Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform
•Automated identification of soybean plants via orbital sensors.•EOS-MODIS, MSI and OLI multitemporal images.•Soybean discrimination by phenological phases.•Spatial distribution of soybean for public policy purposes. Soybean is the main crop of the Brazilian agribusiness. The near-real-time monitori...
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| Veröffentlicht in: | Computers and electronics in agriculture 2020-02, Vol.169, p.105194, Article 105194 |
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| creator | Silva Junior, Carlos Antonio da Leonel-Junior, Antonio Hérbete Sousa Rossi, Fernando Saragosa Correia Filho, Washington Luiz Félix Santiago, Dimas de Barros Oliveira-Júnior, José Francisco de Teodoro, Paulo Eduardo Lima, Mendelson Capristo-Silva, Guilherme Fernando |
| description | •Automated identification of soybean plants via orbital sensors.•EOS-MODIS, MSI and OLI multitemporal images.•Soybean discrimination by phenological phases.•Spatial distribution of soybean for public policy purposes.
Soybean is the main crop of the Brazilian agribusiness. The near-real-time monitoring of this crop is important in the production estimate, identification of the progress, and location of the crops. It is also crucial for governmental surveillance institutions regarding sanitary break. Thus, this study aimed to estimate and map soybean areas in almost real time using temporal series multispectral images and vegetation indices (near-infrared and red) in the Google Earth Engine system in the state of Mato Grosso, Brazil. A multitemporal algorithm of the Perpendicular Vegetation Index (PVI) of MODIS, OLI, and MSI images of the 2016/2017 crop yr−1 was created from the identification of soybean areas using the Perpendicular Crop Enhancement Index (PCEI). The use of the MODIS images for the monitoring of soybean areas using the Google Earth Engine platform was a viable and promising automated alternative for large-scale soybean area estimates. |
| doi_str_mv | 10.1016/j.compag.2019.105194 |
| format | Article |
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Soybean is the main crop of the Brazilian agribusiness. The near-real-time monitoring of this crop is important in the production estimate, identification of the progress, and location of the crops. It is also crucial for governmental surveillance institutions regarding sanitary break. Thus, this study aimed to estimate and map soybean areas in almost real time using temporal series multispectral images and vegetation indices (near-infrared and red) in the Google Earth Engine system in the state of Mato Grosso, Brazil. A multitemporal algorithm of the Perpendicular Vegetation Index (PVI) of MODIS, OLI, and MSI images of the 2016/2017 crop yr−1 was created from the identification of soybean areas using the Perpendicular Crop Enhancement Index (PCEI). The use of the MODIS images for the monitoring of soybean areas using the Google Earth Engine platform was a viable and promising automated alternative for large-scale soybean area estimates.</description><subject>Agriculture</subject><subject>Algorithms</subject><subject>Earth</subject><subject>Image enhancement</subject><subject>Mapping</subject><subject>MODIS</subject><subject>Monitoring</subject><subject>Orbital sensors</subject><subject>PCEI</subject><subject>Real time</subject><subject>Remote sensing</subject><subject>SojaMaps</subject><subject>Soybeans</subject><subject>Vegetation</subject><subject>Vegetation index</subject><subject>Vegetation indices</subject><issn>0168-1699</issn><issn>1872-7107</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9UMtO3DAUtVCRmAJ_wMIS60z9mjjeILVooEhU3cDa8jjXGY8SO7UzRcNX8Ml1lK5ZXd17z0PnIHRDyZoSWn87rG0cRtOtGaGqnDZUiTO0oo1klaREfkGrAmsqWit1gb7mfCBlV41coY9fZhx96HCOpx2YgMfehGk-mAQG-4AH375BnvCPZN59j9_8tMcJhjhBf8IZQoYW-8F0kLEJLR73EGIfu1O1M_PL9F1MhTPgY55lpz3gxxi7HvDWpKK1DZ0PMPtOLqbhCp0702e4_j8v0evD9uX-Z_X8-_Hp_vtzZTkXU8U4iB1v6g0QxnnNnBCqtawW1NmW1pYCaVrGCXOGGy6srC2xtRHSMUfcRvJLdLvojin-OZaA-hCPKRRLzbhslOJcqoISC8qmmHMCp8dUwqaTpkTP3euDXrrXc_d66b7Q7hYalAR_PSSdrYdgofUJ7KTb6D8X-Ae3UZEp</recordid><startdate>202002</startdate><enddate>202002</enddate><creator>Silva Junior, Carlos Antonio da</creator><creator>Leonel-Junior, Antonio Hérbete Sousa</creator><creator>Rossi, Fernando