157 research outputs found
Désagrégation de l'humidité du sol issue des produits satellitaires micro-ondes passives et exploration de son utilisation pour l'amélioration de la modélisation et la prévision hydrologique
De plus en plus de produits satellitaires en micro-ondes passives sont disponibles. Cependant, leur large résolution spatiale (25-50 km) n’en font pas un outil adéquat pour des applications hydrologiques à une échelle locale telles que la modélisation et la prévision hydrologiques. Dans de nombreuses études, une désagrégation d’échelle de l’humidité du sol des produits satellites micro-ondes est faite puis validée avec des mesures in-situ. Toutefois, l’utilisation de ces données issues d’une désagrégation d’échelle n’a pas encore été pleinement étudiée pour des applications en hydrologie. Ainsi, l’objectif de cette thèse est de proposer une méthode de désagrégation d’échelle de l’humidité du sol issue de données satellitaires en micro-ondes passives (Satellite Passive Microwave Active and Passive - SMAP) à différentes résolutions spatiales afin d’évaluer leur apport sur l’amélioration potentielle des modélisations et prévisions hydrologiques. À partir d’un modèle de forêt aléatoire, une désagrégation d’échelle de l’humidité du sol de SMAP l’amène de 36-km de résolution initialement à des produits finaux à 9-, 3- et 1-km de résolution. Les prédicteurs utilisés sont à haute résolution spatiale et de sources différentes telles que Sentinel-1A, MODIS et SRTM. L'humidité du sol issue de cette désagrégation d’échelle est ensuite assimilée dans un modèle hydrologique distribué à base physique pour tenter d’améliorer les sorties de débit. Ces expériences sont menées sur les bassins versants des rivières Susquehanna (de grande taille) et Upper-Susquehanna (en comparaison de petite taille), tous deux situés aux États-Unis. De plus, le modèle assimile aussi des données d’humidité du sol en profondeur issue d’une extrapolation verticale des données SMAP. Par ailleurs, les données d’humidité du sol SMAP et les mesures in-situ sont combinées par la technique de fusion conditionnelle. Ce produit de fusion SMAP/in-situ est assimilé dans le modèle hydrologique pour tenter d’améliorer la prévision hydrologique sur le bassin versant Au Saumon situé au Québec. Les résultats montrent que l'utilisation de l’humidité du sol à fine résolution spatiale issue de la désagrégation d’échelle améliore la représentation de la variabilité spatiale de l’humidité du sol. En effet, le produit à 1- km de résolution fournit plus de détails que les produits à 3- et 9-km ou que le produit SMAP de base à 36-km de résolution. De même, l’utilisation du produit de fusion SMAP/ in-situ améliore la qualité et la représentation spatiale de l’humidité du sol. Sur le bassin versant Susquehanna, la modélisation hydrologique s’améliore avec l’assimilation du produit de désagrégation d’échelle à 9-km, sans avoir recours à des résolutions plus fines. En revanche, sur le bassin versant Upper-Susquehanna, c’est le produit avec la résolution spatiale la plus fine à 1- km qui offre les meilleurs résultats de modélisation hydrologique. L’assimilation de l’humidité du sol en profondeur issue de l’extrapolation verticale des données SMAP n’améliore que peu la qualité du modèle hydrologique. Par contre, l’assimilation du produit de fusion SMAP/in-situ sur le bassin versant Au Saumon améliore la qualité de la prévision du débit, même si celle-ci n’est pas très significative.Abstract: The availability of satellite passive microwave soil moisture is increasing, yet its spatial resolution (i.e., 25-50 km) is too coarse to use for local scale hydrological applications such as streamflow simulation and forecasting. Many studies have attempted to downscale satellite passive microwave soil moisture products for their validation with in-situ soil moisture measurements. However, their use for hydrological applications has not yet been fully explored. Thus, the objective of this thesis is to downscale the satellite passive microwave soil moisture (i.e., Satellite Microwave Active and Passive - SMAP) to a range of spatial resolutions and explore its value in improving streamflow simulation and forecasting. The random forest machine learning technique was used to downscale the SMAP soil moisture from 36-km to 9-, 3- and 1-km spatial resolutions. A combination of host of high-resolution predictors derived from different sources including Sentinel-1A, MODIS and SRTM were used for