Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods-Reference-Cited by-同舟云学术

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Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods

Published:2016-03 Issue: Volume:218-219 Page:74-84
ISSN:0168-1923
Container-title:Agricultural and Forest Meteorology
language:en
Short-container-title:Agricultural and Forest Meteorology

Author:

Johnson Michael D.,Hsieh William W.,Cannon Alex J.,Davidson Andrew,Bédard Frédéric

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Atmospheric Science,Agronomy and Crop Science,Global and Planetary Change,Forestry

Reference33 articles.

1. Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach;Alvarez;Eur. J. Agron.,2009

2. Bayesian neural networks;Bhat,2005

3. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics;Bolton;Agric. Forest Meteorol.,2013

4. Synoptic map-pattern classification using recursive partitioning and principal component analysis;Cannon;Monthly Weather Rev.,2002

5. Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi-arid environment;Fensholt;Int. J. Rem. Sens.,2005

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