Deep Interpolation of Remote Sensing Land Surface Temperature Data with Partial Convolutions.

ABSTRACT: Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST data, such as the MODA11 product using information from the MODIS sensors attached to NASA satellites, data acquisition can be hindered by clouds or cloud shadows, occluding the sensors' view on different areas of the world. This makes it difficult to take full advantage of the high resolution of the data. A common solution to interpolating LST data is statistical interpolation methods, such as fitting polynomials or thin plate spine interpolation. These methods have difficulties in incorporating additional knowledge about the r

SUBMITTER: Huber F 

PROVIDER: S-EPMC10934100 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature