DOI: 10.1016/j.jag.2012.10.010
Scopus记录号: 2-s2.0-84880304045
论文题名: Modelling high resolution rs data with the aid of coarse resolution data and ancillary data
作者: Poggio L ; , Gimona A
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2013
卷: 23, 期: 1 起始页码: 360
结束页码: 371
语种: 英语
英文关键词: Geostatistics
; Image restoration
; Spatio-temporal resolution
Scopus关键词: algorithm
; downscaling
; geostatistics
; image analysis
; kriging
; land surface
; Landsat
; modeling
; MODIS
; pixel
; remote sensing
; spatial resolution
; spatial variation
; surface temperature
; temporal variation
英文摘要: In environmental applications, the data have a large variety of resolutions carrying information at different scales. Various approaches have been used to include in models information from sources at different scales combining multi-resolution products in order to integrate the spatio-temporal variability of sub-pixel pattern. A methodology is proposed for the integration of the results obtained with a geostatistical downscaling algorithm, based on block-to-point-kriging, in a General Additive Models interpolation framework to enhance the spatio-temporal resolution of remote sensing data. This allows a good reproduction of the overall spatial pattern of the target images and of their local values. The developed framework was tested using MODIS land surface temperature (LST) with the thermal band of Landsat in a situation of high contamination of clouds for the high resolution dataset. The method proved to be flexible and able to blend data from different sensors maintaining the finer spatial structure of the higher resolution data. The method combines strengths from different approaches: (1) it uses of information held in covariates to provide more accurate results; (2) it is applicable to a variety of remote sensing products as the method does not rely on predetermined functional relationships; (3) it can cope with cloud-rich high resolution images as only a subset of high resolution pixels is needed. This approach is general and can be used with numerous combinations of high and low resolution images, such as MODIS-derived variables, using related band ratios from Landsat or other higher resolution sensors. This approach is a valuable addition to space-time measuring and modelling of ecosystems functions from remote sensing. © 2012 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79873
Appears in Collections: 气候变化事实与影响
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作者单位: The James Hutton Institute, Craigiebuckler, AB158QH, Aberdeen, Scotland, United Kingdom
Recommended Citation:
Poggio L,, Gimona A. Modelling high resolution rs data with the aid of coarse resolution data and ancillary data[J]. International Journal of Applied Earth Observation and Geoinformation,2013-01-01,23(1)