Transferability and scaling of soil total carbon prediction models in Florida
Author and article information
Abstract
The applicability, transfer, and scalability of visible/near-infrared (VNIR)-derived soil models are still poorly understood. The objectives of this study in Florida, U.S. were to: (i) compare three methods to predict soil total carbon (TC) using five fields (local scale) and a pooled (regional scale) VNIR spectral dataset, (ii) assess the model’s transferability among fields, and (iii) evaluate the up- and down-scaling behavior of TC prediction models. A total of 560 TC-spectral sets were modeled by Partial Least Square Regression (PLSR), Support Vector Machine (SVM), and Random Forest. The transferability and up- and down-scaling of models were limited by the following factors: (i) the spectral data domain, (ii) soil attribute domain, (iii) methods that describe the internal model structure of VNIR-TC relationships, and (iv) environmental domain space of attributes that control soil carbon dynamics. All soil logTC models showed excellent performance based on all three methods with R2 > 0.86, bias < 0.01%, root mean square prediction error (RMSE) = 0.09%, residual predication deviation (RPD) > 2.70% , and ratio of prediction error to inter-quartile range (RPIQ) > 4.54. PLSR performed substantially better than SVM to scale and transfer models. Upscaled soil TC models performed somewhat better in terms of model fit (R2), RPD, and RPIQ, whereas downscaled models showed less bias and smaller RMSE based on PLSR. Given the many factors that can impinge on empirically derived soil spectral prediction models, as demonstrated by this study, more focus on the applicability and scaling of them is needed.
Cite this as
2014. Transferability and scaling of soil total carbon prediction models in Florida. PeerJ PrePrints 2:e494v1 https://doi.org/10.7287/peerj.preprints.494v1Sections
Additional Information
Competing Interests
The authors declare they have no competing interests.
Author Contributions
Sabine Grunwald conceived and designed the experiments, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Congrong Yu performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables.
Xiong Xiong performed the experiments, contributed reagents/materials/analysis tools, prepared figures and/or tables.
Field Study Permissions
The following information was supplied relating to field study approvals (i.e., approving body and any reference numbers):
· Ordway-Swisher: OSR-11-10 (Ordway Swisher Biological Station, University of Florida)
· Econfina: University of Florida property (author team works for the University of Florida and the university owns this property which was used for this research).
· San Felasco State Park: 12131110 (Dept. of Environmental Protection)
· Myakka River State Park: 12131110 (Dept. of Environmental Protection)
· Santa Fe River Ranch (Beef cattle station): University of Florida property (author team works for the University of Florida and the university owns this property which was used for this research).
Data Deposition
The following information was supplied regarding the deposition of related data:
Terrestrial Carbon Information System
(http://terraC.ifas.ufl.edu)
Funding
Dr. Grunwald's Pedometrics, Landscape Analysis, Geographic Information Systems Laboratory provided the support for this study.
