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SDGSAT-1, the world's first science satellite dedicated to assisting the United Nations 2030 Sustainable Development Agenda, has been operational for over two and a half years. It provides valuable data to aid in implementing the Sustainable Development Goals internationally. Through its Open Science Program, the satellite has maintained consistent operations and delivered free data to scientific and technological users from 88 countries. This program has produced a wealth of scientific output, with 72 papers, including 28 on data processing methods and 44 on applications for monitoring progress toward SDGs related to sustainable cities, clean energy, life underwater, climate action, and clean water and sanitation. SDGSAT-1 is equipped with three key instruments: a multispectral imager, a thermal infrared spectrometer, and a glimmer imager, which have enabled ground-breaking research in a variety of domains such as water quality analysis, identification of industrial heat sources, assessment of environmental disaster impacts, and detection of forest fires. The precise measurements and ongoing monitoring made possible by this invaluable data significantly advance our understanding of various environmental phenomena. They are essential for making well-informed decisions on a local and global scale. Beyond its application to academic research, SDGSAT-1 promotes global cooperation and strengthens developing countries’ capacity to accomplish their sustainable development goals. As the satellite continues to gather and distribute data, it plays a pivotal role in developing strategies for environmental protection, disaster management and relief, and resource allocation. These initiatives highlight the satellite’s vital role in fostering international collaboration and technical innovation to advance scientific knowledge and promote a sustainable future.

Tropical forests play a vital role in biodiversity conservation and research on global climate change. However, the complexity and diversity of vegetation types pose challenges to the fine remote sensing - based classification of tropical forests. The claasification of tropical forests in the Jianfengling area, Hainan Province was analyzed using the multi - temporal Landsat8 data of the Google Earth Engine % GEE) platform. Based on the analysis of the impacts of the size and combination of multi - temporal data on the clusification accuracy, this study proposed a clusification method based on multi - temporal Landsat8 images for the vegetation type groups of tropical natural forests, such as typical tropical rain forest, tropical monsoon forest, and evergreen broad - leaved forests. The results are as follows. ! The clusification accuracy of tropical natural forests was significantly improved as the size of multi - temporal data increased. The clusification accuracy of the vegetation type groups of natural forests in Hainan Island reached 91 %. © When the multi - temporal data reached a certain size, the clusification accuracy tended to be stable. Different combinations of multi - temporal data can improve the clusification accuracy of tropical forests, especiaHy when the classification accuracy of individual data involved was low. This finding reflects the broadness of the selection of temporal data. The proposed method, taking advantage of the temporal changes in remote sensing data, provides an effective reference for the remote sensing - based classification of tropical natural forests in Hainan Island. [ABSTRACT FROM AUTHOR]

