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Estimating pasture biomass using sentinel-2 imagery and machine learning

Citation

Chen, Y and Guerschman, J and Shendryk, Y and Henry, D and Harrison, MT, Estimating pasture biomass using sentinel-2 imagery and machine learning, Remote Sensing, 13, (4) Article 603. ISSN 2072-4292 (2021) [Refereed Article]

Copyright Statement

Copyright 2021 Molecular Diversity Preservation International

DOI: doi:10.3390/rs13040603

Abstract

Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R2 of ≈0.6, a root-mean-square error (RMSE) of ≈356 kg dry matter (DM)/ha and a mean absolute error (MAE) of 262 kg DM/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farmís in situ measurement, pasture management and grazing practices have on the modelís predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.

Item Details

Item Type:Refereed Article
Keywords:remote sensing, deep learning, digital agriculture, dairy farming, grazing, grassland 28 biomass, satellite imagery, Sentinel-2, Planet Labs, pasture, native grass, Themeda triandra, phalaris, cocksfoot, wool, stocking rate, pasture variability
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Agriculture, land and farm management
Research Field:Agricultural spatial analysis and modelling
Objective Division:Animal Production and Animal Primary Products
Objective Group:Livestock raising
Objective Field:Sheep for meat
UTAS Author:Harrison, MT (Associate Professor Matthew Harrison)
ID Code:142680
Year Published:2021
Web of Science® Times Cited:3
Deposited By:TIA - Research Institute
Deposited On:2021-02-05
Last Modified:2021-06-25
Downloads:0

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