NOAA/AVHRR

摘要： With the help of the time-series data of NOAA/AVHRR NDVI at 1-kilometer resolution from 1989 to 2008,and the vegetation chart of Xilingol League,this paper abstracted the NDVI time-series curves of eight types of vegetation after filtering the time-series images with the Savitzky-Golay method.After filtering the NDVI time-series curves with the S-G method,the paper estimated the annual beginning,end date and length of growing season of various vegetation using the improved dynamic threshold method,and performed the line-ar fitting.Finally, the accuracy of estimated phenophases and length of growing season of temperate needlegrass arid steppe was verified according to surveyed groud observations.The results indicated that there was significant difference among all vegetation types in beginning of growing season.Six vegetation types showed advanced trend of change in various degree,while temperate grass and forb holophytic meadow and temperate leaflet deciduous woodland showed delayed trend about 4 and 1 d•20 a-1 ,respectively.The end of growing season of all kinds of vegetation showed delayed trend in similar degree and the length of growing sea-son of all kinds of vegetation extended.The end of growing season has greater impact on the length of growing season.Both surveyed and estimated values appeared similar trend of change which were 9 and 7 d•20 a-1 in advance,while end of growing season appeared converse.The RMSE between surveyed and estimated values was 16.59 d for the bginning of growing and 14.40 dfor wilting date,respectively.%利用分辨率为1 km×1 km 1989-2008年长时间序列 NOAA/AVHRR NDVI卫星产品数据以及锡林郭勒盟植被类型图,对时间序列影像进行 Savitzky-Golay 滤波后,提取了8种植被类型多年的归一化植被指数(NDVI)时序曲线,对 NDVI时序曲线进行 S-G滤波后,采用改进的动态阈值法分别估测各植被类型每年生长季的开始、结束时间及长度并进行线性拟合,讨论其变化情况.根据实测数据验证了温带丛生禾草典型草原遥感估测物候期的精确度.研究结果表明,各种植被类型生长季开始时间变化情况差别较大,除温带禾草、杂类草盐生草甸和温带落叶小叶疏林分别有4和1 d•20 a-1的轻微延迟以外,其它6种植被的生长季开始时间均有不同程度的提前趋势,不同植被类型生长季结束时间呈现出了差别较小的延迟趋势,生长季长度均延长,生长季结束时间对生长季长度的影响更大；估测的温带丛生禾草典型草原返青期与观测值变化趋势非常接近,分别提前了9和7 d•20 a-1,而两者的黄枯期则出现了相反的变化趋势.估测值与观测值的均方根误差(RMSE)分别是,生长季开始时间16.59 d,结束时间14.40 d.

摘要： 以2007年数据为例,利用MODIS产品和地面观测数据对NOAA/AVHRR计算得到的总云量进行数据质量评估。评估结果表明,利用NOAA/AVHRR计算的总云量能较好地反映中国总云量的变化特征,总体上在统计意义上要比站点观测总云量略偏低。当以地面观测云为真值,与MODIS云检测产品相比,利用NOAA/AVHRR检测的云像元准确性较高,误判率较低。利用EOF和SVD分解分析NOAA/AVHRR计算总云量与地面观测和MODIS产品的相似度,得到NOAA/AVHRR计算总云量在空间分布上更接近站点观测值,NOAA/AVHRR计算总云量与地面观测总云量SVD分解第一模态时间系数的相关系数(0.96)高于MODIS产品和地面观测总云量间的相关系数(0.76),特别在冬季,NOAA/AVHRR计算总云量明显好于MODIS总云量产品。

