【摘要】 遥感机理模型构建、地表参数反演、遥感产品生产以及真实性检验等均离不开完备的地面先验知识支持，然而目前实验观测、模型模拟等均难以满足观测的完备性需求。目前，基于遥感实验场的数字孪生体生成遥感先验知识以支持遥感基础研究的思路逐渐成熟：突破物理遥感实验场的协同观测技术瓶颈，实现场景三维结构的数字重建；耦合辐射传输、能量平衡和植物生长模型，实现遥感实验场模拟的动态演进；基于物理遥感实验场数字孪生体驱动和约束地表观测数据，通过同化观测与模拟数据反馈优化机理模型，生成精度高、时间连续的完备观测数据集作为先验知识，以支撑遥感机理模型、遥感反演方法和真实性检验等研究。遥感实验场数字孪生体构建方法有望成为小尺度地球系统数字孪生体构建理论的雏形，进而推动地球科学各个学科全面、协同发展。更多还原

【Abstract】 In the context of remote sensing research and application, complete and reliable “ground a priori knowledge” datasets play an essential role in physical-based model construction, land surface parameter inversion, and remote sensing product production and validation.Ill-posed inversion problems, such as the case in which the observation information is less than the inversion target parameters that results in underdetermined inversion parameters, lead to uncertainty in the solution. A priori knowledge is an important support to solving the ill-posed problem of parameter inversion based on physical and empirical models. However, its completeness, accuracy, and timeliness are limited.The traditional methods of obtaining ground a priori knowledge include experimental measurements using various surface/near-surface sensors and numerical simulations using many physical models, such as one-or three-dimensional radiative transfer models. These current methods have their own advantages and disadvantages but cannot meet the need of comprehensive dataset production in spectral, temporal,angular, and spatial aspects for supporting the research and development of remote sensing science and technology when used alone. On the basis of the studies on experimental measurement, modeling of radiative transfer and ecological processes, and land surface parameter inversion and validation, we propose an innovative strategy to support remote sensing research by building a digital twin of the remote sensing experimental field. Several steps are designed for generating remote sensing a priori knowledge on the basis of the digital twin of the remote sensing experimental field. The three-dimensional structure of a scene is digitally reproduced from the surface by multiple experimental measurements of structural descriptors or the near-surface by remotely obtained data, such as the high-resolution visible and near-infrared images and light detection and ranging(lidar) point-clouds from the observation on Unmanned Aerial Vehicle(UAV) or other platforms based on a cooperative observation technology, recorded in a format accessible by simulation models. The systemic evolution of the surface simulations of physical processes can be realized by coupling radiative transfer, energy balance, evapotranspiration, and plant growth modeling theories as a synthesized model and applying the model to an experimental site in virtual space to illuminate and realize the dynamic progression of the remote sensing experiment field. Driven and constrained by the surface/near-surface collaborative observation data processed by data science and statistical methods, such as data fusion and data augmentation, the synthesized model is optimized by the feedback from the data assimilation of observation measurements and corresponding simulation data, increasing the consistency of the simulation results with the actual dynamic evolution of the remote sensing experimental field in the real world. Through the optimized model and the field measurements, a complete and coherent a priori knowledge of the remote sensing experimental field is achieved with high numerical precision and temporal continuity, supporting the development of remote sensing mechanism model construction and remote sensing inversion method and validation and improving the level of basic remote sensing research. The conditions for the development and application of digital twins in remote sensing experimental sites are gradually maturing. The construction of the remote sensing experimental field digital twin is expected to become the rudiment of digital twin construction theory of a small-scale ecosystem, which may in turn promote the comprehensive and collaborative development of various disciplines in geoscience.更多还原