## 1 Introduction  
Changes and losses in global biodiversity have been rapidly accelerating in recent years [1; 2; 3; 4; 5; 6]. Understanding of both current patterns and change tendency is a key concern for scientists, ecologists, and policy-makers. Biodiversity has a multitude of facets that can be quantified, and the most commonly considered one is species richness [7; 8]. Species richness is the number of species in a site, habitat, or clade [1; 9]. Much research has focused on the distribution of animal species richness [10; 11; 12; 13; 14; 15]. In addition, a growing amount of data infrastructure has been constructed for continental-to-global scale species monitoring and analysis [16; 17]. The Global Biodiversity Information Facility (GBIF) and the International Union for Conservation of Nature (IUCN) provides access to millions of current global digitized species records [18; 19; 20]. However, the scale limitation of in situ data has hindered large-scale species monitoring. The collection of in situ data is too costly to be applied in long time-series species monitoring. Remote sensing is a powerful tool in global biodiversity assessment because it enables consistent observations of species across time and space, as well as the tracking of climatic change and other drivers of species change [4]. Based on remote sensing technology, two common methods are used for the observation of species richness: direct species monitoring and indirect species monitoring. In direct species monitoring, the high spatial and spectral resolution of remote sensing data enables individual species to be distinguished from nearby pixels, and the associations of single species can be evaluated based on the individual species [21; 22]. However, this method requires extremely high spatial and spectral resolution remote sensing data, which are generally not available to the general public. Considering the distribution differences between different species or classes, a comprehensive biodiversity assessment on a global scale is too costly to achieve by using direct species monitoring [10; 11; 23; 24]. To increase the monitoring scale and evaluate more species or classes, indirect species monitoring mainly maps the key environmental factors which relate to species distribution using remote sensing data [25; 26]. Compared with direct species monitoring, indirect species monitoring shows less requirement for high spatial and spectral resolution of remote sensing data, and can thus facilitate continuous large-scale species monitoring. Previous studies have found that a variety of factors impact the distribution of animal species, mainly in four categories: energy/productivity [27; 28; 29]; climate [11; 30; 31; 32]; ecosystem texture [33; 34]; and evolutionary history [24]. Energy/productivity, climate, and ecosystem texture can be continuously monitored using remote sensing data. The species-energy hypothesis predicts that more species and higher abundances of individual species will occur where more energy in the form of food is consistently available [35; 36; 37; 38]. Climate change can lead to systematic changes of species distribution [39; 40]. Ecosystem texture determines the physical structure of the environment, and therefore, has a considerable influence on the distributions and interactions of animal species [41; 42; 43]. Based on these theories and hypotheses, recent studies have developed several frameworks, such as Essential Biodiversity Variables (EBVs) and Remote Sensing Essential Biodiversity Variables (RS-EBVs), which have been used to construct a global species-observing system [2; 44]. Meanwhile, many new remote sensing metrics have been developed to monitor species on a global scale [25; 26; 45; 46; 47; 48]. The most outstanding feature of these metrics is that they can be generated on a global scale. Assessment of different remote sensing metrics on a global scale and the development of a multivariate integration index is essential for global biodiversity assessment, but such efforts are missing from the literature.