In August 2023, the USGS National Uncrewed Systems Office (NUSO) participated in a collaborative field campaign to collect remote sensing data over agricultural crop fields in California's Central Valley.
Itiya P Aneece
Itiya Aneece is currently a Research Geographer at the U.S. Geological Survey (USGS) in Flagstaff, AZ, USA.
At the USGS, she is working with the Western Geographic Science Center using hyperspectral and multispectral remote sensing to study globally dominant agricultural crops. She is also working on a variety of projects with the Astrogeology Science Center. Dr. Aneece earned a PhD in Environmental Sciences from the University of Virginia, where she conducted her dissertation research on studying the impacts of invasive plant species on secondary successional dynamics in abandoned agricultural fields using ground-level hyperspectral remote sensing. She has also recently completed a Mendenhall Postdoctoral Fellowship within the Western Geographic Science Center, in which she studied crops using Hyperion hyperspectral satellite data in Google Earth Engine.
Science and Products
Global Food-and-Water Security-support Analysis Data (GFSAD)
Availability, documentation, & community support for an open-source machine learning tool
Increasing data accessibility by adding existing datasets and capabilities to a cutting-edge visualization app to enable cross-community use
Processing a new generation of hyperspectral data on the Cloud using Pangeo
Global Crop Water Productivity and Savings through waterSMART (GCWP)
Global Hyperspectral Imaging Spectral-library of Agricultural-Crops & Vegetation (GHISA)
PlanetScope and DESIS spectral library of agricultural crops in California's Central Valley for the 2020 growing season
In August 2023, the USGS National Uncrewed Systems Office (NUSO) participated in a collaborative field campaign to collect remote sensing data over agricultural crop fields in California's Central Valley.
Crop water productivity from cloud-Based landsat helps assess California’s water savings
New generation hyperspectral sensors DESIS and PRISMA provide improved agricultural crop classifications
New generation hyperspectral data From DESIS compared to high spatial resolution PlanetScope data for crop type classification
Introduction to the Python Hyperspectral Analysis Tool (PyHAT)
Classifying crop types using two generations of hyperspectral sensors (Hyperion and DESIS) with machine learning on the cloud
Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud
Hyperspectral narrowband data propel gigantic leap in the earth remote sensing
Planetary defense preparedness: Identifying the potential for post-asteroid impact time delayed and geographically displaced hazards
A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades
Accuracies achieved in classifying five leading world crop types and their growth stages using optimal Earth Observing-1 Hyperion hyperspectral narrowbands on Google Earth Engine
Spaceborne hyperspectral EO-1 hyperion data pre-processing: Methods, approaches, and algorithms
Crop water productivity estimation with hyperspectral remote sensing
Science and Products
Global Food-and-Water Security-support Analysis Data (GFSAD)
Availability, documentation, & community support for an open-source machine learning tool
Increasing data accessibility by adding existing datasets and capabilities to a cutting-edge visualization app to enable cross-community use
Processing a new generation of hyperspectral data on the Cloud using Pangeo
Global Crop Water Productivity and Savings through waterSMART (GCWP)
Global Hyperspectral Imaging Spectral-library of Agricultural-Crops & Vegetation (GHISA)
PlanetScope and DESIS spectral library of agricultural crops in California's Central Valley for the 2020 growing season
In August 2023, the USGS National Uncrewed Systems Office (NUSO) participated in a collaborative field campaign to collect remote sensing data over agricultural crop fields in California's Central Valley.
In August 2023, the USGS National Uncrewed Systems Office (NUSO) participated in a collaborative field campaign to collect remote sensing data over agricultural crop fields in California's Central Valley.