Study of water cloud model vegetation descriptors in estimating soil moisture in Solani catchment

Water cloud model (WCM) relates the backscatter coefficient (σo) with soil moisture. The backscatter coefficient includes the backscatter coefficient due to vegetation (σoveg), and the backscatter coefficient due to soil (σosoil). The σoveg of WCM depends upon vegetation characteristics. The present study is aimed to investigate the effect of different vegetation descriptors in estimating soil moisture from WCM. The study is carried out in Solani River catchment of India. Envisat Advanced Synthetic Aperture Radar (ASAR) images of three dates were acquired for the study. The field data, volumetric soil moisture from the upper 0–10 cm soil layer, soil texture, soil surface roughness, leaf area index (LAI), leaf water area index, normalized plant water content and average plant height corresponding to satellite pass dates were collected. Genetic algorithm optimization technique is used to estimate the WCM vegetation parameters. The use of LAI as vegetation descriptor results in minimum root mean square error (RMSE) of 1.77 dB between WCM computed backscatter and Envisat ASAR observed backscatter. Also, use of LAI in WCM as vegetation descriptor results in the least RMSE of 4.19%, between estimated and observed soil moisture for the first field campaign, whereas it was 5.64% for the last field campaign which was undertaken after 35 days of first campaign. It is concluded that LAI can be treated as the best vegetation descriptor in studies retrieving soil moisture and backscatter from microwave remote sensing data. Copyright © 2014 John Wiley & Sons, Ltd.

[1]  Y. Inoue,et al.  Inferring the effect of plant and soil variables on C- and L-band SAR backscatter over agricultural fields, based on model analysis , 2007 .

[2]  Wolfgang Wagner,et al.  On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar , 2008, Sensors.

[3]  J. Wigneron,et al.  An empirical calibration of the integral equation model based on SAR data, soil moisture and surface roughness measurement over bare soils , 2002 .

[4]  Thomas J. Schmugge.  Effect of Texture on Microwave Emission from Soils , 1980, IEEE Transactions on Geoscience and Remote Sensing.

[5]  David E. Goldberg,et al.  Designing a competent simple genetic algorithm for search and optimization , 2000 .

[6]  Niko E. C. Verhoest,et al.  Assessment of the operational applicability of RADARSAT-1 data for surface soil moisture estimation , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[7]  J. Chen,et al.  Defining leaf area index for non‐flat leaves , 1992 .

[8]  D. McKinney,et al.  Genetic algorithm solution of groundwater management models , 1994 .

[9]  Mehrez Zribi,et al.  Potential of ASAR/ENVISAT for the characterization of soil surface parameters over bare agricultural fields , 2005 .

[10]  Mehrez Zribi,et al.  Soil moisture estimation using multi‐incidence and multi‐polarization ASAR data , 2006 .

[11]  Irena Hajnsek,et al.  Inversion of surface parameters from polarimetric SAR data , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[12]  F. Ulaby,et al.  Microwave Backscatter Dependence on Surface Roughness, Soil Moisture, and Soil Texture: Part I-Bare Soil , 1978, IEEE Transactions on Geoscience Electronics.

[13]  K. W. Jaggard,et al.  Monitoring leaf area of sugar beet using ERS-1 SAR data , 1996 .

[14]  Venkat Lakshmi,et al.  Soil moisture retrieval using the passive/active L- and S-band radar/radiometer , 2003, IEEE Trans. Geosci. Remote. Sens..

[15]  C. Ojha,et al.  Analysis of virus transport in groundwater and identification of transport parameters. , 2009 .

[16]  Jan G. P. W. Clevers,et al.  Synergy between optical and microwave remote sensing for crop growth monitoring. , 1994 .

[17]  Chandra A. Madramootoo,et al.  Estimation of Unsaturated Hydraulic Parameters from Infiltration and Internal Drainage Experiments , 2010 .

[18]  K. Beven,et al.  Soil moisture estimation over grass-covered areas using AIRSAR. , 1994 .

[19]  M. Th. van Genuchten,et al.  Parameter estimation for unsaturated flow and transport models — A review , 1987 .

[20]  K. S. Hari Prasad,et al.  Estimation of water cloud model vegetation parameters using a genetic algorithm , 2012 .

[21]  Claudia Notarnicola,et al.  Use of radar and optical remotely sensed data for soil moisture retrieval over vegetated areas , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[22]  F. Ulaby,et al.  Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Y. Kerr,et al.  Operational readiness of microwave remote sensing of soil moisture for hydrologic applications , 2007 .

[24]  David G. Mayer,et al.  Robust parameter settings of evolutionary algorithms for the optimisation of agricultural systems models , 2001 .

[25]  Rajat Bindlish,et al.  Parameterization of vegetation backscatter in radar-based, soil moisture estimation , 2001 .

[26]  W. H. Williams,et al.  How Bad Can “Good” Data Really be? , 1978 .

[27]  W. Yeh.  Review of Parameter Identification Procedures in Groundwater Hydrology: The Inverse Problem , 1986 .

[28]  Fawwaz T. Ulaby,et al.  Effects of Vegetation Cover on the Radar Sensitivity to Soil Moisture , 1982, IEEE Transactions on Geoscience and Remote Sensing.

[29]  F. Ulaby,et al.  Vegetation modeled as a water cloud , 1978 .

[30]  Rahman Khatibi,et al.  Identification Problem of Open-Channel Friction Parameters , 1997 .

[31]  Fawwaz T. Ulaby,et al.  Relating the microwave backscattering coefficient to leaf area index , 1984 .

[32]  L. Prévot,et al.  Estimating the characteristics of vegetation canopies with airborne radar measurements , 1993 .

[33]  Jing Li,et al.  A detail-preserving and flexible adaptive filter for speckle suppression in SAR imagery , 2003 .