Phenological characteristics of net ecosystem carbon exchange of Stipa krylovii steppe in Inner Mongolia, China and its remote sensing monitoring

doi:10.13287/j.1001-9332.202403.025

Abstract

Abstract: To accurately monitor the phenology of net ecosystem carbon exchange (NEE) in grasslands with remote sensing, we analyzed the variations in NEE and its phenology in the Stipa krylovii steppe and discussed the remote sensing vegetation index thresholds for NEE phenology, with the observational data from the Inner Mongolia Xilinhot National Climate Observatory’s eddy covariance system and meteorological gradient observation system during 2018-2021, as well as Sentinel-2 satellite data from January 1, 2018 to December 31, 2021. Results showed that, from 2018 to 2021, NEE exhibited seasonal variations, with carbon sequestration occurring from April to October and carbon emission in other months, resulting in an overall carbon sink. The average Julian days for the start date (SCUP) and the end date (ECUP) of carbon uptake period were the 95^th and 259^th days, respectively, with an average carbon uptake period lasting 165 days. Photosynthetically active radiation showed a negative correlation with daily NEE, contributing to carbon absorption of grasslands. The optimal threshold for capturing SCUP was a 10% threshold of the red-edge chlorophyll index, while the normalized difference vegetation index effectively reflected ECUP with a threshold of 75%. These findings would provide a basis for remote sensing monitoring of grassland carbon source-sink dynamics.

Key words: Stipa krylovii steppe, net ecosystem carbon exchange, phenology, remote sensing monitoring, Inner Mongolia

WANG Jin, ZHOU Guangsheng, HE Qijin, ZHOU Li. Phenological characteristics of net ecosystem carbon exchange of Stipa krylovii steppe in Inner Mongolia, China and its remote sensing monitoring[J]. Chinese Journal of Applied Ecology, 2024, 35(3): 659-668.

