基于遥感和GIS的日最高最低气温估算
发布时间：2018-10-15 16:33
【摘要】：气温是气象要素的重要组成部分,广泛用于全球气候变化、资源环境分析及灾害预警等多个领域.随着卫星遥感技术的发展,气温的估算趋向于遥感或遥感和GIS结合的方法.本文以浙江省为研究区域,利用了36个站点2013年逐日每10min一次的自动气象站气温观测数据和MODIS地表温度及其他参数产品,选用多元线性回归(自变量为地表温度、归一化植被指数、地表反照率、经度、纬度和高程)、温度植被指数以及多元线性回归插值方法进行气温估算,建立了研究区日最高气温最低气温估算模型,并比较了几种气温估算方法在研究区的适用性.结果表明:3种方法最高气温估算的决定系数(R~2)分别为0.96、0.91、0.97,均方根误差(R_(MSE))分别为1.84、2.75、1.49℃;多元线性回归和多元线性回归插值法最低气温估算的R~2分别为0.87、0.91,R_(MSE)分别为3.33、2.93℃,两者均为多元线性回归插值法得到的结果最好.空间分布结果显示,多元线性回归插值法能很好地反映由地形不同所带来的细节差异.
[Abstract]:Temperature is an important component of meteorological elements, which is widely used in many fields such as global climate change, resource and environment analysis, disaster warning and so on. With the development of satellite remote sensing technology, the estimation of temperature tends to the method of remote sensing or the combination of remote sensing and GIS. Taking Zhejiang Province as the research area, the temperature observation data of automatic weather station and MODIS surface temperature and other parameter products of 36 stations in 2013 were used to select multiple linear regression (independent variable is surface temperature). Normalized vegetation index, surface albedo, longitude, latitude and elevation), temperature vegetation index and multivariate linear regression interpolation method were used to estimate air temperature. The applicability of several temperature estimation methods in the study area was compared. The results show that the determination coefficient (R2) of the maximum temperature estimation of the three methods is 0.96 ~ 0.91 ~ 0.97, and the root mean square error (R- _ (MSE) is 1.84 ~ 2.75 ~ 1.49 鈩，
本文编号：2273122
[Abstract]:Temperature is an important component of meteorological elements, which is widely used in many fields such as global climate change, resource and environment analysis, disaster warning and so on. With the development of satellite remote sensing technology, the estimation of temperature tends to the method of remote sensing or the combination of remote sensing and GIS. Taking Zhejiang Province as the research area, the temperature observation data of automatic weather station and MODIS surface temperature and other parameter products of 36 stations in 2013 were used to select multiple linear regression (independent variable is surface temperature). Normalized vegetation index, surface albedo, longitude, latitude and elevation), temperature vegetation index and multivariate linear regression interpolation method were used to estimate air temperature. The applicability of several temperature estimation methods in the study area was compared. The results show that the determination coefficient (R2) of the maximum temperature estimation of the three methods is 0.96 ~ 0.91 ~ 0.97, and the root mean square error (R- _ (MSE) is 1.84 ~ 2.75 ~ 1.49 鈩，
本文编号：2273122
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