湄公河三角洲（Mekong River Delta， MRD）生產越南半數以上的稻米，並支撐全國主要的稻米出口，為東南亞重要的糧食生產區。然而，其地勢低平且高度依賴水文與氣候條件，使其對氣候變遷所引發的高溫、降雨異常、乾旱、鹽分入侵與洪水等複合性衝擊高度敏感。傳統氣候脆弱度評估多仰賴間歇性統計資料及空間分布有限的地面氣象觀測，不僅難以刻畫三角洲內部細緻的空間異質性，亦難以將歷史觀測所得之關係延伸至超出歷史變異範圍的未來氣候情境。因此，如何整合具空間連續性之觀測資訊與具情境推估能力之氣候模式資料，以量化不同氣候變遷情境下稻米生產力的時空變化，為評估湄公河三角洲未來糧食安全與氣候調適能力的重要科學課題。 本研究針對上述「觀測—推估」之間的尺度與資訊落差，建立一套以遙測為核心、整合多源衛星觀測與氣候模式推估資料之應用分析架構，系統性評估不同排放情境下，湄公河三角洲各省份及不同稻作季節之氣候變遷衝擊，並進一步推估至2075年之稻米生產力變化。研究架構首先利用多源遙測資料建立歷史基線條件：以MODIS時間序列資料反演稻作物候期，以諧合化（harmonized）Landsat與Sentinel-2影像監測植被狀態與土地鹽分變化；利用Sentinel-1合成孔徑雷達（Synthetic Aperture Radar， SAR）建立歷年農地淹水資訊，並結合年度土地覆蓋資料估算稻作耕作面積。未來氣候條件則採用1995—2075年六組NEX-GDDP-CMIP6全球氣候模式系集資料，建立不同共享社會經濟路徑（Shared Socioeconomic Pathways， SSPs）排放情境下之氣候變遷推估。 在稻作氣候脅迫量化方面，本研究依據遙測反演之稻作物候期，建立五項與稻米生產力密切相關之氣候脅迫指標，包括生長早期強降雨、結穗期強降雨、生殖期熱脅迫、乾旱—鹽分入侵脅迫，以及上游驅動之洪水訊號，以刻畫不同生育階段所承受之氣候風險。針對湄公河流域上游降雨與三角洲下游淹水之空間與時間傳遞關係，進一步利用對數線性轉換函數建立上游強降水與下游淹水範圍之關聯模型。為改善氣候模式與降雨產品空間解析度不足及系統性偏差問題，本研究另發展兩階段FNN–UNet降尺度模型，將每日降水資料降尺度至1 km空間解析度，以提升降雨分布估計的空間細緻度及其對局地氣候脅迫的表徵能力。在產量推估方面，本研究首先利用歷史統計資料分離稻米生產之長期趨勢與氣候變異效應，進而結合線性迴歸與前饋神經網路（Feedforward Neural Network， FNN），以所建立之氣候脅迫指標作為預測因子，分別推估稻米單位面積產量與總產量，藉以建立從「氣候變化—氣候脅迫—稻作面積—稻作產量」之量化鏈結。 歷史分析結果顯示，1995—2024年間湄公河三角洲稻米產量的成長主要來自單位面積產量提升，其中約91%的產量增長可歸因於單位產量的增加，顯示過去三十年間生產效率提升為區域稻米增產的主要驅動因素。然而，未來氣候推估顯示，熱脅迫將逐漸成為影響稻作生產力的主要氣候暴露因子。其中，生殖期熱脅迫呈現最為明顯的增長趨勢，其關鍵暴露日數約為每年27天；至本世紀末，於中度排放情境下將增加至約68天，高度排放情境下更可能達到約107天，顯示稻作生殖階段所面臨的高溫風險將顯著加劇。另一方面，研究亦發現內陸省份之乾旱條件大致維持於現有水平，而沿海省份的降雨不足則呈持續加深趨勢，反映三角洲不同區域所承受的氣候風險具有顯著空間異質性。 就整體產量而言，湄公河三角洲稻米總產量預估將於2035年前後達到約2，750萬公噸的高峰，隨後逐步下降，至2075年約為2，650萬公噸。值得注意的是，中度（SSP2-4.5）與高度排放情境（SSP2-8.5）下的總產量差異並不顯著，顯示單純以三角洲總量衡量氣候變遷衝擊，可能低估其在空間與季節尺度上的結構性變化。進一步分析各省份及不同稻作季節後發現，總產量相對穩定的表象下存在明顯的空間與季節重分配現象：夏秋作於多數省份呈現產量下降，其中以西南沿海地區最為顯著，包括金甌、薄寮與朔莊；相較之下，內陸地區冬春作之產量則大致維持穩定。此結果顯示，氣候變遷對湄公河三角洲稻作生產的影響並非均勻發生，而是受到地理位置、稻作季節與局地水文環境共同調控。 在各項氣候脅迫因子中，上游洪水訊號與強降水對稻米單位面積產量呈現最明確的負向影響，顯示稻作生產不僅受到局地氣候條件控制，亦受到流域尺度水文傳遞所形成之跨區域洪水風險影響。進一步的空間分析顯示，各類氣候風險具有明顯的空間聚集特性：內陸淡水核心區整體具有較高的氣候韌性；相對而言，沿海地區同時暴露於高溫與鹽分入侵等複合性脅迫，並伴隨約12%的稻作耕作面積縮減，使沿海地區成為湄公河三角洲稻米生產系統中氣候暴露程度最高且最具脆弱性的區域。 本研究之核心貢獻在於建立一套以多源遙測觀測為基礎、結合氣候模式推估、深度學習降尺度與產量機制分析之整合性評估架構。相較於傳統依賴稀疏地面觀測與歷史統計資料的氣候脆弱度評估方法，本研究透過目前公開可取得之衛星遙測資料，建立具空間連續性與高解析度的稻作物候、植被、鹽分、淹水及耕作面積資訊，並將其與未來氣候情境及稻米產量模型相互串聯，從而突破歷史觀測資料在空間覆蓋與未來情境推估上的限制。研究結果進一步揭示，氣候變遷對湄公河三角洲稻米生產的主要影響並非單純表現為總產量下降，而是透過高溫、降雨、洪水、乾旱、鹽分入侵及耕作面積變化等多重因素，造成不同省份與稻作季節間的生產力重新分配。因此，本研究所提出之架構可提供具空間明確性、時間連續性及情境推估能力之科學資訊，作為湄公河三角洲糧食安全評估、農業生產管理與氣候變遷調適規劃之重要決策支援工具，並為大尺度氣候變遷衝擊評估提供一套具可操作性與可擴充性的遙測整合分析方法。;The Mekong River Delta (MRD) produces more than half of Vietnam’s rice and accounts for the majority of the country’s rice exports. Its low-lying terrain， however， makes the delta particularly vulnerable to climate change and associated hydroclimatic extremes. Conventional vulnerability assessments largely rely on lagged statistical records and sparsely distributed weather stations， which are insufficient to resolve the pronounced spatial heterogeneity of climate exposure across the delta and provide limited capacity to project impacts beyond the range of historical observations. To address this observation–projection gap， this study develops a transferable， remote-sensing-driven framework that integrates multi-source satellite observations with climate projections to quantify climate-change impacts on rice productivity across provinces and cropping seasons in the MRD under moderate- and high-emission scenarios through 2075. The framework establishes a consistent linkage between observed agricultural conditions and projected future climate stressors. Historical baseline conditions are characterized using multi-source satellite remote sensing， including MODIS-derived rice phenology， harmonized Landsat and Sentinel-2 observations of vegetation and salinity stress， Sentinel-1 synthetic aperture radar (SAR) observations of surface flooding， and annual land-cover maps to quantify cultivated area. Future climate conditions are derived from a six-member NEX-GDDP-CMIP6 ensemble spanning 1995–2075. Satellite-derived phenological windows are used to constrain five crop-specific climate stress indicators: heavy rainfall during the early growth stage， heavy rainfall around flowering， reproductive-stage heat stress， a drought–salinity indicator， and an upstream-driven flood signal. To represent the propagation of hydroclimatic forcing through the river system， a log-linear transfer