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This article presents a study of the relationship among decorrelation phase in synthetic aperture radar (SAR) interferogram, soil moisture, and water content in vegetation with the aim of mitigating the contribution of decorrelation phase in SAR interferometry estimates of terrain displacements. A methodology for the mitigation of the phase bias based on the temporal variation of the vegetation water content is presented. Decorrelation phases are computed using time series of Sentinel-1 images and compared with in situ measurements of soil moisture. It is shown that soil moisture can partially explain the observed values of decorrelation phases pointing out the role of vegetation water content. A new model is proposed to compute the contribution of vegetation to the decorrelation phase based on the normalized difference water index (NDWI) index. The methodology is applied to all short temporal baseline interferograms obtained from the time series of Sentinel-1 SAR images, using the NDWI maps generated from Sentinel-2 multispectral images. The cumulative displacement is computed by integrating the short temporal baseline interferograms, corrected for the land cover and soil moisture changes. It is shown that the proposed methodology can reduce the variance of estimated cumulative displacement in areas covered by vegetation.

ABSTRACT Accurate crop data are essential for reliable irrigation water requirements (IWR) calculations, which can be acquired during the crop growth season for a given region using earth observation (EO) satellite time series. In addition, a relationship between crop coefficients and the NDVI can be established to allow for crop evapotranspiration estimation. The main objective of the present work was to develop a methodology, and its implementation in an application software, to extract crop parameters from EO image time series for a set of parcels of different types of crops, to be used as input data for a soil water balance model to compute IWR. The methodology was tested at two distinct test sites, the Vila Franca de Xira (site I) and Vila Velha de Ródão (site II) municipalities, Portugal. Landsat-7 and −8 images acquired from April to October 2013 were used for site I, while SPOT-5 Take-5 images from April to September 2015 were considered for site II. EO data were used to estimate the basal crop coefficients, planting dates, and crops growth stage lengths. Based on crop, soil and meteorological data, the IWR for the main crops of both test regions were estimated using the IrrigRotation model. The crop coefficient curves obtained from the EO data proved to be reliable for IWR estimation.

As a contribution for sustainability, electric vehicles (EVs) are seen as one of the most effective influences in the transport sector. This paper proposes an improved voltage control of the EV operating as uninterruptible power supply (UPS) in smart homes. With the EV plugged-in into the smart home, it can act as an off-line UPS protecting the electrical appliancesfrom power grid outages. The foremost advantages of the proposed voltage control strategy are comprehensively emphasized, establishing a comparison with the classical approach. Aiming to offer a sinusoidal voltage for linear and nonlinear electrical appliances, a pulse width modulation with a multi-loop control scheme is used. A Kalman filter is usedfor decreasing significantly the time of detecting power outages and, consequently, the transition for the UPS mode. The computer simulations and the acquired experimental results validate the proposed strategy in different conditions of operation.

Global mean sea level rise associated with global warming has a major impact on coastal areas and represents one of the significant natural hazards. The Asia-Pacific region, which has the highest concentration of human population in the world, represents one of the larger areas on Earth being threatened by the rise of sea level. Recent studies indicate a global sea level of 3.2 mm/yr as measured from 20 years of satellite altimetry. The combined effect of sea level rise and local land subsidence, can be overwhelming for coastal areas. The Synthetic Aperture Radar (SAR) interferometry technique is used to process a time series of TerraSAR-X images and estimate the land subsidence in the urban area of Singapore. Interferometric SAR (InSAR) measurements are merged to the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 sea-level rise scenarios to identify projected inundated areas and provide a map of flood vulnerability. Subsiding rates larger than 5 mm/year are found near the shore on the low flat land, associated to areas recently reclaimed or built. The projected flooded map of Singapore are provided for different sea-level rise scenarios. In this study, we show that local land subsidence can increase the flood vulnerability caused by sea level rise by 2100 projections. This can represent an increase of 25% in the flood area in the central area of Singapore for the RCP4.5 scenario.

