Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics

Edson Eyji Sano (), Édson Luis Bolfe, Taya Cristo Parreiras, Giovana Maranhão Bettiol, Luiz Eduardo Vicente, Ieda Del′Arco Sanches and Daniel de Castro Victoria
Additional contact information
Edson Eyji Sano: Brazilian Agricultural Research Corporation, Embrapa Cerrados, Planaltina 73301-970, DF, Brazil
Édson Luis Bolfe: Brazilian Agricultural Research Corporation, Embrapa Agricultura Digital, Campinas 13083-886, SP, Brazil
Taya Cristo Parreiras: Institute of Geosciences, State University of Campinas (UNICAMP), Campinas 13083-855, SP, Brazil
Giovana Maranhão Bettiol: Brazilian Agricultural Research Corporation, Embrapa Cerrados, Planaltina 73301-970, DF, Brazil
Luiz Eduardo Vicente: Brazilian Agricultural Research Corporation, Embrapa Meio Ambiente, Jaguariúna 13917-200, SP, Brazil
Ieda Del′Arco Sanches: National Institute for Space Research (INPE), São José dos Campos 12227-010, SP, Brazil
Daniel de Castro Victoria: Brazilian Agricultural Research Corporation, Embrapa Agricultura Digital, Campinas 13083-886, SP, Brazil

Land, 2023, vol. 12, issue 3, 1-19

Abstract: Farmers in the Brazilian Cerrado are increasing grain production by cultivating second crops during the same crop growing season. The release of PlanetScope (PS) satellite images represents an innovative opportunity to monitor double cropping production. In this study, we analyzed the potential of six PS monthly mosaics from the 2021/2022 crop growing season to discriminate double cropping areas in the municipality of Goiatuba, Goiás State, Brazil. The four multispectral bands of the PS images were converted into normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), green–red normalized difference index (GRNDI), and textural features derived from the gray-level co-occurrence matrix (GLCM). The ten most important combinations of these attributes were used to map double cropping systems and other land use and land cover classes (cultivated pasture, sugarcane, and native vegetation) of the municipality through the Random Forest classifier. Training and validation samples were obtained from field campaigns conducted in October 2021 and April 2022. PS mosaic from February 2022 was the most relevant data. The overall accuracy and Kappa index of the final map were 92.2% and 0.892, respectively, with an accuracy confidence of 81%. This approach can be expanded for mapping and monitoring other agricultural frontiers in the Cerrado biome.

Keywords: Random Forest; gray-level co-occurrence matrix; tropical savanna; land use and land cover mapping; satellite constellation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2073-445X/12/3/581/pdf (application/pdf)
https://www.mdpi.com/2073-445X/12/3/581/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:3:p:581-:d:1083251

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().