Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil

Author:

Epiphanio Rui Dalla Valle1,Formaggio Antonio Roberto2,Rudorff Bernardo Friedrich Theodor2,Maeda Eduardo Eiji3,Luiz Alfredo José Barreto4

Affiliation:

1. Louis Dreyfus Commodities Brasil S.A.

2. Instituto Nacional de Pesquisas Espaciais, Brazil

3. University of Helsinki, Finland

4. Embrapa Meio Ambiente, Brazil

Abstract

The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.

Publisher

FapUNIFESP (SciELO)

Subject

Agronomy and Crop Science,Animal Science and Zoology

Reference33 articles.

1. Avaliação dos modelos SUITS e SAIL no estudo da reflectância da soja (Glycine max (L.) Merril);ANTUNES M.A.H.,1999

2. A semi-automatic technique for multitemporal classification of a given crop within a Landsat scene;BADHWAR G.D.;Pattern Recognition,1982

3. Spring: integrating remote sensing and gis by object-oriented data modeling;CÂMARA G.;Computers & Graphics,1996

4. Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery;CASTILLEJO-GONZÁLEZ I.L.;Computers and Electronics in Agriculture,2009

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