This study introduces a novel satellite image digital surface model (DSM) reconstruction framework grounded in deep learning methodology. The proposed framework effectively utilizes a rational polynomial camera (RPC) model to establish the mapping relationship between image coordinates and geographic coordinates. Given the expansive coverage and abundant ground object data inherent in satellite images, we designed a lightweight deep network model. This model facilitates both coarse and fine estimation of a height map through two distinct stages. Our approach harnesses shallow and deep image information via a feature extraction module, subsequently employing RPC Warping to construct feature volumes for various angles. We employ variance as a similarity metric to achieve image matching and derive the fused cost volume. Following this, we aggregate cost information across different scales and height directions using a regularization module. This process yields the confidence level of the current height plane, which is then regressed to predict the height map. Once the height map from stage 1 is obtained, we gauge the prediction’s uncertainty based on the variance in the probability distribution in the height direction. This allows us to adjust the height estimation range according to this uncertainty, thereby enabling precise height value prediction in stage 2. After conducting geometric consistency detection filtering of fine height maps from diverse viewpoints, we generate 3D point clouds through the inverse projection of RPC models. Finally, we resample these 3D point clouds to produce high-precision DSM products. By analyzing the results of our method’s height map predictions and comparing them with existing deep learning-based reconstruction methods, we assess the DSM reconstruction performance of our proposed framework. The experimental findings underscore the robustness of our method against discontinuous regions, occlusions, uneven illumination areas in satellite imagery, and weak texture regions during height map generation. Furthermore, the reconstructed digital surface model (DSM) surpasses existing solutions in terms of completeness and root mean square error metrics while concurrently reducing the model parameters by 42.93%. This optimization markedly diminishes memory usage, thereby conserving both software and hardware resources as well as system overhead. Such savings pave the way for a more efficient system design and development process.

An anamorphic telescope has different magnifications in the tangential and sagittal directions, and it can be applied to atmospheric remote sensing satellites to effectively improve the spectral sampling rate. However, the initial structure of an anamorphic system is rare and its optimization requires extensive experience, which brings a challenge to the design of anamorphic telescopes. In this study, we propose a design method that is effective in obtaining the initial structure of an anamorphic system and discuss the conversion relationship between the Biconic surface and the XY polynomial surface. The XY polynomial provides design capabilities with an ultrawide field of view (FOV). With this insight, an initial anamorphic system with XY polynomial surfaces is constructed as a good starting point for further ultrawide FOV optimization. Consequently, an off-axis freeform anamorphic telescope with a focal length of 34 mm × 68 mm, and an ultrawide FOV of 110° × 0.24° is designed as an example. The telescope is a Gregorian structure with two concave mirrors as anamorphic elements, and the simulated design exhibits excellent performance. The method provided in this study facilitates the design of remote sensing instruments.

Hyperspectral detection of the change rate of organic matter content in agricultural remote sensing requires a high signal-to-noise ratio (SNR). However, due to the large number and efficiency limitation of the components, it is difficult to improve the SNR. This study uses high-efficiency convex grating with a diffraction efficiency exceeding 50% across the 360–850 nm range, a back-illuminated Complementary Metal Oxide Semiconductor (CMOS) detector with a 95% efficiency in peak wavelength, and silver-coated mirrors to develop an imaging spectrometer for detecting soil organic matter (SOM). The designed system meets the spectral resolution of 10 nm in the 360–850 nm range and achieves a swath of 100 km and a spatial resolution of 100 m at an orbital height of 648.2 km. This study also uses the basic structure of Offner with fewer components in the design and sets the mirrors of the Offner structure to have the same sphere, which can achieve the rapid adjustment of the co-standard. This study performs a theoretical analysis of the developed Offner imaging spectrometer based on the classical Rowland circular structure, with a 21.8 mm slit length; simulates its capacity for suppressing the +2nd-order diffraction stray light with the filter; and analyzes the imaging quality after meeting the tolerance requirements, which is combined with the surface shape characteristics of the high-efficiency grating. After this test, the grating has a diffraction efficiency above 50%, and the silver-coated mirrors have a reflection value above 95% on average. Finally, the laboratory tests show that the SNR over the waveband exceeds 300 and reaches 800 at 550 nm, which is higher than some current instruments in orbit for soil observation. The proposed imaging spectrometer has a spectral resolution of 10 nm, and its modulation transfer function (MTF) is greater than 0.23 at the Nyquist frequency, making it suitable for remote sensing observation of SOM change rate. The manufacture of such a high-efficiency broadband grating and the development of the proposed instrument with high energy transmission efficiency can provide a feasible technical solution for observing faint targets with a high SNR.

