫星亮温数据反演的北极地区海冰密集度具有较高空间分辨率和较高精度,有利于北极地区气候变化的长时间序列研究。展开更多
The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temp...The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.展开更多
文摘目前还没有基于国产卫星的1 km分辨率的全天候陆表温度(LST)产品,FY-3D卫星提供了中分辨率成像仪(MERSI)Ⅱ型1 km分辨率晴空LST产品与微波成像仪(MWRI)25 km全天候LST产品,因此可结合两者优势开展全天候1 km分辨率LST的融合研究。基于地理加权回归(GWR)方法,选择海拔、FY-3D归一化植被指数和归一化建筑指数等建立GWR模型对FY-3D/MWRI 25 km LST降尺度到1 km,并与MERSI 1 km LST进行融合;同时针对MWRI轨道间隙,利用前后1天融合后的云覆盖像元1 km LST进行补值,可以得到接近全天候下的1 km LST。基于以上融合算法,选择了中国区域多个典型日期FY-3D/MERSI和MWRI LST官网产品进行了融合试验,并利用公开发布的全天候1 km LST产品(TPDC LST)对FY-3D 1 km LST融合结果进行了评估。研究结果表明,基于GWR法的LST降尺度方法,可以有效避免传统微波LST降尺度方法中存在的“斑块”效应和局地温度偏低等问题;LST融合结果有值率从融合前的22.4%~36.9%可提高到融合后69.3%~80.7%,融合结果与TPDC LST的空间决定系数为0.503~0.787,均方根误差为3.6~5.8 K,其中晴空为2.6~4.9 K,云下为4.1~6.1 K;分析还表明目前官网产品FY-3D/MERSI和MWRI LST均存在缺值较多与精度偏低等问题,显示其存在较大改进潜力,这有利于进一步改进FY-3D LST融合质量。
文摘利用FY-3D卫星上搭载的微波成像仪(microwave radiation imager,MWRI)的一级亮温数据,结合二级降雨率沿轨产品,基于极化订正温度及散射指数(polarization corrected temperature and scattering index,PCT-SI)综合法,建立了升轨洋面和降轨洋面两种降雨率反演模型,并通过多个台风个例对本研究建立的两种模型进行验证。结果表明,升轨数据与降轨数据反演降雨的效果差异不大,反演的降雨分布区域比二级产品降雨区域略大;两种模型均倾向于高估降雨低值、低估降雨高值;升轨反演模型的相关系数、平均绝对误差和均方根误差分别为0.72632、2.3055mm·h^(−1)和2.7254mm·h^(−1),降轨反演模型的相关系数、平均绝对误差和均方根误差分别为0.73363、1.9079mm·h^(−1)和2.3651mm·h^(−1)。
基金The National Key Research and Development Program of China under contract No.2016YFC1402704the National Natural Science Foundation of China under contract Nos 41941012 and 42076228the Guangdong Basic and Applied Basic Research Foundation under contract No.2019A1515110295。
文摘Sea ice concentration(SIC)is one of the most important indicators when monitoring climate changes in the polar region.With the development of the Chinese satellite technology,the Feng Yun(FY)series has been applied to retrieve the sea ice parameters in the polar region.In this paper,to improve the SIC retrieval accuracy from the passive microwave(PM)data of the Microwave Radiation Imager(MWRI)aboard on the Feng Yun-3 B(FY-3 B)Satellite,the dynamic tie-point(DT)Arctic Radiation and Turbulence Interaction Study(ARTIST)Sea Ice(ASI)(DT-ASI)SIC retrieval algorithm is applied and obtained Arctic SIC data for nearly 10 a(from November 18,2010 to August 19,2019).Also,by applying a land spillover correction scheme,the erroneous sea ice along coastlines in melt season is removed.The results of FY-3 B/DT-ASI are obviously improved compared to that of FY-3 B/NT2(NASA-Team2)in both SIC and sea ice extent(SIE),and are highly consistent with the results of similar products of AMSR2(Advanced Microwave Scanning Radiometer 2)/ASI and AMSR2/DT-ASI.Compared with the annual average SIC of FY-3 B/NT2,our result is reduced by 2.31%.The annual average SIE difference between the two FY-3 Bs is 1.65×10^(6) km^(2),of which the DT-ASI algorithm contributes 87.9%and the land spillover method contributes12.1%.We further select 58 MODIS(Moderate-resolution Imaging Spectroradiometer)cloud-free samples in the Arctic region and use the tie-point method to retrieve SIC to verify the accuracy of these SIC products.The root mean square difference(RMSD)and mean absolute difference(MAD)of the FY-3 B/DT-ASI and MODIS results are 17.2%and 12.7%,which is close to those of two AMSR2 products with 6.25 km resolution and decreased 8%and 7.2%compared with FY-3 B/NT2.Further,FY-3 B/DT-ASI has the most significant improvement where the SIC is lower than 60%.A high-quality SIC product can be obtained by using the DT-ASI algorithm and our work will be beneficial to promote the application of Feng Yun Satellite.
文摘近年来,由于“北极放大”的气候效应,使得北极海冰变化受到了越来越多的关注。而作为海冰被动微波遥感的主要参数,海冰密集度SIC(Sea Ice Concentration)能够表征海冰的主要状态,可用于指导极区走航以及进行不同尺度的海冰变化研究。通过该参数还可以计算出海冰面积、海冰范围等信息,对极区冰情预测以及气候变化研究具有重要意义。本研究探讨了如何利用FY-3B/MWRI(FY-3B/MicroWave Radiometer Imager)较高分辨率通道数据来反演北极地区海冰密集度。基于ASI(ARTIST(Arctic Radiation and Turbulence Interaction STudy)Sea Ice)算法,本研究通过改进算法系点值的方法反演了北极地区海冰密集度,并将反演结果与MWRI海冰密集度产品进行了对比。首先利用Aqua/MODIS(Moderate Resolution Imaging Spectroradiometer)反射率数据获得的海冰密集度对二者进行了验证。结果表明,本研究选用的新系点值ASI算法在全部数据集范围内的平均偏差与MWRI海冰密集度产品相当,但标准偏差和均方根误差均较之明显降低,且在海冰密集度低于95%时精度远高于MWRI产品;然后将二者与不莱梅大学的SIC_UB(Sea Ice Concentration from University of Bremen)海冰密集度产品进行了对比,其中本研究反演海冰密集度与SIC_UB产品的平均偏差和标准偏差分别为3.3%和10.6%,低于MWRI产品与SIC_UB产品之间的5.9%和16.4%;最后,对本研究反演结果、MWRI产品、NSIDC/AMSR-E(National Snow and Ice Data Center/Advanced Microwave Scanning Radiometer-EOS)产品以及SIC_UB产品的日均海冰密集度和海冰面积、海冰范围进行了时间序列对比,结果表明本研究反演海冰密集度的数值在3种统计方式下均显著低于MWRI产品,且较之更接近NSIDC/AMSR-E和SIC_UB产品。本研究利用国产卫星亮温数据反演的北极地区海冰密集度具有较高空间分辨率和较高精度,有利于北极地区气候变化的长时间序列研究。
基金supported by the National Natural Science Foundation of China(Grant Nos.41171260&41030534)
文摘The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.
