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Spectral data source effect on crop state estimation by vegetation indices

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Abstract

Spectral vegetation indices (VIs) are a well-known and widely used method for crop state estimation. The ability to monitor crop state by such indices is an important tool for agricultural management. Even though differences in imagery and point-based spectroscopy are obvious, their impact on crop state estimation by VIs is not well-studied. The aim of this study was to assess the performance level of the selected VIs calculated from spaceborne multispectral imagery and point-based field spectroscopy in application to crop state estimation. For this purpose, irrigated chickpea field was monitored by RapidEye satellite mission and additional measurements by field spectrometer were obtained. Estimated VIs average and coefficient of variation from each observation were compared with physical crop measurements: leaf water content, LAI and chlorophyll level. The results indicate that indices calculated from spaceborne spectral images regardless of the claimed response commonly react on phenology of the irrigated chickpea. This feature makes spaceborne spectral imagery an appropriate data source for monitoring crop development, crop water needs and yield prediction. VIs calculated from field spectrometer were sensitive for estimating pigment concentration and photosynthesis rate. Yet, a hypersensitivity of field spectral measures might lead to a very high variability (up to 69%) of the calculated values. Consequently, the high spatial variability of field spectral measurements depreciates the estimation agricultural field state by average mean only. Nevertheless, the spatial variability might have certain behavior trend, e.g., a significant increase in the active growth or stress and can be an independent feature for field state assessment.

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Acknowledgements

The study is funded by Israel Water Authority, Grant #41683. The authors want to thank the German Aerospace Center (DLR), RESA Science Team, Neustrelitz for the support by providing the satellite data of the RapidEye Science Archive (proposal no. 597) through funding from the German Federal Ministry for Economic Affairs and Energy. We also thank the staff of the DLR (Oberpfaffenhofen), namely Thomas Krauss and Peter Fischer, for the atmospheric correction using the generic processing chain CATENA.

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Authors and Affiliations

  1. Spectroscopy and Remote Sensing Laboratory, The Department of Geography and Environmental Studies, University of Haifa, 3498838, Haifa, Israel

    Maria Polinova & Anna Brook

  2. Remote Sensing Group, Institute of Computer Science, University of Osnabrueck, Wachsbleiche 27, 49090, Osnabrueck, Germany

    Thomas Jarmer

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  1. Maria Polinova
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AB, TJ, and MP proposed the method, designed and performed the experiments; AB, TJ, and MP analyzed the data and provided constructional suggestions; MP wrote the paper.

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Correspondence to Anna Brook.

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This article is part of a Topical Collection in Environmental Earth Sciences on “Learning from spatial data: unveiling the geo-environment through quantitative approaches”, guest edited by Sebastiano Trevisani, Marco Cavalli, Jean Golay, and Paulo Pereira.

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Polinova, M., Jarmer, T. & Brook, A. Spectral data source effect on crop state estimation by vegetation indices. Environ Earth Sci 77, 752 (2018). https://doi.org/10.1007/s12665-018-7932-2

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