Ground-based hyperspectral analysis of the urban nightscape

Airborne hyperspectral cameras provide the basic information to estimate the energy wasted skywards by outdoor lighting systems, as well as to locate and identify their sources. However, a complete characterization of the urban light pollution levels also requires evaluating these effects from the city dwellers standpoint, e.g. the energy waste associated to the excessive illuminance on walls and pavements, light trespass, or the luminance distributions causing potential glare, to mention but a few. On the other hand, the spectral irradiance at the entrance of the human eye is the primary input to evaluate the possible health effects associated with the exposure to artificial light at night, according to the more recent models available in the literature. In this work we demonstrate the possibility of using a hyperspectral imager (routinely used in airborne campaigns) to measure the ground-level spectral radiance of the urban nightscape and to retrieve several magnitudes of interest for light pollution studies. We also present the preliminary results from a field campaign carried out in the downtown of Barcelona.Postprint (author's final draft

PolSAR and PolInSAR model based information estimation

Speckle for multidimensional SAR data may be modeled as the combination of multiplicative and additive noise sources. As demonstrated, the use of this noise model does not corrupt the estimation of physical information from PolInSAR data. The definition of a model based PolInSAR filter allows also the computation of relative errors for estimated heights of forested areas from PolInSAR data.Peer ReviewedPostprint (published version

Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA’s Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky–Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.E.A. was supported by the predoctoral scholarship, grant number ACIF/2019/187, funded by the Generalitat Valenciana and co-funded by the European Social Fund. J.V. and S.B. were supported by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project, grant number 755617. J.V. was additionally supported by a Ramón y Cajal Contract (Spanish Ministry of Science, Innovation and Universities). S.B. was additionally supported by the Generalitat Valenciana SEJIGENT program (SEJIGENT/2021/001) and European Union—NextGenerationEU (ZAMBRANO 21-04)

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016–2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.We gratefully acknowledge the financial support by the European Space Agency (ESA) for airborne data acquisition and data analysis in the frame of the FLEXSense campaign (ESA Contract No. 4000125402/18/NL/NA). The research was also supported by the Action CA17134 SENSECO (Optical synergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu, accessed on: 8 January 2022). This publication is also the result of the project implementation: “Scientific support of climate change adaptation in agriculture and mitigation of soil degradation” (ITMS2014+313011W580) supported by the Integrated Infrastructure Operational Programme funded by the ERDF

Ground-based polarimetric SAR interferometry for the monitoring of terrain displacement phenomena-part II: applications

©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Urban subsidence and landslides are among the greatest hazards for people and infrastructure safety and they require an especial attention to reduce their associated risks. In this framework, ground-based synthetic aperture radar (SAR) interferometry (GB-InSAR) represents a cost-effective solution for the precise monitoring of displacements. This work presents the application of GB-InSAR techniques, particularly with the RiskSAR sensor and its processing chain developed by the Remote Sensing Laboratory (RSLab) of the Universitat Politecnica de Catalunya (UPC), for the monitoring of two different types of ground displacement. An example of urban subsidence monitoring over the village of Sallent, northeastern of Spain, and an example of landslide monitoring in El Forn de Canillo, located in the Andorran Pyrenees, are presented. In this framework, the key processing particularities for each case are deeply analyzed and discussed. The linear displacement maps and time series for both scenarios are showed and compared with in-field data. For the study, fully polarimetric data acquired at X-band with a zero-baseline configuration are employed in both scenarios. The displacement results obtained demonstrate the capabilities of GB-SAR sensors for the precise monitoring of ground displacement phenomena.Peer ReviewedPostprint (author's final draft

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically-based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals (R2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications

Fusing optical and SAR time series for LAI gap filling with multioutput Gaussian processes

