Performance assessment of irrigated sugarcane using remote sensing: A case study of Kasinthula cane growers limited, Malawi

Assessing irrigated sugarcane performance accurately and in a timely manner is crucial for optimizing productivity. This is especially important for ensuring sustainable water use in agriculture and maintaining food and water security in semi-arid regions that are increasingly impacted by climate variability. This study leveraged remote sensing technologies to evaluate the performance of irrigated sugarcane at Kasinthula Cane Growers Limited (KCGL) in Malawi. It specifically applied the PySEBAL to Landsat 8 imagery. The results were validated using FAO's WaPOR datasets. The validated results for PySEBAL and WaPOR were compared to the FAO Penman-Monteith equation. The FAO Penman-Monteith equation uses meteorological to calculate reference evapotranspiration. Key performance indicators (KPIs) like actual evapotranspiration (AET), biomass production, adequacy, equity, and uniformity were analyzed for the 2019 and 2020 seasons. PySEBAL-derived AET showed strong agreement with FAO Penman-Monteith estimates and WaPOR data (R² = 0.83 in 2019; 0.78 in 2020; RMSE = 42.1 mm and 23.8 mm, respectively). Biomass estimates correlated with observed sugarcane yields (R² = 0.74 in 2019; 0.68 in 2020). Centre-pivot irrigation exhibited higher uniformity, while furrow systems demonstrated greater equity. As the first study to compare PySEBAL and WaPOR in a Malawian sugarcane estate, the findings underscore the potential of remote sensing for evidence-based irrigation and water management in data-scarce regions. This study used a combined approach of Penman-Monteith, PySEBAL, and WaPOR to comprehensively evaluate irrigation performance at KCGL. This integrated approach can effectively support operational decisions and enhance water productivity in large-scale sugarcane systems, especially in data-scarce regions.