Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation
Grapevine production worldwide is adversely impacted by climate change, including limited water availability, low-quality or sudden excess of water, and more frequent, severe, and prolonged heatwaves. As a result, grapevine growers require reliable spatial and temporal information on vine water stat...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
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| Series: | Agricultural Water Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377424004992 |
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