downscaling. The downscaled SMAP soil moisture was then assimilated into a physically-based distributed hydrological model for improving streamflow simulation for Susquehanna (larger in size) and Upper Susquehanna (relatively smaller in size) watersheds, located in the United States. In addition, the vertically extrapolated SMAP soil moisture was assimilated into the model. On the other hand, the SMAP and in-situ soil moisture were merged using the conditional merging technique and the merged SMAP/in-situ soil moisture was then assimilated into the model to improve streamflow forecast over the au Saumon watershed. The results show that the downscaling improved the spatial variability of soil moisture. Indeed, the 1-km downscaled SMAP soil moisture presented a higher spatial detail of soil moisture than the 3-, 9- or original resolution (36-km) SMAP product. Similarly, the merging of SMAP and in-situ soil moisture improved the accuracy as well as spatial representation soil moisture. Interestingly, the assimilation of the 9-km downscaled SMAP soil moisture significantly improved the accuracy of streamflow simulation for the Susquehanna watershed without the need of going to higher spatial resolution, whereas for the Upper Susquehanna watershed the 1-km downscaled SMAP showed better results than the coarser resolutions. The assimilation of vertically extrapolated SMAP soil moisture only slightly further improved the accuracy of the streamflow simulation. On the other hand, the assimilation of merged SMAP/in-situ soil moisture for the au Saumon watershed improved the accuracy of streamflow forecast, yet the improvement was not that significant. Overall, this study demonstrated the potential of satellite passive microwave soil moisture for streamflow simulation and forecasting
ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications
Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research
Development of high-precision snow mapping tools for Arctic environments
Le manteau neigeux varie grandement dans le temps et l’espace, il faut donc de nombreux points d’observation pour le décrire précisément et ponctuellement, ce qui permet de valider et d’améliorer la modélisation de la neige et les applications en télédétection.
L’analyse traditionnelle par des coupes de neige dévoile des détails pointus sur l’état de la neige à un endroit et un moment précis, mais est une méthode chronophage à laquelle la distribution dans le temps et l’espace font défaut. À l’opposé sur la fourchette de la précision, on retrouve les solutions orbitales qui couvrent la surface de la Terre à intervalles réguliers, mais à plus faible résolution.
Dans l’optique de recueillir efficacement des données spatiales sur la neige durant les campagnes de terrain, nous avons développé sur mesure un système d’aéronef télépiloté (RPAS) qui fournit des cartes d’épaisseur de neige pour quelques centaines de mètres carrés, selon la méthode Structure from motion (SfM). Notre RPAS peut voler dans des températures extrêmement froides, au contraire des autres systèmes sur le marché. Il atteint une résolution horizontale de 6 cm et un écart-type d’épaisseur de neige de 39 % sans végétation (48,5 % avec végétation).
Comme la méthode SfM ne permet pas de distinguer les différentes couches de neige, j’ai développé un algorithme pour un radar à onde continue à modulation de fréquence (FM-CW) qui permet de distinguer les deux couches principales de neige que l’on retrouve régulièrement en Arctique : le givre de profondeur et la plaque à vent. Les distinguer est crucial puisque les caractéristiques différentes des couches de neige font varier la quantité d’eau disponible pour l’écosystème lors de la fonte. Selon les conditions sur place, le radar arrive à estimer l’épaisseur de neige selon un écart-type entre 13 et 39 %.
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Finalement, j’ai équipé le radar d’un système de géolocalisation à haute précision. Ainsi équipé, le radar a une marge d’erreur de géolocalisation d’en moyenne <5 cm. À partir de la mesure radar, on peut déduire la distance entre le haut et le bas du manteau neigeux. En plus de l’épaisseur de neige, on obtient également des points de données qui permettent d’interpoler un modèle d’élévation de la surface solide sous-jacente. J’ai utilisé la méthode de structure triangulaire (TIN) pour toutes les interpolations. Le système offre beaucoup de flexibilité puisqu’il peut être installé sur un RPAS ou une motoneige.
