2022 Living Planet Symposium

Poster presentation

Harmonization of optical and radar time series towards understanding regional-scale climatological variations on smallholder farming systems in East Africa

1. Adomas Liepa University of Wuerzburg, Germany Speaker
2. Michael Thiel University of Wuerzburg
3. Tobias Ullmann Würzburg University

Smallholder farming accounts for roughly 80% of the available cropland in Africa. Food insecurity and diminished crop yield are chronic issues faced in Africa due to degraded soils, pest outbreaks and extreme weather events related to climate change. In spite of the importance and abundance of smallholder farming systems in Eastern Africa, their agricultural properties 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. Due to the volatile state of crops in Africa and projected negative trends to future yields, better adaptation and mitigation techniques are crucial to face current and future challenges in smallholder farming systems.
With this in mind, 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 analyzing the remote sensing record and meteorological observations. Thereby, several passive multispectral satellite datasets (Landsat 7, Landsat 8 and Sentinel-2) are combined and harmonized to produce a broader time-series and to minimize measurement gaps.
From these harmonized 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 normalized ratio between the two polarizations.
From the harmonized NDVI curves, phenological metrics (e.g. 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, climate indices are utilized to depict the climate fluctuations through time and to compare them to the phenological metrics. Most known examples of these indices include the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and the Pacific Decadal Oscillation (PDO). The contribution will show first results on the effects of large-scale climate fluctuations on smallholder farming systems in East Africa.

Detailed presentation link tinyurl.com/2p9d6v2h

Complete poster session https://express.converia.de/frontend/index.php