Dear Colleagues,

The advance of remote sensing technology, which can quickly access a wide range of real-time land surface spatial information, has provided a powerful means to conduct regional and global measurements for resource surveys, environmental monitoring, and disaster prediction. However, the inconsistency between the spatial resolution of remotely sensed data and that required for remote sensing applications greatly limits the development of the latter. The scale represents the window of perception and the ability of observation and reflects the limitation of knowledge through which a phenomenon may be viewed or perceived. When only certain spatial scales are considered, this can inevitably lead to a geographical misunderstanding of the current status from in situ measurement data on the point, site, or regional scale.

Scale research is of great importance in remote sensing. Generally, most retrieval methods and algorithms are mainly derived at a small scale and assume that the land surface at this scale is homogeneous. However, when these methods and algorithms are used directly on remotely sensed data with different spatial resolutions, there may be inconsistencies among the corresponding products because of the heterogeneity of the land surface. These inconsistencies, which are also regarded as scaling effects, may have great impact on the quality of the products and make the products unreliable. For decades, scientists have been dedicated to answering two important questions about the scale effects and scaling in the quantitative remote sensing: 1) Does the basic laws of physics at the pixel scale still apply to other scales, and if not, how to correct them? 2) What are the laws and connections of geography target features on different resolution scales, and how to upscale and downscale these features?

In fact, there is no generalized scaling method considering both the heterogeneity of land surface and the nonlinearity of the retrieval methods. Research concerning scale is still in its initial stage. With the rapid development of remote sensing technology, this Special Issue focuses on Scale Issues in Remote Sensing: Analysis, Processing, and Modeling. Potential topics include, but are not limited to, the following:

•  Scale effects of remotely sensed products
• Upscaling and downscaling modelling approaches
• Fusion of multi-temporal, multi-angle, and multi-sensor remotely sensed data
• Strategies of multi-scale image processing
• Validation of multi-scale quantitative remote sensing products by in situ measurements
• Evaluation of uncertainty and applicability in upscaled or downscaled products
• Comparison of scale drivers in different domain
• Identification of relevant spatial scales and thresholds for remote sensing models

Dr. Zhao-Liang Li
Dr. Hua Wu
Guest Editors

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• Scale effects
• Scaling
• Fusion
• Upscaling and downscaling modelling
• Multi-scale Validation
• Uncertainty of Scaling