The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 419–424, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 419–424, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-419-2020

  06 Nov 2020

06 Nov 2020

EXPLORING THE POTENTIAL OF HIGH-RESOLUTION PLANETSCOPE IMAGERY FOR PASTURE BIOMASS ESTIMATION IN AN INTEGRATED CROP–LIVESTOCK SYSTEM

A. A. Dos Reis1,2, B. C. Silva1, J. P. S. Werner1, Y. F. Silva1, J. V. Rocha1, G. K. D. A. Figueiredo1, J. F. G. Antunes3, J. C. D. M. Esquerdo3, A. C. Coutinho3, R. A. C. Lamparelli2, and P. S. G. Magalhães2 A. A. Dos Reis et al.
  • 1School of Agricultural Engineering – FEAGRI, University of Campinas – UNICAMP, 13083-875 Campinas, Brazil
  • 2Interdisciplinary Center of Energy Planning – NIPE, University of Campinas – UNICAMP, 13083-896, Campinas, SP, Brazil
  • 3Embrapa Agricultural Informatics, Brazilian Agricultural Research Corporation – Embrapa, 13083-886, Campinas, SP, Brazil

Keywords: Pastureland, Vegetation Indices, Dove satellites, Nano-Satellites, Machine Learning, Random Forest

Abstract. Pasture biomass information is essential to monitor forage resources in grazed areas, as well as to support grazing management decisions. 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. In a preliminary study, we investigated the potential of spectral variables derived from PlanetScope imagery to predict pasture biomass in an area of Integrated Crop-Livestock System (ICLS) in Brazil. Satellite and field data were collected during the same period (May–August 2019) for calibration and validation of the relation between predictor variables and pasture biomass using the Random Forest (RF) regression algorithm. We used as predictor variables 24 vegetation indices derived from PlanetScope imagery, as well as the four PlanetScope bands, and field management information. Pasture biomass ranged from approximately 24 to 656 g m−2, with a coefficient of variation of 54.96%. Near Infrared Green Simple Ratio (NIR/Green), Green Leaf Algorithm (GLA) vegetation indices and days after sowing (DAS) are among the most important variables as measured by the RF Variable Importance metric in the best RF model predicting pasture biomass, which resulted in Root Mean Square Error (RMSE) of 52.04 g m−2 (32.75%). Accurate estimates of pasture biomass using spectral variables derived from PlanetScope imagery are promising, providing new insights into the opportunities and limitations related to the use of PlanetScope imagery for pasture monitoring.