## 5 Conclusions  
CAS CI products from 2001 to 2015 were used to analyse the spatial and temporal variation characteristics of CI in the Sanjiang Plain. Furthermore, the correlation between CI and LAI was explored based on the CAS CI and GLASS LAI products from 2001 to 2015. In addition, two climatic datasets (average annual temperature and annual precipitation) from 2001 to 2015, soil type data, vegetation type data, three topographic data sets (aspect, slope, and elevation), geomorphic type data, and land use type data were used to quantify the driving forces of the spatial distribution of CI. The major conclusions drawn are as follows:  
(1) In terms of temporal variations, the interannual variation of CI in the Sanjiang Plain from 2001 to 2015 was not obvious, and the overall variation exhibited a slight upward trend, with a slow growth rate of 0.021\(\cdot\)10 a\({}^{-1}\) (\(p<0.05\)). However, the seasonal variation was obvious, with the strongest clumping effects in summer and the weakest clumping effects in winter. (2) In terms of spatial variations, lightly improved and lightly degraded areas were the main variation trends of CI\({}_{\text{CS}}\) in the Sanjiang Plain from 2001 to 2015, with area proportions of 46.18% and 42.25%, respectively. The spatial distribution of the multiyear mean CI of each season was related to the spatial distribution of the land use type, and the multiyear mean CI in each season exhibited a slight downward trend with increasing elevation. Moreover, the multiyear mean CI of each season for cultivated land was greater than that for forestland. (3) Temporally, the correlation between the CI time series and LAI time series was not significant in the Sanjiang Plain from 2001 to 2015. Spatially, CI exhibited a negative correlation with LAI. (4) The elevation, geographic type, and slope were the main factors dominating the spatial differentiation of CI. Furthermore, the vegetation type, land use type, and soil type were the secondary driving forces of the spatial distribution of CI. Notably, as single driving factors, climatic factors (average annual temperature and annual precipitation) and aspect had the smallest \(q\) values but still had a notable impact on the spatial differentiation of CI when they interacted with other driving factors. Furthermore, each driving factor had the optimal range/type for foliage clumping effects. The authors' contributions to this article are as follows: conceptualization, Z.Z. ; methodology, K.H. ; software, K.H. and Z.G. ; validation, Z.Z. ; formal analysis, K.H. and Z.Z. ; investigation, Z.Z. and Y.L. ; resources, K.H. ; data curation, K.H. and Z.G. ; writing--original draft preparation, K.H., Z.Z., and H.F.; writing--review and editing, K.H., Z.Z., and H.F.; visualization, K.H. ; supervision, Z.Z. ; project administration, M.G. ; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No.42071085), Open Project of the State Key Laboratory of Cryospheric Science (Grant No. SKLCS 2020\(-\)10), and the National Natural Science Foundation of China (Grant No.41701087). Not applicable. The authors would like to thank the National Earth System Science Data Center, National Science & Technology Infrastructure of China ([[http://www.geodata.cn/](http://www.geodata.cn/)]([http://www.geodata.cn/](http://www.geodata.cn/)) accessed on 11 May 2021) for the CAS (Chinese Academy of Sciences) CI products and the GLASS (Global LAnd Surface Satellite) LAI products, the United States Geological Survey ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) accessed on 11 May 2021) for the SRTM data, and the Resource and Environment Science and Data Center ([[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)) accessed on 11 May 2021) for the other natural factor data and anthropogenic factor data.