A Resolution-Preserving Area Projection Algorithm for Multi-Mission Earth Observation
DOI:
https://doi.org/10.17398/3101-7177.2.175Palabras clave:
Big data, representación geográfica de datos, teledetecciónResumen
We present a fast area projection algorithm for mapping satellite measurements onto geographical grids while preserving the instrument's native spatial resolution. Standard interpolation methods either introduce white noise (Nearest Neighbor), smooth fine-scale features (Bilinear), or are computationally prohibitive. Our approach tessellates each measurement footprint into equal-area sub-elements and performs a single, weighted projection onto the target grid, avoiding the error cascading caused by successive coordinate transformations. As an example, we walidate on SMOS Sea Surface Salinity over three years: area projection methods achieve lower biases and standard deviations than existing operational products, and maintain the correct spectral slope down to ~0.6°—close to the instrument's 35–50 km native resolution. We also apply this approach to Sea Surface Temperature and Brightness Temperature maps, as well as for downscaling in numerical and machine learning models. It offers a general-purpose, computationally efficient solution for Big Data management in Earth observation.
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Derechos de autor 2026 Aina Garcia-Espriu, Cristina González-Haro, Verónica González Gambau, Arnau Ruiz Sebastián, Estrella Olmedo Casal, Antonio Turiel Martínez, Ruben Marti-Burillo (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
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