Incorporation of high accuracy surface modeling into machine learning to improve soil organic matter mapping
Digital soil mapping approaches related to soil organic matter (SOM) are crucial to quantify the process of the carbon cycle in terrestrial ecosystems and thus, can better manage soil fertility. Recently, many studies have compared machine learning (ML) models with traditional statistical models in...
Main Authors: | Bao, Z. (Author), Du, Z. (Author), Li, X. (Author), Wang, Z. (Author), Yue, T. (Author), Zhao, N. (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier B.V.
2021
|
Subjects: | |
Online Access: | View Fulltext in Publisher |
Similar Items
-
Approximating Soil Organic Carbon Stock in the Eastern Plains of Colombia
by: Shauna-kay Rainford, et al.
Published: (2021-07-01) -
Assessment of soil fertility degradation affected by mining disturbance and land use in a coalfield via machine learning
by: Ren, T., et al.
Published: (2021) -
EXTRAPOLATING THE SUITABILITY OF SOILS AS NATURAL REACTORS USING AN EXISTING SOIL MAP: APPLICATION OF PEDOTRANSFER FUNCTIONS, SPATIAL INTEGRATION AND VALIDATION PROCEDURES
by: Yameli Guadalupe Aguilar Duarte, et al.
Published: (2011-04-01) -
Carbon Storage along with Soil Profile: An Example of Soil Chronosequence from the Fluvial Terraces on the Pakua Tableland, Taiwan
by: Chin-Chiang Hsu, et al.
Published: (2021-04-01) -
The Impact of Soil Erosion on the Spatial Distribution of Soil Characteristics and Potentially Toxic Element Contents in a Sloping Vineyard in Tállya, Ne Hungary
by: Manaljav Samdandorj, et al.
Published: (2021-04-01)