Crop area monitoring within an advanced agricultural information system

Abstract

This paper describes a framework for an image processing procedure for operational agricultural crop area estimation. This operational framework has been conceived within the development of an Advanced Agricultural Information System (AAIS) for the “Regione del Veneto “ (RdV ‐ Veneto Region) in northeastern Italy. The objective of this program is to develop the ability to generating timely and accurate area estimates and production information for four major agricultural crops: soybeans, sugar beets, corn, and small grains. AAIS uses state of the art methods in remote sensing and geographic information systems (GIS) technology and integrates a variety of data types including satellite imagery. This paper describes the methodology developed for image and ancillary data processing for the production of crop area statistics. Using a combination of standard unsupervised classification and GIS operations that incorporate knowledge about the agricultural system, a “sequential masking” classification procedure was derived. This sequential masking procedure yielded crop classification accuracies that at the study site level range between 76% and 99% depending on the crop under study. We believe that classification accuracies will improve with full system implementation, along with the incorporation of new and/or improved thematic information and operational experience using AAIS‐based estimation.

Notes

Currently at: Monitoring Tropical Vegetation Institute for Remote Sensing Applications, Joint Research Center, 21020 Ispra, Italy.

Currently at: Desert Research Institute, Reno, NV 89512, U.S.A.