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.

Extracting the latent factors of big time series data is an important means to examine the dynamic complex systems under observation. These low-dimensional and “small” representations reveal the key insights to the overall mechanisms, which can otherwise be obscured by the notoriously high dimensionality and scale of big data as well as the enormously complicated interdependencies amongst data elements. However, grand challenges still remain: (1) to incrementally derive the multi-mode factors of the augmenting big data and (2) to achieve this goal under the circumstance of insufficient a priori knowledge. This study develops an incrementally parallel factorization solution (namely I-PARAFAC) for huge augmenting tensors (multi-way arrays) consisting of three phases over a cutting-edge GPU cluster: in the “giant-step” phase, a variational Bayesian inference (VBI) model estimates the distribution of the close neighborhood of each factor in a high confidence level without the need for a priori knowledge of the tensor or problem domain; in the “baby-step” phase, a massively parallel Fast-HALS algorithm (namely G-HALS) has been developed to derive the accurate subfactors of each subtensor on the basis of the initial factors; in the final fusion phase, I-PARAFAC fuses the known factors of the original tensor and those accurate subfactors of the “increment” to achieve the final full factors. Experimental results indicate that: (1) the VBI model enables a blind factor approximation, where the distribution of the close neighborhood of each final factor can be quickly derived (10 iterations for the test case). As a result, the model of a low time complexity significantly accelerates the derivation of the final accurate factors and lowers the risks of errors; (2) I-PARAFAC significantly outperforms even the latest high performance counterpart when handling augmenting tensors, e.g., the increased overhead is only proportional to the increment while the latter has to repeatedly factorize the whole tensor, and the overhead in fusing subfactors is always minimal; (3) I-PARAFAC can factorize a huge tensor (volume up to 500 TB over 50 nodes) as a whole with the capability several magnitudes higher than conventional methods, and the runtime is in the order of 1/n to the number of compute nodes; (4) I-PARAFAC supports correct factorization-based analysis of a real 4-order EEG dataset captured from a variety of epilepsy patients. Overall, it should also be noted that counterpart methods have to derive the whole tensor from the scratch if the tensor is augmented in any dimension; as a contrast, the I-PARAFAC framework only needs to incrementally compute the full factors of the huge augmented tensor.

Characteristics of radiance spectrum and radiance error distribution after channel center wavelength shift of imaging spectrometers were analyzed in this paper. The results show that shifts of the channel center wavelength can cause shifts of the radiance spectrum, and the shift direction of the two is opposite. The radiance error caused by the channel center wavelength shift is basically distributed near the solar and atmospheric absorption bands. When the center wavelength shifts the same amount towards the positive and negative directions, the absolute value of radiance error is different, which is caused by the asymmetry of spectral absorption region. For 5nm, 10nm and 15nm resolution imaging spectrometers, there is a significant linear relationship between the center wavelength shift and the maximum percent error of radiance. When the center wavelength shift value is less than 10% of the spectral bandwidth, the 10nm imaging spectrometer is most sensitive to the center wavelength shift; when the center wavelength shift is greater than 30%, the 15nm imaging spectrometer is most sensitive to the center wavelength shift.

The imaging Fourier transform spectrometer (IFTS) carried on HJ-1A is the first spaceborne hyperspectral earth observation sensor in China. Shifts in the spectral channel center wavelengths of IFTS may occur after launch due to vibrations, and to changes in instrument temperature and pressure. In this paper, we described an improved on-orbit spectral calibration method, which is based on spectrum angle matching between the spectrometer-measured radiance spectrum and the modeled radiance spectrum at atmospheric absorption bands. Compared with the laboratory calibration result of IFTS, spectral shifts ranged from -3.18 to -3.4 nm were derived depending on cross-track spectral position at 760-nm oxygen bands, and from -3.53 to -3.79 nm at 820-nm water vapor bands. With the updated spectral calibration coefficient, the reflectance of vegetation and desert in our study site were reconstructed by applying a further atmospheric correction, and the strong spikes around the atmospheric absorption bands were almost obviously suppressed.

