Soil moisture index from ERS-SAR and its application to the analysis of spatial patterns in agricultural areas

This page lists all metadata that was entered for this dataset. Only registered users of the TR32DB may download this file.

Feature
Request downloadRequest download
Full Name:
Affiliation:
eMail:
Purpose of use:
 
Bot check:
Type all characters with this
color
.
 
It is case sensitive.
 
 
 
Submit
Citation
Citation Options
Identification
Title:Main Title: Soil moisture index from ERS-SAR and its application to the analysis of spatial patterns in agricultural areas
Description:Abstract: Soil moisture is an important factor influencing hydrological and meteorological exchange processes at the land surface. Synthetic Aperture Radar (SAR) backscatter is strongly affected by the volumetric soil moisture content, thus providing the potential to derive spatially distributed soil moisture information. Archives of historic SAR data exist which use is limited by the lack of corresponding ground truth measurements. This study analyses the potential of using a soil moisture index (SMI) with high spatial resolution to assess the soil moisture status in the absence of ground truth data. The index method is applied to agricultural areas in the catchment of the river Rur in Germany. The SMI was evaluated using antecedent precipitation and the wetting and drying behaviour. The spatial patterns of the SMI were analysed using semivariograms. This study confirms the applicability of a high resolution soil moisture index for monitoring near surface soil moisture changes, to analyse soil moisture patterns and indicates the possibility to complement antecedent precipitation as an input to hydrological models.
Identifier:10.1117/1.JRS.12.022206 (DOI)
Citation Advice:Esch, S., Korres, W., Reichenau, T.G., Schneider, K. (2018): Soil moisture index from ERS-SAR and its application to the analysis of spatial patterns in agricultural areas. Journal of Applied Remote Sensing (JARS). [accepted for publication]
Responsible Party
Creator:Sabrina Esch (Author)
Contributors:Wolfgang Korres (Related Person), Tim G. Reichenau (Related Person), Karl Schneider (Related Person)
Publisher:SPIE - The international society for optics and photonics
Publication Year:2018
Topic
TR32 Topic:Remote Sensing
Related Subproject:C3
Subjects:Keywords: Soil Moisture, SAR
File Details
Filename:SMI_Esch_2017_TR32_db.pdf
Data Type:Text - Article
File Size:2 MB
Date:Accepted: 15.03.2018
Mime Type:application/pdf
Data Format:PDF
Language:English
Status:In Process
Constraints
Download Permission:Only Project Members
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
Specific Information - Publication
Publication Status:Published
Review Status:Peer reviewed
Publication Type:Article
Article Type:Journal
Source:Journal of Applied Remote Sensing
Source Website:https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing?SSO=1
Issue:2
Volume:12
Number of Pages:23 (1 - 23)
Metadata Details
Metadata Creator:Sabrina Esch
Metadata Created:20.03.2018
Metadata Last Updated:20.03.2018
Subproject:C3
Funding Phase:3
Metadata Language:English
Metadata Version:V50
Metadata Export
Metadata Schema:
Dataset Statistics
Page Visits:732
Metadata Downloads:0
Dataset Downloads:1
Dataset Activity
Feature
A download is not possibleDownload