[1] 周伟, 刚成诚, 李建龙, 等. 1982-2010年中国草地覆盖度的时空动态及其对气候变化的响应[J]. 地理学报, 2014, 69(1): 15−30. https://doi.org/10.11821/dlxb201401002 doi:  10.11821/dlxb201401002
[2]

Wang D J, Peng Q Y, Li X Q, et al. A long-term high-resolution dataset of grasslands grazing intensity in China [J]. Scientific Data, 2024, 11(1): 1194. https://doi.org/10.1038/s41597-024-04045-x doi:  10.1038/s41597-024-04045-x
[3]

Yan N N, Zhu W W, Wu B F, et al. Assessment of the grassland carrying capacity for winter-spring period in Mongolia [J]. Ecological Indicators, 2023, 146: 109868. https://doi.org/10.1016/j.ecolind.2023.109868 doi:  10.1016/j.ecolind.2023.109868
[4] 黄季焜, 侯玲玲, 亢楠楠, 等. 草地生态系统服务经济价值评估研究[J]. 中国工程科学, 2023, 25(1): 198−206. https://doi.org/10.15302/J-SSCAE-2023.01.015 doi:  10.15302/J-SSCAE-2023.01.015
[5]

Ma L S, Zheng J H, Pen J, et al. Monitoring and influencing factors of grassland livestock overload in Xinjiang from 1982 to 2020 [J]. Frontiers in Plant Science, 2024, 15: 1340566. https://doi.org/10.3389/fpls.2024.1340566 doi:  10.3389/fpls.2024.1340566
[6] 苏日娜, 俎佳星, 金花, 等. 内蒙古草地生产力及载畜量变化分析[J]. 生态环境学报, 2017, 26(4): 605−612. https://doi.org/10.16258/j.cnki.1674-5906.2017.04.009 doi:  10.16258/j.cnki.1674-5906.2017.04.009
[7]

Li X Y, Zhao C Z, Kang M P, et al. Responses of net primary productivity to phenological dynamics based on a data fusion algorithm in the northern Qinghai-Tibet Plateau [J]. Ecological Indicators, 2022, 142: 109239. https://doi.org/10.1016/j.ecolind.2022.109239 doi:  10.1016/j.ecolind.2022.109239
[8]

Ma R, Xia C L, Liu Y W, et al. Spatiotemporal change of net primary productivity and its response to climate change in temperate grasslands of China [J]. Frontiers in Plant Science, 2022, 13: 899800. https://doi.org/10.3389/fpls.2022.899800 doi:  10.3389/fpls.2022.899800
[9]

Zhang Y J, Huang D, Badgery W B, et al. Reduced grazing pressure delivers production and environmental benefits for the typical steppe of north China [J]. Scientific Reports, 2015, 5(1): 16434. https://doi.org/10.1038/srep16434 doi:  10.1038/srep16434
[10]

Qin P Y, Sun B, Li Z Y, et al. Estimation of grassland carrying capacity by applying high spatiotemporal remote sensing techniques in Zhenglan Banner, Inner Mongolia, China [J]. Sustainability, 2021, 13(6): 3123. https://doi.org/10.3390/su13063123 doi:  10.3390/su13063123
[11]

Huang X T, Luo G P, Ye F P, et al. Effects of grazing on net primary productivity, evapotranspiration and water use efficiency in the grasslands of Xinjiang, China [J]. Journal of Arid Land, 2018, 10(4): 588−600. https://doi.org/10.1007/s40333-018-0093-z doi:  10.1007/s40333-018-0093-z
[12]

Zhang R R, Li Z H, Yuan Y W, et al. Analyses on the changes of grazing capacity in the three-river headwaters region of China under various climate change scenarios [J]. Advances in Meteorology, 2013, 2013: 951261. https://doi.org/10.1155/2013/951261 doi:  10.1155/2013/951261
[13]

Huang L, Ning J, Zhu P, et al. The conservation patterns of grassland ecosystem in response to the forage-livestock balance in North China [J]. Journal of Geographical Sciences, 2021, 31(4): 518−534. https://doi.org/10.1007/s11442-021-1856-6 doi:  10.1007/s11442-021-1856-6
[14]

Yan H, Ran Q W, Hu R H, et al. Machine learning-based prediction for grassland degradation using geographic, meteorological, plant and microbial data [J]. Ecological Indicators, 2022, 137: 108738. https://doi.org/10.1016/j.ecolind.2022.108738 doi:  10.1016/j.ecolind.2022.108738
[15]

Huang X T, Luo G P, He H L, et al. Ecological effects of grazing in the Northern Tianshan Mountains [J]. Water, 2017, 9(12): 932. https://doi.org/10.3390/w9120932 doi:  10.3390/w9120932
[16]

