The Microwave Land Surface Emissivity(MLSE)atlas and instantaneous simulation of all-sky/all-surface MLSE are important prerequisites for satellite data assimilation.A ten-day/month synthesized FengYun-3D MLSE atlas(N...The Microwave Land Surface Emissivity(MLSE)atlas and instantaneous simulation of all-sky/all-surface MLSE are important prerequisites for satellite data assimilation.A ten-day/month synthesized FengYun-3D MLSE atlas(New_FY3D)was constructed by the two global MLSE daily product datasets,clear-sky(FY-3D1)and clear/cloudy(FY-3D2),which were retrieved from the same FY-3D MicroWave Radiation Imager(MWRI)Level-1 brightness temperature(BT)data from 2021 to 2022,respectively.Then,a set of global MLSE label samples based on the New_FY3D,including 14 surface geophysical parameters,was obtained for an instantaneous global MLSE simulation at a 0.10°spatial resolution by adopting the extreme gradient boosting(XGBoost)machine learning method.Finally,the FengYun-3F(FY-3F)MWRI-II BT simulations using the Advanced Radiative Transfer Modeling System(ARMS)based on the above different MLSE products were evaluated.The results show that the New_FY3D atlas performs well,and the BT simulation at the top of atmosphere is better than that of FY-3D1,FY-3D2,and the international mainstream TELSEM2(Version 2.0 for a Tool to Estimate Land Surface Emissivities in the Microwaves)atlas.Surface roughness,vegetation coverage,land cover type,and snow cover are vital parameters for MLSE simulation.The XGBoost model can accurately simulate all-sky/all-surface MLSE instantaneously over the frequency range 10.65–89.0 GHz.The average simulation determination coefficients(R^(2))under clear-sky and cloud-sky conditions are 0.925 and 0.901,respectively,and the average root-mean-square errors(RMSEs)are 0.018 and 0.021,respectively.Large simulation errors occur in permanent wetland,ice and snow,and urban and built-up areas.With a standard deviation of 6.6 K,the BT simulation based on an XGBoost simulated MLSE is better than those based on New_FY3D and TELSEM2.展开更多
