On the public citation graph

An approach for evaluating the impact of gaps and measurement errors on satellite land surface phenology algorithms: Application to 20year NOAA AVHRR data over Canada

https://doi.org/10.1016/j.rse.2015.04.014

0 signed · 97 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/978-90-481-3335-2openalex-snapshot

Phenological research: Methods for environmental and climate change analysis

inferredcites →https://doi.org/10.1007/978-90-481-3335-2_9openalex-snapshot

Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology

inferredcites →https://doi.org/10.1007/s00442-006-0657-zopenalex-snapshot

Use of digital webcam images to track spring green-up in a deciduous broadleaf forest

inferredcites →https://doi.org/10.1016/j.agrformet.2011.09.009openalex-snapshot

Digital repeat photography for phenological research in forest ecosystems

inferredcites →https://doi.org/10.1016/j.agrformet.2014.01.003openalex-snapshot

Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau

inferredcites →https://doi.org/10.1016/j.asr.2007.05.066openalex-snapshot

An automatic procedure to identify key vegetation phenology events using the JRC-FAPAR products

inferredcites →https://doi.org/10.1016/j.cageo.2004.05.006openalex-snapshot

TIMESAT—a program for analyzing time-series of satellite sensor data

inferredcites →https://doi.org/10.1016/j.ecolmodel.2005.11.031openalex-snapshot

A multiyear evaluation of a Dynamic Global Vegetation Model at three AmeriFlux forest sites: Vegetation structure, phenology, soil temperature, and CO2 and H2O vapor exchange

inferredcites →https://doi.org/10.1016/j.jspi.2006.05.014openalex-snapshot

The “Caterpillar”-SSA method for analysis of time series with missing values

inferredcites →https://doi.org/10.1016/j.rse.2002.06.007openalex-snapshot

Systematic corrections of AVHRR image composites for temporal studies

inferredcites →https://doi.org/10.1016/j.rse.2003.11.002openalex-snapshot

Land cover mapping of North and Central America—Global Land Cover 2000

inferredcites →https://doi.org/10.1016/j.rse.2003.11.006openalex-snapshot

Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan

inferredcites →https://doi.org/10.1016/j.rse.2003.11.016openalex-snapshot

Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data

inferredcites →https://doi.org/10.1016/j.rse.2004.03.014openalex-snapshot

A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter

inferredcites →https://doi.org/10.1016/j.rse.2005.10.009openalex-snapshot

Evaluation of MODIS Gross Primary Productivity (GPP) in tropical monsoon regions

inferredcites →https://doi.org/10.1016/j.rse.2006.03.006openalex-snapshot

Normalization of the directional effects in NOAA–AVHRR reflectance measurements for an improved monitoring of vegetation cycles

inferredcites →https://doi.org/10.1016/j.rse.2007.01.004openalex-snapshot

Cross-scalar satellite phenology from ground, Landsat, and MODIS data

inferredcites →https://doi.org/10.1016/j.rse.2007.05.011openalex-snapshot

Interannual vegetation phenology estimates from global AVHRR measurements

inferredcites →https://doi.org/10.1016/j.rse.2007.07.024openalex-snapshot

Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer

inferredcites →https://doi.org/10.1016/j.rse.2008.06.010openalex-snapshot

Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America

inferredcites →https://doi.org/10.1016/j.rse.2008.09.003openalex-snapshot

Noise reduction of NDVI time series: An empirical comparison of selected techniques

inferredcites →https://doi.org/10.1016/j.rse.2009.08.016openalex-snapshot

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

inferredcites →https://doi.org/10.1016/j.rse.2010.04.005openalex-snapshot

Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product

inferredcites →https://doi.org/10.1016/j.rse.2010.08.014openalex-snapshot

Evaluation of compositing period and AVHRR and MERIS combination for improvement of spring phenology detection in deciduous forests

inferredcites →https://doi.org/10.1016/j.rse.2010.12.015openalex-snapshot

Satellite passive microwave remote sensing for monitoring global land surface phenology

inferredcites →https://doi.org/10.1016/j.rse.2011.02.015openalex-snapshot

The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests

inferredcites →https://doi.org/10.1016/j.rse.2011.05.006openalex-snapshot

A multisensor fusion approach to improve LAI time series

inferredcites →https://doi.org/10.1016/j.rse.2011.10.006openalex-snapshot

Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology

inferredcites →https://doi.org/10.1016/j.rse.2012.03.012openalex-snapshot

Ground-based Network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes

inferredcites →https://doi.org/10.1016/j.rse.2012.04.001openalex-snapshot

Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology

inferredcites →https://doi.org/10.1016/j.rse.2012.08.032openalex-snapshot

Identification and correction of systematic error in NOAA AVHRR long-term satellite data record

inferredcites →https://doi.org/10.1016/j.rse.2012.12.027openalex-snapshot

GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production

inferredcites →https://doi.org/10.1016/j.rse.2013.01.010openalex-snapshot

Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements

inferredcites →https://doi.org/10.1016/s0034-4257(02)00078-0openalex-snapshot

Global land cover mapping from MODIS: algorithms and early results

inferredcites →https://doi.org/10.1016/s0034-4257(02)00135-9openalex-snapshot

Monitoring vegetation phenology using MODIS

inferredcites →https://doi.org/10.1016/s0034-4257(96)00137-xopenalex-snapshot

Multitemporal, multichannel AVHRR data sets for land biosphere studies—Artifacts and corrections

