Detecting Arctic snow and ice cover with FY-1D global data     

References： 《极地研究(英文版)》2007,18(1):54-62

Chinese meteorological satellite FY-1D can obtain global data from four spectral channels which include visible channel(0.58-0.68 μm) and infrared channels(0.84-0.89 μm,10.3-11.3 μm,11.5-12.5 μm).2366 snow and ice samples,2024 cloud samples,1602 land samples and 1648 water samples were selected randomly from Arctic imageries.Land and water can be detected by spectral features.Snow-ice and cloud can be classified by textural features.The classifier is Bayes classifier.By synthesizing five d ays classifying result of Arctic snow and ice cover area,complete Arctic snow and ice cover area can be obtained.The result agrees with NOAA/NESDIS IMS products up to 70%.  相似文献   

Estimation of potential evapotranspiration over the Yellow River basin: reference crop evaporation or Shuttleworth–Wallace?     

M. C. Zhou  H. Ishidaira  K. Takeuchi 《水文研究》2007,21(14):1860-1874

Potential evapotranspiration (PET) is a key input to hydrological models. Its estimation has often been via the Penman–Monteith (P–M) equation, most recently in the form of an estimate of reference evapotranspiration (RET) as recommended by FAO‐56. In this paper the Shuttleworth–Wallace (S–W) model is implemented to estimate PET directly in a form that recognizes vegetation diversity and temporal change without reference to experimental measurements and without calibration. The threshold values of vegetation parameters are drawn from the literature based on the International Geosphere–Biosphere Programme land cover classification. The spatial and temporal variation of the LAI of vegetation is derived from the composite NOAA‐AVHRR normalized difference vegetation index (NDVI) using a method based on the SiB2 model, and the Climate Research Unit database is used to provide the required meteorological data. All these data inputs are publicly and globally available. Consequently, the implementation of the S–W model developed in this study is applicable at the global scale, an essential requirement if it is to be applied in data‐poor or ungauged large basins. A comparison is made between the FAO‐56 method and the S–W model when applied to the Yellow River basin for the whole of the last century. The resulting estimates of RET and PET and their association with vegetation types and leaf area index (LAI) are examined over the whole basin both annual and monthly and at six specific points. The effect of NDVI on the PET estimate is further evaluated by replacing the monthly NDVI product with the 10‐day product. Multiple regression relationships between monthly PET, RET, LAI, and climatic variables are explored for categories of vegetation types. The estimated RET is a good climatic index that adequately reflects the temporal change and spatial distribution of climate over the basin, but the PET estimated using the S–W model not only reflects the changes in climate, but also the vegetation distribution and the development of vegetation in response to climate. Although good statistical relationships can be established between PET, RET and/or climatic variables, applying these relationships likely will result in large errors because of the strong non‐linearity and scatter between the PET and the LAI of vegetation. It is concluded that use of the implementation of the S–W model described in this study results in a physically sound estimate of PET that accounts for changing land surface conditions. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics     

《International Journal of Applied Earth Observation and Geoinformation》2017

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienced orbital drift during its duty period, which influenced the AVHRR reflectance measurements. To understand the effect of the orbital drift on the AVHRR-derived RSP dataset, we analyzed the impact of solar zenith angle (SZA) on the RSP metrics in the conterminous United States (CONUS). The AVHRR weekly composites were used to calculate the growing-season median SZA at the pixel level for each year from 1989 to 2014. The results showed that the SZA increased towards the end of each NOAA satellite mission with the highest increasing rate occurring during NOAA-11 (1989–1994) and NOAA-14 (1995–2000) missions. The growing-season median SZA values (44°–60°) in 1992, 1993, 1994, 1999, and 2000 were substantially higher than those in other years (28°–40°). The high SZA in those years caused negative trends in the SZA time series, that were statistically significant (at α = 0.05 level) in 76.9% of the CONUS area. A pixel-based temporal correlation analysis showed that the phenological metrics and SZA were significantly correlated (at α = 0.05 level) in 4.1–20.4% of the CONUS area. After excluding the 5 years with high SZA (>40°) from the analysis, the temporal SZA trend was largely reduced, significantly affecting less than 2% of the study area. Additionally, significant correlation between the phenological metrics and SZA was observed in less than 7% of the study area. Our study concluded that the NOAA satellite orbital drift increased SZA, and in turn, influenced the phenological metrics. Elimination of the years with high median SZA reduced the influence of orbital drift on the RSP time series.  相似文献   

青藏高原非均匀地表区域能量通量的研究   总被引：7，自引：1，他引：7  

马耀明  仲雷  田辉  孙方林  苏中波  Massimo Menenti 《遥感学报》2006,10(4):542-547

卫星遥感在研究青藏高原非均匀地表区域能量通量和蒸发（蒸散）量时有其独到的作用。本文介绍了基于NOAA-14 AVHRR和Landsat TM资料推算藏北高原地区区域地表特征参数、植被参数及区域地表热通量的方案，并把其用于GAME／Tibet（全球能量水循环之亚洲季风青藏高原试验研究）和CAMP／Tibet（“全球协调加强观测计划（CEOP）亚澳季风之青藏高原试验研究”）试验区。并指出了此方法估算青藏高原非均匀地表区域地表能量通量和蒸发（蒸散）量时存在的难点问题和解决问题的可能途径。  相似文献   