海冰密集度是描述海冰特征的重要参数,准确获取海冰密集度对研究全球气候变化具有重要意义。针对北极夏季海冰密集度反演精度较低的问题,本文通过对微波辐射传输模型中的海冰发射率和初始海冰密集度进行优化估算,改善了微波辐射传输模...海冰密集度是描述海冰特征的重要参数,准确获取海冰密集度对研究全球气候变化具有重要意义。针对北极夏季海冰密集度反演精度较低的问题,本文通过对微波辐射传输模型中的海冰发射率和初始海冰密集度进行优化估算,改善了微波辐射传输模型对夏季观测亮温的大气校正效果,从而优化被动微波海冰密集度的反演结果,本研究采用2019年6—9月的FY-3D/MWRI亮温数据,分别利用优化前和优化后的ASI2算法(ASI2和ASI2E),结合固定系点(FTP)与动态系点(DTP),分别获得了4套夏季北极海冰密集度数据(ASI2-FTP、ASI2-DTP、ASI2E-FTP、ASI2E-DTP),并利用14景MODIS影像对结果进行了精度验证。研究结果表明,本研究提出的优化方法可有效提高北极夏季海冰密集度的反演精度,其中该优化方法对基于固定系点的反演改进尤为明显,其优化后的均方根误差(root mean square error,ERMSE)由21.9%减小到15.43%,偏差(bias,|B_(bias)|)由-12.40%下降到-6.01%。4种反演方法中,基于动态系点的算法优化后(ASI2E-DTP)表现尤为明显,其E_(RMSE)和B_(bias)分别为14.33%和-4.53%。展开更多
净初级生产力(Net Primary Productivity,NPP)不仅是估算生态系统固碳释氧、衡量陆地碳循环的主要参数,也是评价生态系统健康状况的主要指标。针对目前国产卫星对草地净初级生产力遥感监测应用较少的情况,本文基于FY-3D/MERSI2资料构建...净初级生产力(Net Primary Productivity,NPP)不仅是估算生态系统固碳释氧、衡量陆地碳循环的主要参数,也是评价生态系统健康状况的主要指标。针对目前国产卫星对草地净初级生产力遥感监测应用较少的情况,本文基于FY-3D/MERSI2资料构建一套内蒙古草地净初级生产力反演模型,结合光能利用率模型与生态过程模型,以遥感数据产品和中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)资料为驱动,通过较严格的云检测算法得到晴空条件下内蒙古草地NPP。研究中引入分辨率较高的格点化气象数据,提升了反演结果的精细化程度;同时还基于观测数据及MODIS产品构建了内蒙古草地生育期不同月份(5—8月)地上生物量及光合有效辐射吸收比率(Fraction Photosynthetic Active Radiation Absorption Ratio,FPAR)与归一化植被指数(Normalized Differ⁃ence Vegetation Index,NDVI)的多种关系模型,基于FY-3D数据直接估算叶面积指数(Leaf Area Index,LAI)及FPAR等过程参数。将反演的关键生态过程参数与MODIS对应产品对比,发现二者具有较好相关性和空间一致性。最后利用2021年6月18个生态气象观测站牧草观测资料与估算结果进行对比验证,二者具有较好的一致性,相关系数为0.86。本研究利用FY-3D/MERSI2反演的NPP能够完整呈现内蒙古地区植被生产力的普遍状态。展开更多
本文结合辐射传输模型和机器学习提出了一种从FY-3D卫星MERSIⅡ传感器光学影像中识别云像元的方法CRMC(Combine Reflectance simulation and Machine learning for Cloud detection)。该方法通过设置变化的地物和大气内在光学特性IOPs(I...本文结合辐射传输模型和机器学习提出了一种从FY-3D卫星MERSIⅡ传感器光学影像中识别云像元的方法CRMC(Combine Reflectance simulation and Machine learning for Cloud detection)。该方法通过设置变化的地物和大气内在光学特性IOPs(Inherent Optical Properties),达到考虑多种下垫面的二项反射特征和不同大气条件下气溶胶和云参数的目的。CRMC方法主要包含3个步骤:(1)通过聚类分析从MODIS二项反射参数产品中分离出11种典型下垫面地表反射参数;(2)将随机设置的气溶胶和云参数以及地表反射率参数(即IOPs)输入SBDART辐射传输模型,得到模拟的反射率值数据集,并以此训练浅层神经网络模型;(3)利用浅层神经网络模型逐像元预测云概率,并根据实际需要确定区分云像元和非云像元的云概率阈值。通过与CALIPSO垂直特性掩膜产品(VFM)逐像元对比验证发现,CRMC方法总正确率为79.6%,且在陆地和海面上分别为78.5%和81.2%。通过与MODIS云掩膜产品横向对比(MYD35)发现,当云阈值设定为0.2时,CRMC方法在陆地,主要是阔叶林、农田、城市和裸土等地表上的云识别效果较好,但在海面的云识别效果仍待进一步提高。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.U2242211)the Hunan Provincial Natural Science Foundation Major Project(Grant No.2021JC0009).
