Fusion of Multi-Temporal PAZ and Sentinel-1 Data for Crop Classification

The accurate identification of crops is essential to help environmental sustainability and support agricultural policies. This study presents the use of a Spanish radar mission, PAZ, to classify agricultural areas with a very high spatial resolution. PAZ was recently launched, and it operates at X band, joining the synthetic aperture radar (SAR) constellation along with TerraSAR-X and TanDEM-X satellites. Owing to its novelty and its ability to classify crop areas (both taking individually its time series and blending with the Sentinel-1 series), it has been tested in an agricultural area of the central-western part of Spain during 2020. The random forest algorithm was selected to classify the time series under five alternatives of standalone/fused data. The map accuracy resulting from the PAZ series standalone was acceptable, but it highlighted the need for a denser time-series of data. The overall accuracy provided by eight PAZ images or by eight Sentinel-1 images was below 60%. The fusion of both sets of eight images improved the overall accuracy by more than 10%. In addition, the exploitation of the whole Sentinel-1 series, with many more observations (up to 40 in the same temporal window) improved the results, reaching an overall accuracy around 76%. This overall performance was similar to that obtained by the joint use of all the available images of the two frequency bands (C and X).This work was funded by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European Funds for Regional Development (EFRD) under Project TEC2017-85244-C2-1-P

Multi-Temporal Dual- and Quad-Polarimetric Synthetic Aperture Radar Data for Crop-Type Mapping

Land-cover monitoring is one of the core applications of remote sensing. Monitoring and mapping changes in the distribution of agricultural land covers provide a reliable source of information that helps environmental sustainability and supports agricultural policies. Synthetic Aperture Radar (SAR) can contribute considerably to this monitoring effort. The first objective of this research is to extend the use of time series of polarimetric data for land-cover classification using a decision tree classification algorithm. With this aim, RADARSAT-2 (quad-pol) and Sentinel-1 (dual-pol) data were acquired over an area of 600 km2 in central Spain. Ten polarimetric observables were derived from both datasets and seven scenarios were created with different sets of observables to evaluate a multitemporal parcel-based approach for classifying eleven land-cover types, most of which were agricultural crops. The study demonstrates that good overall accuracies, greater than 83%, were achieved for all of the different proposed scenarios and the scenario with all RADARSAT-2 polarimetric observables was the best option (89.1%). Very high accuracies were also obtained when dual-pol data from RADARSAT-2 or Sentinel-1 were used to classify the data, with overall accuracies of 87.1% and 86%, respectively. In terms of individual crop accuracy, rapeseed achieved at least 95% of a producer’s accuracy for all scenarios and that was followed by the spring cereals (wheat and barley), which achieved high producer’s accuracies (79.9%-95.3%) and user’s accuracies (85.5% and 93.7%).All RADARSAT-2 images have been provided by MDA and CSA in the framework of the SOAR-EU2 Project ref. 16375. This study was supported by the Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund under projects TEC2017-85244-C2-1-P, ESP2015-67549-C3-3 and ESP2017-89463-C3-3-R

Calibrating a photogrammetric digital frame sensor using a test field

In this paper a twofold calibration approach for a digital frame sensor has been developed which tries to cope with panchromatic and multispectral calibration separately. Although there have been several improvements and developments in calibration of the digital frame sensor, only limited progresses has been made in the context of multispectral image calibration. To this end, a specific photogrammetric flight was executed to try to calibrate the geometric parameters of a large format aerial digital camera. This photogrammetric flight was performed in the “Principado de Asturias” and it has been designed with a Ground Sample Distance of 6 cm, formed by two strips perpendicular between each other, with five images each one and a longitudinal overlap of 60%. Numerous points have been presignalled over the ground, both check points and control points

Influence of Incidence Angle in the Correlation of C-band Polarimetric Parameters with Biophysical Variables of Rain-fed Crops

A multi-temporal field experiment was conducted within the Soil Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain in order to retrieve useful crop information. The objective of this research was to evaluate the potential of polarimetric observations for crop monitoring by exploiting a time series of 20 quad-pol RADARSAT-2 images at different incidence angles (i.e. 25°, 31°, and 36°) during an entire growing season of rain-fed crops, from February to July 2015. The time evolution of 6 crop biophysical variables was gathered from the field measurements, whereas 10 polarimetric parameters were derived from the images. Thus, a subsequent correlation analysis between both datasets was performed. The study demonstrates that the backscattering ratios (HH/VV and HV/VV), the normalized correlation between HH and VV (γHHVV), and the dominant alpha angle (α1), showed significant and relevant correlations with several biophysical variables such as biomass, height, or leaf area index (LAI) at incidence angles of 31° or 36°. The joint use of data acquired with different beams could be exploited effectively to increase the refresh rate of information about crop condition with respect to a single incidence acquisition scheme.This study was supported by the Spanish Ministry of Economy and Competitiveness and the Spanish Ministry of Science, Innovation and Universities, [Projects ESP2015-67549-C3-3, ESP2017-89463-C3-3-R, and TEC2017-85244-C2-1-P] and the European Regional Development Fund (FEDER)

Tutorías online en tiempo real con escritorio compartido y control remoto de aplicaciones y repositorio audiovisual de clases magistrales

Memoria ID-0177. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2014-2015

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De{s}marcaciones : aportes científicos de la Facultad de Ciencias y Humanidades

El libro, revela realidades que solo pueden ser explicados con criterios e indicadores científicos, que se mueven desde su propia singularidad hacia una dimensión crítica, basadas en explicaciones y propuestas diversas, convirtiéndose en ineludible valor social e histórico, para el enriquecimiento de la ciencia y la cultura. Esta genial obra no solo destaca el rigor académico de sus actores, sino que expresan desde las humanidades y las ciencias sociales esos constructos teóricos al servicio de la sociedad, en tal sentido, este conjunto de aportes de los investigadores a través de sus artículos, enaltecen la labor académica de nuestra institución

Adaptar la web con "Mobile First" y mejora del posicionamiento SEO para el Máster en Geotecnologías Cartográficas en Ingeniería y Arquitectura

Memoria ID-037. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2018-2019