My research interests are interdisciplinary in nature, but broadly fall into the common theme of exploring the use of remote sensing and geographical information system (GIS) to solve problems in environmental engineering, water resources management, and other civil engineering areas.
Snow Moeling and Satellite Remote Sensing Data
The CREST-Snow Analysis and Field Experiment (CREST-SAFE) is being carried out using dual polarized microwave (37 and 89 GHz) radiometers along with detailed synchronous observations of snowpack properties with an objective to characterize the behavior of snow-emitted microwave radiation throughout the winter season. The effect of snowpack temperature (related to dry and wet snow conditions) on the microwave brightness temperature will be examined and interpreted. I am principal investigator of this multi-institutional CREST-SAFE Experiment.  (Link Hydrology and Earth System Sciences Paper)
* Basic Snow Research 
with Student: Jonathan Munoz (PhD.. graduated and now Asst Professor at UPRM)
Field Experiments using Microwave Radiometers (37 and 89 GHz) - Preliminary Analysis
- Algorithm Development for Snow Water Equivalent using Microwave Remote Sensing Data
- Synergistic Use of Remote Sensing for Snow Cover and Snow Water Equivalent Estimation
Applied Snow Research 
With Student: Jose Infante Corona (PhD), Carlos Perez (PhD) and Hiram Sanchez (MS)
- Remote Sensing and Ground-Based Weather Forcing Data Analysis for Streamflow Simulation
- Remote Sensing based Snow estimates for Hydrological Models Applications
* Calibration and Validation of snow products
Student: Christine Chen, (MS.. Graduated)
Validation of NOAA Interactive Snow Maps with National Climatic Data Center (NCDC) Data
Assimilation of Remote Sensing Satellite Data in Flash Flood Forecasting System

with Student: Dugwon Seo (PhD.. graduated), and Juan Mejia (MS)
- Evaluation of Operational National Weather Service Gridded Flash Flood Guidance
- Use of microwave soil moisture product to improve FFG System
- Validation of Gridded Flash Flood Guidance System by Statistical Analysis

This study proposed the value of remote sensing data in constraining the state of the system for main-steam and flash flood forecasting. The results from successfully developed technique implied potential improvement of flash flood forecast with SMOS and SMAP satellite sensors assimilation in hydrological modeling.

Development of Climate Adaptation Strategy for Agro-livestock Systems in Nepal
Adaptation for climate change by livestock smallholders in Gandaki river basin inNepal”, USAID Funded
- Spatial and Temporal Variability of Rainfall in the Gandaki River Basin of Nepal Himalaya (publication)
- Development of Probabilistic Precipitation Estimation using Satellite Product
- Recomendations on National Livestock Policy of Nepal: Needs and Opportunities
* Field activities (Workshop, stakeholders Meeting)
.* Project Presentation to USAID
Remote Sensing of Soil Moisture

* Soil Moisture Field Experiment (CREST-SMART) 
Student: Laetitia Kumassi (MS ..graduated)
Field experiment is being performed in a grassland at the NOAA-CREST–Soil Moisture Advanced Radiometric Testbed (CREST-SMART) facility, which includes a mobile L-band dual-polarized radiometer with an in situ soil temperature and soil moisture observation network, located near Millbrook, NY. The research investigates changes in the performance of microwave soil moisture retrieval with diurnal soil temperature variations.

Other Projects:

* Microwave Remote Sensing for Vegetation Monitoring 
Student: Doralee Pellot (MS. Graduated in May 2012)
Evaluation of QuikSCAT Data for Regional Monitoring of Vegetation Phenology
The objective of this study is to evaluate vegetation phenology data based on VIS/IR monitoring and correlate with corresponding data based on microwave radar remote sensing. The relationship between QuikSCAT backscatter, NDVI and LAI will be analyzed across a wide range of global vegetation types and geographical locations. The NDVI and LAI product from MODIS will be used in this study to understand the effect of seasonality.

* Data Assimilation Research 
 4D Variational Data Assimilation Technique for Soil Moisture Estimation.
NPOESS Soil Moisture Data Assimilation Research using WindSAT Data.
* Optical-Microwave (active) synergism for Agricultural and Hydrological application 
- A variational assimilation method for soil moisture using active microwave data.
- Developing soil moisture retrieval model and active microwave SAR data.
- Develop adjoints for soil moisture retrieval model and parameterization.IGARSS'08 Abstract
* Geostatistical techniques for large scale soil moisture variability
Generate variogram and kriging maps from in-situ soil moisture data (Sensors link).
- Compare variogram parameters of in-situ && remote sensing moisture data. (AMS 2008 conference Link)
* Soil moisture retrieval using and remote sensing technique (Poster)
- Soil moisture retrieval using Neural Networks & Fuzzy Logic method. (Journal paper Link)
- Effect of land cover variability on soil moisture retrieval. (Journal paper link)
- Active and passive Microwave remote sensiing for soil moisture.
* Application of Geostationary and Polar Orbiting satellite for Assessment of Forest Fire
- Study of NDVI retrieval from MSG SEVIRI data.
- Time series analysis of NDVI for assessment of forest fire risk
 - Global NDVI assessment using 2 decades of NOAA-AVHRR satellite data.
* Neural Networks algorithm for Snow Cover Identification
- Spatial estimation of snow cover using passive microwave (SSM/I) data.
- Neural Network and filtering algorithm foor mapping snow cover.
* Planned, customized and directed GIS projects.
- Directed image processing, and raster-to-vector conversion.      
- Enhanced the quality control framework immprove product quality.
- Digital database generation using Ortho-PPhotograph and IKONOS images.
* Environmental Impact Assessment
- EIA for an industrial area using Remote Sensing and GIS.
- Prepared technical reports and spreadsheeets for evaluation of projects.
Ph.D. Research

My doctoral research was focused on the application of Neural Network and Fuzzy Logic for retrieval of Soil Moisture using time series of microwave remote sensing data (RADARSAT-1).  This study deals with the application of back-propagation neural network and fuzzy logic in estimating the surface soil moisture using Synthetic Aperture Radar (SAR) data and other impacted variables. In addition A multiple linear regression model has also been developed to establish the relationship SAR data and hydrological variables. The potential of SAR images in spatial soil moisture estimation depends on the ability of these algorithms to define the complex relationship that exists between the backscattered energy and the moisture content of the soil.