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Satellite data

MULTIPLY combines sophisticated data assimilation techniques with radiative transfer models and the rich data available from the Sentinel-1,- 2 and -3 satellites to retrieve continuous estimates of land-surface parameters, and their uncertainties, with high temporal and spatial resolution. The aim of MULTIPLY platform is to facilitate the use of Sentinel data and the latest in land-surface retrieval techniques by a broad range of academic and industry users.

Assimila is contributing to the development of the data assimilation algorithms and techniques for correcting for atmospheric effects in the satellite observations. We also developed techniques for data fusion to enable the production of true multi-sensor information products that combine reflectance data in physically consistent ways, including a combined MODIS/Sentinel-3 Albedo product. Building on these biophysical data products, Assimila demonstrated and validated the MULTIPLY platform for an agricultural test case in collaboration with ADAS, producing Leaf Area Index datasets for integration into a field productivity dashboard.