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A dynamic soil endmember spectrum selection approach for soil and crop residue linear spectral unmixing analysis

Jibo Yue,Q. Tian,S. Tang,Kaijian Xu,Chengquan Zhou

Published 2019 in International Journal of Applied Earth Observation and Geoinformation

ABSTRACT

Abstract Crop residue deposited on the soil surface helps protect against water and wind erosion, improve soil quality and increase soil organic matter and soil carbon storage. Linear spectral mixture analysis (LSMA) is an important technique in field crop residue estimation. Traditionally, only one single or fixed standard crop residue and soil endmember spectrum is performed for each of the presented endmember classes or field. Because the variation in soil endmember spectrum signatures significantly changes with soil moisture (SM), spectral unmixing with fixed endmember spectra can lead to poor accuracy of the abundance of the spectral constituents of pure crop residue. Herein, this paper presents a dynamic soil endmember spectrum selection approach for improving the performance of soil and rice residue spectral unmixing analysis in rice residue cover (RRC) estimation. This new approach uses SM and soil spectral reflectance model to modify soil endmember spectra in each spectral unmixing analysis. Two validation datasets computed results have verified the feasibility and correctness of the dynamic soil endmember method. Results indicated that the distribution of SM in farmland was crucial for RRC estimation. Compared with traditional fixed min and fixed mean soil endmember spectrum methods, the results of the method developed herein showed this method significantly improved RRC estimation accuracy for an SM content lower than 20% (volumetric water content) over the traditional methods tested. Therefore, our proposed approach can be used to improve RRC estimation accuracy in harvest field.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    International Journal of Applied Earth Observation and Geoinformation

  • Publication date

    2019-06-01

  • Fields of study

    Agricultural and Food Sciences, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1016/J.JAG.2019.02.001
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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