Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop-Livestock System Using Textural Information from PlanetScope Imagery

Author(s)

Dos Reis, Aliny A. ^{[1, 2]} ; Werner, Joao P. S. ^[2] ; Silva, Bruna C. ^[2] ; Figueiredo, Gleyce K. D. A. ^[2] ; Antunes, Joao F. G. ^[3] ; Esquerdo, Julio C. D. M. ^[3] ; Coutinho, Alexandre C. ^[3] ; Lamparelli, Rubens A. C. ^[1] ; Rocha, V, Jansle ; Magalhaes, Paulo S. G. ^[1]

Affiliation

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

^[2] V, Univ Campinas UNICAMP, Sch Agr Engn FEAGRI, BR-13083875 Campinas, SP - Brazil

^[3] Brazilian Agr Res Corp Embrapa, Embrapa Agr Informat, BR-13083886 Campinas, SP - Brazil

• Document type: Journal article
• Source: REMOTE SENSING; v. 12, n. 16 AUG 2020.
• Web of Science Citations: 0

Abstract

Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures. (AU)

• FAPESP's process: 18/24985-0 - Methodology for mapping and monitoring different pasture-based Livestock management and Mixed Crop-Livestock systems with remote sensing
• Grantee: Aliny Aparecida dos Reis
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 17/50205-9 - Monitoring integrated crop-livestock systems through remote sensing and precision agriculture for more sustainable production - towards low carbon agriculture
• Grantee: Paulo Sergio Graziano Magalhães
• Support Opportunities: Research Projects - Thematic Grants