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On-board processing has become an inevitable choice for the development of remote-sensing satellites. For the satellites with dual-line array asynchronous push-broom mode, image registration is crucial for pixel localization and alignment between dual-band strip data. However, due to the high computational complexity of feature detection and feature extraction, it is still a challenge to realize real-time registration for on-board edge devices with limited computing power and power consumption. Therefore, this article proposes a hardware-optimized architecture with high-performance and low-energy for on-board registration based on “ARM+FPGA”. On one hand, optimized methods for the image registration algorithm are proposed from both software and hardware perspectives, providing a solution for on-board registration. On the other hand, in the face of the current interface environment of satellites, a design scheme for versatile hardware architecture is proposed. It provides a foundational technical route for hardware deployment of on-board processing. Experimental results show that in this hardware-accelerated architecture, the average acceleration effect of registration algorithm can reach 15 times compared to the unoptimized version. In addition, the average power consumption is reduced by 60%, and the hardware resource utilization is less than 40%. Importantly, the algorithm's accuracy remains unaffected by these optimizations. The on-board intelligent processing payload deployed with this hardware architecture has been successfully launched and validated in February 2022 in the orbit of the MN200Sar-1 satellite from China. It improves the real-time capability for on-board processing, and aims to achieve a new imaging mechanism where target and information transmission replace strip image transmission.

This is the first study to inverse leaf area index (LAI) with photon counting LiDAR (i.e., Ice, Cloud, and Land Elevation Satellite-2/Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS). A new framework composed of three major steps was applied: noise removal algorithm, photon classification algorithm, and the LAI estimation model. To evaluate the feasibility and accuracy of ICESat-2 derived LAI, the MODIS (Moderate Resolution Imaging Spectroradiometer) LAI product at the spatial resolution of 500-m over two sites (Amazon rainforest and Daxing’an mountains forests) were selected for comparison purpose. The results show satisfactory agreements (Amazon: R=0.693, RMSE=2.545; Daxing’an: R=0.626, RMSE=1.893; Two sites together: R=0.667, RMSE=2.433) between MODIS LAI and ICESat-2 derived LAI. Moreover, we also selected the LAI derived from Sentinel-2 to further validate the ICESat-2 derived LAI, and got better results than MODIS LAI (Amazon: R=0.752, RMSE=2.329; Daxing’an: R=0.704, RMSE=1.724). Our study found that: (1) The new applied framework can effectively inverse LAI with ICESat-2. (2) The ICESat-2 derived LAI are reliable. (3) ICESat-2 exists many advantages over MODIS in terms of LAI estimation, for example, it not only can alleviate the issue of saturation of MODIS when LAI is high, but also can mitigate the problem of poor inversion effect of MODIS where the vegetation types are diversity.

The high-precision geometric positioning of optical remote sensing satellites is the prerequisite to determine the application capability of satellite image products. Its positioning accuracy is related to the observation accuracy of each link in the imaging process, including satellite attitude, orbit measurement accuracy, time synchronization accuracy, camera measurement accuracy, and so on. Untimely and inaccurate on-orbit calibration will lead to great geometric positioning errors. To optimize the positioning accuracy of satellite images with the rational function model (RFM) under low positioning accuracy, our paper proposes an improved geometric quality model based on the reorientation of internal and external orientation elements in the RFM model of remote sensing images. By establishing the rational function positioning model, the external orientation model, and the internal orientation model, the original image can be reorientated. Then, we use the improved model to generate uniformly distributed virtual ground control points. By analyzing and verifying the relationship between each rational polynomial coefficient (RPC) and its influence on geometric positioning accuracy, we propose an RPC coefficients optimization method based on image offset correction and positioning dominant coefficients. Finally, we use the small satellite “MN200Sar-1” with low geometric accuracy for experimental verification. The results show that the model can effectively eliminate the errors of internal and external elements in the on-orbit calibration, and the positioning accuracy is improved from one hundred pixels to one pixel. At the same time, the rational polynomial dominant coefficient optimization method can improve geometric positioning accuracy without introducing additional compensation parameters.