In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing to the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsened. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables monitoring of gradual processes. These processes can be monitored using spectral vegetation indices (VI) as their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

This work presents a methodology for the short-term forecast of intense rainfall based on a neural network and the integration of Global Navigation and Positioning System (GNSS) and meteorological data. Precipitable water vapor (PWV) derived from GNSS is combined with surface pressure, surface temperature and relative humidity obtained continuously from a ground-based meteorological station. Five years of GNSS data from one station in Lisbon, Portugal, are processed. Data for precipitation forecast are also collected from the meteorological station. Spaceborne Spinning Enhanced Visible and Infrared Imager (SEVIRI) data of cloud top measurements are also gathered, providing collocated information on an hourly basis. In previous studies it was found that the time-varying PWV is correlated with rainfall and can be used to detected heavy rain. However, a significant number of false positives were found, meaning that the evolution of PWV does not contain enough information to infer future rain. In this work, a nonlinear autoregressive exogenous neural network model (NARX) is used to process the GNSS and meteorological data to forecast the hourly precipitation. The proposed methodology improves the detection of intense rainfall events and reduces the number of false positives, with a good classification score varying from 63% up to 72% and a false positive rate of 36% down to 21%, for the tested years in the dataset. A score of 64% for intense rain events classification with 22% false positive rate is obtained for the most recent years. The method also achieves an almost 100% hit rate for the rain vs no rain detection, with close to no false alarms.

This study evaluates the capability of an unconstrained tomographic algorithm to capture 3D water vapor density variability throughout 2017 in Houston, U.S. The algorithm relies solely on Global Navigation Satellite System (GNSS) observations and does not require an initial guess or other specific constraints regarding water vapor density variability within the tomographic domain. The test domain, featuring 9 km horizontal, 500 m vertical, and 30 min temporal resolutions, yielded remarkable results when compared to data retrieved from the ECMWF Reanalysis v5 (ERA5), regional Weather Research and Forecasting Model (WRF) data, and GNSS-Radio Occultation (RO). For the first time, a time series of Precipitable Water Vapor maps derived from the Interferometric Synthetic Aperture Radar (InSAR) technique was used to validate the spatially integrated water vapor computed by GNSS tomography. Tomographic results clearly indicate the passage of Hurricane Harvey, with integrated water vapor peaking at 60 kg/m2 and increased humidity at altitudes up to 7.5 km. Our findings suggest that GNSS tomography holds promise as a reliable source of atmospheric water vapor data for various applications. Future enhancements may arise from denser and multi-constellation networks.

This paper presents a new approach for detecting deforestation using Sentinel-1 C-band backscattering data. It is based on the temporal analysis of the backscatter intensity and its correlation with the scattering behavior of deforested plots. The backscatter intensity temporal variability is modeled with a logistic function, whose lower and upper boundaries are, respectively, set based on the representative backscatter values for forest and deforested plots. The approach also enables the identification of the date of each deforestation event, corresponding to the inflection point of the logistic curve that best fits the backscatter intensity time series. The methodology was applied to two forest biomes, a tropical forest at Iguazu National Park in Argentina and a temperate forest in the Brăila region in Romania. The optimal flattening parameter was 0.12 for both sites, with an F1-score of 0.93 and 0.71 for the tropical and temperate forests, respectively. The temporal accuracy shows a bias on the estimated date, with a slight delay of 2 months. The results reveal that the Sentinel C-band data can be successfully used for deforestation detection over tropical forests; however, the accuracy for temperate forests might be 20 pp lower, depending on the environmental conditions, such as rainfall, snow and management after logging.

The European Union (EU) member states are expected to develop new procedures, based on automatic earth observation data analysis, for the management and control of direct aids to the farmers, as part of the Common Agricultural Policy (CAP) reform of 2020. Here, we propose an operational monitoring system based on Sentinel-2 surface reflectance (SR) data and machine learning (ML) algorithms, consisting of a hierarchical approach triggering 3 color-coded warning alerts to distinguish among compliant (green), non-compliant (red), and inconclusive (yellow) parcels comparatively to the farmer’s declaration. A Random Forest (RF) model is applied to a 5-day interpolated SR time series to generate a preliminary crop map, where all the parcels whose predicted and declared crop type match are flagged as compliant. Next, a refinement procedure is adopted to improve the discrimination between temporary and permanent crops. At this stage, VI temporal metrics and texture are used as input to a Support Vector Machine (SVM) classifier trained using only the previous compliant parcels. Through a set of decision rules, SVM crop class predictions are flagged as compliant, non-compliant and inconclusive. The system was tested for a significant area in mainland Portugal, using the 2019 Land Parcel Information System (LPIS) data. The system returned 96.5%, 2.5% and 1% of the parcels as compliant, inconclusive, and non-compliant, respectively. Comparison with field inspections for the subsidy control of 2019 revealed that only 1.1% of the correct declarations were classified by the system as non-compliant (5% as admissible value), while less than 5% of the real non-compliant declarations passed through the system (10–20%).

