Spatial uncertainty in multi-year mean phenology based on remote sensing data
- Vol. 26, Issue 9, Pages: 1814-1823(2022)
Published: 07 September 2022
DOI: 10.11834/jrs.20221043
扫 描 看 全 文
浏览全部资源
扫码关注微信
Published: 07 September 2022 ,
扫 描 看 全 文
金佳鑫,季盈盈,郭丰生,于涵,肖园园.2022.基于遥感数据的多年平均物候不确定性研究.遥感学报,26(9): 1814-1823
Jin J X,Ji Y Y,Guo F S,Yu H and Xiao Y Y. 2022. Spatial uncertainty in multi-year mean phenology based on remote sensing data. National Remote Sensing Bulletin, 26(9):1814-1823
多年平均物候能够反映植被生长发育节律的均衡状态,是植被物候模拟与预测的关键参数之一。遥感已广泛用于地表物候监测,是空间多年平均物候信息的重要来源。然而,基于遥感的多年平均物候存在不同计算方法,如先确定每年时序曲线的物候点再求平均值(平均法),以及先求多年平均时序曲线再确定物候点(参考曲线法)。上述方法的结果可能存在差异,但目前尚缺乏对这一不确定性及其影响的认识。针对该问题,本研究利用2001年—2016年遥感植被指数数据,分别在平均法和参考曲线法下提取中国森林生长季起始时间的多年平均值(
<math id="M1"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type=
5.33400011
2.79399991
),比较
<math id="M2"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type=
5.33400011
2.79399991
的差异(
△
<math id="M3"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type=
5.33400011
2.79399991
)及其空间异质性;进一步选取物候研究中常用指标,即以
<math id="M4"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type=
5.33400011
2.79399991
为基础的温度“季前时长PD(Preseason Duration)”,分析
<math id="M5"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type=
5.33400011
2.79399991
不同计算方法对物候—气候关系的潜在影响。结果表明,(1)不同方法下的
<math id="M6"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type=
5.33400011
2.79399991
差异显著,总体上平均法小于参考曲线法(-2.6±2.2 d,占88%),其中存在8.0%和6.0%的有效像元其动态平均法和固定平均法小于参考曲线法超过7 d,主要分布在东南丘陵地区。(2)
△
<math id="M7"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602595&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602587&type=
5.33400011
2.79399991
具有显著的空间异质性,主要表现为随年均温的升高而减小(Slope=0.07 d/℃,
P
<
0.01),随年均降水的增加而增大(Slope=-0.0005 d/mm,
P
<
0.01)。(3)不同方法下的PD存在差异,约40%有效像元的差异(
△
PD)超过5 d(其中近50%的像元
△
PD超过15 d),主要分布在东南丘陵和西南山区。研究结果将为遥感地表物候的模型空间参数化应用提供有益参考。
Multi-year mean phenology reflects the average state of vegetation growth and development rhythm and is one of the key parameters for predicting vegetation phenology. As an important source of spatial multi-year mean phenology
remote sensing is widely used for phenology detection. Different methods of multi-year mean phenology calculation are based on remote sensing. One is determining the phenological point of the annual time series curve first and then calculating the average (referred as the average method)
and another is gaining the multi-year mean time series curve first and then determining the phenological point (referred as the reference curve method). The results of the above methods may be different. However
the uncertainty and its impacts need further elucidation. Hence
this study used the remote sensing vegetation index from 2001 to 2016 to extract the multi-year mean dates of the start of the growing season (
<math id="M8"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602592&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602598&type=
5.92666674
2.70933342
) using two methods in forests in China and detected the differences between the
<math id="M9"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602606&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602604&type=
5.92666674
2.70933342
derived from the two methods (
△
<math id="M10"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602612&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602624&type=
5.92666674
2.70933342
) and the spatial pattern. Furthermore
a commonly used indicator in phenological research
that is
the temperature (Preseason Duration (PD)) based on
<math id="M11"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602628&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602614&type=
5.92666674
