Improvement of pasture biomass modelling using high-resolution satellite imagery and machine learning

Ogungbuyi, Michael Gbenga; Guerschman, Juan; Fischer, Andrew M.; Crabbe, Richard Azu; Ara, Iffat; Mohammed, Caroline; Scarth, Peter; Tickle, Phil; Whitehead, Jason; Harrison, Matthew Tom

doi:10.1016/j.jenvman.2024.120564

Cited by 2 publications

(1 citation statement)

References 54 publications

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“…With the recent development of sensors, remote sensing has been regarded as the best tool for plant biomass estimation. Recent studies have shown that satellite remote sensing can be a good data source for large-scale estimation of various plant biomasses [10]. However, the satellite remote-sensing methods can be easily disturbed by weather, satellite visit time, and other factors.…”

Section: Introductionmentioning

confidence: 99%

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

Hu,

Zhou,

Cao

et al. 2024

Remote Sensing

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Milk vetch (Astragalus sinicus L.) is a winter-growing plant that can enhance soil fertility and provide essential nutrients for subsequent season crops. The fertilizing capacity of milk vetch is closely related to its above-ground biomass. Compared to the manual measurement methods of milk vetch biomass, remote sensing-based estimation methods have the advantages of rapid, noninvasive, and large-scale measurement. However, few studies have been conducted on remote sensing-based estimation of milk vetch biomass. To address this shortcoming, this study proposes combining unmanned aerial vehicle (UAV)-based hyperspectral imagery and machine learning algorithms for accurate estimation of milk vetch biomass. Through the analysis of hyperspectral images and feature selection based on the Pearson correlation and principal component analysis, vegetation indices (VIs), including near-infrared reflectance (NIR), red-edge spectral transform index (RE), and difference vegetation index (DVI), are selected as estimation metrics of the model development process. Four machine learning methods, including random forest (RF), multiple linear regression (MLR), deep neural network (DNN), and support vector machine (SVM), are used to construct the biomass models. The results show that the RF estimation model exhibits the highest coefficient of determination (R2) of 0.950 and the lowest relative root-mean-squared error (RRMSE) of 14.86% among all the models. Notably, the DNN model demonstrates promising performance on the test set, with the R2 and RRMSE values slightly superior and inferior to those of the RF, respectively. The proposed method based on UAV imagery and machine learning can provide an accurate and reliable large-scale estimation of milk vetch biomass.

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Section: Introductionmentioning

confidence: 99%

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

Hu,

Zhou,

Cao

et al. 2024

Remote Sensing

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Integration of Drone and Satellite Imagery Improves Agricultural Management Agility

Ogungbuyi,

Mohammed,

Fischer

et al. 2024

Remote Sensing

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Effective agricultural management hinges upon timely decision-making. Here, we evaluated whether drone and satellite imagery could improve real-time and remote monitoring of pasture management. Using unmanned aerial systems (UAS), we quantified grassland biomass through changes in sward height pre- and post-grazing by sheep. As optical spectral data from Sentinel-2 satellite imagery is often hindered by cloud contamination, we assessed whether machine learning could help improve the accuracy of pasture biomass prognostics. The calibration of UAS biomass using field measurements from sward height change through 3D photogrammetry resulted in an improved regression (R2 = 0.75, RMSE = 1240 kg DM/ha, and MAE = 980 kg DM/ha) compared with using the same field measurements with random forest-machine learning and Sentinel-2 imagery (R2 = 0.56, RMSE = 2140 kg DM/ha, and MAE = 1585 kg DM/ha). The standard error of the mean (SEM) for the field biomass, derived from UAS-measured sward height changes, was 1240 kg DM/ha. When UAS data were integrated with the Sentinel-2-random forest model, SEM reduced from 1642 kg DM/ha to 1473 kg DM/ha, demonstrating that integration of UAS data improved model accuracy. We show that modelled biomass from 3D photogrammetry has significantly higher accuracy than that predicted from Sentinel-2 imagery with random forest modelling (S2-RF). Our study demonstrates that timely, accurate quantification of pasture biomass is conducive to improved decision-making agility, and that coupling of UAS with satellite imagery may improve the accuracy and timeliness of agricultural biomass prognostics.

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Improvement of pasture biomass modelling using high-resolution satellite imagery and machine learning

Cited by 2 publications

References 54 publications

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

Integration of Drone and Satellite Imagery Improves Agricultural Management Agility

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