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Optimizing Optical Coastal Remote-Sensing Products: Recommendations for Regional Algorithm Calibration

Rafael Simão,Juliana Távora,M. S. Salama,E. Fernandes

Published 2024 in Remote Sensing

ABSTRACT

The remote sensing of turbidity and suspended particulate matter (SPM) relies on atmospheric corrections and bio-optical algorithms, but there is no one method that has better accuracy than the others for all satellites, bands, study areas, and purposes. Here, we evaluated different combinations of satellites (Landsat-8, Sentinel-2, and Sentinel-3), atmospheric corrections (ACOLITE and POLYMER), algorithms (single- and multiband; empirical and semi-analytical), and bands (665 and 865 nm) to estimate turbidity and SPM in Patos Lagoon (Brazil). The region is suitable for a case study of the regionality of remote-sensing algorithms, which we addressed by regionally recalibrating the coefficients of the algorithms using a method for geophysical observation models (GeoCalVal). Additionally, we examined the results associated with the use of different statistical parameters for classifying algorithms and introduced a new metric (GoF) that reflects performance. The best performance was achieved via POLYMER atmospheric correction and the use of single-band algorithms. Regarding SPM, the recalibrated coefficients yielded a better performance, but, for turbidity, a tradeoff between two statistical parameters occurred. Therefore, the uncertainties in the atmospheric corrections and algorithms used were analyzed based on previous studies. In the future, we suggest the use of in situ radiometric data to better evaluate atmospheric corrections, radiative transfer modeling to bridge data gaps, and multisensor data merging for compiling climate records.

PUBLICATION RECORD

  • Publication year

    2024

  • Venue

    Remote Sensing

  • Publication date

    2024-04-24

  • Fields of study

    Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs16091497
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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