Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields

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ISSN Print: 2169-267X
ISSN Online: 2169-2688
www.scirp.org/journal/ars
E-mail: ars@scirp.org

"Remote Sensing Derived Phenological Metrics to Assess the Spatio-Temporal Growth Variability in Cropping Fields"
written by Sofanit Araya, Bertram Ostendorf, Greg Lyle, Megan Lewis,
published by Advances in Remote Sensing, Vol.6 No.3, 2017

has been cited by the following article(s):

[1]	Spatially weighted estimation of broadacre crop growth improves gap-filling of Landsat NDVI
	2021

[2]	Exploring the Drivers of Sentinel-2-Derived Crop Phenology: The Joint Role of Climate, Soil, and Land Use
	2021

[3]	Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning
	2021

[4]	Real-Time Monitoring of Crop Phenology in the Midwestern United States Using VIIRS Observations
	Remote Sensing, 2018

[1]	Spatial and Temporal Biomass and Growth for Grain Crops Using NDVI Time Series Remote Sensing, 2022 DOI:10.3390/rs14133071

[2]	Early Prediction of Coffee Yield in the Central Highlands of Vietnam Using a Statistical Approach and Satellite Remote Sensing Vegetation Biophysical Variables Remote Sensing, 2022 DOI:10.3390/rs14132975

[3]	Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI Remote Sensing, 2021 DOI:10.3390/rs13112128

[4]	Exploring the Drivers of Sentinel-2-Derived Crop Phenology: The Joint Role of Climate, Soil, and Land Use Land, 2021 DOI:10.3390/land10060656

[5]	Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning Remote Sensing, 2021 DOI:10.3390/rs13132435

[6]	Real-Time Monitoring of Crop Phenology in the Midwestern United States Using VIIRS Observations Remote Sensing, 2018 DOI:10.3390/rs10101540

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