Understanding microwave backscattering of bare soils by using the inversion of surface parameters, neural networks and genetic algorithm
Other titre : Compréhension de la rétrodiffusion des micro-ondes sur le sol nu en utilisant l'inversion des paramètres de surface, les réseaux de neurones et l'algorithme génétique
Sahebi, Mahmod Reza
Estimates of the physical parameters of the soil surface, namely moisture content and surface roughness, are important for hydrological and agricultural studies, as they appear to be the two major parameters for runoff forecasting in an agricultural watershed. Radar has high potentiality for the remote measurement of soil surface parameters. In particular, the investigation of the radar backscattering response of bare soil surfaces is an important issue in remote sensing because of its capacity for retrieving the desired physical parameters of the surface. The objective of this study is to formulate and to constrain a methodology for solving the inverse problem for the operational retrieval of soil surface roughness and moisture. To separate the effects of the different parameters on the measured signal over complex areas, multi-technique concepts (multi-polarization, multi-angular, multi-sensor, multi-frequency, and multi-temporal) are the main solution. In this work, based on a simulation study, three different configurations, multi-polarization, multi-frequency and multi-angular, are compared to obtain the best configuration for estimating surface parameters and the multi-angular configuration gives the best results. Based on these results, this study was continued according to five different phases: (1) A new index, the NBRI (Normalized radar Backscatter soil Roughness Index), using the multi-angular approach was presented. This index can estimate and classify surface roughness in agricultural fields using two radar images with different incidence angles. (2) A new linear empirical model to estimate soil surface moisture using RADARSAT-1 data was proposed. This model can provide soil moisture with reduced errors of estimation compared to other linear models. (3) Inversion of the surface parameters using nonlinear classical methods. In this case, the Newton-Raphson method, an iterative numerical method, was used in the retrieval algorithm to solve the inverse problem. (4) In this phase, the neural network technique, with a dynamic learning method, was applied to invert the soil surface parameters from the radar data. The results were obtained through performance testing on two different input schemes (one and two data series) and two different databases (theoretical and empirical). The advantage of the multi-angular set with measured data is apparent. These results are the best in this study. (5) Finally, a novel genetic algorithm (GA) was developed to retrieve soil surface parameters. In this study, it is shown that the genetic algorithms, as an optimization technique, can estimate simultaneously soil moisture and surface roughness from only one radar image.