Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil
Estimations of crop area were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from moderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm (MCDA) to estimate soybean crop areas was performed for...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/863141 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832560496700555264 |
|---|---|
| author | Anibal Gusso Damien Arvor Jorge Ricardo Ducati Mauricio Roberto Veronez Luiz Gonzaga da Silveira |
| author_facet | Anibal Gusso Damien Arvor Jorge Ricardo Ducati Mauricio Roberto Veronez Luiz Gonzaga da Silveira |
| author_sort | Anibal Gusso |
| collection | DOAJ |
| description | Estimations of crop area were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from moderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm (MCDA) to estimate soybean crop areas was performed for fields in the Mato Grosso state, Brazil. Using the MCDA approach, soybean crop area estimations can be provided for December (first forecast) using images from the sowing period and for February (second forecast) using images from the sowing period and the maximum crop development period. The area estimates were compared to official agricultural statistics from the Brazilian Institute of Geography and Statistics (IBGE) and from the National Company of Food Supply (CONAB) at different crop levels from 2000/2001 to 2010/2011. At the municipality level, the estimates were highly correlated, with R2=0.97 and RMSD = 13,142 ha. The MCDA was validated using field campaign data from the 2006/2007 crop year. The overall map accuracy was 88.25%, and the Kappa Index of Agreement was 0.765. By using pre-defined parameters, MCDA is able to provide the evolution of annual soybean maps, forecast of soybean cropping areas, and the crop area expansion in the Mato Grosso state. |
| format | Article |
| id | doaj-art-e4a440d50797444996bcfdf07453f20c |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-e4a440d50797444996bcfdf07453f20c2025-02-03T01:27:29ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/863141863141Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, BrazilAnibal Gusso0Damien Arvor1Jorge Ricardo Ducati2Mauricio Roberto Veronez3Luiz Gonzaga da Silveira4Environmental Engineering, Vale do Rio dos Sinos University (UNISINOS), CP 275, São Leopoldo, RS, BrazilIRD-UMR 228 ESPACE-DEV (IRD, UM2, UAG, UR), MTD-Montpellier, 500 rue Jean-François Breton, 34093 Montpellier Cedex, FranceCenter for Remote Sensing and Meteorological Research, Federal University of Rio Grande do Sul (UFRGS), 15044 Porto Alegre, RS, BrazilVizLab—Advanced Visualization Laboratory, Vale do Rio dos Sinos University (UNISINOS), São Leopoldo, BrazilVizLab—Advanced Visualization Laboratory, Vale do Rio dos Sinos University (UNISINOS), São Leopoldo, BrazilEstimations of crop area were made based on the temporal profiles of the Enhanced Vegetation Index (EVI) obtained from moderate resolution imaging spectroradiometer (MODIS) images. Evaluation of the ability of the MODIS crop detection algorithm (MCDA) to estimate soybean crop areas was performed for fields in the Mato Grosso state, Brazil. Using the MCDA approach, soybean crop area estimations can be provided for December (first forecast) using images from the sowing period and for February (second forecast) using images from the sowing period and the maximum crop development period. The area estimates were compared to official agricultural statistics from the Brazilian Institute of Geography and Statistics (IBGE) and from the National Company of Food Supply (CONAB) at different crop levels from 2000/2001 to 2010/2011. At the municipality level, the estimates were highly correlated, with R2=0.97 and RMSD = 13,142 ha. The MCDA was validated using field campaign data from the 2006/2007 crop year. The overall map accuracy was 88.25%, and the Kappa Index of Agreement was 0.765. By using pre-defined parameters, MCDA is able to provide the evolution of annual soybean maps, forecast of soybean cropping areas, and the crop area expansion in the Mato Grosso state.http://dx.doi.org/10.1155/2014/863141 |
| spellingShingle | Anibal Gusso Damien Arvor Jorge Ricardo Ducati Mauricio Roberto Veronez Luiz Gonzaga da Silveira Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil The Scientific World Journal |
| title | Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil |
| title_full | Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil |
| title_fullStr | Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil |
| title_full_unstemmed | Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil |
| title_short | Assessing the MODIS Crop Detection Algorithm for Soybean Crop Area Mapping and Expansion in the Mato Grosso State, Brazil |
| title_sort | assessing the modis crop detection algorithm for soybean crop area mapping and expansion in the mato grosso state brazil |
| url | http://dx.doi.org/10.1155/2014/863141 |
| work_keys_str_mv | AT anibalgusso assessingthemodiscropdetectionalgorithmforsoybeancropareamappingandexpansioninthematogrossostatebrazil AT damienarvor assessingthemodiscropdetectionalgorithmforsoybeancropareamappingandexpansioninthematogrossostatebrazil AT jorgericardoducati assessingthemodiscropdetectionalgorithmforsoybeancropareamappingandexpansioninthematogrossostatebrazil AT mauriciorobertoveronez assessingthemodiscropdetectionalgorithmforsoybeancropareamappingandexpansioninthematogrossostatebrazil AT luizgonzagadasilveira assessingthemodiscropdetectionalgorithmforsoybeancropareamappingandexpansioninthematogrossostatebrazil |