Assessing Suitability of Sentinel-2 Bands for Monitoring of Nutrient Concentration of Pastures with a Range of Species Compositions

Suvarna M Punalekar,Anna Thomson,Anne Verhoef,David J Humphries,Christopher K Reynolds

doi:10.3390/agronomy11081661

Suvarna M Punalekar, Anna Thomson + Show 3 more

Open Access

https://doi.org/10.3390/agronomy11081661

Copy DOI

Abstract

The accurate and timely assessment of pasture quantity and quality (i.e., nutritive characteristics) is vital for effective pasture management. Remotely sensed data can be used to predict pasture quantity and quality. This study investigated the ability of Sentinel-2 multispectral bands, convolved from proximal hyperspectral data, in predicting various pasture quality and quantity parameters. Field data (quantitative and spectral) were gathered for experimental plots representing four pasture types—perennial ryegrass monoculture and three mixtures of swards representing increasing species diversity. Spectral reflectance data at the canopy level were used to generate Sentinel-2 bands and calculate normalised difference indices with each possible band pair. The suitability of these indices for prediction of pasture parameters was assessed. Pasture quantity parameters (biomass and Leaf Area Index) had a stronger influence on overall reflectance than the quality parameters. Indices involving the 1610 nm band were optimal for acid detergent fibre, crude protein, organic matter and water-soluble carbohydrate concentration, while being less affected by biomass or LAI. The study emphasises the importance of accounting for the quantity parameters in the spectral data-based models for pasture quality predictions. These explorative findings inform the development of future pasture quantity and quality models, particularly focusing on diverse swards.

Highlights

Pastures are one of the most important terrestrial ecosystems on earth with currently 26% of the world’s land area and 70% of the world’s agricultural area covered by grasslands
Current methods that farmers use for pasture monitoring include visual observations through field walking, rising plate meters (RPMs) for pasture biomass [7,8] and handheld Near-InfraRed Spectroscopy (NIRS) device surveys and/or laboratory chemical or NIRS analysis for pasture chemical composition that determines its nutritive value and digestibility [9,10,11]
Our findings show that while most of the pasture quality parameters influenced reflectance in the 1610 nm band (Figures 2 and 3), this band is not sensitive to changes in biomass and Leaf Area Index (LAI) (Figure 1)

Summary

Introduction

Pastures are one of the most important terrestrial ecosystems on earth with currently 26% of the world’s land area and 70% of the world’s agricultural area covered by grasslands (http://www.fao.org; accessed on 19 August 2020). Efficient pasture management is one of the key factors governing economic viability of the dairy and ruminant meat industry by ensuring accurate and well-planned pasture allocation for optimal grazing and conservation [3,4,5]. There is a need for an accurate near real-time methods for estimating and predicting pasture quantity and quality, especially in the context of grass-based dairy production systems such as the UK, one of the largest milk producers in Europe [6]. While informative on a local scale, these methods are time, labour and cost intensive [7,11,12]

Objectives

Methods

Findings

Conclusion