Spatial interpolation methods applied in the environmental sciences: A review

By Jin Li, Andrew D. Heap. Environmental Modelling & Software, 2014

868 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/f6a6526aa0b0d99519b96a97f4501c0930cac3af

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Gerard B. M. Heuvelink
2. Tomislav Hengl

In Environmental Engineering

1. Gerard B. M. Heuvelink
2. Mladen Nikolic
3. Aleksandar Sekulić
4. Branislav Bajat
5. M. Kilibarda
6. Tomislav Hengl

In Water Science and Technology

1. Gerard B. M. Heuvelink

Citations in the corpus

• Random Forest Spatial Interpolation (2020, background): “In recent years, more and more use is being made of machine learning (ML) techniques for spatial interpolation [13].”