Soil Carbon Maps

By T. Mueller, F. Pierce. 2003

150 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/5f82c454518af7f808b307eafb8be07d4f3d0f04

How researchers see this paper

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

In Artificial Intelligence

1. Lin F. Yang
2. A-Xing Zhu

In Environmental Engineering

1. Lin F. Yang
2. A-Xing Zhu
3. C. Qin
4. Chenghu Zhou

In Soil Science

1. A-Xing Zhu
2. C. Qin

Citations in the corpus

• Associations between field characteristics and soybean plant performance using canonical correlation analysis (2005, unknown): “Although soil OM content has been estimated by field sampling the interpolation accuracy from a few samples can be considerably improved with interpolation methods that account for secondary variables such as topographic variables (Mueller and Pierce, 2003) or soil reflectance (Konen et al., 2003).”
• Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods (2010, methodology): “Terrain variables have been used widely in soil organic matter (SOM) content mapping as they can be incorporated into geostatistical methods and used as secondary variables (Bell et al., 2000; Mueller and Pierce, 2003).”