TR32-Database: Database of Transregio 32

[1480] - Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany)

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., Montzka, C., Stadler, A., Vereecken, H., 2015. Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany). Remote Sensing, 7 (3), 2808 - 2831. DOI: 10.3390/rs70302808.
Identification
Title(s):Main Title: Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany)
Description(s):Abstract: Leaf Area Index (LAI) is an important variable for numerous processes in various disciplines of bio- and geosciences. In situ measurements are the most accurate source of LAI among the LAI measuring methods, but the in situ measurements have the limitation of being labor intensive and site specific. For spatial-explicit applications (from regional to continental scales), satellite remote sensing is a promising source for obtaining LAI with different spatial resolutions. However, satellite-derived LAI measurements using empirical models require calibration and validation with the in situ measurements. In this study, we attempted to validate a direct LAI retrieval method from remotely sensed images (RapidEye) with in situ LAI (LAIdestr). Remote sensing LAI (LAIrapideye) were derived using different vegetation indices, namely SAVI (Soil Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index). Additionally, applicability of the newly available red-edge band (RE) was also analyzed through Normalized Difference Red-Edge index (NDRE) and Soil Adjusted Red-Edge index (SARE). The LAIrapideye obtained from vegetation indices with red-edge band showed better correlation with LAIdestr (r = 0.88 and Root Mean Square Devation, RMSD = 1.01 & 0.92). This study also investigated the need to apply radiometric/atmospheric correction methods to the time-series of RapidEye Level 3A data prior to LAI estimation. Analysis of the the RapidEye Level 3A data set showed that application of the radiometric/atmospheric correction did not improve correlation of the estimated LAI with in situ LAI.
Identifier(s):DOI: 10.3390/rs70302808
Citation Advice:Ali, M.; Montzka, C.; Stadler, A.; Menz, G.; Thonfeld, F.; Vereecken, H. Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany). Remote Sens. 2015, 7, 2808-2831.
Responsible Party
Creator(s):Author: Muhammad Ali
Author: Carsten Montzka
Author: Anja Stadler
Author: Harry Vereecken
Contributor(s):Researcher: Gunter Menz
Researcher: Frank Thonfeld
Publisher:MDPI, Basel, Switzerland
Topic
TR32 Topic:Remote Sensing
Related Sub-project(s):C6
Subject(s):CRC/TR32 Keywords: LAI, Remote Sensing, Vegetation Index
File Details
File Name:remotesensing_Ali_et_al_2015.pdf
Data Type:Text
File Size:19737 kB (19.274 MB)
Date(s):Available: 2015-03-10
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:Completed
Constraints
Download Permission:Free
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Geographic
North:51.1526149
East:6.8303711
South:50.4583452
West:5.7317383
Measurement Region:RurCatchment
Measurement Location:--RurCatchment--
Specific Informations - Publication
Status:Published
Review:PeerReview
Year:2015
Type:Article
Article Type:Journal
Source:Remote Sensing
Issue:3
Volume:7
Page Range:2808 - 2831
Metadata Details
Metadata Creator:Muhammad Ali
Metadata Created:2016-07-02
Metadata Last Updated:2016-07-02
Subproject:C6
Funding Phase:2
Metadata Language:English
Metadata Version:V41
Dataset Metrics
Page Visits:276
Metadata Downloads:0
Dataset Downloads:7
Dataset Activity
Features