Monthly Remote Sensing Indicators for West Africa predicted to support climate models

Monthly Remote Sensing Indicators for West Africa predicted to support climate models

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Africa | NetCDA | Phenology | project | West Africa

October 1, 2024

Various monthly remote sensing dataset have been published in the context of the LANDSURF project in a spatial resolution of 1 x 1 km which are all based on MODIS and AVHRR datasets covering a period of 40 years which include the following:

Besides the vegetation indices NDVI and LAI we also published a monthly 1x 1 km land cover classification identifying 16 different land cover classes.

In addition, two binary parameters, i.e. forest cover and vegetation cover were contributed to the LANDSURF project. On top we provided a land surface albedo dataset.

For predicting the restrospective indices the STARFM algorithm was utilized in a Python environment.

In addition, the dataset (https://dx.doi.org/10.58160/gGzexcbDikobkyvK) contains climatological and agrometeorological indices of West Africa for 1981-2100 which are based on global and regional climate models (CMIP5 and CORDEX-CORE) calculated by our colleagues from Climatology, Dr. Katrin Ziegler, Dr. Daniel Abel and Prof. Dr. Heiko Paeth . Future greenhouse gas emissions scenarios (low (RCP2.6) and a high (RCP8.5)) were used.

 

 

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