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Article

National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach

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Manaaki Whenua—Landcare Research, Private Bag 11052, Manawatū Mail Centre, Palmerston North 4442, New Zealand
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Te Pūnaha Matatini, A New Zealand Centre of Research Excellence, Private Bag 92019, Auckland 1142, New Zealand
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Manaaki Whenua—Landcare Research, P.O. Box 69040, Lincoln 7640, New Zealand
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Manaaki Whenua—Landcare Research, P.O. Box 10, Wellington 6143, New Zealand
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Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 2872; https://doi.org/10.3390/rs12182872
Received: 11 August 2020 / Revised: 28 August 2020 / Accepted: 1 September 2020 / Published: 4 September 2020
(This article belongs to the Special Issue Digital Mapping in Dynamic Environments)
Understanding the spatial variation of soil pH is critical for many different stakeholders across different fields of science, because it is a master variable that plays a central role in many soil processes. This study documents the first attempt to map soil pH (1:5 H2O) at high resolution (100 m) in New Zealand. The regression framework used follows the paradigm of digital soil mapping, and a limited number of environmental covariates were selected using variable selection, before calibration of a quantile regression forest model. In order to adapt the outcomes of this work to a wide range of different depth supports, a new approach, which includes depth of sampling as a covariate, is proposed. It relies on data augmentation, a process where virtual observations are drawn from statistical populations constructed using the observed data, based on the top and bottom depth of sampling, and including the uncertainty surrounding the soil pH measurement. A single model can then be calibrated and deployed to estimate pH a various depths. Results showed that the data augmentation routine had a beneficial effect on prediction uncertainties, in particular when reference measurement uncertainties are taken into account. Further testing found that the optimal rate of augmentation for this dataset was 3-fold. Inspection of the final model revealed that the most important variables for predicting soil pH distribution in New Zealand were related to land cover and climate, in particular to soil water balance. The evaluation of this approach on those validation sites set aside before modelling showed very good results (R2=0.65, CCC=0.79, RMSE=0.54), that significantly out-performed existing soil pH information for the country. View Full-Text
Keywords: digital soil mapping; soil pH; data augmentation; quantile regression forest digital soil mapping; soil pH; data augmentation; quantile regression forest
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MDPI and ACS Style

Roudier, P.; Burge, O.R.; Richardson, S.J.; McCarthy, J.K.; Grealish, G.; Ausseil, A.-G. National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach. Remote Sens. 2020, 12, 2872. https://doi.org/10.3390/rs12182872

AMA Style

Roudier P, Burge OR, Richardson SJ, McCarthy JK, Grealish G, Ausseil A-G. National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach. Remote Sensing. 2020; 12(18):2872. https://doi.org/10.3390/rs12182872

Chicago/Turabian Style

Roudier, Pierre, Olivia R. Burge, Sarah J. Richardson, James K. McCarthy, Gerard J. Grealish, and Anne-Gaelle Ausseil. 2020. "National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach" Remote Sensing 12, no. 18: 2872. https://doi.org/10.3390/rs12182872

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