The evaluation of modelled or satellite-derived soil moisture (SM) estimates is usually dependent on comparisons against in-situ SM measurements. However, the inherent mismatch in spatial support (i.e., scale) necessitates a cautious interpretation of point-to-pixel comparisons. The upscaling of the in-situ measurements to a commensurate resolution to that of the modelled or retrieved SM will lead to a fairer comparison and statistically more defensible evaluation. In this study, we presented an upscaling approach that combines spatiotemporal fusion with machine learning to extrapolate point-scale SM measurements from 28 in-situ sites to a 100 m resolution for an agricultural area of 100 km by 100 km. We conducted a four-fold cross-validation, which consistently demonstrated comparable correlation performance across folds, ranging from 0.6 to 0.9. The proposed approach was further validated based on a cross-cluster strategy by using two spatial subsets within the study area, denoted as cluster A and B, each of which equally comprised of 12 in-situ sites. The cross-cluster validation underscored the capability of the upscaling approach to map the spatial variability of SM within areas that were not covered by in-situ sites, with correlation performance ranging between 0.6 and 0.8. In general, our proposed upscaling approach offers an avenue to extrapolate point measurements of SM to a spatial scale more akin to climatic model grids or remotely sensed observations. Future investigations should delve into a further evaluation of the upscaling approach using independent data, such as model simulations, satellite retrievals or field campaign data.

There's been a lot of news about GPU upscaling recently. Which graphics cards use which tech? Which game supports which standard? Can some of them be used on GPUs from different manufacturers? Fortunately, we've got Keith May to explain it all on the latest PCWorld YouTube video. To make upscaling as simple as possible, what tech like Nvidia Deep Learning Super Sampling (DLSS) and AMD Fidelity Super Resolution (FSR) do is this: They render the gameplay frames at a smaller size than your monitor's native resolution, pile on some advanced image processing, then scale it back up to native.

This handy development in TV picture processing is able to take content of a lower resolution than your TV's own panel and optimize it to look better, sharper, and more detailed. It may sound a lot like regular old upscaling, and you'd be right – the'AI' part just means the upscaling happens with a greater awareness of context. That's because Al upscaling involves creating new pixels of image information to add detail where there wasn't any before, filling in the gaps to recreate a higher-resolution image, all the while using machine learning to improve the result. Handle this badly and it can look like overcooked picture sharpening, a feature seen in the menus of most TVs, and which we usually advise turning all the way down. But top TV brands – as well as Nvidia, maker of PC graphics hardware – now have compelling 4K and 8K AI upscaling techniques that elevate'AI upscaling' beyond the meaningless marketing buzzword it might have been.