TanSat-2 is the successor to TanSat, China's first dedicated carbon monitoring satellite. Compared to TanSat, TanSat-2 features improved designs with enhanced spatial and temporal coverage, a wider range of measurements, and higher measuring accuracy. TanSat-2 achieves near-daily global coverage by using a medium/medium-low earth orbit (M/MLEO) and large swath design, allowing it to monitor most land areas daily. This M/MLEO elliptical orbit intersects with the Van Allen radiation belts, requiring special radiation analysis and protection designs. TanSat-2 also features higher retrieval accu racy and carbon-pollution collaborative monitoring to identify anthropogenic emissions accurately. TanSat-2 carries three key spectrometers: (1) the Ultra-wide-field Carbon Pollution collab orative monitoring Instrument (UCPI), which provides a 2900 km swath and retrieval accuracy for XCO₂ (1 ppm), XCH₄ (8 ppb), NO2 (1.3×10¹⁵ molec cm⁻²) and SIF (0.3 mW m⁻² Sr⁻¹ nm⁻¹); (2) the Hotspot Greenhouse gas Emission Tracker, with a 500-m nadir resolution, designed for tracking carbon emission hotspots; and (3) the Cloud Aerosol Polarization Imager (CAPI). Additionally, TanSat-2 is also equipped with the On board Intelligent Hotspot Extraction and Distribution Instrument (OHEDI), providing high-performance computing capabilities and Beidou short-message capabilities for near-real-time hotspot detection and distribution. The high-precision global observation from TanSat-2 is expected to provide essential data for global assimilation systems to assess global anthropogenic emissions for global carbon stock and other scientific purposes. The near-real time hotspot detection and distribution capability is also expected to be combined with other space-ground observation programs.

With the development of satellite observation technology, higher resolution and shorter return cycle have also placed higher demands on satellite data processing. The non-tide high-frequency barotropic oscillation in the marginal sea produces large aliasing errors in satellite altimeter observations. In previous studies, the satellite altimeter aliasing correction generally relied on a few bottom pressure data or the model data. Here, we employed the high-frequency tide gauge data to extract the altimeter non-tide aliasing correction in the west Mediterranean Sea. The spatial average method and EOF analysis method were adopted to track the high-frequency oscillation signals from 15 tide gauge records (TGs), and then were used to correct the aliasing errors in the Jason-1 and Envisat observations. The results showed that the EOF analysis method is better than the spatial average method in the altimeter data correction. After EOF correction, 90% of correlation (COR) between TG and sea level of Jason-1 has increased ~5%, and ~3% increase for the Envisat sea level; for the spatial average correction method, only ~70% of Jason-1 and Envisat data at the TGs location has about 2% increase in correlation. The EOF correction reduced the average percentage of error variance (PEL) by ~30%, while the spatial average correction increased the average percentage of PEL by ~20%. After correction by the EOF method, the altimeter observations are more consistent with the distribution of strong currents and eddies in the west Mediterranean Sea. The results prove that the proposed EOF method is more effective and accurate for the non-tide aliasing correction.

Soil monitoring is fundamental for maintaining global soil health, ensuring food security, and achieving sustainable development. While satellite platforms provide invaluable tools for this purpose, the accuracy of soil monitoring heavily relies on the appropriate design of their remote sensing payload parameters. This study focuses on enhancing the accuracy of satellite-based global soil monitoring. Key physicochemical soil parameters—including total nitrogen (TN), soil organic matter (SOM), total salt content (TSS), moisture content (MC), and clay fraction (Clay)—were analyzed. A full-chain analytical validation model integrating “instrument–radiative transfer–soil parameter inversion” was developed. Using spectral measurements and soil sample analyses from the black soil region of Northeast China, the spectral response characteristics of core soil parameters were simulated and cross-validated under varying spectral resolutions and integration times. Results indicate that, under specific parameter configurations, the ‘Linshi-1’ satellite achieved robust TN inversion accuracy with R² > 0.65. SOM consistently exhibited good inversion performance, with RMSE ranging between 5.04 and 5.76 g/kg across various spectral treatments (all < 6 g/kg). TSS inversion demonstrated strong stability, maintaining an RMSE of approximately 0.43–0.44 g/kg at resampled spectral resolutions≥10 nm (corresponding to an SNR > 263). MC inversion accuracy was sensitive to both spectral resolution and regional variations, requiring a resampled resolution below 10 nm for consistently high accuracy. Clay inversion required the highest resolution, achieving an RMSE of less than 6 g/kg only at resampled resolutions of 1 nm or 2 nm (SNR approximately 150–210). These findings guided the design of the ‘Linshi-1’ black soil monitoring satellite system and its hyperspectral payload prototype. This effort establishes a solid theoretical and methodological foundation for future deployment, providing crucial space-based support for China’s black soil resource management and sustainable utilization.