Soil Moisture Estimation in South-Eastern New Mexico Using High Resolution Synthetic Aperture Radar (SAR) Data

Geosciences

A.K.M. Azad Hossain¹  Greg Easson² 

[1]Department of Geology and Geological Engineering, The University of Mississippi, 120A Carrier Hall, University, MS 38677, USAMississippi Mineral Resources Institute, The University of Mississippi, University, MS 38677, USA

关键词: SAR backscatter;    soil moisture estimation;    semi-arid environment;    multiple regressions;    artificial neural networks;    south eastern New Mexico;   

DOI  :  10.3390/geosciences6010001

来源: mdpi

【 摘 要 】

Soil moisture monitoring and characterization of the spatial and temporal variability of this hydrologic parameter at scales from small catchments to large river basins continues to receive much attention, reflecting its critical role in subsurface-land surface-atmospheric interactions and its importance to drought analysis, irrigation planning, crop yield forecasting, flood protection, and forest fire prevention. Synthetic Aperture Radar (SAR) data acquired at different spatial resolutions have been successfully used to estimate soil moisture in different semi-arid areas of the world for many years. This research investigated the potential of linear multiple regressions and Artificial Neural Networks (ANN) based models that incorporate different geophysical variables with Radarsat 1 SAR fine imagery and concurrently measured soil moisture measurements to estimate surface soil moisture in Nash Draw, NM. An artificial neural network based model with vegetation density, soil type, and elevation data as input in addition to radar backscatter values was found suitable to estimate surface soil moisture in this area with reasonable accuracy. This model was applied to a time series of SAR data acquired in 2006 to produce soil moisture data covering a normal wet season in the study site.

【 授权许可】

CC BY   
© 2016 by the authors; licensee MDPI, Basel, Switzerland.