The availability of satellite optical information is often hampered by the natural presence of clouds, which can be problematic for many applications. Persistent clouds over agricultural fields can mask key stages of crop growth, leading to unreliable yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery which can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge. In this work, we propose the use of Multi-Output Gaussian Process (MOGP) regression, a machine learning technique that learns automatically the statistical relationships among multisensor time series, to detect vegetated areas over which the synergy between SAR-optical imageries is profitable. For this purpose, we use the Sentinel-1 Radar Vegetation Index (RVI) and Sentinel-2 Leaf Area Index (LAI) time series over a study area in north west of the Iberian peninsula. Through a physical interpretation of MOGP trained models, we show its ability to provide estimations of LAI even over cloudy periods using the information shared with RVI, which guarantees the solution keeps always tied to real measurements. Results demonstrate the advantage of MOGP especially for long data gaps, where optical-based methods notoriously fail. The leave-one-image-out assessment technique applied to the whole vegetation cover shows MOGP predictions improve standard GP estimations over short-time gaps (R 2 of 74% vs 68%, RMSE of 0.4 vs 0.44 [m 2 m −2 ]) and especially over long-time gaps (R 2 of 33% vs 12%, RMSE of 0.5 vs 1.09 [m 2 m −2 ])

Polarimetric differential SAR Interferometry with ground-based sensors

Las técnicas de Interferometría Diferencial se basan en la combinación de varias imágenes SAR con distinta separación temporal y permiten la recuperación de las componentes lineales y no-lineales del proceso de deformación ocurrida en el entorno de interés durante el entero periodo de observación. Condición imprescindible para una correcta estimación de los fenómenos geodéticos es la elevada estabilidad de la plataforma que embarca el sensor SAR. Por esta razón, a nivel operativo se utilizan datos SAR satelitales.El objetivo de la Polarimetría SAR es describir el entorno de interés analizando las propiedades de la señal que éste dispersa cuando se utilizan diferentes combinaciones de polarización de las antenas transmisora y receptora, definidas canales polarimétricos. La polarimetría interferométrica SAR junta la capacidad de la polarimetría de separar mecanismos de dispersión independientes con la sensibilidad de la Interferometría a la altura de los correspondientes centros de fase, y permite describir la distribución volumétrica de los dispersores dentro de la escena observada. Debido a la falta de conjuntos de datos polarimétricos SAR satelitales que cubran tramos temporales suficientemente largos, hay aún un gran interés en las mejoras que la polarimetría podría aportar a técnicas ya consolidadas como las de Interferometría Diferencial.La actividad de investigación que se presentará en esta tesis doctoral abarca, por primera vez conjuntamente, las dos áreas de la Polarimetría SAR y de la Interferometría Diferencial utilizando el sensor radar terrestre de corto alcance (gbSAR) desarrollado por la Universitat Politècnica de Catalunyua (UPC). El trabajo constará de dos bloques principales.El primer bloque describirá las técnicas que se han desarrollado para convertir el sistema UPC gbSAR en un instrumento operativo y simplificar la utilización de sus adquisiciones, incluyendo la formulación matemática de los principios de funcionamiento del sistema, la cadena de procesado de los raw data y su calibración polarimétrica, los procedimientos de georeferenciación, y las técnicas de compensación de los artefactos atmosféricos presentes en sus medidas diferenciales.La segunda parte se ocupará de demostrar los beneficios que los datos SAR polarimétricos ofrecen respecto a la medición de un único canal polarimétrico para aplicaciones diferenciales. A fin de llevar a cabo esta tarea, se analizarán los datos gbSAR adquiridos durante una campaña de medidas de un año realizada en el pueblo de Sallent, en Cataluña, afectado por un fenómeno de subsidencia. En esta parte se analizarán tres temas principales. El primero es el comportamiento no estacionario en tiempo del entorno urbano bajo la geometría de observación del sensor terrestre. Se estudiarán en detalle los efectos de su inestabilidad y se propondrá una técnica de filtrado novedosa entallada a las propiedades de los blancos deterministas con el fin de preservar la información de la fase diferencial. El segundo tema abarca el problema de los efectos de troposfera en datos diferenciales con separación temporal superior al mes y de su separación de las variaciones de fase inducidas por el proceso de deformación. El tercer tema es la utilización de toda la información polarimétrica diferencial. Con fin de superar las limitaciones propias de las técnicas DInSAR clásicas, se propondrá un nuevo modelo polarimétrico de dispersión y se