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Remote Sens. 2019, 11(7), 890; https://doi.org/10.3390/rs11070890

UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Lab for Remote Sensing of Crop Phenotyping, Wuhan University, Wuhan 430079, China
3
College of Life Sciences, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Received: 14 February 2019 / Revised: 7 April 2019 / Accepted: 8 April 2019 / Published: 11 April 2019
(This article belongs to the Special Issue Progress on the Use of UAS Techniques for Environmental Monitoring)
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Abstract

Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular network (TIN), which was directly built from structure from motion (SfM) point clouds. Growing degree days (GDD) was used as the meteorological feature. Three models were used to estimate rice AGB, including the simple linear regression (SLR) model, simple exponential regression (SER) model, and machine learning model (random forest). Compared to models that do not use structural and meteorological features (NDRE, R2 = 0.64, RMSE = 286.79 g/m2, MAE = 236.49 g/m2), models that include such features obtained better estimation accuracy (NDRE*Hcv/GDD, R2 = 0.86, RMSE = 178.37 g/m2, MAE = 127.34 g/m2). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features. View Full-Text
Keywords: unmanned aerial vehicle (UAV); above ground biomass (AGB); triangulated irregular network (TIN); growing degree days (GDD) unmanned aerial vehicle (UAV); above ground biomass (AGB); triangulated irregular network (TIN); growing degree days (GDD)
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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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Jiang, Q.; Fang, S.; Peng, Y.; Gong, Y.; Zhu, R.; Wu, X.; Ma, Y.; Duan, B.; Liu, J. UAV-Based Biomass Estimation for Rice-Combining Spectral, TIN-Based Structural and Meteorological Features. Remote Sens. 2019, 11, 890.

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