OnTheUseOfMultitemporalSeriesOfCOSMODataForClassificationAndSurfaceParameterRetrievalOverAgriculturalSites.ppt
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The document presents research using multi-temporal COSMO-SkyMed SAR data for land cover classification and surface parameter retrieval over agricultural sites. Algorithms were developed for classification, leaf area index retrieval, and soil moisture content retrieval. The algorithms were applied to a 2010 dataset over Foggia, Italy, showing potential for crop classification, wheat leaf area index mapping, and soil moisture mapping of bare fields using X-band SAR. Future work involves validating the algorithms and assessing their improvement of land process models when coupled with SAR-derived information.
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The document presents a new algorithm for locally adaptive template sizes in image matching to measure displacement of Earth surface masses. The algorithm aims to select template sizes that contain optimal signal-to-noise ratio without geometric distortion, and can identify distinct features. It evaluates template size based on peaks in signal-to-noise ratio and normalized cross-correlation to select matchable templates. The algorithm improves displacement estimation accuracy compared to fixed template sizes, especially in noisy areas. It filters out non-matchable templates to help automate the matching process.
BAUP_IGARSS_2011.pdf
Radar data from ALOS, Radarsat-2 and TerraSAR-X satellites were used to monitor agricultural surfaces as part of the MCM Experiment. The objectives were to develop new approaches for high resolution surface monitoring using optical, thermal and radar data and to improve estimates of crop biophysical parameters. Ground measurements were made quasi-synchronously with satellite acquisitions. Preliminary results showed potential for soil moisture estimation and vegetation monitoring using different frequencies, with X-band showing higher potential for soil moisture mapping. Further analysis of multi-sensor data and modeling is needed to fully characterize capabilities for agriculture applications.
IGARSS_LIU_XU_2011.ppt
The Principal Component-based Radiative Transfer Model (PCRTM):
- Is a key tool for analyzing hyperspectral remote sensing data as it can handle the large numbers of spectral channels while traditional models are too slow.
- Greatly reduces the number of radiative transfer calculations needed through use of principal components, making it 3-4 orders of magnitude faster than line-by-line models.
- Has been successfully applied to analyze data from instruments like IASI, AIRS, CrIS, and is being extended to sensors like OCO and SCIAMACHY.
75315682 hse leak-detection
This document reviews detectors for potential use in detecting leaks and spills at fuel storage sites like Buncefield. It discusses gas detectors, liquid hydrocarbon detectors, and standards related to their use. Key points made include:
- Gas detectors are commonly used offshore but less so at UK fuel storage sites currently, though one major company has specified them.
- Standards exist for gas detectors regarding performance, use, and functional safety, but they focus on instrumentation and don't consider detector coverage area.
- Techniques for estimating detector coverage are important for assessing risk mitigation effectiveness but require further investigation and validation.
- Infrared gas detectors tend to have lower failure rates than catalytic detectors and are preferred for fixed monitoring
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Sabaghy_Workshop
This document presents a method for combining active and passive microwave observations to retrieve soil moisture at high spatial resolution. The study uses PALSAR SAR data at 100m resolution and AMSR-E passive microwave data at 25km resolution over a heterogeneous area in the Netherlands. A change detection algorithm is applied to downscale the coarse resolution AMSR-E soil moisture to match the SAR observations. Validation against in situ soil moisture measurements shows reasonable accuracy, with RMSE of around 0.06 m3/m3. Soil moisture maps are produced at 1km, 5km and 10km resolutions, showing the different information extracted at various scales.
McNairn soil moisture IGARSS 2011 v2.ppt
The document summarizes research on using RADARSAT-2 satellite data to monitor soil moisture for agricultural risk reduction in Canada. It finds that a calibrated Integral Equation Model can estimate regional soil moisture with an average error of 3.23% and detect changes in soil moisture over time. However, site-specific estimates have higher errors of 7.71% due to spatial variability not captured. Further analysis is needed to reduce errors and better quantify relative changes in soil moisture.