文摘The Microwave Land Surface Emissivity(MLSE)atlas and instantaneous simulation of all-sky/all-surface MLSE are important prerequisites for satellite data assimilation.A ten-day/month synthesized FengYun-3D MLSE atlas(New_FY3D)was constructed by the two global MLSE daily product datasets,clear-sky(FY-3D1)and clear/cloudy(FY-3D2),which were retrieved from the same FY-3D MicroWave Radiation Imager(MWRI)Level-1 brightness temperature(BT)data from 2021 to 2022,respectively.Then,a set of global MLSE label samples based on the New_FY3D,including 14 surface geophysical parameters,was obtained for an instantaneous global MLSE simulation at a 0.10°spatial resolution by adopting the extreme gradient boosting(XGBoost)machine learning method.Finally,the FengYun-3F(FY-3F)MWRI-II BT simulations using the Advanced Radiative Transfer Modeling System(ARMS)based on the above different MLSE products were evaluated.The results show that the New_FY3D atlas performs well,and the BT simulation at the top of atmosphere is better than that of FY-3D1,FY-3D2,and the international mainstream TELSEM2(Version 2.0 for a Tool to Estimate Land Surface Emissivities in the Microwaves)atlas.Surface roughness,vegetation coverage,land cover type,and snow cover are vital parameters for MLSE simulation.The XGBoost model can accurately simulate all-sky/all-surface MLSE instantaneously over the frequency range 10.65–89.0 GHz.The average simulation determination coefficients(R^(2))under clear-sky and cloud-sky conditions are 0.925 and 0.901,respectively,and the average root-mean-square errors(RMSEs)are 0.018 and 0.021,respectively.Large simulation errors occur in permanent wetland,ice and snow,and urban and built-up areas.With a standard deviation of 6.6 K,the BT simulation based on an XGBoost simulated MLSE is better than those based on New_FY3D and TELSEM2.
文摘海冰密集度是描述海冰特征的重要参数,准确获取海冰密集度对研究全球气候变化具有重要意义。针对北极夏季海冰密集度反演精度较低的问题,本文通过对微波辐射传输模型中的海冰发射率和初始海冰密集度进行优化估算,改善了微波辐射传输模型对夏季观测亮温的大气校正效果,从而优化被动微波海冰密集度的反演结果,本研究采用2019年6—9月的FY-3D/MWRI亮温数据,分别利用优化前和优化后的ASI2算法(ASI2和ASI2E),结合固定系点(FTP)与动态系点(DTP),分别获得了4套夏季北极海冰密集度数据(ASI2-FTP、ASI2-DTP、ASI2E-FTP、ASI2E-DTP),并利用14景MODIS影像对结果进行了精度验证。研究结果表明,本研究提出的优化方法可有效提高北极夏季海冰密集度的反演精度,其中该优化方法对基于固定系点的反演改进尤为明显,其优化后的均方根误差(root mean square error,ERMSE)由21.9%减小到15.43%,偏差(bias,|B_(bias)|)由-12.40%下降到-6.01%。4种反演方法中,基于动态系点的算法优化后(ASI2E-DTP)表现尤为明显,其E_(RMSE)和B_(bias)分别为14.33%和-4.53%。
文摘净初级生产力(Net Primary Productivity,NPP)不仅是估算生态系统固碳释氧、衡量陆地碳循环的主要参数,也是评价生态系统健康状况的主要指标。针对目前国产卫星对草地净初级生产力遥感监测应用较少的情况,本文基于FY-3D/MERSI2资料构建一套内蒙古草地净初级生产力反演模型,结合光能利用率模型与生态过程模型,以遥感数据产品和中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)资料为驱动,通过较严格的云检测算法得到晴空条件下内蒙古草地NPP。研究中引入分辨率较高的格点化气象数据,提升了反演结果的精细化程度;同时还基于观测数据及MODIS产品构建了内蒙古草地生育期不同月份(5—8月)地上生物量及光合有效辐射吸收比率(Fraction Photosynthetic Active Radiation Absorption Ratio,FPAR)与归一化植被指数(Normalized Differ⁃ence Vegetation Index,NDVI)的多种关系模型,基于FY-3D数据直接估算叶面积指数(Leaf Area Index,LAI)及FPAR等过程参数。将反演的关键生态过程参数与MODIS对应产品对比,发现二者具有较好相关性和空间一致性。最后利用2021年6月18个生态气象观测站牧草观测资料与估算结果进行对比验证,二者具有较好的一致性,相关系数为0.86。本研究利用FY-3D/MERSI2反演的NPP能够完整呈现内蒙古地区植被生产力的普遍状态。
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。