N-FINDR is a widely used endmember extraction algorithm in hyperspectral imagery. Nevertheless, its computational complexity is high. Plaza's parallel implementation of N FINDR, namely, P-FINDR, demonstrates an excellent way to improve the computing performance of N-FINDR by incorporating with parallel computing technique. In this paper, three parallel implementation patterns, i.e., synchronous, asynchronous and grouping asynchronous pattern, are presented. Two versions of N-FINDR, i.e. iterative N-FINDR and successive N-FINDR, are considered to be implemented in parallel by these patterns respectively. Thus obtains six parallel N-FINDR algorithms. In experiment, both solution quality and parallel performance of these algorithms are compared and suitable patterns for parallel implementation of N-FINDR are obtained.

The Three Gorges Reservoir is the largest water resource project in west China. With water environment change greatly, water quality of the Three Gorges Reservoir needs monitoring urgently. With the properties of high spatial resolution, multi-angle observation and hyperspectral imagery, CHRIS offers a precious data resource for monitoring water quality. We apply CHRIS imagery to detect total suspended substance concentration of the Three Gorges. The method is simplified bio-optic model. The results showed the distribution total suspended substance concentration in the Three Gorges at 17thApril of 2007, which agree with the actual situation. The total suspended substance concentrations retrieved by the near-infrared method are quite close to the measured data. This study could be helpful to monitor water quality with hyperspectral remote sensing imagery just having near-infrared channels.

HJ-1A is one member of the Environment and Disaster Monitoring Microsatellite Constellation, which carries a HyperSpectral Imager (HSI) with the spectral resolution about 5nm from 450nm to 950nm. Retrieving columnar water vapor content is essential to the quantitative applications of hypespectral imagery. The ideal best band (940nm) for water vapor retrieval has lower signal-to-noise(S/N) value. In this paper, we choose another weak water absorption band (820nm) to retrieval the columnar water vapor content. Two common water vapor retrieval algorithms, called the Continuum Interpolated Band Ratio (CIBR) and the Atmospheric Precorrected Differential Absorption (APDA), are implemented and compared with each other. Simulation results show that these two algorithms have less difference in the accuracy of water vapor retrieval because the weak water vapor absorption effect in 820nm The water vapor contents derived from HJ-1A HSI by CIBR algorithm are compared with MODIS products and a systematic error may exist in the radiometric calibration of HSI.

HJ-1A and HJ-1B, the first micro-satellite constellation for Environment and Disaster Monitoring of China, was successfully launched in China on September 6, 2008. The same multispectral imagers named HJ-1/CCD with four bands (R, G, B, Nir) and large swath are installed on both HJ-1A and HJ-1B. The HJ-1/CCD is the main sensor of the constellation. In this paper, data sets includes six image pairs of HJ-1A/CCD and Terra/Modis and field reflectance spectra of Dunhuang Calibration Site are acquired. Cross calibration is taken to get the calibration results of HJ-1A/CCD relative to Terra/Modis. Based on the calibration results, the radiometric performance of HJ-1A/CCD is evaluated. Taking into that the good radiometric performance of Terra/Modis, it is obvious that HJ-1A/CCD has a radiometric attenuation during its second half of the first year. The max attenuation occurred on the near infrared band is about 18%. The min attenuation occurred on the blue band is about 8%. There maybe some inevitable errors about the data sets and produced during the processing. More images should be acquired and more attention should be paid to get the true radiometric attenuation.

To fulfill the objectives of Beijing-1 micro-satellite on-orbit calibration experiment, two in-flight calibration experiments were carried out on September 3 and 13, 2008 at Dunhuang Calibration Site. Two simultaneous datasets including Beijing-1 data and the in-situ surface reflectance and atmospheric measurements, were acquired at the calibration area. During the period of the calibration experiments, some dark sea images were captured to calculate the dark current of Biejing-1. With the reflectance of calibration targets, the atmospheric optical depths, and the water vapor constraints, the 6S radiative transfer code was used to calculate the equivalent radiance of Beijing-1 to the top of the atmosphere (TOA). The reflectance calibration algorithm was taken to calculate the absolute radiometric calibration coefficients of Beijing-1 multi-spectral cameras. The calibration results of Sep. 3 and that of Sep. 13 are very similar. The biggest relative error between the two results is only 3.77%. This proves the calibration results are reasonable.