References

[1] 季波, 何建龙, 王占军, 等. 宁夏天然草地植被碳储量特征及构成. 应用生态学报, 2021, 32(4): 1259-1268
[2] 王卓妮, 袁佳双, 庞博, 等. IPCC AR6 WGIII 报告减缓主要结论、亮点和启示. 气候变化研究进展, 2022, 18(5): 531
[3] White MA, de Beurs KM, Didan K, et al. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Global Change Biology, 2009, 15: 2335-2359
[4] Piao SL, Fang JY, Zhou LM, et al. Variations in satellite-derived phenology in China’s temperate vegetation. Global Change Biology, 2006, 12: 672-685
[5] Sparks TH, Menzel A. Observed changes in seasons: An overview. International Journal of Climatology, 2002, 22: 1715-1725
[6] 竺可桢. 中国近五千年来气候变迁的初步研究. 中国科学, 1973, 16(2): 168-189
[7] Wang XH, Piao SL, Ciais P, et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature, 2014, 506: 212
[8] Delgado-Baquerizo M, Eldridge DJ, Ochoa V, et al. Soil microbial communities drive the resistance of ecosystem multifunctionality to global change in drylands across the globe. Ecology Letters, 2017, 20: 1295-1305
[9] Chen H, Zhu QY, Peng CH, et al. The impacts of climate change and human activities on biogeochemical cycles on the Qinghai-Tibetan Plateau. Global Change Biology, 2013, 19: 2940-2955
[10] Huang YY, Chen YX, Castro-Izaguirre N, et al. Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science, 2018, 362: 80
[11] Richardson AD, Keenan TF, Migliavacca M, et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agricultural and Forest Meteorology, 2013, 169: 156-173
[12] Cleland EE, Allen JM, Crimmins TM, et al. Phenological tracking enables positive species responses to climate change. Ecology, 2012, 93: 1765-1771
[13] Caparros-Santiago JA, Rodriguez-Galiano V, Dash J. Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 171: 330-347
[14] Yu HY, Luedeling E, Xu JC. Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107: 22151-22156
[15] 张玉静, 杨秀春, 郭剑, 等. 呼伦贝尔草原物候变化及其与气象因子的关系. 干旱区地理, 2019, 42(1): 144-153
[16] Piao SL, Ciais P, Friedlingstein P, et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature, 2008, 451: 49-52
[17] 牟敏杰, 朱文泉, 王伶俐, 等. 基于通量塔净生态系统碳交换数据的植被物候遥感识别方法评价. 应用生态学报, 2012, 23(2): 319-327
[18] 严燕儿, 赵斌, 郭海强, 等. 生态系统碳通量估算中耦合涡度协方差与遥感技术研究进展. 地球科学进展, 2008, 23(8): 884-894
[19] 周昊强, 包刚, 金胡格吉乐吐, 等. 柽柳灌丛关键物候参数多种植被指数遥感提取的适用性:基于CO₂通量观测和Sentinel-2数据. 应用生态学报, 2021, 32(12): 4315-4326
[20] 刘宇霞. 植被物候变化遥感反演及生态系统碳循环作用机理. 硕士论文. 北京: 中国科学院大学(中国科学院遥感与数字地球研究所), 2017
[21] Zhou L, Wang Y, Jia QY, et al. Increasing temperature shortened the carbon uptake period and decreased the cumulative net ecosystem productivity in a maize cropland in Northeast China. Field Crops Research, 2021, 267: 108150
[22] Wang Q, Liu X, Wang ZY, et al. Time scale selection and periodicity analysis of grassland drought monitoring index in Inner Mongolia. Global Ecology and Conservation, 2022, 36: e02138
[23] 张峰, 周广胜, 王玉辉. 内蒙古克氏针茅草原植物物候及其与气候因子关系. 植物生态学报, 2008, 32(6): 1312-1322
[24] 田磊, 朱毅, 李欣, 等. 不同降水条件下内蒙古荒漠草原主要植物物候对长期增温和氮添加的响应. 植物生态学报, 2022, 46(3): 290-299
[25] Poulter B, Frank D, Ciais P, et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature, 2014, 509: 600-603
[26] 薛海丽, 张钦, 唐海萍. 近60a内蒙古不同草原类型区极端气温和干旱事件特征分析. 干旱区地理, 2018, 41(4): 701-711
[27] Delegido J, Verrelst J, Alonso L, et al. Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 2011, 11: 7063-7081
[28] Gitelson AA, Viña A, Ciganda V, et al. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 2005, 32: 2005GL022688
[29] Roerink GJ, Menenti M, Verhoef W. Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 2000, 21: 1911-1917
[30] Gitelson AA, Gritz Y, Merzlyak MN. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 2003, 160: 271-282
[31] Wu C, Niu Z, Gao S. The potential of the satellite derived green chlorophyll index for estimating midday light use efficiency in maize, coniferous forest and grassland. Ecological Indicators, 2012, 14: 66-73