摘要： Our current knowledge of the sea fog distribution are mostly obtained from the coastal weather stations, ships,and buoys observational data.However these data are limited in spatial distribution,quality control and repr-esentativeness.Thus we are lack of a comprehensive understanding of the distribution of sea fog.Satellite data are known as uniform spacial distribution,wide coverage and relatively consistent quality.Under cloudless conditions, satellite data have the advantages of monitoring large-scale,offshore sea fog.Base on the frequency of fog and cloud together with the percentage distribution,we obtained comprehensive seasonal variation characteristics of the fog in the Yellow Sea and Bohai Sea from 1989 to 2008 .Besides the conclusions confirms those found in the literature, this work also revealed the following facts:(1)The seasonal variation in fog frequency of the Yellow Sea is more significant than that in the Bohai sea.(2)The fog in the Bohai Sea and the Yellow Sea occur more frequently than expected in winter.(3)In the course of sea fog formation and dissipation,there are the phenomena of eastward propagation of sea fog coverage.(4)In the spring and summer fog season,there are two sea fog-prone areas,loca-ted in the middle of Yellow Sea and the West Korea Bay respectively.And the annual maximum fog occurrence is in the West Korea Bay.Furthermore,in the case of sufficient sample population,based on the statistical analysis of the frequency of clouds and sea fog,the datasets generated by the algorithm can be used to estimate the average probability of the fog occurrence in the Yellow Sea and Bohai Sea for the next 20 years.%目前对海上雾分布的认识多基于沿岸测站和海上船舶、浮标观测，但这些数据非常稀少，且存在代表性和数据质量方面的问题，因此一直缺乏对海雾分布更全面、清晰的了解。卫星遥感数据空间均一、覆盖范围广、质量一致，具有对无云条件下大范围、离岸海雾监测的优势。本文通过分析算法检测出的1989－2008年黄渤海海雾及云的频数、分布百分率信息，得到了黄渤海海雾季节变化的较全面特征。除印证其他资料或研究的结论外，还发现：（1）黄海海雾频数随季节变化的幅度较渤海明显；（2）黄海、渤海海域存在冬季海雾多发时段；（3）海雾生消过程中有覆盖区变化的东传特征；（4）春夏雾季中存在黄海中部和西朝鲜湾两处海雾多发区，其中西朝鲜湾也是全年海雾最多的海域。另外，在样本充足的情况下，通过对检测出的低云、中高云覆盖百分率和海雾频数的分析统计，还能估算出黄海、渤海部分季节20年海雾发生的平均概率。

摘要： 为了得到更精确的渤海海冰密集度反演参数，采用辽东湾不同类型海冰 ASD 实测数据，在分析光谱特征的基础上，针对 NOAA/AVHRR 数据确定出合适海冰密集度反演算法阈值。继而，基于线性光谱混合模型的多通道反演算法进行了一系列算法试验。同时实现了基于 LandSat5-TM 数据的渤海海冰密集度场反演，并利用该结果与 AVHRR 单通道和多通道算法得到的海冰密集度反演结果进行比对分析。定量误差分析结果表明，当单通道算法或组合算法中包含1通道时，与 Landsat5-TM反演结果的平均误差为正值，包含2通道且不包含1通道时，平均误差为负值；同时使用这两个通道较只包含其一的各种组合算法的平均误差明显偏小；在各种组合算法中，1245四个通道组合反演的海冰密集度结果误差最小，可应用于渤海 AVHRR 数据海冰密集度反演。%To achieve the improved retrieval parameter of sea ice concentration(SIC)in the Bohai Sea,spectral char-acteristics of different types of sea ice are obtained and analyzed using in-situ ASD measurements in the Liaodong Bay.Appropriate threshold is thus determined for the retrieval of AVHRR data and then the SIC is calculated.In this paper,muti-band retrieval algorithm based on linear spectral mixture model (LSMM)is used for conducting a series of band experiments.The retrieved SIC using LandSat5-TM data is used to compare with the AVHRR SIC of different combinational algorithms.The results of error quantitative analysis indicate that the mean error (ME) of the algorithms involving band1 is positive,but the ME turns to negative when the algorithms involve band2 in-stead of band1;the ME of the algorithms which include both of two bands is less than the those which include only band1 or band2;among these algorithms,the 1245 joint-band algorithm has the minimum errors:it is concluded that the 1245 joint-band algorithm can be used for the retrieval of AVHRR SIC in the Bohai Sea.