Saragosa</creator><creator>Correia Filho, Washington Luiz Félix</creator><creator>Santiago, Dimas de Barros</creator><creator>Oliveira-Júnior, José Francisco de</creator><creator>Teodoro, Paulo Eduardo</creator><creator>Lima, Mendelson</creator><creator>Capristo-Silva, Guilherme Fernando</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>202002</creationdate><title>Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform</title><author>Silva Junior, Carlos Antonio da ; Leonel-Junior, Antonio Hérbete Sousa ; Rossi, Fernando Saragosa ; Correia Filho, Washington Luiz Félix ; Santiago, Dimas de Barros ; Oliveira-Júnior, José Francisco de ; Teodoro, Paulo Eduardo ; Lima, Mendelson ; Capristo-Silva, Guilherme Fernando</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c334t-23e4b3865e023362f449dc2641fcd16c1e08d2302fa3a34c76c0c6a47f2f0f573</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Agriculture</topic><topic>Algorithms</topic><topic>Earth</topic><topic>Image enhancement</topic><topic>Mapping</topic><topic>MODIS</topic><topic>Monitoring</topic><topic>Orbital sensors</topic><topic>PCEI</topic><topic>Real time</topic><topic>Remote sensing</topic><topic>SojaMaps</topic><topic>Soybeans</topic><topic>Vegetation</topic><topic>Vegetation index</topic><topic>Vegetation indices</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Silva Junior, Carlos Antonio da</creatorcontrib><creatorcontrib>Leonel-Junior, Antonio Hérbete Sousa</creatorcontrib><creatorcontrib>Rossi, Fernando Saragosa</creatorcontrib><creatorcontrib>Correia Filho, Washington Luiz Félix</creatorcontrib><creatorcontrib>Santiago, Dimas de Barros</creatorcontrib><creatorcontrib>Oliveira-Júnior, José Francisco de</creatorcontrib><creatorcontrib>Teodoro, Paulo Eduardo</creatorcontrib><creatorcontrib>Lima, Mendelson</creatorcontrib><creatorcontrib>Capristo-Silva, Guilherme Fernando</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers and electronics in agriculture</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Silva Junior, Carlos Antonio da</au><au>Leonel-Junior, Antonio Hérbete Sousa</au><au>Rossi, Fernando Saragosa</au><au>Correia Filho, Washington Luiz Félix</au><au>Santiago, Dimas de Barros</au><au>Oliveira-Júnior, José Francisco de</au><au>Teodoro, Paulo Eduardo</au><au>Lima, Mendelson</au><au>Capristo-Silva, Guilherme Fernando</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform</atitle><jtitle>Computers and electronics in agriculture</jtitle><date>2020-02</date><risdate>2020</risdate><volume>169</volume><spage>105194</spage><pages>105194-</pages><artnum>105194</artnum><issn>0168-1699</issn><eissn>1872-7107</eissn><abstract>•Automated identification of soybean plants via orbital sensors.•EOS-MODIS, MSI and OLI multitemporal images.•Soybean discrimination by phenological phases.•Spatial distribution of soybean for public policy purposes.
Soybean is the main crop of the Brazilian agribusiness. The near-real-time monitoring of this crop is important in the production estimate, identification of the progress, and location of the crops. It is also crucial for governmental surveillance institutions regarding sanitary break. Thus, this study aimed to estimate and map soybean areas in almost real time using temporal series multispectral images and vegetation indices (near-infrared and red) in the Google Earth Engine system in the state of Mato Grosso, Brazil. A multitemporal algorithm of the Perpendicular Vegetation Index (PVI) of MODIS, OLI, and MSI images of the 2016/2017 crop yr−1 was created from the identification of soybean areas using the Perpendicular Crop Enhancement Index (PCEI). The use of the MODIS images for the monitoring of soybean areas using the Google Earth Engine platform was a viable and promising automated alternative for large-scale soybean area estimates.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.compag.2019.105194</doi></addata></record> |
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| subjects | Agriculture Algorithms Earth Image enhancement Mapping MODIS Monitoring Orbital sensors PCEI Real time Remote sensing SojaMaps Soybeans Vegetation Vegetation index Vegetation indices |
| title | Mapping soybean planting area in midwest Brazil with remotely sensed images and phenology-based algorithm using the Google Earth Engine platform |
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