首页 >  2020, Vol. 24, Issue (7) : 1993-2002

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引用本文:

DOI:

10.11834/jrs.20209062

收稿日期:

2019-03-05

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高分五号热红外传感器多通道SST反演
崔文杰1,2,李家国2,李忠1,朱利3,王殿忠4,张宁5,6
1.防灾科技学院, 廊坊 065000;2.中国科学院遥感与数字地球研究所, 北京 100101;3.环境保护部卫星环境应用中心, 北京 100094;4.北京空间机电研究所, 北京 100094;5.住房和城乡建设部城乡规划管理中心, 北京 100835;6.中国科学院地理科学与资源研究所, 北京100101
摘要:

2018-05高分五号(GF-5)卫星发射升空,其上搭载的全谱段成像仪在热红外8—13 μm谱段范围内具有4个温度反演通道(B09,B10,B11,B12),空间分辨率设计优于40 m,在国内民用传感器领域实现了由单通道向多通道、中空间分辨率向高空间分辨率的跨越式突破,使得GF-5卫星热红外数据在地表热环境遥感领域具有极其重要应用价值。本研究基于GF-5的4个热红外通道的通道响应函数,利用全球742条TIGR(Thermodynamic Initial Guess Retrieval)探空廓线数据,进行不同观测角度、水汽含量和海表发射率条件下的MODTRAN4.0(Moderate resolution atmospheric Transmittance and Radiance code4.0)辐射传输过程模拟,基于模拟结果分别对两通道、三通道和四通道劈窗算法海表温度SST( Sea Surface Temperature)反演系数进行修订,并分析观测角度、水汽含量和海表发射率对不同通道组合的精度影响,并通过GF-5卫星实际反演的SST结果进行验证。GF-5全谱段成像仪SST反演两通道劈窗算法组合共有6种,即B09-B10、B09-B11、B09-B12、B10-B11、B10-B12、B11-B12;三通道劈窗算法组合共有4种,即B09-B10-B11、B09-B10-B12、B09-B11-B12、B10-B11-B12;四通道劈窗算法组合1种,即B09-B10-B11-B12。通过对不同通道组合形式研究发现,水汽含量对SST反演精度有较大的影响,且温度反演的精度随着水汽含量的增加而降低;其次是观测角度,SST反演精度随着观测天顶角的增大而降低;最后是发射率的影响,两通道、三通道和四通道劈窗算法SST反演精度随着发射率的变化总体在0.1 K以内变化。最后以大亚湾核电站周围海域为验证区,用GF-5热红外遥感影像进行SST的反演并做误差分析,结果表明B09-B10通道SST反演实际误差为0.57 K,反演精度较高,实际误差与理论模拟误差相差0.24 K,差异的来源主要包括辐射定标和传感器噪声等要素影响,其他通道形式反演精度有待于传感器响应稳定后进一步验证。

Simulation of sea surface temperature retrieval based on GF-5 thermal infrared data
Abstract:

A Tropical Cyclone (TC) is one of the most destructive meteorological disasters. The strong winds and heavy precipitation have significant effect on people’s lives, property, and social and economic development. Therefore, the accuracy of the path and intensity prediction of TCs is always an important consideration in meteorological research. However, considering the complexity and variability of typhoon cloud patterns, the existing objective methods are usually based on statistical linear regression. Moreover, they still have deficiencies in expressing the dynamic changes of the complex characteristics of TC cloud patterns. The deep learning algorithm performs well in high-dimensional nonlinear modeling and accurately identifies the input mode with displacement and slight deformation. This algorithm finds significance in TC monitoring with dynamic changes over time. To develop TC intensity estimation technology further in the field of satellite remote sensing, this study applied a new machine-learning technology to analyze and to study the TC intensity of FY-4A/AGRI data from China’s second-generation stationary meteorological satellite. First, a deep Convolution Neural Network (CNN) model was used to distinguish effectively and estimate quantitatively the TC intensity level and center wind speed. The images of day and night were placed into the convolution sampling channel of the CNN to obtain and combine same-size spectral features. Then, multilayer convolution, pooling, nonlinear mapping, and other operations were used to mine the input characteristics deeply. Finally, the TC intensity was estimated. The experiment was divided into the TC intensity classification test and the quantitative estimation test of the TC center maximum wind speed. The CNN model was used to convert the recognition of the TC intensity into the pattern recognition of satellite cloud images, which could classify and identify the TC level. The experiment found that the recognition accuracy of the TC intensity was all above 95% regardless of the overall classification accuracy or the respective accuracy of day and night statistics. Compared with k-nearest neighbor, error back-propagation neural network, multiple linear regression, support vector machine, and other classical classification algorithms, it improves by 7-16 percentage points. Moreover, the CNN is also superior to the classical algorithm in terms of classification accuracy. The CNN model comprises two fully connected network layers (each layer has three neurons). The TC wind speed can be quantitatively estimated by prior training samples of the network parameters. Compared with the data of Tropical Cyclone 2017 Yearbook, the mean absolute error of the wind speed was 1.75 m/s, and the root mean square error of the wind speed was 2.04 m/s, which were lower than the corresponding errors of Deviation Angle Variance Technique (DAVT) by 85.70% and 84.38%. Thus, the CNN algorithm has a high application prospect in the quantitative estimation of typhoon intensity. As the first second-generation Chinese geostationary meteorological satellite to be launched, FY-4A has its advantages of multichannel structure and high spatial and temporal resolution. On the basis of these features, the advantages of the techniques of the deep neural network, and the flexible structure of CNN, this study proposes an improved CNN model that is tailor-made for FY-4A data. The model has the capacity to mine the morphological characteristic of typhoons deeply and effectively and achieve high-precision typhoon intensity estimation. This model has positive research value and application prospect for the quantitative estimation of typhoon intensity.

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