MPE data validation
The document summarizes a study that evaluated EUMETSAT's Multi-sensor Precipitation Estimate (MPE) products from Meteosat-8 and Meteosat-9 satellites in comparison with ground-based radar data over Northwestern Europe. Categorical and continuous verification statistics were used to assess differences in spatial distribution and rainfall values between the MPE products and radar data. The results showed that MPE from Meteosat-9 had higher accuracy scores and was better at estimating rainfall values, though both products tended to overestimate during heavy rainfall events. Recommendations included further validation of MPE products over different regions and timescales.
DRONES IN HYDROLOGY
A network of scientists is cooperating within the COST Action "Harmonious" to promote UAS monitoring strategies and establish harmonized practices. The action has 36 partner countries and aims to transfer advances in UAS methods through a global network. It has five working groups focused on data processing, vegetation monitoring, soil moisture, streamflow monitoring, and harmonization. The overall goal is to increase the capacity to describe water resources and assist management through consistent UAS monitoring of key variables.
A strategy for estimating the components of the carbon and water budgets for ...
A strategy for estimating the components of the carbon and water budgets for ...Integrated Carbon Observation System (ICOS)
This document summarizes a strategy for estimating carbon and water budgets for croplands at the plot scale over large areas using remote sensing data and a crop model. The objectives are to analyze ecosystem services like yield, biomass, evapotranspiration, and net CO2 fluxes to calculate annual carbon and water budgets and test the effects of management practices. A multi-temporal remote sensing data assimilation scheme was developed to run the SAFYE-CO2 crop model without needing detailed ground data by using Sentinel satellite imagery. The approach provides good estimates of fluxes compared to observations and performs well compared to other models without requiring management data. It can help quantify the effects of practices like cover crops on carbon storage and other benefits.TU2.L10 - NEXT-GENERATION GLOBAL PRECIPITATION PRODUCTS AND THEIR APPLICATIONS
The document summarizes the goals and capabilities of the upcoming Global Precipitation Measurement (GPM) mission. GPM will provide next-generation global precipitation data products through a constellation of passive microwave sensors calibrated to the GPM Core Observatory's radar and radiometer. This will improve accuracy for light rain and snow and provide higher resolution and more frequent observations. Ground validation efforts and applications research are important to maximize the scientific and societal benefits of GPM precipitation data.
TH4.L10.1: SMOS SMAP SYNERGISMS FOR THE RETRIEVAL OF SOIL MOISTURE
SMOS and SMAP are satellite missions aimed at globally mapping soil moisture. While SMOS uses passive L-band microwave radiometry with coarse 30-55km resolution, SMAP will use an active L-band radar to achieve higher 9-36km resolution. Both have a 3-day revisit time but SMAP has greater sensitivity. There are opportunities for synergism by intercomparing the two datasets to validate algorithms and identify the optimal approach for long-term soil moisture monitoring, especially for addressing issues like freeze/thaw detection where SMAP's active radar is better suited.
IRJET- Soil Water Forecasting System using Deep Neural Network Regression Model
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4_bindlish_igarss2011.pptx
The document evaluates SMAP soil moisture algorithms using SMOS data. SMOS data was reprocessed to simulate SMAP observations at 40 degrees incidence angle. The Single Channel Algorithm was implemented using this SMOS/SMAP data set with different ancillary datasets. Initial results show the SCA using MODIS data performs well compared to in situ observations. Using vegetation polarization also performs satisfactorily, though vegetation parameters may need to be specific to land cover types to reduce bias in different areas. Overall results indicate SMAP algorithms can meet the target 0.04 m3/m3 accuracy requirement with further analysis and research. This evaluation will help select and develop the SMAP passive level 2 soil moisture algorithm.
TU3.L09 - SPACEBORNE FULLY POLARIMETRIC TIME-SERIES DATASETS FOR LAND COVER A...
