Tracking the magnitude of climate change and variability with remote sensing data to improve targeting of climate smart agricultural technologies

Quantifying the magnitude and significance of climate change variables over space and time in Africa is challenging due to sparse distribution of weather stations and poor quality of existing data. Time series climate data generated from remote sensing platforms could provide plausible alternative for measuring the trends of climate change in data limiting context. This study utilise time series remote sensing data for rainfall, maximum temperature and minimum temperature to investigate the magnitude and significance of spatial-temporal trends over six countries in West Africa. A modified Mann-Kendall test and Theil-Sen’s slope are utilised to test the significance and the magnitude of trends respectively for period between 1981 and 2017. June to September rainfall along the Sahel, Sudan and northern Guinea savanna agro-ecological zones revealed a significant increase (0.1 – 3 mm yr $^{-1}$) that peaked in August. Extreme temperatures for period between August and October remained stable while significant positive trend (0.005 – 0.07°C yr $^{-1}$) was observed in rest of months. Areas experiencing significant drying and warming trends are earmarked as priority for targeting appropriate climate smart agricultural technologies. The widespread significant increase of extreme temperatures justifies increased investments in measures to cope with heat stress.