Stefania Di Tommaso

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Stefania Di Tommaso

  • Research Data Analyst

Y2E2 Bldg, 473 VIA ORTEGA
Dept. Center on Food Security - Room 349
Stanford, CA 94305

 

Biography

Stefania joined FSE as a research data analyst in March 2018 where she works with David Lobell on designing, implementing, and applying new satellite-based monitoring techniques to study several aspects of food security. 

Her current focuses include estimates of crop yields, crop classification, and detection of management practices in Africa and India using a variety of satellite sensors including Landsat (NASA/USGS), Sentinel 1 and 2 (ESA), combined with crop modeling and machine learning techniques.

publications

Journal Articles
November 2021

Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops

Author(s)
cover link Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops
Journal Articles
November 2020

Changes in the drought sensitivity of US maize yields

Author(s)
cover link Changes in the drought sensitivity of US maize yields
Journal Articles
September 2020

Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning

Author(s)
cover link Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning

In The News

Green corn and soybean field
News

Lasering In on Corn Fields

Mapping crops around the globe is key to estimating production and developing targeted management strategies. New research utilized data from NASA's Global Ecosystem Dynamics Investigation (GEDI) technology and developed an algorithm to distinguish between maize and other crops with high accuracy and produce crop maps across the globe.
cover link Lasering In on Corn Fields
Dense rows of yellow corn under a blue sky
News

NASA Harvest Partners At Stanford Expand Lidar Applications To Create Wall-To-Wall Crop Type Mapping

NASA Harvest partners at Stanford’s Center on Food Security and the Environment (FSE) recently published a study on their efforts integrating lidar (Light Detection and Ranging) and optical earth observation (EO) data to improve crop type mapping in areas with low training data availability.
cover link NASA Harvest Partners At Stanford Expand Lidar Applications To Create Wall-To-Wall Crop Type Mapping