Pasture quality assessment through NDVI obtained by remote sensing: a validation study in the mediterranean silvo-pastoral ecosystem

Monitoring the evolution of pasture availability and quality throughout the growing season is the basis of grazing management in extensive Mediterranean livestock systems. Remote sensing (RS) is an innovative tool that, among many other applications, is being developed for detailed spatial and temporal pasture quality assessment. The aim of the present study is to evaluate the potential of satellite images (Sentinel-2) to assess indicators of pasture quality (pasture moisture content, PMC, crude protein, CP and neutral detergent fiber, NDF) using the normalized difference vegetation index (NDVI). Field measurements were conducted over three years at eight representative fields of the biodiversity and variability of dryland pastures in Portugal. A total of 656 georeferenced pasture samples were collected and processed in the laboratory. The results show a significant correlation between pasture quality parameters (PMC, CP and NDF) obtained in standard laboratory methods and NDVI satellite-derived data (R2 of 0.72, 0.75, and 0.50, respectively). The promising findings obtained in this large-scale validation study (three years and eight fields) encourage further research (i) to test and develop other vegetation indexes for monitoring pasture nutritive value; (ii) to extend this research to pastures of the other Mediterranean countries, building large and representative datasets and developing more robust and accurate monitoring models based on freely available Sentinel-2 images; (iii) to implement an extension program for agricultural managers to popularize the use of these technological tools as the basis of grazing and pasture management.

This work was funded by National Funds through FCT (Foundation for Science and Technology) under the Project UIDB/05183/2020. This work was also supported by the projects PDR2020−101-030693 and PDR2020−101-031244 (“Programa 1.0.1-Grupos Operacionais”).

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