Source: GEORGE MASON UNIVERSITY submitted to
FACT: MACHINE-LEARNING-BASED IN-SEASON CROP MAPPING AND ASSOCIATED CLOUDBASED BIGDATA CYBERINFRASTRUCTURE TO SUPPORT USDA NASS DECISION MAKING
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
NEW
Funding Source
Reporting Frequency
Annual
Accession No.
1025609
Grant No.
2021-67021-34151
Project No.
VA.W-2020-08826
Proposal No.
2020-08826
Multistate No.
(N/A)
Program Code
A1541
Project Start Date
Feb 25, 2021
Project End Date
Feb 24, 2024
Grant Year
2021
Project Director
Di, L.
Recipient Organization
GEORGE MASON UNIVERSITY
4400 UNIVERSITY DRIVE
FAIRFAX,VA 22030
Performing Department
(N/A)
Non Technical Summary
The National Agricultural Statistics Service (NASS) of U.S. Department of Agriculture produces annually a digital product, called Cropland Data Layer (CDL), covering the contigious U.S. since 2008. The product maps what crop grows in each field at ~95% accuracy for major crops. CDL is distributed to 60,000 decision makers via CropScape web service system and is one of the key data products for many agricultural decision makings. However, currently there are two major problems associated with CDL production and distribution: 1) the current-year CDL is not available to public until Feb/March next year, making many in-season agricultural decisions impossible; and 2) CropScape on traditional servers cannot meet the peak user requests, especially when a new CDL is released. This project will solve the two problems by 1) producing pre- and in-season CDL-like products through smart algorithms that can learn from historical CDL products and the current satellite observations of the ground; 2) enhancing CropScape to make the new products and the smart algorithms available and easily usable by decision makers. The project will enhance agricultural decision making by providing timely and accurate in-season CDL-like products to NASS and 60,000 unique decision makers through the CropScape cyberinfrastructure.
Animal Health Component
0%
Research Effort Categories
Basic
30%
Applied
35%
Developmental
35%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
9030120303050%
9010120209030%
4020120208020%
Goals / Objectives
The goal of the project is to facilitate timely and informed agricultural decision making by developing the capability of generating and providing in-season CDL-like crop maps for CONUS through easy-to-use cyberinfrastructure. The specific objectives of this project is to 1) develop bigdata classification algorithms to automatically derive in-season CDL for CONUS; 2) enhance CropScape by implementing the algorithms as web services; and 3) migrate the enhanced CropScape to a cloud for better user support. In-season CDL means to have CDL-compatible product with reasonable accuracy at beginning of a growing season, continue to improve the product with season progress, and reach the accuracy similar to NASS CDL around early July.
Project Methods
The project will use historical USDA/NASS Cropland Data Layer (CDL) data and current satellite observations to automatically classify and generate pre- and in-season crop planting classification maps of CONUS that has the same classification schema as the CDL. The major satellite observations will be Landsat 8 data. In order to obtain the complete coverage of CONUS and sufficient repetitions over time, other data sources and observations will be considered as well, particularly Sentinel-2 and Sentinel-1 data. Four major activities will be carried out to accomplish the overall goal of producing the pre- and in-season cropland classification from satellite observations and historical CDL data. They are (1) extraction of "trusted pixel" cropland predictions from over 10-year annual historic CDL as ground truth, (2) pre- and in-season cropland classification from satellite observations with optimal deep-learning algorithms, (3) cloud computing for supporting deep-learning classification, and (4) user-experience-centric distribution of cropland classification products using standard geospatial Web services and Web applications on cloud.