Lin, S. et al. Underestimated interannual variability of terrestrial vegetation production by terrestrial ecosystem models. Glob. Biogeochem. Cycles. https://doi.org/10.1029/2023gb007696 (2023).
Shi, S. et al. Analysis of ecological environment quality heterogeneity across different landform types in Myanmar and its driving forces. Ecol. Indic. https://doi.org/10.1016/j.ecolind.2024.112755 (2024).
Wessely, J. et al. Climate warming may increase the frequency of cold-adapted haplotypes in alpine plants. Nat. Clim. Change 12, 77–82. https://doi.org/10.1038/s41558-021-01255-8 (2022).
Zhang, M. et al. Application of the optimal parameter geographic detector model in the identification of influencing factors of ecological quality in Guangzhou, China. Land 11, 1303 (2022).
Beigaite, R. et al. Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes. Glob. Change Biol. 28, 3557–3579. https://doi.org/10.1111/gcb.16110 (2022).
Li, J. et al. Evaluation of geomorphological classification uncertainty using rough set theory: A case study of Shaanxi Province, China. Earth Surf. Processes Landf. 49, 4532–4548 (2024).
Liu, D., Chen, H., Zhang, H., Geng, T. & Shi, Q. Spatiotemporal evolution of landscape ecological risk based on geomorphological regionalization during 1980–2017: A case study of Shaanxi Province, China. Sustainability 12, 941 (2020).
Zhu, L., Li, Z., Su, H. & Wang, X. Temporal and spatial distribution of ancient sites in Shaanxi Province using geographic information systems (GIS). Herit. Sci. 9, 1–10 (2021).
Kattel, G. R. Climate warming in the Himalayas threatens biodiversity, ecosystem functioning and ecosystem services in the 21st century: Is there a better solution?. Biodivers. Conserv. 31, 2017–2044 (2022).
Balist, J., Malekmohammadi, B., Jafari, H. R., Nohegar, A. & Geneletti, D. Detecting land use and climate impacts on water yield ecosystem service in arid and semi-arid areas. A study in Sirvan River Basin-Iran. Appl. Water Sci. 12, 1–14 (2022).
Wang, L. et al. Urban warming increases the temperature sensitivity of spring vegetation phenology at 292 cities across China. Sci. Total Environ. 834, 155154–155154. https://doi.org/10.1016/j.scitotenv.2022.155154 (2022).
Gampe, D. et al. Increasing impact of warm droughts on northern ecosystem productivity over recent decades. Nat. Clim. Change 11, 772–779 (2021).
Haile, G. G. et al. Projected impacts of climate change on drought patterns over East Africa. Earth’s Future https://doi.org/10.1029/2020ef001502 (2020).
Xiong, Y. et al. Influence of human activities and climate change on wetland landscape pattern—A review. Sci. Total Environ. 879, 163112 (2023).
Su, B., Su, Z. & Catena, Z. Trade-off analyses of plant biomass and soil moisture relations on the loess Plateau. CATENA 197, 104946–104946. https://doi.org/10.1016/j.catena.2020.104946 (2020).
Li, Y. et al. Biophysical impacts of earth greening can substantially mitigate regional land surface temperature warming. Nat. Commun. 14, 121 (2023).
Xu HanQiu, X. H. A remote sensing index for assessment of regional ecological changes (2013).
Zhang, H. et al. Combing remote sensing information entropy and machine learning for ecological environment assessment of Hefei-Nanjing-Hangzhou Region, China. J. Environ. Manag. https://doi.org/10.1016/j.jenvman.2022.116533 (2022).
Zhang, H., Ma, C. & Liu, P. Dynamic evaluation of the ecological evolution and quality of arid and semi-arid deserts in the Aibugai River Basin based on an improved remote sensing ecological index. Ecol. Inform. 82, 102727 (2024).
Zhang, L., Liu, Q., Wang, J., Wu, T. & Li, M. Constructing ecological security patterns using remote sensing ecological index and circuit theory: A case study of the Changchun-Jilin-Tumen region. J. Environ. Manag. 373, 123693 (2025).
Du, Z. et al. Ecological health assessment of Tibetan alpine grasslands in Gannan using remote sensed ecological indicators. 1–19 (2024).
Zenghui, S. et al. Effects of large-scale land consolidation projects on ecological environment quality: A case study of a land creation project in Yan’an, China. Environ. Int. 183, 108392 (2024).
Chen, S. et al. Vegetation change and eco-environmental quality evaluation in the loess plateau of China from 2000 to 2020. Remote Sens. https://doi.org/10.3390/rs15020424 (2023).
