Extended model prediction of high-resolution soil organic matter over a large area using limited number of field samples
•A two-stage approach was developed to extend model prediction of soil organic matter.•The local SOM model had high model performance but weak capability of generalization.•Building a stable extended model only required 20% of 386 field samples.•The optimal extended model improved model accuracy wit...
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Veröffentlicht in: | Computers and electronics in agriculture 2020-02, Vol.169, p.105172, Article 105172 |
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Zusammenfassung: | •A two-stage approach was developed to extend model prediction of soil organic matter.•The local SOM model had high model performance but weak capability of generalization.•Building a stable extended model only required 20% of 386 field samples.•The optimal extended model improved model accuracy with 28–29% of R2.
Detailed soil organic matter (SOM) spatial distribution maps are essential for soil management and forestry operations. However, mapping of spatial SOM distribution over a large area is a difficult challenge, especially in regions where field samples are difficult to obtain. The objective of this research was to develop a two-stage approach to map SOM content with 10 m-resolution in Yunfu, South China with an area of 7785 km2. In the first stage, using 10-fold cross-validation 511 artificial neural network (ANN) models were built to map SOM content based on 318 field samples from three of five sub-areas of Yunfu (ANN model area). Results indicated that the optimal ANN model with six DEM-derived variables as model inputs, i.e. ANN6, had a good model performance in ANN model area, 5.6 g/kg of root mean squared error (RMSE), 0.81 of R2, and 84.1% of relative overall accuracy (ROA) ± 10%, and the best generalization capability in the rest two of five sub-areas of Yunfu (extended model area), with 7.7 g/kg of RMSE, 0.58 of R2, and 60.7% of ROA ± 10%. In the second stage, using the reverse k-fold cross-validation extended models were developed to adapt ANN6-produced SOM content to fit field samples in the extended model areas. Results indicated the optimal extended model only required 20% of 386 field samples (5-fold) to build a stable and significant linear relationship between ANN6-produced SOM content and measured SOM content from the extended model area, and improved model accuracy with 9–21% of RMSE, 28–29% of R2, and 6–21% of ROA ± 10%. Thus, the two-stage method is a viable way to generate SOM content over a large area with limited number of field samples. |
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ISSN: | 0168-1699 1872-7107 |
DOI: | 10.1016/j.compag.2019.105172 |