Normalization　of　satellite　images　collected　under　various　atmospheric　conditions　is　critical　for　the　comprehensive,　long-term　global　surveillance　of　terrestrial　surface　alterations.　This　study　utilized　remote　sensing　data　from　the　Sentinel-2A　Multispectral　Instrument　(MSI)　in　polar　orbit　and　the　Landsat-8　Operational　Land　Imager　(OLI)　sensors,　with　multispectral　global　coverage　of　10-30　m,　to　derive　reflectance　products　using　inversion　algorithms.　Validation　and　assessment　were　conducted　using　synchronous　surface　measurement　spectra　collected　from　four　sites　across　three　Chinese　provinces　in　2019.　We　corrected　surface　reflectance　and　derived　vegetation　indices　across　blue,　green,　red,　near-infrared　(NIR),　and　two　short-wave　infrared　(SWIR)　bands　and　normalized　discrepancies.　The　phenological　spatial　distribution　map　for　late　rice　in　Jiangxi　Province　was　constructed　using　normalized　data　outcomes.　A　robust　linear　correlation　in　reflectance　across　corresponding　bands　of　the　two　satellite　sensors　was　observed.　The　NIR　and　SWIR　bands　showed　the　most　significant　difference　because　of　differences　in　their　spectral　response　functions.　A　high　degree　of　congruence　was　observed　between　Landsat-8　OLI　and　Sentinel-2　MSI　sensor　reflectance　products,　with　root　mean　square　error　values　consistently　below　0.05.　The　derived　conversion　equations　were　highly　accurate　for　harmonizing　data　from　both　sensor　systems.