Digital twin of remote sensing experiment field：Theory and key technology

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. © 2023 National Remote Sensing Bulletin. All rights reserved.