摘要： Using 2008-2013 NOAA/AVHRR 1B satellite data and weather observation data,preliminary research of haze indentification is carried out by digitizing image color processing technology,frequency distribution figure of apparent reflectance and technology of apparent reflectance threshold of visible light channel.And the distinguish indices were applied in haze monitoring of Beijing and surrounding area.The results show that haze is easily distinguished by the color composition of red,green and blue channels with NOAA/AVHRR bands 1,2,1 or bands 4,2,1 pattern and the colors of remote sensing images are mainly grey,purple and blue.Generally,it is difficult to distinguish haze from mist only by remote sensing ima-ges,but with the help of prior knowledge,surrounding environment information and image color,haze can be identified.Apparent reflectance of the first channel of NOAA/AVHRR 1B can be used as spectral index of haze identification.The apparent reflectance values for haze identification in winter,spring,summer, autumn are 0.15-0.32,0.15-0.30,0.14-0.30,0.14-0.32,respectively.And the verification analy-sis shows that accuracy of haze identification using these spectral indices is 82%.The color indices and re-flectance indices are effectively applied in haze monitoring over Beijing and surrounding area from January to March 2013.%利用2008-2013年 NOAA/AVHRR1B 卫星资料和气象观测资料，应用米氏散射理论、图像色彩处理技术、频数分布图和可见光通道表观反射率阈值技术，开展了北京地区霾的遥感识别研究，并在京津冀地区进行了应用。研究结果表明：易于识别的霾在 NOAA/AVHRR 遥感图像上一般以1、2、1波段或4、2、1波段进行红、绿、蓝三通道假彩色合成，图像颜色以灰色、紫色和蓝色三大系列为主；一般仅靠遥感图像难以识别霾与轻雾，但借助先验知识、周围环境和图像色彩，可在一定程度上区分霾与轻雾；NOAA/AVHRR 图像的第一波段表观反射率作为光谱指标可以对霾进行较好识别，反射率识别指标值分别为：冬季0．15～0．32，春季0．15～0．30，夏季0．14～0．30，秋季0．14～0．32；指标对霾的有效识别准确率为82％。利用上述建立的颜色指标和光谱指标可以较好地对2013年1-3月发生在京津冀地区的雾霾进行有效监测。

摘要： 极地海冰反照率直接影响极区的热收支,反照率的变化对地气系统热量收支平衡及气候变化等的研究具有重要意义.本文采用由美国国家海洋与大气管理局NOAA (National Oceanic and Atmospheric Administration)发射的NOAA卫星携带的先进的甚高分辨率辐射仪AVHRR (Advanced Very High Resolution Radiometer) Level-1B (L1B)数据,经宽带反射率转换、各向异性校正、大气订正、云检测等处理,得到4km宽带晴空地表反照率产品.将AVHRR反照率与北冰洋地表热收支SHEBA (Surface Heat Budget of the Arctic Ocean)实验数据进行印证,印证结果显示在冰雪冻结期二者平均偏差为-0.07,标准偏差为0.05.本文处理了2008年-2010年的AVHRR数据,结合第4次北极科学考察现场观测数据研究了北极冰面月平均反照率的变化,从降雪和冰脊两个方面分析了反照率的变化,结果显示反照率在冰雪融化过程中变化约为0.3,变化较大且较为迅速,表面粗糙的多年冰海域和较为平滑的一年冰海域的反照率在雪融化时期变化约为0.2且变化相对缓慢.研究结果表明,由冰雪融化引起的反照率变化较为快速且幅度较大,是引起北极反照率变化的主导因素.

摘要： 10.3969/j.issn.1009-5470.2012.04.001%　　白天,太阳辐射将海面上层加热,会出现海表温度日变化的情况,该变化对海气热交换以及海洋生态等的研究具有重要意义,且在不同海域有着不尽相同的变化规律.文章首先介绍了海表温度日变化经验和数值模型,然后在西北太平洋海域范围内,利用美国国家海洋和大气管理局(National Oceanic and Atmospheric Administration, NOAA)的改进型甚高分辨率辐射计(Advanced Very High Resolution Radiometer, AVHRR)海表温度数据、美国宇航局“水”卫星Aqua上先进微波扫描辐射计(Advanced Microwave Scanning Radiometer for EOS, AMSR-E)的海面风速和经计算得到的太阳辐射强度数据,通过对已有经验模型系数进行重新回归拟合,得到在该海域 NOAA AVHRR 海表温度数据日变化的经验模型.验证结果显示,重新回归系数后的模型在西北太平洋海域内计算所得的海表温度日变化大小与 AVHRR 数据本身计算所得结果相比,其平均偏差为−0.01°C,标准偏差为0.22°C,可以在该海域内较好地对NOAA AVHRR海表温度数据进行日变化校正.

