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.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 01/2015; 8(1):332-349. DOI:10.1109/JSTARS.2014.2361862 · 2.83 Impact Factor
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ABSTRACT: A methodology to retrieve soil moisture (SM) from SMOS data is presented. The method uses a Neural Network (NN) to find the statistical relationship linking the input data to a reference SM dataset. The input data is composed of passive microwaves (L-band SMOS brightness temperatures, \Tb's) complemented with active microwaves (C-band ASCAT backscattering coefficients), and MODIS NDVI. The reference SM data used to train the NN are ECMWF model predictions. The best configuration of SMOS data to retrieve SM using a NN is using \Tb's measured with both H and V polarizations for incidence angles from 25$^\circ$ to 60$^\circ$. The inversion of soil moisture can be improved by $\sim 10 \%$ by adding MODIS NDVI and ASCAT backscattering data and by an additional $\sim 5 \%$ by using local information on the maximum and minimum record of SMOS Tb's (or ASCAT backscattering coefficients) and the associated SM values. The NN inverted SM is able to capture the temporal and spatial variability of the SM reference dataset. The temporal variability is better captured when either adding active microwaves or using a local normalization of SMOS Tb's. The NN SM products have been evaluated against in situ measurements, giving results of comparable or better (for some NN configurations) quality to other SM products. The NN used in this study allows to retrieve SM globally on a daily basis. These results open interesting perspectives such as a near real time processor and data assimilation in weather prediction models.IEEE Transactions on Geoscience and Remote Sensing 01/2015; in press. · 2.93 Impact Factor
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ABSTRACT: In this study, the Artificial Bee Colony Optimization (ABCO) algorithm has been proposed to estimate the atmospheric duct in maritime environment. The radar sea clutter power is calculated by the parabolic equation method. In order to validate the accuracy and robustness of ABCO algorithm, the experimental and simulation study are respectively carried out in the current research. In the simulation study, the statistical analysis of the estimation results in term of the mean squared error (MSE), mean absolute deviation (MAD) and mean relative error (MRE) are presented to analyze the optimization performance with different noise standard deviation, and the comparative study of the performance of ABCO and particle swarm optimization (PSO) algorithm are also shown. The investigation presented indicate that the ABCO algorithm can be accurately and effectively utilized to estimate the evaporation duct and surface-based duct using refractivity from clutter (RFC) technique in maritime environment. In addition, the performance of ABCO algorithm is clearly superior to that of the PSO algorithm according to the statistical analysis results, especially for the four-parameter surface-based duct estimation.Progress In Electromagnetics Research 01/2013; 135:183-199. DOI:10.2528/PIER12110104 · 5.30 Impact Factor