Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA

Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA

Currently, accurate information on crop area coverage is vital for food security and industry, and there is strong demand for timely crop mapping. In this study, we used MODIS time series data to investigate the effect of the time series length on crop mapping. Eight time series with different lengt...

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Bibliographic Details
Main Authors: Pengyu Hao, Yulin Zhan, Li Wang, Zheng Niu, Muhammad Shakir
Format: Article
Language:English
Published: MDPI AG 2015-04-01
Series:Remote Sensing
Subjects:
time series length
MODIS
feature selection
Random Forest
classification certainty
Online Access:http://www.mdpi.com/2072-4292/7/5/5347

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