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首页> 外文期刊>International journal of remote sensing >MODIS land surface temperature composite data and their relationships with climatic water budget factors in the central Great Plains
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MODIS land surface temperature composite data and their relationships with climatic water budget factors in the central Great Plains

机译:大平原中部MODIS地表温度综合数据及其与气候水收支因子的关系

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摘要

Daily land surface temperatures (T_s) derived from moderate resolution imaging spectroradiometer (MODIS) data were correlated with concurrent climatic water budget variables. Using a climatic water budget program, four daily water budget factors—percentage soil moisture (SM), actual/potential evapotranspiration ratio (AE/PE), moisture deficit (MD), and moisture deficit/potential evapotranspiration ratio (MD/PE)—were calculated at six weather stations across western and central Kansas. Correlation analysis showed that T_s deviations from air temperature had a significant relationship with the water budget factors. To do the analysis on a weekly basis, daily MODIS data were integrated into three different types of weekly composites, including maximum T_s, driest-day, and maximum T_s deviation (from maximum air temperature, or max T_a). Results showed that the maximum T-s deviation (T_s-max T_a) temperature composite had the highest correlation with the climatic water budget parameters. Time-integrated, or cumulative values and the moving average of the T_s deviation were meaningful measures of the relationship, but effective moving average periods varied spatially. Correction for different data acquisition times of MODIS thermal imagery improved the representativeness of signals for surface moisture conditions. The driest-day composite was most sensitive to time correction. After time correction, its relationship with soil moisture content improved by 11.1% on average, but the degree of correlation improvement varied spatially. Despite this improvement, the driest-day composite dataset did not have as strong a correlation with water budget factors as that of the maximum T_s deviation composite method.
机译:中分辨率成像光谱仪(MODIS)数据得出的每日地表温度(T_s)与同时存在的气候水预算变量相关。使用气候用水预算程序,可以得出四个每日用水预算因子-土壤湿度百分比(SM),实际/潜在蒸散率(AE / PE),水分亏缺(MD)和水分亏缺/潜在蒸散率(MD / PE)-在堪萨斯州西部和中部的六个气象站进行了计算。相关分析表明,T_s与气温的偏差与水预算因素有显着关系。为了每周进行分析,将每日MODIS数据集成到三种不同类型的每周组合中,包括最大T_s,最干燥日和最大T_s偏差(与最高气温或最大T_a的偏差)。结果表明,最大T-s偏差(T_s-max T_a)温度合成值与气候水预算参数的相关性最高。时间积分或累积值以及T_s偏差的移动平均值是该关系的有意义的度量,但是有效的移动平均值周期在空间上有所不同。通过对MODIS热成像仪不同数据采集时间进行校正,可以提高表面湿度条件下信号的代表性。最干旱的一天对时间校正最敏感。经过时间校正后,其与土壤含水量的关系平均提高了11.1%,但相关程度的提高在空间上有所不同。尽管有这种改进,但最干旱天的综合数据集与水预算因子的相关性不如最大T_s偏差综合方法强。

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