Design and Performance Estimation of a Photonic Integrated Beamforming Receiver for Scan-On-Receive Synthetic Aperture Radar

Synthetic aperture radar is a remote sensing technology finding applications in a wide range of fields, especially related to Earth observation. It enables a fine imaging that is crucial in critical activities, like environmental monitoring for natural resource management or disasters prevention. In this picture, the scan-on-receive paradigm allows for enhanced imaging capabilities thanks to wide swath observations at finer azimuthal resolution achieved by beamforming of multiple simultaneous antenna beams. Recently, solutions based on microwave photonics techniques demonstrated the possibility of an efficient implementation of beamforming, overcoming some limitations posed by purely electronic solutions, offering unprecedented flexibility and precision to RF systems. Moreover, photonics-assisted RF beamformers can nowadays be realized as integrated circuits, with reduced size and power consumption with respect to digital beamforming approaches. This paper presents the design analysis and the challenges of the development of a hybrid photonic-integrated circuit as the core element of an X-band scan-on-receive spaceborne synthetic aperture radar. The proposed photonic-integrated circuit synthetizes three simultaneous scanning beams on the received signal, and performs the frequency down-conversion, guaranteeing a compact 15 cm2-form factor, less than 6 W power consumption, and 55 dB of dynamic range. The whole photonics-assisted system is designed for space compliance and meets the target application requirements, representing a step forward toward a deeper penetration of photonics in microwave applications for challenging scenarios, like the observation of the Earth from space

Towards a 20m global building map from Sentinel-1 SAR Data

This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.Peer ReviewedPostprint (published version

Sentinel-1 InSAR coherence for land cover mapping: a comparison of multiple feature-based classifiers

This article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain—interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Doñana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.Peer ReviewedPostprint (published version

Design and performance estimation of a photonic integrated beamforming receiver for scan-on-receive synthetic aperture radar

Synthetic aperture radar is a remote sensing technology finding applications in a wide range of fields, especially related to Earth observation. It enables a fine imaging that is crucial in critical activities, like environmental monitoring for natural resource management or disasters prevention. In this picture, the scan-on-receive paradigm allows for enhanced imaging capabilities thanks to wide swath observations at finer azimuthal resolution achieved by beamforming of multiple simultaneous antenna beams. Recently, solutions based on microwave photonics techniques demonstrated the possibility of an efficient implementation of beamforming, overcoming some limitations posed by purely electronic solutions, offering unprecedented flexibility and precision to RF systems. Moreover, photonics-assisted RF beamformers can nowadays be realized as integrated circuits, with reduced size and power consumption with respect to digital beamforming approaches. This paper presents the design analysis and the challenges of the development of a hybrid photonic-integrated circuit as the core element of an X-band scan-on-receive spaceborne synthetic aperture radar. The proposed photonic-integrated circuit synthetizes three simultaneous scanning beams on the received signal, and performs the frequency down-conversion, guaranteeing a compact 15 cm2-form factor, less than 6 W power consumption, and 55 dB of dynamic range. The whole photonics-assisted system is designed for space compliance and meets the target application requirements, representing a step forward toward a deeper penetration of photonics in microwave applications for challenging scenarios, like the observation of the Earth from space

SENBYGG

Rapporten dokumenterer resultater fra prosjektet SENBYGG der Statens kartverk er oppdragsgiver. Prosjektet har som formål å detektere bygningsendringer (nybygg, tilbygg eller revet bygg) ved hjelp av radarsatellittene Sentinel-1 A og B. I prosjektet har vi studert flere mulige metoder for endringsdeteksjon. Radarsatellitter (SAR) har relativt god oppløsning (10m) men betydelig med støy. For å redusere støyen midler vi bilder over samme område for hvert kalenderår. Endringer detekteres ved å sammenligne tilbakespredning fra to etterfølgende år. I prosjektet har vi påvist at det er fult mulig å detektere bygningsendringer i SAR bilder basert på årlige middelbilder. Man kan også redusere tidsintervallene noe, men antall feil vil øke siden støyene i bildene da blir mer merkbar.SENBYGGpublishedVersio

Mapping of multitemporal rice (Oryza sativa L.) growth stages using remote sensing with multi-sensor and machine learning : a thesis dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand

Figure 2.1 is adapted and re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Rice (Oryza Sativa) plays a pivotal role in food security for Asian countries, especially in Indonesia. Due to the increasing pressure of environmental changes, such as land use and climate, rice cultivation areas need to be monitored regularly and spatially to ensure sustainable rice production. Moreover, timely information of rice growth stages (RGS) can lead to more efficient of inputs distribution from water, seed, fertilizer, and pesticide. One of the efficient solutions for regularly mapping the rice crop is using Earth observation satellites. Moreover, the increasing availability of open access satellite images such as Landsat-8, Sentinel-1, and Sentinel-2 provides ample opportunities to map continuous and high-resolution rice growth stages with greater accuracy. The majority of the literature has focused on mapping rice area, cropping patterns and relied mainly on the phenology of vegetation. However, the mapping process of RGS was difficult to assess the accuracy, time-consuming, and depended on only one sensor. In this work, we discuss the use of machine learning algorithms (MLA) for mapping paddy RGS with multiple remote sensing data in near-real-time. The study area was Java Island, which is the primary rice producer in Indonesia. This study has investigated: (1) the mapping of RGS using Landsat-8 imagery and different MLAs, and their rigorous performance was evaluated by conducting a multitemporal analysis; (2) the temporal consistency of predicting RGS using Sentinel-2, MOD13Q1, and Sentinel-1 data; (3) evaluating the correlation of local statistics data and paddy RGS using Sentinel-2, PROBA-V, and Sentinel-1 with MLAs. The ground truth datasets were collected from multi-year web camera data (2014-2016) and three months of the field campaign in different regions of Java (2018). The study considered the RGS in the analysis to be vegetative, reproductive, ripening, bare land, and flooding, and MLAs such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN) were used. The temporal consistency matrix was used to compare the classification maps within three sensor datasets (Landsat-8 OLI, Sentinel-2, and Sentinel-2, MOD13Q1, Sentinel-1) and in four periods (5, 10, 15, 16 days). Moreover, the result of the RGS map was also compared with monthly data from local statistics within each sub-district using cross-correlation analysis. The result from the analysis shows that SVM with a radial base function outperformed the RF and ANN and proved to be a robust method for small-size datasets (< 1,000 points). Compared to Sentinel-2, Landsat-8 OLI gives less accuracy due to the lack of a red-edge band and larger pixel size (30 x 30 m). Integration of Sentinel-2, MOD13Q1, and Sentinel-1 improved the classification performance and increased the temporal availability of cloud-free maps. The integration of PROBA-V and Sentinel-1 improved the classification accuracy from the Landsat-8 result, consistent with the monthly rice planting area statistics at the sub-district level. The western area of Java has the highest accuracy and consistency since the cropping pattern only relied on rice cultivation. In contrast, less accuracy was noticed in the eastern area because of upland rice cultivation due to limited irrigation facilities and mixed cropping. In addition, the cultivation of shallots to the north of Nganjuk Regency interferes with the model predictions because the cultivation of shallots resembles the vegetative phase due to the water banks. One future research idea is the auto-detection of the cropping index in the complex landscape to be able to use it for mapping RGS on a global scale. Detection of the rice area and RGS using Google Earth Engine (GEE) can be an action plan to disseminate the information quickly on a planetary scale. Our results show that the multitemporal Sentinel-1 combined with RF can detect rice areas with high accuracy (>91%). Similarly, accurate RGS maps can be detected by integrating multiple remote sensing (Sentinel-2, Landsat-8 OLI, and MOD13Q1) data with acceptable accuracy (76.4%), with high temporal frequency and lower cloud interference (every 16 days). Overall, this study shows that remote sensing combined with the machine learning methodology can deliver information on RGS in a timely fashion, which is easy to scale up and consistent both in time and space and matches the local statistics. This thesis is also in line with the existing rice monitoring projects such as Crop Monitor, Crop Watch, AMIS, and Sen4Agri to support disseminating information over a large area. To sum up, the proposed workflow and detailed map provide a more accurate method and information in near real-time for stakeholders, such as governmental agencies against the existing mapping method. This method can be introduced to provide accurate information to rice farmers promptly with sufficient inputs such as irrigation, seeds, and fertilisers for ensuring national food security from the shifting planting time due to climate change

City-scale Modelling of Factors Affecting Urban Pluvial Flood Hazard in Rapidly Developing Cities Using Global Data