The document summarizes a project using spaceborne fully polarimetric time-series datasets from Radarsat-2 and ALOS for land cover analysis and monitoring of wetlands. The objectives were to delineate wetlands and map vegetation, identify agricultural practices, and determine water cycles and levels. Ground truth campaigns were conducted including soil moisture and roughness measurements. Initial qualitative analysis of the Radarsat-2 data showed polarimetric descriptors and classifications could distinguish flooded areas with 78-94% accuracy. Optical data from ALOS provided complementary information and 88-99% classification accuracy. Further inversion of parameters like soil moisture from time-series datasets and data fusion of radar and optical data was outlined.
Geoland2 Workshop - 01 - sousa
GMES initial operations on Land monitoring 2011 - 2013
Ana Sousa - European Environment Agency (EEA)
Parma, 16 novembre 2011. Nell'ambito della XV Conferenza Italiana ASITA si svolge il Workshop "GMES Land products developed in Geoland2: requirements and examples of products for analysis at a European and regional level."
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A knowledge-based model for identifying and mapping tropical wetlands and pea...
This presentation was presented during the 2 Parallel session on Theme 3.1, Managing SOC in: Soils with high SOC – peatlands, permafrost, and black soils, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Thomas Gumbricht, from Center for International Forestry Research – Indonesia, in FAO Hq, Rome
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
- Researchers developed a two-phase system to simulate gridded AMSR-E BT data using the Community Land Model, a microwave emissivity model, and calibration with SCE-UA algorithm and AMSR-E observations.
- The system calibrates microwave wetland surface emissivity and land emission model parameters to minimize differences between simulated and observed BT.
- Experiments showed the calibrated wetland emissivities transferred well to another location and improved soil moisture assimilation when used to assimilate AMSR-E BT data.
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
- Researchers developed a two-phase system to simulate gridded AMSR-E BT data using the Community Land Model (CLM), a microwave emissivity model, and calibration with SCE-UA algorithm and AMSR-E observations.
- The system calculates sub-grid land states from CLM, simulates BT from these states, and calibrates model parameters and wetland emissivity to minimize differences from observed AMSR-E BT.
- Experiments showed the calibrated wetland emissivities transferred well to another location and improved soil moisture estimates
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
- Researchers developed a two-phase system to simulate gridded AMSR-E BT data using the Community Land Model (CLM), a microwave emissivity model, and calibration with SCE-UA algorithm and AMSR-E observations.
- The system calculates sub-grid land states from CLM, simulates BT from these states, and calibrates model parameters and wetland emissivity to minimize differences from observed AMSR-E BT.
- Experiments showed the calibrated wetland emissivities transferred well to another location and improved soil moisture estimates
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
- Researchers developed a two-phase system to simulate gridded AMSR-E BT data using the Community Land Model (CLM), a microwave emissivity model, and calibration with SCE-UA algorithm and AMSR-E observations.
- The system calibrates LandEM parameters and microwave wetland emissivity to minimize differences between simulated and observed BTs.
- Experiments showed the calibrated wetland emissivities transferred well to another location, and soil moisture assimilation using the simulated BT data improved estimates.
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
- Researchers developed a two-phase system to simulate gridded AMSR-E BT data using the Community Land Model (CLM), a microwave emissivity model, and calibration with SCE-UA algorithm and AMSR-E observations.
- The system calculates sub-grid land states from CLM, simulates BT from these states, and calibrates model parameters and wetland emissivity to minimize differences from observed AMSR-E BT.
- Experiments showed the calibrated wetland emissivities transferred well to another location and improved soil moisture estimates
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
- Researchers developed a two-phase system to simulate gridded AMSR-E BT data using the Community Land Model, a microwave emissivity model, and calibration with SCE-UA algorithm and AMSR-E observations.
- The system calibrates parameters and wetland emissivity to minimize differences between simulated and observed BT. Calibrated wetland emissivities showed good transferability to another location.
