[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126702-en":3,"doc-seo-126702-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126702,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Mapping Agricultural Intensification in the Brazilian Savanna - A Machine Learning Approach Using Harmonized Data from Landsat Sentinel-2","Agricultural intensification in Brazil’s Cerrado (especially the Cerrado–Amazon transition) is increasingly used to raise productivity while limiting new land clearing. Growing demand for sustainable practices drives the need for accurate geospatial monitoring, and remote sensing combined with pixel-wise artificial intelligence can deliver such information. This work presents a methodological framework using HLS-derived spectral indices (NDVI, NDWI, SAVI) and machine learning models (RF, ANN, XGBoost) to map intensification across three hierarchical levels in Sorriso, Mato Grosso (2021–2022).","International Journal of  \nGeo-Information  \nArticle  \nMapping Agricultural Intensification in the Brazilian Savanna: A Machine Learning Approach Using Harmonized Data from Landsat Sentinel-2  \n􀂒 dson Luis Bolfe 1,2, *, Taya Cristo Parreiras 2, Lucas Augusto Pereira da Silva 3, Edson Eyji Sano 4, Giovana Maranh¢o Bettiol 4, Daniel de Castro Victoria 1, Ieda Del'Arco Sanches 5 and Luiz Eduardo Vicente 6  \nCitation: Bolfe, É .L.; Parreiras, T.C.; Silva, L.A.P.d.; Sano, E.E.; Bettiol, G.M.; Victoria, D.d.C.; Sanches, I.D.; Vicente, L.E. Mapping Agricultural Intensification in the Brazilian Savanna: A Machine Learning Approach Using Harmonized Data from Landsat Sentinel-2 . ISPRS Int. J. Geo-Inf. 2023, 12, 263. [https://](https://)[ ](https://)[doi.org/10.3390/ijgi12070263](doi.org/10.3390/ijgi12070263)  \nAcademic Editors: Wolfgang Kainz, Diego Gonz¡lez-Aguilera and Pablo Rodr½guez-Gonz¡lvez  \nReceived: 26 April 2023  \nRevised: 29 June 2023  \nAccepted: 30 June 2023  \nPublished: 2 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Brazilian Agricultural Research Corporation (Embrapa Agricultura Digital), Campinas 13083-886, Brazil;  \ndaniel.victoria@embrapa.br  \n2 Institute of Geosciences, State University of Campinas (Unicamp), Campinas 13083-855, Brazil; [t234520@dac.unicamp.br](t234520@dac.unicamp.br)  \n3 Institute of Geography, Federal University of Uberl¥ndia (UFU), Uberl¥ndia 38408-100, Brazil; [lucasagusto@ufu.br](lucasagusto@ufu.br)  \n4 Brazilian Agricultural Research Corporation (Embrapa Cerrados), Planaltina 73301-970, Brazil;  \nedson.sano@embrapa.br (E.E.S.); giovana.bettiol@embrapa.br (G.M.B.)  \n5 National Institute for Space Research (INPE), S¢o Jos² dos Campos 12227-010, Brazil; [ieda.sanches@inpe.br](ieda.sanches@inpe.br)  \n[6](6 Brazilian Agricultural Research Corporation)[ Brazilian Agricultural Research Corporation](6 Brazilian Agricultural Research Corporation) ([Embrapa Meio Ambiente](Embrapa Meio Ambiente)), Jaguarióna 13820-000, Brazil;  \nluiz.vicente@embrapa.br  \n* Correspondence: edson.bolfe@embrapa.br  \nAbstract: Agricultural intensification practices have been adopted in the Brazilian savanna (Cerrado), mainly in the transition between Cerrado and the Amazon Forest, to increase productivity while reducing pressure for new land clearing. Due to the growing demand for more sustainable practices, more accurate information on geospatial monitoring is required. Remote sensing products andartificial intelligence models for pixel-by-pixel classification have great potential. Therefore, we developed a methodological framework with spectral indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Soil-Adjusted Vegetation Index (SAVI)) derived from the Harmonized Landsat Sentinel-2 (HLS) and machine learning algorithms (Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost)) to map agricultural intensification considering three hierarchical levels, i.e., temporary crops (level 1), the number of crop cycles (level 2), and the crop types from the second season in double-crop systems (level 3) in the 2021–2022 crop growing season in the municipality of Sorriso, Mato Grosso State, Brazil. All models were statistically similar, with an overall accuracy between 85 and 99% . The NDVI was the most suitable index for discriminating cultures at all hierarchical levels. The RF-NDVI combination mapped best at level 1, while at levels 2 and 3, the best model was XGBoost-NDVI. Our results indicate the great potential of combining HLS data and machine learning to provide accurate geospatial information for decision-makers in monitoring a","cbCaikfRsPn3h7dR","https://ap.wps.com/l/cbCaikfRsPn3h7dR","pdf",5736223,1,21,"English","en",105,"# Abstract\n## Study approach\n## Results and model performance\n# Introduction\n## Study area and agricultural context\n## Intensification practices and remote sensing challenges\n## Remote sensing opportunities","[{\"question\":\"What data and spectral indices are used to map agricultural intensification?\",\"answer\":\"The framework uses Harmonized Landsat Sentinel-2 (HLS) and derives spectral indices including NDVI, NDWI, and SAVI for pixel-wise classification.\"},{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"The study evaluates Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost) for different hierarchical mapping levels.\"},{\"question\":\"How are agricultural intensification levels defined and how do models perform across them?\",\"answer\":\"Three hierarchical levels are mapped: level 1 temporary crops, level 2 number of crop cycles, and level 3 crop types from the second season in double-crop systems. Models are statistically similar with overall accuracy between 85% and 99%, with RF+NDVI best at level 1 and XGBoost+NDVI best at levels 2 and 3.\"}]","Mapping Agricultural Intensification in the Brazilian Savanna - A Machine Learning Approach Using Harmonized Data from Landsat Sentinel-2 | PDF",1785934309,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"mapping-agricultural-intensication-in-the-brazilian-savanna-a-machine-learning-approach-using-harmonized-data-from-landsat-sentinel-2","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/mapping-agricultural-intensication-in-the-brazilian-savanna-a-machine-learning-approach-using-harmonized-data-from-landsat-sentinel-2/126702/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data and spectral indices are used to map agricultural intensification?","Question",{"text":75,"@type":76},"The framework uses Harmonized Landsat Sentinel-2 (HLS) and derives spectral indices including NDVI, NDWI, and SAVI for pixel-wise classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are evaluated in the study?",{"text":80,"@type":76},"The study evaluates Random Forest (RF), Artificial Neural Networks (ANNs), and Extreme Gradient Boosting (XGBoost) for different hierarchical mapping levels.",{"name":82,"@type":73,"acceptedAnswer":83},"How are agricultural intensification levels defined and how do models perform across them?",{"text":84,"@type":76},"Three hierarchical levels are mapped: level 1 temporary crops, level 2 number of crop cycles, and level 3 crop types from the second season in double-crop systems. Models are statistically similar with overall accuracy between 85% and 99%, with RF+NDVI best at level 1 and XGBoost+NDVI best at levels 2 and 3.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]