Estimation of Soil Moisture during Different Growth Stages of Summer Maize under Various Water Conditions Using UAV Multispectral Data and Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Area
2.2. Experimental Design
2.3. Acquisition of Field Data
2.3.1. Acquisition of Multispectral Image
2.3.2. Acquisition of Ground Data
- Leaf Area Index (LAI)
- 2.
- Soil Moisture Content (SMC)
2.4. Acquisition of Spectral Information
2.4.1. Image Processing
2.4.2. Vegetation Index
2.5. Data Processing
2.5.1. Grey Relational Analysis
2.5.2. Sample Set Partitioning Based on Joint X–Y Distance
2.6. Modeling Methods
2.7. Statistical Analysis
3. Results
3.1. Temporal and Spatial Characteristics Analysis of Soil Moisture Data
3.2. SMC Modeling at Different Growth Stages
3.2.1. Screening Variables for Various Stages Based on GRA (Grey Relational Analysis)
3.2.2. Establishment and Verification of RF Model for Various Stages
3.2.3. Establishment and Verification of PSO-SVM Model for Various Stages
3.3. SMC Modeling in Different Fields
3.3.1. Screening Variables for Various Fields Based on GRA (Grey Relational Analysis)
3.3.2. Establishment and Verification of RF Model for Various Fields
3.3.3. Establishment and Verification of PSO-SVM Model for Various Fields
4. Discussion
5. Conclusions
- (1)
- The soil moisture fluctuated in the shape of an “M” during the maize growth period. Temporally, the SMC is relatively higher at the tasselling, silking, and milk stages but relatively lower at the jointing, blister, and maturing stages, which is essentially in line with the fact that maize is moisture-sensitive during the middle time of its growth period;
- (2)
- The modeling paradigms for UAV-based SMC estimation were recommended: In the early growth stages of maize (jointing, tasselling, and silking stages), the optimal combinations of input features comprise vegetation indexes (NDVI, GNDVI, OSAVI, TVI) and NIR reflectance; At the blister and milk stages, a combination of spectral reflectance (G, R, RE), vegetation indexes (NDVI, GNDVI, OSAVI, TVI), and LAI is more suitable. At the maturing stage, the green (G) and red (R) bands, along with vegetation indexes (NDVI, GNDVI, OSAVI, TVI), are preferred. Across multiple fields with varying moisture conditions, vegetation indexes (NDVI, GNDVI, OSAVI, TVI) and spectral reflectance (G, RE, NIR) are suggested;
- (3)
- Both RF and PSO-SVM models performed best at the jointing stage, while subsequently, their accuracy gradually deteriorated as the maize grew. Overall, RF performed better at capturing soil moisture differences across different growth stages, while PSO-SVM has a distinct advantage in reproducing the spatial variability of soil moisture.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Vegetation Indexes | Formula | References |
---|---|---|
NDVI | Haboudane et al., 2004 [25] | |
RVI | Sims and Gamon 2002 [26] | |
GNDVI | Yao et al., 2017 [27] | |
OSAVI | Haboudane et al., 2002 [28] | |
TVI | Broge and Leblanc 2001 [29] | |
MSAVI | Tian et al., 2011 [30] |
Input Variable | Remarks |
---|---|
G | 560 nm ± 16 nm |
R | 650 nm ± 16 nm |
RE | 730 nm ± 16 nm |
NIR | 860 nm ± 26 nm |
LAI | |
NDVI | |
RVI | |
GNDVI | |
OSAVI | |
TVI | |
MSAVI |
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Chen, Z.; Chen, H.; Dai, Q.; Wang, Y.; Hu, X. Estimation of Soil Moisture during Different Growth Stages of Summer Maize under Various Water Conditions Using UAV Multispectral Data and Machine Learning. Agronomy 2024, 14, 2008. https://doi.org/10.3390/agronomy14092008
Chen Z, Chen H, Dai Q, Wang Y, Hu X. Estimation of Soil Moisture during Different Growth Stages of Summer Maize under Various Water Conditions Using UAV Multispectral Data and Machine Learning. Agronomy. 2024; 14(9):2008. https://doi.org/10.3390/agronomy14092008
Chicago/Turabian StyleChen, Ziqiang, Hong Chen, Qin Dai, Yakun Wang, and Xiaotao Hu. 2024. "Estimation of Soil Moisture during Different Growth Stages of Summer Maize under Various Water Conditions Using UAV Multispectral Data and Machine Learning" Agronomy 14, no. 9: 2008. https://doi.org/10.3390/agronomy14092008