Algorithm for Soybean Classification Using Medium Resolution Satellite Images
An accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for soybean classificati...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2012-10-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/4/10/3127 |
| _version_ | 1818958309911166976 |
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| author | Anibal Gusso Jorge Ricardo Ducati |
| author_facet | Anibal Gusso Jorge Ricardo Ducati |
| author_sort | Anibal Gusso |
| collection | DOAJ |
| description | An accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for soybean classification over the Rio Grande do Sul State, Brazil, was developed as an objective, automated tool. It is based on reflectance from medium spatial resolution images. The classification method was called the RCDA (Reflectance-based Crop Detection Algorithm), which operates through a mathematical combination of multi-temporal optical reflectance data obtained from Landsat-5 TM images. A set of 39 municipalities was analyzed for eight crop years between 1996/1997 and 2009/2010. RCDA estimates were compared to the official estimates of the Brazilian Institute of Geography and Statistics (IBGE) for soybean area at a municipal level. Coefficients R2 were between 0.81 and 0.98, indicating good agreement of the estimates. The RCDA was also compared to a soybean crop map derived from Landsat images for the 2000/2001 crop year, the overall map accuracy was 91.91% and the Kappa Index of Agreement was 0.76. Due to the calculation chain and pre-defined parameters, RCDA is a timesaving procedure and is less subjected to analyst skills for image interpretation. Thus, the RCDA was considered advantageous to provide thematic soybean maps at local and regional scales. |
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| format | Article |
| id | doaj.art-621edfe7d7584719912bce5b3fcb2b48 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:23:42Z |
| publishDate | 2012-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-621edfe7d7584719912bce5b3fcb2b482022-12-21T19:42:26ZengMDPI AGRemote Sensing2072-42922012-10-014103127314210.3390/rs4103127Algorithm for Soybean Classification Using Medium Resolution Satellite ImagesAnibal GussoJorge Ricardo DucatiAn accurate estimation of soybean crop areas while the plants are still in the field is highly necessary for reliable calculation of real crop parameters as to yield, production and other data important to decision-making policies related to government planning. An algorithm for soybean classification over the Rio Grande do Sul State, Brazil, was developed as an objective, automated tool. It is based on reflectance from medium spatial resolution images. The classification method was called the RCDA (Reflectance-based Crop Detection Algorithm), which operates through a mathematical combination of multi-temporal optical reflectance data obtained from Landsat-5 TM images. A set of 39 municipalities was analyzed for eight crop years between 1996/1997 and 2009/2010. RCDA estimates were compared to the official estimates of the Brazilian Institute of Geography and Statistics (IBGE) for soybean area at a municipal level. Coefficients R2 were between 0.81 and 0.98, indicating good agreement of the estimates. The RCDA was also compared to a soybean crop map derived from Landsat images for the 2000/2001 crop year, the overall map accuracy was 91.91% and the Kappa Index of Agreement was 0.76. Due to the calculation chain and pre-defined parameters, RCDA is a timesaving procedure and is less subjected to analyst skills for image interpretation. Thus, the RCDA was considered advantageous to provide thematic soybean maps at local and regional scales.http://www.mdpi.com/2072-4292/4/10/3127remote sensingclassificationcrop areareflectance |
| spellingShingle | Anibal Gusso Jorge Ricardo Ducati Algorithm for Soybean Classification Using Medium Resolution Satellite Images Remote Sensing remote sensing classification crop area reflectance |
| title | Algorithm for Soybean Classification Using Medium Resolution Satellite Images |
| title_full | Algorithm for Soybean Classification Using Medium Resolution Satellite Images |
| title_fullStr | Algorithm for Soybean Classification Using Medium Resolution Satellite Images |
| title_full_unstemmed | Algorithm for Soybean Classification Using Medium Resolution Satellite Images |
| title_short | Algorithm for Soybean Classification Using Medium Resolution Satellite Images |
| title_sort | algorithm for soybean classification using medium resolution satellite images |
| topic | remote sensing classification crop area reflectance |
| url | http://www.mdpi.com/2072-4292/4/10/3127 |
| work_keys_str_mv | AT anibalgusso algorithmforsoybeanclassificationusingmediumresolutionsatelliteimages AT jorgericardoducati algorithmforsoybeanclassificationusingmediumresolutionsatelliteimages |