Two　groups　of　retrieval　algorithms,　physics　based　and　machine　learning　(ML)　based,　each　consisting　of　two　independent　approaches,　have　been　developed　to　retrieve　cloud　base　height　(CBH)　and　its　diurnal　cycle　from　Himawari-8　geostationary　satellite　observations.　Validations　have　been　conducted　using　the　joint　CloudSat/Cloud-Aerosol　Lidar　with　Orthogonal　Polarization　(CALIOP)　CBH　products　in　2017,　ensuring　independent　assessments.　Results　show　that　the　two　ML-based　algorithms　exhibit　markedly　superior　performance　(the　optimal　method　is　with　a　correlation　coefficient　of　R　＞　0.91　and　an　absolute　bias　of　approximately　0.8　km)　compared　to　the　two　physics-based　algorithms.　However,　validations　based　on　CBH　data　from　the　ground-based　lidar　at　the　Lijiang　station　in　Yunnan　Province　and　the　cloud　radar　at　the　Nanjiao　station　in　Beijing,　China,　explicitly　present　contradictory　outcomes　(R　＜　0.60).　An　identifiable　issue　arises　with　significant　underestimations　in　the　retrieved　CBH　by　both　ML-based　algorithms,　leading　to　an　inability　to　capture　the　diurnal　cycle　characteristics　of　CBH.　The　strong　consistence　observed　between　CBH　derived　from　ML-based　algorithms　and　the　spaceborne　active　sensors　of　CloudSat/CALIOP　may　be　attributed　to　utilizing　the　same　dataset　for　training　and　validation,　sourced　from　the　CloudSat/CALIOP　products.　In　contrast,　the　CBH　derived　from　the　optimal　physics-based　algorithm　demonstrates　good　agreement　in　diurnal　variations　in　CBH　with　ground-based　lidar/cloud　radar　observations　during　the　daytime　(with　an　R　value　of　approximately　0.7).　Therefore,　the　findings　in　this　investigation　from　ground-based　observations　advocate　for　the　more　reliable　and　adaptable　nature　of　physics-based　algorithms　in　retrieving　CBH　from　geostationary　satellite　measurements.　Nevertheless,　under　ideal　conditions,　with　an　ample　dataset　of　spaceborne　cloud　profiling　radar　observations　encompassing　the　entire　day　for　training　purposes,　the　ML-based　algorithms　may　hold　promise　for　still　delivering　accurate　CBH　outputs.