Yang, Y. & Li, H. Spatiotemporal dynamic decoupling states of eco-environmental quality and land-use carbon emissions: A case study of Qingdao City, China. Ecol. Inform. 75, 101992 (2023).
Sun, C. et al. Ecological quality assessment and monitoring using a time-series remote sensing-based ecological index (ts-RSEI). GIScience Remote Sens. 59, 1793–1816 (2022).
Liu, S. et al. Spatial-temporal changes in vegetation cover in a typical semi-humid and semi-arid region in China: Changing patterns, causes and implications. Ecol. Indic. 98, 462–475 (2019).
Zhou, T. et al. Seasonal dynamics of soil water content in the typical vegetation and its response to precipitation in a semi-arid area of Chinese Loess Plateau. J. Arid Land 13, 1015–1025 (2021).
Wang, B. & Cheng, W. Geomorphic influences on land use/cover diversity and pattern. CATENA 230, 107245 (2023).
Xu, H. et al. Effects of geomorphic spatial differentiation on vegetation distribution based on remote sensing and geomorphic regionalization. Remote Sens. 16, 1062 (2024).
Zhou, Q., Luo, Y., Zhou, X., Cai, M. & Zhao, C. Response of vegetation to water balance conditions at different time scales across the karst area of southwestern China—A remote sensing approach. Sci. Total Environ. 645, 460–470 (2018).
Fei, L., Ya, L. & Shuang, M. Regional difference of grain production potential change and its influencing factors: A case-study of Shaanxi Province, China. J. Agric. Sci. 157, 1–11 (2019).
Liu, S. & Yao, S. The effect of precipitation on the cost-effectiveness of Sloping land conversion program: A case study of Shaanxi Province, China. Ecol. Indic. 132, 108251 (2021).
Gorelick, N. et al. Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Shouzhang, P. (ed Center National Tibetan Plateau Data) (National Tibetan Plateau Data Center, 2020).
Peng, S., Gang, C., Cao, Y. & Chen, Y. Assessment of climate change trends over the Loess Plateau in China from 1901 to 2100. Int. J. Climatol. 38, 2250–2264 (2018).
Peng, S. et al. Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011–2100. Agric. For. Meteorol. 233, 183–194 (2017).
Peng, S., Ding, Y., Liu, W. & Li, Z. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst. Sci. Data 11, 1931–1946 (2019).
Shui, J., Ren, J., Peng, S. & Zhan, X. A dataset of 1 km-spatial-resolution monthly mean temperature and monthly precipitation in the loess plateau from 1901 to 2014. Sci. Data Bank 4, 133–142 (2019).
Liu, L., Chen, Y., Peng, S. & Han, Q. J. E. I. Improving forest carbon sequestration through thinning strategies under soil conservation constraints: A case study in Shaanxi Province, China. Ecol. Indic. 166, 112291 (2024).
Wang, R. et al. Mapping 30-m resolution bioclimatic variables during 1991–2020 climate normals for Hubei province, the Yangtze river middle reaches. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 17, 4646–4662 (2024).
Zheng, Z., Wu, Z., Chen, Y., Guo, C. & Marinello, F. Instability of remote sensing based ecological index (RSEI) and its improvement for time series analysis. Sci. Total Environ. 814, 152595 (2022).
Gong, C., Lyu, F. & Wang, Y. Spatiotemporal change and drivers of ecosystem quality in the Loess Plateau based on RSEI: A case study of Shanxi, China. Ecol. Indic. 155, 111060 (2023).
Mallick, J. et al. Analysing the trend of rainfall in Asir region of Saudi Arabia using the family of Mann-Kendall tests, innovative trend analysis, and detrended fluctuation analysis. Theor. Appl. Climatol. 143, 823–841 (2021).
Zhou, J., Deitch, M. J., Grunwald, S. & Screaton, E. Do the Mann-Kendall test and Theil-Sen slope fail to inform trend significance and magnitude in hydrology?. Hydrol. Sci. J. 68, 1241–1249 (2023).
Long, Y., Jiang, F., Deng, M., Wang, T. & Sun, H. Spatial-temporal changes and driving factors of eco-environmental quality in the Three-North region of China. J. Arid Land 15, 231–252 (2023).
Ren, Z., Tian, Z., Wei, H., Liu, Y. & Yu, Y. Spatiotemporal evolution and driving mechanisms of vegetation in the Yellow River Basin, China during 2000–2020. Ecol. Indic. 138, 108832 (2022).
Wang, K. et al. Sustainability of eco-environment in semi-arid regions: Lessons from the Chinese Loess Plateau. Environ. Sci. Policy 125, 126–134 (2021).