In the Mediterranean region, a significant decline in the vitality of vegetation has been observed in the last two decades, with a high forest mortality rate for several species. The increase in mortality has been attributed to water stress resulting from an increase in temperature and long periods of drought. To detect and quantify the impact of these events on tree mortality, an efficient and easy-to-use methodology for rapid damage assessment is required. Our study aims to assess the potential of high spatial resolution multispectral images from the Pleiades constellation to detect and map cork oak mortality in a pasture environment with multiple forest species. An approach based on change detection and the use of an unsupervised classifier is proposed to detect mortality at the cork oak level. The change in the values observed for three vegetation indices, NDVI, RGI, and GNDVI, between two epochs is used in an unsupervised classification algorithm to estimate the dead tree class. The classification results are accurate, with precision and recall values higher than 90%. Detailed cork oak mortality mapping is of significant use in comprehending ecosystem change as a result of tree mortality and for the implementation of mitigation mechanisms for the ongoing desertification process.

Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation.

The skills of CODAR SeaSonde coastal high-frequency radars (HFR) located in the West Iberian Peninsula on measuring wave parameters are compared to in situ (buoy) and satellite altimeters (SA) wave observations. Significant wave heights (SWH), wave periods, and wave directions are compared over a time window of 36-months, from January 2017 to December 2019. The ability of HFR systems to capture extreme wave events is also assessed by comparing SWH measurements during the Emma storm, which hit the Iberian Peninsula in March 2018. The analysis presented in this study shows a slight overestimation of the SWH by the HFR systems. Comparisons with in situ observations revealed correlation coefficients (R) of the order of 0.69–0.87, biases below 0.60 m, root-mean-squared errors (RMSE) between 0.89 m to 1.18 m, and a slope regression between 1.01 and 1.26. Using buoy observations as reference ground truth, the comparisons with SA revealed Rs higher than 0.94, biases under 0.19 m, and RMSEs between 0.17 m and 0.42 m. Since in situ observations do not overlap all the HFR range cells (RC), and its correlation coefficients with SA have shown good agreement (R > 0.94), Sentinel-3 SA (SRAL) SWH measurements are further used for the validation of the HFR systems SWH observations. The comparison between the HFR and the SA collocated SWH observations allowed the evaluation of the ability of the radars to retrieve wave data as a function of the distance to the coast, particularly during extreme wave events. The comparison of the lower frequency (4.86 MHz) HFR coastal radars with the SA measurements showed an R of 0.94–0.99, a negative but reduced bias (−0.37), and an RMSE of 0.53 m. The higher frequency HFR systems (12–13.5 MHz) showed R between 0.53 and 0.82, and a clear overestimation of the SWH by the HFR sites.

In this study, a methodology to estimate the intertidal bathymetry from multispectral remote sensing images is presented. The technique is based on the temporal variability of the water and the intertidal zone reflectance and their correlation with the tidal height. The water spectral behavior is characterized by high absorption at the infrared (IR) band or radiation with higher wavelengths. Due to tidal cycles, pixels on the intertidal zone have higher temporal variability on the near IR spectral reflectance. The variability of IR reflectivity in time is modeled through a sigmoid function of three parameters, where the inflection parameter corresponds to the pixel elevation. The methodology was tested at the Tagus river estuary in Lisbon, Portugal, and at the Bijagós archipelago, in the West African nation of Guinea-Bissau. Multispectral images from Sentinel-2 satellites were used, after atmospheric corrections from ACOLITE processor and the derived bathymetric model validated with in situ data. The presented method does not require additional depth data for calibration, and the output can generate intertidal digital elevation models at 10 m spatial resolution, without any manual editing by the operator. The results show a standard deviation of 0.34 m at the Tagus tidal zone, with −0.50 m bias, performing better than the Stumpf ratio transform algorithm, also applied to the test areas to derive intertidal bathymetry. This methodology can be used to update intertidal elevation models with clear benefits to monitoring of intertidal dynamics, morphodynamic modeling, and cartographic update.

The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth’s atmosphere’s global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.

