Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Experimental Details
2.2. Field Data Collection
2.3. UAV Image Acquisition
2.4. Processing of UAV Spectral Images
2.5. AGB and LAI Retrieval Methods
2.5.1. Baseline Retrieval Methods
2.5.2. Linear Mixed-effect Model
2.6. Comparison of Different Models
3. Results
3.1. Relationships between AGB and LAI with VIs Extracted from Multispectral Images
3.2. Determination of Model Parameters for ANN, RF and LME Models
3.3. Comparative Analysis of Four Models Based on Predictive Capability
3.4. Comparison of LME Models with Different Random Effects
4. Discussion
4.1. Advantages of Fixed-Wing UAVs
4.2. Relationship between Growth Parameters and Vegetation Indices
4.3. Advantages of the LME Model for Estimating Rice Growth Parameters
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Experiment | Transplanting | Cultivars | N Rate (kg ha−1) | Planting Patterns | Plot Size | Sampling Stages |
---|---|---|---|---|---|---|
EXP.1 2017 | 14-Jun | Nanjing-9108 | N0(0) | PMT | 150 m2 | Stem elongation (25-Jul) |
(Japonica) | N1(135) | CMT | (15 m × 10 m) | Booting (10-Aug) | ||
Yongyou-2640 | N2(270) | DS | Heading (23-Aug) | |||
(Indica/Japonica | N3(405) | Flowering (1-Sep) | ||||
Hybrid) | Grain filling (14-Sep) | |||||
EXP.2 2018 | 18-Jun | Nanjing-9108 | N0(0) | PMT | 300 m2 | Tillering (22-Jul) |
(Japonica) | N1(135) | CMT | (20 m × 15 m) | Stem elongation (28-Jul) | ||
Yongyou-2640 | N2(270) | Panicle initiation (12-Aug) | ||||
(Indica/Japonica | N3(405) | Booting (19-Aug) | ||||
Hybrid) | Heading (31-Aug) | |||||
Grain filling (21-Sep) |
Type | Variable | N | Mean | Minimum | Maximum | SD | CV (%) |
---|---|---|---|---|---|---|---|
Growth | AGB (t ha−1) | 648 | 9.05 | 0.54 | 22.83 | 5.22 | 58 |
parameters | LAI | 648 | 4.81 | 0.40 | 14.83 | 2.49 | 52 |
Vegetation Index | Formulation | Application | Reference |
---|---|---|---|
NDVI | AGB & LAI | [39] | |
NDRE | AGB & LAI | [40] | |
CIRE | AGB & LAI | [41] | |
MSAVI | AGB & LAI | [42] | |
OSAVI | AGB & LAI | [43] | |
MCARI1 | AGB & LAI | [44] | |
MTVI2 | AGB | [45] | |
DATT | AGB | [46] | |
GNDVI | LAI | [47] | |
MTVI1 | LAI | [45] |
Type | Variable | N | Mean | Minimum | Maximum | SD | CV (%) |
---|---|---|---|---|---|---|---|
NDVI | 648 | 0.87 | 0.51 | 0.94 | 0.07 | 8 | |
NDRE | 648 | 0.33 | 0.05 | 0.47 | 0.09 | 28 | |
CIRE | 648 | 1.02 | 0.11 | 1.76 | 0.38 | 37 | |
MSAVI | 648 | 0.65 | 0.16 | 0.91 | 0.18 | 27 | |
Vegetation | OSAVI | 648 | 0.73 | 0.31 | 0.90 | 0.11 | 16 |
indices | MCARI1 | 648 | 0.31 | 0.01 | 0.91 | 0.17 | 55 |
MTVI1 | 648 | 0.61 | 0.15 | 1.13 | 0.18 | 29 | |
MTVI2 | 648 | 0.69 | 0.16 | 0.97 | 0.19 | 28 | |
DATT | 648 | 0.52 | 0.12 | 0.66 | 0.11 | 22 | |
GNDVI | 648 | 0.74 | 0.39 | 0.85 | 0.08 | 11 |
Growth Index | Growth Stage | ANN Model Parameter | |
---|---|---|---|
Number of Hidden Neurons | Training Algorithm | ||
WS | 27 | Scaled Conjugate Gradient | |
AGB | Pre-HD | 21 | |
Post-HD | 23 | ||
WS | 17 | Scaled Conjugate Gradient | |
LAI | Pre-HD | 15 | |
Post-HD | 14 |
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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
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 StyleWang, 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