inferredcites →https://doi.org/10.1021/ac034173topenalex-snapshot

A Perfect Smoother

inferredcites →https://doi.org/10.1029/2006jg000179openalex-snapshot

Evaluating water stress controls on primary production in biogeochemical and remote sensing based models

inferredcites →https://doi.org/10.1029/2009jg001138openalex-snapshot

Impacts of including forest understory brightness and foliage clumping information from multiangular measurements on leaf area index mapping over North America

inferredcites →https://doi.org/10.1029/96jd03988openalex-snapshot

Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer

inferredcites →https://doi.org/10.1029/97gb00330openalex-snapshot

A continental phenology model for monitoring vegetation responses to interannual climatic variability

inferredcites →https://doi.org/10.1038/386698a0openalex-snapshot

Increased plant growth in the northern high latitudes from 1981 to 1991

inferredcites →https://doi.org/10.1046/j.1466-822x.2003.00017.xopenalex-snapshot

Land use and cover with intensity of agriculture for Canada from satellite and census data

inferredcites →https://doi.org/10.1080/014311600750020000openalex-snapshot

Developments in the 'validation' of satellite sensor products for the study of the land surface

inferredcites →https://doi.org/10.1080/01431160500168686openalex-snapshot

An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data

inferredcites →https://doi.org/10.1080/01431160512331326657openalex-snapshot

A statistical framework for the analysis of long image time series

inferredcites →https://doi.org/10.1080/01431161.2010.550330openalex-snapshot

Linking ground-based to satellite-derived phenological metrics in support of habitat assessment

inferredcites →https://doi.org/10.1080/01431169408954055openalex-snapshot

SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum

inferredcites →https://doi.org/10.1080/014311699211499openalex-snapshot

Surface phenology and satellite sensor-derived onset of greenness: An initial comparison

inferredcites →https://doi.org/10.1080/07038992.2000.10874778openalex-snapshot

Aerosol Optical Depth for Atmospheric Correction of AVHRR Composite Data

inferredcites →https://doi.org/10.1098/rspa.1998.0193openalex-snapshot

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

inferredcites →https://doi.org/10.1109/igarss.2012.6352745openalex-snapshot

Near-real time estimates of leaf area index from AVHRR time series data

inferredcites →https://doi.org/10.1109/tgrs.2002.802519openalex-snapshot

Seasonality extraction by function fitting to time-series of satellite sensor data

inferredcites →https://doi.org/10.1109/tgrs.2003.811557openalex-snapshot

A comparison of BRDF models for the normalization of satellite optical data to a standard sun-target-sensor geometry

inferredcites →https://doi.org/10.1109/tgrs.2006.876030openalex-snapshot

Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: proposition of the CEOS-BELMANIP

inferredcites →https://doi.org/10.1111/j.1365-2486.2005.00949.xopenalex-snapshot

Land surface phenology and temperature variation in the International Geosphere–Biosphere Program high‐latitude transects

inferredcites →https://doi.org/10.1111/j.1365-2486.2006.01311.xopenalex-snapshot

Phenology model from surface meteorology does not capture satellite‐based greenup estimations

inferredcites →https://doi.org/10.1111/j.1365-2486.2009.01910.xopenalex-snapshot

Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006

inferredcites →https://doi.org/10.1175/jtech1987.1openalex-snapshot

SPARC: New Cloud, Snow, and Cloud Shadow Detection Scheme for Historical 1-km AVHHR Data over Canada

inferredcites →https://doi.org/10.1525/bio.2011.61.7.6openalex-snapshot

Spring Flowering Response to Climate Change between 1936 and 2006 in Alberta, Canada

inferredcites →https://doi.org/10.1890/070217openalex-snapshot

Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century

inferredcites →https://doi.org/10.2307/3235884openalex-snapshot

Measuring phenological variability from satellite imagery

inferredcites →https://doi.org/10.5194/bg-10-4055-2013openalex-snapshot

A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products

inferredcites →https://doi.org/10.5589/m05-024openalex-snapshot

Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies

inferredcites →https://doi.org/10.7551/mitpress/3206.001.0001openalex-snapshot

Gaussian Processes for Machine Learning

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.4684596openalex-extracted

Global Land Surface Phenology Monitoring Using the Fy3d/Mersi-Ⅱ Ndvi Time Series

inferred← citeshttps://doi.org/10.1007/s13253-018-00338-yopenalex-snapshot

Bayesian Dynamic Linear Models for Estimation of Phenological Events from Remote Sensing Data

inferred← citeshttps://doi.org/10.1007/s43538-026-00706-0openalex-snapshot

Crop modelling for impact assessment and identifying adaptation strategies under changing climate scenarios: a comprehensive review

