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A Better Understanding of Our Earth through Remote Sensing
Article

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

1
NOAA-Cooperative Remote Sensing Science & Technology Center (NOAA-CREST), City University of New York, NY 10031 USA
2
American University in Dubai P.O. Box: 28282 Dubai, United Arab Emirates
3
Co-operative Institute for Research in the Atmosphere (CIRA), Colorado State University, Fort Collins, CO 80521 USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2009, 1(1), 3-21; https://doi.org/10.3390/rs1010003
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)
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
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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. https://doi.org/10.3390/rs1010003

AMA 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 Sensing. 2009; 1(1):3-21. https://doi.org/10.3390/rs1010003

Chicago/Turabian Style

Lakhankar, Tarendra, Hosni Ghedira, Marouane Temimi, Manajit Sengupta, Reza Khanbilvardi, and Reginald Blake. 2009. "Non-parametric Methods for Soil Moisture Retrieval from Satellite Remote Sensing Data" Remote Sensing 1, no. 1: 3-21. https://doi.org/10.3390/rs1010003

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