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Reliable estimation of carbon stocks and their temporal and spatial changes is essential for the formulation of climate policy, ecosystem management, carbon accounting, and carbon-credit assessment. Conventional field measurements provide detailed information but are constrained by sampling requirements, cost, labour, spatial heterogeneity, and limitations in continuous monitoring. Remote sensing provides a complementary approach by enabling spatially continuous observations of vegetation characteristics, land-cover change, biomass, and other carbon-related variables across different spatial and temporal scales. Advances in multispectral, hyperspectral, synthetic aperture radar, LiDAR, thermal, and high-resolution satellite observations have extended the capability for carbon estimation. Their integration with geographic information systems, field observations, statistical techniques, and machine-learning algorithms enables precise spatial assessment of above-ground biomass, below-ground biomass, soil organic carbon, forest carbon, agricultural carbon, mangrove carbon, and blue-carbon resources. However, uncertainties associated with sensor characteristics, field-data quality, model selection, spatial variability, temporal dynamics, and scaling remain acknowledged limitations. Converting spatial carbon estimates into carbon credits requires consideration of baseline conditions, additionality, permanence, leakage, uncertainty, verification, and measurement, reporting, and verification requirements. This review critically examines remote sensing technologies, carbon-estimation models, spatial mapping approaches, machine-learning techniques, carbon-accounting frameworks, and their integration into carbon-credit assessment. It places particular emphasis on multisource data fusion, emerging Earth-observation technologies, artificial intelligence, uncertainty assessment, and digital MRV systems. Major methodological, technological, and policy gaps are identified, along with opportunities for developing transparent, spatially explicit, scientifically robust, and scalable carbon-credit assessment frameworks.","碳固存是减缓气候变化的基础，因为陆地和水生生态系统将碳储存在植被、生物量、土壤和地壳中。可靠估算碳储量及其时空变化，对于制定气候政策、生态系统管理、碳核算和碳信用评估至关重要。传统野外测量可提供详细信息，但受采样要求、成本、劳动力、空间异质性以及连续监测局限性等因素制约。遥感提供了一种互补方法，能够在不同时空尺度上对植被特征、土地覆盖变化、生物量及其他碳相关变量进行空间连续观测。多光谱、高光谱、合成孔径雷达、激光雷达、热红外和高分辨率卫星观测的进步，拓展了碳估算能力。这些技术与地理信息系统、野外观测、统计技术和机器学习算法的集成，使得对地上生物量、地下生物量、土壤有机碳、森林碳、农业碳、红树林碳和蓝碳资源进行精确空间评估成为可能。然而，与传感器特性、野外数据质量、模型选择、空间变异性、时间动态和尺度转换相关的不确定性仍是公认的局限。将空间碳估算转化为碳信用，需要考虑基线条件、额外性、永久性、泄漏、不确定性、核查以及测量、报告和核查要求。本综述批判性地审视了遥感技术、碳估算模型、空间制图方法、机器学习技术、碳核算框架及其在碳信用评估中的集成。特别强调了多源数据融合、新兴地球观测技术、人工智能、不确定性评估和数字化MRV系统。识别了主要的方法学、技术和政策差距，以及开发透明、空间明确、科学稳健且可扩展的碳信用评估框架的机遇。","Journal of Geography Environment and Earth Science International","2026-09-25T00:00:00Z","论文",false,{"impact":416,"substance":361,"depth":583,"authority":416,"freshness":584,"relevant":455,"comment":585},16,9,"系统综述遥感碳汇估算与碳信用评估的方法、不确定性与数字MRV进展，对农业碳汇与碳交易信息化有参考价值，但属综述类论文，公共影响有限。",[587],{"name":578,"url":575},[589,590,591,592,593],"机器学习","遥感","碳汇","碳信用","数字MRV",{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},[596],{"code":160,"slug":161,"name":162,"path":163,"parent_code":128},{"status":598,"locked":581,"version":4,"method":599,"evidence":600},"classified","rules",{"candidates":601,"agricultural_evidence":603},[602,610,617,623,629,635],{"code":32,"score":361,"supported":603,"title":604,"abstract":606,"tags":609},true,[605],"remote sensing",[590,605,607,608],"multispectral","hyperspectral",[590],{"code":160,"score":611,"supported":603,"title":612,"abstract":614,"tags":616},15,[613],"carbon sequestration",[615,613],"农业碳",[591],{"code":24,"score":618,"supported":603,"title":619,"abstract":620,"tags":622},7,[],[589,621],"artificial intelligence",[589],{"code":144,"score":624,"supported":603,"title":625,"abstract":626,"tags":628},6,[],[627,145],"土壤",[],{"code":40,"score":624,"supported":603,"title":630,"abstract":631,"tags":634},[],[632,633],"地理信息","geographic information",[],{"code":184,"score":636,"supported":603,"title":637,"abstract":638,"tags":640},3,[],[639],"气候变化",[],[642,643],"遥感 碳汇 估算","碳信用 评估 遥感","遥感碳汇估算-3554","10.9734\u002Fjgeesi\u002F2026\u002Fv30i91125",{"doi":645,"openalex_id":647,"authors":648,"venue":578,"cited_by_count":424,"oa_url":575,"card":658,"direction":664,"ingested_from":665},"W7214392294",[649,651,654,656],{"name":650,"orcid":11},"Aishwarya Desai",{"name":652,"orcid":653},"Himalaya Ganachari","https:\u002F\u002Forcid.org\u002F0000-0002-6157-9242",{"name":655,"orcid":11},"Sangita Shinde",{"name":657,"orcid":11},"Sachinkumar Nandgude",{"tldr":659,"method":660,"finding":661,"direction":662,"opportunity":663},"综述遥感碳储量估算技术及其在碳信用评估中的应用与挑战。","综述多光谱、高光谱、SAR、LiDAR等遥感与GIS、机器学习融合方法。","遥感可空间连续估算多类碳库，但不确定性及MRV要求制约碳信用转化。","农业绿色发展与碳","可研究多源数据融合与AI驱动的数字MRV，降低碳信用评估不确定性。","农业遥感与作物表型","openalex","2026-09-26T23:30:30.002197Z",{"technologies":668,"keywords":679},[669,670,672,674,676,678],{"name":590,"count":636},{"name":671,"count":69},"区块链",{"name":673,"count":69},"遥感监测",{"name":675,"count":455},"农产品溯源",{"name":677,"count":455},"地理信息系统",{"name":589,"count":455},[680,682,684,686,688,690,692,694,696,698,700,702],{"name":681,"count":345},"农业碳排放",{"name":683,"count":524},"数字乡村",{"name":685,"count":584},"智慧农业",{"name":687,"count":624},"低碳农业",{"name":689,"count":400},"农业碳减排",{"name":691,"count":400},"土壤固碳",{"name":693,"count":400},"数字农业",{"name":695,"count":636},"农业数字化",{"name":697,"count":636},"农业碳汇",{"name":699,"count":636},"农业绿色发展",{"name":701,"count":636},"碳交易",{"name":703,"count":636},"碳足迹"]