TR32-Database: Database of Transregio 32

[1037] - Biomass-Corrected Quantitative Soil Moisture Estimation from Dual Polarimetric ALOS PALSAR Data

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Koyama, C. N., Schneider, K., 2014. Biomass-Corrected Quantitative Soil Moisture Estimation from Dual Polarimetric ALOS PALSAR Data. IEEE Transactions on Geoscience and Remote Sensing, 1 - 56.
Title(s):Main Title: Biomass-Corrected Quantitative Soil Moisture Estimation from Dual Polarimetric ALOS PALSAR Data
Description(s):Abstract: Up to date there is still no easy-to-use operational retrieval model available which allows robust quantitative estimation of soil moisture under vegetation by means of widely available EO SAR data. To improve soil moisture retrieval procedures for the next generation of high-resolution, low frequency SAR satellite missions ALOS-2 and SAOCOM-1A/B, the use of their polarimetric capabilities is essential. However, frequent quad-polarization PolSAR coverage of the earth surface is due to current downlink limitations still not possible. In this paper, we introduce dual-polarization L-band SAR soil moisture retrieval algorithms for three different land cover types, i.e. bare soil, sugar beet, and winter wheat. The simple semi-empirical soil moisture model aims not only for accurate quantitative soil moisture estimations for a wide range of surface roughness states and vegetation conditions, but in addition, allows accurately deriving mv at high spatial resolution rendering remote sensing of within-field spatial heterogeneities possible. Key for the development of this novel approach is the coherent-on-receive dual polarimetry mode (FBD) of ALOS PALSAR. By applying dual polarimetric H/alpha decomposition, the polarization information is exploited to increase the amount of radar observables. The potential to derive information on biomass and surface roughness is investigated based on correlation analyses. We found high correlations between PALSAR observables and in situ measurements of surface soil moisture, surface roughness, and crop biomass of sugar beet and winter wheat. Using these empirical equations, surface roughness ks was estimated with a RMS error of 0.11. Sugar beet total fresh weight and winter wheat above-ground fresh weight were estimated with RMS errors of 2.7 kg/m² and 0.8 kg/m², respectively. The quality of the estimates allows correcting the horizontally co-polarized backscattering coefficients for the surface roughness and vegetation effects. The accuracy of soil moisture retrievals improves from 4.5 to 3.6 Vol.-% using the roughness correction for bare soil and from >10.0 to 4.2 and 3.9 Vol.-% using the biomass corrections for sugar beet and winter wheat, respectively. These significant improvements satisfy the requirements for hydrological and meteorological applications and give a promising outlook for operational high-resolution soil moisture retrieval in the upcoming sensor generation.
Responsible Party
Creator(s):Author: Christian N. Koyama
Author: Karl Schneider
TR32 Topic:Remote Sensing
Subject(s):CRC/TR32 Keywords: ALOS, Soil Moisture
Topic Category:Enviroment
File Details
File Name:Koyama_and_Schneider_2014_submitted_to_IEEE_Transactions.pdf
Data Type:Text
File Size:6978 kB (6.814 MB)
Date(s):Available: 2014-09-08
Mime Type:application/pdf
Data Format:PDF
Status:In Process
Download Permission:OnlyOwnSubproject
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
North:-no map data
Measurement Region:RurCatchment
Measurement Location:--RurCatchment--
Specific Informations - Publication
Article Type:Journal
Source:IEEE Transactions on Geoscience and Remote Sensing
Page Range:1 - 56
Metadata Details
Metadata Creator:Sabrina Esch
Metadata Created:2014-09-08
Metadata Last Updated:2014-09-08
Funding Phase:2
Metadata Language:English
Metadata Version:V40
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Dataset Downloads:2
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