Study on the predicted model of crop leaf water status by the NIR band of ground reflectance and spaceborne hyperspectral images

Quanjun Jiao; Xue Liu; Bo Liu; Xia Zhang; Bing Zhang

doi:10.1117/12.756091

8 January 2008 Study on the predicted model of crop leaf water status by the NIR band of ground reflectance and spaceborne hyperspectral images

Quanjun Jiao, Xue Liu, Bo Liu, Xia Zhang, Bing Zhang

Author Affiliations +

Proceedings Volume 6835, Infrared Materials, Devices, and Applications; 68351H (2008) https://doi.org/10.1117/12.756091
Event: Photonics Asia 2007, 2007, Beijing, China

Abstract

Crop leaf water content can be a valuable biochemical parameter to diagnose crop water stress. The leaf water content characterizes some spectral absorption features in NIR band. Some researchers have proved that it was feasible to retrieve leaf water content utilizing those spectral absorption features. Measured leaf water content data of several sorts of winter wheat and the corresponding reflectance, CHRIS images were collected. Even 974 nm, 1160 nm and 1440 nm were absorption feature bands of foliar water, the predicted accuracies of leaf water content only using these bands were not satisfying. Four popular indices of vegetation water content including NDWI, SR, WI and REP were used to build the predict model and evaluated though relativity analysis. SR and REP opposed the stronger predicted accuracy of leaf water content than other spectral indices. Limited to the band position setting and band spectral function of CHRIS, SR and REP were modified fit to the band setting of CHRIS. The predicted model based on ground reflectance were made to adaptable to the band spectral function of CHRIS sensors, and the results showed that wide band spectral function indeed caused a lower accuracy of crop water content than narrow band spectral function.

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Quanjun Jiao, Xue Liu, Bo Liu, Xia Zhang, and Bing Zhang "Study on the predicted model of crop leaf water status by the NIR band of ground reflectance and spaceborne hyperspectral images", Proc. SPIE 6835, Infrared Materials, Devices, and Applications, 68351H (8 January 2008); https://doi.org/10.1117/12.756091

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KEYWORDS

Reflectivity

Vegetation

Sensors

Absorption

Dielectrophoresis

Remote sensing

Near infrared

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