On the public citation graph
Automated detection of phenological transitions for yellow flowering plants such as Brassica oilseeds
0 signed · 41 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0034-4257(95)00136-oopenalex-snapshot
The robustness of canopy gap fraction estimates from red and near-infrared reflectances: A comparison of approaches
inferredcites →https://doi.org/10.1016/j.indcrop.2012.06.021openalex-snapshot
Rapid estimation of seed yield using hyperspectral images of oilseed rape leaves
inferredcites →https://doi.org/10.1016/j.isprsjprs.2019.08.007openalex-snapshot
Automatic canola mapping using time series of sentinel 2 images
inferredcites →https://doi.org/10.1016/j.jag.2012.07.020openalex-snapshot
A visible band index for remote sensing leaf chlorophyll content at the canopy scale
inferredcites →https://doi.org/10.1016/j.rse.2009.08.014openalex-snapshot
Detecting trend and seasonal changes in satellite image time series
inferredcites →https://doi.org/10.1016/j.rse.2010.10.011openalex-snapshot
Analysis of monotonic greening and browning trends from global NDVI time-series
inferredcites →https://doi.org/10.1016/j.rse.2016.06.016openalex-snapshot
Spectral considerations for modeling yield of canola
inferredcites →https://doi.org/10.1016/j.rse.2018.10.012openalex-snapshot
Estimating canola phenology using synthetic aperture radar
inferredcites →https://doi.org/10.1016/j.rse.2020.111660openalex-snapshot
Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series
inferredcites →https://doi.org/10.1016/s0034-4257(02)00135-9openalex-snapshot
Monitoring vegetation phenology using MODIS
inferredcites →https://doi.org/10.1016/s0168-1923(02)00196-xopenalex-snapshot
Spatial estimation of air temperature differences for landscape-scale studies in montane environments
inferredcites →https://doi.org/10.1078/0176-1617-01176openalex-snapshot
Wide Dynamic Range Vegetation Index for Remote Quantification of Biophysical Characteristics of Vegetation
inferredcites →https://doi.org/10.1080/01431160110107806openalex-snapshot
Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction
inferredcites →https://doi.org/10.1080/01431160903578812openalex-snapshot
Do flowers affect biomass estimate accuracy from NDVI and EVI?
inferredcites →https://doi.org/10.1080/01431161.2015.1047994openalex-snapshot
Spectral indices for yellow canola flowers
inferredcites →https://doi.org/10.1080/014311697217819openalex-snapshot
An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, U.S.A.
inferredcites →https://doi.org/10.1093/jxb/erh038openalex-snapshot
High temperature stress of Brassica napus during flowering reduces micro- and megagametophyte fertility, induces fruit abortion, and disrupts seed production
inferredcites →https://doi.org/10.1094/pd-90-0946openalex-snapshot
Influence of Temperature, Relative Humidity, Ascospore Concentration, and Length of Drying of Colonized Dry Bean Flowers on White Mold Development
inferredcites →https://doi.org/10.1137/0717021openalex-snapshot
Monotone Piecewise Cubic Interpolation
inferredcites →https://doi.org/10.14358/pers.71.5.595openalex-snapshot
Agricultural Applications of High-Resolution Digital Multispectral Imagery
inferredcites →https://doi.org/10.18520/cs/v116/i2/291-298openalex-snapshot
Mapping Spring Canola and Spring Wheat using Radarsat-2 and Landsat-8 Images with Google Earth Engine
inferredcites →https://doi.org/10.2134/agronj2004.1139openalex-snapshot
Monitoring Maize ( Zea mays L.) Phenology with Remote Sensing
inferredcites →https://doi.org/10.3389/fpls.2018.00293openalex-snapshot
Mapping the Flowering of an Invasive Plant Using Unmanned Aerial Vehicles: Is There Potential for Biocontrol Monitoring?
inferredcites →https://doi.org/10.3390/rs2102369openalex-snapshot
Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology
inferredcites →https://doi.org/10.4141/cjps89-057openalex-snapshot
THE DETERMINATION AND VERIFICATION OF A BASELINE TEMPERATURE FOR THE GROWTH OF WESTAR SUMMER RAPE
inferredcites →https://doi.org/10.5589/m09-003openalex-snapshot
Yellow flowers can decrease NDVI and EVI values: evidence from a field experiment in an alpine meadow
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.4779347openalex-extracted
In-Season Unsupervised Mapping and Planted Area Estimation of Major Crops in War-Affected Ukraine
inferred← citeshttps://doi.org/10.1002/agg2.70320openalex-snapshot
Threshold‐based classification of rapeseed using multi‐spectral vegetation indices from Sentinel‐2
inferred← citeshttps://doi.org/10.1002/ppj2.70016openalex-snapshot
Unmanned aerial vehicle phenotyping of agronomic and physiological traits in mungbean
inferred← citeshttps://doi.org/10.1002/sae2.70087openalex-snapshot
Comparative Performance of Multi‐Spectral Vegetation Indices for Phenology‐Based Rapeseed Classification
inferred← citeshttps://doi.org/10.1007/s12517-022-10961-2openalex-snapshot
UAV-based multispectral image analytics for generating crop coefficient maps for rice
inferred← citeshttps://doi.org/10.1016/j.agrformet.2025.110788openalex-snapshot
Unlock genotype-environment-management interaction via field phenotypic insights for multi-scale prediction of winter rapeseed flowering in the Yangtze River Basin
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2021.12.001openalex-snapshot
Seamless and automated rapeseed mapping for large cloudy regions using time-series optical satellite imagery
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2026.04.058openalex-snapshot
A multi-task Transformer for integrated in-season rapeseed mapping and flowering retrieval using Sentinel-1 and Sentinel-2 data
inferred← citeshttps://doi.org/10.1080/15481603.2022.2163576openalex-snapshot
Mapping rapeseed in China during 2017-2021 using Sentinel data: an automated approach integrating rule-based sample generation and a one-class classifier (RSG-OC)
inferred← citeshttps://doi.org/10.1109/jstars.2024.3374232openalex-snapshot
Unveiling the Invasion: Advancing Ecological Mapping of Heterotheca Subaxillaris Through Integrated Remote Sensing Techniques With Drones and Satellites
inferred← citeshttps://doi.org/10.21203/rs.3.rs-5018229/v1openalex-snapshot
Vegetation growth conditions strongly indicate coffee flowering anomalies
inferred← citeshttps://doi.org/10.3389/fpls.2021.780250openalex-snapshot
Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population
inferred← citeshttps://doi.org/10.3390/rs16173197openalex-snapshot
Early-Stage Mapping of Winter Canola by Combining Sentinel-1 and Sentinel-2 Data in Jianghan Plain China
inferred← citeshttps://doi.org/10.3390/rs17132127openalex-snapshot
Canola Yield Estimation Using Remotely Sensed Images and M5P Model Tree Algorithm
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.