2.70933342
was used to explore the potential impact of the
<math id="M12"><mtext> </mtext><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602638&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602631&type=
6.60400009
2.79399991
derived from different methods on the phenology–climate relationship. Results show that (1) the
<math id="M13"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602637&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602634&type=
5.92666674
2.70933342
derived from different methods was significant different. The
<math id="M14"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602646&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602654&type=
5.92666674
2.70933342
of the average method was generally smaller than that of the reference curve method (-2.6±2.2 days
accounting for 88%). The pixels with
△
<math id="M15"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602650&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602660&type=
5.92666674
2.70933342
>
7 between the dynamic average method and the reference method and that between the fixed average method and the reference method accounted for 8.0% and 6.0% of the effective pixels
respectively
which are mainly distributed in the southeastern hilly area. (2) A significant spatial heterogeneity of
△
<math id="M16"><mover><mrow><mi mathvariant="normal">S</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">S</mi></mrow><mo>¯</mo></mover></math>
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602671&type=
http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=42602669&type=
5.92666674
2.70933342
showed a decrease with the increase of the annual average temperature (
Slope
=0.07 days/℃
P
<
0.01) and the decrease of the average annual precipitation (
Slope
=-0.0005 days/mm
P
<
0.01). (3) The PD derived from different methods was distinct. Approximately 40% of the effective pixels show a difference with PD
>
5 days
and a half of them show a difference with PD
>
15 days
which are mainly located in the southeast hills and the southwest mountains. Overall
the achievements of this study provide a beneficial reference for the spatial parameterization of satellite-based phenology for modeling.
多年平均物候物候季前时长遥感地表物候时序数据森林
multi-year average phenologyphenological preseason durationremote sensing surface phenologytime seriesforest
Cao P Y, Zhang L M, Li S G and Zhang J H. 2016. Review on vegetation phenology observation and phenological index extraction. Advances in Earth Science, 31(4): 365-376
曹沛雨, 张雷明, 李胜功, 张军辉. 2016. 植被物候观测与指标提取方法研究进展. 地球科学进展, 31(4): 365-376 [DOI: 10.11867/j.issn.1001-8166.2016.04.0365http://dx.doi.org/10.11867/j.issn.1001-8166.2016.04.0365]
Cao R Y, Shen M G, Zhou J and Chen J. 2018. Modeling vegetation green-up dates across the Tibetan Plateau by including both seasonal and daily temperature and precipitation. Agricultural and Forest Meteorology, 249: 176-186 [DOI: 10.1016/j.agrformet.2017.11.032http://dx.doi.org/10.1016/j.agrformet.2017.11.032]
Chuine I. 2000. A unified model for budburst of trees. Journal of Theoretical Biology, 207(3): 337-347 [DOI: 10.1006/jtbi.2000.2178http://dx.doi.org/10.1006/jtbi.2000.2178]
Dai J H, Wang H J and Ge Q S. 2013a. The decreasing spring frost risks during the flowering period for woody plants in temperate area of eastern China over past 50 years. Journal of Geographical Sciences, 23(4): 641-652 [DOI: 10.1007/s11442-013-1034-6http://dx.doi.org/10.1007/s11442-013-1034-6]
Dai J H, Wang H J and Ge Q S. 2013b. Multiple phenological responses to climate change among 42 plant species in Xi’an, China. International Journal of Biometeorology, 57(5): 749-758 [DOI: 10.1007/s00484-012-0602-2http://dx.doi.org/10.1007/s00484-012-0602-2]