[32] Clevers JGPW, Kooistra L, Van den Brande MMM. Using sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sensing, 2017, 9: 405
[33] Baldocchi DD, Black TA, Curtis PS, et al. Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: A synthesis of FLUXNET data. International Journal of Biometeorology, 2005, 49: 377-387
[34] 李净, 刘红兵, 李彩云, 等. 基于GIMMS 3g NDVI的近30年中国北部植被生长季始期变化研究. 地理科学, 2017, 37(4): 620-629
[35] 晨阳, 李慧融, 李冬楠, 等. 锡林浩特草原净生态系统碳交换量特征及源区分布. 应用生态学报, 2023, 34(6): 1509-1516
[36] 薛红喜, 李琪, 黄瑜, 等. 土壤环境因子对克氏针茅草地生态系统碳通量的影响. 地理科学, 2014, 34(11): 1385-1390
[37] 李岩, 干珠扎布, 胡国铮, 等. 增温对青藏高原高寒草原生态系统碳交换的影响. 生态学报, 2019, 39(6): 2004-2012
[38] 丁明军, 张镱锂, 刘林山, 等. 青藏高原植物返青期变化及其对气候变化的响应. 气候变化研究进展, 2011, 7(5): 317
[39] Wang N, Quesada B, Xia LL, et al. Effects of climate warming on carbon fluxes in grasslands: A global meta-analysis. Global Change Biology, 2019, 25: 1839-1851
[40] Shi LN, Lin ZR, Tang SM, et al. Interactive effects of warming and managements on carbon fluxes in grasslands: A global meta-analysis. Agriculture, Ecosystems & Environment, 2022, 340: 108178
[41] 齐建东, 黄俊尧. 基于深度学习的草地生态系统净碳交换模拟. 农业机械学报, 2020, 51(6): 152-161
[42] Du Q, Liu HZ, Li YH, et al. The effect of phenology on the carbon exchange process in grassland and maize cropland ecosystems across a semiarid area of China. Science of the Total Environment, 2019, 695: 133868
[43] 文双雅, 高倩文, 高志强, 等. 稻油两熟农田生态系统净碳交换特征及其主要影响因子研究. 农业现代化研究, 2022, 43(1): 162-171
[44] Wofsy SC, Goulden ML, Munger JW, et al. Net exchange of CO₂ in a mid-latitude forest. Science, 1993, 260: 1314-1317
[45] Fan YZ, Zhang XZ, Wang JS, et al. Effect of solar radiation on net ecosystem CO₂ exchange of alpine meadow on the Tibetan Plateau. Journal of Geographical Sciences, 2011, 21: 666-676
[46] 王松年, 王云琦, 王凯, 等. 缙云山针阔叶混交林涡相关适用性及碳通量变化特征. 林业科学研究, 2022, 35(4): 93-102
[47] Clevers JGPW, Gitelson AA. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and-3. International Journal of Applied Earth Observation and Geoinformation, 2013, 23: 344-351
[48] Maleki M, Arriga N, Barrios JM, et al. Estimation of gross primary productivity (GPP) phenology of a short-rotation plantation using remotely sensed indices derived from Sentinel-2 images. Remote Sensing, 2020, 12: 2104
[49] Dash J, Curran PJ. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Advances in Space Research, 2007, 39: 100-104
[50] Ren J, Campbell JB, Shao Y. Estimation of SOS and EOS for Midwestern US corn and soybean crops. Remote Sensing, 2017, 9: 722
[51] Piao SL, Liu Q, Chen A, et al. Plant phenology and global climate change: Current progresses and challenges. Global Change Biology, 2019, 25: 1922-1940
[52] Richardson AD, Anderson RS, Arain MA, et al. Terrestrial biosphere models need better representation of vegetation phenology: Results from the North American Carbon Program Site Synthesis. Global Change Biology, 2012, 18: 566-584
[53] Sonnentag O, Hufkens K, Teshera-Sterne C, et al. Digital repeat photography for phenological research in forest ecosystems. Agricultural and Forest Meteorology, 2012, 152: 159-177
[54] Huang X, Liu JH, Zhu WQ, et al. The optimal threshold and vegetation index time series for retrieving crop phenology based on a modified dynamic threshold method. Remote Sensing, 2019, 11: 2725
[55] Wu, CY, Peng, DL, Soudani K, et al. Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites. Agricultural and Forest Meteorology, 2017, 233: 171-182
[56] D’Odorico P, Gonsamo A, Gough CM et al. The match and mismatch between photosynthesis and land surface phenology of deciduous forests. Agricultural and Forest Meteorology, 2015, 214-215: 25-38
[57] Bai Y, Cotrufo MF. Grassland soil carbon sequestration: Current understanding, challenges, and solutions. Science, 2022, 377: 603-608
[58] 范瑛, 李小雁, 李广泳. 基于遥感数据的内蒙古草原灌丛物候变化研究. 干旱气象, 2014, 36(6): 902-908
[59] 郭剑, 陈实, 徐斌, 等. 基于SPOT-VGT数据的锡林郭勒盟草原返青期遥感监测. 地理研究, 2017, 36(1): 37-48