The prevalence of aphid in winter wheat field has a significant impact on the production of winter wheat. An effective and timely forewarning of the scope and severity of the disease at a regional scale will not only reduce yield losses but also alert the stakeholders to take effective preventive measures. Forecasting aphid occurrence with Remote Sensing has great advantages over traditional methods and meteorological data, such as lower cost, simpler operation, more real-time and higher resolution. Chinese HJ-1A/1B data which have a high revisit frequency (<4 days )as well as 30 m spatial resolution, in addition to a full band set covering visible, near-infrared, short-wave infrared, mid-infrared and thermal infrared spectral ranges, which thus significantly expand opportunities of remote sensing application in predicting crop diseases and pest. According to the literature research and field investigation found that apart from crops growth status was closely related to wheat aphid occurrence, development, and dispersal, the environmental conditions of field, such as temperature and humidity, also have a certain impact on its susceptibility to pest. In this study, we retrieve two parts of information from multi-temporal HJ-CCD optical data and HJ-IRS thermal infrared data, including Ratio vegetation index (RVI) and Normalized Difference Vegetation Index (NDVI) representing grow status of wheat, Perpendicular Drought Index (PDI) and Land Surface Temperature (LST) that represent the environmental characteristics. An independent t-test analysis was used to test the difference between pest and healthy samples based on calibration data. Those vegetation and environmental factors that failed to show a statistical significant (p-value <0.001) were eliminated. Five remotely sensed variables (NDVI-T1, RVI-T1, LST-T1, LST-T2, and PDI-T2) were identified as optimal explanatory variables for developing the pest forecasting model. The relevance vector machine (RVM) model was established to predict aphid occurrence of filling stage of wheat in the Beijing suburbs, which is a machine learning algorithm and commonly used to improve business decisions, detect disease, and forecast weather credit to its superior learning ability when on a small datasets. The results obtained from the RVM model are compared with prediction models developed using logistic regression (LR) and support vector machine (SVM) techniques. Goodness-of-fit values of the calibrated models were evaluated through the p-value of Spearman test. In addition, the association between observed outcome of pest occurrence and predicted outcome of pest occurrence was evaluated by using three different measures: Somers'D, Goodman-Kruskal gamma, and Kendall's Tau-c. The RVM model produced a higher Spearman p-value than that the LR model and the SVM model did, indicating that the RVM model had a better performance to predict the aphid than the other two models. Moreover, the values of Somers'D, Goodman-Kruskal Gamma, and Kendall's Tau-c were all higher with the RVM model than those with the remaining model, which further demonstrated the better performance of the RVM model on the training datasets. To further evaluate the difference of performances of the three types of models, we obtain accuracy of models respectively based on validation samples. The results showed that: accuracy by using RVM algorithm is the highest among the three methods, with lower omission and commission than the other two algorithms. Furthermore, the overall prediction accuracy and the kappa coefficient of the RVM model are 87.5%, 0.71respectively, have shown better performance over the SVM model (79.2%,0.55) and the LR model (75.0%,0.44). Additionally, the RVM model needs less computation work and faster prediction speed. These results revealed that the RVM model with high accuracy level and prediction efficiency is much more favorable in forecast the pest occurrence at a regional scale effective and timely. It indicated that the result of this research can be used in different regions. [ABSTRACT FROM AUTHOR]

Shrub encroachment has been a wide phenomenon across the arid and semi-arid grasslands in Inner Mongolia, China. Although numerous studies have investigated the effect of this phenomenon on community composition, ecosystem structure, and nutrient cycling, reports on the distribution patterns of shrub encroachment are limited. A recent development in satellite remote sensing enables accurate assessment of shrub distribution and its dynamics at large scales. In this paper, the combined ground survey in Xianghuangqi, four satellite images (with spatial resolution of 5.8 m) of ZY-3, covering nearly the whole area and taken between July and August in 2013, were used to identify the shrub distribution in this region. It should be noted that the shrub here indicated the shrub-grass mosaic due to the mixed pixel effect, and the identification was weak when the coverage of shrub was on low levels. The NDVI threshold method was first used to extract the vegetation coverage area, and then three traditional pixel-oriented methods (Support vector machine, Maximum likelihood and Mahalanobis distance), compared with the object-oriented method, were used for the classification of images. Object-oriented method is different from the traditional one, in that the classification is not based on the spectral characteristics of individual pixel, but relies on the image object with spatial texture and shape and size characteristics. Ground survey data were used to compare the accuracy level of these methods. It indicated that the shrub recognition accuracy by using support vector machine algorithm is the highest among the three pixel-oriented methods, with higher producer accuracy and user accuracy than the other two algorithms. Furthermore, the overall classification accuracy of this algorithm is 81.15% higher than that of the maximum likelihood (73.33%) and the Mahalanobis distance (61.77%). However, the overall recognition accuracy by using the object-oriented approach (combined scale 97) was up to 89.24%. It also revealed that the proportion of shrub omission and commission decreased while the combined scale of object increased. These results suggest that the object-oriented method, with high accuracy level, is much more favorable in shrub extraction from grassland background. [ABSTRACT FROM AUTHOR]