NASA Harvest News
In this video, NASA Harvest sits down with highly experienced food security specialists, senior market analysts, economists, and remote sensing experts to discuss the near and potential long term impacts of the Russian invasion of Ukraine on global food security and market volatility. In the face of crises that impact global supply chains and the agrifood system on the whole, we discuss how Earth observation data plays a critical role in agricultural monitoring when ground access is limited.
The NASA Earth Observatory showcases almost two decades of global cropland maps recently produced by Harvest partners from the Global Land Analysis and Discovery (GLAD) lab at the University of Maryland. These maps show global cropland has expanded by an area equivalent to the country of Egypt between 2003 and 2019. This research will assist policymakers in ensuring food production needs are met while also protecting local ecosystems, biodiversity, and in meeting carbon sequestration goals.
A team of remote sensing researchers, including Harvest's Dr. Mehdi Hosseini, recently evaluated frequently used machine learning models on their ability to accurately estimate common crop characteristics across a variety of commodity crops. Focusing on wheat, canola, and soybeans, the team explored the selected model's accuracy and speed for individual and multi-crop mapping.
Sarah Brennan, Deputy Program Manager for the Water Resources and Agriculture Applications areas within NASA’s Applied Science Program, was selected for this honor based on her dedication and success in positively impacting NASA operations. Sarah Brennan is a tireless advocate for the Harvest program and has been instrumental in helping the consortium grow since its inception.
NASA Harvest partners at the Stanford University's Center on Food Security and the Environment, were featured on NASA's Earth Observatory Blog for their work utilizing NASA's GEDI mission to map maize. While GEDI was originally designed for global forest canopy mapping, the team at Stanford realized that given the significant height difference between maize and other crops, GEDI can be repurposed to help monitor this globally important commodity crop.
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NASA Harvest's AI Lead Dr. Hannah Kerner will be giving the keynote address at Planet's AI4EO Food Security Challenge Ceremony. AI4EO, a program within the European Space Agency, tasked participants in the Challenge with developing new ML and AI techniques for crop identification using Planet and Sentinel-1 and -2 data. In addition to the the keynote address, the ceremony will feature pitch presentations from the Top 4 selected solutions. Registration is free and open to the public.
NASA’s Applied Remote Sensing Training (ARSET) is building off previous agricultural monitoring workshops with a 4 part online advanced training on the use of synthetic aperture radar (SAR) and optical imagery for the mapping of crops and crop characteristics. Training will use data from Sentinel-1, RadarSat, SAOCOM, and Sentinel-2. Participants will also learn about radar polarimetry and receive training in the use of the Sentinel Application Platform (SNAP) and Python for Jupyter Notebooks. Training available in English and Spanish.
NASA’s Applied Remote Sensing Training (ARSET) is hosting a 3-part online training on the tracking of emissions and removals of carbon dioxide and methane from the atmosphere. The training is in support of the 2023 Global Stocktake – an effort by parties to the Paris Agreement to compile national inventories of GHG budgets. At the end of the training, participants will be able to describe how CO2 and methane budgets are derived using atmospheric measurements and explore the data and products necessary to model these budgets. 
Harvest's John Bolten of NASA Goddard Space Flight Center is convening a session at the American Geophysical Union's Frontiers in Hydrology meeting. The meeting will take place June 19-24 in San Juan, Puerto Rico. The session, Remote Sensing Applications for Agricultural Water Management, is looking for work that utilizes remote sensing products, numerical modeling, and machine learning to advance decision making around agricultural water management.