81
Views
8
CrossRef citations to date
0
Altmetric
Miscellany

Estimating inter-annual crop area variation using multi-resolution satellite sensor images

&
Pages 2641-2647 | Received 24 Mar 2003, Accepted 03 Nov 2003, Published online: 13 May 2010
 

Abstract

The work aimed at developing a methodology which integrates high and low spatial resolution satellite sensor data to assess inter-annual crop area variation on a regional scale. The methodology is based on the use of high spatial resolution images of the main crops for a training year joint to a long-term series of low spatial resolution NDVI images. These data allow the identification of crop specific NDVI profiles for the training year, which can be compared to low spatial resolution images of other study years to detect NDVI differences convertible into crop area variation. The method was preliminarily tested in Tuscany (central Italy), for which five Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+) frames and a 13-year series of 10-day National Oceanic Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites were available together with reference agricultural statistics. This test demonstrated the suitability of the method for inter-annual winter crop area change estimation and revealed its potential for further improvement.

Acknowledgments

The authors thank Dr J. Delincé, Dr F.J. Gallego, Dr G. Genovese, Dr H. Kerdiles and Dr P. Loudjani for their helpful comments on the first draft of this Letter.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 689.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.