The implementation of the United Nations (UN) 2030 Agenda for Sustainable Development (2030 Agenda), with its 17 sustainable development goals (SDGs), faces challenges such as insufficient data, limited research methodologies, and uneven progress across regions. Earth observation (EO), particularly scientific satellites, offers unique advantages in supporting global sustainable development by providing objective, dynamic, and large-scale datasets for SDG evaluations and policymaking, as well as by facilitating the study of Earth’s environmental systems and their interactions with human activity. Sustainable Development Science Satellite 1 (SDGSAT-1), the world’s first scientific satellite dedicated to supporting the 2030 Agenda, was designed and developed by the International Research Center of Big Data for Sustainable Development Goals (CBAS). Its three advanced EO sensors, i.e., Thermal Infrared Spectrometer (TIS), Glimmer Imager (GLI), and Multispectral Imager (MSI), furnish high-quality data, enabling continuous monitoring of human activity and environmental changes to bolster SDG-related research and global sustainability initiatives. As of November 2025, SDGSAT-1 has collected over 480 000 global terrain coverage images since its launch in November 2021. All its datasets have been shared free of charge with the Global Scientific Community through the SDGSAT-1 Open Science Program initiated in September 2022. The datasets have enabled researchers from more than 110 countries, 10 UN agencies, and various international organizations to publish over 180 scientific articles, 17 UN reports, and numerous public data products. These have demonstrated applications in urban development, disaster response, environmental monitoring, agriculture, and marine conservation. This article reviews the technical innovations and mission specifications of the SDGSAT-1 satellite, demonstrates its contribution in leveraging space technology for SDG monitoring and evaluation, and discusses the future evolution of development of EO systems, specifically the planned Sustainable Development Satellite Constellation, for supporting the global achievement of SDGs.

As the first satellite specifically designed to serve Sustainable Development Goals (SDGs), the Sustainable Development Science Satellite 1 (SDGSAT-1) has attracted numerous attention by providing panchromatic glimmer imagery with a unprecedented clarity of 10 meters. However, unsatisfactory positional accuracy of glimmer imagery hinder further fine-grained studies. This study identifies the cause of these positional errors as misalignments generated during the data production process, and thus proposes a correction method based on deep learning object detection and cross-correlation. The method achieves accurate measurements of the position, direction, and distance of the misalignment, which are used to correct the glimmer imagery and to effectively improve the positional accuracy. In this study, we (1) propose a simulated misalignment dataset which demonstrated with excellent performance during the training of multiple deep learning object detection models, reaching up to 99.44% average precision (AP); (2) propose pixel brightness correlation coefficient based on cross-correlation and validate its effectiveness to characterize misalignments; (3) improve detection efficiency by the threshold design of window average brightness and object detection confidence; (4) explore the reasons for the misalignment suggests that it is the difference between the satellite’s designed operating time and the actual operating time that leads to the Charge-coupled Device(CCD) stitching flaw. Our proposed method can contribute to evaluating and improving the positional accuracy of SDGSAT-1 panchromatic glimmer imagery, providing a way to obtain datasets with good spatial consistency and to conduct related research.

The Sustainable Development Science Satellite-1 (SDGSAT-1) is a new-generation night-light satellite equipped with an advanced Glimmer Imager (GLI) sensor capable of acquiring multispectral, high-resolution night-time light (NTL) images. It has been widely applied to various fields, yield valuable insights in supporting human activity monitoring and sustainable development research. However, the Level 4A GLI images still suffer from spatial misalignment issues, including seam misalignment between the images captured by different cameras in the same scene; registration errors between images and basic geographic information data (i.e. OpenStreetMap road networks) and spatial inconsistencies between the panchromatic (Pan) and RGB bands. The existing studies mainly relied on manual registration or algorithms to mitigate misalignment effects, lacking efficient and systematic solutions. To effectively address this issue, in this work, an automatic geometric registration algorithm called road-guided image registration (RGIR), which uses the OSM road network as a spatial reference. Ground control points (GCPs) are selected via a three-step process: point match, line match and precise match. The RGIR algorithm could achieve high-precision registration and effectively correct the three typical spatial misalignment problems commonly found in GLI images. The experimental results demonstrated that RGIR can achieve sub-pixel accuracy across various scenarios and exhibits strong robustness. Overall, our work provides a workable technical approach to solving the existing spatial misalignment problem in GLI images, laying a solid foundation for its reliable application in multi-temporal analyses and large-scale sustainable development analyses.

