利用MODIS图像估算西班牙穆尔西亚地区最低和最高气温月平均图的一种简单方法

IF 0.4 Q4 REMOTE SENSING
A. Galdón-Ruíz, Guillermo Fuentes-Jaque, Jesús Soto, L. Morales-Salinas
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引用次数: 1

摘要

气温记录是由相距几公里的气象站网络获取的。在复杂的地形中,气象站的代表性可能会因较平坦的山谷而减弱,最近的气象站可能与附近的地方没有关系。本文提出了一种利用MODIS地表温度(LST)和归一化植被指数(NDVI)影像估算最低和最高气温空间分布的简单方法。利用地理加权回归方程得到的MODIS昼夜地表温度产品与气象站气温记录之间存在较强的相关性,且结果可靠。然后,结果允许以海拔和NDVI作为描述变量对局部回归系数进行空间插值,从而获得整个地区的最低和最高气温图。大多数气象站的气温估计值与实测值相比没有显著差异。结果表明:47个站点的最低气温与最低夜的相关性(R2 = 0.69 ~ 0.82)和最高气温与最低日的相关性(R2 = 0.70 ~ 0.87)较强;统计模型的均方根误差(RMSE)分别为1.0°C和0.8°C。此外,每对数据之间的相关性在95%水平上显著(p<0.01)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A simple method for the estimation of minimum and maximum air temperature monthly mean maps using MODIS images in the region of Murcia, Spain
Air temperature records are acquired by networks of weather stations which may be several kilometres apart. In complex topographies the representativeness of a meteorological station may be diminished in relation to a flatter valley, and the nearest station may have no relation to a place located near it. The present study shows a simple method to estimate the spatial distribution of minimum and maximum air temperatures from MODIS land surface temperature (LST) and normalized difference vegetation index (NDVI) images. Indeed, there is a strong correlation between MODIS day and night LST products and air temperature records from meteorological stations, which is obtained by using geographically weighted regression equations, and reliable results are found. Then, the results allow to spatially interpolate the coefficients of the local regressions using altitude and NDVI as descriptor variables, to obtain maps of the whole region for minimum and maximum air temperature. Most of the meteorological stations show air temperature estimates that do not have significant differences compared to the measured values. The results showed that the regression coefficients for the selected locations are strong for the correlations between minimum temperature with LSTnight (R2 = 0.69–0.82) and maximum temperature with LSTday (R2 = 0.70–0.87) at the 47 stations. The root mean square errors (RMSE) of the statistical models are 1.0 °C and 0.8 °C for night and daytime temperatures, respectively. Furthermore, the association between each pair of data is significant at the 95% level (p<0.01).
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来源期刊
Revista de Teledeteccion
Revista de Teledeteccion REMOTE SENSING-
CiteScore
1.80
自引率
14.30%
发文量
11
审稿时长
10 weeks
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