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.

Spatiotemporal fusion (STF) plays a critical role in remote sensing and Earth observation by integrating high spatial resolution details with high temporal consistency, serving as an essential tool for environmental monitoring and postdisaster assessment. Recently, numerous STF methods, especially deep learning-based approaches, have been proposed and shown promising results. However, most of them struggle with effectively capturing complex spatiotemporal interactions as well as balancing computational efficiency and interpretability. To address these challenges, we propose an innovative STF framework, frequency-selected differential fusion Transformer (FSDFormer), which comprehensively processes multiscale spatiotemporal data while maintaining linear computational complexity. FSDFormer integrates three key components: the spatial enhanced agent Transformer (SEAT) block, the frequency-adaptive filtering network (FAFN), and the spatiotemporal differential fusion (SDF) block. The SEAT block employs a novel attention mechanism to reduce computational complexity, while the FAFN enhances feature extraction by introducing frequency-domain filtering in the forward network. Grounded in a spatiotemporal dependency-aware fusion strategy, the SDF block leverages a two-stage mechanism to excavate and fuse temporal and spatial information sequentially. Extensive experiments show that FSDFormer delivers superior performance on three STF datasets, outperforming state-of-the-art (SOTA) methods in accuracy, robustness, and generalizability. The code is available athttps://github.com/Sichen-Lu/FSDFormer

NDVI is the most widely applied vegetation index, it can indicate the information of growth status, LAI, et al. However, NDVI is also affected by the atmosphere conditions, soil background, view conditions and so on. In this paper, we analyses the effects of chlorophyll, surface soil moisture, the ratio of sky light, view zenith and Sun zenith on NDVI under different LAI using the leaf radiative transfer model PROSPECT and canopy reflectance model SAIL, and find the sensitive factors and ranges.

This paper mainly proposed using multi-temporal spectral data with given weight to estimate yield using contribution ratios of different stages to improve yield estimation, then use stepwise regression to build models for yield estimation. The contribution ratio is calculated by principal component analysis respectively. Result show that this methodology reflects the crop growth status, physiological characters at different stages, and improves the accuracy obviously.

Crown cover fraction is an important ecological and environmental parameter. In this paper, an algorithm to estimate crown cover fraction is derived according to the characteristics of soil and vegetation spectra and the definition of crown cover fraction. Furthermore, a more simple method, which is called maximal gradient difference method, is proposed to calculate crown cover fraction. Once the crown cover fraction is calculated, the background spectra of pixels that are partly covered by vegetation can be recovered under the assumption of linear spectra mixing. Using the above method, we've successfully retrieved the background information of a TM image of Colorado State. The method we proposed is simple, easy to performance, and can separate the effect of soil and vegetation effectively, so it's possible to replace the method of estimating crown cover fraction from NDVI, which is used widely at present.

At XiaoTangShan Precision Agriculture Experiment Base, suburban of Beijing city, we obtained the hyperspectral data of wheat canopy by field measurement and synchronal relative foliage water content (RFWC) at lab, according to the different growth stage of wheat, in the spring of 2002. In this paper, we extracted spectral feature parameters firstly. Then, the correlation analysis between the spectral features parameters and RFWC was made. two spectral feature parameters, which have good relativity to foliage water content, to make linear regression models between RFWC and spectral feature parameter according to the growth stages of wheat.

LAI (Leaf Area Index) is an important parameter for plant growth status detecting. The traditional method to measure leaves' area in field is time and labor consuming. In some remote sensing applications, LAI might be needed more intensively such as daily and weekly. Crop simulation models can be effective tools for simulating LAI, crop and soil water statuses, crop growth and development parameters at the daily step. Ground LAI measurements for winter wheat were carried out in several fields of Shunyi county, Beijing, China at several days' interval during the crop growth season. The CERES-wheat model is run under local soil, weather and management conditions of a field site to simulate daily LAI values. The simulated LAI corresponded comparatively well with the measured ones at the early stage. The results suggested that the technique might be promising for estimating LAI, this make it possible to develop an approach to assimilate LAI of winter wheat between ground measurements and CERES-wheat simulation.