Estimating the Grape Basal Crop Coefficient in the Subhumid Region of Northwest China Based on Multispectral Remote Sensing by Unmanned Aerial Vehicle
How to quickly and accurately obtain the basal crop coefficient is the key to estimating evapotranspiration in sparse vegetation. To enhance the accuracy of vineyard evapotranspiration estimation in the subhumid region of Northwest China, this study utilized the actual evapotranspiration (<i>E...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Horticulturae |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2311-7524/11/2/217 |
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| Summary: | How to quickly and accurately obtain the basal crop coefficient is the key to estimating evapotranspiration in sparse vegetation. To enhance the accuracy of vineyard evapotranspiration estimation in the subhumid region of Northwest China, this study utilized the actual evapotranspiration (<i>ET</i><sub>c</sub>) measured by the Bowen ratio system as the reference standard. The reference crop evapotranspiration (<i>ET</i><sub>o</sub>) was calculated using the Penman formula, and the grape crop coefficient (<i>K</i><sub>c</sub>) was subsequently derived. The FAO-56 dual crop coefficient method was then employed to determine the soil evaporation coefficient (<i>K</i><sub>e</sub>) and the water stress coefficient (<i>K</i><sub>s</sub>), leading to the acquisition of the basal crop coefficient (<i>K</i><sub>cb</sub>). Concurrently, multispectral remote sensing images captured by unmanned aerial vehicle (UAV) were used to gather grape spectral data, from which the reflectance of multiple bands was extracted to compute four vegetation indices: the Normalized Difference Vegetation Index (<i>NDVI</i>), the Soil-Adjusted Vegetation Index (<i>SAVI</i>), the Ratio Vegetation Index (<i>RVI</i>), and the Difference Vegetation Index (<i>DVI</i>). Relationship models between the grape basal crop coefficient (<i>K</i><sub>cb</sub>) and these vegetation indices were established using univariate linear regression, polynomial regression, and multiple linear regression. These models were then used to estimate vineyard evapotranspiration and validate the accuracy of the UAV multispectral remote sensing in estimating the grape <i>K</i><sub>cb</sub>. The results indicated that: (1) The growth stage, type of vegetation index, and modeling method were three significant factors influencing the fitting accuracies of the relationship models between the grape basal crop coefficient (<i>K</i><sub>cb</sub>) and vegetation indices. These model fitting accuracies had a notable impact on the estimation accuracies of evapotranspiration. (2) The application of UAV-based multispectral remote sensing to estimate the grape basal crop coefficient in the subhumid region of Northwest China was feasible. Compared to the <i>K</i><sub>cb</sub> values recommended by the FAO-56, the <i>K</i><sub>cb</sub> values derived from the UAV data improved the estimation accuracies of evapotranspiration by more than 11% in 2021 and 13% in 2022. |
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| ISSN: | 2311-7524 |