Reconstructing intra-annual cropping dynamics on irrigation schemes in data sparse environments by fusing Landsat and MODIS imagery . 

<p>Governments and engineers have promoted the construction of large-scale, formalised, irrigation schemes across Africa for nearly 100 years. These developments are designed to increase food production and reduce the vulnerability of agriculture to climate shocks. Yet over the past decades, many irrigation schemes have deteriorated or completely failed; due to a wide range of problems from faulty infrastructure to unexpectedly severe climate shocks. Understanding the drivers of successes and challenges on irrigation schemes is complicated by limited long-term statistics. Meanwhile, for historic Earth-observation based analysis, the Landsat archive remains poor for large areas of Africa, and MODIS imagery is too coarse for meaningull mapping.</p><p>Here, we demonstrate a multi-sensor fusion methodology to map the expansion and intensification dynamics of irrigation schemes in the 21st century. Our methodology produces monthly Landsat-like images from the fusion of Landsat 5, Landsat 7 SLC-off, and MODIS imagery, which are classified into cropped area estimates. First, we use the StarFM fusion algorithm to generate monthly Landsat-like images from MODIS composites, based on temporally co-located MODIS and cloud free Landsat 5 or Landsat 7 SLC-on images. Next, we adjust these Landsat-like images against Landsat 7 SLC-off pixels by iteratively reweighting within a spatiotemporal Generalised Additive Model. Finally, we classify the derived monthly, Landsat-like, time-series data using a Random Forest classification model, mapping the number of crops harvested per year for the 2000-2020 period.</p><p>We test this methodology against two irrigation schemes in West Africa: the Office du Niger scheme in Mali and the Bagre Irrigation Scheme in Burkina Faso. For both sites, the mapped areas correlate with official statistics on cropped areas. Our data highlight infrastructure improvement and expansion on the Office du Niger, and the resilience of the scheme to rainfall variability. Whilst on the Bagre scheme, we show a vulnerability to large rainfall deficits, and a recent expansion in cropping frequency on newly developed extensions. This methodology is applicable to many areas where the Landsat archive is limited, but intra-annual mapping is required.</p>