Contribución de la interferometría SAR diferencial (InSAR) al estudio de la subsidencia del terreno de la Vega Media del Segura (Murcia): experiencias y tendencias futuras

XVII Congreso de la Asociación Española de Teledetección. Murcia 3-7 octubre 2017La Vega Media del Segura (VMS) se localiza en el sector este de la Cordillera Bética. El valle está relleno por sedimentos recientes (Holoceno-Plioceno) potencialmente deformables que han sido depositados por la acción de los ríos Segura y Guadalentín. La extracción de agua subterránea de los niveles permeables que constituyen el acuífero conlleva la consolidación de los materiales deformables, dando lugar a asientos de la superficie del terreno. La Interferometría SAR diferencial (InSAR) es una técnica remota que permite monitorizar de forma efectiva y precisa amplias extensiones del territorio. En este trabajo se describe las diferentes experiencias llevadas a cabo por los autores en la VMS, que han permitido avanzar en el entendimiento del funcionamiento hidrogeológico del acuífero para la comprensión del comportamiento geomecánico del subsuelo, así como para monitorizar los desplazamientos del terreno desde el año 1994 usando imágenes ERS, ENVISAT y TerraSAR-X, contribuyendo de forma efectiva al estudio, caracterización y modelización del fenómeno. Por último, se describen las tareas futuras a desarrollar haciendo uso de nuevos sensores SAR con el fin de asegurar la continuidad de la información disponible para el estudio de este fenómeno a lo largo del tiempo.Departamento de Ingeniería Civil, Universidad de Alicante, EspañaGeohazards InSAR Laboratory and Modeling Group, Instituto Geológico y Minero de España, EspañaDepartamento de Física, Ingeniería de Sistemas y Teoría de la Señal, Universidad de Alicante, EspañaDepartamento de Teoria Senyal i Comunicacions, Universitat Politècnica de Catalunya, EspañaDepartment of Earth Sciences, Environment and Resources, University of Naples, EspañaDares Technology, Barcelona, Españ

A Complete Procedure for Crop Phenology Estimation With PolSAR Data Based on the Complex Wishart Classifier

A new methodology to estimate the growth stages of agricultural crops using the time series of polarimetric synthetic aperture radar (PolSAR) images is proposed. The methodology is based on the complex Wishart classifier and both phenological intervals and training areas are identified measuring the distances among polarimetric covariance matrices obtained from the time series of PolSAR data. Consequently, the computation of PolSAR features, which is the main step of state-of-the-art methods, is no longer needed, and the proposed approach can be applied in the same way to any crop type. Experiments undertaken on a dense time series of fully polarimetric C-band RADARSAT-2 images, collected at incidence angles ranging from 23° to 39°, in ascending/descending orbit passes, demonstrate that the proposed methodology can be successfully applied to retrieve the phenological stages of four different crop types. In addition, the effect of combining beams corresponding to different sensor's configurations has been evaluated, showing that it affects the retrieval accuracies. Validation with ground data shows the following: overall accuracy is between 54% and 86%; producer's accuracy (PA) and user's accuracy (UA) range between 21% and 100% and between 33% and 100%, respectively

Retrieval of phenological stages of onion fields during the first year of growth by means of C-band polarimetric SAR measurements

The phenological stages of onion fields in the first year of growth are estimated using polarimetric observables and single-polarization intensity channels. Experiments are undertaken on a time series of RADARSAT-2 C-band full-polarimetric synthetic aperture radar (SAR) images collected in 2009 over the Barrax region, Spain, where ground truth information about onion growth stages is provided by the European Space Agency (ESA)-funded agricultural bio/geophysical retrieval from frequent repeat pass SAR and optical imaging (AgriSAR) field campaign conducted in that area. The experimental results demonstrate that polarimetric entropy or copolar coherence when used jointly with the cross-polarized intensity allows unambiguously distinguishing three phenological intervals

Sincohmap: land-cover and vegetation mapping using multi-temporal sentinel-1 interferometric coherence

InSAR coherence is a promising parameter for land-cover classification and mapping. The ESA SEOM SInCohMap project is devised to test and analyze multi-temporal InSAR coherence potentialities exploiting dense multitemporal data from the Sentinel-1 constellation. In the framework of the project, this paper shows the first classification results using machine learning algorithms over a two-year period of InSAR coherence data. The evaluation is performed on the test site of Doñana (Seville, Southwestern Spain), mainly an agricultural area where different land covers can be identified. Classification results exploiting InSAR coherence shows accuracies around 80 % for this site.Peer ReviewedPostprint (published version

Assessing hypertemporal SENTINEL-1 COHERENCE maps for LAND COVER monitoring

This paper presents the main concepts and the initial analysis of the ESA SEOM project SInCohMap “EXPLOITATION OF SENTINEL-1 INTERFEROMETRIC COHERENCE FOR LAND COVER AND VEGETATION MAPPING”. The project evaluates the performance of using the interferometric coherence of S-1 time series for land cover and vegetation mapping. One of the main objectives of the project is to quantify the impact in using S-1 InSAR (Interferometric Synthetic Aperture Radar) data relative to traditional land cover and vegetation mapping using optical data (especially Sentinel-2, hereafter named S-2) or SAR-based (Synthetic Aperture Radar) approaches. In this framework, a Round-Robin consultation is used to assess the performances of the different classification methodologies.Peer Reviewe

Assessing hypertemporal SENTINEL-1 COHERENCE maps for LAND COVER monitoring

This paper presents the main concepts and the initial analysis of the ESA SEOM project SInCohMap “EXPLOITATION OF SENTINEL-1 INTERFEROMETRIC COHERENCE FOR LAND COVER AND VEGETATION MAPPING”. The project evaluates the performance of using the interferometric coherence of S-1 time series for land cover and vegetation mapping. One of the main objectives of the project is to quantify the impact in using S-1 InSAR (Interferometric Synthetic Aperture Radar) data relative to traditional land cover and vegetation mapping using optical data (especially Sentinel-2, hereafter named S-2) or SAR-based (Synthetic Aperture Radar) approaches. In this framework, a Round-Robin consultation is used to assess the performances of the different classification methodologies.Peer ReviewedPostprint (published version

Sincohmap: land-cover and vegetation mapping using multi-temporal sentinel-1 interferometric coherence

InSAR coherence is a promising parameter for land-cover classification and mapping. The ESA SEOM SInCohMap project is devised to test and analyze multi-temporal InSAR coherence potentialities exploiting dense multitemporal data from the Sentinel-1 constellation. In the framework of the project, this paper shows the first classification results using machine learning algorithms over a two-year period of InSAR coherence data. The evaluation is performed on the test site of Doñana (Seville, Southwestern Spain), mainly an agricultural area where different land covers can be identified. Classification results exploiting InSAR coherence shows accuracies around 80 % for this site.Peer Reviewe

New microwave-based missions applications for rainfed crops characterization

A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSSR) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.Peer Reviewe

NEW MICROWAVE-BASED MISSIONS APPLICATIONS FOR RAINFED CROPS CHARACTERIZATION

A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops