Mountains provide essential ecosystem services to billions of people and are home to a majority of the global biodiversity hotspots. However, mountain ecosystems are particularly sensitive to climate and environmental changes. The protection and sustainable management of mountain ecosystems are thus of great importance and are listed as a Sustainable Development Goal (SDG 15.4) of the United Nations 2030 Agenda for sustainable development. The mountain green cover index (MGCI, SDG 15.4.2), which is defined by measuring the changes of green vegetation in mountain areas, is one of the two SDG indicators for monitoring the conservation status of mountain environments. However, as a country indicator, it is challenging to use the current MGCI data to quantify the detailed changes in highly heterogeneous mountain areas within each country, and correspondingly, the measures is limited when supporting sustainable development and protection strategy decision-making for mountain environments. In this paper, a new global high resolution gridded-MGCI calculation method that depicts the varying details in the MGCI from both the spatial and temporal domains was proposed based on 30-m Landsat-8 Operational Land Imager (OLI) images and the Google Earth Engine (GEE) cloud computing platform. In the method, first, a grid-based MGCI calculation model was proposed by that considers the true surface area instead of the planimetric area of each mountain pixel. The global green vegetation cover was then extracted using all available 30-m Landsat-8 satellite observations within the calendar year on the GEE platform via a new frequency- and phenology-based algorithm. The mountain true surface area was finally calculated and introduced into the MGCI calculation model for global MGCI mapping. The results showed that the green vegetation cover extracted from 30 m Landsat images can reach an overall accuracy of 95.56%. In general, 69.73% of the global mountain surface had 1.05 times more surface area than planimetric area. The average difference between the MGCIs considering the surface area and planimetric area can reach 11.89%. According to the statistics of the global grid MGCI, 68.79% of the global mountain area had an MGCI higher than 90%, 16.94% of the global mountain area had no vegetation cover and 3.81% of mountain area had an MGCI lower than 10%. The proposed MGCIs were further aggregated at the country level and compared with the Food and Agriculture Organization (FAO) MGCI baseline data from 2017. The comparison indicated good consistency between the two datasets, with an R² of 0.9548 and a mean absolute difference of 4.26%. The new MGCI calculation method was based all available Landsat-8 observations from a year, which reduced the dependence of the MGCI on the updating frequency of the land cover product. Furthermore, the method has great potential for getting the spatio-temporal continuous MGCI with a high spatial resolution for characterizing explicit mountain vegetation dynamics and vegetation-climate change interactions to advance our understanding of global mountain changes. The new MGCI data will be available on the CASEarth data-sharing platform.

山区为数十亿人提供了必不可少的生态系统服务，并且是全球大多数生物多样性热点的家园。但是，山区生态系统对气候和环境变化特别敏感。因此，山区生态系统的保护和可持续管理非常重要，被列为《联合国2030年可持续发展议程》的可持续发展目标（SDG 15.4）。通过测量山区绿色植被的变化而定义的山地绿色覆盖指数（MGCI，SDG 15.4.2）是用于监测山区环境保护状况的两个SDG指标之一。但是，作为一个国家指标，使用当前的MGCI数据来量化每个国家/地区内高度异质山区的详细变化具有挑战性，因此，在支持山区环境的可持续发展和保护战略决策时，这些措施是有限的。本文基于30米Landsat-8作战陆地成像仪（OLI）图像和Google Earth，提出了一种新的全局高分辨率网格化MGCI计算方法，该方法可描述时空域中MGCI中的变化细节。引擎（GEE）云计算平台。在该方法中，首先，提出了一种基于网格的MGCI计算模型，该模型考虑了真实的表面积而不是每个山像素的平面面积。然后，通过基于频率和物候的新算法，利用GEE平台上的日历年内所有可用的30 m Landsat-8卫星观测数据提取全球绿色植被覆盖度。最终计算出了山的真实表面积，并将其引入用于全球MGCI映射的MGCI计算模型中。结果表明，从30 m Landsat影像中提取的绿色植被覆盖度可达到95.56％的总体精度。一般而言，全球山地面积的69.73％比表面积大1.05倍。考虑到表面积和平面面积的MGCI之间的平均差异可以达到11.89％。根据全球网格MGCI的统计，全球山区的68.79％的MGCI高于90％，全球山区的16.94％的植被没有覆盖，而3.81％的山区的MGCI低于10％。拟议的MGCI在国家一级得到进一步汇总，并与2017年粮食及农业组织（FAO）MGCI基准数据进行了比较。0.9548中的第^2个，平均绝对差为4.26％。新的MGCI计算方法基于一年中所有可用的Landsat-8观测值，从而减少了MGCI对土地覆盖产品更新频率的依赖性。此外，该方法具有获得具有高空间分辨率的时空连续MGCI的巨大潜力，可用于表征显性山地植被动态和植被-气候变化的相互作用，从而加深我们对全球山地变化的认识。新的MGCI数据将在CASEarth数据共享平台上提供。