## 4 Discussion
### Further Research  
The 2030 Sustainable Development Goals (SDGs) are currently less than 10 years away. Given the long time it takes for ecosystems to recover from conservation to benefits, only urgent and effective action to halt and reverse global land degradation will make it possible to meet the growing global demand for agriculture and, thus, achieve the 2030 SDGs. Focusing on the lack of methodology in assessing SDG 15.3 progress, this paper used an internationally shared dataset to enable the tracking and monitoring of SDG 15.3.1 indicators to evaluate trends in the growth trend of LDN in the Han River Basin. The study can provide important information to support the understanding of SDG 15.3 achievement at a regional scale. The methodology adopted in this paper can also be replicated and operationalized on a global scale. The results are globally consistent and comparable, which can help to clarify the global contribution of LDN growth in the region. The findings of this paper on the achievement and trends of LDN growth in the Han River Basin are broadly consistent with those of [PERSON] and [PERSON] (2016) [43] on the spatial distribution and temporal trends of land productivity. The NPP data in both studies were based on the MOD17A3 dataset, which was obtained using a model combining ecological processes and remote sensing data (Biome-BGC model). The MOD17A3 NPP dataset compensates to some extent for the deficiencies of ecophysiological models in non-homogeneous regions and helps provide feedback on regional land productivity, carbon sink capacity, and ecosystem quality. The multi-year average of land NPP derived in this study (486 g C\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\)) was higher than that gained by [PERSON] and [PERSON] (2016) [43] (439 g C\(\cdot\)m\({}^{-2}\)\(\cdot\)a\({}^{-1}\)), mainly due to the inconsistent time period. This study was based on 2001-2019, whereas their work was based on 2000-2012. Therefore, this study better represented the impact of the implementation of China's South-to-North Water Diversion Project (the middle route started supplying water on 12 December 2014) on land NPP and the ecology of the water source area. This paper explored the drivers of land NPP change and their spatial differentiation using an MGWR model based on regular grids, which helps identify the dominant influencing factors at given locations. The study reflected that land NPP change in the Han River Basin resulted from a combination of natural elements and human activities. Changes in land NPP are usually nonlinear and are often influenced by multiple factors. Most previous studies have explored the influence of climatic factors on land NPP changes [34; 41; 42; 44], while the combined effects of climate change and human activities have been less considered [43]. The study by [PERSON] and [PERSON] (2016) [43] elaborated the effects of temperature, precipitation, elevation, slope, and human activities, respectively. Their finding that the precipitation had a more substantial impact on land NPP than the temperature was consistent with present studies [42; 43; 68]. However, these studies were more oriented toward descriptive statistical analysis and did not spatially elucidate the dominant factors of spatial differentiation. Therefore, there were similarities and differences in the conclusions. Several studies have discussed anthropogenic impact concerning land-use patterns or land-use intensity [30; 31; 32; 33], but there is no direct relationship between land NPP and land use. Land NPP may remain constant for land-use changes, while land use may stay the same for land NPP changes. Baseline determination and dynamic monitoring of degraded land are key tasks in achieving SDG target 15.3 (LDN) [8], which can be directly used to assess the progress of LDN and provide support for land-use planning. Further research can be geared toward the demand for geographic data at different spatial scales in the assessment of SDG 15.3 progress.