Surface melt plays a vital role in impacting the polar mass balance and global sea level rise. Over the past decades, synthetic aperture radar (SAR) imagery has garnered considerable attention due to its capacity to provide high-precision and long-term information. However, the traditional SAR-based large-scale surface melt detection methods utilizing co-orbit normalization predominantly depend on reference images and the precise spatial registration to mitigate geometric distortions arising from diverse incidence angles. Consequently, both the absence of reference imagery and the movement of ice sheets and shelves present challenges to the method. In this study, we address this issue by developing a reference-free deep learning network integrating the Convolutional Block Attention Module (CBAM) into DeepLabv3+ to automatically detect surface melt and establishing the surface melt dataset based on multi-temporal Sentinel-1 SAR imagery, encompassing diverse surface conditions of the Antarctic. Our model achieves an accuracy of 95.67%, surpassing the reference-based method and an advanced deep learning-based approach by 4.23% and 4.67%, respectively. Moreover, compared to 500 m resolution UMelt product and the kilometer-level results obtained from Advanced Scatterometer (ASCAT) and Special Sensor Microwave Imager Sounder (SSMIS), our model demonstrates the capability to accurately capture the small-scale melting patterns of ice shelves with a higher spatial resolution of 40 m. Notably, our findings underscore the dispensability of reference imagery in traditional methods through the formidable information extraction capabilities of deep learning. We finally applied the proposed method to extract and analyze the spatiotemporal characteristics of surface melt on the Larsen C Ice Shelf during the 2019/2020 period. The corresponding code of this study is at https://github.com/Tangyu35/Surface-melt-detection.

The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals (SDGs) that form part of the United Nations 2030 Sustainable Development Agenda. In terms of anthropogenic factors threatening the conservation of heritage properties, such a metric aids in the assessment of achievements toward heritage sustainability solving the problem of insufficient data availability. Therefore, in this study, 589 cultural World Heritage List (WHL) properties from 115 countries were analyzed, encompassing globally distributed and statistically significant samples of “monuments and groups of buildings” (73.2%), “sites” (19.3%), and “cultural landscapes” (7.5%). Land-cover changes in the WHL properties between 2015 and 2020 were automatically extracted from big data collections of high-resolution satellite imagery accessed via Google Earth Engine using intelligent remote sensing classification. Sustainability indexes (SIs) were estimated for the protection zones of each property, and the results were employed, for the first time, to assess the progress of each country toward SDG Target 11.4. Despite the apparent advances in SIs (10.4%), most countries either exhibited steady (20.0%) or declining (69.6%) SIs due to limited cultural investigations and enhanced negative anthropogenic disturbances. This study confirms that land-cover changes are among serious threats for heritage conservation, with heritage in some countries wherein the need to address this threat is most crucial, and the proposed spatiotemporal monitoring approach is recommended.

Using the Moon as an Earth observation platform for remote sensing offers the benefits of a high orbital altitude and vast surface area, which could provide continuous Earth observation capabilities over great temporal and spatial scales. Over the course of China’s follow-up lunar missions in the next three Five-year plans, the Earth observation instruments will be put on the Moon. However, the understanding of the characteristics of Moon-based Earth observations remains limited. Here, the observational characteristics for a moon-based platform related to the Earth ellipsoid model is studied, which advances previous studies with a spherical Earth assumption. We perform three analyses. First, an integrated coordination transformation equation, which denotes the geometric relationship between a Moon-based platform and the target on Earth is established based on numerical ephemerides and Earth orientation parameters. Second, the explicit expression for the intersection between the line of sight of the sensor and the Earth oblate spheroid is formulated, and the formulae of uncertainties are given. Lastly, a theoretical visible area on the Earth ellipsoid observed from the sensor is derived based on the geometrical relationship between the observation position and the Earth ellipsoid; two special situations are obtained via explicit expressions and series expansion. Based on this, the optimum radius for the spherical assumption of the Earth is obtained. The simulation and analyses reveal that the proposed mathematical derivation aimed at the Earth spheroid can be used to improve the accuracy of studies focused on the geometrical characteristics of Moon-based Earth observations.

