Seamlessly combine map layers (fill the gaps of one layer using the second one)

Question

I am creating air quality maps using two approaches.

One of them gives slightly better results when validated against measurements but there are significant spatial gaps present in the output.

The other approach gives a spatially continuous layer.

My goal is to combine the two layers by taking as much information as possible from the first layer and fill in the missing data with information from the second layer. I would like to avoid simply averaging the layers as this 1) does not retain the information from the more "valuable" layer, 2) leads to sharp breaks between the layers at some of the edges (as shown in the last picture where unnatural squares appear).

Is there any more sophisticated method to combine data in this way?

For example by assigning weights to the border data to combine it gradually.

Ultimately I would like to accomplish this in R.

Answer 1

Just for visual purpose, you can create a linear model to adjust data. Sample data:

library(RStoolbox)
library(raster)

data(lsat)

hq <- lsat[[2]]
lq <- lsat[[3]]

hq[1:(88970/2)] <- NA

plot(mosaic(hq,lq, fun = mean))

Mosaic doesn't 'look good', so adjust data of continuous layer with lm:

df <- as.data.frame(stack(hq,lq))

names(df) <- c('y','x')

lm_model <- lm(y~x,df)
coefficients(lm_model)

## (Intercept)           x 
## 14.7880063   0.5354473 

lq2 <- calc(lq, fun = function(x) x*0.5354473 + 14.7880063)

lq2 <- mask(lq2, mask = is.na(hq), maskvalue = 0)

plot(mosaic(hq,lq2, fun = mean))

I used mask to obtain a layer with data from continuous layer where the first layer has no values.