基于NOAA/POES卫星观测的磁层相对论电子起源的初探   总被引：1，自引：0，他引：1       下载免费PDF全文

谢伦  涂蔚超  张辉  濮祖荫 《地球物理学报》2007,50(6):1632-1638

本文利用低高度极轨卫星NOAA/POES的观测数据，并结合ACE卫星和Polar卫星的观测结果，研究分析了磁层相对论电子的起源. NOAA/POES卫星对于不同地磁活动时期相对论电子的分布和起源进行了较为详细观测, 分析结果表明（1） 亚暴期间注入磁层的能量电子可以为与磁暴相关的磁层高能电子暴提供种子电子；（2）太阳质子事件期间太阳风中的能量电子也可以为磁层中的相对论电子提供所需要的源.  相似文献   

基于人工神经网络的赤潮卫星遥感方法研究   总被引：7，自引：1，他引：7  

楼琇林  黄韦艮 《遥感学报》2003,7(2):125-130

根据赤潮的卫星遥感探测机理，应用人工神经网络技术，建立和利用NOAA AVHRR可见光和热红外波段遥感数据的BP神经网络赤潮信息提取模型。应用实例显示。基于该人工神经网络方法可以提取赤潮发生地点和范围等信息，赤潮探测正确率达到78.5%。研究结果表明，应用人工神经网络方法提取赤潮信息是可行的。本文中建立的BP赤潮信息提取模型适当修改后可移植应用于其它传感器遥感数据进行赤潮信息提取。  相似文献   

德令哈6．6级地震前卫星红外长波辐射OLR的分析   总被引：4，自引：0，他引：4  

秦松涛  孙洪斌 《高原地震》2003,15(3):42-44,51

应用NOAA卫星资料反演的地震长波辐射值OLR对2003年4月17日发生在青海省德令哈西的6．6级地震进行了追踪研究。结果显示：2003年1月起OLR距平值出现明显的高值异常区域。  相似文献   

应用遥感数据研究中国植被生态系统与气候的关系   总被引：48，自引：2，他引：48  

赵茂盛  符淙斌  延晓冬  温刚 《地理学报》2001,56(3):287-296

应用1982-1994年NOAA/AVHRR的归一化植被指数（NDVI）资料和587个气象台站的数据对我国不同类型植被生态系统和气候的关系进行研究，首先将我国的植被类型划分为21类，在此基础上分别研究了不同时间尺度下我国不同区域，不同植被类型和气候的关系。结果表明：在多年平均状态下，植被生态系统NDVI水平主要受水分条件的影响；年内变化上，温度对植被生态系统季相变化化起着比降水略大的作用，年降水量造成了植被季相响应的差异，在年际变化上，分别研究了4个季节和整个生长期尺度上的关系，一般情形为温度和降水对植被的年际波动起着大致相反的作用，不同植被类型在不同的生长时期（季节）对气候的变化响应方式也不同，发现在植被的生长期，我国南方和北方的植被生态系统对温度和降水的响应方式相反；同时存在2个植被-气候敏感区，分别为我国北方的典型草原到森林的过渡区和云南中部部分区域。  相似文献   

NOAA相邻卫片的自动镶嵌     

韦玉春  张增峰  李伟 《测绘工程》2001,10(1):46-48

图象镶嵌是个麻烦而且费时的工作。本文通过分析相邻图象的关系，给出了图象自动镶嵌的算法，即通过寻找相邻图象象素最小偏差平方和出现的位置来自动确定镶嵌需要偏移的行列数，这种方法容易实现。通过对NOAA图象镶嵌的试验，表明该算法是有效的。  相似文献   

利用NOAA NDVI数据集监测冬小麦生育期的研究   总被引：34，自引：2，他引：34  

辛景峰  宇振荣  P.M.Driessen 《遥感学报》2001,5(6):442-447

探索了利用NDVI研究作物生育期的方法，对黄淮海冬麦区的返青期、抽穗期、成熟期进行了估测，并利用地面实际观测资料进行了验证。结果表明，NDVI数据对大范围农作物生育期监测是可行的。冬小麦遥感反青期由南到北依次推迟，符合春季绿波由南到北推移规律。对冬小麦遥感生育期年际变化分析表明，黄淮海平原返青期变化相对较大，而抽穗期和成熟期变化较小。根据历年月平均温度与返青期分析，冬小麦返青日期与2月份平均温度密切相关。对于局部地区，利用5d合成1km分辨率数据，且按农业生态分区分别制定生育期判别标准，估测效果将更好。  相似文献