Fallow ID: Leveraging Sentinel-2 multi-year time series for the characterization and mapping of fallow land in West Africa

Thanks to its primary ability of restoring soil fertility, fallow practices are an integral part in cropping systems worldwide. More recently, their importance has been further pointed out, considering the related implications on climate change mitigation through carbon sequestration and biodiversity conservation (Dayamba et al., 2016; Ringius, 2002). Mapping fallow land has hence become a key challenge to assess the impact of such practices on the sustainability of agricultural systems. Sentinel-2 missions have enhanced the ability for mapping agricultural land due to its high revisit time (5-day) and spatial resolution (10-meter pixels), easing the task of monitoring cropping practices and allowing land mapping in areas with relatively small plots. This has an even greater importance in West African countries where the financing for building a reliable and robust agricultural statistics system might be lacking. Nonetheless, fallow land mapping has been, for the most part, overlooked in mainstream land cover products with little to no discrimination between active cropped land and fallowed land. Still, these global/regional land cover products are widely used as a basic information for many crops monitoring tasks, including yield estimation and forecasting in food security early warning systems (Nakalembe et al., 2021). Few studies have proposed a land cover mapping methodology specifically for fallow fields and, to the best of our knowledge, only one has provided tests of such a method across the Sahel, reporting that more than 50% of cropland class in most of the global land cover products are fallow fields (Tong et al., 2020). However, the unsupervised methodology they used relies on a strong hypothesis on seasonal NDVI profiles (i.e. cropped fields have in general a lower NDVI than fallow fields across the cropping season) whose pertinence at both local scales and outside the Sahelian area may be questionable. In this study, we present the outcomes of a first exploratory analysis on fine scale, remote sensing-based characterization of fallow practices, carried out over a study case located in the Sudanian region of Burkina Faso. Leveraged data consist of a Sentinel-2 multi-year image time series, appropriately pre-processed and coupled with detailed, small scale in-situ data derived from a recently published agricultural land cover database for the 2015-2021 period (Jolivot et al., 2021), built up during the JECAM experiment of the GEOGLAM network (http://jecam.org/). In order to test the suitability of the Tong et al. (2020) underlying NDVI hypothesis for our study area, we first replicated the aforementioned reference methodology, but did not reach satisfying accuracies, with both producer and user accuracy below 50% and highly overestimating the proportion of fallow land when validating with JECAM database. We then provide an expertise-based exploratory analysis of fallow-field NDVI profiles in order to come up with a more suitable set of hypotheses which could be used in the definition of a novel methodology for remote sensing based fallow mapping. Our preliminary results highlight that seasonal NDVI-based fallow discrimination approaches are not sufficient for discriminating fallow fields from other cropped areas (Figure 1). Conversely, we come out with several evidences that multi-year NDVI fallow characterization might a more suitable approach, for example by showing that transitions of fields from cropped to fallow and vice versa may have a measurable impact on vegetation index dynamics over multiple years.