Assessing the capability of MODIS to monitor mixed pastures with high-intensity grazing at a fine-scale. Issue 20 (18th October 2022)

• Record Type: Journal Article
• Authors: da Silva, Yane de Freitas; Aparecida Dos Reis, Aliny; Sampaio Werner, João Paulo; Vasconcelos Valadares, Rafael; Campbell, Eleanor E.; Augusto Camargo Lamparelli, Rubens; Magalhães, Paulo Sérgio Graziano; Figueiredo, Gleyce Kelly Dantas Araújo
• Is Part Of: Geocarto international. Volume 37:Issue 20(2022)
• Page Start: 6033
• Page End: 6051
• Publication Date: 2022-10-18
• Subjects: Aboveground biomass -- canopy height -- Sentinel-2 -- pasture management -- Random Forest; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2021.1926559
• Languages: English
• ISSNs: 1010-6049

Abstract

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 (R 2 : Sentinel-2 = 38%; MODIS = 42%) and CH (R 2 : 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.