Special Section on Remote Sensing and Sensor Networks for Promoting Agro-Geoinformatics, Part 2

Potential of ensemble tree methods for early-season prediction of winter wheat yield from short time series of remotely sensed normalized difference vegetation index and in situ meteorological data

[+] Author Affiliations
Stien Heremans, Jos Van Orshoven

KU Leuven (University of Leuven), Department of Earth and Environmental Sciences, Celestijnenlaan 200E, BE-3001 Leuven, Belgium

Qinghan Dong, Lieven Bydekerke

Flemish Institute of Technological Research (VITO), Department of Remote Sensing, Boeretang 200, BE-2400 Mol, Belgium

Beier Zhang

Anhui Institution for Economical Research, Hefei 230001, China

J. Appl. Remote Sens. 9(1), 097095 (Mar 12, 2015). doi:10.1117/1.JRS.9.097095
History: Received August 7, 2014; Accepted February 12, 2015
Text Size: A A A

Abstract.  We aimed at analyzing the potential of two ensemble tree machine learning methods—boosted regression trees and random forests—for (early) prediction of winter wheat yield from short time series of remotely sensed vegetation indices at low spatial resolution and of in situ meteorological data in combination with annual fertilization levels. The study area was the Huaibei Plain in eastern China, and all models were calibrated and validated for five separate prefectures. To this end, a cross-validation process was developed that integrates model meta-parameterization and simple forward feature selection. We found that the resulting models deliver early estimates that are accurate enough to support decision making in the agricultural sector and to allow their operational use for yield forecasting. To attain maximum prediction accuracy, incorporating predictors from the end of the growing season is, however, recommended.

Figures in this Article
© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Stien Heremans ; Qinghan Dong ; Beier Zhang ; Lieven Bydekerke and Jos Van Orshoven
"Potential of ensemble tree methods for early-season prediction of winter wheat yield from short time series of remotely sensed normalized difference vegetation index and in situ meteorological data", J. Appl. Remote Sens. 9(1), 097095 (Mar 12, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.097095


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.