摘要： 采用我国地面报资料、国际船舶报资料和云卫星(CloudSat)产品,分别对多通道动态阈值云检测方法的云检测结果进行了检验。结果表明,动态阈值云检测方法可用于长时间序列卫星观测数据的云检测,不仅运行速度较快,可实现全自动,且精度较高,陆地上空云检测准确率为82.8%(地面报资料),海洋上空云检测准确率为91.5%(船舶报资料)。此外,还利用CloudSat资料检验不同观测时间差对NOAA系列卫星云检测结果检验的影响。

摘要： 通过对夜间雾的NOAA/AVHRR红外光谱特征分析,建立基于NOAA/AVHRR遥感数据的夜间雾遥感监测模型.利用该模型对2005年冬季和2008年秋季2个时次江西省范围内的夜间雾分布格局进行监测,经地面气象观测资料检验表明,该模型监测夜间雾的准确率达69.2%.该模型的应用可弥补常规气象监测方法的不足,基本实现对夜间雾的宏观、动态、连续监测.%Through analyzing the infrared spectral characteristics of nighttime fog in NOAA/AVHRR data,the nighttime fog detection model has been established base on NOAA/AVHRR data. The distribution of nighttime fog in winter of 2005 and autumn of 2008 in Jiangxi Province has been detected using the model.And the result is validated by comparing to surface meteorological observation records. The analysis indicates that the model can accurately monitor the distribution of nighttime fog and its accuracy is 69.2％. Application of this model can remedy the inadeguacy of the meteorological observation and detect nighttime fog widely, dynamically and continuously.

摘要： [目的]研究青藏高原地表反照率的时空分布和变化特征.[方法]通过对NOAA/AVHRR数据反演的青藏高原地区地表反照率的不同算法进行验证,确认由Stroeve给出的反演结果最接近于观测值.基于Stroeve的反演算法.利用1982～2000年的NOAA/AVHRR数据,反演得到1982～2000年青藏高原地区地表反照率的空间分布.[结果]青藏高原地区年均地表反照率的分布与高原自然地理带的分布特征大致吻合;高原区域年均地表反照率呈缓慢下降趋势,不同地区的变化趋势有所不同;高原地区月均地表反照率有明显的地带性,且随时间的变化比较明显.[结论]该研究有助于改进气候模式中地面反射率的参数化方案,揭示局地和区域气候变化的内在机制,提高中长期气候预报的水平.%This paper aims to study the temporal and spatial variation of surface Albedo in Tibetan. [ Methods ] By using NOAA/rnAVHRR data, different algorithms were used to retrieve surface albedo in Tibetan Plateau, and it showed that the result of retrieval algorithm rnfrom Stroeve was mostly close to observed value. Based on retrieval algorithm from Stroeve, the spatial distribution of surface albedo in Tibetan rnPlateau was obtained by means of NOAA/AVHRR data during 1982 -2000. [ Resuts] The results revealed that the distribution of annual mean rnsurface albedo in Tibetan Plateau was identical with that of geographical zone in plateau area; annual mean surface albedo in plateau area rnshowed slight decrease trend which was different in various regions; monthly surface albedo in plateau area had obviously zonal distribution and rnchanged with time evidently. [ Conclusions] Our results will be helpful to improving the parameterization scheme of surface albedo in climate rnmodel, revealing the internal mechanism of local and regional climate change and enhancing the level of long-term climate forecast.

