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Remote Sens. 2017, 9(1), 11; doi:10.3390/rs9010011

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

1
Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100094, China
2
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Jixian Zhang, Xiangguo Lin, Nicolas Baghdadi, Richard Gloaguen and Prasad S. Thenkabail
Received: 5 September 2016 / Revised: 6 December 2016 / Accepted: 21 December 2016 / Published: 27 December 2016
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
View Full-Text   |   Download PDF [2874 KB, uploaded 27 December 2016]   |  

Abstract

The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (H C V of digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the H C V combined with H m e a n performed better than one LiDAR-derived metric alone in biomass estimation (R2 = 0.835, RMSE = 374.655 g/m2, RMSECV = 393.573 g/m2). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R2 = 0.883, RMSE = 321.092 g/m2, RMSECV = 337.653 g/m2) compared with LiDAR or hyperspectral data alone. View Full-Text
Keywords: airborne LiDAR; hyperspectral data; biomass; maize; partial least squares (PLS) regression airborne LiDAR; hyperspectral data; biomass; maize; partial least squares (PLS) regression
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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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MDPI and ACS Style

Wang, C.; Nie, S.; Xi, X.; Luo, S.; Sun, X. Estimating the Biomass of Maize with Hyperspectral and LiDAR Data. Remote Sens. 2017, 9, 11.

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