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Near Real-Time Crop Mapping at Field-Scale by Blending Crop Phenometrics with Growth Magnitude from Multiple Temporal and Spatial Satellite Observations

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inferredcites →https://doi.org/10.1007/978-3-030-92365-5_22openalex-extracted
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inferredcites →https://doi.org/10.1016/0034-4257(79)90013-0openalex-extracted
Red and photographic infrared linear combinations for monitoring vegetation
inferredcites →https://doi.org/10.1016/j.compag.2020.105864openalex-extracted
Agricultural crop discrimination in a heterogeneous low-mountain range region based on multi-temporal and multi-sensor satellite data
inferredcites →https://doi.org/10.1016/j.compag.2023.107768openalex-extracted
Preseason crop type prediction using crop sequence boundaries
inferredcites →https://doi.org/10.1016/j.isprsjprs.2018.09.006openalex-extracted
A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies
inferredcites →https://doi.org/10.1016/j.isprsjprs.2020.01.012openalex-extracted
Development and evaluation of a new algorithm for detecting 30 m land surface phenology from VIIRS and HLS time series
inferredcites →https://doi.org/10.1016/j.isprsjprs.2022.01.023openalex-extracted
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inferredcites →https://doi.org/10.1016/j.isprsjprs.2022.12.005openalex-extracted
In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time series
inferredcites →https://doi.org/10.1016/j.isprsjprs.2022.12.016openalex-extracted
End-to-end learned early classification of time series for in-season crop type mapping
inferredcites →https://doi.org/10.1016/j.isprsjprs.2022.12.019openalex-extracted
Estimation of vegetation traits with kernel NDVI
inferredcites →https://doi.org/10.1016/j.isprsjprs.2023.06.012openalex-extracted
CropSow: An integrative remotely sensed crop modeling framework for field-level crop planting date estimation
inferredcites →https://doi.org/10.1016/j.jag.2021.102374openalex-extracted
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inferredcites →https://doi.org/10.1016/j.rse.2005.07.008openalex-extracted
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inferredcites →https://doi.org/10.1016/j.rse.2008.06.006openalex-extracted
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inferredcites →https://doi.org/10.1016/j.rse.2013.08.023openalex-extracted
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MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types
inferredcites →https://doi.org/10.1016/j.rse.2017.09.020openalex-extracted
Evaluation of the VIIRS BRDF, Albedo and NBAR products suite and an assessment of continuity with the long term MODIS record
inferredcites →https://doi.org/10.1016/j.rse.2018.02.045openalex-extracted
A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach
inferredcites →https://doi.org/10.1016/j.rse.2018.06.047openalex-extracted
Generation and evaluation of the VIIRS land surface phenology product
inferredcites →https://doi.org/10.1016/j.rse.2018.09.002openalex-extracted
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Remote sensing for agricultural applications: A meta-review
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Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages
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inferredcites →https://doi.org/10.1016/s0034-4257(02)00096-2openalex-extracted
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inferredcites →https://doi.org/10.1109/lgrs.2006.875433openalex-extracted
The Global Impact of Clouds on the Production of MODIS Bidirectional Reflectance Model-Based Composites for Terrestrial Monitoring
inferredcites →https://doi.org/10.1111/j.2517-6161.1977.tb01600.xopenalex-extracted
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In-Season Major Crop-Type Identification for US Cropland from Landsat Images Using Crop-Rotation Pattern and Progressive Data Classification
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Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities
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Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites
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