Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library
By Z. Shi, W. Ji, R. V. Viscarra Rossel, S. Chen, Y. Zhou. 2015
186 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Artificial Intelligence, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/75f35f86d4e4ac5337a8f820687ef4146e0d52b6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Analytical Chemistry
In Artificial Intelligence
In Environmental Engineering
Citations in the corpus
- Accurate and Precise Prediction of Soil Properties from a Large Mid-Infrared Spectral Library (2019, methodology): “Visible (vis; 400–700 nm), near-infrared (NIR; 700–2500 nm), and mid-infrared (MIR; 2500–25,000 nm) regions have been used widely to characterize soil minerals and organic matter at the global [5,7,8], regional [9,10], national [11,12] and local scale [13].”