Performance of Satellite remote sensing (SRS) to monitor dry and wet extreme events and crop yield prediction in Senegal

Studying the relationship between dry/wet extreme events and crop yields can help us understand the impact of the intensification of the water cycle on agricultural production.

Four datasets are based on satellite data (TRMM-3B42 V7, CMORPH V1.0, TAMSAT V3, and CHIRPS V2. 0), two on reanalysis products (NCEP-CFSR and ERA5), and three on rain gauge observations (CPC Unified V1.0/RT and a 65-rain-gauge network regridded by using two kriging methods, namely ordinary kriging, OK, and block kriging, BK). The occurrence of dry extreme events is less in products using infrared measurement techniques than in products coupling infrared and microwave. The amplitude of wet extreme events shows the greatest variation between products.

Using these dry and wet indicators with remote sensing indicators (NDVI, SPI3, WSI and RG), we analysed the ability of dry and wet spells alone or combined with these remote sensing indicators to provide intraseasonal forecasts covering the period 1991–2010. Regarding the timing of prediction, millet yield can be forecast as early as July with an accuracy of 40% of the mean yield but the best forecast is obtained in early September (80%), at the peak of crop development (accuracy of 100 kg/ha i.e. 20% of the mean yield). However, the estimated yields show biases over some years identified as extremely deficient or in oversupply in terms of agricultural yields. Our findings provide guidance in choosing the most suitable SRS for implementing early warning systems (EWSs) using a multi-risk approach and integrating effective dry/wet spell indicators for monitoring and detecting extreme events.