基于亮温和SVM模型的干球温度推算方法
Calculation method for dry-bulb temperature on the basis of brightness temperature and SVM model
- 2015年19卷第1期 页码:172-178
纸质出版日期: 2015
DOI: 10.11834/jrs.20153061
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纸质出版日期: 2015 ,
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[1]林奕桐,叶骏菲,汪嘉杨,王永前.基于亮温和SVM模型的干球温度推算方法[J].遥感学报,2015,19(01):172-178.
LIN Yitong, YE Junfei, WANG Jiayang, et al. Calculation method for dry-bulb temperature on the basis of brightness temperature and SVM model[J]. Journal of Remote Sensing, 2015,19(1):172-178.
干球温度(气温)是地面气象观测中所要测定的常规要素之一。目前基于遥感数据获取该量的方法多采用线性拟合或直接应用遥感反演的温度近似代替干球温度
但是由于下垫面复杂
导致误差较大。本文提出用支持向量机(SVM)模型进行干球温度推算。选择广西省南宁市为研究区域
首先通过遥感反演温度与气象实测温度的对比
证明了利用遥感数据推算干球温度的可能性。然后
构建了针对干球温度的SVM推算模型。最后
尝试了分别使用表观亮温和遥感反演地温作为SVM模型的输入进行干球温度的推算。结果表明
SVM模型推算的干球温度与实测值更为接近
和传统方法相比
精度得到明显提高;且用表观亮温进行推算更为简单
更适合业务化的应用。
Dry-bulb temperature
which can represent the regional characteristics of thermal conditions
is one of the conventional meteorological elements measured over surfaces. Such measurement serves an important function in studying plant physiology
hydrology
the atmosphere
and the environment. Dry-bulb temperatures are usually calculated through linear fitting to original remote sensing data or approximate temperatures retrieved from remote sensing data. These methods are suitable for homogeneous areas with a stable atmospheric stratification and circulation pattern. However
a linear relation does not exist between surface temperature retrieved from remote sensing data and the actual dry temperature because of the limitations of algorithms and the complexity of the underlying surface. Dry-bulb temperatures cannot be calculated accurately with the use of traditional retrieval algorithms. Therefore
a support vector machine( SVM) model was proposed in this study to calculate dry-bulb temperatures.Nanning City was selected as the research area. First
the temperature retrieved from remote sensing data was compared with in situ data. The relations among brightness temperature
temperature retrieved from remote sensing data
and actual dry-bulb temperature were confirmed. Calculating dry-bulb temperature by remote sensing data was a reasonable approach. Second
the actual dry-bulb temperature in some stations and the corresponding temperatures retrieved from remote sensing data( at the same time and geographical location) were taken as modeling samples. The SVM prediction model with a strong learning capability and nonlinear processing capability was developed to retrieve dry-bulb temperatures. Finally
the dry-bulb temperature was calculated by using remote sensing brightness temperature and the temperature retrieved from remote sensing data as the input parameters of the SVM model.For the data obtained on May 12 and November 20
2008
the absolute errors of the traditional method( using Tslinear translation with surface temperature retrieved from remote sensing data) to calculate the dry-bulb temperature are 2. 050112 ℃ and1. 3437564 ℃; the absolute errors of SVM models of brightness temperature are 0. 91915 ℃ and 0. 40294 ℃; and the absolute errors of SVM models of surface temperature are 0. 73802 ℃ and 0. 55002 ℃. The precision of the SVM models is higher than that of traditional methods.The conclusions are as below:( 1) Actual dry-bulb temperatures
brightness temperatures
and surface temperatures retrieved by remote sensing are well correlated. Using remote sensing data to predict dry-bulb temperatures is a feasible approach. As determinative factors of regional change for dry-bulb temperature are complex
relations among dry-bulb temperatures
brightness temperatures
and surface temperatures are not linear. Using traditional linear methods may result in large biases. The SVM model with nonlinear processing capacity is more suitable than traditional methods for calculating dry-bulb temperature.( 2) The absolute errors of the SVM model are more reasonable and smaller than those of traditional methods.( 3) The results of the SVM model
which used brightness temperature and retrieval surface temperature as input parameters
are comparable. The retrieval process for surface temperature is complex; therefore
brightness temperature can be taken as input for SVM directly instead of retrieving surface temperature.( 4) Given that November is a non-flood season in Guangxi
nonadiabatic heating of heat flux equation is less affected by convection and turbulence. The main factor for dry-bulb temperature is surface thermal radiation; the absolute error for the November data is significantly smaller than that of May even when using the same SVM models.
亮度温度干球温度支持向量机南宁
brightness temperaturedry-bulb temperaturesupport vector machinesNanning
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