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CO 2 Flux Model Assessment and Comparison between an Airborne Hyperspectral Sensor and Orbital Multispectral Imagery in Southern Amazonia

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  • João Lucas Della-Silva

    (Programa de Pós-Graduação em Biodiversidade e Biotecnologia da Amazônia Legal (BIONORTE), State University of Mato Grosso (UNEMAT), Sinop 78555-000, Mato Grosso, Brazil)

  • Carlos Antonio da Silva Junior

    (Department of Geography, State University of Mato Grosso (UNEMAT), Sinop 78555-000, Mato Grosso, Brazil)

  • Mendelson Lima

    (Department of Biology, State University of Mato Grosso (UNEMAT), Alta Floresta 78580-000, Mato Grosso, Brazil)

  • Paulo Eduardo Teodoro

    (Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, Mato Grosso do Sul, Brazil)

  • Marcos Rafael Nanni

    (Department of Agronomy, State University of Maringá (UEM), Maringá 87030-120, Paraná, Brazil)

  • Luciano Shozo Shiratsuchi

    (AgCenter, School of Plant, Environmental and Soil Sciences, Louisiana State University (LSU), Baton Rouge, LA 70808, USA)

  • Larissa Pereira Ribeiro Teodoro

    (Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, Mato Grosso do Sul, Brazil)

  • Guilherme Fernando Capristo-Silva

    (Postgraduate Program in Agronomy, Federal University of Mato Grosso (UFMT), Sinop 78555-000, Mato Grosso, Brazil)

  • Fabio Henrique Rojo Baio

    (Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, Mato Grosso do Sul, Brazil)

  • Gabriel de Oliveira

    (Department of Earth Sciences, University of South Alabama, Mobile, AL 36688, USA)

  • José Francisco de Oliveira-Júnior

    (Institute of Atmospheric Sciences, Federal University of Alagoas (UFAL), Maceió 57072-970, Alagoas, Brazil)

  • Fernando Saragosa Rossi

    (Department of Agronomy, State University of São Paulo (UNESP), Jaboticabal 14884-900, São Paulo, Brazil)

Abstract

In environmental research, remote sensing techniques are mostly based on orbital data, which are characterized by limited acquisition and often poor spectral and spatial resolutions in relation to suborbital sensors. This reflects on carbon patterns, where orbital remote sensing bears devoted sensor systems for CO 2 monitoring, even though carbon observations are performed with natural resources systems, such as Landsat, supported by spectral models such as CO 2 Flux adapted to multispectral imagery. Based on the considerations above, we have compared the CO 2 Flux model by using four different imagery systems (Landsat 8, PlanetScope, Sentinel-2, and AisaFenix) in the northern part of the state of Mato Grosso, southern Brazilian Amazonia. The study area covers three different land uses, which are primary tropical forest, bare soil, and pasture. After the atmospheric correction and radiometric calibration, the scenes were resampled to 30 m of spatial resolution, seeking for a parametrized comparison of CO 2 Flux, as well as NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index). The results obtained here suggest that PlanetScope, MSI/Sentinel-2, OLI/Landsat-8, and AisaFENIX can be similarly scaled, that is, the data variability along a heterogeneous scene in evergreen tropical forest is similar. We highlight that the spatial-temporal dynamics of rainfall seasonality relation to CO 2 emission and uptake should be assessed in future research. Our results provide a better understanding on how the merge and/or combination of different airborne and orbital datasets that can provide reliable estimates of carbon emission and absorption within different terrestrial ecosystems in southern Amazonia.

Suggested Citation

  • João Lucas Della-Silva & Carlos Antonio da Silva Junior & Mendelson Lima & Paulo Eduardo Teodoro & Marcos Rafael Nanni & Luciano Shozo Shiratsuchi & Larissa Pereira Ribeiro Teodoro & Guilherme Fernand, 2022. "CO 2 Flux Model Assessment and Comparison between an Airborne Hyperspectral Sensor and Orbital Multispectral Imagery in Southern Amazonia," Sustainability, MDPI, vol. 14(9), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5458-:d:807200
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    References listed on IDEAS

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    1. Chen, Yun & Guerschman, Juan P & Cheng, Zhibo & Guo, Longzhu, 2019. "Remote sensing for vegetation monitoring in carbon capture storage regions: A review," Applied Energy, Elsevier, vol. 240(C), pages 312-326.
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    Cited by:

    1. Tássia Fraga Belloli & Diniz Carvalho de Arruda & Laurindo Antonio Guasselli & Christhian Santana Cunha & Carina Cristiane Korb, 2025. "Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest," Land, MDPI, vol. 14(3), pages 1-22, March.

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