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Applications of amsr-e Measurements for Tropical Cyclone Predictions PartⅠ: Retrieval of Sea Surface Temperature and Wind Speed

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Advances in Atmospheric Sciences 2008年第2期25卷 227-245页

作者： Banghua YAN Fuzhong WeNG NOAA Joint Center for Satellite Data Assimilation Camp Springs MD USA NOAA/NeSDIS/Center for Satellite Applications and Research Camp Springs MD USA

existing satellite microwave algorithms for retrieving Sea Surface Temperature （SST） and Wind （SSW） are applicable primarily for non-raining cloudy conditions. With the launch of the earth Observing System （eOS） Aqua satellite in 2002, the Advanced Microwave Scanning Radiometer （amsroe） onboard provides some unique measurements at lower frequencies which are sensitive to ocean surface parameters under adverse weather conditions. In this study, a new algorithm is developed to derive SST and SSW for hurricane predictions such as hurricane vortex analysis from the amsroe measurements at 6.925 and 10.65 GHz. In the algorithm, the effects of precipitation emission and scattering on the measurements are properly taken into account. The algorithm performances are evaluated with buoy measurements and aircraft dropsonde data. It is found that the root mean square （RMS） errors for SST and SSW are about 1.8 K and 1.9 m s^- 1, respectively, when the results are compared with the buoy data over open oceans under precipitating clouds （e.g., its liquid water path is larger than 0.5 mm）, while they are 1.1 K for SST and 2.0 m s^-1 for SSW, respectively, when the retrievals are validated against the dropsonde measurements over warm oceans. These results indicate that our newly developed algorithm can provide some critical surface information for tropical cycle predictions. Currently, this newly developed algorithm has been implemented into the hybrid variational scheme for the hurricane vortex analysis to provide predictions of SST and SSW fields.

关键词： Ocean surface temperature ocean surface wind amsr-e hurricane vortex analysis

Cross-calibration of brightness temperature obtained by FY-3B/MWRI using Aqua/amsr-e data for snow depth retrieval in the Arctic

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Acta Oceanologica Sinica 2021年第1期40卷 43-53页

作者： Haihua Chen Lele Li Lei Guan College of Information Science and engineering Ocean University of ChinaQingdao 266100China

This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/amsr-e L2A.The monthly parameters of the cross-calibration were determined and evaluated using robust linear regression.The snow depth in case of seasonal ice was calculated by using parameters of the crosscalibration of data from the MWRI Tb.The correlation coefficients of the H/V polarization among all channels Tb of the two sensors were higher than 0.97.The parameters of the monthly cross-calibration were useful for the snow depth retrieval using the MWRI.Data from the MWRI Tb were cross-calibrated to the amsr-e baseline.Biases in the data of the two sensors were optimized to approximately 0 K through the cross-calibration,the standard deviations decreased significantly in the range of 1.32 K to 2.57 K,and the correlation coefficients were as high as 99%.An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI Tb data had been well calibrated.Furthermore,the results of the cross-calibration were evaluated by data on the daily average Tb at 18.7 GHz,23.8 GHz,and 36.5 GHz(V polarization),and at 89 GHz(H/V polarization),and were applied to the snow depths retrieval in the Arctic.The parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average Tb.The results of the snow depths were compared with those of the calibrated MWRI and amsr-e products.Biases of 0.18 cm to 0.38 cm were observed in the monthly snow depths,with the standard deviations ranging from 4.19 cm to 4.80 cm.

关键词： FY-3B amsr-e brightness temperature(T^(b)) cross-calibration snow depth Arctic

Digitizing the thermal and hydrological parameters of land surface in subtropical China using amsr-e brightness temperatures

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International Journal of Digital earth 2017年第7期10卷 687-700页

作者： Yongxian Su Xiuzhi Chen Hua Su Liyang Liu Jishan Liao Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangzhouPeople’s Republic of China Jiangsu Key Laboratory of Agricultural Meteorology College of Applied MeteorologyNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China Key Laboratory of Vegetation Restoration and Management of Degraded ecosystems South China Botanical GardenChinese Academy of SciencesGuangzhouPeople’s Republic of China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of education National Engineering Research Centre of Geo-spatial Information TechnologyFuzhou UniversityFuzhouPeople’s Republic of China

Digitizing the land surface temperature(T_(s))and surface soil moisture(m _(v))is essential for developing the intelligent Digital earth.Here,we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_(s) and m_(v) using the dual-polarized T_(b) of Aqua satellite advanced microwave scanning radiometer(amsr-e)C-band(6.9 GHz)based on the simplified radiative transfer equation.Validation using in situ T_(s) and m_(v) in southern China showed the average root mean square errors(RMSe)of T s and m_(v) retrievals reach 2.42 K(R^(2)=0.61,n=351)and 0.025 g cm^(−3)(R^(2)=0.68,n=663),respectively.The results were also validated using global in situ T_(s)(n=2362)and m_(v)(n=1657)of International Soil Moisture Network.The corresponding RMSe are 3.44 k(R 2=0.86)and 0.039 g cm^(−3)(R^(2)=0.83),respectively.The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_(s)(R^(2)=0.57;RMSe=2.91 k)and eCV_SM m_(v)(R^(2)=0.51;RMSe=0.045 g cm^(−3)),respectively.Overall,this paper indicates an effective way to jointly modeling T_(s) and m_(v) using passive microwave remote sensing.

