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author:

Wu, Xiaoping (Wu, Xiaoping.) [1] | Xu, Hanqiu (Xu, Hanqiu.) [2] (Scholars:徐涵秋) | Jiang, Qiaoling (Jiang, Qiaoling.) [3]

Indexed by:

EI PKU CSCD

Abstract:

This paper aims at an analysis on the consistency of the top of atmosphere (TOA) reflectance among GF-1 PMS2, GF-2 PMS1 and Landsat-8 operational land imager(OLI) sensor data based on two synchronous image pairs. The result shows that TOA reflectance of GF-1 PMS2 and GF-2 PMS1 sensors has a high degree of agreement. Nevertheless, this paper also finds that TOA reflectance of either GF-1 PMS2 or GF-2 PMS1 data is less consistent with that of Landsat-8 OLI data, especially in the near-infrared band. In general, the rank of TOA reflectance in the blue, green and red bands of three sensors data is as follows: GF-2 PMS1>GF-1 PMS2>Landsat-8 OLI, while the relationship in the near-infrared band is: Landsat-8 OLI>GF-1 PMS2>GF-2 PMS1. The coversion models among the three sensors data were obtained through regression analysis. The validation shows that the conversion equations can significantly reduce the difference in the near-infrared band among the three sensors. It is also found that when the image to be converted has similar land cover types and proportions with the image on which the conversion model was developed, the conversion accuracy can be improve. © 2020, Editorial Board of Geomatics and Information Science of Wuhan University. All right reserved.

Keyword:

Image enhancement Infrared devices Reflection Regression analysis

Community:

  • [ 1 ] [Wu, Xiaoping]College of Environment and Resources, Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou; 350116, China
  • [ 2 ] [Wu, Xiaoping]Institute of Remote Sensing Information Engineering, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou; 350116, China
  • [ 3 ] [Xu, Hanqiu]College of Environment and Resources, Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou; 350116, China
  • [ 4 ] [Xu, Hanqiu]Institute of Remote Sensing Information Engineering, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou; 350116, China
  • [ 5 ] [Jiang, Qiaoling]College of Environment and Resources, Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou; 350116, China
  • [ 6 ] [Jiang, Qiaoling]Institute of Remote Sensing Information Engineering, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou; 350116, China

Reprint 's Address:

  • 徐涵秋

    [xu, hanqiu]college of environment and resources, key laboratory of spatial data mining & information sharing of ministry of education, fuzhou university, fuzhou; 350116, china;;[xu, hanqiu]institute of remote sensing information engineering, fujian provincial key laboratory of remote sensing of soil erosion, fuzhou university, fuzhou; 350116, china

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Source :

Geomatics and Information Science of Wuhan University

ISSN: 1671-8860

CN: 42-1676/TN

Year: 2020

Issue: 1

Volume: 45

Page: 150-158

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 12

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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