Tang Q H, Liu X C, Zhou Y Y, et al. Climate change and water security in the northern slope of the Tianshan Mountains [J]. Geography and Sustainability, 2022, 3(3): 246−257. https://doi.org/10.1016/j.geosus.2022.08.004 doi:  10.1016/j.geosus.2022.08.004
[17]

江康威, 张青青, 王亚菲, 等. 天山北坡中段草地生态系统多功能性对放牧的响应 [J]. 生态学报, 2024, 44(8): 3440−3456. https://doi.org/10.20103/j.stxb.202308111737 doi:  10.20103/j.stxb.202308111737
[18]

Chen J C, Chen Y P, Liu Z, et al. Validation of MODIS LAI product using upscaling sentinel-2 decameter-scale LAI and field measured LAI[C]//IGARSS 2023 - 2023 IEEE international geoscience and remote sensing symposium. Pasadena: IEEE, 2023: 5119−5122. https://doi.org/10.1109/IGARSS52108.2023.10281427
[19] 陈惺, 王军邦, 何启凡, 等. 未来气候情景下中国植被净初级生产力稳定性及气候影响[J]. 地理学报, 2023, 78(3): 694−713. https://doi.org/10.11821/dlxb202303012 doi:  10.11821/dlxb202303012
[20]

Griffiths P, Kuemmerle T, Baumann M, et al. Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites [J]. Remote Sensing of Environment, 2014, 151: 72−88. https://doi.org/10.1016/j.rse.2013.04.022 doi:  10.1016/j.rse.2013.04.022
[21]

Cleveland W S. Robust locally weighted regression and smoothing scatterplots [J]. Journal of the American Statistical Association, 1979, 74(368): 829−836. https://doi.org/10.1080/01621459.1979.10481038 doi:  10.1080/01621459.1979.10481038
[22]

Zhu J P, Xie A H, Qin X, et al. An assessment of ERA5 reanalysis for Antarctic near-surface air temperature [J]. Atmosphere, 2021, 12(2): 217. https://doi.org/10.3390/atmos12020217 doi:  10.3390/atmos12020217
[23]

Ryu Y, Jiang C Y, Kobayashi H, et al. MODIS-derived global land products of shortwave radiation and diffuse and total photosynthetically active radiation at 5km resolution from 2000 [J]. Remote Sensing of Environment, 2018, 204: 812−825. https://doi.org/10.1016/j.rse.2017.09.021 doi:  10.1016/j.rse.2017.09.021
[24]

Pan L, Xiao X M, Pan B H, et al. Interannual variations and trends of gross primary production and transpiration of four mature deciduous broadleaf forest sites during 2000–2020 [J]. Remote Sensing of Environment, 2024, 304: 114042. https://doi.org/10.1016/j.rse.2024.114042 doi:  10.1016/j.rse.2024.114042
[25]

Ma X L, Huete A, Yu Q, et al. Parameterization of an ecosystem light-use-efficiency model for predicting savanna GPP using MODIS EVI [J]. Remote Sensing of Environment, 2014, 154: 253−271. https://doi.org/10.1016/j.rse.2014.08.025 doi:  10.1016/j.rse.2014.08.025
[26]

Xiangming Xiao. Light absorption by leaf chlorophyll and maximum light use efficiency [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(7): 1933−1935. https://doi.org/10.1109/TGRS.2006.874796 doi:  10.1109/TGRS.2006.874796
[27]

Dye D G. Spectral composition and quanta‐to‐energy ratio of diffuse photosynthetically active radiation under diverse cloud conditions [J]. Journal of Geophysical Research: Atmospheres, 2004, 109(D10): D10203. https://doi.org/10.1029/2003JD004251 doi:  10.1029/2003JD004251
[28]

Zhang Y, Xiao X, Jin C, et al. Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America [J]. Remote Sensing of Environment, 2016, 183: 154−169. https://doi.org/10.1016/j.rse.2016.05.015 doi:  10.1016/j.rse.2016.05.015
[29]

Xiao X, Zhang Q, Braswell B, et al. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data [J]. Remote Sensing of Environment, 2004, 91(2): 256−270. https://doi.org/10.1016/j.rse.2004.03.010 doi:  10.1016/j.rse.2004.03.010
[30]

Zhang L X, Hu Z M, Fan J W, et al. A meta-analysis of the canopy light extinction coefficient in terrestrial ecosystems [J]. Frontiers of Earth Science, 2014, 8(4): 599−609. https://doi.org/10.1007/s11707-014-0446-7 doi:  10.1007/s11707-014-0446-7
[31]

Zhang Y, Xiao X M, Wu X C, et al. A global moderate resolution dataset of gross primary production of vegetation for 2000–2016 [J]. Scientific Data, 2017, 4(1): 170165. https://doi.org/10.1038/sdata.2017.165 doi:  10.1038/sdata.2017.165
[32]