关键词： Surface soil moisture land surface temperature physical-based radiative transfer model amsr-e brightness temperatures

Retrieving the antarctic sea-ice concentration based on amsr-e 89 GHz data

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Acta Oceanologica Sinica 2013年第9期32卷 38-43页

作者： YU Qinglong WANG Hui WAN Liying BI Haibo National Marine environmental Forecasting Center State Oceanic Administration Institute of Remote Sensing Applications Chinese Academy of Sciences Graduate School Chinese Academy of Sciences

Sea-ice concentration is a key item in global climate change research. Recent progress in remotely sensed sea-ice concentration product has been stimulated by the use of a new sensor, advanced microwave scan- ning radiometer for eOS （amsr-e）, which offers a spatial resolution of 6 km×4 km at 89GHz, A new inver- sion algorithm named LASI （linear ASI） usingamsr-e 89GHz data was proposed and applied in the antarc- tic sea areas. And then comparisons between the LASI ice concentration products and those retrieved by the other two standard algorithms, ASI （arctic radiation and turbulence interaction study sea-ice algorithm） and bootstrap, were made. Both the spatial and temporal variability patterns of ice concentration differ- ences, LASI minus ASI and LASI minus bootstrap, were investigated. Comparative data suggest a high result consistency, especially between LASI and ASI. On the other hand, in order to estimate the LASI ice concen- tration errors introduced by the tie-points uncertainties, a sensitivity analysis was carried out. Additionally an LASI algorithm error estimation based on the field measurements was also completed. The errors suggest that the moderate to high ice concentration areas （〉70%） are less affected （never exceeding 10%） than those in the low ice concentration. LASI and ASI consume 75 and 112 s respectively when processing the same amsr-e time series thourghout the year 2010. To conclude, by using the LASI algorithm, not only the sea- ice concentration can be retrieved with at least an equal quality as that of the two extensively demonstrated operational algorithms, ASI and bootstrap, but also in a more efficient way than ASI.

关键词： sea-ice concentration amsr-e antarctic LASI algorithm

A simplified physically-based algorithm for surface soil moisture retrieval using amsr-e data

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Frontiers of earth Science 2014年第3期8卷 427-438页

作者： Jiangyuan ZeNG Zhen LI Quan CHeN Haiyun BI Key Laboratory of Digital earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing 100094 China University of Chinese Academy of Sciences Beijing 100049 China

A simplified physically-based algorithm for surface soil moisture inversion from satellite microwave radiometer data is presented. The algorithm is based on a radiative transfer model, and the assumption that the optical depth of the vegetation is polarization independent. The algorithm combines the effects of vegetation and roughness into a single parameter. Then the microwave polarization difference index （MPDI） is used to eliminate the effects of surface temperature, and to obtain soil moisture, through a nonlinear iterative procedure. To verify the present algorithm, the 6.9 GHz dual-polarized brightness temperature data from the Advanced Micro- wave Scanning Radiometer （amsr-e） were used. Then the soil moisture values retrieved by the present algorithm were validated by in-situ data from 20 sites in the Tibetan Plateau, and compared with both the NASA amsr-e soil moisture products, and Soil Moisture and Ocean Salinity （SMOS） soil moisture products. The results show that the soil moisture retrieved by the present algorithm agrees better with ground measurements than the two satellite products. The advantage of the algorithm is that it doesn＇t require field observations of soil moisture, surface roughness, or canopy biophysical data as calibration parameters, and needs only single-frequency brightness temperature observations during the whole retrieval process.

关键词： passive microwave remote sensing soilmoisture inversion amsr-e SMOS

Applications of an amsr-e RFI Detection and Correction Algorithm in 1-DVAR over Land

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Journal of Meteorological Research 2014年第4期28卷 645-655页

作者：吴莹翁富忠 Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration Nanjing University of Information Science & Technology School of Atmospheric Physics Nanjing University of Information Science & Technology NOAA/NeSDIS/Center for Satellite Application and Research College ParkMD 20742 USA

Land retrievals using passive microwave radiometers are sensitive to small fluctuations in land brightness temperatures. As such, the radio-frequency interference （RFI） signals emanating from man-made microwave radiation transmitters can result in large errors in land retrievals. RFI in C-and X-band channels can con-taminate remotely sensed measurements, as experienced with the Advanced Microwave Scanning Radiometer （amsr-e） and the WindSat sensor. In this work, applications of an RFI detection and correction algorithm in retrieving a comprehensive suite of geophysical parameters from amsr-e measurements using the one-dimensional variational retrieval （1-DVAR） method are described. The results indicate that the values of retrieved parameters, such as land skin temperature （LST）, over these areas contaminated by RFI are much higher than those from the global data assimilation system （GDAS） products. The results also indicate that the differences between new retrievals and GDAS products are decreased evidently through taking into account the RFI correction algorithm. In addition, the convergence metric （χ2） of 1-DVAR is found to be a new method for identifying regions where land retrievals are affected by RFI. For example, in those regions with much stronger RFI, such as europe and Japan, χ2 of 1-DVAR is so large that convergence cannot be reached and retrieval results may not be reliable or cannot be obtained. Furthermore,χ2 also decreases with the RFI-corrected algorithm for those regions with moderate or weak RFI. The results of RFI detected byχ2 are almost consistent with those identified by the spectral difference method.