Ces outils épaulent la modélisation du couvert neigeux en fournissant des données sur un secteur, plutôt que sur un seul point. Les données peuvent servir à entraîner et à valider les modèles. Ainsi améliorés, ils peuvent, par exemple, permettre de prédire la taille, le niveau de santé et les déplacements de populations d’ongulés, dont la survie dépend de la qualité de la neige. (Langlois et coll., 2017.) Au même titre que la validation de modèles de neige, les outils présentés permettent de comparer et de valider d’autres données de télédétection (par ex. satellites) et d’élargir notre champ de compréhension. Finalement, les cartes ainsi créées peuvent aider les écologistes à évaluer l’état d’un écosystème en leur donnant accès à une plus grande quantité d’information sur le manteau neigeux qu’avec les coupes de neige traditionnelles.Abstract: Snow is highly variable in time and space and thus many observation points are needed to describe the present state of the snowpack accurately. This description of the state of the snowpack is necessary to validate and improve snow modeling efforts and remote sensing applications. The traditional snowpit analysis delivers a highly detailed picture of the present state of the snow in a particular location but lacks the distribution in space and time as it is a time-consuming method. On the opposite end of the spatial scale are orbital solutions covering the surface of the Earth in regular intervals, but at the cost of a much lower resolution. To improve the ability to collect spatial snow data efficiently during a field campaign, we developed a custom-made, remotely piloted aircraft system (RPAS) to deliver snow depth maps over a few hundred square meters by using Structure-from-Motion (SfM). The RPAS is capable of flying in extremely low temperatures where no commercial solutions are available. The system achieves a horizontal resolution of 6 cm with snow depth RMSE of 39% without vegetation (48.5% with vegetation) As the SfM method does not distinguish between different snow layers, I developed an algorithm for a frequency modulated continuous wave (FMCW) radar that distinguishes between the two main snow layers that are found regularly in the Arctic: “Depth Hoar” and “Wind Slab”. The distinction is important as these characteristics allow to determine the amount of water stored in the snow that will be available for the ecosystem during the melt season. Depending on site conditions, the radar estimates the snow depth with an RMSE between 13% and 39%. v Finally, I equipped the radar with a high precision geolocation system. With this setup, the geolocation uncertainty of the radar on average < 5 cm. From the radar measurement, the distance to the top and the bottom of the snowpack can be extracted. In addition to snow depth, it also delivers data points to interpolate an elevation model of the underlying solid surface. I used the Triangular Irregular Network (TIN) method for any interpolation. The system can be mounted on RPAS and snowmobiles and thus delivers a lot of flexibility. These tools will assist snow modeling as they provide data from an area instead of a single point. The data can be used to force or validate the models. Improved models will help to predict the size, health, and movements of ungulate populations, as their survival depends on it (Langlois et al., 2017). Similar to the validation of snow models, the presented tools allow a comparison and validation of other remote sensing data (e.g. satellite) and improve the understanding limitations. Finally, the resulting maps can be used by ecologist to better asses the state of the ecosystem as they have a more complete picture of the snow cover on a larger scale that it could be achieved with traditional snowpits
Examining Ecosystem Drought Responses Using Remote Sensing and Flux Tower Observations
Indiana University-Purdue University Indianapolis (IUPUI)Water is fundamental for plant growth, and vegetation response to water availability influences water, carbon, and energy exchanges between land and atmosphere. Vegetation plays the most active role in water and carbon cycle of various ecosystems. Therefore, comprehensive evaluation of drought impact on vegetation productivity will play a critical role for better understanding the global water cycle under future climate conditions.
