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Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield

https://doi.org/10.3390/agriengineering3030044

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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A global study of GPP focusing on light‐use efficiency in a random forest regression model

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Phenology of ecosystem processes : applications in global change research

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Estimating PAR absorbed by vegetation from bidirectional reflectance measurements

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Terrain analysis and remote sensing

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Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database

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Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments

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Fusion of Sentinel-2 and PlanetScope time-series data into daily 3 m surface reflectance and wheat LAI monitoring

inferredcites →https://doi.org/10.1016/j.rama.2021.01.006openalex-snapshot

Using Satellite-Based Vegetation Data for Short-Term Grazing Monitoring to Inform Adaptive Management

inferredcites →https://doi.org/10.1016/j.rse.2003.12.013openalex-snapshot

Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

inferredcites →https://doi.org/10.1016/j.rse.2014.08.025openalex-snapshot

Parameterization of an ecosystem light-use-efficiency model for predicting savanna GPP using MODIS EVI

inferredcites →https://doi.org/10.1016/j.rse.2014.09.017openalex-snapshot

The need for a common basis for defining light-use efficiency: Implications for productivity estimation

inferredcites →https://doi.org/10.1080/01431161.2012.746483openalex-snapshot

Modelling gross primary production in semi-arid Inner Mongolia using MODIS imagery and eddy covariance data

inferredcites →https://doi.org/10.1080/01431161.2019.1697004openalex-snapshot

Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data

inferredcites →https://doi.org/10.1109/tgrs.2003.810682openalex-snapshot

A progressive morphological filter for removing nonground measurements from airborne LIDAR data

inferredcites →https://doi.org/10.1111/gfs.12517openalex-snapshot

Prediction of aboveground biomass and dry‐matter content in Brachiaria pastures by combining meteorological data and satellite imagery

inferredcites →https://doi.org/10.21000/jasmr01010065openalex-snapshot

STABILIZATION OF THE MEAN AS A DEMONSTRATION OF SAMPLE ADEQUACY

inferredcites →https://doi.org/10.2307/2401901openalex-snapshot

Solar Radiation and Productivity in Tropical Ecosystems

inferredcites →https://doi.org/10.2307/2845365openalex-snapshot

Northern Australian Savannas: Management for Pastoral Production

inferredcites →https://doi.org/10.3390/agriengineering2040035openalex-snapshot

Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

inferredcites →https://doi.org/10.3390/agriengineering3030044openalex-snapshot

Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield

inferredcites →https://doi.org/10.3390/rs11050595openalex-snapshot

Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite

inferredcites →https://doi.org/10.3390/rs12040600openalex-snapshot

Monitoring Grass Phenology and Hydrological Dynamics of an Oak–Grass Savanna Ecosystem Using Sentinel-2 and Terrestrial Photography

inferredcites →https://doi.org/10.3390/rs12132160openalex-snapshot

Long-Term Grass Biomass Estimation of Pastures from Satellite Data

inferredcites →https://doi.org/10.3390/rs13040603openalex-snapshot

Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning

inferredcites →https://doi.org/10.3390/rs9060531openalex-snapshot

Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass

inferredcites →https://doi.org/10.5194/isprs-archives-xlii-3-w2-15-2017openalex-snapshot

ASSESSING LANDSAT FRACTIONAL GROUND-COVER TIME SERIES ACROSS AUSTRALIA’S ARID RANGELANDS: SEPARATING GRAZING IMPACTS FROM CLIMATE VARIABILITY

inferredcites →https://doi.org/10.5281/zenodo.2556737openalex-snapshot

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What points at this work

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inferred← citeshttps://doi.org/10.2139/ssrn.4617637openalex-extracted

Supplement to the Article: Influence of spatial and temporal variability on the performance of machine learning-based pasture biomass prediction

inferred← citeshttps://doi.org/10.1007/s11119-023-10079-9openalex-snapshot

Prediction of pasture yield using machine learning-based optical sensing: a systematic review

inferred← citeshttps://doi.org/10.1016/j.atech.2025.101057openalex-snapshot

Remote sensing for pasture biomass quantity and quality assessment: Challenges and future prospects

inferred← citeshttps://doi.org/10.1016/j.ecoinf.2023.102007openalex-snapshot

Unmanned aerial systems accurately map rangeland condition indicators in a dryland savannah

inferred← citeshttps://doi.org/10.1016/j.ecoinf.2026.103663openalex-snapshot

UAV-based remote sensing for rangeland monitoring, a generalized and transparent workflow with an Australian lead

inferred← citeshttps://doi.org/10.1109/icsscna68616.2026.11546888openalex-snapshot

Image2Biomass: Multi-Target Biomass Regression from Pasture Images with Pretrained ConvNeXt

inferred← citeshttps://doi.org/10.3390/agriengineering8080317openalex-snapshot

Pasture Biomass Monitoring in Queensland Rangelands with UAV and Satellite Cascades

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