Accurate Characterization of Soil Moisture in Wheat Fields with an Improved Drought Index from Unmanned Aerial Vehicle Observations
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Collection
2.2.1. UAV Data
2.2.2. Ground Measurement
2.3. Data Preprocessing
2.4. Improved Drought Index Construction
2.4.1. Construction of Structured Vegetation Index
2.4.2. NLST-SNDVI Space and Drought Index Construction
- (1).
- Resample the NLST and SNDVI images from five periods to the same resolution (1 cm × 1 cm).
- (2).
- Combine the NLST and SNDVI images from each period with the near-surface air temperature measured in each sub-area and place them into a two-dimensional Cartesian coordinate system by pixel. The triangle/trapezoid formed by scattered points is the NLST-SNDVI space, and the upper boundary of this space is the dry edge and the lower boundary is the wet edge (Figure 6).
- (3).
- Fit the upper/lower edges of NLST, i.e., the corresponding dry/wet edges, through statistical regression methods. This study intends to compare the fitting effects of four statistical regression methods: linear regression, quadratic function, exponential function, and logarithmic function.
- (4).
- Calculate iTVDI using the following formula:
2.5. Evaluation
3. Results
3.1. Spatial Distribution of Crop Height
3.2. NLST-SNDVI Space and Dry/Wet Edge
3.3. Comparison of the Improved Drought Index with In Situ Soil Moisture
3.4. Spatial Analysis of Improved Drought Index
4. Discussion
5. Conclusions
- (1).
- The structured spectral indices exhibit better resistance to spectral saturation, making the NLST-SNDVI space closer to expectations than the NLST-NDVI space, with higher fitting accuracy for wet and dry edges.
- (2).
- The iTVDI calculated based on the NLST-SNDVI space can effectively characterize soil moisture content, showing a significant correlation with measured surface soil moisture content.
- (3).
- The global Moran’s I calculated based on iTVDI deviations ranges between 0.18 and 0.30, all reaching significant levels, indicating that iTVDI has good spatial applicability.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Space | Edge | Fitting Function | Date | ||||
---|---|---|---|---|---|---|---|
31/03/2024 | 15/04/2024 | 23/04/2024 | 13/05/2024 | 24/05/2024 | |||
NLST-NDVI | Dry | Linear | 0.60 | 0.21 | 0.40 | 0.06 | 0.69 |
Logarithmic | 0.49 | 0.13 | 0.31 | 0.02 | 0.41 | ||
Exponential | 0.56 | 0.19 | 0.37 | 0.06 | 0.58 | ||
Quadratic | 0.84 | 0.68 | 0.73 | 0.23 | 0.89 | ||
Wet | Linear | 0.87 | 0.65 | 0.89 | 0.88 | 0.03 | |
Logarithmic | 0.91 | 0.74 | 0.93 | 0.95 | 0.12 | ||
Exponential | 0.91 | 0.82 | 0.96 | 0.94 | 0.05 | ||
Quadratic | 0.91 | 0.84 | 0.97 | 0.96 | 0.64 | ||
NLST-SNDVI | Dry | Linear | 0.96 | 0.91 | 0.97 | 0.88 | 0.95 |
Logarithmic | 0.94 | 0.67 | 0.82 | 0.59 | 0.86 | ||
Exponential | 0.99 | 0.81 | 0.96 | 0.77 | 0.94 | ||
Quadratic | 0.99 | 0.97 | 0.97 | 0.98 | 0.97 | ||
Wet | Linear | 0.00 | 0.69 | 0.12 | 0.30 | 0.44 | |
Logarithmic | 0.06 | 0.40 | 0.00 | 0.43 | 0.20 | ||
Exponential | 0.00 | 0.75 | 0.15 | 0.32 | 0.49 | ||
Quadratic | 0.35 | 0.88 | 0.72 | 0.59 | 0.64 |
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Cheng, M.; Lu, X.; Liu, Z.; Yang, G.; Zhang, L.; Sun, B.; Wang, Z.; Zhang, Z.; Shang, M.; Sun, C. Accurate Characterization of Soil Moisture in Wheat Fields with an Improved Drought Index from Unmanned Aerial Vehicle Observations. Agronomy 2024, 14, 1783. https://doi.org/10.3390/agronomy14081783
Cheng M, Lu X, Liu Z, Yang G, Zhang L, Sun B, Wang Z, Zhang Z, Shang M, Sun C. Accurate Characterization of Soil Moisture in Wheat Fields with an Improved Drought Index from Unmanned Aerial Vehicle Observations. Agronomy. 2024; 14(8):1783. https://doi.org/10.3390/agronomy14081783
Chicago/Turabian StyleCheng, Minghan, Xintong Lu, Zhangxin Liu, Guanshuo Yang, Lili Zhang, Binqian Sun, Zhian Wang, Zhengxian Zhang, Ming Shang, and Chengming Sun. 2024. "Accurate Characterization of Soil Moisture in Wheat Fields with an Improved Drought Index from Unmanned Aerial Vehicle Observations" Agronomy 14, no. 8: 1783. https://doi.org/10.3390/agronomy14081783