基于MODIS热红外波段与投影寻踪模型的水汽反演方法

doi:10.6046/gtzyyg.2018.03.17

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摘要

基于热红外波段的水汽反演方法可以进行夜间观测,但是精度稍低,因此提出基于MODIS热红外通道结合投影寻踪模型的水汽反演方法。通过变量选取实验和结果对比实验,选取出最适宜的模型输入变量及变量组合,建立了投影寻踪模型水汽反演方法; 应用该方法反演了美国南部地区2015年夏季与中国山西省2011年7月份的水汽含量,并与GPS测量水汽数据进行了对比。结果表明: 在美国南部地区,基于投影寻踪模型的水汽反演算法反演得到的水汽含量与GPS测量水汽含量的均方根误差(root mean square error,RMSE)为2.478 mm; 在山西省RMSE为1.408 mm; 与MODIS热红外水汽产品数据相比,具有更高的精度,且弥补了近红外夜间无法工作的缺陷,更具有业务化推广的潜力。

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	林奕桐
	叶骏菲
	王永前
	钟仕全

关键词 ：亮度温度, 水汽含量, 投影寻踪

Abstract：

Precipitable water vapor retrieval methods using MODIS data are mainly based on near infrared and thermal infrared data. Compared with the thermal infrared methods, the near infrared methods have higher inversion accuracy. The near infrared water vapor retrieval method is only applicable to daytime; by contrast, the thermal infrared water vapor data can be obtained both day and night. Therefore, the thermal infrared data are more suitable for operational applications. It is of great significance to improve the accuracy of thermal infrared water vapor retrieval methods. By means of variable selection experiments and results comparing experiments, the precision of variable associations were tested with the optimal substitution variable associations selected, and the water vapour retrieval method based on projection pursuit model has been found. The inversion experiment over the 2015 summer water vapor in the South United States and July 2011 in Shanxi Province of China were carried out through projection pursuit model with inverse results validated by the the water vapor detection data(W_GPS). According to the results obtained, in South United States, the root-mean-square error was 2.478 mm based on the water vapor retrieval model of brightness temperature and projection pursuit. In Shanxi province of China, the root-mean-square error was 1.408 mm. Compared with thermal infrared vapor product of MODIS, it had higher accuracy; compared with MODIS near infrared water vapor product, it had higher accuracy and temporal resolution. This method has the potential of business promotion.

Key words： brightness temperature water vapor column projection pursuit

收稿日期: 2017-02-22 出版日期: 2018-09-10

TP79

基金资助:国家自然科学基金项目“基于多源遥感数据的陆地上空高时空分辨率大气水汽反演研究”(41471305);“暴雨定点、定量模块化智能计算集合预报建模理论方法研究”(41575051);四川省教育厅创新团队项目“遥感定量反演及其气象应用”(16TD0024);重庆市气象局开放基金项目“基于多源卫星的重庆市大气环境质量遥感信息提取及综合评价研究”(kfjj-201402)

通讯作者: 王永前

作者简介: 林奕桐(1990-),男,学士,助理工程师,主要从事遥感应用研究。Email: drift_lin@163.com。

引用本文:

林奕桐, 叶骏菲, 王永前, 钟仕全. 基于MODIS热红外波段与投影寻踪模型的水汽反演方法[J]. 国土资源遥感, 2018, 30(3): 120-127.
Yitong LIN, Junfei YE, Yongqian WANG, Shiquan ZHONG. Water vapor retrieval method based on MODIS thermal infrared band and projection pursuit model. Remote Sensing for Land & Resources, 2018, 30(3): 120-127.

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https://www.gtzyyg.com/CN/10.6046/gtzyyg.2018.03.17 或 https://www.gtzyyg.com/CN/Y2018/V30/I3/120

Fig.1 $T 31$ , $T 32$ , $T — 31$ , $T — 32$ 及 $W GPS$ 的归一化统计

Fig.2-1 $T 31$ , $T 32$ , $T — 31$ 和 $T — 32$ 分别与 $W GPS$ 的线性关系

Fig.2-2 $T 31$ , $T 32$ , $T — 31$ 和 $T — 32$ 分别与 $W GPS$ 的线性关系

Fig.3 部分亮温四则运算及 $W GPS$ 的归一化统计

Fig.4 $W GPS$ 与部分亮温四则运算的线性关系

Fig.5 美国南部地区水汽反演结果与 $W GPS$ 对比

Fig.6 中国山西省水汽反演结果与 $W GPS$ 对比

Fig.7 3组模型水汽反演结果与 $W GPS$ 的相关性分析

Fig.8 投影寻踪模型水汽反演结果与 $W GPS$ 的相关性分析

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