摘要： 本文利用1982~2000年NOAA-AVHRR数据资料,研究中国区域地表反照率的空间分布及其随时间的变化规律.研究结果表明中国区域年均地表反照率在空间分布上有很大的差异,瑗珲-腾冲线以西地区地表反照率明显大于瑗珲-腾冲线以东地区;年平均地表反照率呈现缓慢下降趋势,但是不同地区的变化趋势有所不同,年均地表反照率显著降低的区域主要集中在华北平原,年均地表反照率显著增加的区域主要集中在长白山和大兴安岭北部;不同季节地表反照率存在很大的差异,冬季最大,春夏季次之,秋季最小,且冬季平均地表反照率波动幅度比较大;不同地表覆盖的地表反照率的年际变化也存在比较显著的差异.

摘要： 本文利用1982~2000年NOAA-AVHRR数据资料,研究中国区域地表反照率的空间分布及其随时间的变化规律.研究结果表明中国区域年均地表反照率在空间分布上有很大的差异,瑗珲-腾冲线以西地区地表反照率明显大于瑗珲-腾冲线以东地区;年平均地表反照率呈现缓慢下降趋势,但是不同地区的变化趋势有所不同,年均地表反照率显著降低的区域主要集中在华北平原,年均地表反照率显著增加的区域主要集中在长白山和大兴安岭北部;不同季节地表反照率存在很大的差异,冬季最大,春夏季次之,秋季最小,且冬季平均地表反照率波动幅度比较大;不同地表覆盖的地表反照率的年际变化也存在比较显著的差异.

摘要： 本文利用1982~2000年NOAA-AVHRR数据资料,研究中国区域地表反照率的空间分布及其随时间的变化规律.研究结果表明中国区域年均地表反照率在空间分布上有很大的差异,瑗珲-腾冲线以西地区地表反照率明显大于瑗珲-腾冲线以东地区;年平均地表反照率呈现缓慢下降趋势,但是不同地区的变化趋势有所不同,年均地表反照率显著降低的区域主要集中在华北平原,年均地表反照率显著增加的区域主要集中在长白山和大兴安岭北部;不同季节地表反照率存在很大的差异,冬季最大,春夏季次之,秋季最小,且冬季平均地表反照率波动幅度比较大;不同地表覆盖的地表反照率的年际变化也存在比较显著的差异.

摘要： 本文为研究城市生态环境,使用EOS/MODIS的第1、2和31波段数据计算得到归一化植被指数NDVIe和亮温Tse,用NOAA/AVHRR的第1、2和4波段数据计算得到NDVIa和Tsa;建立环境遥感模型,计算出城市生态环境综合指数K.结果表明,使用NDVIe得到的K指数图象分辨率较高;K指数能够反映出城市生态环境时空变化的综合特征.MODIS的中分辨率数据与MOAA的低分辨率数据结合使用是可行的.

摘要： 本文为研究城市生态环境,使用EOS/MODIS的第1、2和31波段数据计算得到归一化植被指数NDVIe和亮温Tse,用NOAA/AVHRR的第1、2和4波段数据计算得到NDVIa和Tsa;建立环境遥感模型,计算出城市生态环境综合指数K.结果表明,使用NDVIe得到的K指数图象分辨率较高;K指数能够反映出城市生态环境时空变化的综合特征.MODIS的中分辨率数据与MOAA的低分辨率数据结合使用是可行的.

摘要： 本文为研究城市生态环境,使用EOS/MODIS的第1、2和31波段数据计算得到归一化植被指数NDVIe和亮温Tse,用NOAA/AVHRR的第1、2和4波段数据计算得到NDVIa和Tsa;建立环境遥感模型,计算出城市生态环境综合指数K.结果表明,使用NDVIe得到的K指数图象分辨率较高;K指数能够反映出城市生态环境时空变化的综合特征.MODIS的中分辨率数据与MOAA的低分辨率数据结合使用是可行的.

摘要： 本文为研究城市生态环境,使用EOS/MODIS的第1、2和31波段数据计算得到归一化植被指数NDVIe和亮温Tse,用NOAA/AVHRR的第1、2和4波段数据计算得到NDVIa和Tsa;建立环境遥感模型,计算出城市生态环境综合指数K.结果表明,使用NDVIe得到的K指数图象分辨率较高;K指数能够反映出城市生态环境时空变化的综合特征.MODIS的中分辨率数据与MOAA的低分辨率数据结合使用是可行的.