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: 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(6):1607-1621. DOI:10.5194/hess-16-1607-2012 · 3.64 Impact Factor
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ABSTRACT: Soil moisture retrieval is one of the most challenging problems in the context of biophysical parameter estimation from remotely sensed data. Typically, microwave signals are used thanks to their sensitivity to variations in the water content of soil. However, especially in the Alps, the presence of vegetation and the heterogeneity of topography may significantly affect the microwave signal, thus increasing the complexity of the retrieval. In this paper, the effectiveness of RADARSAT2 SAR images for the estimation of soil moisture in an alpine catchment is investigated. We first carry out a sensitivity analysis of the SAR signal to the moisture content of soil and other target properties (e.g., topography and vegetation). Then we propose a technique for estimating soil moisture based on the Support Vector Regression algorithm and the integration of ancillary data. Preliminary results are discussed both in terms of accuracy over point measurements and effectiveness in handling spatially distributed data.Applied and Environmental Soil Science 01/2011; 2011. DOI:10.1155/2011/175473
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ABSTRACT: Soil moisture is an important indicator of the land surface environment. The combination of land surface temperature (LST) and normalized difference vegetation index (NDVI) could enhance the ability of extracting information on soil moisture conditions. In this study, we employed multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data products of LST, NDVI, and land cover types to obtain the information about soil moisture for the greater Changbai Mountains. We selected nine time periods in 2007 for inversion of the soil moisture conditions and focused the analysis on four critical time periods. According to the spatial pattern of the LST and NDVI, we established the ??wet-edge?? and ??dry-edge?? equations and determined the relative parameters. We obtained the temperature-vegetation dryness index (TVDI) using the wet-edge and dry-edge relationships to reveal temporal changes of the land surface soil moisture conditions of the study area. We also analyzed the relationship between different land cover types in five TVDI classes. This paper demonstrates that TVDI is an effective indicator to detect soil moisture status in the greater Changbai Mountains region.IEEE Transactions on Geoscience and Remote Sensing 07/2010; 48(6-48):2509 - 2515. DOI:10.1109/TGRS.2010.2040830 · 2.93 Impact Factor