The Use of Spectral Databases for Remote Sensing of Agricultural Crops

Authored by: Andreas Hueni , Lola Suarez , Laurie A. Chisholm , Alex Held

Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation

Print publication date:  December  2018
Online publication date:  December  2018

Print ISBN: 9781138058545
eBook ISBN: 9781315164151
Adobe ISBN:

10.1201/9781315164151-7

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Abstract

Remote sensing is one of the emerging technologies used in precision agriculture to monitor plant health status, targeted correction of any deficit or infestation to increase food production at minimized operational cost, and environmental pressure. Among the effects to be mitigated are water stress, nutrient stress, and diseases. These stress factors have an impact on the leaf pigment composition, water content, plant structure, and internal biophysical processes such as fluorescence or heat dissipation. Remote sensing can be used to infer or directly measure the influence of stressors on the plants. Differences between species and in the element disposition within the canopy or row structure, complicate the application of algorithms across species and locations. Spectral databases storing spectral data with stress and structure-specific metadata are an important tool to test hypotheses and algorithms without the implicit need to carry out dedicated field experiments. Such spectral data collections would enable basic research of plant traits and their connection with spectral information content. Furthermore, these repositories would increase the usability of existing resources and foster community collaboration, facilitating long-term crop monitoring at regional scales.

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