Assessing the capability of MODIS to monitor mixed pastures with high-intensity grazing at a fine-scale

Abstract

MODIS time series carries valuable long-term data essential to support several studies such as biogeochemical modelling. However, there is a lack of validation studies applying MODIS data at a fine-scale to monitor pasture management practices. In this study, we assessed the potential of MODIS sensor in monitoring at a fine-scale four intensively managed mixed-pastures fields located in São Paulo State, Brazil. The MODIS spectral response was compared with Sentinel-2, and the ability of the two sensors in predicting aboveground biomass (AGB) and canopy height (CH) was assessed using the Random Forest algorithm. EVI images from MODIS and Sentinel-2 were correlated with field measurements of AGB and CH. The prediction performance of AGB (R²: Sentinel-2 = 38%; MODIS = 42%) and CH (R²: Sentinel-2 = 69%; MODIS = 85%) models was superior using EVI data from MODIS than Sentinel-2, highlighting MODIS ability to monitor small and intensively managed pasture fields.

Keywords:

• Aboveground biomass
• canopy height
• Sentinel-2
• pasture management
• Random Forest

Acknowledgements

The authors would like to thank the owner, manager and staff of the Campina farm (CV Nelore Mocho) for their support and assistance. We are also grateful to the undergraduate and graduate students, postdoctoral researchers, in particular to EMBRAPA researcher, Dr. João Francisco Gonçalves Antunes for his collaboration in discussions on theoretical concepts of satellites, and technicians for helping with the field data collection and preparation and NIPE and FEAGRI/UNICAMP by the infrastructure support provided for this project development.

Conflicts of interest

The authors declare no conflict of interest.

Data availability statement

The data that support the findings of this study are available from the corresponding author, YFS, upon reasonable request.

Additional information

Funding

This research was funded by FAPESP (Process numbers 2017/50205-9, 2018/24707-0 and 2018/24985-0) and in part by CNPQ (Process numbers 167705/2018-0).