SAR phenology across major West-African land cover types

Kurzfassung

West Africa is an important hotspot of global change facing huge environmental and societal challenges. These include climate and land use change, migration, and conflicts, all of which are a major threat to food security. Food security – and a number of other envisaged achievements of the sustainable development goals (SDG) – depends largely on wise natural resource management. For several reasons, the West African environment and its changes are still poorly understood, although a large number of scientific studies have been conducted over the past years thanks to the establishment of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL) and other initiatives. However, most of the studies are focused on only few study sites, only very few aim at large-scale assessments of climate change impacts or land use. Little is known about vegetation structure, which plays a crucial role in the estimation of the greenhouse gas budget and the carbon sequestration potential of complex ecosystems such as agroforestry systems. Agroforestry systems are a mixture of different land uses, characterized by a certain tree cover and crops in between. Ideally, the trees do not only shade the fields but also provide fruits that can be used as food or to feed animals. Among the consistent datasets that provide more detailed information about vegetation properties throughout the region is the Copernicus Global Land Cover product (Buchhorn et al. 2020). It is available for multiple years (2015-2019) and provides rich information with regard to vegetation, particularly forests. The spatial resolution is 100 m. However, West African ecosystems are diverse and complex. This complexity is also true for agroforestry systems, which are important agricultural production zones and at the same time fulfill numerous ecosystem services. Unfortunately, none of the well-established nor the recent land cover and land use products such as the beforementioned Copernicus product are able to adequately resolve agroforestry systems. Even the WorldCover 2020 product (Zanaga et al. 2021) with 10 m spatial resolution is not suitable to differentiate between cropland areas, forest cover, shrubland and agroforestry systems. While our hypothesis is that the spatial resolution of the Copernicus Sentinel satellites is limiting the classification of single trees, we expect differences in the phenology within agroforestry systems that can be mapped by means of remote sensing. Phenology, the characteristic, often seasonal life cycle of plants, is an important plant species trait and hence one of the essential biodiversity variables. Many methods exist to retrieve phenology from optical remote sensing data. While the resulting information aids in differentiating plant species or plant functional types, satellite-derived products are usually different from what can be observed in the field. West Africa experiences a strong climate gradient from the hot and dry Sahara Desert region to the moist Guinean forest ecozone. In terms of optical remote sensing, capabilities to retrieve dense time series is limited by frequent cloud cover, particularly in the southern part of the region. Therefore, we propose to use Sentinel-1 Synthetic Aperture Radar (SAR) data to retrieve phenology at pixel level (10 m spatial resolution). In recent years, Sentinel-1 SAR data is increasingly used to characterize phenology of field crops. Little is known about phenology of West African vegetation, particularly non-crops. Consequently, we sampled all classes of the Copernicus Land Cover product covering the ECOWAS region in West Africa and explored Sentinel-1 time series. Our pre-processing includes radiometric terrain correction, speckle filtering and time series smoothing using a Savitzky-Golay filter. For West Africa, only data in ascending orbit is available, resulting in a reduced temporal resolution compared to other regions. From the two polarizations, VV and VH, we computed several well-established indices (e.g. VH/VV ratio, radar vegetation index). We sampled the whole region, resulting in 250 samples per land cover class. For each sampling point we extracted the time series of the backscatter as well as the indices and tested their similarity. As the Copernicus product also provides fractions of each class, we were able to explore the relationship between fractional tree cover (and others) and the SAR backscatter and indices, respectively. Our results show that some of the classes are no longer separable at high spatial resolution (e.g. open evergreen forest vs. closed evergreen forest). After adequate join of similar classes, we were able to use the backscatter information as well as the uncorrelated indices to map Copernicus land cover classes at high spatial resolution (10 m) with acceptable accuracy. From the smoothed time series, we derived phenological parameters such as start of season, end of season and length of season, greenup and senescence. While a direct link to ground phenology is challenging, we are able to map groups of similar phenological behavior, which is important for a more comprehensive characterization of vegetation.