Accurate　information　on　tea　plantation　distribution　provides　scientific　support　for　land　use　planning　and　optimization　of　planting　layouts,　contributing　to　the　sustainable　development　of　the　tea　industry.　Multimodal　remote　sensing　features　of　tea　plantation　were　constructed　based　on　RGB　bands　from　GF　-　2　PMS　imagery,　NDVI　calculated　from　Sentinel　-　2　optical　imagery,　phenological　characteristics　derived　from　Sentinel　-　1　time-series　SAR　data,　including　growth　amplitude,　GA,　and　growth　length,　GL),　and　slope　aspect,　slope　gradient,　and　curvature　calculated　from　GF　-　7　stereo　imagery.　The　optimal　feature　combination　was　selected　through　a　random　forest　feature　selection　algorithm.　A　dualbranch　network　model,　multi-modal　information　parallel　branch　network　(MIPBNet),　was　built　by　using　a　multi-network　joint　learning　strategy,　with　attentional　multiscale　lightweight　encoder-decoder　network　(AMLNet)　as　the　first　branch　and　Vanilla　AMLNet　as　the　second　branch.　A　feature　fusion　module　(dual-branch　feature　fusion　block,　DBFF)　was　utilized　for　feature-level　fusion　at　the　end　of　the　decoder,　and　a　composite　loss　function　was　employed　for　optimization　training.　The　research　findings　were　as　follows:　the　combination　of　NDVI,　GA,　slope　aspect,　and　slope　gradient　best　improved　classification　accuracy　and　was　identified　as　the　optimal　multi-modal　feature　set.　When　RGB　data　was　sequentially　augmented　with　NDVI,　GA,　slope　aspect,　and　slope　gradient,　experiments　showed　a　significant　reduction　in　both　omitted　and　falsely　extracted　tea　plantation　areas,　with　an　improvement　in　overall　accuracy　(OA)　of　3.　11%.　Compared　with　typical　semantic　segmentation　models　such　as　UNet,　UNeXt,　and　Segformer,　the　singlebranch　AMLNet　within　MIPBNet　achieved　superior　tea　plantation　extraction　results.　©　2025　Chinese　Society　of　Agricultural　Machinery.　All　rights　reserved.