When calculating water leaving radiance or remote sensing reflectance from water surface measured spectra, the results are affected by the reflected skylight, which are the product of skylight radiance and the air-water surface reflectance of skylight. The skylight radiance can be measured over water surface by spectrometer. But the air-water surface reflectance is affected by multi factors, and is hard to be determined. Presently, there are some methods to estimate the value of air-water surface reflectance of skylight over ocean waters. However, when calculating the air-water surface reflectance of skylight over turbid inland waters, these methods all have some problems. In this paper, we develop an improved method to calculate air-water surface reflectance of skylight over turbid inland waters. This method is based on the spectral characteristics of turbid inland waters that water leaving radiance in shortwave infrared wavelength region can be assumed zero, which is verified by the data acquired in Taihu Lake on April 25 to 27, 2007. In this method, two atmospheric windows (1190 ~ 1330 nm and 1500 ~ 1700 nm) in shortwave infrared wavelength region are used. The main strategy of this method is to use the data in 1190 ~ 1330 nm wavelength region to calculate air-water surface reflectance of skylight first. When no reasonable result is obtained, the data in 1500 ~ 1700 nm wavelength region is used. With this method, the air-water surface reflectances of skylight on the 31 sampling stations are calculated. Then, water leaving radiance and remote sensing reflectance are calculated. The results of air-water surface reflectance of skylight and remote sensing reflectance on the 31 sampling stations are reasonable. Then, the advantages and limitations of the improved method are analyzed.

In late June 2008, Enteromorpha prolifera (E.P) bloomed in Qingdao marine, including Qingdao Olympic Sailing Center. The large scale and the fast extending speed of the E.P bloom were rare in history. The E.P bloomed just before Beijing 2008 Olympics, so it attracted the most attention around the world. Monitoring E.P from remote sensing data has the advantages of rapidness, wide coverage, low cost. But commonly used optical remote sensing is easily affected by cloud, which limits the monitoring timelines. In this paper, the theory of E.P monitoring from optical remote sensing and SAR remote sensing were analyzed. Then, the technical routes of E.P monitoring by exploiting the synergy of active and passive remote sensing data were presented. Because the SAR images are not affected by cloud, the timelines can be increased exploiting the by synergy of active and passive remote sensing data. MODIS and RADARSAT were used to monitor E.P. The monitoring results were used to analyze the beginning and spread of E.P. The monitoring results from MODIS and RADARSAT were compared.

This paper creates an operational swift solution for automatic analyzing water quality in case II waters. The HJ-1 satellites of China was successfully launched, this provides a good opportunity for case II water (inland and coastal water) environment monitoring. However, there is no operational monitoring system in China and abroad. The traditional processing methods were developed for expert users, which including many complex operations and user interaction, not suitable for business operation. To overcome this problem, an automatic monitoring procedure was developed by us. With this approach, one can get monitoring reports from original HJ-1 satellites images or MODIS images with ¿one-click¿. That means, you do not have to concern the complex data processing procedure, and what you need to do is specify the water quality monitoring area, the specific space-borne sensor and the remote sensing images. According to the study area, sensor type and image file, the operational procedure will automatically get parameters from the integrated XML files and database indexed by area and sensor type. The processing procedure does not need any user intervention, which greatly improve the processing efficiency. This paper gives a complete flow chart of data processing in the automatic monitoring mode using XML files and databases. Moreover, in order to verify the ability of the operational monitoring approach, experiments were conducted in Tai'hu, Chao'hu, and Dian'chi lakes using MODIS QKM images and the HJ-1 multi-spectral images. Tests show both the efficiency and accuracy of this approach is relatively high.

The aerosol optical depth (AOD) measured by sun photometer is obtained after subtracting the Rayleigh optical depth, ozone optical depth and other absorption gas optical depth from total optical depth. Wherein, it was according to Beer's attenuation law. However, the band filter function of sun photometers should be considered when retrieving AODs. In this paper, it was systemically analyzed the influence of sun photometer filter function on retrieving aerosol optical depth. Numerous study showed that the band filter function has a significant impact on retrieving AODs: 1)The uncertainty of AODs may become large when the filter function is not well desgined; 2) Under large zenith observation condition, it may introduce non-neglected errors if Rayleigh scattering optical depth is calculated directly at central wavelength without filter function considered; 3) The band weighted absorption coefficients of O3and NO2almost hold constant when gas amounts increase, except for questionable designed band filter; 4) These weak absorption optical depths can not be ignored such as H2O absorption optical depths in 1020nm &1640nm band and CH4, CO2absorption optical depths in 1640nm band.