Este artigo, Parte 2 do mesmo estudo, apresenta os resultados do experimento “Sensação de continuidade de um trecho musical” (CHAIB et. al. 2013) analisados através da técnica estatística Análise Fatorial de Correspondências (AFC). O objetivo principal dessa análise é o de observar, através de índices, a influência do gesto na transmissão de sensações de continuidade, suspensão e conclusão sobre a performance em percussão. Uma vez alcançado o objetivo será possível generalizar os resultados em performances realizadas sobre os mesmos moldes das apresentadas no experimento.

Este artigo, Parte 1 do mesmo estudo, mostra como foi realizado um tratamento descritivo, através de uma análise percentual, dos dados experimentais obtidos ao observar a influência do gesto corporal sobre as sensações de continuidade, suspensão e conclusão de um trecho musical em percussão. Dentro da complexa relação estabelecida entre o percussionista e o texto musical apresentado em grande parte das produções para percussão, procuramos compreender até que ponto o corpo poderá ser um agente auxiliador no processo de transmissão de sensações específicas sobre a música executada em uma performance percussiva. A complexidade deste processo experimental nos obrigou a dividir os resultados obtidos em dois artigos. Este trabalho trata-se da 1ª parte de publicação dos dados adquiridos e analisados a partir dos pesos percentuais.

In this work, we study the capability of the ground surface to generate Persistent Scatterers (PS) based on the lithology, slope and aspect angles. These properties affect the scattering behavior of the Synthetic Aperture Radar (SAR) signal, the interferometric phase stability and, as a consequence, the PS generation. Two-time series of interferometric SAR data acquired by two different SAR sensors in the C-band are processed to generate independent PS datasets. The region north of Lisbon, Portugal, characterized by sparse vegetation and lithology diversity, is chosen as study area. The PS frequency distribution is obtained in terms of lithology, slope and aspect angles. This relationship could be useful to estimate the expected PS density in landslide-prone areas, being lithology, slope and aspect angles important landslide predisposing factors.

PHOTOGRAMMETRIC TECHNIQUES AND GIS ENVIRONMENT FOR SEA CLIFF RETREAT ASSESSMENT (FORTE NOVO–TRAFAL AREA, ALGARVE). High-accuracy photogrammetric techniques were used in order to assess the sea cliff retreat in the area of Forte Novo – Trafal (Algarve) in the 1991-2001 period. They yielded an estimate of the top cliff retreat for Forte Novo of 22.7m, and of 8.4m for Trafal. The sediment volume that has been lost from the cliffs is around 121.467m3. The relative precision obtained in this study by the photogrammetric measurement of the errors in horizontal position for some control elements is of 0.60m, and it is this value that determines the accuracy of the retreat rates for the ten-year period.

Optical and microwave images have been combined for land cover monitoring in different agriculture scenarios, providing useful information on qualitative and quantitative land cover changes. This study aims to assess the complementarity and interoperability of optical (SPOT-5 Take-5) and synthetic aperture radar (SAR) (Sentinel-1A) data for crop parameter (basal crop coefficient (Kcb) values and the length of the crop’s development stages) retrieval and crop type classification, with a focus on crop water requirements, for an irrigation perimeter in Angola. SPOT-5 Take-5 images are used as a proxy of Sentinel-2 data to evaluate the potential of their enhanced temporal resolution for agricultural applications. In situ data are also used to complement the Earth O=observation (EO) data. The Normalized Difference Vegetation Index (NDVI) and dual (VV + VH) polarization backscattering time series are used to compute the Kcb curve for four crop types (maize, soybean, bean and pasture) and to estimate the length of each phenological growth stage. The Kcb values are then used to compute the crop’s evapotranspiration and to subsequently estimate the crop irrigation requirements based on a soil water balance model. A significant R2 correlation between NDVI and backscatter time series was observed for all crops, demonstrating that optical data can be replaced by microwave data in the presence of cloud cover. However, it was not possible to properly identify each stage of the crop cycle due to the lack of EO data for the complete growing season.

A barragem de Paradela, construída no Rio Cávado no de 1956/58, é uma barragem de enrocamento com 11O m de altura. A estanqueidade do seu paramento era assegurada por lajes de betão armado, articuladas. Os importantes assentamentos observados durante a exploração dos primeiros anos estão na origem da fissuração da cortina de montante e do aumento das infiltrações. O presente artigo mostra como, depois de várias intervenções infrutíferas, se efectuou a reparação geral em 1980. A impermeabilização do paramento de montante ficou assegurado graças à aplicação de uma membrana "in situ", processo RODIMPERM. Os diferentes aspectos e fases desta reparação encontram-se brevemente descritos, bem como os resultados obtidos.