• Submit to Sensors Login Register
• MDPI
• Journals A-Z
• For Authors
• For Editors
• About
• Open Access Policy

• Abstract
• Full-Text PDF [300 KB]

• Article Statistics
• Google Scholar
• Order Reprints

• Cite this article
• Export to BibTeX
• Export to EndNote

• [+] on DOAJ
• Choi, M
• Jacobs, JM.
• [+] on Google Scholar
• Choi, M
• Jacobs, JM.
• [+] on PubMed
• Choi, M
• Jacobs, JM.

•  CiteULike
•  Facebook
•  Mendeley
•  Twitter

This article is

• freely available
• re-usable

Sensors 2008, 8(4), 2617-2627; doi:10.3390/s8042617

Article

Temporal Variability Corrections for Advanced Microwave Scanning Radiometer E (AMSR-E) Surface Soil Moisture: Case Study in Little River Region, Georgia, U.S.

Minha Choi ^1,*  and Jennifer M. Jacobs ²

¹ Research Physical Scientist, USDA-ARS Hydrology & Remote Sensing Lab., Beltsville, MD 20705, U.S.A. ² Associate Professor of Civil Engineering, University of New Hampshire, Durham, NH 03824, U.S.A

* Author to whom correspondence should be addressed.

Received: 3 December 2007 / Accepted: 8 April 2008 / Published: 14 April 2008

(This article belongs to the Special Issue Remote Sensing of Land Surface Properties, Patterns and Processes)

Download PDF Full-Text [300 KB, uploaded 16 September 2008 11:02 CEST]

Abstract: Statistical correction methods, the Cumulative Distribution Function (CDF) matching technique and Regional Statistics Method (RSM) are applied to adjust the limited temporal variability of Advanced Microwave Scanning Radiometer E (AMSR-E) data using the Common Land Model (CLM). The temporal variability adjustment between CLM and AMSR-E data was conducted for annual and seasonal periods for 2003 in the Little River region, GA. The results showed that the statistical correction techniques improved AMSR-E’s limited temporal variability as compared to ground-based measurements. The regression slope and intercept improved from 0.210 and 0.112 up to 0.971 and -0.005 for the non-growing season. The R² values also modestly improved. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) products were able to identify periods having an attenuated microwave brightness signal that are not likely to benefit from these statistical correction techniques.

Keywords: Temporal variability; Statistical correction; Advanced Microwave Scanning Radiometer E (AMSR-E); Common Land Model (CLM); Leaf Area Index (LAI)

Article Statistics

Cite This Article