Please wait a minute...
img

Wechat

Remote Sensing Technology and Application  2020, Vol. 35 Issue (1): 132-140    DOI: 10.11873/j.issn.1004-0323.2020.1.0132
    
Sea Surface Temperature Inversion of the Southern South China Sea from MODIS and Temporal and Spatial Variation Analysis
Guizhou Zheng(),Liangchao Xiong,Yanwen Liao,Hongping Wang
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Download:  HTML  PDF (5930KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  

The sea surface temperature in the southern South China Sea has a significant influence on the climate change of China land. In the paper, on the basis of the geometric correction and cloud removal of MODIS basic data in the southern South China Sea, the atmospheric transmittance was calculated by MODTRAN Model, and the brightness temperature was calculated by the radiance intensity of the MODIS 31, 32 channels. The split-window algorithm was used to retrieve the sea surface temperature in the southern South China Sea. Finally, the accuracy was evaluated byR 2, SSE, RMSE and the regression analysis between retrieved temperature and the products temperature or ground measured temperature.R 2 is lager than 0.8. SSE and RMSE are all smaller. The inversion accuracy is good. The research showed the distinct seasonal variation of lower temperature in autumn and winter and higher temperature in spring and summer. The research still showed the fundamental variation of temperature with declines from the near shore to the center of the sea, and lowest temperature over the deep basin. The sea surface temperature was affected by variations of weather. The sea surface temperature was positively correlated with El Ni?o, and was negatively correlated with La Ni?a.

Key words:  Southern South China Sea      MODIS      Sea surface temperature inversion      Split-window algorithm      Temporal and spatial temperature variation     
Received:  12 October 2018      Published:  01 April 2020
ZTFLH:  TP79  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Guizhou Zheng
Liangchao Xiong
Yanwen Liao
Hongping Wang

Cite this article: 

Guizhou Zheng,Liangchao Xiong,Yanwen Liao,Hongping Wang. Sea Surface Temperature Inversion of the Southern South China Sea from MODIS and Temporal and Spatial Variation Analysis. Remote Sensing Technology and Application, 2020, 35(1): 132-140.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.1.0132     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I1/132

