Soil moisture retrieval using GNSS-R

the UPC Passive Remote Sensing Lab (RSLab) has developed theoretical models exploiting this technique for soil moisture retrieval. These models are based on the interference pattern produced by interaction between the direct GPS signal and the reflected GPS signal coming from the soil surface. The properties of the received interference pattern are modified by changes in geophysical parameters, such as the soil texture, moisture, roughness and thickness of soil layers. Changes in each of the parameters will induce a characteristic change in the interference pattern. Therefore, by identifying the correct observable change in the pattern, the soil moisture can be inferred. So as to get enough experimental results to validate the theoretical models mentioned above, a ground-based instrument, called the Soil Moisture Interference-pattern GNSS Observations at L-band (SMIGOL) Reflectometer, was placed in a wheat field at Palau d’Anglesola in Lleida province, from January to October 2008. Measurements have been carried out at the different stages in the growth of wheat. Those were then compared to the results produced by the theoretical model by means of a simulator, implemented using Matlab. By fixing geophysical variables in the simulator so as to represent as closely as possible the characteristics of the monitored soil, it is able to produce a theoretical interference pattern. It is then possible to compare this pattern with the experimental results to test the validity of the models. The simulator was in an initial stage so that most of the parameterisation had to be done manually, which takes a lot of time and introduces errors in accuracy. The presented PFC arises in this framework, so that the objective is to automate the simulator taking as input raw data produced by the SMIGOL Reflectometer and producing as output the soil moisture and topography of the monitored site, with minimum human input and delivering a fast response. Having a more accurate and faster simulator gives more results to analyse and better theoretical models for soil moisture retrieval

Radar interferometry techniques for the study of ground subsidence phenomena: a review of practical issues through cases in Spain

Subsidence related to multiple natural and human-induced processes affects an increasing number of areas worldwide. Although this phenomenon may involve surface deformation with 3D displacement components, negative vertical movement, either progressive or episodic, tends to dominate. Over the last decades, differential SAR interferometry (DInSAR) has become a very useful remote sensing tool for accurately measuring the spatial and temporal evolution of surface displacements over broad areas. This work discusses the main advantages and limitations of addressing active subsidence phenomena by means of DInSAR techniques from an end-user point of view. Special attention is paid to the spatial and temporal resolution, the precision of the measurements, and the usefulness of the data. The presented analysis is focused on DInSAR results exploitation of various ground subsidence phenomena (groundwater withdrawal, soil compaction, mining subsidence, evaporite dissolution subsidence, and volcanic deformation) with different displacement patterns in a selection of subsidence areas in Spain. Finally, a cost comparative study is performed for the different techniques applied.The different research areas included in this paper has been supported by the projects: CGL2005-05500-C02, CGL2008-06426-C01-01/BTE, AYA2 010-17448, IPT-2011-1234-310000, TEC-2008-06764, ACOMP/2010/082, AGL2009-08931/AGR, 2012GA-LC-036, 2003-03-4.3-I-014, CGL2006-05415, BEST-2011/225, CGL2010-16775, TEC2011-28201, 2012GA-LC-021 and the Banting Postdoctoral Fellowship to PJG

Incorporating Multiresolution Analysis With Multiclassifiers And Decision Fusion For Hyperspectral Remote Sensing

The ongoing development and increased affordability of hyperspectral sensors are increasing their utilization in a variety of applications, such as agricultural monitoring and decision making. Hyperspectral Automated Target Recognition (ATR) systems typically rely heavily on dimensionality reduction methods, and particularly intelligent reduction methods referred to as feature extraction techniques. This dissertation reports on the development, implementation, and testing of new hyperspectral analysis techniques for ATR systems, including their use in agricultural applications where ground truthed observations available for training the ATR system are typically very limited. This dissertation reports the design of effective methods for grouping and down-selecting Discrete Wavelet Transform (DWT) coefficients and the design of automated Wavelet Packet Decomposition (WPD) filter tree pruning methods for use within the framework of a Multiclassifiers and Decision Fusion (MCDF) ATR system. The efficacy of the DWT MCDF and WPD MCDF systems are compared to existing ATR methods commonly used in hyperspectral remote sensing applications. The newly developed methods’ sensitivity to operating conditions, such as mother wavelet selection, decomposition level, and quantity and quality of available training data are also investigated. The newly developed ATR systems are applied to the problem of hyperspectral remote sensing of agricultural food crop contaminations either by airborne chemical application, specifically Glufosinate herbicide at varying concentrations applied to corn crops, or by biological infestation, specifically soybean rust disease in soybean crops. The DWT MCDF and WPD MCDF methods significantly outperform conventional hyperspectral ATR methods. For example, when detecting and classifying varying levels of soybean rust infestation, stepwise linear discriminant analysis, results in accuracies of approximately 30%-40%, but WPD MCDF methods result in accuracies of approximately 70%-80%

