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Full-Text Articles in Engineering
Using Microwave Brightness Temperature Diurnal Cycle To Improve Emissivity Retrievals Over Land, Hamidreza Norouzi, William Rossow, Marouane Temimi, Catherine Prigent, Marzieh Azarderakhsh, Sid Boukabara, Reza Khanbilvardi
Using Microwave Brightness Temperature Diurnal Cycle To Improve Emissivity Retrievals Over Land, Hamidreza Norouzi, William Rossow, Marouane Temimi, Catherine Prigent, Marzieh Azarderakhsh, Sid Boukabara, Reza Khanbilvardi
Publications and Research
To retrieve microwave land emissivity, infrared surface skin temperatures have been used as surface physical temperature since there is no global information on physical vegetation/soil temperature profiles. However, passive microwave emissions originate fromdeeper layerswith respect to the skin temperature. So, this inconsistency in sensitivity depths between skin temperatures and microwave temperaturesmay introduce a discrepancy in the determined emissivity. Previous studies showed that this inconsistency can lead to significant differences between day and night retrievals of land emissivity which can exceed 10%. This study proposes an approach to address this inconsistency and improve the retrieval of land emissivity using microwave observations …
Analysis Of The Effects Of Snowpack Properties On Satellite Microwave Brightness Temperature And Emissivity Data, Tarendra Lakhankar, Amir E. Azar, Narges Shahroudi, Alfred Powell, Reza Khanbilvardi
Analysis Of The Effects Of Snowpack Properties On Satellite Microwave Brightness Temperature And Emissivity Data, Tarendra Lakhankar, Amir E. Azar, Narges Shahroudi, Alfred Powell, Reza Khanbilvardi
Publications and Research
Spatial variations of snowpack properties are an essential component in flood predictions and water resource management. Satellite microwave remote sensing has shown great potential in retrieving snowpack properties such as: snow depth, snow grain size, and snow density. In this research, we investigate the potential of microwave emissivity which is highly influenced by snowpack properties. Brightness temperature and emissivity data generated from HUT (Helsinki University of Technology) microwave emission of snow model were evaluated with satellite microwave measurements. The comparison of the real measurements (in-situ and satellite) with the modeled results shows that the scattering signature (19GHz-37GHz and 19GHz-85GHz) shows …
A Multi-Temporal Analysis Of Amsr-E Data For Flood And Discharge Monitoring During The 2008 Flood In Iowa, Marouane Temimi, Teodosio Lacava, Tarendra Lakhankar, Valerio Tramutoli, Hosni Ghedira, Riadh Ata, Reza Khanbilvardi
A Multi-Temporal Analysis Of Amsr-E Data For Flood And Discharge Monitoring During The 2008 Flood In Iowa, Marouane Temimi, Teodosio Lacava, Tarendra Lakhankar, Valerio Tramutoli, Hosni Ghedira, Riadh Ata, Reza Khanbilvardi
Publications and Research
The objective of this work is to demonstrate the potential of using passive microwave data to monitor flood and discharge conditions and to infer watershed hydraulic and hydrologic parameters. The case study is the major flood in Iowa in summer 2008. A new Polarisation Ratio Variation Index (PRVI) was developed based on a multi-temporal analysis of 37 GHz satellite imagery from the Advanced Microwave Scanning Radiometer (AMSR-E) to calculate and detect anomalies in soil moisture and/or inundated areas. The Robust Satellite Technique (RST) which is a change detection approach based on the analysis of historical satellite records was adopted. A …
Sensitivity Analysis Of B-Factor In Microwave Emission Model For Soil Moisture Retrieval: A Case Study For Smap Mission, Dugwon Seo, Tarendra Lakhankar, Reza Khanbilvardi
Sensitivity Analysis Of B-Factor In Microwave Emission Model For Soil Moisture Retrieval: A Case Study For Smap Mission, Dugwon Seo, Tarendra Lakhankar, Reza Khanbilvardi
Publications and Research
Sensitivity analysis is critically needed to better understand the microwave emission model for soil moisture retrieval using passive microwave remote sensing data. The vegetation b-factor along with vegetation water content and surface characteristics has significant impact in model prediction. This study evaluates the sensitivity of the b-factor, which is function of vegetation type. The analysis is carried out using Passive and Active L and S-band airborne sensor (PALS) and measured field soil moisture from Southern Great Plains experiment (SGP99). The results show that the relative sensitivity of the b-factor is 86% in wet soil condition and 88% in high vegetated …