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

Della-Silva, João Lucas; da Silva Junior, Carlos Antonio; Lima, Mendelson; Teodoro, Paulo Eduardo; Nanni, Marcos Rafael; Shiratsuchi, Luciano Shozo; Teodoro, Larissa Pereira Ribeiro; Capristo-Silva, Guilherme Fernando; Baio, Fabio Henrique Rojo; de Oliveira, Gabriel; de Oliveira-Júnior, José Francisco; Rossi, Fernando Saragosa

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 CO2 monitoring, even though carbon observations are performed with natural resources systems, such as Landsat, supported by spectral models such as CO2Flux adapted to multispectral imagery. Based on the considerations above, we have compared the CO2Flux 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 CO2Flux, 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 CO2 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.

MATO Grosso (Brazil : State); NORMALIZED difference vegetation index; CARBON dioxide; REMOTE sensing; GRASSLAND soils; DETECTORS; TROPICAL forests; CARBON cycle

Sustainability (2071-1050), 2022, Vol 14, Issue 9, p5458

2071-1050

Academic Journal

10.3390/su14095458