| Technology and Methodology |
|
|
|
|
|
Method for Landsat dense time series data format unification and surface reflectance conversion |
| SHEN Wenjuan, LI Mingshi |
| Forestry School, Nanjing Forestry University, Nanjing 210037, China |
|
|
|
|
Abstract This paper introduces a program called landsat ecosystem disturbance adaptive processing system (LEDAPS) for the image stacks creation of the atmospherically corrected Landsat dense time series standard products from 1987 to 2011. Landsat images were first calibrated to top-of-atmosphere (TOA) reflectance by using solar zenith, Sun-Earth distance, TM or ETM+ bandpass, and solar irradiance (using the MODTRAN solar output model). The interpolated aerosol optical thickness (AOT) which was interpolated spatially between the "dark dense vegetation (DDV)" using a spline algorithm, ozone, atmospheric pressure, and water vapor were supplied to the 6S radioactive transfer algorithm to convert TOA reflectance into ground surface reflectance for each 30 m pixel. The algorithm was applied to the LEDAPS standard data of Landsat7 ETM+ and non-standard data of Landsat5 TM to illustrate the data choice, data format unification and the algorithm implementation of the dense Landsat time series. Finally, a method for the validation of the corrected images was provided. The results show that the surface reflectance products resulting from the LEDAPS processing could effectively reduce the influence caused by ozone, water vapor, and aerosol particles in the atmosphere on the true image surface reflectance. The surface reflectivity is more precise and provides standard products for multiple scientific applications, such as land cover change or forest disturbance dynamic characterization and remote sensing based biophysical parameters retrieval, thus beneficial to formulating criteria for processing sequence image data in China.
|
| Keywords
remote sensing
satellite images
MapGIS
mine
|
|
|
|
Issue Date: 17 September 2014
|
|
|
| [1] Cohen W B,Goward S N.Landsat's role in ecological applications of remote sensing[J].Bioscience,2004,54(6):535-545.[2] Kennedy R E,Cohen W B,Schroeder T A.Trajectory-based change detection for automated characterization of forest disturbance dynamics[J].Remote Sensing of Environment,2007,110(3):370-386.[3] Huang C Q,Goward S N,Masek J G,et al.Development of time series stacks of Landsat images for reconstructing forest disturbance history[J].International Journal of Digital Earth,2009,2(3):195- 218.[4] Masek J G,Vermote E F,Saleous N,et al.A Landsat surface reflectance dataset for North America,1990-2000[J].Geoscience and Remote Sensing Letters,2006,3(1):68-72.[5] Wolfe R,Masek J,Saleous N,et al.LEDAPS:Mapping North American disturbance from the Landsat record[C]//Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium.IEEE,2004.[6] 亓雪勇,田庆久.光学遥感大气校正研究进展[J].国土资源遥感,2005,17(4):1-6. Qi X Y,Tian Q J.The advances in the study of atmospheric correction for optical remote sensing[J].Remote Sensing for Land and Resources,2005,17(4):1-6.[7] 赵春江,宋晓宇,王纪华,等.基于6S模型的遥感影像逐像元大气纠正算法[J].光学技术,2007,33(1):11-15. Zhao C J,Song X Y,Wang J H,et al.An algorithm based on 6S model for removing atmospheric effects form satellite imagery pixel by pixel[J].Optical Technique,2007,33(1):11-15.[8] 徐永明,覃志豪,陈爱军.基于查找表的MODIS逐像元大气校正方法研究[J]. 武汉大学学报:信息科学版,2010,35(8):959- 962. Xu Y M,Qin Z H,Chen A J.A pixel-by-pixel atmospheric correction algorithm for MODIS data based on look-up table[J].Geomatics and Information Science of Wuhan University,2010,35(8):959-962.[9] 杨静学,王云鹏,杨勇.基于高程或气溶胶厚度与6S模型校正参数回归方程的遥感图像大气校正模型[J].遥感技术与应用,2009,24(3):331-340. Yang J X,Wang Y P,Yang Y.Atmospheric correction model of remote sensing image by regression analysis between elevation/aerosol optical thickness and correction parameters of 6S model[J].Remote Sensing Technology and Application,2009,24(3):331-340.[10] Vermote E F,Saleous N E,Justice C O,et al.Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces:Background,operational algorithm,and validation[J].Journal of Geophysical Research:Atmospheres(1984-2012),1997,102(D14):17131-17141.[11] Kaufman Y J,Wald A E,Remer L A,et al.The MODIS 2.1μm channel-correlation with visible reflectance for use in remote sensing of aerosol[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(5):1286-1298.[12] Vermote E F,Saleous N,Justice C O.Atmospheric correction of MODIS data in the visible to middle infrared:First results[J].Remote Sensing of Environment,2002,83:97-111.[13] Ouaidrari H,Vermote E F.Operational atmospheric correction of Landsat TM data[J].Remote Sensing of Environment,1999,70(1):4-15.[14] Irish R R.Landsat7 automatic cloud cover assessment:Algorithms for multispectral,hyperspectral,and ultraspectral imagery[J].Proceedings of SPIE,2000,4049:348-355.[15] Department of the Interior U S.Geological Survey.Landsat climate data record(CDR)surface reflectance product guide version 2.0[EB/OL].[2013-03-27].http://landsat.usgs.gov/documents.[16] 姚薇,李志军,姚珙,等.Landsat卫星遥感影像的大气校正方法研究[J].大气科学学报,2011,34(2):251-256. Yao W,Li Z J,Yao G,et al.Atmospheric correction model for Landsat images[J].Transactions of Atmospheric Sciences,2011,34(2):251-256. |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
2014,
Vol. 26
