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ssrn.4571009

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

inferredcites →https://doi.org/10.1002/saj2.20018openalex-extracted

Soil organic carbon more strongly related to soil moisture than soil temperature in temperate grasslands

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.2021.106614openalex-extracted

Estimating pasture quality of Mediterranean grasslands using hyperspectral narrow bands from field spectroscopy by Random Forest and PLS regressions

inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.03.017openalex-extracted

Potential of hyperspectral data and machine learning algorithms to estimate the forage carbon-nitrogen ratio in an alpine grassland ecosystem of the Tibetan Plateau

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

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.2012.07.011openalex-extracted

Spectral resampling based on user-defined inter-band correlation filter: C3 and C4 grass species classification

inferredcites →https://doi.org/10.1016/j.rse.2017.10.011openalex-extracted

Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region

inferredcites →https://doi.org/10.1016/j.rse.2019.111273openalex-extracted

Grassland ecosystem services in a changing environment: The potential of hyperspectral monitoring

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

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

inferredcites →https://doi.org/10.1080/01431161.2014.967885openalex-extracted

Investigating the spectral and ecological characteristics of grassland communities across an ecological gradient of the Inner Mongolian grasslands with in situ hyperspectral data

inferredcites →https://doi.org/10.1080/01431161.2015.1024893openalex-extracted

Predicting C3 and C4 grass nutrient variability using in situ canopy reflectance and partial least squares regression

inferredcites →https://doi.org/10.1109/tgrs.2021.3105482openalex-extracted

Estimating the Forage Neutral Detergent Fiber Content of Alpine Grassland in the Tibetan Plateau Using Hyperspectral Data and Machine Learning Algorithms

inferredcites →https://doi.org/10.1111/grs.12112openalex-extracted

Estimation of herbage biomass and nutritive status using band depth features with partial least squares regression in Inner Mongolia grassland, China

inferredcites →https://doi.org/10.11686/cyxb2019207openalex-extracted

Hyperspectral remote sensing progress for forage nutritional quality and quantity in natural grassland

inferredcites →https://doi.org/10.3390/rs10020269openalex-extracted

Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model

inferredcites →https://doi.org/10.3390/rs12182929openalex-extracted

Mapping the Forage Nitrogen-Phosphorus Ratio Based on Sentinel-2 MSI Data and a Random Forest Algorithm in an Alpine Grassland Ecosystem of the Tibetan Plateau

inferredcites →https://doi.org/10.3390/rs14092086openalex-extracted

Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass

inferredcites →https://doi.org/10.3390/rs8070540openalex-extracted

Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

inferredcites →https://doi.org/10.3964/j.issn.1000-0593(2019)10-3245-06openalex-extracted

Research on Crude Protein of Pasture Based on Hyperspectral Imaging

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