In-situ meteorological measurements and the eddy covariance flux tower network, which provide meteorological data, and estimates of ecosystem productivity and respiration are remarkable tools to assess the impacts of drought on ecosystem carbon and water cycles. In regions with limited in-situ observations, remote sensing can be a very useful tool to monitor ecosystem drought status since it provides continuous observations of relevant variables linked to ecosystem function and the hydrologic cycle. However, the detailed understanding of ecosystem responses to drought is still lacking and it is challenging to quantify the impacts of drought on ecosystem carbon balance and several factors hinder our explicit understanding of the complex drought impacts. This dissertation addressed drought monitoring, ecosystem drought responses, trends of vegetation water constraint based on in-situ metrological observations, flux tower and multi-sensor remote sensing observations. This dissertation first developed a new integrated drought index applicable across diverse climate regions based on in-situ meteorological observations and multi-sensor remote sensing data, and another integrated drought index applicable across diverse climate regions only based on multi-sensor remote sensing data. The dissertation also evaluated the applicability of new satellite dataset (e.g., solar induced fluorescence, SIF) for responding to meteorological drought. Results show that satellite SIF data could have the potential to reflect meteorological drought, but the application should be limited to dry regions. The work in this dissertation also accessed changes in water constraint on global vegetation productivity, and quantified different drought dimensions on ecosystem productivity and respiration. Results indicate that a significant increase in vegetation water constraint over the last 30 years. The results highlighted the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate
Remote Sensing of Precipitation: Part II
Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products
The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library
Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications
Remote Sensing Monitoring of Land Surface Temperature (LST)
This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research
Remote Sensing of Land Surface Phenology
Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects
Space Systems: Emerging Technologies and Operations
SPACE SYSTEMS: EMERGING TECHNOLOGIES AND OPERATIONS is our seventh textbook in a series covering the world of UASs / CUAS/ UUVs. Other textbooks in our series are Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA\u27s Advanced Air Assets, 1st edition. Our previous six titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols et al., 2021) (Nichols R. K. et al., 2020) (Nichols R. et al., 2020) (Nichols R. et al., 2019) (Nichols R. K., 2018)
Our seventh title takes on a new purview of Space. Let\u27s think of Space as divided into four regions. These are Planets, solar systems, the great dark void (which fall into the purview of astronomers and astrophysics), and the Dreamer Region. The earth, from a measurement standpoint, is the baseline of Space. It is the purview of geographers, engineers, scientists, politicians, and romantics. Flying high above the earth are Satellites. Military and commercial organizations govern their purview. The lowest altitude at which air resistance is low enough to permit a single complete, unpowered orbit is approximately 80 miles (125 km) above the earth\u27s surface. Normal Low Earth Orbit (LEO) satellite launches range between 99 miles (160 km) to 155 miles (250 km). Satellites in higher orbits experience less drag and can remain in Space longer in service. Geosynchronous orbit is around 22,000 miles (35,000 km). However, orbits can be even higher. UASs (Drones) have a maximum altitude of about 33,000 ft (10 km) because rotating rotors become physically limiting. (Nichols R. et al., 2019) Recreational drones fly at or below 400 ft in controlled airspace (Class B, C, D, E) and are permitted with prior authorization by using a LAANC or DroneZone. Recreational drones are permitted to fly at or below 400 ft in Class G (uncontrolled) airspace. (FAA, 2022) However, between 400 ft and 33,000 ft is in the purview of DREAMERS.
In the DREAMERS region, Space has its most interesting technological emergence. We see emerging technologies and operations that may have profound effects on humanity. This is the mission our book addresses. We look at the Dreamer Region from three perspectives:1) a Military view where intelligence, jamming, spoofing, advanced materials, and hypersonics are in play; 2) the Operational Dreamer Region; whichincludes Space-based platform vulnerabilities, trash, disaster recovery management, A.I., manufacturing, and extended reality; and 3) the Humanitarian Use of Space technologies; which includes precision agriculture wildlife tracking, fire risk zone identification, and improving the global food supply and cattle management.
Here’s our book’s breakdown:
SECTION 1 C4ISR and Emerging Space Technologies. C4ISR stands for Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance. Four chapters address the military: Current State of Space Operations; Satellite Killers and Hypersonic Drones; Space Electronic Warfare, Jamming, Spoofing, and ECD; and the challenges of Manufacturing in Space.
SECTION 2: Space Challenges and Operations covers in five chapters a wide purview of challenges that result from operations in Space, such as Exploration of Key Infrastructure Vulnerabilities from Space-Based Platforms; Trash Collection and Tracking in Space; Leveraging Space for Disaster Risk Reduction and Management; Bio-threats to Agriculture and Solutions From Space; and rounding out the lineup is a chapter on Modelling, Simulation, and Extended Reality.
SECTION 3: Humanitarian Use of Space Technologies is our DREAMERS section. It introduces effective use of Drones and Precision Agriculture; and Civilian Use of Space for Environmental, Wildlife Tracking, and Fire Risk Zone Identification.
SECTION 3 is our Hope for Humanity and Positive Global Change. Just think if the technologies we discuss, when put into responsible hands, could increase food production by 1-2%. How many more millions of families could have food on their tables?
State-of-the-Art research by a team of fifteen SMEs is incorporated into our book. We trust you will enjoy reading it as much as we have in its writing. There is hope for the future.https://newprairiepress.org/ebooks/1047/thumbnail.jp
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