Next Article in Journal
Investigation on Accuracies of Real Time Kinematic GPS for GIS Applications
Next Article in Special Issue
Polarimetric Emission of Rain Events: Simulation and Experimental Results at X-Band
Previous Article in Journal
A Better Understanding of Our Earth through Remote Sensing
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2009, 1(1), 3-21;

Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data

NOAA-Cooperative Remote Sensing Science & Technology Center (NOAA-CREST), City University of New York, NY 10031 USA
American University in Dubai P.O. Box: 28282 Dubai, United Arab Emirates
Co-operative Institute for Research in the Atmosphere (CIRA), Colorado State University, Fort Collins, CO 80521 USA
Author to whom correspondence should be addressed.
Received: 20 February 2009 / Revised: 24 March 2009 / Accepted: 25 March 2009 / Published: 27 March 2009
(This article belongs to the Special Issue Microwave Remote Sensing)
Full-Text   |   PDF [658 KB, uploaded 19 June 2014]   |  


Satellite remote sensing observations have the potential for efficient and reliable mapping of spatial soil moisture distributions. However, soil moisture retrievals from active microwave remote sensing data are typically complex due to inherent difficulty in characterizing interactions among land surface parameters that contribute to the retrieval process. Therefore, adequate physical mathematical descriptions of microwave backscatter interaction with parameters such as land cover, vegetation density, and soil characteristics are not readily available. In such condition, non-parametric models could be used as possible alternative for better understanding the impact of variables in the retrieval process and relating it in the absence of exact formulation. In this study, non-parametric methods such as neural networks, fuzzy logic are used to retrieve soil moisture from active microwave remote sensing data. The inclusion of soil characteristics and Normalized Difference Vegetation Index (NDVI) derived from infrared and visible measurement, have significantly improved soil moisture retrievals and reduced root mean square error (RMSE) by around 30% in the retrievals. Soil moisture derived from these methods was compared with ESTAR soil moisture (RMSE ~4.0%) and field soil moisture measurements (RMSE ~6.5%). Additionally, the study showed that soil moisture retrievals from highly vegetated areas are less accurate than bare soil areas. View Full-Text
Keywords: Soil moisture; Remote Sensing; Neural Network; Fuzzy Logic Soil moisture; Remote Sensing; Neural Network; Fuzzy Logic

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Lakhankar, T.; Ghedira, H.; Temimi, M.; Sengupta, M.; Khanbilvardi, R.; Blake, R. Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data. Remote Sens. 2009, 1, 3-21.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



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