There is increasing demand for gridded products of topographical, meteorological and climatological variables with high quality and spatial resolution from many different disciplines such as agriculture, construction, biodiversity planning, forestry and risk assessment and decision making in environmental management. In this report discuss application of some common methods to rainfall interpolation by considering rainfall spatial variability. Some common geostatistical interpolation methods are including Thiessen polygon, inverse distance weighting (IDW), Linear regression, Ordinary kriging (OK), Regression Krigging and Simple kriging with varying local means (SKlm),
Samarasinghe, J. (2023). A Regression Kriging Application for Rainfall Data Interpolation. Geostatistical Approach. Afribary. Retrieved from https://afribary.com/works/a-regression-kriging-application-for-rainfall-data-interpolation
Samarasinghe, Jayantha "A Regression Kriging Application for Rainfall Data Interpolation. Geostatistical Approach" Afribary. Afribary, 31 Mar. 2023, https://afribary.com/works/a-regression-kriging-application-for-rainfall-data-interpolation. Accessed 29 Apr. 2024.
Samarasinghe, Jayantha . "A Regression Kriging Application for Rainfall Data Interpolation. Geostatistical Approach". Afribary, Afribary, 31 Mar. 2023. Web. 29 Apr. 2024. < https://afribary.com/works/a-regression-kriging-application-for-rainfall-data-interpolation >.
Samarasinghe, Jayantha . "A Regression Kriging Application for Rainfall Data Interpolation. Geostatistical Approach" Afribary (2023). Accessed April 29, 2024. https://afribary.com/works/a-regression-kriging-application-for-rainfall-data-interpolation