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Extraction of sub-pixel C3/C4 emissions of solar-induced chlorophyll fluorescence (SIF) using artificial neural network

O. Kira,Ying Sun

Published 2020 in Isprs Journal of Photogrammetry and Remote Sensing

ABSTRACT

Abstract Solar-induced chlorophyll fluorescence (SIF) is a signal directly and functionally related to photosynthetic activity and thus holds great promise for large-scale agricultural monitoring. However, the coarse spatial resolution of existing satellite SIF observations usually consist of mixed SIF signals contributed by different crop types with distinct phenology (modulated by management practices) and varying SIF emission capacities, which impedes effective utilization of existing SIF records for large-scale agricultural applications. This study makes the first effort to overcome this challenge by developing a sub-pixel SIF extraction framework for corn and soybean in the US Corn Belt as a case study. Here we developed a machine learning (ML) based sub-pixel SIF extraction framework using Orbiting Carbon Observatory 2 (OCO-2), whose high-resolution SIF acquired along orbits at nadir enables the identification of relatively pure pixels dominated by single corn or soybean crops, facilitating validation of the developed framework. To achieve this, we first generated artificially mixed SIF pixels from pure pixels randomly weighted by fractional area coverage. We then employed a standard feed forward artificial neural network (ANN) to estimate sub-pixel SIF for corn and soybean respectively, using the following predictors: total mixed SIF, spectral reflectance of corn/soybean (from Moderate Resolution Imaging Spectroradiometer MODIS), and the fractional area coverage of corn/soybean (derived from CropScape-Cropland Data Layer). Our results demonstrated that the estimated sub-pixel SIF could successfully reproduce the original pure SIF values constituting the mixed pixel, with a normalized root mean squared error (NRMSE) of

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Isprs Journal of Photogrammetry and Remote Sensing

  • Publication date

    2020-03-01

  • Fields of study

    Agricultural and Food Sciences, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1016/j.isprsjprs.2020.01.017
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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