日本地球惑星科学連合2018年大会

講演情報

[EE] Eveningポスター発表

セッション記号 M (領域外・複数領域) » M-IS ジョイント

[M-IS05] Satellite Land Physical Processes Monitoring at Medium and High Resolution

2018年5月23日(水) 17:15 〜 18:30 ポスター会場 (幕張メッセ国際展示場 7ホール)

コンビーナ:Jean-Claude Roger(University of Maryland College Park)、Shinichi Sobue(Japan Aerospace Exploration Agency)、Eric Vermote

[MIS05-P03] Improving the cross-calibration of Landsat8OLI, Sentinel 2 and MODIS sensors through spectral adjustment methods

*Jose Luis Villaescusa1Belen Franch1,2Jean-Claude Roger1,2Eric Vermote1,2Christopher Justice1 (1.University of Maryland College Park、2.NASA Goddard Flight Space Center)

キーワード:Cross-calibration, Spectral Adjustment, RSR, Landsat8 OLI, Sentinel 2, MODIS

Cross-calibration between sensors is necessary to bring measurements to a common radiometric scale; it allows a more complete monitoring of land surface processes and enhances data continuity and harmonization. However, differences in the Relative Spectral Response (RSR) of sensors generate uncertainties in the process. For this reason, compensating for these differences is of great importance and can be achieved by using statistical regression methods between analogous bands. In this paper, we analyze the difference between different statistical regression functions obtained with the use of a combination of ground, airborne and satellite measurements, on the accuracy of the spectral adjustment for Landsat8, Sentinel 2 and MODIS sensors. In particular, we compare the linear regression intercalibration methods with multilinear regression, a quadratic and a proposed exponential SBAF (spectral band adjustment factor) dependence with the NDVI for the green, red and NIR bands, and the NDVI. We then propose a correction combining the different methods for specific bands. Results on simulated data show that the best performance is obtained from an empirically corrected exponential model, which can improve the uncertainties due to the spectral differences by up to 50% in the green band, 22% in the red band and 32% in the NIR band when the sensors considered have spectrally dissimilar analogous bands.