A5003803532- Soil Moisture and Remote Sensing
- Geophysics and Gravity Measurements
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Precipitation Measurement and Analysis
- Seismic Imaging and Inversion Techniques
- Computational Physics and Python Applications
- Climate variability and models
- Remote Sensing and Land Use
- Climate change and permafrost
- Flood Risk Assessment and Management
- Hydrology and Drought Analysis
- Advanced SAR Imaging Techniques
Universität der Bundeswehr München
2023
The Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) is providing surface soil moisture data record products based a change detection technique applied the Advanced Scatterometer (ASCAT) on-board series of Metop satellites. At moment two three satellites are still operational (Metop-B Metop-C), while first satellite (Metop-A), launched in 2007, completed its mission November 2021. Thus, latest ASCAT product covers period more than 15 years...
The United Nations' Intergovernmental Panel on Climate Change (IPCC) has reported an increase in the frequency and intensity of heavy precipitation events globally, primarily driven by human-induced climate change. South Southeast Asian Monsoon, particularly over India, is one affected regions, which experienced significant changes patterns. Characterized a seasonal reversal wind rainfall, Indian summer monsoon land-sea thermal contrasts atmospheric dynamics influenced Himalayas, Tibetan...
Many developing countries strongly depend on agriculture, but the sector is challenged by increasing occurrence of droughts.  Unfortunately, advanced agricultural drought monitoring that can trigger early warning and action still not widely available for many even though it crucial to stakeholders including local regional governments, NGOs, farmers, vulnerable households. Classic tools often rely precipitation data, which are influenced density station data. Recently, satellite soil...
Sentinel-1, a pair of Synthetic Aperture Radar (SAR) sensors, provides valuable all-weather and day-night imaging capabilities, enabling continuous monitoring vegetation dynamics even in the presence cloud cover. SAR sensors excel at penetrating canopies providing information on crucial factors like structure, biomass, moisture content. However, most remote sensing studies have primarily relied optical data, benefiting from longer historical datasets but facing challenges due to atmospheric...
Agriculture faces increased challenges due to intense and frequent droughts caused by climate change. Accurate timely monitoring of drought conditions, therefore, becomes paramount taking quick decisive actions towards its impact mitigation. Agricultural occur prolonged periods low rainfall high temperatures, which lead soil moisture deficits, increasing plant water stress adversely affecting crops. This study explores the potential use a new demonstrational ASCAT surface (SSM) product...
Droughts are characterized by periods of below-average precipitation leading to an imbalance in the hydrological cycle and reduced water availability.In last decades, higher average temperatures shifts annual rainfall patterns have increased frequency, intensity, length droughts across globe. With majority its population living rural areas a high economic dependency on rain-fed agriculture, Mozambique is particularly vulnerable droughts, as shortages devastating environmental, agricultural,...
Most scientific studies dealing with the retrieval of soil moisture data from Synthetic Aperture Radar (SAR) focus on formulation, training, and validation models used to convert backscatter measurements into data, while paying little attention how are preprocessed. This is insofar surprising given that topography Earth surface in combination variable SAR imaging geometry may introduce strong orbit-related geometric effects obscure signal time series. Furthermore, mechanisms characterized by...
This paper focuses on the estimation of interferometric SAR parameters, a step that precedes entire processing chain to produce derived information such as digital elevation models and ground displacement. Deep learning, especially convolutional neural networks (CNN), has revolutionized image denoising recently received considerable attention. However, traditional supervised approaches require labeled images for training, which are generally unavailable or inaccurate, in remote sensing...