The precise positioning of dynamic and static objects such as vehicles and pedestrians is a key technology. A global navigation satellite system signal is the primary signal source required to achieve precise positioning, and the optimal estimation method used in precise positioning is Kalman filtering (KF). Standard KF can only achieve optimal estimation results under the conditions that the mathematical model has been determined and the noise characteristics are known. However, when there are measurement outliers and prior noise information deviations, the accuracy of KF cannot be guaranteed. To effectively solve the above problems, a combined adaptive robust Kalman filter (CARKF) algorithm is proposed. First, the influence of measurement outliers on KF accuracy is resisted using the robust Kalman filter method. Then, the influence of an anomalous innovation on its autocorrelation sequence and cross-correlation sequence is resisted by the robust estimator for correlated observations based on the IGGIII method, to ensure the correct estimation of the correlation function sequence of the filtered innovation statistics. Finally, the autocovariance least-squares method is used to eliminate the coupling effect between noise covariances and accurately estimate the unknown noise covariance information. Moreover, an iterative strategy is adopted to eliminate the effect of errors caused by prior noise covariance deviations. The results of the three experiments show that the CARKF method can not only effectively resist the influence of measurement outliers on filtering accuracy and noise covariance valuation but also overcome the influence of errors caused by prior noise deviations and accurately simultaneously estimate two kinds of unknown noise covariance information.

The lack of ground control points (GCPs) affects the elevation accuracy of digital surface models (DSMs) generated by optical satellite stereo images and limits the application of high-resolution DSMs. It is a feasible idea to use ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) laser altimetry data to improve the elevation accuracy of optical stereo images, but it is necessary to accurately match the two types of data. This paper proposes a DSM registration strategy based on terrain similarity (BOTS), which integrates ICESat-2 laser altimetry data without GCPs and improves the DSM elevation accuracy generation from optical satellite stereo pairs. Under different terrain conditions, Worldview-2, SV-1, GF-7, and ZY-3 stereo pairs were used to verify the effectiveness of this method. The experimental results show that the BOTS method proposed in this paper is more robust when there are a large number of abnormal points in the ICESat-2 data or there is a large elevation gap between DSMs. After fusion of ICESat-2 data, the DSM elevation accuracy extracted from the satellite stereo pair is improved by 73~92%, and the root mean square error (RMSE) of Worldview-2 DSM reaches 0.71 m.

Atmospheric refraction is an important factor that affects the positioning accuracy of optical high-resolution satellite imaging. Current calculation methods do not consider regional change characteristics in the atmosphere; the atmospheric division model is rough and does not conform to reality. This article studies atmospheric refraction in optical remote sensing satellite observations, provides a formula for calculating the strict refraction of a multilayer atmosphere, and shows a new method for calculating atmospheric refraction error. We use a ray-tracing method to calculate the light propagation path in the atmosphere and continuously layer the atmosphere according to the height difference of 100 m. Different from general calculation methods, we prove that a regional atmospheric model can calculate the atmospheric refraction index more accurately than using the empirical model. As the off-nadir angle gradually increases, the calculation results in this article are better than the current commonly used methods. We use WorldView-2 images of the Qinghai–Tibet Plateau region in China for the experiments. When the off-nadir angle is less than 32°, the positioning accuracy improves by 6–47%. Compared with the standard atmospheric model, the regional atmospheric model improves positioning accuracy by 2–18%. This method reflects the continuous variation in the atmospheric refractive index with the vertical distribution of the atmosphere and amended regional meteorological conditions. Model errors caused by overly simple atmospheric division are avoided, andthe positioning accuracy of optical remote sensing images is increased.

Traditional methods for acquiring high-resolution Digital Elevation Models (DEMs) are costly and laborious. Deep learning-based image super-resolution (SR) offers a promising alternative, but requires substantial training data. High-resolution DEMs, however, are often scarcer than satellite images at the same resolution. Recognizing the strong correlation between DEM grayscale images and high-resolution satellite imagery, we propose a novel method called EMASA-SR: Enhanced DEM Image super-resolution Reconstruction using Texture Transfer. It leverages texture information from satellite images to enhance the resolution of low-resolution DEMs. We address limitations of existing texture transfer methods by integrating a pyramid pooling module (PPM) and selective kernel convolution (SKC) into the network. PPM strengthens feature extraction for complex terrain objects, while SKC minimizes texture loss and feature confusion. Our experiments used 10 m Sentinel-2 remote sensing images and AW3D30 DEM data to upscale 30 m DEMs to 10 m resolution. Validation with ground-truth elevation data and ICESat-2 laser altimetry data revealed significant improvements. Compared to the original DEM, EMASA-SR achieved a 21.42%-37.44% reduction in elevation RMSE and a 23.30%-38.99% decrease in MAE. Moreover, it outperformed other super-resolution methods, achieving a 2.87%-28.27% reduction in RMSE and a 7.83%-30.04/% decrease in MAE.

This paper collects the nighttime light data and six control variables of 77 cities in the eastern China region from 2000 to 2020 and analyzes the spatio-temporal changes of urban sprawl and carbon emissions in the eastern China region. Using the baseline regression model, the correlation and influencing factors between urban sprawl and carbon emissions are explored. The results show that although the impact of urban sprawl on carbon emissions in the eastern China region is positive, it is the result of a trade-off of various factors. Different factors have different impacts on carbon emissions, and urban expansion of different city sizes also has different impacts on carbon emissions in the eastern China region. This paper uses nighttime light data to measure the urban sprawl index more finely, directly explores its impact on carbon emissions from the perspective of urban sprawl and conducts an in-depth analysis of multiple external factors and different city types, providing references for decision-makers to construct sustainable low-carbon city development models and low-carbon city planning systems from different dimensions.