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Article

Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts

1
Agronomy Department, University of Almería, 04120 Almería, Spain
2
Centro de Investigación de Colecciones Científicas de la Universidad de Almería (CECOUAL), University of Almería, 04120 Almería, Spain
3
Estación Experimental de Zonas Áridas (EEZA), Consejo Superior de Investigaciones Científicas, 04120 Almería, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1350; https://doi.org/10.3390/rs11111350
Received: 3 May 2019 / Revised: 28 May 2019 / Accepted: 2 June 2019 / Published: 5 June 2019
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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 from the 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 (R2 > 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. View Full-Text
Keywords: Biocrusts; biological soil crust; chlorophyll quantification; hyperspectral; random forest; remote sensing Biocrusts; biological soil crust; chlorophyll quantification; hyperspectral; random forest; remote sensing
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MDPI and ACS Style

Román, J.R.; Rodríguez-Caballero, E.; Rodríguez-Lozano, B.; Roncero-Ramos, B.; Chamizo, S.; Águila-Carricondo, P.; Cantón, Y. Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts. Remote Sens. 2019, 11, 1350. https://doi.org/10.3390/rs11111350

AMA Style

Román JR, Rodríguez-Caballero E, Rodríguez-Lozano B, Roncero-Ramos B, Chamizo S, Águila-Carricondo P, Cantón Y. Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts. Remote Sensing. 2019; 11(11):1350. https://doi.org/10.3390/rs11111350

Chicago/Turabian Style

Román, José R., Emilio Rodríguez-Caballero, Borja Rodríguez-Lozano, Beatriz Roncero-Ramos, Sonia Chamizo, Pilar Águila-Carricondo, and Yolanda Cantón. 2019. "Spectral Response Analysis: An Indirect and Non-Destructive Methodology for the Chlorophyll Quantification of Biocrusts" Remote Sensing 11, no. 11: 1350. https://doi.org/10.3390/rs11111350

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