Fig.1  The cloud removal of MODIS(the red box area is the research area)
Fig.2  The relationship between atmospheric transmittance and atmospheric water vapor
Fig.3  The results of temperature inversion
Fig.4  Temperature regression analysis
各组温度 R 2 SSE RMSE
实测温度与反演温度 0.923 1.025 0.1581
产品温度与实测温度 0.8672 1.190 0.1703
反演温度与产品温度 0.8159 1.322 0.1794
Table 1  Accuracy evaluation index
Fig.5  The change trend of monthly mean sea surface temperature in 2014
Fig.6  The change trend of 96 months temperature from 2007 to 2014
Fig.7  Monthly mean sea surface temperature contour map for the study area in 2014
1 Tan Kun , Liao Zhihong , Du Peijun .Algorithm for Retrieving Surface Temperature Considering HJ-1 Images and Ground Sensor Network Data[J].Geomatics and Information Scicnce of Wuhan University,2016,41(2):148-155.谭琨, 廖志宏, 杜培军.顾及地面传感器观测数据的遥感影像地面温度反演算法[J].武汉大学学报·信息科学版,2016,41(2):148-155.
2 Anding D , Kauth R .Estimation of Sea Surface Temperature from Space[J].Remote Sensing of Environment,1970,1(4):217-220.
3 Chen Hong , Xu Hua , Li Jiaguo ,et al .Design and Implementation of Sea Surface Temperature Retrieval System based on MODIS[J].Remote Sensing Information,2009(2):76-80.陈宏,许华,李家国, 等 .基于MODIS的海表面温度反演系统设计与实现[J].遥感信息,2009(2):76-80.
4 Price J C .Land Surface Temperature Measurements from the Split Window Channels of the NOAA 7 Advanced very High Resolution Radiometer[J].Journal of Geophysical Research,1984,89(D5):7231-7237.
5 Becker F , Li Z L .Towards A Local Split Window Method over Land Surfaces[J].International Journal of Remote Sensing,1990,11(3):369-393.
6 Zhang Yong , Yu Tao , Gu Xingfa ,et al .Land Surface Temperature Retrieval from CBERS-02 IBM SS Thermal Infrared Data and Its Applications in Quantitative Analysis of Urban Heat Island Effect[J].Journal of Remote Sensing,2006,10(5):789-797.张勇,余涛,顾行发, 等 .CBERS- 02 IRM SS热红外数据地表温度反演及其在城市热岛效应定量化分析中的应用[J].遥感学报,2006,10(5):789-797.
7 Wan Z M , Dozier J .A Generalized Split-window Algorithm for Retrieving Land-surface Temperature from Space[J].IEEE Transactions on Geoscience and Remote Sensing,1996,34(4):892-905.
8 Becker F , Li Z L .Surface Temperature and Emissivity at Various Scales: Definition, Measurement and Related Problems[J].Remote Sensing Review,1995,12(3-4):225-253.
9 Qin Z , Dall’Olmo G , Karnieli A ,et al .Derivation of Split Window Algorithm and Its Sensitivity Analysis for Retrieving Land Surface Temperature from NOAA-AVHRR Data[J].Journal of Geophysical Research,2001,106 (D19):22655-22670.
10 Qin Z H , Karnieli A .A Mono-window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region[J].International Journal of Remote Sensing,2001,22(18):3719-3746.
11 Qin Zhihao , Zhang M , Arnon K .Mono-window Algorithm for Retrieving Land Surface Temperature from Landsat TM 6 Data[J].Acta Geographica Sinica,2001(4):456-466.覃志豪, Zhang M, Arnon K.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001(4):456-466.
12 Jimenez-Munoz J C , Sobrino J A .A Generalized Single-channel Method for Retrieving Land Surface Temperature from Remote Sensing Data[J].Journal of Geophysical Research,2003,108(22):1-9.
13 Wan Z M , Li Z L .A Physics-based Algorithm for Retrieving Land-surface Emissivity and Temperature from EOS/MODIS Data [J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(4):980-996.
14 Liu Chao , Li Hua , Du Yongming ,et al .Practical Split-window Algorithm for Retrieving Land Surface Temperature from Himawari 8 AHI Data[J].Journal of Remote Sensing,2017,21(5):702-714.刘超,历华,杜永明, 等 .Himawari 8 AHI数据地表温度反演的实用劈窗算法[J].遥感学报,2017,21(5):702-714.
15 Zhai Shaoyi , Huang Dui , Wang Wenzhong ,et al .Land Surface Temperature Retrieval Using Improved Splitting Window Algorithm based on Landsat 8 Data[J].Journal of China Hydrology,2019,39(5):8-13.翟劭燚,黄对,王文种, 等 .改进的劈窗算法结合Landsat 8热红外数据反演地表温度研究[J].水文,2019,39(5):8-13.
16 Wu Liang , Yao Kun .Land Surface Temperature Retrieval of Landsat 8 Images based on Split Window Algorithm[J].Power Systems and Big Data,2018,21(4):18-25.吴亮, 姚昆.基于劈窗算法的Landsat 8影像地表温度反演[J].电力大数据,2018,21(4) :18-25.
17 Mao Kebiao , Qin Zhihao , Shi Jiancheng ,et al .The Research of Split-window Algorithm on the MODIS[J].Geomatics and Information Science of Wuhan University,2005,30(8):703-707
17 毛克彪,覃志豪,施建成,等 .针对 MODIS 影像的劈窗算法研究[J].武汉大学学报·信息科学版,2005,30(8):703-707.
18 Min W B , Li Y Q , Li B .Relation Analysis of Remotely Sensed Temperature, Soil Surface and Air Temperature over Alpine Meadow[J].Science Technology and Engineering,2013,13(12):3497-3504.
18 闵文彬,李跃清,李宾 .高山草甸遥感温度和地、气温度的关系分析[J].科学技术与工程,2013,13(12):3497-3504.
19 Mao Kebiao , Qin Zhihao , Wang Jianming ,et al .Low Tran Retrieval of Atmospheric Water Content and Transmittance Computation of MODIS Band 31 and 32[J].Remote Sensing for Land and Resources,2005,63(1):26-29.毛克彪, 覃志豪, 王建明, 武胜利.针对MODIS数据的大气水汽含量反演及 31和32波段透过率计算[J].国土资源遥感,2005,63(1):26-29.
[1] . A New Direct Solution of Range-Doppler model for SAR Image Location[J]. , , (): 0 .
[2] . Monitoring Surface Deformation in Changzhou City Using COSMO-SkyMed Data[J]. , , (): 0 .
[3] Rui YANG Su Yang. U-Net neural networks and its application in high resolution satellite image classification[J]. Remote Sensing Technology and Application, 0, (): 0 .
[4] . An improved Hyperspectral Image Clasification Algorithm Based On Multinomial Logistic Regression[J]. Remote Sensing Technology and Application, 0, (): 0 .
[5] yingchun Fu. A comparative study of urban heterogeneity vegetation coverage estimation model[J]. Remote Sensing Technology and Application, 0, (): 0 .
[6] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 11 -12 .
[7] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 1 -7 .
[8] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 8 -10 .
[9] . [J]. Remote Sensing Technology and Application, 1986, 1(1): 65 -66 .
[10] . [J]. Remote Sensing Technology and Application, 1987, 2(1): 40 -50 .