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Chlorophyll Retrieval Using Ground Based Hyperspectral Data from a Tropical Area of India Using Regression Algorithms

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Remote Sensing Applications in Environmental Research

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Abstract

Newly emerged hyperspectral techniques make it possible to acquire images in narrow and continuous spectral bands, providing significant improvements when compared with broad bands. In this work total chlorophyll, chlorophyll-a and chlorophyll-b were estimated, which have been used to find an empirical relationship with the hyperspectral data. The vegetation indices used in this study are Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil Adjustment Vegetation Index (SAVI) and Structure Insensitive Pigment Index (SIPI). These indices were calculated on the first derivative of reflectance obtained from the field. The REIP value was calculated using a four point interpolation technique. The REIP value for fertilizer plots was found higher than for control, indicating a better health of crop in the fertilizer plots. Stepwise regression analysis was employed to estimate the linear regression algorithm for chlorophyll retrieval. The analysis shows that REIP and NDVI served as best predictor for total chlorophyll and chlorophyll-b estimation, while REIP was found suitable for Chlorophyll-a estimation. A global sensitivity analysis method was developed to estimate the uncertainty associated with the proposed chlorophyll retrieval algorithms.

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Acknowledgment

Authors are highly thankful to the University Grant Commission (UGC), India for providing financial support. Authors are also greatful to Space Application Centre, Indian Space Research Organization, Ahmedabad, India for providing technical support and information.

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Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology, New Delhi, 110016, India

    M. Gupta

  2. Department of Civil Engineering, University of Bristol, Bristol, BS8 1TR, UK

    Prashant K. Srivastava

  3. School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    S. Mukherjee

  4. Department of Botany, M S University of Baroda, Vadodara, Gujarat, India

    G. Sandhya Kiran

Authors
  1. M. Gupta
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  2. Prashant K. Srivastava
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  3. S. Mukherjee
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  4. G. Sandhya Kiran
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Corresponding author

Correspondence to M. Gupta .

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Editors and Affiliations

  1. Hydrological Sciences, ESSIC/NASA GSFC, Greenbelt, Maryland, USA

    Prashant K. Srivastava

  2. School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India

    Saumitra Mukherjee

  3. Civil Engineering, Indian Institute of Technology (IIT), New Delhi, India

    Manika Gupta

  4. Atmospheric Sciences, CIRA/NOAA, College Park, Maryland, USA

    Tanvir Islam

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© 2014 Springer International Publishing Switzerland

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Gupta, M., Srivastava, P.K., Mukherjee, S., Sandhya Kiran, G. (2014). Chlorophyll Retrieval Using Ground Based Hyperspectral Data from a Tropical Area of India Using Regression Algorithms. In: Srivastava, P., Mukherjee, S., Gupta, M., Islam, T. (eds) Remote Sensing Applications in Environmental Research. Society of Earth Scientists Series. Springer, Cham. https://doi.org/10.1007/978-3-319-05906-8_10

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