inferred← citeshttps://doi.org/10.1016/j.agrformet.2016.11.193openalex-snapshot

Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites

inferred← citeshttps://doi.org/10.1016/j.agrformet.2018.03.003openalex-snapshot

Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery

inferred← citeshttps://doi.org/10.1016/j.agrformet.2024.110253openalex-snapshot

Factors and effects of inter-individual variability in silver birch phenology using dense LiDAR time-series

inferred← citeshttps://doi.org/10.1016/j.aiia.2025.10.008openalex-snapshot

Advancing UAV-based wheat phenology monitoring: A dual-mode framework integrating time-series reconstruction, noise augmentation, and deep learning for robust BBCH estimation

inferred← citeshttps://doi.org/10.1016/j.compag.2024.109212openalex-snapshot

Real-time monitoring of maize phenology with the VI-RGS composite index using time-series UAV remote sensing images and meteorological data

inferred← citeshttps://doi.org/10.1016/j.inffus.2017.06.005openalex-snapshot

Validation of synthetic daily Landsat NDVI time series data generated by the improved spatial and temporal data fusion approach

inferred← citeshttps://doi.org/10.1016/j.jag.2019.101974openalex-snapshot

Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests

inferred← citeshttps://doi.org/10.1016/j.jag.2021.102352openalex-snapshot

Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary?

inferred← citeshttps://doi.org/10.1016/j.jag.2022.102814openalex-snapshot

Quantitative estimation for the impact of mining activities on vegetation phenology and identifying its controlling factors from Sentinel-2 time series

inferred← citeshttps://doi.org/10.1016/j.rsase.2026.102183openalex-snapshot

Investigating the phenology of two invasive macrophytes across environmental gradients using satellite time series

inferred← citeshttps://doi.org/10.1016/j.scitotenv.2024.171895openalex-snapshot

Responses of phenology to preseason drought and soil temperature for different land cover types on the Mongolian Plateau

inferred← citeshttps://doi.org/10.1080/01431161.2020.1755738openalex-snapshot

A phenology-based method for identifying the planting fraction of winter wheat using moderate-resolution satellite data

inferred← citeshttps://doi.org/10.1080/01431161.2023.2189033openalex-snapshot

On the temporal mismatch between in-situ and satellite-derived spring phenology of European beech forests

inferred← citeshttps://doi.org/10.1080/01431161.2023.2217982openalex-snapshot

Local interpretation of machine learning models in remote sensing with SHAP: the case of global climate constraints on photosynthesis phenology

inferred← citeshttps://doi.org/10.1080/15481603.2020.1723279openalex-snapshot

Investigating the impact of the temporal resolution of MODIS data on measured phenology in the prairie grasslands

inferred← citeshttps://doi.org/10.1093/forsci/fxac054openalex-snapshot

Quantitative Assessment of Climate Change Impacts on Forest Ecosystems

inferred← citeshttps://doi.org/10.1109/access.2020.3043788openalex-snapshot

Multi-Climate Factors and the Preceding Growth Stage of Vegetation Co-Regulated the Variation of the End of Growing Season in Northeast Inner Mongolia, China

inferred← citeshttps://doi.org/10.1109/jstars.2020.2968809openalex-snapshot

Spatial Downscaling of MODIS Land Surface Temperature Based on Geographically Weighted Autoregressive Model

inferred← citeshttps://doi.org/10.1109/tgrs.2022.3183212openalex-snapshot

A Taylor Expansion Algorithm for Spatial Downscaling of MODIS Land Surface Temperature

inferred← citeshttps://doi.org/10.1117/12.2680350openalex-snapshot

Towards optimising the derivation of phenological phases of different crop types over Germany using optical high resolution image time series

inferred← citeshttps://doi.org/10.21203/rs.3.rs-913668/v1openalex-snapshot

Quantitative Assessment of Climate Change Impacts On Forest Ecosystem

inferred← citeshttps://doi.org/10.3389/fpls.2025.1558990openalex-snapshot

Extraction of maize growth stages in the Sanjiang Plain of China from 2003 to 2022 and their spatio-temporal changes in response to meteorological variables

inferred← citeshttps://doi.org/10.3390/cli5020037openalex-snapshot

Evaluating Vegetation Growing Season Changes in Northeastern China by Using GIMMS LAI3g Data

inferred← citeshttps://doi.org/10.3390/rs13081580openalex-snapshot

Downscaling Land Surface Temperature Based on Non-Linear Geographically Weighted Regressive Model over Urban Areas

inferred← citeshttps://doi.org/10.3390/rs15102559openalex-snapshot

Evaluating the Consistency of Vegetation Phenological Parameters in the Northern Hemisphere from 1982 to 2015

inferred← citeshttps://doi.org/10.3390/rs8040267openalex-snapshot

Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series

inferred← citeshttps://doi.org/10.3390/rs9010016openalex-snapshot

Using MODIS Data to Predict Regional Corn Yields

inferred← citeshttps://doi.org/10.3390/rs9090968openalex-snapshot

Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands

inferred← citeshttps://doi.org/10.3390/rs9121271openalex-snapshot

Performance of Smoothing Methods for Reconstructing NDVI Time-Series and Estimating Vegetation Phenology from MODIS Data

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.