function is developed to relate upstream heavy precipitation to downstream flood extent. In parallel， a two-stage feedforward neural network–U-Net (FNN–UNet) model downscales daily precipitation to 1-km resolution， thereby improving the spatial representation of precipitation and reducing systematic biases associated with coarse-resolution products. For production attribution and projection， long-term yield trends are first separated from interannual climate variability using historical production records. The resulting detrended yield anomalies are then modeled using a hybrid linear-regression and feedforward neural network (FNN) approach driven by the climate-stress indicators. This formulation provides a quantitative linkage between projected climate stressors and both yield response and total rice production， while allowing the relative influence of individual stressors to be examined. Historical decomposition indicates that approximately 91% of the increase in MRD rice production from 1995 to 2024 was attributable to rising yield rather than expansion of cultivated area， highlighting yield improvement as the dominant historical driver of production growth. Future projections under the Shared Socioeconomic Pathways (SSPs) indicate that reproductive-stage heat stress will become the dominant exposure signal. The number of critical heat-stress days is projected to increase from approximately 27 days per year historically to 68 days under SSP2-4.5 (middle of the road) and 107 days under SSP5-8.5 (fossil-fueled development) by the late twenty-first century. Meanwhile， drought conditions in the inland delta remain broadly near their historical range， whereas rainfall deficits intensify along the coastal belt， indicating increasingly divergent hydroclimatic trajectories across the region. At the delta-wide scale， however， the projected change in total production remains relatively modest. Production is projected to peak at approximately 27.5 million tons around 2035 before gradually declining to about 26.5 million tons by 2075， with limited separation between the moderate- and high-emission scenarios. This aggregate stability masks substantial spatial and seasonal redistribution. At the provincial and seasonal scales， Summer–Autumn rice production declines across most provinces， with the strongest reductions projected along the southwestern coastal belt， particularly in Ca Mau， Bac Lieu， and Soc Trang. In contrast， the inland Winter–Spring crop remains broadly stable or improves. The resulting pattern therefore represents a redistribution of production across seasons and locations rather than a uniform decline across the delta. Among the climate stressors， the upstream flood signal and heavy precipitation exhibit the clearest negative associations with detrended yield， providing the strongest evidence for stressor-specific attribution within the modeling framework. The spatial distribution of risk is likewise highly concentrated. The freshwater-dominated inland core retains comparatively high resilience， whereas the coastal belt experiences compound heat and salinity stress together with an approximately 12% reduction in cultivated area. Consequently， coastal rice production emerges as the most climate-exposed component of the MRD production system. Although the individual datasets and analytical components employed in this study are established， the principal contribution lies in their integration into a coherent， end-to-end assessment architecture. Built entirely upon openly accessible Earth observation data and coupled with climate-model projections， the framework links remotely sensed crop phenology， vegetation and salinity conditions， flood dynamics， cultivated area， climate-stress indicators， and production responses within a common spatially explicit framework. This integration addresses a fundamental challenge in climate-impact assessment: reconciling the spatial detail of observation with the forward-looking capacity of climate projection. The resulting province- and season-resolved evidence provides a scalable basis for identifying emerging climate hotspots， diagnosing the dominant drivers of production vulnerability， and supporting targeted food-security and climate-adaptation planning across the Mekong River Delta.
