Competencies

Interpersonal Communication

• Experience doing collaboration in multidisciplinary and diverse teams
• Proactive and curiosity-driven
• Intercultural Communication skills
• Native Bengali speaker
• Advanced English speaker
• Intermediate French speaker
• Beginner Arabic speaker
• Empathic Listening

Teaching and Mentoring

-Supported undergraduate teaching as both an independent instructor and teaching assistant, contributing to syllabus development, grading, recitation materials, and individual student support.

-Contributed to curriculum design for courses such as Biophysics and Sustainable Watershed Management, and supervised student teams in senior design projects.

-Mentored multiple undergraduate students through NSF REU, honors thesis, and independent research projects, offering training in remote sensing, GEE, R, and Python.

-Guided students in processing eddy covariance flux data using EddyPro and Campbell Scientific software, and supported data analysis workflows using Python.

-Provided hands-on training in GIS and remote sensing, including ArcGIS ModelBuilder, Sentinel-5P analysis, and large-scale land use and environmental datasets.

Scientific Communication

• Journal Articles
• Conference Proceedings
• Group Meetings
• Technical Data Documentation

Information Science

Programming Languages

• R
• Javascript (Google Earth Engine)
• Python
• Latex

Software

• QGIS
• ArcGIS
• Eddy Pro
• MS Office

Data Analytics

• Geo-spatial Analysis
• Time Series Analysis
• Descriptive Statistics
• Process Based Models: Vegetation Photosynthesis Model

Machine Learning and Statistical Analysis

• Random Forest
• SciKit Learn
• XGBoost
• Linear Regression
• Double Logistic Regression Analysis
• Harmonic Function Analysis
• Non Linear Regression
• GAM Regression

Field sensor deployment and calibration

• LI 7500
• LI 7700
• Campbell Sonic aenonometer

Deep Learning

• PyTorch
• TensorFlow

Published Data

Cumulative GPP Data of Rice in Arkansas (500m)

Authors: Riasad Bin Mahbub (Contact), Michele Reba, Benjamin Runkle

Overview:
High-resolution dataset of Arkansas rice regions (2008–2020) including: crop frequency, county shapefiles, environmental variables (temperature, PAR), MODIS indices (EVI, LSWI), and GPP modeled with VPM. Includes six rice ecological zones.

🔗 Access Dataset

Google Earth Engine Workflow

Mapping Crop Rotation and Monoculture Patterns

This Google Earth Engine script identifies and visualizes crop rotation patterns using the USDA Cropland Data Layer (CDL) from 2008–2020. It employs the agkit4ee module to detect monoculture and rotation sequences (e.g., rice–soybean, corn–soybean) for major crops such as rice, corn, soybean, cotton, and wheat. The resulting output provides color-coded map layers showing where each cropping pattern occurs across the agricultural landscape.

🔗 Link to the Code

Modeling 2020 Crop Frequency and Intensity Across Arkansas

This Google Earth Engine script models and visualizes the 2020 spatial distribution and frequency of major crops across Arkansas using multi-year data (2008–2020) from the USDA Cropland Data Layer (CDL). Using the agkit4ee module, it estimates occurrence frequencies for corn, cotton, rice, soybean, wheat, and double-cropped wheat–soybean systems.

The workflow produces color-coded frequency maps, generates histograms of rice distribution, and exports each crop’s 2020 intensity layer to Google Drive as GeoTIFFs. These outputs help assess spatial cropping intensity and dominant crop patterns across the state.

🔗 Link to the Code