Remote Sensing Image Downscaling

Dear Colleagues,

In the early 1970s, one of the first applications of remote sensing was to determine “what is there”, that is, to classify the cover of the land. In the 1980s, some researchers realised that the pixel is a problematic concept in relation to land cover, because commonly a pixel covers more than one class. This led to the so-called “mixed pixel” approaches that estimated the proportion of each land cover class in each pixel instead of allocating each pixel to one class only. However, in the 1990s, it was realised that the allocation of proportions is only a partial solution (and a frustrating point at which to stop) because in reality those land cover proportions represent hard classes which have a spatial position within the pixel. Moreover, where the number of classes is large, it is very difficult to visualise such proportion maps, that is, to compress many class proportions into a single visual map. This led to methods for image downscaling (also termed super-resolution mapping and sub-pixel mapping) which produce a single thematic class map at a finer spatial resolution than the original data. Over the last two decades, many advances have been made. At the same time, the goal of downscaling was extended to continua, that is, increasing the spatial resolution of images of reflectance using change-of-support geostatistics and related techniques.

The advent of long historical time-series of remotely sensed images from sensors, such as AVHRR, MODIS and MERIS, has meant that the focus of remote sensing image downscaling has shifted from handling one-time image sets to extensive time-series of images. The requirement for downscaling solutions for time-series of images has been amplified by the Copernicus programme and especially the Sentinel series of satellites. The objective is to find the most suitable method of utilizing the available temporal information (e.g., temporal covariance structure) in such time-series and integrate this with the available spatial information and covariate information. This is problematic because the time-series of a given pixel, or set of pixels, may involve abrupt land cover changes, which requires a non-stationary model.

This Special Issue aims to showcase a wide range of new developments in remote sensing image downscaling. The scope includes both image downscaling for land cover classification and downscaling of continua. Contributions which provide new downscaling solutions for extensive time-series of remotely sensed images are particularly encouraged. Contributions may focus on, but are not limited to:

1. New methods for the creation of downscaled image time-series and fine resolution change detection;
2. New (e.g., Bayesian, machine learning, and computationally efficient) approaches to spatial downscaling;
3. Image downscaling in operational applications (e.g., agricultural monitoring and crop yield forecasting, deforestation, urbanization, vegetation phenology, spatial epidemiology);
4. Assessment of the information content and uncertainty in downscaled products.

Prof. Peter M. Atkinson
Guest Editor

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