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 political and socioeconomic instability renders traditional methods for land mapping/inventory expensive and dangerous (Nakalembe, 2021; Sahajpal et al., 2020). 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 crop 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 compare to 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. 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 (see attach figures).
In parallel, we also performed a data-driven analysis in which we use common machine learning techniques to provide automatic fallow mapping through supervised image classification. The rationale of this part of the study is two-fold : (1) assess the potential of supervised classification and build a “baseline” set of fallow maps for the period covered by the reference database, and (2) exploit a larger sets of variables, radiometric (i.e. derived from Sentinel-2 time series - vegetation, water and soil indices) as well as other types of geo-spatial data (such as soil type or rainfall data), and explore their correlation with the dynamics of agricultural practices and crop vegetation development. Although no clear hypothesis can be made yet for the design of a novel methodology suitable for upscaling, we come up with further clues that integrating multi-year strategies into state-of-the-art land cover mapping techniques may be a promising approach in tackling the complex, yet key task of fallow monitoring in West African agrosystems.
Dayamba, S. D., Djoudi, H., Zida, M., Sawadogo, L., & Verchot, L. (2016). Biodiversity and carbon stocks in different land use types in the Sudanian Zone of Burkina Faso, West Africa. Agriculture, Ecosystems & Environment, 216, 61–72. https://doi.org/10.1016/j.agee.2015.09.023
Jolivot, A., […], Gaetano, R., […] Leroux, L., […] Bégué, A. (2021). Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries. Earth System Science Data [Preprint]. https://doi.org/10.5194/essd-2021-125
Nakalembe C et al. (2021). A review of satellite-based global agricultural monitoring systems available for Africa, Global Food Security, 29 : 100543. https://doi.org/10.1016/j.gfs.2021.100543
Ringius, L. (2002). Soil carbon sequestration and the CDM: Opportunities and challenges for Africa. Climatic Change, 54(4), 471–495.
Sahajpal, R., Fontana, L., Lafluf, P., Leale, G., Puricelli, E., O’Neill, D., Hosseini, M., Varela, M., & Reshef, I. (2020). Using machine-learning models for field-scale crop yield and condition modeling in Argentina. 49º Jornadas Argentinas InformáTica, Congr. Argentino AgroinformáTica, 1–6.
Tong, X., Brandt, M., Hiernaux, P., Herrmann, S., Rasmussen, L. V., Rasmussen, K., Tian, F., Tagesson, T., Zhang, W., & Fensholt, R. (2020). The forgotten land use class: Mapping of fallow fields across the Sahel using Sentinel-2. Remote Sensing of Environment, 239, 111598. https://doi.org/10.1016/j.rse.2019.111598