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Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index

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Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index

dc.contributor.author	Pasqualotto Vicente, Nieves
dc.contributor.author	Delegido Gómez, Jesús
dc.contributor.author	Wittenberghe, Shari Van
dc.contributor.author	Verrelst, Jochem
dc.contributor.author	Rivera-Caicedo, Juan Pablo
dc.contributor.author	Moreno Méndez, José F.
dc.date.accessioned	2023-06-29T10:19:39Z
dc.date.available	2023-06-29T10:19:39Z
dc.date.issued	2018
dc.identifier.citation	Pasqualotto Vicente, Nieves Delegido Gómez, Jesús Wittenberghe, Shari Van Verrelst, Jochem Rivera-Caicedo, Juan Pablo Moreno Méndez, José F. 2018 Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index International Journal Of Applied Earth Observation And Geoinformation 67 69 78
dc.identifier.uri	https://hdl.handle.net/10550/88586
dc.description.abstract	Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (<30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.
dc.language.iso	eng
dc.relation.ispartof	International Journal Of Applied Earth Observation And Geoinformation, 2018, vol. 67, p. 69-78
dc.subject	Teledetecció
dc.subject	Aigua Qualitat
dc.title	Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index
dc.type	journal article
dc.date.updated	2023-06-29T10:19:40Z
dc.identifier.doi	10.1016/j.jag.2018.01.002
dc.identifier.idgrec	125017
dc.rights.accessRights	open access