Urban Agglomeration (UA) is regarded as an emerging complex urban system in China. The development of UA demands a reasonable scale structure, which can be investigated by Zipf’s law. However, few studies have been conducted to quantify the optimal scale of UA and how its development deviates from the optimal scale. With the continuous urban expansion, the problem of UAs’ scale structure has received increasing attention. In this study, we propose a method based on Zipf’s law for estimating the theoretical optimal scale of UAs in China and assessing the deviation rate from their optimal scales. Twelve typical UAs in China are selected, and their development is assessed via urban impervious surface data from 2000 to 2018. The results show that the average deviation rate of the investigated UAs decreased from 3.40% in 2000 to 2.32% in 2018, demonstrating that these UAs are on a positive evolution trajectory. Furthermore, according to the development stage, we make recommendations on “large cities vs. medium/small-sized cities and promoting vs. restraining” to each UA based on its size. The conceptual and analytical knowledge, as well as the results from this study, are expected to offer valuable insights and new references for regulating and managing UAs’ development in China.

With the advancement of China's satellite technology, the HuanJingJianZai-2 A/B (HJ-2 A/B) satellites, equipped with whisk-broom infrared sensors, represent a significant leap forward in environmental monitoring and Earth observation capabilities. This technological leap, however, introduces new challenges in calibration. The unique structure of the HJ-2 A/B infrared spectroradiometer (IRS) necessitates innovative calibration techniques, as traditional methods primarily focused on exterior orientation parameters (EOPs) and often overlooked the importance of interior orientation accuracy, which is essential for accurate multispectral band registration and color rendering. Addressing this gap, we introduce an innovative multi-focal-plane-array joint calibration method specifically designed for whisk-broom cameras. Our method involves selecting a master band from each focal plane array for accurate focal length calibration and deriving ground control points from image matching and altitude interpolation for comprehensive bundle adjustment. This adjustment refines EOPs and interior orientation parameters (IOPs), ensuring globally optimal EOPs and enhanced IOPs calibration stability. The application of our method to the HJ-2 A/B IRS yielded substantial improvements in georeferencing and band registration accuracies, surpassing traditional methods. This paper details the multi-focal-plane-array joint calibration method, describes the IRS and experimental setup, presents the experimental results, and concludes with the implications and potential applications of our findings.

The exterior orientation parameters (EOPs) provided by the self-developed position and orientation system (POS) of the first Chinese airborne three-line scanner mapping system, AMS-3000, are impacted by jitter, resulting in waveform distortions in rectified images. This study introduces a Gaussian Markov EOP refinement method enhanced by cubic spline interpolation to mitigate stochastic jitter errors. Our method first projects tri-view images onto a mean elevation plane using POS-provided EOPs to generate Level 1 images for dense matching. Matched points are then back-projected to the original Level 0 images for the bundle adjustment based on the Gaussian Markov model. Finally, cubic spline interpolation is employed to obtain EOPs for lines without observations. Experimental comparisons with the piecewise polynomial model (PPM) and Lagrange interpolation model (LIM) demonstrate that our method outperformed these models in terms of geo-referencing accuracy, EOP refinement metric, and visual performance. Specifically, the line fitting accuracies of four linear features on Level 1 images were evaluated to assess EOP refinement performance. The refinement performance of our method showed improvements of 50%, 45.1%, 29.9%, and 44.6% over the LIM, and 12.9%, 69.2%, 69.6%, and 49.3% over the PPM. Additionally, our method exhibited the best visual performance on these linear features.