TR32-Database: Database of Transregio 32

[602] - Estimation and validation of Leaf Area Index time series for crops on 5m scale from space

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

Features
Citation
Ali, M., 2012. Estimation and validation of Leaf Area Index time series for crops on 5m scale from space. PhD Report, Institute of Bio and Geosciences Agrosphere (IBG-3), Research Center Jülich, Jülich, Germany. Accessed from http://www.tr32db.uni-koeln.de/data.php?dataID=602 at 2017-10-23.
Identification
Title(s):Main Title: Estimation and validation of Leaf Area Index time series for crops on 5m scale from space
Description(s):Abstract: Time series of Leaf Area Index (LAI) is of utmost importance for various disciplines of bio and geosciences where satellite remote sensing makes LAI estimation possible for large areas in less time and resources. Remote sensing LAI, validated against in situ LAI, is used as base for calculating LAI for large areas e.g. on catchment scale. To have an in situ LAI reference for satellite data, two different LAI meters i.e. LAI2200 and delta-T SunScan and one destructive method was used at different places inside winter wheat fields at two different location in the vicinity of Rur river. To test the impact of radiometric normalization for calculating vegetation indices on a time series of satellite images, pre and post radiometric normalization LAI was compared. Prior to vegetation indices calculation, radiometric normalization was applied to the time series of RapidEye data but here is no sufficient improvement in correlation coefficient between remote sensing based LAI and destructive LAI except than smoothening the LAI temporal plot and removing invalid below zero LAI values. Utilization of the unique red-edge spectral band (0.690 – 0.730 μm) in several vegetation indices was also tested in this paper for accurate estimation of LAI, but it did not show any promising results. SAVI (Soil Adjusted vegetation Index) presents good correlation between remote sensing based predicted LAI against destructive LAI for two test sites in winter wheat fields (for Selhausen R² = 0.73 and for Merzenhausen R² = 0.97). More precise and high resolution estimation of LAI for large areas is of vital importance for improving hydrological models and soil moisture estimation in radiometric transfer models.
Responsible Party
Creator(s):Author: Muhammad Ali
Publisher:CRC/TR32 Database (TR32DB)
Topic
TR32 Topic:Remote Sensing
Subject(s):CRC/TR32 Keywords: PhD Report
File Details
File Name:Report2_Ali_2012.pdf
Data Type:Text
File Size:658 kB (0.643 MB)
Date(s):Available: 2012-12-31
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:OnlyTR32
General Access and Use Conditions:For internal use only.
Access Limitations:For internal use only.
Licence:TR32DB Data policy agreement
Geographic
North:51.5915435
East:7.4236328
South:50.2058789
West:5.2263672
Measurement Region:NorthRhine-Westphalia
Measurement Location:--NorthRhine-Westphalia--
Specific Informations - Report
Report Date:31st of December, 2012
Report Type:PhD Report
Report City:Jülich, Germany
Report Institution:Institute of Bio and Geosciences Agrosphere (IBG-3), Research Center Jülich
Number Of Pages:24
Period of Pages:1 - 24
Further Informations:TR32 Student Report Phase II
Metadata Details
Metadata Creator:Muhammad Ali
Metadata Created:2013-12-04
Metadata Last Updated:2013-12-04
Subproject:C6
Funding Phase:2
Metadata Language:English
Metadata Version:V40
Dataset Metrics
Page Visits:164
Metadata Downloads:0
Dataset Downloads:3
Dataset Activity
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