demostrarán las ventajas de la nueva formulación enseñando la mejor estimación del proceso de subsidencia en Sallent. En la parte final de este apartado se explorará también el potencial de las técnicas polarimétricas de optimización de la coherencia para aplicaciones diferenciales.Differential SAR interferometry (DInSAR) deals with the combination of multi-temporal SAR images for the estimation of the linear and non-linear components of the deformation process within an area of interest during the whole observation period. A high stability of the platform is required for a reliable estimation of the geodetic phenomena. Accordingly, space-borne SAR images are operatively employed for DInSAR estimation, air-borne DInSAR still constituting a challenging research issue. SARPolarimetry aims at charactering the illuminated area through the analysis of its response under different combinations of transmitting and receiving antennas polarization, called polarimetric channels. The Polarimetric SAR Interferometry joins the capability of Polarimetry to separate independent scattering mechanisms and the sensitivity of Interferometry to the corresponding phase centers' elevation, making it possible to describe the volumetric distribution of the scatterers within the observed area. Owing to the lack of long-time collections of polarimetric space-borne SAR data, the studies carried out in this research field have been mainly based on air-borne acquisitions. Yet, there is a great expectation for the improvements that polarimetry may bring to assessed single-polarization techniques such as the DinSAR.The research described in this PhD dissertation fills for the first time the gap between SAR Polarimetry and SAR Differential Interferometry through the employment of an X-band ground-based SAR (gbSAR) sensor developed by the Remote Sensing Lab of the Universitat Politècnica de Catalunya (UPC).The work is divided into two main blocks. The first part deals with the algorithms that have been developed to make the UPC system operative and its acquisitions easy to use. Summarily, they include the mathematical formulation of the sensor's working principles, the raw data processing chain and the polarimetric calibration method, the geocoding procedures, and the techniques compensating for the atmospheric artefacts affecting gbSAR zero-baseline acquisitions.The second part is concerned with demonstrating the benefits that polarimetric SAR measurements provide with respect to single-polarization data for differential applications. In order to cope with this task, the data sets acquired during a one-year measurement campaign carried out in the village of Sallent, northeastern Spain, are analyzed. The experiment was focused on monitoring the subsidence phenomenon affecting a district of the village with the UPC gbSAR sensor. Three main issues are here argued. The first one is the time non-stationary behaviors characterizing the urban environment at X-band in the gbSAR observation geometry. Their effects are analyzed in detail and a novel non-stationary filtering technique tailored to deterministic scatterers' properties is introduced to preserve the differential phase information. The second one is the compensation of the troposphere changes in long-time span gbSAR differential interferograms. A new technique is worked out to effectively separate the differential phase variations due to the atmospheric artefacts from the deformation components. The third one is the use of the whole polarimetric differential information. A novel polarimetric differential scattering model is put forward to relax the constraints of an advanced DInSAR technique, the Coherent Pixel Technique, and to propose an innovative polarimetric approach. The advantages offered by Polarimetric DInSAR are demonstrated in terms of quality of the deformation-rate map describing the subsidence phenomenon in Sallent. In the end, the potentials of coherence-optimization techniques for the further improvement of the deformation process estimation are stressed

Forest parameter estimation in the Pol-InSAR context employing the multiplicative–additive speckle noise model

This paper addresses the problem of speckle noise on single baseline polarimetric SAR interferometry (Pol-InSAR) on the basis of the multiplicative–additive speckle noise model. Considering this speckle noise model, a novel filtering technique is defined and studied in terms of simulated and experimental Pol-InSAR data. As demonstrated, the use of the multiplicative–additive speckle noise model does not lead to a corruption of the useful information but to an improvement of its estimation. The performance of the algorithm is analyzed in terms of the physical parameters retrieved from the filtered data, that in this work correspond to the forest height and the ground phase. In case of experimental data, the retrieved forest height is compared and validated against Lidar ground truth measurements.Peer ReviewedPostprint (published version