Friday 08 Mar 2019: Using remote sensing to refine water-use estimates, explore air quality, and prediction of the soil moisture
Prof. Alireza Farid - Ferdowsi University of Mashhad, Iran
Harrison 170 13:30-14:30
Estimation of forest structural attributes, such as the Leaf Area Index (LAI), is an important step in identifying the amount of water use in riparian forest areas. In this study, small-footprint lidar (light detecting and ranging) data were used to estimate biophysical properties of young, mature, and old cottonwood trees. Different metrics (tree height, height of median energy, ground return ratio, and canopy return ratio) were derived. These metrics were incorporated into a stepwise regression procedure to predict field-derived LAI for different age classes of cottonwoods. This research applied the Penman-Monteith model to estimate transpiration of the cottonwood clusters using lidar-derived canopy metrics.
Monitoring air quality is crucial for Middle East countries such as Iran, where dust and polluted aerosol sources heavily influence local air quality. This research presents an assessment of NASA’s Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol data over large cities. We examined the Cloud and Aerosol Discrimination (CAD) score values, extinction coefficient, and the CALIOP Vertical Feature Mask (VFM) data product and Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Aerosol Optical Depth (DBOD) at wavelength of 0.55 μm. The ground-based PM10 measurements were analyzed for different time periods, seasons, and years. We investigated the profiles of the particle backscatter and extinction coefficient, as well as information about the determined feature types (e.g., clouds or aerosols) and aerosol subtypes (e.g., dust, and smoke) from the VFM data product in 2 months of August 2009 and July 2013. Additionally, the correlations of the PM10 concentration, MODIS AOD, and MODIS DBOD were investigated for January 2005 to December 2014. Furthermore, the CAD algorithm should be modified and calibrated based on local measurements, MODIS-DBOD, and ground-based PM10 measurements.
Prediction of Root-Zone Soil Moisture (RZSM) at regional scales is a critical issue in surface hydrology. A significant number of satellites (SMOS, SSM/I, AMSR-E, TRMM/TMI) retrieve Surface Soil Moisture (SSM) using passive microwave remote sensing. In particular, the recently developed Soil Moisture Analytical Relationship (SMAR) can relate the surface soil moisture to the moisture of deeper layer using a relationship derived from a soil water balance equation where infiltration is estimated based on the relative fluctuations of soil moisture in the surface soil layer. In this study, the SMAR model is tested on two research databases in Africa and North America, where field measurements at different depths are available. Furthermore, the TRMM/TMI Satellite is selected to retrieve the satellite SSM data of the studied regions using the Land Parameter Retrieval Model (LPRM). Both remotely sensed SSM and field measurements are used within the SMAR model to explore their ability in reproducing the RZSM and also to explore the existing difference in model parameterization moving from one dataset to the other.
I am currently an Associate Professor of Water Engineering at Ferdowsi University of Mashhad, Iran. I graduated, both MSc and PhD degrees, from the Center for the Sustainability of semi-Arid Hydrology and Riparian Areas (SAHRA) and the Department of Hydrology and Water Resources (HWR), both located at the University of Arizona, USA. My primary research work is in surface hydrology, specifically in the study of rainfall estimation from satellite infrared imagery over different regions of the United States of America using Artificial Neural Networks (ANNs) and its incorporation into the General Circulation Model (GCM). After completing my PhD, I had a position as a senior hydrologist at a consulting company in Tucson, Arizona. With my work experience in hydrology consulting, I have continued my research work on hydrology and using remote sensing technique on forestry, ecological and hydrologic applications (modeling evapotranspiration, infiltration, and soil moisture) that will improve management of hydrologic resources, timber harvest, and ecological models.