- A soil moisture data assimilation system using the simulated BT data produced more accurate soil moisture estimates than the land model alone.
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
- Researchers developed a two-phase system to simulate gridded AMSR-E BT data using the Community Land Model (CLM), a microwave emissivity model, and calibration with SCE-UA algorithm and AMSR-E observations.
- The system calculates sub-grid land states from CLM, simulates BT from each patch, and calibrates wetland emissivity to minimize differences from observed BT.
- Results showed the calibrated wetland emissivities transferred well to another location and improved soil moisture estimates when assimilated using an ensemble Kalman filter
2 ShengleiZhang_IGARSS2011_MO3.T04.2.ppt
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
- Researchers developed a two-phase system to simulate gridded AMSR-E BT data using the Community Land Model (CLM), a microwave emissivity model, and calibration with SCE-UA algorithm and AMSR-E observations.
- The system calculates sub-grid land states from CLM, simulates BT from these states, and calibrates model parameters and wetland emissivity to minimize differences from observed AMSR-E BT.
- Experiments showed the calibrated wetland emissivities transferred well to another location and improved soil moisture estimates
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- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
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- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
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Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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OnTheUseOfMultitemporalSeriesOfCOSMODataForClassificationAndSurfaceParameterRetrievalOverAgriculturalSites.ppt
- 1. “ On the use of multi-temporal series of COSMO-SkyMed data for LANDcover classification and surface parameter retrieval over agricultural sites ( COSMOLAND )” Anna Balenzano (1), Giuseppe Satalino (1), Antonella Belmonte (1), Guido D’Urso (2), Fulvio Capodici (2), Vito Iacobellis (3), Andrea Gioia (3), Michele Rinaldi (4), Sergio Ruggieri (4) and Francesco Mattia (1) (1) Consiglio Nazionale delle Ricerche ( CNR ) – Istituto di Studi sui Sistemi Intelligenti per l’Automazione ( ISSIA ), Bari, Italy (2) Dipartimento di Ingegneria Agraria e Agronomia del Territorio - Università Federico II (UniNa) (3) Politecnico di Bari (PoliBa) – Dipartimento di Ingegneria delle Acque e di Chimica (4) Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Unità di Ricerca sui Sistemi Colturali per Ambienti Caldo-Aridi (CRA-SCA) Acknowledgement : the research is supported by the Italian Space Agency under contract I/051/09/0. COSMO-SkyMed data were provided by ©ASI in the framework of ©CSK AO 2161. SPOT data were obtained from CNES (2010) Distribution Spot Image ISIS-368
- 5. RGB compositions of the COSMO HH images (left) and of HV images (right) over Foggia. R: 20100403, G:20100427, B:20100521 Sensitivity of multi-temporal X band data to crops Product derived from original COSMO-SkyMed products ©ASI- Agenzia Spaziale Italiana (2010) Temporal X-band backscatter signature of crops is significantly different and provides useful information for classification. HH pol. HV pol.
- 10. Quantitative retrieval of m v N SAR acquisitions N-1 linear equations in N unknown pp coefficients Under determined linear system infinite number of solutions Solution found through Least Square minimization subject to the linear constraints: Details in Balenzano et al., IEEE JSTARS 2011
- 12. SMOSAR transforms dense time series of N SAR images into N- m v maps over agricultural areas with vegetation cover not dominated by volume scattering SMOSAR (Soil Moisture retrieval from multi-temporal SAR data) algorithm based on the alpha approximation European Space Agency project "GMES Sentinel-1 Soil Moisture Algorithm Development“ poster section in area K on Wed.
Editor's Notes
- This talk regards
- Colured polygons of fields with different crops can be clearly noticed. Temporal dependence of the SAR signal to the crop classes. This means that the temporal backscatter signature of crops such as wheat and sugar beet is significantly different and provides useful information for the classification. Anche in banda X c’e’ un’interazione con la canopy che evolve nel tempo in modo significativo
- Based on the apriori information regarding the crop types
- Rather smooth