The　leaf　area　index　(LAI)　is　significant　for　research　on　global　climate　change　and　ecological　environment.　China　HJ-1　satellite　has　a　revisit　cycle　of　four　days,　providing　CCD　data　(HJ-1　CCD)　with　a　resolution　of　30　m.　However,　the　HJ-1　CCD　is　incapable　of　obtaining　observations　at　multiple　angles.　This　is　problematic　because　single　angle　observations　provide　insufficient　data　for　determining　the　LAI.　This　article　proposes　a　new　method　for　determining　LAI　using　HJ-1　CCD　data.　The　proposed　method　uses　background　knowledge　of　dynamic　land　surface　processes　that　are　extracted　from　MODerate　resolution　Imaging　Spectroradiometer　(MODIS)　LAI　1-km　resolution　data.　To　process　the　uncertainties　that　arise　from　using　two　data　sources　with　different　spatial　resolutions,　the　proposed　method　is　implemented　in　a　dynamitic　Bayesian　network　scheme　by　integrating　a　LAI　dynamic　process　model　and　a　canopy　reflectance　model　with　remotely　sensed　data.　Validation　results　showed　that　the　determination　coefficient　between　estimated　and　measured　LAI　was　0.791,　and　the　RMSE　was　0.61.　This　method　can　enhance　the　accuracy　of　the　retrieval　results　while　retaining　the　time　series　variation　characteristics　of　the　vegetation　LAI.　The　results　suggest　that　this　algorithm　can　be　widely　applied　to　determining　high-resolution　leaf　area　indices　using　data　from　China　HJ-1　satellite　even　if　information　from　single　angle　observations　are　insufficient　for　quantitative　application.