Flooding is the most prevalent disaster worldwide accounting for 43% of all recorded global disaster events in the past 20 years leading up to 2018 Choy (2018). While migration from rural settlements to urban areas often mirrors economic advancement, it also presents socioeconomic and environmental challenges. Rapid urban growth strains existing infrastructure and also discourages the preservation of natural habitat in favour of building more developments causing urban flooding. Climate change and urbanisation have been reported as the major contributors to the increasing damaging effects of flooding to lives and livelihoods worldwide (Aerts et al., 2014). There is lack of adequate research focused on the dual impacts of climate change and urbanisation on urban flooding and water quality in rapidly developing urban areas of the world – a gap that will result in an increase in fluvial and pluvial flood risk, and further reduction in water quality (Miller and Hutchins, 2017). This study highlights the importance of the use of free global datasets in the development of a city-scale 2D hydraulic model that assesses the impact of land use change and climate change on urban pluvial flooding in rapidly developing cities. This thesis presents three key results chapters assessing the ability of a simplified city-scale hydrological and hydraulic models to estimate urban pluvial flood inundation in a large catchment, before going on to establish the impacts of climate change and land use change on flood hazard. Topography has been identified as a key dataset of estimating flood extent (Horritt and Bates, 2001) and models of flood extent rely on DEMs in order to simulate paths of water flow, flood extent and depth. Errors in Digital Elevation Models (DEMs) can substantially affect the results of flood models (Stephens et al., 2012, Hawker et al., 2018). Therefore, in order to increase the accuracy of the outputs from the hydrological and hydraulic models used in this study, a methodology for the correction of building error in DEMs was developed in Chapter 4 for removing building elevation artefacts from six global DEMs namely: (i) NASADEM, (ii) SRTM, (iii) MERIT, (iv) ALOS, (v) TanDEM-X 12 m, and (vi) TanDEM-X 90 m. The findings show that the removal of building elevation artefacts/error from global DEMs resulted in the improvement of the vertical height accuracy of global DEMs. The findings show that building density has an influence on vertical accuracy of global digital elevation models (DEMs). This finding was a key step prior to research undertaken in subsequent chapters. In chapter 5, a city-scale hydrological and hydraulic model of the Nairobi catchment is built. The purpose of creating the models is to use the raw DEM and corrected global DEM derived in Chapter 4 to estimate the impact of land use change and climate change on urban pluvial flood hazard at city-scale level using global datasets. The HEC-RAS software is used to create five categories of models for the extreme rainfall event of 1st to 13th of March 2018; a baseline model (S1-Baseline), S2-2000LU model, S3-CP4uplift model, S4-P25uplift model, and the S5-RawDEM model. The five sets of models are created in 2D and make use of the diffusive wave equation for simplification. The results showing a lot of promises by providing evidence for the hypothesis that urban flood models built at city-scale level using free global datasets have a good level of skill and are proficient enough to accurately estimate urban flood inundation depth and extent in rapidly developing cities characterised by sparsity of data. Chapter 6 discusses the results of the flood inundations and flood hazard vulnerability maps from the HEC-RAS 2D hydraulic model under 5 different scenarios. It is found that topography plays an important role in flood inundation maps and that the accuracy of flood inundation maps can be improved simply by using urban corrected DEMs over raw DEMs as key input data when conducting both hydrological and hydraulic modelling. The findings also show that urban pluvial flooding is affected by both change in climate change and land use change, however, climate change is found to contribute significantly to surface water runoff and exacerbate the problems of urban flooding. The results also show that the baseline model using the urban corrected DEM as input data produced flood inundation and flood hazard vulnerability maps with better accuracy in comparison to a similar baseline model using the raw DEM as key input data. Chapter 7 further explored the influence of climate change and land use change that is due to rapid urbanization on urban pluvial flood hazard. Chapter 7 focuses on the synthesis of the results obtained from the results of the 5 set of 2D hydraulic models discussed in chapter 6. Results demonstrated that climate change had more influence on urban pluvial flood hazard than land use change. It is found that climate change, rather than land use change is a bigger threat to urban area in terms of flood risk. Specifically, the effects induced by climate change under the CP4 and P25 climate rainfall models are much higher than the effects induced by land use change due to urbanisation in Nairobi from 2000 to 2020. The findings show that the changes caused by current and future changes in rainfall intensities and frequencies are most likely to render most large urban areas vulnerable to extreme rainfall and pluvial flooding due to lack of resilience in existing drainage infrastructure and flood mitigation systems. Assessment of land use changes alone cannot fully account for hydrological and hydraulic alterations in the urban context and it is important for policy makers and people with responsibilities for managing urban flood risks to consider adaptation and mitigation strategies that considers increasing threat of urban flooding emanating from increased runoff from climate change rainfall. This thesis has subsequently enhanced our understanding of the value of free global hydrological and hydraulic models developed at city-scale to model the impacts of climate change and land use change on urban pluvial flood hazard in data-sparse context of rapidly developing cities where availability of high-quality data for urban flood studies are a rarity. Finally, one of the key findings of this study is that in the context of conducting urban flood modelling in data sparse regions in rapidly developing cities across the world, it is possible to leverage the opportunities provided by the growing availability of free, global datasets to develop urban flood models. Traditional urban flood models rely on the use of high-quality datasets as key input data and require computers with high computational efficiency to run detailed flood inundation models. Most importantly, the study has demonstrated that it is possible to achieve a trade-off between complexity and resolution by the use of simplified 2D hydraulic flood models that use global dataset as key input data

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium