Elsevier

Geoderma

Volume 323, 1 August 2018, Pages 31-40
Geoderma

Soil respiration and its temperature sensitivity (Q10): Rapid acquisition using mid-infrared spectroscopy

https://doi.org/10.1016/j.geoderma.2018.02.031Get rights and content
Under a Creative Commons license
open access

Highlights

  • 124 soil samples were taken from different soil- and land use types.

  • Soil respiration (SR) was measured at 4 soil moisture levels and 5 temperatures.

  • SR at standardized temperature and moisture is predictable by MIRS.

  • Q10 is predictable by MIRS for croplands and grasslands.

  • MIRS-Random Forest allows predicting SR across various weather conditions.

Abstract

Spatial patterns of soil respiration (SR) and its sensitivity to temperature (Q10) are one of the key uncertainties in climate change research but since their assessment is very time-consuming, large data sets can still not be provided. Here, we investigated the potential of mid-infrared spectroscopy (MIRS) to predict SR and Q10 values for 124 soil samples of diverse land use types taken from a 2868 km2 catchment (Rur catchment, Germany/Belgium/Netherlands). Soil respiration at standardized temperature (25 °C) and soil moisture (45% of maximum water holding capacity, WHC) was successfully predicted by MIRS coupled with partial least square regression (PLSR, R2 = 0.83). Also the Q10 value was predictable by MIRS-PLSR for a grassland submodel (R2 = 0.75) and a cropland submodel (R2 = 0.72) but not for forested sites (R2 = 0.03). In order to provide soil respiration estimates for arbitrary conditions of temperature and soil moisture, more flexible models are required that can handle nonlinear and interacting relations. Therefore, we applied a Random Forest model, which includes the MIRS spectra, temperature, soil moisture, and land use as predictor variables. We could show that SR can be simultaneously predicted for any temperature (5–25 °C) and soil moisture level (30–75% of WHC), indicated by a high R2 of 0.73. We conclude that the combination of MIRS with sophisticated statistical prediction tools allows for a novel, rapid acquisition of SR and Q10 values across landscapes and thus to fill an important data gap in the validation of large scale carbon modeling.

Keywords

Heterotrophic soil respiration
Environmental soil classes
PLSR
Random Forest

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