Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site and Experimental Design
2.2. Field Data Collection
2.3. UAV Camera System for Multispectral Imagery and Data Collection
2.4. Crop Growth Model
2.5. Data Assimilation and Technical Processes
2.6. Vegetation Index Calculation and Model Evaluation
3. Result
3.1. LAI Estimation Using Vegetation Indices
3.2. Comparison of Observation Yield and Estimated Yield
3.3. Yield Mapping
4. Discussion
4.1. Accuracy of LAI Inversion and its Influence on Yield Estimation Accuracy
4.2. Uncertainties in the Estimated Crop Yield
4.3. Yield Estimation and Data Assimilation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Treatment | Applied Water Depth (mm) | |||
---|---|---|---|---|
Late Vegetative (07.04–07.27) | Reproductive (07.28–08.18) | Maturation (08.19–09.4) | Total | |
TRT1 | 98.2 (100%) | 86.3 (100%) | 71.7 (100%) | 256.2 (100%) |
TRT2 | 98.2 (100%) | 63.9 (74%) | 71.7 (100%) | 233.8 (91%) |
TRT3 | 98.2 (100%) | 46.6 (54%) | 71.7 (100%) | 216.5 (85%) |
TRT4 | 72.6 (74%) | 63.9 (74%) | 71.7 (100%) | 208.2 (81%) |
TRT5 | 72.6 (74%) | 86.3 (100%) | 71.7 (100%). | 230.6 (90%) |
Band No. | Name | Center Wavelength | Bandwidth | Panel Reflectance |
---|---|---|---|---|
1 | Blue | 475 nm | 20 nm | 0.57 |
2 | Green | 560 nm | 20 nm | 0.57 |
3 | Red | 668 nm | 10 nm | 0.56 |
4 | NIR | 840 nm | 40 nm | 0.51 |
5 | RedEdge | 717 nm | 10 nm | 0.55 |
Parameter | Description | Notation | Unit | Initial Value or Range a | |
---|---|---|---|---|---|
Fixed | Climatic efficiency | Ratio of incoming photosynthetically active radiation to global radiation | ε | - | 0.48 |
Light-interception coefficient | Coefficient in Beer’s Law | K | - | 0.5 | |
Optimal temperature for crop growth | The optimal temperature for crop functions | Topt | °C | 30 | |
Minimum temperature for crop growth | The minimum temperature below which crop growth stops | Tmin | °C | 10 | |
Maximum temperature for crop growth | The maximum temperature above which crop growth stops | Tmax | °C | 47 | |
Specific leaf area | The ratio of leaf area to dry leaf mass | SLA | m2/g | 0.024 | |
Initial aboveground biomass | The aboveground mass at emergence | DAM0 | g/m2 | 3.7 | |
Root growth rate | Increase of root depth over time | Rgrt | cm·°C−1·Day−1 | 0.22 | |
Root length/weight ratio | The ratio of root length to root dry weight | Rrt | cm/g | 0.98 | |
Bare soil albedo | Albedo of bare soils | SALB | - | 0.16 | |
Free | Day of Emergence | The day of the year when the dry biomass of the crop is 2.5 g/m2 | D0 | day | 120–160 |
Leaf Partitioning Coefficient 1 | Initial fraction of daily accumulated dry biomass partitioned to leaf at the emergence | PLa | - | 0.1–0.4 | |
Leaf Partitioning Coefficient 2 | PLa and PLb together determine the cumulated GDD when LAI reaches peak value | PLb | - | 0.001–0.01 | |
Leaf Senescence Coefficient 1 | Cumulated GDD when leaf senescence starts (LAI decreases) | SenA | °C·Day | 500–1200 | |
Leaf Senescence Coefficient 2 | Determines the rate of leaf senescence | SenB | °C·Day | 2000–15,000 | |
Effective Light Use Efficiency | The ratio between produced dry biomass and APAR | ELUE | g/MJ | 2.5–5 |
Vegetation Indices | Equation |
---|---|
Normalized difference vegetation index | NDVI = (NIR − R)/(NIR + R) |
Optimized soil-adjusted vegetation index | OSAVI = (NIR − R)/(NIR + R + X) (x = 0.16) |
Green normalized difference vegetation index | GNDVI = (NIR − G)/(NIR + G) |
Enhanced vegetation index without a blue band | EVI2 = 2.5 (NIR − R)/(NIR + 2.4R + 1) |
Modified secondary soil adjusted vegetation index | |
Enhanced vegetation index | EVI = 2.5 (NIR − R)/(NIR + 6.0R − 7.5B + 1) |
Vegetation Index | Optimal Model | Validation Models | ||
---|---|---|---|---|
R2 | RMSE | R2 | RMSE | |
NDVI | 0.823 | 0.646 | 0.784 | 0.659 |
OSAVI | 0.799 | 0.669 | 0.772 | 0.682 |
GNDVI | 0.792 | 0.697 | 0.743 | 0.718 |
EVI2 | 0.724 | 0.774 | 0.690 | 0.791 |
MSAVI2 | 0.741 | 0.728 | 0.734 | 0.742 |
EVI | 0.877 | 0.609 | 0.795 | 0.621 |
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Peng, X.; Han, W.; Ao, J.; Wang, Y. Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield. Remote Sens. 2021, 13, 1094. https://doi.org/10.3390/rs13061094
Peng X, Han W, Ao J, Wang Y. Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield. Remote Sensing. 2021; 13(6):1094. https://doi.org/10.3390/rs13061094
Chicago/Turabian StylePeng, Xingshuo, Wenting Han, Jianyi Ao, and Yi Wang. 2021. "Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield" Remote Sensing 13, no. 6: 1094. https://doi.org/10.3390/rs13061094