On the public citation graph

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

https://doi.org/10.2139/ssrn.4617637
0 signed · 29 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1002/rse2.149openalex-extracted
Target‐oriented habitat and wildlife management: estimating forage quantity and quality of semi‐natural grasslands with Sentinel‐1 and Sentinel‐2 data
inferredcites →https://doi.org/10.1002/rse2.182openalex-extracted
Remote sensing data fusion as a tool for biomass prediction in extensive grasslands invaded by L. polyphyllus
inferredcites →https://doi.org/10.1007/s41064-020-00120-1openalex-extracted
Estimating Grassland Parameters from Sentinel-2: A Model Comparison Study
inferredcites →https://doi.org/10.1016/j.anifeedsci.2020.114670openalex-extracted
Use of traditional, modern, and hybrid modelling approaches for in situ prediction of dry matter yield and nutritive characteristics of pasture using hyperspectral datasets
inferredcites →https://doi.org/10.1016/j.biombioe.2018.04.016openalex-extracted
Using remote sensing to estimate forage biomass and nutrient contents at different growth stages
inferredcites →https://doi.org/10.1016/j.compag.2019.03.038openalex-extracted
Estimation of yield and quality of legume and grass mixtures using partial least squares and support vector machine analysis of spectral data
inferredcites →https://doi.org/10.1016/j.compag.2020.105880openalex-extracted
Predicting pasture biomass using a statistical model and machine learning algorithm implemented with remotely sensed imagery
inferredcites →https://doi.org/10.1016/j.compag.2022.107122openalex-extracted
Estimating pasture aboveground biomass under an integrated crop-livestock system based on spectral and texture measures derived from UAV images
inferredcites →https://doi.org/10.1016/j.inpa.2021.04.012openalex-extracted
A near infrared spectroscopy calibration for the prediction of fresh grass quality on Irish pastures
inferredcites →https://doi.org/10.1016/j.rse.2020.111830openalex-extracted
Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry
inferredcites →https://doi.org/10.1071/wf19026openalex-extracted
An efficient method for estimating dormant season grass biomass in tallgrass prairie from ultra-high spatial resolution aerial imaging produced with small unmanned aircraft systems
inferredcites →https://doi.org/10.1080/03650340.2017.1296138openalex-extracted
Soil moisture index improves models of forage production in central Iran
inferredcites →https://doi.org/10.1080/10106049.2021.1926559openalex-extracted
Assessing the capability of MODIS to monitor mixed pastures with high-intensity grazing at a fine-scale
inferredcites →https://doi.org/10.1080/22797254.2021.1901063openalex-extracted
Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data
inferredcites →https://doi.org/10.1109/jstars.2016.2561618openalex-extracted
Modeling Managed Grassland Biomass Estimation by Using Multitemporal Remote Sensing Data—A Machine Learning Approach
inferredcites →https://doi.org/10.1111/gfs.12439openalex-extracted
Canopy height measurements and non‐destructive biomass estimation of Lolium perenne swards using UAV imagery
inferredcites →https://doi.org/10.1111/gfs.12517openalex-extracted
Prediction of aboveground biomass and dry‐matter content in Brachiaria pastures by combining meteorological data and satellite imagery
inferredcites →https://doi.org/10.3389/fpls.2022.950720openalex-extracted
Within and combined season prediction models for perennial ryegrass biomass yield using ground- and air-based sensor data
inferredcites →https://doi.org/10.3390/agriengineering3020021openalex-extracted
Predicting Key Grassland Characteristics from Hyperspectral Data
inferredcites →https://doi.org/10.3390/agriengineering3030044openalex-extracted
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/rs11151835openalex-extracted
Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques
inferredcites →https://doi.org/10.3390/rs12122017openalex-extracted
The Fusion of Spectral and Structural Datasets Derived from an Airborne Multispectral Sensor for Estimation of Pasture Dry Matter Yield at Paddock Scale with Time
inferredcites →https://doi.org/10.3390/rs12162534openalex-extracted
Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery
inferredcites →https://doi.org/10.3390/rs13040603openalex-extracted
Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning
inferredcites →https://doi.org/10.3390/rs13173459openalex-extracted
Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning
inferredcites →https://doi.org/10.3390/rs14133066openalex-extracted
Herbage Mass, N Concentration, and N Uptake of Temperate Grasslands Can Adequately Be Estimated from UAV-Based Image Data Using Machine Learning
inferredcites →https://doi.org/10.3390/rs9010055openalex-extracted
Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives
inferredcites →https://doi.org/10.3390/s21123971openalex-extracted
Convolutional Neural Networks to Estimate Dry Matter Yield in a Guineagrass Breeding Program Using UAV Remote Sensing
inferredcites →https://doi.org/10.5194/bg-19-2699-2022openalex-extracted
Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.