itrogen variability assessment of pasture fields under an integrated crop-livestock system using UAV, PlanetScope, and Sentinel-2 dat

Autor(es)

Pereira, F. R. da S. ^{[1, 2]} ; de Lima, J. P. ^[3] ; Freitas, R. G. ^[3] ; Dos Reis, A. A. ^[1] ; do Amaral, L. R. ^[3] ; Figueiredo, G. K. D. A. ^[3] ; Lamparelli, R. A. C. ^{[3, 1]} ; Magalhaes, P. S. G. ^{[3, 1]}

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Interdisciplinary Ctr Energy Planning, BR-13083896 Campinas, SP - Brazil

^[2] Fed Inst Educ Sci & Technol Alagoas, BR-57120000 Satuba, Alagoas - Brazil

^[3] Univ Estadual Campinas, Sch Agr Engn, BR-13083875 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: COMPUTERS AND ELECTRONICS IN AGRICULTURE; v. 193, FEB 2022.
• Citações Web of Science: 0

Resumo

In agricultural production, nitrogen (N) deficiency reduces yield, while overapplication may have an unwanted impact on the natural environment and farm finances. Frequent field N monitoring is impractical due to the time and cost required for traditional laboratory analysis. However, remotely sensed data are an alternative to evaluate and monitor crop nutrition status throughout the growing season. This study evaluates the spatial distribution of N in pasture fields cultivated under an integrated crop-livestock system (ICLS) using unmanned aerial vehicle (UAV) and satellites data. We assessed the performance of UAV, PlanetScope, and Sentinel-2A platforms to predict the N parameters: plant N content (PNC), aboveground biomass (AGB), and nutritional nitrogen index (NNI). Moreover, we also simulated a UAV device with a visible light sensor (i.e., red-green-blue (RGB)) as a costly alternative to near-infrared (NIR) sensors to monitor N status. Finally, we assessed whether combining the information from these platforms would improve the N predictions in our study area. The UAV, PlanetScope, and Sentinel-2A data were able to estimate N parameters in the studied pasture fields using the random forest regression algorithm. The UAV multispectral data resulted in the best prediction accuracies (R2: 0.84-PNC, 0.70-AGB, and 0.84-NNI). The combination of UAV\_RGB with either PlanetScope (R2: 0.79-PNC, 0.67AGB, 0.77-NNI) or Sentinel-2A (R2: 0.76-PNC, 0.57-AGB, 0.69-NNI) improved the performance of the three platforms individually (UAV\_RGB, PlanetScope or Sentinel-2A). The association between high spatial and spectral resolutions contributes to the highest prediction accuracy in estimating N variability in pasture fields using remote sensing data. Our results suggest that remote sensing techniques are a reliable approach for N monitoring in commercial pasture fields under ICLS. (AU)

• Processo FAPESP: 17/50205-9 - Monitoramento de sistemas integrados lavoura-pecuária por meio de sensoriamento remoto e agricultura de precisão para uma produção mais sustentável - rumo à agricultura de baixo carbono
• Beneficiário: Paulo Sergio Graziano Magalhães
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/24985-0 - Metodologia para o mapeamento e monitoramento de diferentes sistemas de manejo de pastagens para Pecuária e sistemas mistos de Agricultura-Pecuária com sensoriamento remoto
• Beneficiário: Aliny Aparecida dos Reis
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado