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Remote Sens. 2009, 1(2), 80-91;

Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active Microwave 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, UAE
Author to whom correspondence should be addressed.
Received: 3 April 2009 / Revised: 11 May 2009 / Accepted: 13 May 2009 / Published: 13 May 2009
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This study addresses the issue of the variability and heterogeneity problems that are expected from a sensor with a larger footprint having homogenous and heterogeneous sub-pixels. Improved understanding of spatial variability of soil surface characteristics such as land cover and vegetation in larger footprint are critical in remote sensing based soil moisture retrieval. This study analyzes the sub-pixel variability (standard deviation of sub-grid pixels) of Normalized Difference Vegetation Index and SAR backscatter. Back-propagation neural network was used for soil moisture retrieval from active microwave remote sensing data from Southern Great Plains of Oklahoma. The effect of land cover heterogeneity (number of different vegetation species within pixels) on soil moisture retrieval using active microwave remote sensing data was investigated. The presence of heterogeneous vegetation cover reduced the accuracy of the derived soil moisture using microwave remote sensing data. The results from this study can be used to characterize the uncertainty in soil moisture retrieval in the context of Soil Moisture Active and Passive (SMAP) mission which will have larger footprint. View Full-Text
Keywords: soil moisture; land-cover; heterogeneity; RADAR; neural network soil moisture; land-cover; heterogeneity; RADAR; neural network

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

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Lakhankar, T.; Ghedira, H.; Temimi, M.; Azar, A.E.; Khanbilvardi, R. Effect of Land Cover Heterogeneity on Soil Moisture Retrieval Using Active Microwave Remote Sensing Data. Remote Sens. 2009, 1, 80-91.

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