[1509] - Improvement of soil respiration estimates by mid-infrared spectroscopy

All available metadata of the dataset is listed below. Some features are available, e.g. download of dataset or additional description file.

By downloading files from this dataset you accept the license terms of TR32DB Data policy agreement and TR32DBData Protection Statement.
Adequate reference when this dataset will be discussed or used in any publication or presentation is mandatory. In this case please contact the dataset creator.
VERY IMPORTANT!
Due to the speed of the filesystem and depending on the size of the archive and the file to be extracted, it may take up to thirty (!) minutes until a download is ready! Beware of that when confirming since you may not close the tab because otherwise, you will not get your file!
Features
Citation
Meyer, N., Welp, G., Amelung, W., 2016. Improvement of soil respiration estimates by mid-infrared spectroscopy. PhD Report, INRES-Soil science, Bonn. Accessed from https://www.tr32db.uni-koeln.de/data.php?dataID=1509 at 2019-07-20.
Citation Options
Export as: Select the file format for your download.Citation style: Select the displayed citation style.
Identification
Title(s):Main Title: Improvement of soil respiration estimates by mid-infrared spectroscopy
Subtitle: 3rd PhD report
Description(s):Abstract: Semi-annual PhD report. Spatial patterns of soil respiration (SR) and their sensitivity to temperature (Q10) are one of the key uncertainties in climate change research but their assessment is too time-consuming to conduct research on large data sets. Here, we investigate the potential of mid-infrared spectroscopy (MIRS) coupled with partial-least square regression (PLSR) to predict SR and Q10 values for 108 soil samples of diverse land use types. Predictions of SR delivered excellent accuracy for the entire data set (R² = 0.90). The prediction accuracy of the Q10 value was excellent for a cropland submodel (R² = 0.88), and acceptable for a grassland submodel (R² = 0.66), but poor accuracy was obtained by a general model including the entire data set (R² = 0.23). In a second step, we showed that SR at any temperature and soil moisture level can be directly predicted by a random forests model which includes the MIRS spectra, temperature, soil moisture, and land use as predictor variables (R² = 0.81). Although our approach to assess soil respiration still requires soil sampling, the analytical time is largely reduced compared to soil respiration measurements at various temperatures and soil moisture levels.
Responsible Party
Creator(s):Author: Nele Meyer
Author: Gerd Welp
Author: Wulf Amelung
Publisher:CRC/TR32 Database (TR32DB)
Topic
TR32 Topic:Soil
Related Sub-project(s):B3
Subject(s):CRC/TR32 Keywords: Soil CO2 efflux, Soil Respiration, Soil, Soil Temperature
File Details
File Name:Meyer_PhDReport.pdf
Data Type:Text
File Size:453 kB (0.442 MB)
Date(s):Created: 2016-09-30
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:In Process
Constraints
Download Permission:OnlyTR32
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:TR32DB Data policy agreement
Geographic
North:-no map data
East:-
South:-
West:-
Measurement Region:RurCatchment
Measurement Location:--RurCatchment--
Specific Informations - Report
Report Date:30th of September, 2016
Report Type:PhD Report
Report City:Bonn
Report Institution:INRES-Soil science
Metadata Details
Metadata Creator:Nele Meyer
Metadata Created:2016-10-11
Metadata Last Updated:2017-07-26
Subproject:B3
Funding Phase:3
Metadata Language:English
Metadata Version:V42
Dataset Metrics
Page Visits:393
Metadata Downloads:0
Dataset Downloads:1
Dataset Activity
Features