Land use–land cover (LULC) is an important feature for ecological environment research, land resource management and evaluation. Although global high-resolution LULC data sets are booming, their regional performances were still evaluated in limited regions. To demonstrate the local applicability of global LULC data products, six emerging LULC data products were evaluated and compared in Guangxi, China. The six products used are European Space Agency GlobCover (ESAGC), ESRI Land Use–Land Cover (ESRI–LULC), Finer Resolution Observation and Monitoring of Global Land Cover (FROM–GLC), the China Land Cover Dataset (CLCD), the Global Land Cover product with Fine Classification System at 30 m (GLC_FCS30) and GlobeLand30 (GLC30). Reference data were obtained from the local government statistical yearbook and high-resolution remote sensing images on Google Earth. The results showed that CLCD, ESRI–LULC and GLC30 were found to agree well with the forest reference data, with the highest correlation coefficient of 0.999. For the cropland areas, GLC30, CLCD and ESAGC agreed well with the reference data, and the highest correlation coefficient was 0.957. Combined with the comparison with the high-resolution images obtained by Google Earth, we finally concluded that ESAGC, CLCD and GLC30 can best represent the LULCs in Guangxi. Furthermore, the spatial consistency analysis showed that three or more products identified the same LULC type as high as 96.98% of the area. We suggest that majority voting might be applied to global LULC products to provide fused products with better performances on a regional or local scale to avoid the error caused by a single data product.

The High Mountain Asia (HMA) region, ranging from the Hindu Kush and Tien Shan in the west to the Himalaya in the south with an altitude between 2000 and 8844 m, holds the largest reservoir of glaciers and snow outside Earth Polar Regions. In the last decades, numerous glaciers and lake areas there have undergone tremendous changes with water redistribution. In order to increase understanding of the pattern of distribution of water resources, and their dynamic changes at the basin scale, a watershed classification based on the water replenishment patterns dataset was constructed. The input dataset are from the Randolph Glacier Inventory V.6.0 and the vector data of rivers and streams. Four datasets were thus obtained: Glacier-fed and Runoff-fed Drainage Area (GRDA), Glacier-fed and Runoff-free Drainage Area (GDA), Glacier-free and Runoff-fed Drainage Area (RDA), and the Glacier-free and Runoff-free Drainage Area (NGRDA), and the numbers of these four types of basins are 87, 107, 32, and 448 separately. The statistical results show GRDA has the largest surface area, accounting for 82.2% of the total basin area in HMA, mainly in the region of the basin with outflow rivers or streams. Dominated by small basins, the GDA area accounts for the smallest area, only 3.86% and the RDA accounts for 5.62%. For NGRDA, most are with small areas, accounting for 8.32%, and mainly distributes in the closed basin of the Qiangtang Plateau. This dataset provides a fundamental classified data source for research on water resources, climate, ecology, and environment in HMA. The published data are available at https://data.4tu.nl/download/uuid:d07d748f-d10b-4308-9626-199ef05cc9af/ and http://www.dx.doi.org/10.11922/sciencedb.923.

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.