Next Article in Journal
TopoLAP: Topology Recovery for Building Reconstruction by Deducing the Relationships between Linear and Planar Primitives
Previous Article in Journal
Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications
Article

Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles

by 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4, 1,2,3,4 and 1,2,3,4,*
1
National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
2
Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University, Nanjing 210095, China
3
Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
4
Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1371; https://doi.org/10.3390/rs11111371
Received: 19 May 2019 / Revised: 3 June 2019 / Accepted: 4 June 2019 / Published: 8 June 2019
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
The accurate estimation of aboveground biomass (AGB) and leaf area index (LAI) is critical to characterize crop growth status and predict grain yield. Unmanned aerial vehicle (UAV) -based remote sensing has attracted significant interest due to its high flexibility and easiness of operation. The mixed effect model introduced in this study can capture secondary factors that cannot be captured by standard empirical relationships. The objective of this study was to explore the potential benefit of using a linear mixed-effect (LME) model and multispectral images from a fixed-wing UAV to estimate both AGB and LAI of rice. Field experiments were conducted over two consecutive years (2017–2018), that involved different N rates, planting patterns and rice cultivars. Images were collected by a compact multispectral camera mounted on a fixed-wing UAV during key rice growth stages. LME, simple regression (SR), artificial neural networks (ANN) and random forests (RF) models were developed relating growth parameters (AGB and LAI) to spectral information. Cultivar (C), growth stage (S) and planting pattern (P) were selected as candidates of random effects for the LME models due to their significant effects on rice growth. Compared to other regression models (SR, ANN and RF), the LME model improved the AGB estimation accuracy for all stage groups to varying degrees: the R2 increased by 0.14–0.35 and the RMSE decreased by 0.88–1.80 t ha−1 for the whole season, the R2 increased by 0.07–0.15 and the RMSE decreased by 0.31–0.61 t ha−1 for pre-heading stages and the R2 increased by 0.21–0.53 and the RMSE decreased by 0.72–1.52 t ha−1 for post-heading stages. Further analysis suggested that the LME model also successfully predicted within the groups when the number of groups was suitable. More importantly, depending on the availability of C, S, P or combinations thereof, mixed effects could lead to an outperformance of baseline retrieval methods (SR, ANN or RF) due to the inclusion of secondary effects. Satisfactory results were also obtained for the LAI estimation while the superiority of the LME model was not as significant as that for AGB estimation. This study demonstrates that the LME model could accurately estimate rice AGB and LAI and fixed-wing UAVs are promising for the monitoring of the crop growth status over large-scale farmland. View Full-Text
Keywords: aboveground biomass; leaf area index; vegetation index; linear mixed-effect model; UAV multispectral image; remote sensing; rice aboveground biomass; leaf area index; vegetation index; linear mixed-effect model; UAV multispectral image; remote sensing; rice
Show Figures

Graphical abstract

MDPI and ACS Style

Wang, Y.; Zhang, K.; Tang, C.; Cao, Q.; Tian, Y.; Zhu, Y.; Cao, W.; Liu, X. Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles. Remote Sens. 2019, 11, 1371. https://doi.org/10.3390/rs11111371

AMA Style

Wang Y, Zhang K, Tang C, Cao Q, Tian Y, Zhu Y, Cao W, Liu X. Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles. Remote Sensing. 2019; 11(11):1371. https://doi.org/10.3390/rs11111371

Chicago/Turabian Style

Wang, Yanyu, Ke Zhang, Chunlan Tang, Qiang Cao, Yongchao Tian, Yan Zhu, Weixing Cao, and Xiaojun Liu. 2019. "Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles" Remote Sensing 11, no. 11: 1371. https://doi.org/10.3390/rs11111371

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop