Soil Moisture Retrieval From Remotely Sensed Data: Neural Network Approach Versus Bayesian Method
ABSTRACT Neural network (NN) approaches and statistical methods, based on a Bayesian procedure, are applied and compared in soil moisture (SM) retrieval from remotely sensed data. The principles and the practical implementations of Bayesian procedures and NNs are briefly discussed in terms of the advantages and disadvantages of each. Experimental tests are carried out by using the same set of training and test data for each method. The methodologies have been applied to two sets of data to retrieve SM from bare soils and to verify their accuracy. One data set contains scatterometer and radiometer data acquired on a variety of agricultural fields in different polarizations, frequencies, and incidence angles. The other is made up of five experiments carried out with a C-band scatterometer on rough and smooth soils at different polarizations and incidence angles. There are significant similarities in the performance of each method; they both retrieve the same features and trends in the analyzed data sets. Algorithm performances change according to SM level and data configuration. The main difficulties are found in retrieving low SM values, and in this case, the error on estimates is reduced when the data with two polarizations or two incidence angles are inserted in the inversion procedure. One major difference between the methodologies is that the NN performance improves, with respect to the Bayesian method, when more inputs are presented as two polarizations or two incidence angles in the training phase.
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ABSTRACT: This paper deals with the tuning of the free parameters of the Support Vector Regression technique used for the retrieval of geo/bio-physical variables from remotely sensed data. We propose to address this task in the framework of the multi-objective optimization. A multi-objective function is defined based on a set of two (or more) metrics (e.g., mean squared error MSE and determination coefficient R2 ) that quantify from different (and sometimes competing) perspectives the goodness of a given parameter configuration. Then the metrics are jointly optimized according to the concept of Pareto optimality. This allows preserving the meaning of each metric and deriving multiple optimal solutions to the tuning problem. Each solution leads to a different optimal trade-off among the considered metrics. The main advantages of the proposed multi-objective parameter optimization approach with respect to traditional mono-objective strategies are: (1) the intrinsic improved robustness and efficiency, since multiple metrics are jointly exploited in the tuning of the free parameters of the considered regression method; and (2) the possibility to select the parameter configuration that leads to the desired trade-off among different criteria and thus best meets both the application constraints and the requirements of the specific estimation problem. The experimental analysis was focused on the challenging application domain of soil moisture retrieval from microwave remotely sensed data. The results obtained on data sets associated with two different operative conditions are very promising and show the effectiveness of the proposed approach in comparison with more traditional tuning strategies based on a single metric and its usefulness in defining estimation systems for real application domains.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 01/2012; 5(5):1495-1508. · 2.87 Impact Factor
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ABSTRACT: The purpose of this study was to develop an approach to estimate soil surface parameters from C-band polarimetric SAR data in the case of bare agricultural soils. An inversion technique based on multi-layer perceptron (MLP) neural networks was introduced. The neural networks were trained and validated on a noisy simulated dataset generated from the Integral Equation Model (IEM) on a wide range of surface roughness and soil moisture, as it is encountered in agricultural contexts for bare soils. The performances of neural networks in retrieving soil moisture and surface roughness were tested for several inversion cases using or not using a-priori knowledge on soil parameters. The inversion approach was then validated using RADARSAT-2 images in polarimetric mode. The introduction of expert knowledge on the soil moisture (dry to wet soils or very wet soils) improves the soil moisture estimates, whereas the precision on the surface roughness estimation remains unchanged. Moreover, the use of polarimetric parameters α1 and anisotropy were used to improve the soil parameters estimates. These parameters provide to neural networks the probable ranges of soil moisture (lower or higher than 0.30 cm3 cm-3) and surface roughness (root mean square surface height lower or higher than 1.0 cm). Soil moisture can be retrieved correctly from C-band SAR data by using the neural networks technique. Soil moisture errors were estimated at about 0.098 cm3 cm-3 without a-priori information on soil parameters and 0.065 cm3 cm-3 (RMSE) applying a-priori information on the soil moisture. The retrieval of surface roughness is possible only for low and medium values (lower than 2 cm). Results show that the precision on the soil roughness estimates was about 0.7 cm. For surface roughness lower than 2 cm, the precision on the soil roughness is better with an RMSE about 0.5 cm. The use of polarimetric parameters improves only slightly the soil parameters estimates.Hydrology and Earth System Sciences 01/2012; 16:1607-1621. · 3.59 Impact Factor
Conference Paper: Multiobjective model selection for non-linear regression techniques.[Show abstract] [Hide abstract]
ABSTRACT: This paper proposes to model the critical issue of the choice of the free parameters of a supervised non-linear regression technique (the so called model selection issue) as a multiobjective optimization problem. In this framework, the multi-objective function is made up of a set of two or more quality metrics (e.g., MSE, R<sup>2</sup>, etc.) computed on the test (or validation) samples. A set of solutions is derived according to the concept of Pareto optimality. The advantages of the proposed approach with respect to the traditional ones (which typically optimize a single scalar metric) are mainly two: (1) the capability to derive solutions which jointly optimize the set of metrics considered and represent different possible optimal tradeoffs among them; and (2) the possibility for the user to effectively select the model that optimizes the requirements of the specific retrieval problem. Results achieved for the specific application of soil moisture estimation from microwave remotely sensed data with the Support Vector Regression (SVR) technique are reported. These results show the effectiveness of the proposed approach.IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2010, July 25-30, 2010, Honolulu, Hawaii, USA, Proceedings; 01/2010