Nighttime light plays an important role in social economy and human activities by observing artificial light at night. Accurate radiometric calibration is the basis of quantitative applications and influences the accuracy of surface information revision. A nighttime light payload, named Glimmer Imager (GLI) is onboard the Sustainable Development Goals Satellite (SDGSAT-1). The payload can acquire nighttime light observations with a spatial resolution of 10 m in the panchromatic bands, and 40 m in Red/Green/Blue bands. In this paper, we proposed a vicarious radiometric calibration method for the GLI of SDGSAT-1 based on point light source, and conducted experiment in the Dunhuang radiometric calibration site. Atmospheric profile, aerosol optical depth (AOD), and total column water vapor content were measured during the in-situ measurements, and were used to simulate the at-sensor radiance at the entrance of satellite payload via MODerate resolution atmospheric TRANsmission (MOTRAN). Our result shows that there is a strong linear correlation between the simulated atsensor radiance and the DN value, with R2larger than 0.91. Moreover, uncertainties of the vicarious radiometric calibration induced by relative spectral response shift, AOD content measurements, atmospheric profile measurements, AOD type assumption, and specialized point light itself are also discussed. Results show that the overall uncertainty of vicarious radiometric calibration for nighttime light payload onboard SDGSAT-1 based on point light sources is between 4.00% and 5.50%. The comparison between GLI/SDGSAT-1 and DNB/VIIRS at Athens, Denver, and Milan shows a strong correlation with R2larger than 0.80. Our analysis reveals the feasibility of vicarious radiometric calibration method based on point light source for nighttime light payload.

The absolute radiometric accuracy of thermal infrared channels of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the FengYun-4A (FY4A) satellite was assessed with field experiment data in a vicarious calibration on 18, 2019, and August 20, 2019, in Lake Qinghai. Continuous water temperature records were collected by an unmanned surface vehicle equipped with a radiometer during the experimental period. We proposed a spectral matching method based on atmospheric transfer code to create consistent equivalent spectral radiance, and the results predicted by atmospheric transfer simulation were convolved with instrument response functions to obtain predicted brightness temperature for comparison with satellite-derived temperature. Our results indicated that AGRI had an average temperature bias of 0.12 K with an RMSE of 0.17 and 0.61 K with an RMSE of 0.22 K in band 12 and band 13 on August 18, while these biases decreased to -0.01 K with an RMSE of 0.13 and -0.48 K with an RMSE of 0.17 K on August 20, respectively. This radiometric accuracy indicated that AGRI TIR bands are well functional compared with their preflight requirement of less than 1 K. The uncertainty analysis also suggested that atmospheric conditions would alter the calibration accuracy by influencing the transmittance and path thermal radiance. Meanwhile, cloud cover also requires more attention to select the appropriate concurrent satellite pixels to reduce the possible cloud edge effects. It is important and necessary for FY4A AGRI to conduct more intense automatic observations and obtain more data to improve the accuracy of radiometric calibration and monitor the operational status of satellite instruments.

In this paper, we describe a method for radiometric calibration of Fengyun-3D (FY-3D) MERSI-II TIR, bands 24 and 25 that combines vicarious and cross-calibration of the corresponding bands of Aqua MODIS, bands 31 and 32. A field campaign was conducted on lake Qinghai, China, on August 18, 2019. The surface measurements were performed before and after the passing time of Terra MODIS and FY-3D MERSI-II. Path radiance and transmittance were then calculated via the radiative transfer code MODTRAN 4.0, and the difference caused by the relative spectral response (RSR) of the sensors was eliminated by the spectral matching. Our experimental results indicate that the obtained radiometric calibration accuracy of FY-3D MERSI-II is stable during on-orbit operation.

The International Space Station Agriculture Camera (ISSAC), Sponsored by the National Aeronautics and Space Administration's (NASA) Education Office and developed by University of North Dakota, aims to demonstrate the applications of precision farming and natural resource management from the International Space Station (ISS). With a designed Ground Sampling Distance (GSP) of ~20 meters, an accurate geo-referencing algorithm is important for near-real time delivery of images that can be used immediately. One outstanding feature of ISSAC is its tilting capability in cross-track direction so that a wide area can be covered. At a relatively low altitude, the ISS, where the ISSAC will be hosted, experiences a large variation in attitude. Additionally, the optics window on ISS where the ISSAC sees through is not at the center of mass of the ISS. All these factors need to be taken care of in the algorithm. Evaluated by the commercial navigation software, Satellite Tool Kit (STK, Analytical Graphics, Inc.), the proposed algorithm can achieve theoretical accuracy within 0.5 meters.