Next Article in Journal
Global Land Cover Mapping: A Review and Uncertainty Analysis
Next Article in Special Issue
Land Surface Temperature Retrieval Using Airborne Hyperspectral Scanner Daytime Mid-Infrared Data
Previous Article in Journal
Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV
Previous Article in Special Issue
An Assessment of Polynomial Regression Techniques for the Relative Radiometric Normalization (RRN) of High-Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2014, 6(12), 12055-12069; doi:10.3390/rs61212055

First Results of Estimating Surface Soil Moisture in the Vegetated Areas Using ASAR and Hyperion Data: The Chinese Heihe River Basin Case Study

1
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
2
Research Center of Flood and Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
*
Author to whom correspondence should be addressed.
Received: 30 July 2014 / Revised: 5 November 2014 / Accepted: 7 November 2014 / Published: 3 December 2014
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
View Full-Text   |   Download PDF [2448 KB, uploaded 3 December 2014]   |  

Abstract

This study introduces a new approach to estimate surface soil moisture in vegetated areas using Synthetic Aperture Radar (SAR) and hyperspectral data. To achieve this, the Michigan Microwave Canopy Scattering (MIMICS) model was initially used to simulate backscatter from vegetated surfaces containing various canopy water contents, across three frequency bands (i.e., L, S, and C). Using this simulated dataset, the influence of the canopy water content on the backscattered signals was further analyzed. In addition, we developed a modified Water-Cloud model which adds in the crown-ground interaction term. Finally, a soil moisture retrieval model for an agricultural region was developed. Alternating polarization data with ASAR and Hyperion hyperspectral data were used to retrieve soil moisture and validate the feasibility of the retrieval model. The field measured data from the Heihe river basin was used to confirm the proposed model. Results revealed an average absolute deviation (AAD) and average absolute relative deviation (AARD) of 0.051 cm3∙cm−3 and 19.7%, respectively, between the estimated soil moisture and the field measurements. View Full-Text
Keywords: advanced integrated equation model (AIEM); ASAR; Hyperion; michigan microwave canopy scattering (MIMICS); surface soil moisture; water-cloud model advanced integrated equation model (AIEM); ASAR; Hyperion; michigan microwave canopy scattering (MIMICS); surface soil moisture; water-cloud model
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Song, X.; Ma, J.; Li, X.; Leng, P.; Zhou, F.; Li, S. First Results of Estimating Surface Soil Moisture in the Vegetated Areas Using ASAR and Hyperion Data: The Chinese Heihe River Basin Case Study. Remote Sens. 2014, 6, 12055-12069.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top