人工智能理论与技术，特别是深度学习，展现出桥接宏观与微观认知的巨大潜力，为传统遥感范式中的跨尺度同构映射和高保真信息处理开辟了创新路径，从而推动经典物理遥感向量子遥感范式的深刻转型。本文提出一种基于深度学习的遥感参数反演新范式理论，其核心在于将深度学习的多层神经元结构视作微观世界的“量子积分传输单元”，从而有机耦合物理辐射传输过程与高维统计测度，实现量子级涨落与宏观观测的无缝桥接。逻辑分析显示，该范式以硅基计算的指数级信息粒度细化为基石，打破传统手工设计坐标系的约束，实现通用坐标系的自动生成，并逐步逼近自然界本征粒度水平，以最小化能量信息损失。进一步而言，深度学习实现了从量子相互作用到宏观理解的连续衔接，其信息处理分辨率随计算能力的提升而逐步逼近自然界本征水平，并在跨尺度映射中显著降低失真。计算能力和能源将成为核心生产要素，而深度学习作为“通用坐标系生成器”，在20世纪末至21世纪初经历了质变加速。这一范式理论标志着人类认知史与数学史中延续近三千年的“坐标系构建运动”的里程碑式突破，正式开启“量子”坐标系和量子遥感纪元。展望未来，软硬件的协同演进将促使深度学习孕育出多粒度新型“量子”语言，成为人类集体认知的新感官与新大脑，对遥感技术创新以及农业气象监测等领域产生深远影响。

Artificial intelligence theories and technologies, particularly deep learning, demonstrate immense potential in bridging macroscopic and microscopic cognition, opening innovative pathways for cross-scale isomorphic mapping and high-fidelity information processing in traditional remote sensing paradigms, thereby propelling the profound transformation from classical physical remote sensing to quantum remote sensing paradigms. This paper proposes a new paradigm theory for remote sensing parameter inversion based on deep learning, with its core being to regard the multi-layer neuron structure of deep learning as "quantum integral transmission units" in the microscopic world, thereby organically coupling the physical radiative transfer process with high-dimensional statistical measures, achieving seamless bridging between quantum-level fluctuations and macroscopic observations. Logical analysis shows that this paradigm is based on the exponential refinement of information granularity in silicon-based computing, breaking the constraints of traditional manual coordinate system design, realizing the automatic generation of universal coordinate systems, and gradually approaching the intrinsic granularity level of nature to minimize energy information loss. Furthermore, deep learning achieves a continuous connection from quantum interactions to macroscopic understanding, with its information processing resolution gradually approaching the intrinsic level of nature as computing power increases, and significantly reducing distortion in cross-scale mapping. Computing power and energy will become core production factors, while deep learning, as a "universal coordinate system generator," experienced qualitative acceleration from the late 20th century to the early 21st century. This paradigm theory marks a milestone breakthrough in the "coordinate system construction movement" that has lasted nearly three thousand years in human cognitive history and mathematical history, formally ushering in the quantum remote sensing era. Looking to the future, the collaborative evolution of software and hardware will prompt deep learning to nurture a multi-granularity new "quantum" language, becoming a new sensory organ and new brain for human collective cognition, producing profound impacts on remote sensing technology innovation and fields such as agricultural meteorological monitoring.