Automated Cropland Classification Algorithm (ACCA) for California using multi-sensor remote sensing
Links
- More information: Publisher Index Page (via DOI)
- Download citation as: RIS | Dublin Core
Abstract
Increasing pressure to feed the growing population with scarce water resources requires accurate and routine cropland mapping. This paper develops and implements a rule-based automated cropland classification algorithm (ACCA) using multi-sensor remote sensing data. Pixel-by-pixel accuracy assessments showed that ACCA produced an overall accuracy of 96 percent (Khat = 0.8) when tested using independent data layers. Furthermore, ACCA-generated county cropland areas showed high agreement (R-square values 0.94) when compared with three independent data sources: (a) US Department of Agriculture (USDA) cropland data layer derived cropland areas, (b) county specific crop acreage data from the Farm Service Agency, and (c) the Census of Agriculture data for the 58 counties in California. Our results demonstrate the ability of ACCA to generate cropland extent and areas over space and time, in an automated fashion with high degree of accuracies year after year, greatly contributing to food and water security analysis and decision making.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Automated Cropland Classification Algorithm (ACCA) for California using multi-sensor remote sensing |
| Series title | Photogrammetric Engineering & Remote Sensing |
| DOI | 10.14358/PERS.80.1.81 |
| Volume | 80 |
| Issue | 1 |
| Year Published | 2014 |
| Language | English |
| Publisher | American Society for Photogrammetry and Remote Sensing |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center, Western Geographic Science Center |
| Description | 10 p. |
| First page | 81 |
| Last page | 90 |
| Country | United States |
| State | California |
| Google Analytic Metrics | Metrics page |