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.

Nighttime light (NTL) data holds irreplaceable value in research related to human activities, sustainable development, etc. However, the temporal and spatial continuity of high-resolution NTL data is challenging to meet the demands of large-scale applications. Spatiotemporal data fusion methods, by integrating low-resolution and high-resolution data, can fill in missing high-resolution data. Nevertheless, previous spatiotemporal data fusion research has primarily focused on multispectral data and face challenges when directly applied to NTL data. This research proposes a spatiotemporal fusion method specifically for NTL data, named the "LIght ON spatiotemporal data fusion" (LION) model. Experimental results indicate that LION demonstrated potential in predicting abrupt changes in NTL.

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 accurate radiometric calibration is very important for remote sensing optical sensors. Numerous astronomical measurements and model analyses have shown that the Moon is extremely stable in reflectance properties and can serve as a great absolute reference source. Jilin-1 satellite has made long-period observations of the Moon over a wide range of phase angles. However, the spectra displayed fluctuations after radiometric calibration with the provided coefficients. This is inconsistent with the lunar reflectance spectra, which are smooth and generally no sharp absorption. In this study, we conducted radiometric calibration for PMS2 onboard Jilin-1 Guangpu02 (GP02) satellite using the Moon as the calibration source. The calibration results were validated based on different lunar geological terrain, i.e., maria and highlands. The lunar phase curve obtained through the application of the lunar calibration coefficients derived in this study show good agreement with the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and PLEIADES data. The results indicate that: 1) the spectra become much smoother after lunar calibration, consistent with the characteristics of lunar spectra; 2) the lunar calibration results demonstrate exceeding linearity, with average linear fit uncertainty of 0.69% and goodness-of-fit$({R{^{2}}})$of 0.9997 for the calibration coefficients; and 3) the average relative difference between the phase curves of PMS2 and SeaWiFS is 3.6% and 4.66% with PLEIADES. The obtained results confirm the effectiveness of our lunar calibration method. Although this method was specifically applied to the PMS2, the lunar calibration method is expected to be applicable to other Earth-orbiting satellites.

The Advanced Hyperspectral Imager (AHSI) is the second hyperspectral imager of China, which is also one of the most important payloads onboard the ZY1E satellite. In order to monitor the radiometric calibration status since its launch on 12 Sep. 2019, and thus provide supplementary on-board radiometric calibration, this paper conducts several vicarious radiometric calibration experiments for the ZY-1E AHSI. Five satellite observations over Dunhuang radiometric calibration site, one of the most relevant China Radiometric Calibration Sites (CRCS), were used. Surface reflectance, radiosonde data, aerosol optical depth (AOD) loading, and water vapor content were used to simulate the top of atmosphere (TOA) radiance at the entrance pupil of the satellite via MODerate resolution atmospheric TRANsmission (MODTRAN). Our results show that the vicarious radiometric calibration coefficients are relatively constant with the official coefficients: the mean relative differences are 5.55%, 5.64%, 7.02%, 5.07%, and 5.30% on 11 Jan, 17 Jan, 12 Feb, 07 May, and 23 Nov, 2021, respectively. The vicarious radiometric calibration coefficients were found in good agreement with the official ones according to the validation analysis based on different surface types, i.e., water and vegetation. Uncertainties of vicarious radiometric calibration due to AOD assumptions, AOD measurements, water vapor measurements, radiosonde data measurements, relative spectral response (RSR) shifts and surface reflectance measurements are also discussed in detail. The obtained results show that the ZY-1E AHSI exhibits good on-orbit radiometric status.

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.

In some remote sensing applications such as change detection, satellite images with both high spatial and high temporal resolution are required. However, no single satellite sensor can currently provide such images due to technical specifications. To solve this problem, spatio-temporal fusion provides a cost-effective solution. In this paper, we propose a new spatio-temporal fusion approach, based on convolutional neural networks (CNNs), for Landsat and MODIS image fusion. Specifically, the proposed approach utilizes CNNs to model the heterogeneity of fine pixels from the coarse MODIS images. Here, the heterogeneity of fine pixels is defined as the difference between the reflectance changes obtained from the two types of images. After that, two transition-predicted images can be obtained using the trained CNNs, which are then fused in order to obtain a fi-nal prediction. In our newly proposed approach, CNNs are only used to learn the heterogeneity of fine pixels rather than the whole images, thus providing a more stable and less time-consuming strategy as compared to other available approaches. We evaluated the proposed approach on a public spatio-temporal fusion dataset and the obtained results suggest that our newly developed method achieves state-of-the-art performance.