Estimation of water cloud model vegetation parameters using a genetic algorithm

Abstract The water cloud model is used to account for the effect of vegetation water content on radar backscatter data. The model generally comprises two parameters that characterize the vegetated terrain, A and B, and two bare soil parameters, C and D. In the present study, parameters A and B were estimated using a genetic algorithm (GA) optimization technique and compared with estimates obtained by the sequential unconstrained minimization technique (SUMT) from measured backscatter data. The parameter estimation was formulated as a least squares optimization problem by minimizing the deviations between the backscatter coefficients retrieved from the ENVISAT ASAR image and those predicted by the water cloud model. The bias induced by three different objective functions was statistically analysed by generating synthetic backscatter data. It was observed that, when the backscatter coefficient data contain no errors, the objective functions do not induce any bias in the parameter estimation and the true parameters are uniquely identified. However, in the presence of noise, these objective functions induce bias in the parameter estimates. For the cases considered, the objective function based on the sum of squares of normalized deviations with respect to the computed backscatter coefficient resulted in the best possible estimates. A comparison of the GA technique with the SUMT was undertaken in estimating the water cloud model parameters. For the case considered, the GA technique performed better than the SUMT in parameter estimation, where the root mean squared error obtained from the GA was about half of that obtained by the SUMT. Editor D. Koutsoyiannis; Associate editor L. See Citation Kumar, K., Hari Prasad, K.S. and Arora, M.K., 2012. Estimation of water cloud model vegetation parameters using a genetic algorithm. Hydrological Sciences Journal, 57 (4), 776–789.

[1]  Leon S. Lasdon,et al.  Optimization in engineering design , 1967 .

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

[3]  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.

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

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

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

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

[8]  F. Ulaby,et al.  Active Microwave Soil Moisture Research , 1986, IEEE Transactions on Geoscience and Remote Sensing.

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

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

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  T. Schmugge,et al.  Vegetation effects on the microwave emission of soils , 1991 .

[13]  Uri Shani,et al.  Analyses of infiltration events in relation to determining soil hydraulic properties by inverse problem methodology , 1991 .

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

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

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

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

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

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

[20]  W. Brutsaert,et al.  Aspects of soil moisture variability in the Washita '92 study region , 1999 .

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

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

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

[24]  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 .

[25]  Madan K. Jha,et al.  Estimation of Aquifer Parameters from Pumping Test Data by Genetic Algorithm Optimization Technique , 2003 .

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

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

[28]  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.

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

[30]  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 .

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

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

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