## 1 Introduction  
Land degradation is defined as a persistent decline in terrestrial ecosystem productivity during a period and it is characterized by vegetation degradation, water loss, soil erosion and desertification [1]. It has profoundly affected the ecosystem and human well-being and induced a series of environmental and social problems, including carbon emissions, climate change, biodiversity loss, sandstorms and food and water security [2, 3, 4]. Land development represents land improvement and is characterized by vegetation restoration, terrestrial ecosystem productivity and vegetation cover growth. It is beneficial to natural environment protection and socioeconomic development [5]. The world is facing the issue of land degradation and development (LDD) and it has attracted strong attention from concerned individuals and the scientific community [6].  
Quick and accurate monitoring of LDD dynamics is key to ensure the sustainability of land resources. The development of remote sensing (RS) technology provides an important resource for monitoring the spatiotemporal dynamics of LDD processes and plays a vital role in the mitigation of land degradation, especially at large scales (e.g., national scale, continent scale, and global scale) [4, 7]. Remotely sensed vegetation indices have been widely applied to reflect LDD processes in previous studies. One of the most common indices is normalized difference vegetation index (NDVI) [6]. NDVI reflects the vegetation conditions of terrestrial ecosystems based on the normalized difference between red and near-infrared red (NIR) radiation of healthy vegetation. However, there are two main flaws when using NDVI to represent the biomass and production of terrestrial ecosystems [8]. First, the relationships between NDVI, biomass and productivity are not linear, and may become saturated in high vegetation cover areas. In addition, NDVI reflects the greenness instead of the photosynthesis of vegetation. This means that NDVI may not be very sensitive to a substantial increase or decline in vegetation, and therefore does not necessarily reflect the actual biomass and productivity dynamics in particular regions [8]. These issues result in the uncertainty of NDVI in evaluating the dynamics of LDD.  
To improve the reliability of NDVI in monitoring LDD processes, some studies have attempted to construct a new index by integrating NDVI with transformation parameters. For instance, multiplying NDVI by NIR, [PERSON] et al. [9] proposed the NIRv and [PERSON] et al. [8] built a kernel NDVI (kNDVI) using the theory of kernel methods based on machine learning. Both new indices showed higher correlations with gross primary productivity (GPP) and sun-induced chlorophyll fluorescence (SIF) than the original NDVI at specific temporal scales [8; 9]. However, the abovementioned new indices may not be suitable at the large regional scale and over longer period because the calculation process is complex and time-consuming. Therefore, the question remains whether we can create a more convenient and simpler index to quickly and accurately monitor LDD processes at wide spatiotemporal scales. NPP (net primary productivity), as an important remotely sensed parameter, can accurately reflect ecosystem productivity and biomass, and has been individually used to explore LDD processes at different spatiotemporal scales [10]. Theoretically, NDVI can reveal LDD processes if it is integrated with NPP. However, no previous research has attempted this.  
In this study, we introduced a LDD index (LDDI) by integrating NDVI and NPP based on the Euclidean distance method to detect the LDD processes in China from 1985 to 2015 and explored its relationship to climate change and human activities. This study can offer quick and accurate monitoring of LDD at large spatiotemporal scales and provide a deeper understanding of LDD processes and their driving mechanisms and eventually contribute to land use sustainability in China and other countries around the world.