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Spectral response analysis: An indirect and non-destructive methodology for the chlorophyll quantification of biocrusts

Remote Sensing, ISSN: 2072-4292, Vol: 11, Issue: 11
2019
  • 33
    Citations
  • 0
    Usage
  • 63
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    33
    • Citation Indexes
      33
  • Captures
    63
  • Mentions
    1
    • Blog Mentions
      1
      • 1

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Remote Sensing, Vol. 11, Pages 1350: Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

Remote Sensing, Vol. 11, Pages 1350: Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts Remote Sensing doi: 10.3390/rs11111350 Authors:

Article Description

Chlorophyll a concentration (Chla) is a well-proven proxy of biocrust development, photosynthetic organisms' status, and recovery monitoring after environmental disturbances. However, laboratory methods for the analysis of chlorophyll require destructive sampling and are expensive and time consuming. Indirect estimation of chlorophyll a by means of soil surface reflectance analysis has been demonstrated to be an accurate, cheap, and quick alternative for chlorophyll retrieval information, especially in plants. However, its application to biocrusts has yet to be harnessed. In this study we evaluated the potential of soil surface reflectance measurements for non-destructive Chla quantification over a range of biocrust types and soils. Our results revealed that fromthe different spectral transformation methods and techniques, the first derivative of the reflectance and the continuum removal were the most accurate for Chla retrieval. Normalized difference values in the red-edge region and common broadband indexes (e.g., normalized difference vegetation index (NDVI)) were also sensitive to changes in Chla. However, such approaches should be carefully adapted to each specific biocrust type. On the other hand, the combination of spectral measurements with non-linear random forest (RF) models provided very good fits (R > 0.94) with a mean root mean square error (RMSE) of about 6.5 μg/g soil, and alleviated the need for a specific calibration for each crust type, opening a wide range of opportunities to advance our knowledge of biocrust responses to ongoing global change and degradation processes from anthropogenic disturbance.

Bibliographic Details

José Raúl Román; Emilio Rodríguez-Caballero; Borja Rodríguez-Lozano; Beatriz Roncero-Ramos; Sonia Chamizo; Pilar Águila-Carricondo; Yolanda Cantón

MDPI AG

Earth and Planetary Sciences

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