Incorporating Multiresolution Analysis With Multiclassifiers And Decision Fusion For Hyperspectral Remote Sensing

The ongoing development and increased affordability of hyperspectral sensors are increasing their utilization in a variety of applications, such as agricultural monitoring and decision making. Hyperspectral Automated Target Recognition (ATR) systems typically rely heavily on dimensionality reduction methods, and particularly intelligent reduction methods referred to as feature extraction techniques. This dissertation reports on the development, implementation, and testing of new hyperspectral analysis techniques for ATR systems, including their use in agricultural applications where ground truthed observations available for training the ATR system are typically very limited. This dissertation reports the design of effective methods for grouping and down-selecting Discrete Wavelet Transform (DWT) coefficients and the design of automated Wavelet Packet Decomposition (WPD) filter tree pruning methods for use within the framework of a Multiclassifiers and Decision Fusion (MCDF) ATR system. The efficacy of the DWT MCDF and WPD MCDF systems are compared to existing ATR methods commonly used in hyperspectral remote sensing applications. The newly developed methods’ sensitivity to operating conditions, such as mother wavelet selection, decomposition level, and quantity and quality of available training data are also investigated. The newly developed ATR systems are applied to the problem of hyperspectral remote sensing of agricultural food crop contaminations either by airborne chemical application, specifically Glufosinate herbicide at varying concentrations applied to corn crops, or by biological infestation, specifically soybean rust disease in soybean crops. The DWT MCDF and WPD MCDF methods significantly outperform conventional hyperspectral ATR methods. For example, when detecting and classifying varying levels of soybean rust infestation, stepwise linear discriminant analysis, results in accuracies of approximately 30%-40%, but WPD MCDF methods result in accuracies of approximately 70%-80%

Railway deformation detected by DInSAR over active sinkholes in the Ebro Valley evaporite karst, Spain

Subsidence was measured for the first time on railway tracks in the central sector of Ebro Valley (NE Spain) using Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques. This area is affected by evaporite karst and the analysed railway corridors traverse active sinkholes that produce deformations in these infrastructures. One of the railway tracks affected by slight settlements is the Madrid-Barcelona high-speed line, a form of transport infrastructure highly vulnerable to ground deformation processes. Our analysis based on DInSAR measurements and geomorphological surveys indicates that this line shows dissolution-induced subsidence and compaction of anthropogenic deposits (infills and embankments). Significant sinkhole-related subsidence was also measured by DInSAR techniques on the Castejón-Zaragoza conventional railway line. This study demonstrates that DInSAR velocity maps, coupled with detailed geomorphological surveys, may help in the identification of the railway track sections that are affected by active subsidence

A new kernel method for hyperspectral image feature extraction

Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required

Exploitation of large archives of ERS and ENVISAT C-band SAR data to characterize ground deformations

In the last few years, several advances have been made in the use of radar images to detect, map and monitor ground deformations. DInSAR (Differential Synthetic Aperture Radar Interferometry) and A-DInSAR/PSI (Advanced DInSAR/Persistent Scatterers Interferometry) technologies have been successfully applied in the study of deformation phenomena induced by, for example, active tectonics, volcanic activity, ground water exploitation, mining, and landslides, both at local and regional scales. In this paper, the existing European Space Agency (ESA) archives (acquired as part of the FP7-DORIS project), which were collected by the ERS-1/2 and ENVISAT satellites operating in the microwave C-band, were analyzed and exploited to understand the dynamics of landslide and subsidence phenomena. In particular, this paper presents the results obtained as part of the FP7-DORIS project to demonstrate that the full exploitation of very long deformation time series (more than 15 years) can play a key role in understanding the dynamics of natural and human-induced hazards. © 2013 by the authors