Soil moisture estimation under vegetation from PALSAR FBD data by means of polarimetric decomposition techniques

This page lists the DOI metadata that was entered for this dataset. You can download the dataset.

The full metadata can be seen here.

Feature

Citation

Citation Options

Identification

*Title:*	Main Title: Soil moisture estimation under vegetation from PALSAR FBD data by means of polarimetric decomposition techniques
*Descriptions:*	Abstract: The disturbing effects caused by vegetation and surface roughness are major impediments to accurate quantitative retrievals of soil moisture from Synthetic Aperture Radar (SAR). With most of the operational spaceborne systems it is not possible to separate the different scattering contributions of the soil and vegetation components. In this study we use the coherent-on-receive dual-pol standard acquisitions (FBD343) of Phased Array type L-band Synthetic Aperture Radar (PALSAR) aboard the Advanced Land Observing Satellite (ALOS 'Daichi') acquired over an arable land test site in Western Germany. By applying a PolSAR decomposition technique, namely the H/A/Alpha decomposition, we exploit the phase information to increase the amount of observables. The potential to derive information on biomass and surface roughness from the dual-pol data is investigated based on correlation analyses between PALSAR observables and in-situ measurements. High sensitivities towards surface roughness and crop biomass could be ascertained. Using these findings, we estimate surface roughness ks and sugar beet total wet weight with RMS errors of 0.11 and 2.66 kg/m?, respectively. The good quality of the estimates allows correcting the backscattering coefficients for the surface roughness and vegetation effects. The accuracy of soil moisture retrievals could be increased from 4.5 to 3.6 Vol.-% using the roughness correction for bare soil and from > 10.0 to 3.6 Vol.-% using the biomass correction for sugar beet. The results give a promising outlook in terms of the possibility to develop an operational soil moisture retrieval model for PALSAR data collected in the Fine Beam Dual Polarization (FBD) mode. Series Information: Proceedings on the Workshop of Remote Sensing Methods for Change Detection and Process Modelling, 18-19 November 2010, University of Cologne, Germany, Kölner Geographische Arbeiten, 92, pp. 63-70
*Identifier:*	10.5880/TR32DB.KGA92.9 (DOI)
*Related Resource:*	Is Part Of 0454-1294 (ISBN)

Responsible Party

*Creators:*	Christian N. Koyama (Author), Peter Fiener (Author), Karl Schneider (Author)
*Contributors:*	Victoria Lenz-Wiedemann (Editor), Georg Bareth (Editor), Transregional Collaborative Research Centre 32 (Meteorological Institute, University of Bonn) (Data Manager), University of Cologne (Regional Computing Centre (RRZK)) (Hosting Institution)
*Publisher:*	Geographisches Institut der Universität zu Köln - Kölner Geographische Arbeiten
*Publication Year:*	2011

Topic

Subjects:

Keywords: ALOS, PALSAR, Polarimetry, Soil Moisture, Vegetation, Surface Roughness

File Details

*Filename:*	Koyama_et_al_2011_KGA92.pdf
*Data Type:*	Text - Book Section
*Sizes:*	1686 Kilobytes 8 Pages
*File Size:*	1.6 MB
*Dates:*	Created: 18.11.2010 Issued: 05.10.2010
*Mime Type:*	application/pdf
*Language:*	English

Constraints

*Download Permission:*	Free
*Licence:*	[Creative Commons] Attribution 4.0 International (CC BY 4.0)

Geographic

Metadata Details

*Metadata Creator:*	Constanze Curdt
*Metadata Created:*	05.08.2013
*Metadata Last Updated:*	11.05.2021
*Subproject:*	C3
*Funding Phase:*	2
*Metadata Language:*	English
*Metadata Version:*	V50

Metadata Export

Metadata Schema:

Dataset Statistics

*Page Visits:*	1277
*Metadata Downloads:*	0
*Dataset Downloads:*	18

Dataset Activity

Feature

Download

top ∧