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Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data.

, , , , and . Remote Sensing, 5 (8): 3729-3748 (2013)

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Data Mining in Precision Agriculture: Management of Spatial Information., and . IPMU, volume 6178 of Lecture Notes in Computer Science, page 350-359. Springer, (2010)Novel Approaches to Model Assessment and Interpretation in Geospatial Machine Learning.. GISTAM, page 7. SCITEPRESS, (2024)RQGIS: Integrating R with QGIS for Statistical Geocomputing., , and . R J., 9 (2): 409 (2017)Spatial Variable Importance Assessment for Yield Prediction in Precision Agriculture., and . IDA, volume 6065 of Lecture Notes in Computer Science, page 184-195. Springer, (2010)Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest.. IGARSS, page 5372-5375. IEEE, (2012)Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data., , , , and . Remote Sensing, 5 (8): 3729-3748 (2013)Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data., , , , and . CoRR, (2018)A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management., , , and . Int. J. Geogr. Inf. Sci., 29 (4): 667-689 (2015)Interpreting machine-learning models in transformed feature space with an application to remote-sensing classification.. Mach. Learn., 112 (9): 3455-3471 (September 2023)2019-feature-selection., , , , and . (April 2019)