Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

Remote Sensing

Jakob Geipel¹  Johanna Link²  Wilhelm Claupein²  Arko Lucieer²  Pablo J. Zarco-Tejada²  Uwe Rascher²  Georg Bareth² 

[1] Institute of Crop Science, University of Hohenheim, Fruwirthstr. 23, Stuttgart 70599, Germany;

关键词: corn;    crop coverage;    crop height;    crop surface model;    CSM;    UAS;    yield;   

DOI  :  10.3390/rs61110335

来源: mdpi

【 摘 要 】

Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs) enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS) to capture standard RGB imagery datasets for corn grain yield prediction at three early- to mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i) unclassified mean heights, (ii) crop-classified mean heights and (iii) a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients R² of up to 0.74, whereas model (iii) performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04 m·px⁻¹). Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.