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.
Dr. Itiya Aneece is a Research Geographer at the U.S. Geological Survey Western Geographic Science Center using remote sensing to study globally dominant agricultural crops. She 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 in abandoned agricultural fields using ground-level hyperspectral remote sensing. As a Mendenhall Postdoc, she used Hyperion images to study globally major agricultural crops using Google Earth Engine. Her research interests include big data analysis, machine learning, cloud computing, and agricultural study.
Science and Products
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...
Python Hyperspectral Analysis Tool (PyHAT)
The Python Hyperspectral Analysis Tool (PyHAT) provides access to data processing, analysis, and machine learning capabilities for spectroscopic applications. It includes a GUI so you can get straight to analyzing data without writing any code. Or, if you are comfortable writing code, PyHAT can be imported just like any other Python package.
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...
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:
Drone-based hyperspectral mapping of 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.
Science and Products
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...
Python Hyperspectral Analysis Tool (PyHAT)
The Python Hyperspectral Analysis Tool (PyHAT) provides access to data processing, analysis, and machine learning capabilities for spectroscopic applications. It includes a GUI so you can get straight to analyzing data without writing any code. Or, if you are comfortable writing code, PyHAT can be imported just like any other Python package.
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...
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:
Drone-based hyperspectral mapping of 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.
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.