Landsberg J J, Waring R H, Williams M. The assessment of NPP/GPP ratio [J]. Tree Physiology, 2020, 40(6): 695−699. https://doi.org/10.1093/treephys/tpaa016 doi:  10.1093/treephys/tpaa016
[33] 王昭生. 1949-2008年东亚区域陆地生态系统NPP和GPP的时空格局演变[D]. 湘潭: 湖南科技大学, 2012. doi: 10.7666/d.D290678
[34] 张千羚. 基于NPP数据的锡林郭勒盟牧区草地资源承载力估算研究[J]. 赤峰学院学报(自然科学版), 2024, 40(2): 1−5. https://doi.org/10.3969/j.issn.1673-260X.2024.02.002 doi:  10.3969/j.issn.1673-260X.2024.02.002
[35]

Yang Y H, Fang J Y, Ma W H, et al. Large‐scale pattern of biomass partitioning across China’s grasslands [J]. Global Ecology and Biogeography, 2010, 19(2): 268−277. https://doi.org/10.1111/j.1466-8238.2009.00502.x doi:  10.1111/j.1466-8238.2009.00502.x
[36] 莫兴国, 刘文, 孟铖铖, 等. 青藏高原草地产量与草畜平衡变化[J]. 应用生态学报, 2021, 32(7): 2415−2425. https://doi.org/10.13287/j.1001-9332.202107.002 doi:  10.13287/j.1001-9332.202107.002
[37]

Shiyomi M, Akiyama T, Wang S P, et al. A grassland ecosystem model of the Xilingol steppe, Inner Mongolia, China [J]. Ecological Modelling, 2011, 222(13): 2073−2083. https://doi.org/10.1016/j.ecolmodel.2011.03.028 doi:  10.1016/j.ecolmodel.2011.03.028
[38]

Zhu Q A, Chen H, Peng C H, et al. An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau [J]. Nature Communications, 2023, 14(1): 6406. https://doi.org/10.1038/s41467-023-42099-4 doi:  10.1038/s41467-023-42099-4
[39]

Ellis J E, Swift D M. Stability of African pastoral ecosystems: alternate paradigms and implications for development [J]. Journal of Range Management, 1988, 41(6): 450−459. https://doi.org/10.2307/3899515 doi:  10.2307/3899515
[40]

Ji F K, Xi G L, Xie Y W, et al. Estimating grassland carrying capacity in the Source Area of Nujiang River and Selinco Lake, Tibetan Plateau (2001–2020) based on multisource remote sensing [J]. Remote Sensing, 2024, 16(20): 3790. https://doi.org/10.3390/rs16203790 doi:  10.3390/rs16203790
[41]

Wang Y G, Luo G P, Li C F, et al. Grazing weakens the carbon sequestration capacity of dry temperate grassland ecosystems in Central Asia [J]. CATENA, 2025, 248: 108608. https://doi.org/10.1016/j.catena.2024.108608 doi:  10.1016/j.catena.2024.108608
[42]

Zhao M S, Running S W, Nemani R R. Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses [J]. Journal of Geophysical Research: Biogeosciences, 2006, 111(G1): G01002. https://doi.org/10.1029/2004JG000004 doi:  10.1029/2004JG000004
[43]

Huang X T, Luo G P, Ma Z, et al. Modeling the effect of grazing on carbon and water use efficiencies in grasslands on the Qinghai–Tibet Plateau [J]. BMC Ecology and Evolution, 2024, 24(1): 26. https://doi.org/10.1186/s12862-024-02215-4 doi:  10.1186/s12862-024-02215-4
[44]

Li C F, Han Q F, Xu W Q. Contribution of climate change and grazing on carbon dynamics in central Asian pasturelands [J]. Remote Sensing, 2022, 14(5): 1210. https://doi.org/10.3390/rs14051210 doi:  10.3390/rs14051210
[45]

Reinermann S, Asam S, Kuenzer C. Remote sensing of grassland production and management—a review [J]. Remote Sensing, 2020, 12(12): 1949. https://doi.org/10.3390/rs12121949 doi:  10.3390/rs12121949
[46]

Lulandala L, Bargués‐Tobella A, Masao C A, et al. Excessive livestock grazing overrides the positive effects of trees on infiltration capacity and modifies preferential flow in dry miombo woodlands [J]. Land Degradation & Development, 2022, 33(4): 581−595. https://doi.org/10.1002/ldr.4149 doi:  10.1002/ldr.4149
[47]

Chen H, Zhao X R, Chen X J, et al. Seasonal changes of soil microbial C, N, P and associated nutrient dynamics in a semiarid grassland of north China [J]. Applied Soil Ecology, 2018, 128: 89−97. https://doi.org/10.1016/j.apsoil.2018.04.008 doi:  10.1016/j.apsoil.2018.04.008