Challenges in methane column retrievals from AVIRIS-NG imagery over spectrally cluttered surfaces: A sensitivity analysis

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Abstract

A comparison between efforts to detect methane anomalies by a simple band ratio approach from the Airborne Visual Infrared Imaging Spectrometer-Classic (AVIRIS-C) data for the Kern Front oil field, Central California, and the Coal Oil Point marine hydrocarbon seep field, offshore southern California, was conducted. The detection succeeded for the marine source and failed for the terrestrial source, despite these sources being of comparable strength. Scene differences were investigated in higher spectral and spatial resolution collected by the AVIRIS-C successor instrument, AVIRIS Next Generation (AVIRIS-NG), by a sensitivity study. Sensitivity to factors including water vapor, aerosol, planetary boundary layer (PBL) structure, illumination and viewing angle, and surface albedo clutter were explored. The study used the residual radiance method, with sensitivity derived from MODTRAN (MODerate resolution atmospheric correction TRANsmission) simulations of column methane (XCH4 ). Simulations used the spectral specifications and geometries of AVIRIS-NG and were based on a uniform or an in situ vertical CH4 profile, which was measured concurrent with the AVIRIS-NG data. Small but significant sensitivity was found for PBL structure and water vapor; however, highly non-linear, extremely strong sensitivity was found for surface albedo error. For example, a 10% decrease in the surface albedo corresponded to a 300% XCH4 increase over background XCH4 to compensate for the total signal, less so for stronger plumes. This strong non-linear sensitivity resulted from the high percentage of surface-reflected radiance in the airborne at-sensor total radiance. Coarse spectral resolution and feedback from interferents like water vapor underlay this sensitivity. Imaging spectrometry like AVIRIS and the Hyperspectral InfraRed Imager (HyspIRI) candidate satellite mission, have the advantages of contextual spatial information and greater at-sensor total radiance. However, they also face challenges due to their relatively broad spectral resolution compared to trace gas specific orbital sensors, e.g., the Greenhouse gases Observing SATellite (GOSAT), which is especially applicable to trace gas retrievals over scenes with high spectral albedo variability. Results of the sensitivity analysis are applicable for the residual radiance method and CH4 profiles used in the analysis, but they illustrate potential significant challenges in CH4 retrievals using other approaches.

Figures

  • Table 1. Nomenclature.
  • Figure 1. (A) True color imagery of Airborne Visual Infrared Imaging Spectrometer-Classic (AVIRIS-C) data acquired on 19 June 2008. (B) Band ratio (σ) of at-sensor reflectance (ρ t) for the 2298 and 2058 nm bands, σ = ρ t(2298)/ρ t(2058) for AVIRIS-C data in (A), black rectangle outline shows clear plume structure. (C) True color imagery of AVIRIS-C data acquired on 6 June 2013. (D) σ for AVIRIS-C data in (C). Data key on figure.
  • Figure 2. True color imagery for AVIRIS-NG image of the Kern Front oil field, near Bakersfield, central California on 4 September 2014. The land cover types for the three pixels selected for sensitivity analysis are noted, by Ar, Bsl, and Rbl, which are for Asphalt road, Brown sandy loam, and Reddish brown sandy loam, respectively. Pixels in the red box are used to investigate the effect of subpixel heterogeneity on albedo and XCH4.
  • Figure 3. (A) In situ methane, CH4, and wind data for 4 September 2014 collected by CIRPAS for the Kern Front and Kern River oil fields. Data key on panel. (B) Derived in situ and uniform CH4 profiles, which have the same column-averaged CH4 within a boundary layer of 2.0 km.
  • Figure 5. Surface albedo (ρs) for (A) 500 to 2500 nm, (B) 2139 to 2299 nm, for three common scene elements: asphalt road (Ar), brown sandy loam (Bsl), and red-brown sandy loam (Rbl), respectively. Data key on figure. Spectra are from Environment for Visualizing Images (ENVI) library.
  • Figure 5. Surface albedo (ρs) for (A) 500 to 2500 nm, (B) 2139 to 2299 nm, for three common scene elements: asphalt road (Ar), brown sandy loam (Bsl), and red-brown sandy loam (Rbl), respectively. Data key on figure. Spectra are from Environment for Visualizing Images (ENVI) library.
  • Figure 9. Column retrieval error (S(k = 1, α = −10%)) where k = 1 signifies the plume profile (Figure 3) for (A) solar zenith angle (θs) when viewing zenith angle (θv) and relative sun-senor azimuth angle (φ) are equal to 8°, 90° respectively, (B) θv when θs and φ are equal to 40°, 90° respectively, and (C) φ when θs and θv are equal to 40°, 8° respectively.
  • Figure 9. Column retrieval error (S(k = 1, α = −10%)) where k = 1 signifies the plume profile (Figure 3) for (A) solar zenith angle (θs) when viewing zenith angle (θv) and relative sun-senor azimuth angle (φ) are equal to 8°, 90° respectively, (B) θv when θs and φ are equal to 40°, 90° respectively, and (C) φ when θs and θv are equal to 40°, 8° respectively.

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Zhang, M., Leifer, I., & Hu, C. (2017). Challenges in methane column retrievals from AVIRIS-NG imagery over spectrally cluttered surfaces: A sensitivity analysis. Remote Sensing, 9(8), 1–21. https://doi.org/10.3390/rs9080835

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