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Remote Sens. 2018, 10(12), 2015; https://doi.org/10.3390/rs10122015

Optimal Hyperspectral Characteristics Determination for Winter Wheat Yield Prediction

1,2,3
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1,2,3,* , 1,2,3
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1
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
2
Beijing Key Laboratory of Spatial Information Integration and 3S Application, Peking University, Beijing 100871, China
3
Mapping and Geo-information for Geographic Information Basic Softwares and Applications, Engineering Research Center of National Administration of Surveying, Beijing 100871, China
4
Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Received: 16 October 2018 / Revised: 8 December 2018 / Accepted: 10 December 2018 / Published: 12 December 2018
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Abstract

Crop growth in different periods influences the final yield. This study started from the agronomic mechanism of yield formation and aimed to extract useful spectral characteristics in different phenological phases, which could directly describe the final yield and dynamic contributions of different phases to the yield formation. Hyperspectral information of the winter wheat canopy was acquired during three important phases (jointing stage, heading stage, and grain-filling stage). An enhanced 2D correlation spectral analysis method modified by mutual information was proposed to identify the sensitive wavebands. The selected wavebands performed well with good mechanism interpretation and close correlation with important crop growth parameters and main physiological activities related to yield formation. The quantitative contribution proportions of plant growth in three phases to the final yield were estimated by determining the coefficients of partial least square models based on full spectral information. They were then used as single-phase weight factors to merge the selected wavebands. The support vector machine model based on the weighted spectral dataset performed well in yield prediction with satisfactory accuracy and robustness. This result would provide rapid and accurate guidance for agricultural production and would be valuable for the processing of hyperspectral remote sensing data. View Full-Text
Keywords: hyperspectral; winter wheat; yield; mutual information; 2D correlation spectra hyperspectral; winter wheat; yield; mutual information; 2D correlation spectra
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, Y.; Qin, Q.; Ren, H.; Sun, Y.; Li, M.; Zhang, T.; Ren, S. Optimal Hyperspectral Characteristics Determination for Winter Wheat Yield Prediction. Remote Sens. 2018, 10, 2015.

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