Hybrid phenology matching model for robust crop phenological retrieval
Published 2021 View Full Article
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Title
Hybrid phenology matching model for robust crop phenological retrieval
Authors
Keywords
Phenology, Remote sensing, Agriculture, Crop progress, Planting date
Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 181, Issue -, Pages 308-326
Publisher
Elsevier BV
Online
2021-09-29
DOI
10.1016/j.isprsjprs.2021.09.011
References
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- Sowing date detection at the field scale using CubeSats remote sensing
- (2019) Yuval Sadeh et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- The Global Gridded Crop Model Intercomparison phase 1 simulation dataset
- (2019) Christoph Müller et al. Scientific Data
- Innovative pheno-network model in estimating crop phenological stages with satellite time series
- (2019) Chunyuan Diao ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- A review of vegetation phenological metrics extraction using time-series, multispectral satellite data
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- (2018) Andrew D. Richardson et al. Scientific Data
- Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery
- (2018) Feng Gao et al. Remote Sensing
- Real-Time Monitoring of Crop Phenology in the Midwestern United States Using VIIRS Observations
- (2018) Lingling Liu et al. Remote Sensing
- Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean
- (2018) Chunhua Liao et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Monitoring crop phenology using a smartphone based near-surface remote sensing approach
- (2018) Koen Hufkens et al. AGRICULTURAL AND FOREST METEOROLOGY
- Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France
- (2017) Giacinto Manfron et al. International Journal of Applied Earth Observation and Geoinformation
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- Estimation of SOS and EOS for Midwestern US Corn and Soybean Crops
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- (2017) Xingmei Xu et al. Remote Sensing
- Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications
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- Proximal NDVI derived phenology improves in-season predictions of wheat quantity and quality
- (2016) Troy S. Magney et al. AGRICULTURAL AND FOREST METEOROLOGY
- A hybrid approach for detecting corn and soybean phenology with time-series MODIS data
- (2016) Linglin Zeng et al. REMOTE SENSING OF ENVIRONMENT
- A logistic-based method for rice monitoring from multitemporal MODIS-Landsat fusion data
- (2016) Nguyen-Thanh Son et al. European Journal of Remote Sensing
- Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data
- (2016) Meha Jain et al. Remote Sensing
- Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes
- (2015) Ming Chen et al. Journal of Geophysical Research-Biogeosciences
- Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics
- (2013) Douglas K. Bolton et al. AGRICULTURAL AND FOREST METEOROLOGY
- Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models
- (2013) Felix Kogan et al. International Journal of Applied Earth Observation and Geoinformation
- MODIS-based corn grain yield estimation model incorporating crop phenology information
- (2013) Toshihiro Sakamoto et al. REMOTE SENSING OF ENVIRONMENT
- An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States
- (2013) David M. Johnson REMOTE SENSING OF ENVIRONMENT
- The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies
- (2012) C. Rosenzweig et al. AGRICULTURAL AND FOREST METEOROLOGY
- Climate change, phenology, and phenological control of vegetation feedbacks to the climate system
- (2012) Andrew D. Richardson et al. AGRICULTURAL AND FOREST METEOROLOGY
- Impact of input data resolution and extent of harvested areas on crop yield estimates in large-scale agricultural modeling for maize in the USA
- (2012) Christian Folberth et al. ECOLOGICAL MODELLING
- Testing farm management options as climate change adaptation strategies using the MONICA model
- (2012) C. Nendel et al. EUROPEAN JOURNAL OF AGRONOMY
- Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa
- (2012) K. Waha et al. GLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS
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- (2008) Chris Funk et al. REMOTE SENSING OF ENVIRONMENT
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