ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 215-219, 2012
https://doi.org/10.5194/isprsannals-I-7-215-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
17 Jul 2012
SOYBEAN CROP AREA ESTIMATION AND MAPPING IN MATO GROSSO STATE, BRAZIL
A. Gusso1 and J. R. Ducati2,1 1CEPSRM-UFRGS, Center for Remote Sensing and Meteorological Research, Federal University of Rio Grande do Sul, 15044, Porto Alegre, Brazil
2Institute of Physics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
Keywords: Crop Area, Algorithm, LUCC mapping, Remote Sensing, MODIS Abstract. Evaluation of the MODIS Crop Detection Algorithm (MCDA) procedure for estimating historical planted soybean crop areas was done on fields in Mato Grosso State, Brazil. MCDA is based on temporal profiles of EVI (Enhanced Vegetation Index) derived from satellite data of the MODIS (Moderate Resolution Imaging Spectroradiometer) imager, and was previously developed for soybean area estimation in Rio Grande do Sul State, Brazil. According to the MCDA approach, in Mato Grosso soybean area estimates can be provided in December (1st forecast), using images from the sowing period, and in February (2nd forecast), using images from sowing and maximum crop development period. The results obtained by the MCDA were compared with Brazilian Institute of Geography and Statistics (IBGE) official estimates of soybean area at municipal level. Coefficients of determination were between 0.93 and 0.98, indicating a good agreement, and also the suitability of MCDA to estimations performed in Mato Grosso State. On average, the MCDA results explained 96% of the variation of the data estimated by the IBGE. In this way, MCDA calibration was able to provide annual thematic soybean maps, forecasting the planted area in the State, with results which are comparable to the official agricultural statistics.
Conference paper (PDF, 4493 KB)


Citation: A. Gusso and J. R. Ducati: SOYBEAN CROP AREA ESTIMATION AND MAPPING IN MATO GROSSO STATE, BRAZIL, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., I-7, 215-219, https://doi.org/10.5194/isprsannals-I-7-215-2012, 2012.

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