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排序方式: 共有191条查询结果,搜索用时 31 毫秒
1.
冬季浙闽沿岸水分布的短期变动与风的关系初探 总被引:6,自引:0,他引:6
利用1999年7月至2003年5月期间的遥感数据,包括AVHRR海表层温度、QuikSCAT风场和风应力数据,在分析4年内月平均遥感温度场和风场特征与历年现场观测所获得的认识一致的基础上,选取2002年002-008天这一连续晴空的时段,尝试建立简单的沿岸冷水影响面积表征方法,初步探讨了冬季台湾海峡浙闽沿岸水分布的短期变动与风应力之间的关系。结果表明,风是决定冬季台湾海峡海表层温度逐日变动的关键因素,日平均SST与风应力的相关系数R2达到0·90。采用温度法(SST≤17℃)和温度空间距平法(≤-1℃)表征的浙闽沿岸水影响面积的变化趋势基本一致,而且影响面积的逐日变动与风应力显著相关,二者的相关系数R2分别达到0·90和0·91。 相似文献
2.
沙尘源区AVHRR数据地表温度时序变化与沙尘干量TSP数据的对比分析——以2001年春季北方强沙尘过程为例 总被引:7,自引:11,他引:7
根据沙尘暴起沙、传输与降沉模型系统对参数的需求,中日亚洲沙尘暴项目在源区、传输区和降沉区布设了先进的仪器设备,其中沙尘干量TSP数据是主要获取的数据,同时利用气象卫星获取沙尘过程的云图和反演沙尘过程的地表物理参数。由于不同土地利用/覆盖对沙尘暴过程中起沙的贡献不一样,研究中用局地分裂窗算法反演NOAA/AVHRR热红外波段数据的地面温度参数;取得大范围的时序地面温度数据后参照1:10万土地利用/覆盖类型重采样图层选点提取地表温度,形成不同点时间序列变化曲线;最后将不同地类的地表温度时序变化曲线与观测点沙尘干量TSP时序变化曲线对比分析,发现两者具有较好的对应关系,说明沙尘暴过程地表温度变化现象与沙尘有密切的关系。结果表明,利用遥感数据反演地面温度参数可以作为沙尘预报模型的重要参数。 相似文献
3.
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
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
4.
将要用来处理来自即将开始运行的EOSMODIS和MISR仪器的BRDF/反照率乘积的反照率反演程序包中的BRDF模型在该文中被用来对来自AVHRR数据的SAHEL地区的方向反射数据进行建模。模型被证明能够很好地描述观测到的数据。同时也讨论了核的选择和导出模型的参数所含的信息内容等问题。 相似文献
5.
Seasonal and inter-annual relationships between vegetation and climate in central New Mexico, USA 总被引:8,自引:0,他引:8
Jeremy L. Weiss David S. Gutzler Julia E. Allred Coonrod Clifford N. Dahm 《Journal of Arid Environments》2004,57(4):507-534
Linear correlations between seasonal and inter-annual measures of meteorological variables and normalized difference vegetation index (NDVI) are calculated at six nearby yet distinct vegetation communities in semi-arid New Mexico, USA Monsoon season (June–September) precipitation shows considerable positive correlation with NDVI values from the contemporaneous summer, following spring, and following summer. Non-monsoon precipitation (October–May), temperature, and wind display both positive and negative correlations with NDVI values. These meteorological variables influence NDVI variability at different seasons and time lags. Thus vegetation responds to short-term climate variability in complex ways and serves as a source of memory for the climate system. 相似文献
6.
Discrimination between climate and human-induced dryland degradation 总被引:21,自引:0,他引:21
In this study we present a technique to discriminate between climate or human-induced dryland degradation, based on evaluations of AVHRR NDVI data and rainfall data. Since dryland areas typically have high inter-annual rainfall variations and rainfall has a dominant role in determining vegetation growth, minor biomass trends imposed by human influences are difficult to verify. By performing many linear regression calculations between different periods of accumulated precipitation and the annual NDVImax, we identify the rainfall period that is best related to the NDVImax and by this the proportion of biomass triggered by rainfall. Positive or negative deviations in biomass from this relationship, expressed in the residuals, are interpreted as human-induced. We discuss several approaches that use either a temporally fixed NDVI peaking time or an absolute one, a best mean rainfall period for the entire drylands or the best rainfall period for each individual pixel. Advantages and disadvantages of either approach or one of its combinations for discriminating between climate and human-induced degradation are discussed. Depending on the particular land-use either method has advantages. To locate areas with a high likelihood of human-induced degradation we therefore recommend combining results from each approach. 相似文献
7.
8.
确定像元地理坐标是几何纠正及卫星数据应用的基础。经验插值方法常用于计算高分辨率辐射计AVHRR1B定位数据,研究插值模型的定位误差具有重要意义。该文分析了AVHRR像元空间位置和像元大小,利用AVHRR扫描线的圆心角θ表示像元的位置,建立了像元位置θ和像元大小Δθ的表达模型;根据分段线性插值模型,计算插值后的像元位置θafter和像元大小Δθafter,获得分段线性插值模型的像元位置和大小的误差模型。 相似文献
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10.
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