关键词： microwave remote sensing radio-frequency interference （RFI） amsr-e 1-DVAR

Flood and Waterlogging Monitoring over Huaihe River Basin by amsr-e Data Analysis

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Chinese Geographical Science 2008年第3期18卷 262-267页

作者： ZHeNG Wei LIU Chuang XIN Zhongbao WANG Zhengxing Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China Graduate University of the Chinese Academy of Sciences Beijing 100049 China National Satellite Meteorological Center Beijing 100081 China

In this paper, we investigated the potential of Advanced Microwave Scanning Radiometer for earth Observation System (amsr-e) for flood monitoring. The proposed approach was based on the polarized ratio index (PRI), which was computed by using amsr-e data at 37GHz, vertically and horizontally polarized brightness temperature values and the water surface fraction (WSF) got by using the PRI at 37GHz. Moderate Resolution Imaging Spectrora-diometer (MODIS) data were used to validate the WSF values. The analysis of flood and waterlogging using the WSF map on July 6, 2003 indicates that the use of WSF for flood and waterlogging disaster assessment is feasible. Utilizing the correlation of WSF derived from amsr-e and water area derived from MODIS, the water area of the Huaihe River Basin were computed by only using amsr-e data in the summer of 2003, which overcame the influence of cloud on water estimation using MODIS data during flood.

关键词：洪水水涝淮河盆地 amsr-e 数据分析

利用微波辐射计amsr-e的京津冀地区大气水汽反演

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武汉大学学报（信息科学版） 2015年第4期40卷 479-486页

作者：王永前施建成刘志红冯文兰成都信息工程学院资源环境学院四川成都610225 重庆市气象科学研究所重庆401147 中国科学院遥感与数字地球研究所遥感科学国家重点实验室北京10010

发展了微波遥感水汽反演算法,对于裸露地表,通过极化差比值形式消除地表信息对大气水汽反演的干扰;针对非裸露地表,首先反演了地表发射率并对不同波段地表发射率之间的关系进行分析,进而建立了非裸露地表上空大气水汽的反演算法。本文... 详细信息

发展了微波遥感水汽反演算法,对于裸露地表,通过极化差比值形式消除地表信息对大气水汽反演的干扰;针对非裸露地表,首先反演了地表发射率并对不同波段地表发射率之间的关系进行分析,进而建立了非裸露地表上空大气水汽的反演算法。本文算法的反演结果与GPS探测结果的对比显示均方根误差为7.4mm,与MODIS大气水汽产品空间分布特征的对比也显示了两者较高的区域一致性。最后对京津冀平原地区和山地地区的水汽进行了时间序列的分析。

关键词：京津冀地区 amsr-e 大气水汽亮温极化差比

利用amsr-e被动微波数据反演地表温度的神经网络算法

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高技术通讯 2009年第11期19卷 1195-1200页

作者：毛克彪王道龙李滋睿张立新周清波唐华俊李丹丹中国农业科学院农业资源与农业区划研究所/农业部资源遥感与数字农业重点开放实验室/呼伦贝尔草原生态系统国家野外科学观测研究站北京100081 遥感科学国家重点实验室北京100101 中国科学院大气物理研究所东亚区域气候-环境重点实验室北京100029 北京师范大学地理学与遥感科学学院北京100875

结合对地观测卫星AQUA多传感器/多分辨率的特点,研究了利用amsr-e被动微波数据反演地表温度的神经网络算法。MODIS地表温度(LST)产品被作为地表温度实测数据,对应的平均温度被用作对应amsr-e像元的实际地表温度,从而克服由于amsr-e像元... 详细信息

结合对地观测卫星AQUA多传感器/多分辨率的特点,研究了利用amsr-e被动微波数据反演地表温度的神经网络算法。MODIS地表温度(LST)产品被作为地表温度实测数据,对应的平均温度被用作对应amsr-e像元的实际地表温度,从而克服由于amsr-e像元尺度太大和云的影响而难以获得地表实测数据的难点。反演结果分析表明,利用神经网络能够精确地由amsr-e数据反演地表温度。当使用5个频率10个通道反演时,反演精度最高,说明使用更多的通道能更好地消除土壤水分、粗糙度、大气和其它因素的影响。相对于MODIS温度产品,用此算法反演的平均误差约低于2K。

关键词：地表温度(LST) 神经网络(NN) amsr-e MODIS