Combining 2 raster data sets with different extent, resolution and point regularity

Question

I'm having trouble merging two raster datasets that have different resolution and extent, and have irregular point data. Below is info on each raster. I need these datasets to merge according to the grid points so that I can run a fire spread model (which requires both wind and slope data).

I have tried converting to normal dataframes (using rasterToPoints) before merging but the differences lead to loss of a lot of grid points.

I have tried to align the rasters with project raster and rasterize, but I haven't managed to get it to work. If anyone has an idea that could help, I would really appreciate your answer!

'''

Data from https://globalwindatlas.info/downloads/gis-files

> wind <- raster("ZAF_wind-speed_10m.tif"); wind
class      : RasterLayer 
dimensions : 11271, 11804, 133042884  (nrow, ncol, ncell)
resolution : 0.0025, 0.0025  (x, y)
extent     : 13.33407, 42.84407, -50.31423, -22.13673  (xmin, xmax, ymin, ymax)
crs        : +proj=longlat +datum=WGS84 +no_defs 
source     : C:/Users/s2000128/Documents/Flammability modelling/ZAF/ZAF_wind-speed_10m.tif 
names      : ZAF_wind.speed_10m  

Data from https://datacatalog.worldbank.org/dataset/world-slope-model

> slope <- raster("ZAF1_msk_alt.grd"); slope
class      : RasterLayer 
dimensions : 1548, 1992, 3083616  (nrow, ncol, ncell)
resolution : 0.008333333, 0.008333333  (x, y)
extent     : 16.4, 33, -34.9, -22  (xmin, xmax, ymin, ymax)
crs        : +proj=longlat +ellps=WGS84 +no_defs 
source     : C:/Users/s2000128/Documents/Flammability modelling/slope_deg_0/slope_deg/ZAF1_msk_alt.grd 
names      : ZAF1_msk_alt 
values     : -26, 3264  (min, max)

Here are some examples of what I have tried:

1

windresampled <- projectRaster(wind,slope,method = 'ngb'); windresampled

2

wind_points <- rasterToPoints(wind); 
coordinates(wind_points) = ~x+y; 
proj4string(wind_points) = CRS("+init=epsg:4326"); 
gridded(wind_points) = TRUE; 
g_wind <- raster(wind_points); g_wind; 
extent(wind) <- c(16.4,33, -34.9,-22); 
res(wind) <- c(0.0025, 0.0025); 
r_wind <- rasterize(wind_points, g_wind, field = wind_points$z, fun = mean, na.rm = TRUE); r_wind