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Predicting Key Grassland Characteristics from Hyperspectral Data

https://doi.org/10.3390/agriengineering3020021

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What this work says

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

inferredcites →https://doi.org/10.1016/1011-1344(93)06963-4openalex-snapshot

Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves

inferredcites →https://doi.org/10.1016/j.biosystemseng.2016.01.014openalex-snapshot

Active learning system for weed species recognition based on hyperspectral sensing

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Crop reflectance monitoring as a tool for water stress detection in greenhouses: A review

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Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments

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Crop signalling: A novel crop recognition technique for robotic weed control

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Spectral differentiation of sugarcane from weeds

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Real-time weed-crop classification and localisation technique for robotic weed control in lettuce

inferredcites →https://doi.org/10.1016/j.compag.2011.03.009openalex-snapshot

Extended-altitude, aerial mapping of crop NDVI using an active optical sensor: A case study using a Raptor™ sensor over wheat

inferredcites →https://doi.org/10.1016/j.ecolind.2020.106114openalex-snapshot

Using the random forest model and validated MODIS with the field spectrometer measurement promote the accuracy of estimating aboveground biomass and coverage of alpine grasslands on the Qinghai-Tibetan Plateau

inferredcites →https://doi.org/10.1016/j.isprsjprs.2014.04.005openalex-snapshot

Retrieval of leaf water content spanning the visible to thermal infrared spectra

inferredcites →https://doi.org/10.1016/j.jag.2012.06.006openalex-snapshot

Hyperspectral determination of feed quality constituents in temperate pastures: Effect of processing methods on predictive relationships from partial least squares regression

inferredcites →https://doi.org/10.1016/j.jag.2013.04.004openalex-snapshot

Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements

inferredcites →https://doi.org/10.1016/j.jag.2015.03.002openalex-snapshot

Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States

inferredcites →https://doi.org/10.1016/j.meatsci.2009.10.030openalex-snapshot

Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection

inferredcites →https://doi.org/10.1016/j.proenv.2011.09.112openalex-snapshot

Hyperspectral Remote Sensing Estimation Models of Aboveground Biomass in Gannan Rangelands

inferredcites →https://doi.org/10.1016/j.rse.2014.03.011openalex-snapshot

Estimation of water-related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral response

inferredcites →https://doi.org/10.1016/j.rse.2021.112353openalex-snapshot

Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network

inferredcites →https://doi.org/10.1016/s0176-1617(96)80285-9openalex-snapshot

Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm

inferredcites →https://doi.org/10.1038/srep27790openalex-snapshot

Hyperspectral Imaging for Determining Pigment Contents in Cucumber Leaves in Response to Angular Leaf Spot Disease

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The world’s road to water scarcity: shortage and stress in the 20th century and pathways towards sustainability

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Evaluating an active optical sensor for quantifying and mapping green herbage mass and growth in a perennial grass pasture

inferredcites →https://doi.org/10.1080/014311698216071openalex-snapshot

Biomass estimation on grazed and ungrazed rangelands using spectral indices

inferredcites →https://doi.org/10.1109/cama.2014.7003402openalex-snapshot

Proximal soil moisture sensing of dairy pasture

inferredcites →https://doi.org/10.1117/12.681640openalex-snapshot

Estimating fresh grass/herb biomass from HYMAP data using the red edge position

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World population stabilization unlikely this century

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1616a Main factors influencing occupational solar uv exposure

inferredcites →https://doi.org/10.1155/2017/1353691openalex-snapshot

Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

inferredcites →https://doi.org/10.1371/journal.pone.0116205openalex-snapshot

Hyperspectral Imaging for Mapping of Total Nitrogen Spatial Distribution in Pepper Plant

inferredcites →https://doi.org/10.21427/8x00-8q48openalex-snapshot

Multi-Spectral Visual Crop Assessment Under Limited Data Constraints

inferredcites →https://doi.org/10.33584/jnzg.2015.77.482openalex-snapshot

Mapping nutrient concentration in pasture using hyperspectral imaging

inferredcites →https://doi.org/10.3390/agriengineering3020021openalex-snapshot

Predicting Key Grassland Characteristics from Hyperspectral Data

inferredcites →https://doi.org/10.3390/rs4123892openalex-snapshot

Evaluation of Broadband and Narrowband Vegetation Indices for the Identification of Archaeological Crop Marks

inferredcites →https://doi.org/10.3390/rs70505329openalex-snapshot

Spectral Index for Quantifying Leaf Area Index of Winter Wheat by Field Hyperspectral Measurements: A Case Study in Gifu Prefecture, Central Japan

inferredcites →https://doi.org/10.3390/rs8020109openalex-snapshot

A Combination of Plant NDVI and LiDAR Measurements Improve the Estimation of Pasture Biomass in Tall Fescue (Festuca arundinacea var. Fletcher)

inferredcites →https://doi.org/10.3390/rs9010055openalex-snapshot

Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

inferredcites →https://doi.org/10.3964/j.issn.1000-0593openalex-snapshot

Combined use of Infrared and Raman spectra in the characterization of orthoclase under different hydrostatic pressure

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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.compag.2023.108463openalex-snapshot

Onfield estimation of quality parameters in alfalfa through hyperspectral spectrometer data

inferred← citeshttps://doi.org/10.1016/j.jag.2025.104857openalex-snapshot

Modeling grassland parameters with hyperspectral satellite data: Comparison of sensors, acquisition times and spectral transformations

inferred← citeshttps://doi.org/10.1109/metroagrifor52389.2021.9628816openalex-snapshot

Potential of in-field Vis/NIR hyperspectral imaging to monitor quality parameters of alfalfa

inferred← citeshttps://doi.org/10.3390/rs17071120openalex-snapshot

Hyperspectral Data Can Classify Plant Functional Groups Within New Zealand Hill Farm Pasture

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