Song, Y. et al. Land space change process and its eco-environmental effects in the Guanzhong Plain urban agglomeration of China. Land 11, 1547 (2022).
Hussain, S. et al. Photosynthesis research under climate change. Photosynthesis Res. 150, 5–19 (2021).
Yuan, B. et al. Spatiotemporal change detection of ecological quality and the associated affecting factors in Dongting Lake Basin, based on RSEI. J. Clean. Prod. 302, 126995 (2021).
Abbass, K. et al. A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environ. Sci. Pollut. Res. 29, 42539–42559 (2022).
Fang, Q. et al. Effects of climatic disturbance on the trade-off between the vegetation pattern and water balance based on a novel model and accurately remotely sensed data in a Semiarid Basin. Remote Sens. 16, 2132 (2024).
Naif, S., Mahmood, D. & Al-Jiboori, M. (2020).
Shi, S. et al. Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the loess plateau. Sci. Total Environ. 755, 142419–142419. https://doi.org/10.1016/j.scitotenv.2020.142419 (2021).
Oliveira, B. F., Moore, F. C. & Dong, X. Biodiversity mediates ecosystem sensitivity to climate variability. Commun. Biol. https://doi.org/10.1038/s42003-022-03573-9 (2022).
Aguirre-Gutiérrez, J. et al. Functional susceptibility of tropical forests to climate change. Nat. Ecol. Evol. 6, 878–889 (2022).
Pan, Z., Gao, G. & Fu, B. Spatiotemporal changes and driving forces of ecosystem vulnerability in the Yangtze River Basin, China: Quantification using habitat-structure-function framework. Sci. Total Environ. 835, 155494 (2022).
Huang, L. Advances and perspectives on soil water research in China’s Loess Plateau. Earth-Sci. Rev. 199, 102962 (2019).
Liu, Z., Wang, J., Wang, X. & Wang, Y. Understanding the impacts of ‘grain for green’ land management practice on land greening dynamics over the loess plateau of China. Land Use Policy 99, 105084–105084. https://doi.org/10.1016/j.landusepol.2020.105084 (2020).
Song, W., Feng, Y. & Wang, Z. Ecological restoration programs dominate vegetation greening in China. Sci. Total Environ. 848, 157729 (2022).
Jiang, F. et al. Loess plateau evapotranspiration intensified by land surface radiative forcing associated with ecological restoration. Agric. For. Meteorol. 311, 108669–108669. https://doi.org/10.1016/j.agrformet.2021.108669 (2021).
Zhang, S. et al. Using the geodetector method to characterize the spatiotemporal dynamics of vegetation and its interaction with environmental factors in the Qinba mountains, China. Remote Sens. 14, 5794 (2022).
Wang, Q. et al. Enhancing resilience against geological hazards and soil erosion through sustainable vegetation management: A case study in Shaanxi Province. J. Clean. Prod. 423, 138687 (2023).
Liu, X. et al. Vegetation dynamics in Qinling-Daba Mountains in relation to climate factors between 2000 and 2014. J. Geograph. Sci. 26, 45–58 (2016).
Su, J., Fan, L., Yuan, Z., Wang, Z. & Wang, Z. Vegetation dynamics and their response patterns to drought in Shaanxi province, China. Forests 14, 1528 (2023).
Seddon, N. et al. Getting the message right on nature-based solutions to climate change. Glob. Change Biol. 27, 1518–1546 (2021).
Wang, B. et al. Vegetation dynamics and their relationships with climatic factors in the Qinling Mountains of China. Ecol. Indic. 108, 105719 (2020).
Gu, L., Yan, J., Li, Y. & Gong, Z. Spatial–temporal evolution and correlation analysis between habitat quality and landscape patterns based on land use change in Shaanxi Province, China. Ecol. Evol. 13, e10657 (2023).
Zhao, G., Liang, R., Li, K., Wang, Y. & Pu, X. Study on the coupling model of urbanization and water environment with basin as a unit: A study on the Hanjiang Basin in China. Ecol. Indic. https://doi.org/10.1016/j.ecolind.2021.108130 (2021).
Wardeh, Y. et al. Review of the optimization techniques for cool pavements solutions to mitigate Urban Heat Islands. Build. Environ. 223, 109482 (2022).
Albert, J. S. et al. Human impacts outpace natural processes in the Amazon. Science 379, eabo5003 (2023).
Jiang, R. et al. Spatiotemporal variability of extreme precipitation in Shaanxi province under climate change. Theor. Appl. Climatol. 130, 831–845 (2017).
Cao, S. et al. Spatiotemporal characteristics of drought and its impact on vegetation in the vegetation region of Northwest China. Ecol. Indic. 133, 108420 (2021).