Digital mapping of soil organic matter and cation exchange capacity in a low relief lanscape using LiDAR data

Authors: RAHMANI, SHAMS - Purdue University; ACKERSON, JASON - Purdue University; SCHULZE, DARRELL - Purdue University; Adhikari, Kabindra; Libohova, Zamir

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/30/2022
Publication Date: 5/31/2022
Citation: Rahmani, S.R., Ackerson, J., Schulze, D., Adhikari, K., Libohova, Z. 2022. Digital mapping of soil organic matter and cation exchange capacity in a low relief lanscape using LiDAR data. Agronomy Journal. https://doi.org/10.3390/agronomy12061338.
DOI: https://doi.org/10.3390/agronomy12061338

Interpretive Summary: Soil organic matter (SOM) and cation exchange capacity (CEC) are important for soil health. Accurate and detailed maps of OM and CEC are needed for precision farm management. The current maps from USDA Soil Survey Geographic (SSURGO) data do not support precision farm management. Using high resolution elevation maps in combination with topography and soil analysis, detailed maps of SOM and CEC were produced. The maps were like SSURGO but provided more details on the distribution of SOM and CEC at field level. This study was conducted at Purdue University, Agronomy Center for Research and Education (ACRE) and can be applied to USDA-ARS Research centers.

Technical Abstract: Soil organic matter content (OM) and cation exchange capacity (CEC) are important agronomic and edaphic soil properties. Accurate, high-resolution spatial information of OM and CEC are needed for high-resolution farm management and agronomic research. The objectives of this study were to: 1) map soil OM and CEC in a low relief area using only lidar elevation-based terrain attributes, and 2) compare the prediction accuracy of OM and CEC maps created by universal kriging (UK), Cubist, and random forest (RF). For this study, 174 soil samples were collected from 0 to 10 cm depth. The topographic wetness index (TWI), topographic position index (TPI), multi resolution valley bottom flatness (MrVBF), and multi resolution ridge top flatness indices (MrRTF) generated from the lidar data were used as covariates in model predictions. Based on an independent evaluation, no major differences were found in the prediction performance of all selected models. For OM, the predictive models provide results with r2 (0.44 – 0.45), RMSE (0.8 – 0.83 %), bias (0 – 0.22 %), and concordance (0.56 – 0.58). For CEC, the r2 ranged from 0.39 – 0.44, RMSE ranged from 3.62 – 3.74 cmolc kg-1, bias ranged from 0 – 0.17 cmolc kg-1, and concordance ranged from 0.55 – 0.57. We also compared the results to the USDA Soil Survey Geographic (SSURGO) data. For both OM and CEC, SSURGO was comparable with our predictive models, except for few map units where both OM and CEC were either higher or lower than predictive models.