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Potential of Time-Series Sentinel 2 Data for Monitoring Avocado Crop Phenology

https://doi.org/10.3390/rs14235942
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Preformation of Node Number in Vegetative and Reproductive Proleptic Shoot Modules of Persea (Lauraceae)
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Multitemporal Remote Sensing
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Deriving Crop Phenology Metrics and Their Trends Using Times Series NOAA-AVHRR NDVI Data
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Architectural analysis of tree form in a range of avocado cultivars
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QPhenoMetrics: An open source software application to assess vegetation phenology metrics
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Jujube yield prediction method combining Landsat 8 Vegetation Index and the phenological length
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Image time series processing for agriculture monitoring
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A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter
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Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data
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Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine
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Biochemical bases of appearance and texture changes in fresh-cut fruit and vegetables
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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.2005.03.008openalex-snapshot
A crop phenology detection method using time-series MODIS data
inferredcites →https://doi.org/10.1016/j.rse.2006.11.025openalex-snapshot
AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982–2005
inferredcites →https://doi.org/10.1016/j.rse.2007.05.017openalex-snapshot
Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil
inferredcites →https://doi.org/10.1016/j.rse.2012.08.009openalex-snapshot
Global phenological response to climate change in crop areas using satellite remote sensing of vegetation, humidity and temperature over 26years
inferredcites →https://doi.org/10.1016/j.rse.2014.06.012openalex-snapshot
Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change
inferredcites →https://doi.org/10.1016/j.rse.2017.06.031openalex-snapshot
Google Earth Engine: Planetary-scale geospatial analysis for everyone
inferredcites →https://doi.org/10.1016/j.rse.2018.08.022openalex-snapshot
A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter
inferredcites →https://doi.org/10.1016/j.rse.2018.10.031openalex-snapshot
Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping
inferredcites →https://doi.org/10.1016/j.scienta.2012.06.021openalex-snapshot
‘Hass’ avocado tree growth on four rootstocks in California. II. Shoot and root growth
inferredcites →https://doi.org/10.1016/j.scienta.2013.01.008openalex-snapshot
Potential applications of remote sensing in horticulture—A review
inferredcites →https://doi.org/10.1016/j.scienta.2013.09.051openalex-snapshot
Phenological growth stages of avocado (Persea americana) according to the BBCH scale
inferredcites →https://doi.org/10.1016/j.scienta.2015.12.009openalex-snapshot
Physiological factors affecting flower and fruit abscission of ‘Hass’ avocado
inferredcites →https://doi.org/10.1016/j.scienta.2020.109379openalex-snapshot
States of phenological development of avocado (Persea americana Mill.) based on the BBCH scale extended and its relationship to the incidence of anthracnose in field conditions
inferredcites →https://doi.org/10.1016/s0034-4257(01)00223-1openalex-snapshot
An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction
inferredcites →https://doi.org/10.1016/s0034-4257(02)00096-2openalex-snapshot
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
inferredcites →https://doi.org/10.1016/s0034-4257(96)00112-5openalex-snapshot
A comparison of vegetation indices over a global set of TM images for EOS-MODIS
inferredcites →https://doi.org/10.1021/ac60214a047openalex-snapshot
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
inferredcites →https://doi.org/10.1079/9781845937010.0000openalex-snapshot
The avocado: botany, production and uses
inferredcites →https://doi.org/10.1080/01431160802632249openalex-snapshot
Multi-year monitoring of rice crop phenology through time series analysis of MODIS images
inferredcites →https://doi.org/10.1080/14620316.1994.11516473openalex-snapshot
Aspects of delayed harvest of ‘Hass’ avocado (Persea americanaMill.) fruit in a cool subtropical climate. I. Fruit lipid and fatty acid accumulation
inferredcites →https://doi.org/10.1109/rsete.2012.6260634openalex-snapshot
Monitoring Vegetation Phenology Using MODIS Time-Series Data
inferredcites →https://doi.org/10.1109/tgrs.2012.2223475openalex-snapshot
Monitoring Vegetation Dynamics Inferred by Satellite Data Using the PhenoSat Tool
inferredcites →https://doi.org/10.1111/gcb.14619openalex-snapshot
Plant phenology and global climate change: Current progresses and challenges
inferredcites →https://doi.org/10.21273/jashs.123.4.537openalex-snapshot
Inflorescence and Flower Development of the `Hass' Avocado (Persea americana Mill.) during “On” and “Off” Crop Years
inferredcites →https://doi.org/10.21273/jashs.133.1.3openalex-snapshot
The Relationship Between Flower and Fruit Abscission and Alternate Bearing of ‘Hass’ Avocado
inferredcites →https://doi.org/10.2134/agronj2006.0370copenalex-snapshot
Application of Spectral Remote Sensing for Agronomic Decisions
inferredcites →https://doi.org/10.22430/22565337.1232openalex-snapshot
Classification of Hass avocado (persea americana mill) in terms of its ripening via hyperspectral images
inferredcites →https://doi.org/10.2503/jjshs.59.487openalex-snapshot
Studies on the Bearing Behavior and Yield Composition of the Avocado Tree
inferredcites →https://doi.org/10.25165/ijabe.v7i6.1433openalex-snapshot
Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China.
