Harvest Speaker Series - Dr. Ritvik Sahajpal
On Friday December 11, 2020 Dr. Ritvik Sahajpal continued the HARVEST speaker series by discussing earth observation data and machine learning at NASA Harvest. His presentation was entitled “Harvesting What We Sow: Usage of Earth Observation Data and Machine Learning at NASA Harvest”. Below is a short bio on Dr. Sahajpal and an abstract of his talk as well as a recording of the presentation.
Dr. Ritvik Sahajpal
Dr. Ritvik Sahajpal is an Associate Research Professor at the Department of Geographical Sciences at University of Maryland, crop condition co-lead at NASA Harvest, and a member of the data advisory council at Foundation for Food and Agriculture Research (FFAR). His research expertise is broadly related to using Earth observation data to monitor crop yields from field to global scales, modeling the impacts of conservative agriculture practices on soil health and crop yield, and mapping land-use and land-cover change and modeling their impacts on the carbon-climate system. Dr. Sahajpal uses both machine learning and data driven agro-ecosystem modeling techniques in his work. His research has been funded by NASA, FFAR, USAID and published in journals like Nature, Environmental Research Letters, Geoscientific Model Development and Science of the Total Environment. Previously, he was a post-doc at the Forest and Wildlife Ecology Department at the University of Wisconsin-Madison, and finished his PhD in Geographical Sciences at the University of Maryland in 2014.
Harvesting What We Sow: Usage of Earth Observation Data and Machine Learning at NASA Harvest
NASA Applied Sciences launched NASA Harvest in 2017 as its food security & agriculture program. NASA Harvest operates as a multidisciplinary consortium led and operated by a Hub at the University of Maryland, College Park. Three years since its inception, NASA Harvest has expanded to include over 50 institutions as partners and affiliates and targets improving agricultural land use, productivity, and sustainability through the improvement of satellite-based methods and their adoption by users in the public and private sector alike. NASA Harvest envisions reduced food insecurity, stable markets, and environmentally and socially responsible agricultural practices, all underpinned through the use of satellite-based Earth observations. As part of this talk, I will provide examples of our work at NASA Harvest integrating satellite-based Earth Observation data and process-based and machine learning models to address food security challenges: 1. Field to Global scale crop yield forecasting 2. Optimization of crop data collection by combining optimization and forecasting models 3. Mapping croplands across a variety of geographies. This talk will highlight these and other NASA Harvest impact cases. It will also include a forward look at priorities and challenges that lie ahead.