Title: The Use of MODIS data to derive acreage estimations for larger fields: a case study in the South-Western Rostov Region of Russia
Authors: FRITZ SteffenMASSART MICHELSAVIN IGORGALLEGO PINILLA FranciscoREMBOLD FELIX
Citation: International Journal of Applied Earth Observation and Geoinformation vol. 10 no. 4 p. 453-466
Publisher: ELSEVIER
Publication Year: 2008
JRC Publication N°: JRC42505
ISSN: 0303-2434
URI: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6X2F-4RWHH0S-1&_user=4900406&_coverDate=12%2F31%2F2008&_rdoc=7&_fmt=high&_orig=browse&_srch=doc-info(%23toc%237269%232008%23999899995%23700820%23FLA%23display%23Volume)&_cdi=7269&_sort=d&_docanchor
http://publications.jrc.ec.europa.eu/repository/handle/JRC42505
DOI: 10.1016/j.jag.2007.12.004
Type: Articles in Journals
Abstract: Recent developments in remote sensing technology, in particular improved spatial and temporal resolution, open new possibilities for estimating crop acreage over larger areas. Remotely sensed data allow in some cases the estimation of crop acreage statistics independently of sub-national survey statistics, which are sometimes biased and incomplete. This work focuses on the use of MODIS data acquired in 2001/2002 over the Rostov Oblast in Russia, by the Azov Sea. The region is characterized by large agricultural fields of around 75 hectares on average. This paper presents a methodology to estimate crop acreage using the MODIS 16-day composite NDVI product. Particular emphasis is placed on a good quality crop mask and a good quality validation dataset. In order to have a second dataset which can be used for cross checking the MODIS classification a Landsat ETM time series for 4 different dates in the season of 2002 was acquired and classified. We attempted to distinguish 5 different crop types and achieved satisfactory and good results for winter crops. 360 fields were identified to be suitable for the training and validation of the MODIS classification using a maximum likelihood classification. A novel method based on a pure pixel field sampling is introduced. This novel method is compared with the traditional hard classification of mixed pixels and was found to be superior.
JRC Institute:Institute for the Protection and Security of the Citizen

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