Reza M. Khanbilvardi

Combination of passive and active microwave data for soil moisture estimates

Various remote sensing techniques have been evaluated and proven to be a valuable source of information for different hydrological applications. For example, with the actual Earth observation satellites, we can observe the entire river basin in rather than sparse points and provide unique information about properties of the surface or shallow layers of the Earth. Furthermore, the actual remote sensing sensors offer the potential of measuring new hydrologic variables not generally possible with traditional techniques such as soil moisture, snow status, land cover parameters etc. Previous researches in microwave remote sensing technology indicate that surface soil moisture can be inferred with remote sensing systems operating in the microwave region of the electromagnetic spectrum. The ability to estimate soil moisture in the upper surface layer by microwave remote sensing (active and passive) has been demonstrated under a variety of the topographic and land-cover conditions. The primary intent of this project is to produce a spatial estimation of soil moisture from active microwave data with sufficient spatial and temporal resolution using neural networks. The derived soil moisture was analyzed in conjunction with vegetation data to understand the effect of land cover on the soil moisture variation. This paper describes the first steps in evaluating the performance of the neural network classification and presents some of the early results.

An automated approach for sea ice mapping and ice concentration determination for the future GOES-R Advanced Baseline Imager (ABI)

The aim of this work is to develop an automated approach for sea ice mapping and ice concentration determination using visible/infrared measurements provided by a geostationary satellite. This study is a part of the algorithm development activities of the future GOES-R ABI sensor. The data used in this study as prototype of the future GOES-R ABI sensor are provided by the SEVIRI sensor onboard of the METEOSAT Second Generation (MSG) satellite. The algorithm developed in this study is completely autonomous. Images of the prospective GOES-R will be the sole input of the algorithm which returns as an output ice charts and ice concentration maps. To achieve the ultimate objective of this study, two issues have been addressed. Firstly, it was necessary to accurately detect and map clouds over the study area in order to estimate ice fraction exclusively over cloud-free pixels. This primary step has been performed using the same proxy data provided by the SEVIRI instrument. Secondly, reflectances of all the Caspian Sea pixels were simulated for all the possible sun-satellite geometries and for pure ice and ice free pixels. The neural network was used in order to obtain these reflectances. An exhaustive sample has been selected using MODIS images to train the network. The obtained water and ice reflectances have been used to estimate the ice fraction as a ratio of the difference of the observed reflectance at channel R01 (0.6 mum) and the water reflectance over the difference of the ice reflectance and water reflectance.

A global passive microwave based wetness index for the monitoring of soil moisture and inundation

The objective of study is to implement an automated microwave based index to detect and monitor extreme soil wetness and flooding conditions on global scale. The proposed index is based on the Polarization Ratio (PR) that is determined from brightness temperature measures from the AMSR-E sensor. The Robust Satellite Technique (RST) is then applied to the PR to determine a Polarization Ratio Variational Index (PRVI) which is sensitive to extreme hydrological conditions in term of wetness but also drought conditions. The PRVI was determined at different frequencies ranging from the 37 GHz to the 6.9 GHz. The index was tested during extreme flooding events in Asia and Europe as well as in Northern America. The index was implemented globally using observation from passive microwave instruments. The analysis of the results on global scale shows that the index was sensitive to extreme hydrological event and that false alarms were mostly issued over northern snow covered regions only. This implies that the proposed index can be used to assess and delineate flooding conditions.