Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling
By Hua Wu, Zhao-Liang Li. Italian National Conference on Sensors, 2009
337 citations recorded by the source corpus.
With the development of quantitative remote sensing, scale issues have attracted more and more the attention of scientists. Research is now suffering from a severe scale discrepancy between data sources and the models used. Consequently, both data interpretation and model application become difficult due to these scale issues. Therefore, effectively scaling remotely sensed information at different scales has already become one of the most important research focuses of remote sensing. The aim of this paper is to demonstrate scale issues from the points of view of analysis, processing and modeling and to provide technical assistance when facing scale issues in remote sensing. The definition of scale and relevant terminologies are given in the first part of this paper. Then, the main causes of scale effects and the scaling effects on measurements, retrieval models and products are reviewed and discussed. Ways to describe the scale threshold and scale domain are briefly discussed. Finally, the general scaling methods, in particular up-scaling methods, are compared and summarized in detail.
Subfields: Atmospheric Science, Ecology, Media Technology
View Paper: https://www.semanticscholar.org/paper/fdfe298afe61c4b160fddb1e55425b6cb37e505b
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Citations in the corpus
- Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review (2019, background): “Assuming that the retrieval models and algorithms are derived at small scales, when the same models are utilized at large scales, uncertainties may occur [99].”
- Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats (2013, positive): “To make the movingwindow size determination more reasonable, future trends should focus on the improved semi-variogram modeling (Garrigues et al., 2006) as well as local variances, fractals, and the wavelet transform (Wu & Li, 2009).”