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Tropentag, September 14 - 16, 2022, Prague

"Can agroecological farming feed the world? Farmers' and academia's views."


Spatial and temporal patterns of smallholder farming systems in East Africa

Adomas Liepa1, Michael Thiel1, Tobias Ullmann2

1University of Wuerzburg, Dept. of Remote Sensing, Germany
2University of Wuerzburg, Dept. of Physical Geography, Germany


Abstract


Smallholder farming accounts for roughly 80% of the available cropland in Africa. Despite the importance and abundance of smallholder farming systems in Eastern Africa, their spatial and temporal patterns remain sparsely measured and understood.
In this regard, knowledge on crop phenology is highly relevant for a multitude of environmental and social topics, such as application of nutrients, management of land use, water distribution and irrigation. Furthermore, information on crop phenology is essential in combating food insecurity and understanding the impact of climate change on smallholder farming systems.
This research aims at (i) determining phenological variations at field-level in East African smallholder farming systems via a combination of datasets collected by multiple earth-observation missions and (ii) investigating the impact of regional climatological trends on the field-level variation by jointly analysing the remote sensing record and meteorological observations. Thereby, several passive multispectral satellite datasets (Landsat 7, Landsat 8 and Sentinel-2) and Synthetic Aperture Radar (SAR) data of the Sentinel-1 mission are combined and harmonised to produce a broader time-series and to minimise measurement gaps.
From these harmonised series, the Normalized Difference Vegetation Index (NDVI) was selected as a proxy to track farming field phenology through time. Furthermore, active Synthetic Aperture Radar (SAR) data from the Sentinel-1 mission is added to further increase the number of observations and to close remaining observational gaps in the series. SAR data are incorporated by applying a multivariate regression allowing to extract pseudo-NDVI values. This was achieved by expressing the Sentinel-2 NDVI as a function of VV and VH backscatter, as well as, local incidence angle and the normalised ratio between the two polarisations.
From the harmonised NDVI curves, phenological metrics, such as start, end, midpoint, duration, and peak of the growing season, are computed for selected smallholder fields to obtain a continues depiction of the phenological cycles. Finally, temporal and spatial patterns are compared with observed climate variables. The contribution will show first results on the effects of large-scale climate fluctuations on smallholder farming systems in East Africa.


Keywords: Agriculture, farming, Landsat 7, Landsat 8, NDVI, phenology, Sentinel-1, Sentinel-2, smallholder, systems


Contact Address: Adomas Liepa, University of Wuerzburg, Dept. of Remote Sensing, Pommergasse 4a, 97070 Wuerzburg, Germany, e-mail: adomas.liepa@uni-wuerzburg.de


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