Agriculture is an important source of income for many countries in the Global South, where it may account for as much as 25% of GDP. Precipitation is crucial for agriculture in countries like Ghana, where ~95% of farming is rainfed. Accurate rainfall observations are limited in Ghana. The sparse rain gauge network and the lack of weather radars make remote sensing methods a potentially attractive alternative source of rainfall data. Radar satellites, such as Sentinel-1, emit radiation that passes through the atmosphere and is scattered back to the satellite by the Earth’s surface. The backscatter measured by the satellite is correlated with the wetness of the soil but the existence of vegetation hinders straightforward quantification of soil moisture. By choosing sites with a simple and, more or less, constant phenology, it may be possible to eliminate the effect of vegetation on backscatter. Soccer field may qualify as sites with such a simple and constant phenology. The main objective of this study is to use the Sentinel-1 data over soccer fields and assess them as rainfall detectors. A machine learning approach will be used to reach this objective. This research assessed the stability and the generalization capabilities of a classification model (rain/no rain). The model was trained with and applied to different locations and periods (2019 & 2020). Ground observations from 53 Ghanaian (TAHMO) and 1 Greek stations were used. Soccer fields in Ghana and Greece were selected and their suitability as rainfall detectors was checked based on the correlation between modeled soil moisture and backscatter strength. The rain/no rain classification of the soccer fields was made with a stacked classifier that was trained and validated with both spaceborne and ground data. The classifier was tested on six different datasets from Greece and Ghana 2019 and 2020. The stability of the model was assessed by a Leave-p out cross-validation approach. The generalization in space was tested by using different environments. The generalization in time was tested by using different time periods. The results showed that the classification was stable. The minimum and maximum performances for the different testing datasets were 0.43 to 0.85. The median performance of the algorithm in Ghana for 2020 is 67%. The stacked classifier was found to have the best performance compared to other classifiers. Finally, the performance of the stacked classifier was competitive in comparison with the performance of the well-known IMERG algorithm. The study showed that there is a potential for using radar backscatter from suitable fields to detect rainfall. The classifier is stable and can be generalized in time and space under certain conditions.

Rain-fed agriculture is the main source of food in Ghana therefore improving quantitative rainfall estimates is essential for local farmers to predict crop growth using vegetation models. Rainfall dynamics in the tropics is an ongoing topic of research due to their complexity and sub-grid precipitation variability. At the same time, tropical areas such as Ghana are the most affected by a lack of proper rain gauge network coverage. Traditional methods rely on empirical assumptions and statistical theories that require continuous calibration and still struggle to accurately represent local variability. The aim of this paper is to demonstrate the potential of a Deep Learning (DL) approach using bi-spectral information of water vapor imagery (WV) and thermal infrared (TIR) as a starting point to develop an effective alternative to the Cold Cloud Duration (CCD) method which is a widely applied statistical technique by satellite rainfall products like Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Tropical Applications of Meteorology using SATellite data (TAMSAT) that are specifically designed for Africa. WV inhibition of low-level features assures the correct depiction of strong convective motions usually related to heavy rainfall which is crucial in tropical areas where convective rainfall is dominant. The addition of WV 7.3𝜇m is particularly beneficial in North Ghana as tropical systems are advecting dry air from the nearby Sahara desert creating discontinuities in precipitation events which translates into dry intrusions and dry slots seen in the images of the WV channel. The developed Deep learning model showed strong performances in binary classification where it outperformed IMERG-Final false alarms count resulting in lower rainfall overestimation (FBias < 2.0), although further research is needed to overcome the very poor relation between GEO-IR images and actual rainfall estimates at the surface.