inferredcites →https://doi.org/10.3103/s1060992x19030093openalex-snapshot
Agriculture Phenology Monitoring Using NDVI Time Series Based on Remote Sensing Satellites: A Case Study of Guangdong, China
inferredcites →https://doi.org/10.3390/horticulturae8010011openalex-snapshot
Assessing the Potential of Sentinel-2 Derived Vegetation Indices to Retrieve Phenological Stages of Mango in Ghana
inferredcites →https://doi.org/10.3390/rs11212479openalex-snapshot
Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform
inferredcites →https://doi.org/10.3390/rs12081313openalex-snapshot
Integrating Landsat-8 and Sentinel-2 Time Series Data for Yield Prediction of Sugarcane Crops at the Block Level
inferredcites →https://doi.org/10.3390/rs13132510openalex-snapshot
Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine
inferredcites →https://doi.org/10.3390/rs9040317openalex-snapshot
Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards
inferredcites →https://doi.org/10.3390/s16122099openalex-snapshot
Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products
inferredcites →https://doi.org/10.5154/r.rchsh.1999.05.095openalex-snapshot
FLOWERS, FRUlTLETS AND FRUlT DROP IN AVOCADO TREES
inferredcites →https://doi.org/10.5194/isprs-archives-xli-b8-1037-2016openalex-snapshot
TIME SERIES ANALYSIS OF REMOTE SENSING OBSERVATIONS FOR CITRUS CROP GROWTH STAGE AND EVAPOTRANSPIRATION ESTIMATION
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
inferredcites →https://doi.org/10.5721/eujrs20144743openalex-snapshot
Examining the relationship between the Enhanced Vegetation Index and grapevine phenology

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.5071980openalex-extracted
inferred← citeshttps://doi.org/10.1016/j.jag.2023.103434openalex-snapshot
Early-Season forecasting of citrus block-yield using time series remote sensing and machine learning: A case study in Australian orchards
inferred← citeshttps://doi.org/10.1016/j.rsase.2025.101524openalex-snapshot
Quantification and attribution of spectral variation in irrigated perennial tree crops
inferred← citeshttps://doi.org/10.1109/igarss55030.2025.11242341openalex-snapshot
Improved yield forecasting of individual Sugarcane crops using an evolved remote sensing and Machine Learning approach
inferred← citeshttps://doi.org/10.2139/ssrn.5084963openalex-snapshot
Day: Data-Driven Machine Learning Framework for Avocado Yield Forecasting
inferred← citeshttps://doi.org/10.2139/ssrn.5249847openalex-snapshot
Thirty Years' of Crop Composition Change: Analysing Multi-Decade Trends in Australia's Southern Murray-Darling Basin
inferred← citeshttps://doi.org/10.2139/ssrn.7226942openalex-snapshot
Block-Level Avocado Yield Forecasting from Two-Year Lagged Sentinel-1, Sentinel-2, and Climate Predictors Using Gradient Boosting and Tabular Foundation Models
inferred← citeshttps://doi.org/10.3389/fagro.2024.1419479openalex-snapshot
Application of UAV and satellite technologies for assessing phytophthora root rot severity in avocado orchards
inferred← citeshttps://doi.org/10.3389/fpls.2026.1759956openalex-snapshot
Research progress on precision orchard yield estimation based on multi-source information perception sensors
inferred← citeshttps://doi.org/10.3390/horticulturae12040481openalex-snapshot
Spectral Phenology, Climate, and Topography as Determinants of Vigor, Yield, and Fruit Quality in Avocado (cv. Semil-34)
inferred← citeshttps://doi.org/10.3390/rs15123075openalex-snapshot
Integrating Remote Sensing and Weather Variables for Mango Yield Prediction Using a Machine Learning Approach
inferred← citeshttps://doi.org/10.3390/rs17040703openalex-snapshot
MangiSpectra: A Multivariate Phenological Analysis Framework Leveraging UAV Imagery and LSTM for Tree Health and Yield Estimation in Mango Orchards
inferred← citeshttps://doi.org/10.3390/rs17243935openalex-snapshot
Machine Learning Approaches for Assessing Avocado Alternate Bearing Using Sentinel-2 and Climate Variables—A Case Study in Limpopo, South Africa
inferred← citeshttps://doi.org/10.5141/jee.24.118openalex-snapshot
A review of remote sensing applications in flower phenology detection
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