On the public citation graph

Radiative Transfer Models-Guided Spectral Super-Resolution for Enhanced Alfalfa Crude Protein Prediction from Sentinel-2 Imagery

https://doi.org/10.2139/ssrn.6462446
0 signed · 32 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/978-1-4615-8389-9_3openalex-snapshot
The Association of Official Agricultural Chemists (AOAC)
inferredcites →https://doi.org/10.1016/0377-8401(95)00837-3openalex-snapshot
Standardization of procedures for nitrogen fractionation of ruminant feeds
inferredcites →https://doi.org/10.1016/j.compag.2018.02.016openalex-snapshot
Deep learning in agriculture: A survey
inferredcites →https://doi.org/10.1016/j.compag.2021.106485openalex-snapshot
A synergic method of Sentinel-1 and Sentinel-2 images for retrieving soil moisture content in agricultural regions
inferredcites →https://doi.org/10.1016/j.compag.2023.108306openalex-snapshot
CatBoost algorithm for estimating maize above-ground biomass using unmanned aerial vehicle-based multi-source sensor data and SPAD values
inferredcites →https://doi.org/10.1016/j.fcr.2023.109175openalex-snapshot
Combining vegetation, color, and texture indices with hyperspectral parameters using machine-learning methods to estimate nitrogen concentration in rice stems and leaves
inferredcites →https://doi.org/10.1016/j.isprsjprs.2018.09.018openalex-snapshot
Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
inferredcites →https://doi.org/10.1016/j.isprsjprs.2019.01.011openalex-snapshot
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
inferredcites →https://doi.org/10.1016/j.isprsjprs.2019.04.015openalex-snapshot
Deep learning in remote sensing applications: A meta-analysis and review
inferredcites →https://doi.org/10.1016/j.jag.2023.103533openalex-snapshot
An improved fusion of Landsat-7/8, Sentinel-2, and Sentinel-1 data for monitoring alfalfa: Implications for crop remote sensing
inferredcites →https://doi.org/10.1016/j.rse.2010.09.011openalex-snapshot
Modeling directional–hemispherical reflectance and transmittance of fresh and dry leaves from 0.4μm to 5.7μm with the PROSPECT-VISIR model
inferredcites →https://doi.org/10.1016/j.rse.2012.05.013openalex-snapshot
Estimating crop biophysical properties from remote sensing data by inverting linked radiative transfer and ecophysiological models
inferredcites →https://doi.org/10.1016/j.rse.2014.03.034openalex-snapshot
Incorporating spatial information in spectral unmixing: A review
inferredcites →https://doi.org/10.1016/j.tplants.2018.11.007openalex-snapshot
Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture
inferredcites →https://doi.org/10.1038/s43586-022-00184-wopenalex-snapshot
Principal component analysis
inferredcites →https://doi.org/10.1098/rsta.2015.0202openalex-snapshot
Principal component analysis: a review and recent developments
inferredcites →https://doi.org/10.1109/igarss47720.2021.9554521openalex-snapshot
Physics-Aware Machine Learning for Geosciences and Remote Sensing
inferredcites →https://doi.org/10.1109/isai.2016.0094openalex-snapshot
Using NIR with Support Vector Regression to Predict the Crude Protein of Alfalfa
inferredcites →https://doi.org/10.1109/jstars.2022.3220875openalex-snapshot
Domain Adaptation in Remote Sensing Image Classification: A Survey
inferredcites →https://doi.org/10.1109/jstars.2024.3493237openalex-snapshot
Feature Guided Masked Autoencoder for Self-Supervised Learning in Remote Sensing
inferredcites →https://doi.org/10.1109/mgrs.2016.2548504openalex-snapshot
Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances
inferredcites →https://doi.org/10.1109/mgrs.2017.2762087openalex-snapshot
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art
inferredcites →https://doi.org/10.1109/mgrs.2018.2867592openalex-snapshot
Mini-UAV-Borne Hyperspectral Remote Sensing: From Observation and Processing to Applications
inferredcites →https://doi.org/10.1109/tgrs.2020.3045790openalex-snapshot
Physically Constrained Transfer Learning Through Shared Abundance Space for Hyperspectral Image Classification
inferredcites →https://doi.org/10.1109/tgrs.2023.3297363openalex-snapshot
Inversion of a New Designed ANN-Based 3-D-RTM Emulator by Continuous MCMC Technique to Monitor Crop Biophysical Properties Using Sentinel-2 Images
inferredcites →https://doi.org/10.1109/tgrs.2024.3478248openalex-snapshot
Improving Red Solar-Induced Chlorophyll Fluorescence Retrieval Using a Data-Driven Reflectance Reconstruction Method
inferredcites →https://doi.org/10.1109/tgrs.2025.3643447openalex-snapshot
A Radiative Transfer-Driven Deep Learning Framework for Accurate Estimation of Rice Growth Parameters Using Multisource UAV Data
inferredcites →https://doi.org/10.1109/tip.2005.854479openalex-snapshot
Super-resolution reconstruction of hyperspectral images
inferredcites →https://doi.org/10.1109/tip.2017.2687128openalex-snapshot
Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images
inferredcites →https://doi.org/10.1109/tnnls.2020.2966319openalex-snapshot
Why ResNet Works? Residuals Generalize
inferredcites →https://doi.org/10.1186/s13007-021-00750-5openalex-snapshot
Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods
inferredcites →https://doi.org/10.3390/rs17213606openalex-snapshot
Physical-Guided Transfer Deep Neural Network for High-Resolution AOD Retrieval
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.