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.

How to extract the landslides triggered by earthquakes rapidly and accurately is of great significance in current earthquake remote sensing application. Based on the earthquake emergency response, an automatic seismic landslide detection method is proposed in this paper. In the method, a simple and effective automatic thresholding algorithm is designed by using a single-temporal remote sensing image, which is different from traditional change detection methods. The gray scale, shape and texture features of landslides are taken into account in landslide detection, along with several rules and experts' knowledge. In the experiments using high-resolution aerial images, major influencing objects, such as lakes, roads, buildings and cultivated land are effectively removed and more than 80% seismic landslides are extracted successfully. Different from traditional change detection methods based on images before and after the earthquake, the simple and efficient method proposed in the paper is only based on seismic-post remote sensing imagery and is more suitable for earthquake disaster emergency responses.

Remotely sensed mineralized alteration information is of great significance to geological prospecting and extracting such alteration information in high vegetated areas is difficult. Based on ASTER data, the following processes are performed. First, the ASTER data are geometrically corrected and atmospherically corrected, and water and shadow are masked. Then, principal component analysis method is applied to extract ferric contamination information, which is enhanced and classified by thresholding. Finally, mineralization anomaly regions are delineated with reference to known ore deposits and related alterations. In the fieldwork, the extracted alteration information was verified, proving that the ASTER data are of capacity to acquire minerals' spectral characteristics in the short wave infrared range, which effectively facilitate geological prospecting in densely vegetated regions.

In this article, a newsegmentation method based on traditional region growing (RG) is proposed for high resolutionremote sensing imagery. This method takes regional minima from horizontal and vertical gradient maps of the image as seeds for the following region growing processing. The new method consists of several steps as follows: (1)deriving a morphological gradient map from the input multispectral image, (2) morphologically filtering the gradient image to remove local minima with small depthand extracting regional minima of flat areas in the resulting filtered image as seeds, (3) segmenting the multispectral image using the RG approach with reference to the seeds, and (4) merge the resulting initial segments to yield asegmentation map. In a test with a WorldView-2 multispectral image, the proposed method offered segmentation maps with nearly the same accuracy as several current methods.

Various multispectral (MS) and panchromatic (PAN) fusion (or pan-sharpening) algorithms were developed to produce an enhanced MS image of high spatial resolution. Regarding the novelty in both the PAN and MS bands of the WV-2 imagery, the objective of this study is to assess the performance of nine state-of-the-art pan-sharpening methods for the WV-2 imagery, using both image quality indices and information indices that used for urban information extraction. The comparison of the four quality indices (RASE, ERGAS, SAM, and Q4) demonstrated that the HR method performed the best for the WV-2 MS and PAN images. However, the comparison of the four information indices showed that a higher quality at data level does not signify better information preservation for object recognition.

The case study presents an information index model for landslide susceptibility mapping in Tonggu County, Jiangxi Province, China. More than 100 landslides were identified from the 2.5-m fused SPOT imagery, and about 60 percent of them were verified in field investigation. It is proved that the landslides are more likely to occur in granite areas, which are strongly weathered, and the areas close to faults. The high elevation accuracy of 75.6% was reached using a receiver operating characteristic (ROC). The result indicates that the south and parts of the northeast of Tonggu County, including Paipu-Yongning and Daduan-Guqiao Towns, are highly susceptible to damages from landslides.

Image segmentation is the basis of object-based information extraction from remote sensing imagery. Image segmentation based on multiple features, multi-resolution, and spatial context is one current research focus. Combining graph theory based optimization with the multi-scale image segmentation framework of the eCognition software, a multi-scale image segmentation method is proposed in this paper. In this method, a coherent enhancement anisotropic diffusion filtering approach and a minimum spanning tree segmentation algorithm are employed to initially segment the image. After that, the resulting segments are merged regarding minimum heterogeneity criteria, which are based on both the spectral characteristics and the shape parameters of segments. Two test images were used for visual and quantitative comparisons of the proposed method with the multi-scale segmentation method FNEA employed in the eCognition software. The results show that the proposed method is effective, and is more sensitive to subtle spectral differences than the FNEA.

A data conflation method was developed based on a multiple-point geostatistical method. Geostatistics can quantify cross-correlation from different sources of data when integrating geospatial information. Multiple-point geostatistics (MPG) is a development of geostatistics. Pattern-based MPG can capture similar patterns using spatial correlation in the form of training image, and then reproduces the area of interest in a local window using sequential simulation. In the proposed method, MPG simulation was applied to the traditional geostatistical prediction. This method was tested on digital elevation models (DEMs). The aim was to simulate an image at a fine spatial resolution by conflating sparsely sampled elevation data and digital raster elevation at a coarser spatial resolution. The MPG simulation approach was compared to traditional geostatistical conflation using four different methods. The results show that the proposed method can achieve a more precise prediction than the benchmarks.