Geoscience and Remote Sensing

Remote sensing of environment research has explored the benefits of using synthetic aperture radar imagery systems for a wide range of land and marine applications since these systems are not affected by weather conditions and therefore are operable both daytime and nighttime. The design of image processing techniques for  synthetic aperture radar applications requires tests and validation on real and synthetic images. The GRSS benchmark database supports the desing and analysis of algorithms to deal with SAR and PolSAR data.

Last Updated On: 
Tue, 02/08/2022 - 17:46
Citation Author(s): 
Nobre, R. H.; Rodrigues, F. A. A.; Rosa, R.; Medeiros, F.N.; Feitosa, R., Estevão, A.A., Barros, A.S.

Mapping millions of buried landmines rapidly and removing them cost-effectively is supremely important to avoid their potential risks and ease this labour-intensive task. Deploying uninhabited vehicles equipped with multiple remote sensing modalities seems to be an ideal option for performing this task in a non-invasive fashion. This report provides researchers with vision-based remote sensing imagery datasets obtained from a real landmine field in Croatia that incorporated an autonomous uninhabited aerial vehicle (UAV), the so-called LMUAV.


Slow moving motions are mostly tackled by using the phase information of Synthetic Aperture Radar (SAR) images through Interferometric SAR (InSAR) approaches based on machine and deep learning. Nevertheless, to the best of our knowledge, there is no dataset adapted to machine learning approaches and targeting slow ground motion detections. With this dataset, we propose a new InSAR dataset  for Slow SLIding areas DEtections (ISSLIDE) with machine learning. The dataset is composed of standardly processed interferograms and manual annotations created following geomorphologist strategies.


We used  Sentinel-2 images to create the dataset In order to estimate sequestered carbon in the above-ground forest Biomass.  Moreover, fieldwork was completed to gather related forest biomass volume. The clipped image has a size of 1115 × 955 pixels and consists of bands 3, 4, and 8, which correspond to green, red, and near-infrared.


The  Sentinel-2 L2A multispectral data cubes include two regions of interest (roi1 and roi2) each of them containing 92 scenes across Switzerland within T32TLT, between 2018 and 2022, all band at 10m resolution These areas of interest show a diverse landscape, including regions covered by forests that have undergone changes, agriculture and urban areas.


Progress toward the use of S-band SoOp in sea surface remote sensing was demonstrated in a 2012-2013 experiment based at the Harvest Oil Platform located at 34.469° N and 120.682° W, roughly 11 km from Point Conception, Santa Barbara, CA. Satellite transmissions from the XM-radio service were observed, using one channel each from the “Rhythm” (located above 85°W) and the “Blues” (115°W) satellites. Each downlink channel had a bandwidth of 1.886 MHz with a symbol rate of 1.64 Msps in Quadrature Phase Shift Key (QPSK) modulation.


The dataset contains fitted three-pole Debye dielectric model parameters of 567 soil spectra. Three soils of loamy sand, sandy loam, and silt loam textures were tested. Of each soil, 20 samples of various water contents were prepared with the use of distilled water and potassium chloride solutions, 5 samples for each liquid. Air-dry samples were also prepared. Dielectric spectra were obtained with the use of a six-channel coaxial-transmission-line cell system at 9 controlled temperature steps from 0.5 to 40°C in the 0.02 – 3 GHz frequency range.


In precision agriculture, detecting productive crop fields is an essential practice that allows the farmer to evaluate operating performance separately and compare different seed varieties, pesticides, and fertilizers. However, manually identifying productive fields is often time-consuming, costly, and subjective. Previous studies explore different methods to detect crop fields using advanced machine learning algorithms to support the specialists’ decisions, but they often lack good quality labeled data.


A computational experiment has been performed in order to evaluate systematic errors of atmospheric Total Water Vapor (TWV) and integral Liquid Water Content (LWC) microwave radiometric retrieval from satellites by means of dual-frequency method (inverse problem).  The errors under consideration may arise due to the non-linearity of brightness temperature level on true liquid water and effective cloud temperature dependencies and due to neglecting the spatial distribution of cumulus clouds in the satellite microwave radiometer antenna field-of-view (FOV).