TR32-Database: Database of Transregio 32

[1482] - Spatial Heterogeneity of Leaf Area Index (LAI) and its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

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

Features
Citation
Reichenau, T. G., Korres, W., Montzka, C., Fiener, P., Wilken, F., Wilken, F., Waldhoff, G., Schneider, K., 2016. Spatial Heterogeneity of Leaf Area Index (LAI) and its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA). PLOS ONE, 11 (7), 1 - 24. DOI: 10.1371/journal.pone.0158451.
Identification
Title(s):Main Title: Spatial Heterogeneity of Leaf Area Index (LAI) and its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)
Description(s):Abstract: The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.
Identifier(s):DOI: 10.1371/journal.pone.0158451
Relation(s):References: DOI: 10.5880/TR32DB.20
References: DOI: 10.5880/TR32DB.21
References: DOI: 10.5880/TR32DB.22
References: DOI: 10.5880/TR32DB.23
References: DOI: 10.5880/TR32DB.7
Responsible Party
Creator(s):Author: Tim G. Reichenau
Author: Wolfgang Korres
Author: Carsten Montzka
Author: Peter Fiener
Author: Florian Wilken
Author: Florian Wilken
Author: Guido Waldhoff
Author: Karl Schneider
Contributor(s):Producer: University of Cologne, Institute of Geography
Producer: Transregional Collaborative Research Centre 32, Meteorological Institute, University of Bonn
Funder: Transregional Collaborative Research Centre 32, Meteorological Institute, University of Bonn
Funding Reference(s):Deutsche Forschungsgemeinschaft (DFG): CRC/TRR 32: Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling and Data Assimilation
Publisher:Public Library of Science
Topic
TR32 Topic:Vegetation
Related Sub-project(s):C3, B5, C6, Z1
Subject(s):CRC/TR32 Keywords: LAI, Crop/s, Agriculture, Winter Wheat, Sugar Beet, Maize, DANUBIA Simulation System, Remote Sensing
Topic Category:Farming
File Details
File Name:reichenau_et_al_2016.PDF
Data Type:Text
Size(s):1 Datasets
File Size:124 kB (0.121 MB)
Date(s):Date Accepted: 2016-06-28
Available: 2016-07-08
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:Free
Download Information:accepted, final version pending
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:Creative Commons Attribution 4.0 International (CC BY 4.0)
Geographic
North:-no map data
East:-
South:-
West:-
Measurement Region:RurCatchment
Measurement Location:Juelicher Boerde
Specific Informations - Publication
Status:Accepted
Review:PeerReview
Year:2016
Type:Article
Article Type:Journal
Source:PLOS ONE
Issue:7
Volume:11
Number Of Pages:24
Page Range:1 - 24
Metadata Details
Metadata Creator:Tim G. Reichenau
Metadata Created:2016-07-04
Metadata Last Updated:2016-09-08
Subproject:C3
Funding Phase:3
Metadata Language:English
Metadata Version:V41
Dataset Metrics
Page Visits:277
Metadata Downloads:0
Dataset Downloads:5
Dataset Activity
Features