Hi Susanta Mahato !

You can use machine learning techniques to do the downscaling, the methodology utilized in the article below will work good!

hope it helps!!

http://www.mdpi.com/2072-4292/8/3/215

Dear Susanta,

In R statistical software there are easy and difficult ways of downscaling/interpolation.

1) Easiest: use disaggregate(x, fact) function of package 'raster'. It can simply disaggregate the RasterLayer/RasterStack/RasterBrick, or you can set parameter 'method' to 'bilinear' for bilinear interpolation.

2) More difficult: you can use idw() function of package 'gstat' for IDW (Inverse Distance Weighted) and Tps() function of package 'fields' for TPS (Thin Plate Spline). Use raster::interpolate() after training the TPS model.

3) Difficult: Several type of kriging are available from package 'gstat'. Fitting a semivariogram model (fit.variogram()) will be needed before running function krige() or (functions gstat() and predict()).

If you have auxiliary variables, I recommend you to use regression kriging. This post may be of interest: https://www.researchgate.net/post/Can_I_downscale_climate_data_from_Worldclim_which_will_be_used_in_species_distribution_modelling#view=5908286b5b49523cc909005f

For more interpolation methods, please refer to this site:

http://rspatial.org/analysis/rst/4-interpolation.html

Here you can find a very good overview of the GIS packages of R:

https://cran.r-project.org/web/views/Spatial.html

HTH,

Ákos

Thank you sir... If I have any issue regarding the work then I will contact you...

Hello,

You should try loadeR in R environment

Susanta,

You should try the Package ‘downscale’.