Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system
Mbulisi Sibanda, Helen S. Ndlovu, Kiara Brewer, Siphiwokuhle Buthelezi, Trylee Nyasha Matongera, Onisimo Mutanga, John Odidndi, Alistair Clulow, Vimbayi Grace Petrova Chimonyo, Tafadzwanashe Mabhaudhi
Abstract
Most rural communities reside in marginal environments and are directly or indirectly participating in smallholder agriculture under rainfed conditions for subsistence. Maize is the main staple, grown in these smallholder croplands for subsistence purposes. Despite the role of these smallholder farming activities in bolstering food security and alleviating hunger, these croplands are often impacted by climate variability as well as extreme weather events such as hailstorms. This results in reduced crop productivity and yields. Robust spatially explicit monitoring techniques such as remotely piloted aircraft systems (RPAS) based remote sensing technologies could be instrumental in understanding the impact and extent of crop damage to devise adequate response mechanisms suitable for bolstering crop productivity in a spatially explicit manner. It is in this regard, that this study sought to evaluate the utility of drone-derived multispectral data in estimating crop productivity elements (Equivalent water thickness (EWT), Chlorophyll content, and leaf area index (LAI)) in maize smallholder croplands based on the random forest regression algorithm. A hailstorm occurred in the study area during the reproductive stage 2 to 3 and 3 to 4. EWT, Chlorophyll content, and LAI were measured before and after the storm. Results of this study showed that there could be optimally estimated EWT, Chlorophyll content, and LAI based on the red edge and its spectral derivatives. Specifically, EWT was estimated to a rRMEs 2.7% and 59%, RMSEs of 5.31gm−2 and 27.35 gm-2, R2 of 0.88 and 0.77 while Chlorophyll exhibited rRMSE of 28% and 25%, RMSEs of 87.4 µmol m−2 and 76.2 µmol m−2 and R2 of 0.89 and 0.80 and LAI yielded a rRMSE of 10.9% and 15.2%, RMSEs of 0.6 m2/m2 and 0.19 m2/m2.before and after the hail damage, respectively. Overall, the study underscores the potential of RPAS-based remote sensing as a valuable resource for assessing crop damage and responding to the impact of hailstorms on crop productivity in smallholder croplands. This offers a means to enhance agricultural resilience and adaptability in the face of these natural disasters.