3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning. Issue 1 (4th September 2020)

• Record Type: Journal Article
• Authors: Rentschler, Tobias; Werban, Ulrike; Ahner, Mario; Behrens, Thorsten; Gries, Philipp; Scholten, Thomas; Teuber, Sandra; Schmidt, Karsten
• Is Part Of: Vadose zone journal. Volume 19:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-09-04
• Subjects: Soil science -- Periodicals; Zone of aeration -- Periodicals; Groundwater flow -- Periodicals; Groundwater flow; Zone of aeration; Periodicals; Electronic journals; 631.4
• DOI: 10.1002/vzj2.20062
• Languages: English
• ISSNs: 1539-1663

Abstract

Abstract: Soil organic C (SOC) and soil moisture (SM) affect the agricultural productivity of soils. For sustainable food production, knowledge of the horizontal as well as vertical variability of SOC and SM at field scale is crucial. Machine learning models using depth‐related data from multiple electromagnetic induction (EMI) sensors and a gamma‐ray spectrometer can provide insights into this variability of SOC and SM. In this work, we applied weighted conditioned Latin hypercube sampling to calculate 25 representative soil profile locations based on geophysical measurements on the surveyed agricultural field, for sampling and modeling. Ten additional random profiles were used for independent model validation. Soil samples were taken from four equal depth increments of 15 cm each. These were used to approximate polynomial and exponential functions to reproduce the vertical trends of SOC and SM as soil depth functions. We modeled the function coefficients of the soil depth functions spatially with Cubist and random forests with the geophysical measurements as environmental covariates. The spatial prediction of the depth functions provides three‐dimensional (3D) maps of the field scale. The main findings are (a) the 3D models of SOC and SM had low errors; (b) the polynomial function provided better results than the exponential function, as the vertical trends of SOC and SM did not decrease uniformly; and (c) the spatial prediction of SOC and SM with Cubist provided slightly lower error than with random forests. Hence, we recommend modeling the second‐degree polynomial with Cubist for 3D prediction of SOC and SM at field scale. Core Ideas: Multi‐depth ECa and gamma‐ray spectrometry describe vertical trends of SOC and soil moisture. Machine learning models can predict vertical trends of SOC and soil moisture spatially. Cubist models of polynomial depth functions provide accurate 3D maps at field scale.