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)
The GFSAD is a NASA funded project (2023-2028) to provide highest-resolution global cropland data and their water use that contributes towards global food-and-water security in the twenty-first century. The GFSAD products are derived through multi-sensor remote sensing data (e.g., Landsat-series, Sentinel-series, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland...
Availability, documentation, & community support for an open-source machine learning tool
We will make cutting-edge spectral analysis and machine learning algorithms available to remote sensing and chemical quantification communities, regardless of the user’s programming skills, by releasing, documenting, presenting, and developing tutorials for the Python Hyperspectral Analysis Tool.
Increasing data accessibility by adding existing datasets and capabilities to a cutting-edge visualization app to enable cross-community use
We will collate and publish existing datasets from collaborators and ingest them into a visualization app to help researchers with machine learning model-building and hypothesis-making. These data collation and app development methods could help other researchers increase their data accessibility.
Processing a new generation of hyperspectral data on the Cloud using Pangeo
We aim to migrate our research workflow from a closed system to an open framework, increasing flexibility and transparency in our science and accessibility of our data. Our hyperspectral data of agricultural crops are crucial for training/ validating machine learning algorithms to study food security, land use, etc. Generating such data is resource-intensive and requires expertise, proprietary
Global Crop Water Productivity and Savings through waterSMART (GCWP)
The waterSMART (Sustain and Manage America’s Resources for Tomorrow) project places technical information and tools in the hands of stakeholders that allow them to answer pertinent questions regarding water availability. Two goals of waterSMART are to 1) establish water availability and its use based on an understanding of the past and present water users and to 2) project water availability and...
Global Hyperspectral Imaging Spectral-library of Agricultural-Crops & Vegetation (GHISA)
This webpage showcases the key research advances made in hyperspectral remote sensing of agricultural crops and vegetation over the last 50 years. There are three focus areas:
Science and Products
- Science
Global Food-and-Water Security-support Analysis Data (GFSAD)
The GFSAD is a NASA funded project (2023-2028) to provide highest-resolution global cropland data and their water use that contributes towards global food-and-water security in the twenty-first century. The GFSAD products are derived through multi-sensor remote sensing data (e.g., Landsat-series, Sentinel-series, MODIS, AVHRR), secondary data, and field-plot data and aims at documenting cropland...Availability, documentation, & community support for an open-source machine learning tool
We will make cutting-edge spectral analysis and machine learning algorithms available to remote sensing and chemical quantification communities, regardless of the user’s programming skills, by releasing, documenting, presenting, and developing tutorials for the Python Hyperspectral Analysis Tool.Increasing data accessibility by adding existing datasets and capabilities to a cutting-edge visualization app to enable cross-community use
We will collate and publish existing datasets from collaborators and ingest them into a visualization app to help researchers with machine learning model-building and hypothesis-making. These data collation and app development methods could help other researchers increase their data accessibility.Processing a new generation of hyperspectral data on the Cloud using Pangeo
We aim to migrate our research workflow from a closed system to an open framework, increasing flexibility and transparency in our science and accessibility of our data. Our hyperspectral data of agricultural crops are crucial for training/ validating machine learning algorithms to study food security, land use, etc. Generating such data is resource-intensive and requires expertise, proprietaryGlobal Crop Water Productivity and Savings through waterSMART (GCWP)
The waterSMART (Sustain and Manage America’s Resources for Tomorrow) project places technical information and tools in the hands of stakeholders that allow them to answer pertinent questions regarding water availability. Two goals of waterSMART are to 1) establish water availability and its use based on an understanding of the past and present water users and to 2) project water availability and...Global Hyperspectral Imaging Spectral-library of Agricultural-Crops & Vegetation (GHISA)
This webpage showcases the key research advances made in hyperspectral remote sensing of agricultural crops and vegetation over the last 50 years. There are three focus areas: - Data
- Publications
- Multimedia