Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Area and Observational Data
2.2. Acquisition and Processing of Remote Sensing Data
2.2.1. Satellite Remote Sensing Data
2.2.2. UAV Multi-Spectral Remote Sensing Data
2.3. Spectral Index
2.4. Model Building and Precision Evaluation
2.5. Correction of Satellite Image Reflectance
3. Results
3.1. Analysis of the Correlation between Spectral Band and Remote Sensing Indexes
3.2. Inversion Model for Relative Soil Moisture at Different Depths Based on UAV Upscaling Correction
3.3. Agricultural Drought Grade of Winter Wheat during Seedling Stage in the Huaibei Plain of Anhui Province
4. Discussion
4.1. Research Model and Accuracy
4.2. Integrated UAV and Satellite
4.3. Agricultural Drought Monitoring at Seedling Stage of Winter Wheat
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Indexes | Full Name | Calculation Formulas |
---|---|---|
NDVI | Normalized difference vegetation index | (IR − R)/(NIR + R) |
OSAVI | Optimized soil adjusted vegetation index | 1.16 (NIR − R)/(NIR + R + 0.16) |
PDI | Perpendicular drought index | |
NDWI | Normalized difference water index | (NIR − G)/((NIR + G)) |
SAVI | Soil adjusted vegetation index | SAVI = (1 + L) × (NIR − R)/(NIR + R + L) |
SAVI2 | Soil adjusted vegetation index 2 | NIR/(B + b/a) |
DVI | Deviation vegetation index | NIR − R |
EVI | Enhanced vegetation index | 2.5 (NIR − R)/(1 + NIR + 6R − 7.5B) |
RVI | Ratio vegetation index | NIR1/R |
TVI | Triangular vegetation index | 0.5 [120 (NIR − G) − 200 (R − G)] |
Model | Depth/cm | Before The Upscaling Correction | After the Upscaling Correction | ||
---|---|---|---|---|---|
R2 | RMSE (%) | R2 | RMSE (%) | ||
RF | 0~10 | 0.4202 | 6.3264 | 0.4812 | 5.7113 |
10~20 | 0.4511 | 5.4521 | 0.5036 | 4.5925 | |
20~40 | 0.3859 | 5.8890 | 0.4287 | 5.0533 | |
0~20 | 0.4602 | 5.2537 | 0.5407 | 4.4246 | |
0~40 | 0.4067 | 6.0221 | 0.4502 | 5.3914 | |
XGBoost | 0~10 | 0.4652 | 5.5759 | 0.5724 | 4.5383 |
10~20 | 0.5004 | 5.1284 | 0.6402 | 4.0134 | |
20~40 | 0.4518 | 5.4983 | 0.5248 | 4.8602 | |
0~20 | 0.5519 | 4.8031 | 0.6863 | 3.8823 | |
0~40 | 0.4602 | 4.9235 | 0.5613 | 4.2846 |
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Yang, X.; Gao, F.; Yuan, H.; Cao, X. Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage. Sensors 2024, 24, 5715. https://doi.org/10.3390/s24175715
Yang X, Gao F, Yuan H, Cao X. Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage. Sensors. 2024; 24(17):5715. https://doi.org/10.3390/s24175715
Chicago/Turabian StyleYang, Xiaohui, Feng Gao, Hongwei Yuan, and Xiuqing Cao. 2024. "Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage" Sensors 24, no. 17: 5715. https://doi.org/10.3390/s24175715