Dense and widespread tropical rainforests across Southeast Asia are crucial for the global carbon cycle. A new generation geostationary satellite, Himawari-8/9, and onboard sensor, the Advanced Himawari Imager (AHI), enables hyper-temporal vegetation monitoring in this cloud-prone region. However, AHI’s fixed viewing geometry varies spatially, making consistent vegetation monitoring challenging across a wide area at a seasonal scale. This study evaluated four different sun-target-sensor geometry conditions using two-band enhanced vegetation index (EVI2) to achieve near-uniform geometry and enhance the ability to monitor vegetation activities: (i) nadir condition, which used nadir viewing and solar geometry at local solar noon; (ii) local solar noon (LSN) condition, which used pixel-by-pixel fixed viewing and solar geometry at local solar noon; (iii) temporally-constant scattering angle (T-CSA) condition, which used pixel-by-pixel fixed viewing and solar geometry corresponding to the scattering angle which meets the criterion (Gao et al 2024 Remote Sens. Environ.315 114407); and (iv) spatially-constant scattering angle (S-CSA) condition, which used pixel-by-pixel fixed viewing and solar geometry corresponding to a uniform scattering angle of 140°. Among these, the S-CSA condition most effectively mitigated angular artifacts. The derived EVI2 showed higher correlations with tower-based gross primary productivity (GPP), indicating it better captured seasonal variations in vegetation activity. It also demonstrated spatially consistent temporal variations, least affected by AHI’s unique observation geometry. Applying a near-uniform sun-target-sensor geometry based on the S-CSA condition improves monitoring capability in Southeast Asia and enhances our understanding of vegetation dynamics.

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