4.7 Article

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

期刊

REMOTE SENSING
卷 11, 期 11, 页码 -

出版社

MDPI
DOI: 10.3390/rs11111350

关键词

Biocrusts; biological soil crust; chlorophyll quantification; hyperspectral; random forest; remote sensing

资金

  1. Spanish National Plan for Research
  2. European Union ERDF funds
  3. project H2020-MSCA-RISE-GYPWORLD (European Union's Horizon 2020 research and innovation program under the Marie Slodowska-Curie Grant) [777803]
  4. FPU predoctoral fellowship from the Educational, Culture and Sports Ministry of Spain [FPU14/05806]
  5. Juan de la Cierva incorporacion fellowship [IJCI-2016-29274]
  6. Hipatia postdoctoral fellowship - University of Almeria
  7. foundation Tatiana Perez de Guzman el Bueno
  8. RESUCI project [CGL2014-59946-R]
  9. DINCOS project [CGL2016-78075-P]
  10. REBIOARID project [RTI2018-101921-B-I00]
  11. Marie Curie Actions (MSCA) [777803] Funding Source: Marie Curie Actions (MSCA)

向作者/读者索取更多资源

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 (R-2 > 0.94) with a mean root mean square error (RMSE) of about 6.5 mu g/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.

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