The Belt and Road initiative has a significant focus on infrastructure, trade, and economic development across a vast region, and it also provides significant opportunities for sustainable development. The combined pressure of climate variability, intensified use of resources, and the fragility of ecosystems make it very challenging, however, to achieve future sustainability. To develop the path in a sustainable way, it is important to have a comprehensive understanding of these issues across nations and evaluate them in a scientific and well-informed approach. In this context, the Digital Belt and Road (DBAR) program was initiated as an international venture to share expertise, knowledge, technologies, and data to demonstrate the role of Earth observation science and technology and big Earth data applications to support large-scale development. In this paper, we identify pressing challenges, present the research priorities and foci of the DBAR program, and propose solutions where big Earth data can make significant contributions. This paper calls for further joint actions and collaboration to build a digital silk road in support of sustainable development at national, regional and global levels.

Synthetic aperture radar (SAR) interferometric baseline parameters form important input for SAR interferometry. In this paper, a nonlinear error model is established for the SAR interferometric baseline and parameterized as a polynomial based on the natural nonlinearity of the orbit of a satellite. Unlike conventional models, the proposed model takes into account the nonlinear part of the baseline error. A theoretical derivation is performed based on the imaging geometry of interferometric SAR, and the results of the analysis show that the parameters of the nonlinear baseline error model can be obtained from the relationship between the orbit, the nominal baseline, the baseline error, and the residual interferogram phase. A sample data set from the Japanese Earth Resources Satellite-1 (JERS-1) L-band SAR is used to validate the proposed model, and the results indicated that the compensation of the residual interferogram phase of the test data is superior to that provided by conventional models.

Interferometric SAR is an emerging earth observation technique, especially useful in cartography and surface subsidence survey, Differential SAR Interferometry (DInSAR) provides an alternative and complementary method to investigate the land subsidence phenomena, which can provide two dimensional deformations on the line of sight (LOS) of radar on areas of thousands square kilometers. In order to obtain accurate surface subsidence, precise orbit must be provided so as to compensate the reference surface phase and topographic phase. This paper proposed a new method for SAR interferometric baseline rectification specifically for SAR satellites those have no precise orbits. The method based on the imaging geometry of interferometric SAR, during the processing we setup a coordinate system called RXA, where R is a vector directing from the scene center to the corresponding position on the orbit, A is the vector of satellite velocity, X is normal to both R and A to construct a right hand coordinate system. There are two steps in our baseline rectification process, which are perpendicular baseline rectification and parallel baseline rectification, and the singular value of phase frequency was removed by the DIA method. In our study two InSAR pairs are processed, the results show the new proposed method could compensate not only the linear part of the orbit error but also nonlinear part of the orbit error.

In consideration of the importance of reference phase compensation to repeat orbit SAR interferometry and the significance of the reference phase to parameter selection and baseline reestimation in InSAR processing, the effect of reference surface difference that caused reference phase change is studied as well as the method of baseline reestimation. This paper gives out the analytical form of the reference phase and reference phase frequency in different reference surface, studies the difference of the reference phase and reference phase frequency characteristic and at last analyses the possible methods in baseline reestimation using reference phase characteristic. The paper also setup the theoretic basis for baseline reestimation based on reference phase characteristic. The study shows that the baseline reestimation could be achieved by utilizing the characteristics of reference phase, and the proper surface of reference phase must be selected since different surface of reference phase will cause different reference phase.

In this paper, the time series multi-image stack processing technique is implemented based on the ERS-1, ERS-2 SAR data set of cities of Las Vegas in America. A single master approach is used in the stack data processing based on the permanent scatterers processing technique invented by Ferretti et al. [1,2]. After the differential phase model establishment and stable points selection, linear subsidence velocity and digital elevation model errors are estimated, non-linear subsidence velocity and atmospheric artifacts related to each SAR acquisition are separated, so a land subsidence history covering all the SAR data acquisitions in each city can be achieved. In our research, more test data in cities of China will be implemented in the next step.