Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach

By Qi-quan Li, T. Yue, Changquan Wang, Wen-jiang Zhang, Yong Yu, Bing Li, Juan Yang, Genchuan Bai. 2013

126 citations recorded by the source corpus.

Subfields: Environmental Chemistry, Environmental Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/63c8537ca88ff9fa26718390057a1f51b8295266

How researchers see this paper

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

In Environmental Engineering

1. Jan G. P. W. Clevers
2. Gustau Camps-Valls
3. Jochem Verrelst
4. José F. Moreno
5. Jordi Muñoz-Marí
6. Frank Veroustraete
7. Juan Pablo Rivera

Citations in the corpus

• Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (2015, background): “Alternative powerful, yet more complex, structures involve recurrent artificial neural networks (RANNs) and radial basis function neural networks (RBFNN), which only recently went into application to map vegetation properties (Chai et al., 2012; Li et al., 2013; Wang et al., 2013).”