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Potencial produtivo da açúcar: modelo metodológico baseado em índices biofísicos com dados Sentinel-2 MSI
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A atividade canavieira representa um segmento do agronegócio com maior dimensão de cultivo em escala global de produção, posicionando o Brasil como maior produtor mundial de cana-de-açúcar. O estudo objetivou estruturar um modelo metodológico para avaliar o potencial produtivo de cultivares de cana-de-açúcar e sua relação com à eficiência do uso da luz para captura de dióxido de carbono atmosférico (CO2). Sendo as variáveis de entrada do modelo uma série temporal dos índices biofísicos de vegetação (fração da radiação fotossinteticamente ativa absorvida – fAPAR e Índice de área foliar – IAF) derivados de Open Data Sentinel-2, processados no software Open Source Sentinel Application Plataform-SNAP. O trabalho foi desenvolvido na Energética Serranópolis-Goiás nas safras agrícolas 2020 e 2021 sobre um mosaico de 17.387,60 hectares de cana-de-açúcar, distribuído sobre 48 fazendas e 773 talhões que abrigam 24 diferentes cultivares com número de cortes no ciclo de produção, variando entre 1º ao 10º. Os produtos resultantes da combinação do uso das geotecnologias (Sentinel-2 e SNAP) e modelo metodológico de avaliação do potencial produtivo da cana-de-açúcar em escala de cultivo comercial foram: a comprovação da eficácia dos índices fAPAR e IAF como métricas robustas para discriminar e identificar em escala regional de campo, cultivares de cana-de-açúcar mais produtivos e também mais eficientes na captura de dióxido de carbono atmosférico (CO2), gerando créditos na descarbonização do processo produtivo no segmento canavieiro. Portanto, contribuindo para orientar a reordenação no censo de cultivares através da ampliação da participação de materiais genéticos mais produtivos (CVSP7870 e RB988082) e consequentemente, incrementando a produtividade média da Energética Serranópolis em Tonelada de Colmos por Hectares (+3,79) e Tonelada de Açúcar por Hectare (+1,05). Além da criação de base de dados com assinaturas espectro-morfofisiológicas dos cultivares de cana-de-açúcar.
The sugarcane activity represents a segment of agribusiness with the largest dimension of cultivation on a global production scale, positioning Brazil as the world's largest producer of sugarcane. The study aimed to structure a methodological model to evaluate the productive potential of sugarcane cultivars and its relationship with the efficiency of the use of light to capture atmospheric carbon dioxide (CO2). As the input variables of the model are a time series of biophysical indices of vegetation (fraction of absorbed photosynthetically active radiation – fAPAR and Leaf Area Index – LAI) derived from Open Data Sentinel-2, processed in the Open Source Sentinel Application Platform-SNAP software. The work was carried out at Energética Serranópolis-Goiás in the 2020 and 2021 agricultural seasons on a land mosaic of 17,387.60 hectares of sugarcane, distributed over 48 farms and 773 fields that house 24 different sugarcana cultivars with a number of cuts in the production cycle, ranging from 1st to 10th. The outputs resulting from the combination of the use of geotechnologies (Sentinel-2 and SNAP) and the methodological model for evaluating the productive potential of sugarcane on a commercial cultivation scale were: the proof of the effectiveness of the fAPAR and IAF indices as robust metrics for discriminate and identify, on a regional field scale, sugarcane cultivars that are most productive and also most efficient in capturing atmospheric carbon dioxide (CO2), generating credits in the decarbonization of the production process in the sugarcane segment. Therefore, contributing to guide the reordering in the census of cultivars through the expansion of the participation of most productive genetic materials (CVSP7870 and RB988082) and consequently, increasing the average productivity of Energética Serranópolis in Tonnes of stalks per Hectares (+3.79) and Tonnes of Sugar per Hectare (+1.05). In addition to creating a database with spectro-morphophysiological signatures of sugarcane cultivars.
The sugarcane activity represents a segment of agribusiness with the largest dimension of cultivation on a global production scale, positioning Brazil as the world's largest producer of sugarcane. The study aimed to structure a methodological model to evaluate the productive potential of sugarcane cultivars and its relationship with the efficiency of the use of light to capture atmospheric carbon dioxide (CO2). As the input variables of the model are a time series of biophysical indices of vegetation (fraction of absorbed photosynthetically active radiation – fAPAR and Leaf Area Index – LAI) derived from Open Data Sentinel-2, processed in the Open Source Sentinel Application Platform-SNAP software. The work was carried out at Energética Serranópolis-Goiás in the 2020 and 2021 agricultural seasons on a land mosaic of 17,387.60 hectares of sugarcane, distributed over 48 farms and 773 fields that house 24 different sugarcana cultivars with a number of cuts in the production cycle, ranging from 1st to 10th. The outputs resulting from the combination of the use of geotechnologies (Sentinel-2 and SNAP) and the methodological model for evaluating the productive potential of sugarcane on a commercial cultivation scale were: the proof of the effectiveness of the fAPAR and IAF indices as robust metrics for discriminate and identify, on a regional field scale, sugarcane cultivars that are most productive and also most efficient in capturing atmospheric carbon dioxide (CO2), generating credits in the decarbonization of the production process in the sugarcane segment. Therefore, contributing to guide the reordering in the census of cultivars through the expansion of the participation of most productive genetic materials (CVSP7870 and RB988082) and consequently, increasing the average productivity of Energética Serranópolis in Tonnes of stalks per Hectares (+3.79) and Tonnes of Sugar per Hectare (+1.05). In addition to creating a database with spectro-morphophysiological signatures of sugarcane cultivars.
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Sequestro de carbono atmosférico Eficiência do uso da luz Parâmetros biofísicos de vegetação Sensoriamento remoto Atmospheric carbon sequestration Light use efficiency Remote sensing