Multispectral, Thermal, and Hyperspectral Sensing Data Depict Stomatal Conductance in Grapevine
Climate-driven water challenges in the Pacific Northwest necessitate precise irrigation for sustainable vineyard management. In such scenarios, conservation of water using different approaches, including subsurface irrigation, becomes critical. Detecting crop water status becomes key to evaluating a...
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MDPI AG
2025-01-01
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| author | Kesevan Veloo Carlos Zúñiga Espinoza Alberto Espinoza Salgado Pete W. Jacoby Sindhuja Sankaran |
| author_facet | Kesevan Veloo Carlos Zúñiga Espinoza Alberto Espinoza Salgado Pete W. Jacoby Sindhuja Sankaran |
| author_sort | Kesevan Veloo |
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| description | Climate-driven water challenges in the Pacific Northwest necessitate precise irrigation for sustainable vineyard management. In such scenarios, conservation of water using different approaches, including subsurface irrigation, becomes critical. Detecting crop water status becomes key to evaluating and managing such approaches. This study examines how multispectral, thermal, and hyperspectral proximal sensing data depict irrigation-induced variations in stomatal conductance in Cabernet Sauvignon vineyards during 2016 and 2017. The roles of individual and combined sensing modalities were analyzed, with key contributions including the identification of indices that characterize stomatal conductance. Data were collected at the following growth stages: 80 and 44 days before harvest (DBH) in 2016; and 64, 44, and 8 DBH in 2017. The vegetation indices analyzed included the green normalized difference vegetation index (GNDVI) and leaf area index (LAI) from multispectral data, crop water stress index (CWSI) from thermal data, and normalized difference spectral indices (NDSI) from hyperspectral data. Pearson’s correlations at 80 and 44 DBH (2016) showed significant relationships between normalized stomatal conductance and multispectral indices (LAI: <i>r</i> = 0.59 to 0.66, GNDVI: <i>r</i> = 0.41 to 0.50, both <i>p</i> < 0.01). NDSI pairs (1380 nm with 1570 nm, 1570 nm with 1810 nm) at 80 DBH showed significant correlations (<i>r</i> = −0.27, 0.31, both <i>p</i> < 0.05). In 2017, the thermal data showed the strongest correlation with normalized stomatal conductance (<i>r</i> = −0.83) at 44 DBH. In the same year, NDSI pairs exhibited stronger correlations than multispectral indices as the DBH decreased (1380 nm with 1570 nm: <i>r</i> = −0.58 to −0.69, 1570 nm with 1810 nm: <i>r</i> = 0.64 to 0.48, both <i>p</i> < 0.05). Combining LAI with these NDSI pairs improved stomatal conductance predictions (2016: <i>R</i><sup>2</sup> = 0.37–0.50; 2017: <i>R</i><sup>2</sup> = 0.51–0.63, both <i>p</i> < 0.01). These results demonstrate the precision of a multimodal sensing approach, particularly integrating multispectral and hyperspectral data, to improve irrigation strategies and promote sustainable viticulture. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-ffeff66eec3f45a5be03adf581e1c7bc2025-01-10T13:20:21ZengMDPI AGRemote Sensing2072-42922025-01-0117113710.3390/rs17010137Multispectral, Thermal, and Hyperspectral Sensing Data Depict Stomatal Conductance in GrapevineKesevan Veloo0Carlos Zúñiga Espinoza1Alberto Espinoza Salgado2Pete W. Jacoby3Sindhuja Sankaran4Department of Biological System Engineering, Washington State University, Pullman, WA 99164, USADepartment of Biological System Engineering, Washington State University, Pullman, WA 99164, USAInstituto de Investigaciones Agropecuarias (INIA), 2280454 La Cruz, ChileDepartment of Crop and Soil Science, Washington State University, Pullman, WA 99164, USADepartment of Biological System Engineering, Washington State University, Pullman, WA 99164, USAClimate-driven water challenges in the Pacific Northwest necessitate precise irrigation for sustainable vineyard management. In such scenarios, conservation of water using different approaches, including subsurface irrigation, becomes critical. Detecting crop water status becomes key to evaluating and managing such approaches. This study examines how multispectral, thermal, and hyperspectral proximal sensing data depict irrigation-induced variations in stomatal conductance in Cabernet Sauvignon vineyards during 2016 and 2017. The roles of individual and combined sensing modalities were analyzed, with key contributions including the identification of indices that characterize stomatal conductance. Data were collected at the following growth stages: 80 and 44 days before harvest (DBH) in 2016; and 64, 44, and 8 DBH in 2017. The vegetation indices analyzed included the green normalized difference vegetation index (GNDVI) and leaf area index (LAI) from multispectral data, crop water stress index (CWSI) from thermal data, and normalized difference spectral indices (NDSI) from hyperspectral data. Pearson’s correlations at 80 and 44 DBH (2016) showed significant relationships between normalized stomatal conductance and multispectral indices (LAI: <i>r</i> = 0.59 to 0.66, GNDVI: <i>r</i> = 0.41 to 0.50, both <i>p</i> < 0.01). NDSI pairs (1380 nm with 1570 nm, 1570 nm with 1810 nm) at 80 DBH showed significant correlations (<i>r</i> = −0.27, 0.31, both <i>p</i> < 0.05). In 2017, the thermal data showed the strongest correlation with normalized stomatal conductance (<i>r</i> = −0.83) at 44 DBH. In the same year, NDSI pairs exhibited stronger correlations than multispectral indices as the DBH decreased (1380 nm with 1570 nm: <i>r</i> = −0.58 to −0.69, 1570 nm with 1810 nm: <i>r</i> = 0.64 to 0.48, both <i>p</i> < 0.05). Combining LAI with these NDSI pairs improved stomatal conductance predictions (2016: <i>R</i><sup>2</sup> = 0.37–0.50; 2017: <i>R</i><sup>2</sup> = 0.51–0.63, both <i>p</i> < 0.01). These results demonstrate the precision of a multimodal sensing approach, particularly integrating multispectral and hyperspectral data, to improve irrigation strategies and promote sustainable viticulture.https://www.mdpi.com/2072-4292/17/1/137proximal sensingNDSIwater stressvineyard monitoring |
| spellingShingle | Kesevan Veloo Carlos Zúñiga Espinoza Alberto Espinoza Salgado Pete W. Jacoby Sindhuja Sankaran Multispectral, Thermal, and Hyperspectral Sensing Data Depict Stomatal Conductance in Grapevine Remote Sensing proximal sensing NDSI water stress vineyard monitoring |
| title | Multispectral, Thermal, and Hyperspectral Sensing Data Depict Stomatal Conductance in Grapevine |
| title_full | Multispectral, Thermal, and Hyperspectral Sensing Data Depict Stomatal Conductance in Grapevine |
| title_fullStr | Multispectral, Thermal, and Hyperspectral Sensing Data Depict Stomatal Conductance in Grapevine |
| title_full_unstemmed | Multispectral, Thermal, and Hyperspectral Sensing Data Depict Stomatal Conductance in Grapevine |
| title_short | Multispectral, Thermal, and Hyperspectral Sensing Data Depict Stomatal Conductance in Grapevine |
| title_sort | multispectral thermal and hyperspectral sensing data depict stomatal conductance in grapevine |
| topic | proximal sensing NDSI water stress vineyard monitoring |
| url | https://www.mdpi.com/2072-4292/17/1/137 |
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