System vicarious calibration of GCOM-C/SGLI visible and near-infrared channels

Abstract

Vicarious calibration coefficients (kv) of Second-generation GLobal Imager (SGLI) for ocean color processing were derived using in-situ radiometric buoy measurements from the Marine Optical BuoY (MOBY) and the BOUée pour l'acquiSition d'une Série Optique à Long termE (BOUSSOLE). Two aerosol-model look up tables (LUTs) used in the GCOM-C aerosol retrieval algorithm (LUT-A) and in the previous version of ocean color atmospheric correction algorithm (LUT-B) were tested in the procedures to calculate kv and retrieve remote sensing reflectance (Rrs) and aerosol optical thickness (AOT). Bias of the processed Rrs compared to AERONET-OC Rrs was reduced by applying the determined kv (i.e., corrected SGLI radiance = original SGLI radiance/kv). LUT-A yielded smaller AOT bias compared to AERONET-OC AOT; on the other hand, LUT-B gave smaller Rrs noise due to gentle slope of the aerosol reflectance even though it caused AOT overestimation. When kv was derived by adjusting to the AOT measurements, kv was about 1.1 by LUT-A and 1.2 by LUT-B in the near-infrared (NIR) channel. However, the kv in the NIR channel was close to 1.0 when AOT and land surface reflectance measurements of Radiometric Calibration Network (RadCalNet) were used. The LUT-A with kv from MOBY and BOUSSOLE are currently adopted for the SGLI standard ocean color processing. Improvement is needed, however, to design an optimal LUT suitable for both aerosol and ocean color purposes.

Perspective: Improving the accuracy of plant phenology observations and land-cover and land-use detection by optical satellite remote-sensing in the Asian tropics

“…The SeaWiFS (1.13-km at a daily interval) 17 and Aqua-MODIS satellites, which were launched around 2000, observe ocean color and allow for estimation of chlorophyll concentration (O'Reilly et al, 1998;Schollaert et al, 2003;Gregg and Casey, 2004; 16 https://www.eorc.jaxa.jp/ALOS/jp/dataset/lulc_j.htm 17 https://oceancolor.gsfc.nasa.gov/SeaWiFS/ Siswanto and Tanaka, 2014;Groom et al, 2019). In addition, the Second Generation Global Imager (SGLI) on board the Global Change Observation Mission-Climate (GCOM-C) satellite (250m at 2-day intervals) 18 improved the accuracy and precision of ocean color observations (Murakami, 2016;Matsuoka et al, 2021). Further research should examine, for instance, the relationship between the interannual variation of deforestation and the spatiotemporal variation of the chlorophyll concentrations in the coastal areas of some river basins.…”

Feasibility of cross-calibrating ocean-color sensors in polar orbit using an intermediary geostationary sensor of reference

“…Both 250 m and 1 km (resampled) level-1b are provided, but only 1-km data is used in the analyses. Like MODIS, SGLI level-1b data is vicariously calibrated, using the detailed procedure described in Murakami et al (2022).…”

“…At longer wavelengths, i.e., 857, 1,610, and 2,257 nm, however, the AHI image noise increases to more than 3%, which will eventually propagate to the cross-calibration coefficients. Consequently, even with very accurate spectral band matching (for example, at 857 and 1,610 nm, see Table 2), these longer wavelengths are not of interest in this study, even more as for ocean color applications the SVC (Franz et al, 2007;Murakami et al, 2022) makes assumptions about radiometric calibration in the near infrared and does not consider SWIR measurements (only bands in the visible are adjusted for calibration). We keep 639 nm as the spectral matching RMSD is less than 3% for some SGLI/MODIS band combinations (Table 2).…”

“…As such, the about 4% difference in the SGLI/MODIS-A and SGLI/ MODIS-T calibration for the band combination using 490 and 510 nm would result in a much larger difference on the water-leaving signal than the acceptable uncertainty for ocean color applications, i.e., 5% (IOCCG, 2013), if the same atmospheric correction scheme were applied to SGLI and MODIS data. It is quite possible that such differences may introduce significant discrepancies between the downstream ocean-color products by SGLI and MODIS-A and -T. As indicated in Murakami et al (2022), however, the atmospheric correction scheme applied to SGLI is different from that used for MODIS-A and MODIS-T, which may eventually compensate for the differences in TOA signals. In addition, SVC of SGLI was performed using the in situ measurements from both MOBY and BOUSSOLE, as compared to the OBPG vicarious calibration that only utilized MOBY measurements.…”

“…In addition, SVC of SGLI was performed using the in situ measurements from both MOBY and BOUSSOLE, as compared to the OBPG vicarious calibration that only utilized MOBY measurements. In Table 2 of their paper, Murakami et al (2022) reported differences of vicarious calibration gains depending on the in situ datasets and aerosol LUTs used. About 1% difference in the gains were found for SGLI at 443, 490, and 530 nm while the gains at 672 nm were almost identical when using LUT-A and MOBY + BOUSSOLE, i.e., the method applied to SGLI, versus using LUT-B and MOBY, i.e., the method applied to MODIS-A and -T. Consequently, the different atmospheric correction and SVC applied to SGLI and MODIS-A and -T may reduce the impact of TOA signal discrepancy on the derived ocean-color products.…”