Validating the predictive power of statistical models in retrieving leaf dry matter content of a coastal wetland from a Sentinel-2 image

Abebe Mohammed Ali*, Roshanak Darvishzadeh, Kasra Rafiezadeh Shahi, Andrew Skidmore

*Corresponding author for this work

    Research output: Contribution to journalArticle

    1 Citation (Scopus)
    1 Downloads (Pure)

    Abstract

    Leaf dry matter content (LDMC), the ratio of leaf dry mass to its fresh mass, is a key plant trait, which is an indicator for many critical aspects of plant growth and survival. Accurate and fast detection of the spatiotemporal dynamics of LDMC would help understanding plants' carbon assimilation and relative growth rate, and may then be used as an input for vegetation process models to monitor ecosystems. Satellite remote sensing is an effective tool for predicting such plant traits non-destructively. However, studies on the applicability of remote sensing for LDMC retrieval are scarce. Only a few studies have looked into the practicality of using remotely sensed data for the prediction of LDMC in a forest ecosystem. In this study, we assessed the performance of partial least squares regression (PLSR) plus 11 widely used vegetation indices (VIs), calculated based on different combinations of Sentinel-2 bands, in predicting LDMC in a coastal wetland. The accuracy of the selected methods was validated using LDMC, destructively measured in 50 randomly distributed sample plots at the study site in Schiermonnikoog, the Netherlands. The PLSR applied to canopy reflectance of Sentinel-2 bands resulted in accurate prediction of LDMC (coefficient of determination (R2) = 0.71, RMSE = 0.033). PLSR applied to the studied VIs provided an R2 of 0.70 and RMSE of 0.033. Four vegetation indices (enhanced vegetation index(EVI), specific leaf area vegetation index (SLAVI), simple ratio vegetation index (SRVI), and visible atmospherically resistant index (VARI)) computed using band 3 (green) and band 11 of the Sentinel-2 performed equally well and achieved a good measure of accuracy (R2 = 0.67, RMSE = 0.034). Our findings demonstrate the feasibility of using Sentinel-2 surface reflectance data to map LDMC in a coastal wetland.

    Original languageEnglish
    Article number1936
    Pages (from-to)1-17
    Number of pages17
    JournalRemote Sensing
    Volume11
    Issue number16
    DOIs
    Publication statusPublished - 2 Aug 2019

    Bibliographical note

    Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • LDMC
    • PLSR
    • vegetation indices
    • Sentinel-2
    • wetland

    Fingerprint Dive into the research topics of 'Validating the predictive power of statistical models in retrieving leaf dry matter content of a coastal wetland from a Sentinel-2 image'. Together they form a unique fingerprint.

  • Cite this