Dai J H, Xu Y J, Wang H J, Alatalo J, Tao Z X and Ge Q S. 2019. Variations in the temperature sensitivity of spring leaf phenology from 1978 to 2014 in Mudanjiang, China. International Journal of Biometeorology, 63(5): 569-577 [DOI: 10.1007/s00484-017-1489-8http://dx.doi.org/10.1007/s00484-017-1489-8]
Deng G R, Zhang H Y, Yang L B, Zhao J J, Guo X Y, Hong Y, Wu R H and Dan G. 2020. Estimating frost during growing season and its impact on the velocity of vegetation greenup and withering in northeast China. Remote Sensing, 12(9): 1355 [DOI: 10.3390/rs12091355http://dx.doi.org/10.3390/rs12091355]
Ding M J, Zhang Y L, Sun X M, Liu L S, Wang Z F and Bai W Q. 2013. Spatiotemporal variation in alpine grassland phenology in the Qinghai-Tibetan Plateau from 1999 to 2009. Chinese Science Bulletin, 58(3): 396-405 [DOI: 10.1007/s11434-012-5407-5http://dx.doi.org/10.1007/s11434-012-5407-5]
Fu Y H, Zhao H F, Piao S L, Peaucelle M, Peng S S, Zhou G Y, Ciais P, Huang M T, Menzel A, Peñuelas J, Song Y, Vitasse Y, Zeng Z Z and Janssens I A. 2015. Declining global warming effects on the phenology of spring leaf unfolding. Nature, 526(7571): 104-107 [DOI: 10.1038/nature15402http://dx.doi.org/10.1038/nature15402]
Garonna I, De Jong R, De Wit A J W, Mücher C A, Schmid B and Schaepman M E. 2014. Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982—2011). Global Change Biology, 20(11): 3457-3470 [DOI: 10.1111/gcb.12625http://dx.doi.org/10.1111/gcb.12625]
Ge Q S, Wang H J, Rutishauser T and Dai J H. 2015. Phenological response to climate change in China: a meta-analysis. Global Change Biology, 21(1): 265-274 [DOI: 10.1111/gcb.12648http://dx.doi.org/10.1111/gcb.12648]
Geng L Y and Ma M G. 2014. Advance in method comparison of reconstructing remote sensing time series data sets. Remote Sensing Technology and Application, 29(2): 362-368
耿丽英, 马明国. 2014. 长时间序列NDVI数据重建方法比较研究进展. 遥感技术与应用, 29(2): 362-368 [DOI: 10.11873/j.issn.1004-0323.2014.2.0362http://dx.doi.org/10.11873/j.issn.1004-0323.2014.2.0362]
He J, Yang K, Tang W J, Lu H, Qin J, Chen Y Y and Li X. 2020. The first high-resolution meteorological forcing dataset for land process studies over China. Scientific Data, 7(1): 25 [DOI: 10.1038/s41597-020-0369-yhttp://dx.doi.org/10.1038/s41597-020-0369-y]
Huang W L, Zhang Q, Kong D D, Gu X H, Sun P and Hu P. 2019. Response of vegetation phenology to drought in Inner Mongolia from 1982 to 2013. Acta Ecologica Sinica, 39(13): 4953-4965
黄文琳, 张强, 孔冬冬, 顾西辉, 孙鹏, 胡畔. 2019. 1982—2013年内蒙古地区植被物候对干旱变化的响应. 生态学报, 39(13): 4953-4965 [DOI:10.5846/stxb201801150118http://dx.doi.org/10.5846/stxb201801150118]
Huang Y, Jiang N, Shen M G and Guo L. 2020. Effect of preseason diurnal temperature range on the start of vegetation growing season in the Northern Hemisphere. Ecological Indicators, 112: 106161 [DOI: 10.1016/j.ecolind.2020.106161http://dx.doi.org/10.1016/j.ecolind.2020.106161]
Jeong S J, Ho C H, Gim H J and Brown M E. 2011. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982—2008. Global Change Biology, 17(7): 2385-2399 [DOI: 10.1111/j.1365-2486.2011.02397.xhttp://dx.doi.org/10.1111/j.1365-2486.2011.02397.x]
Jin J, Jiang H, Peng C, Zhang X, Wang Y, and Wang J. 2014. Climate change contribution to forest growth in eastern China over past two decades. Terrestrial Atmospheric and Oceanic Sciences, 25(1): 49-60 [DOI: 10.3319/TAO.2013.08.20.01(Ahttp://dx.doi.org/10.3319/TAO.2013.08.20.01(A)]
Keenan T F, Gray J, Friedl M A, Toomey M, Bohrer G, Hollinger D Y, Munger J W, O’Keefe J, Schmid H P, Wing I S, Yang B and Richardson A D. 2014. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nature Climate Change, 4(7): 598-604 [DOI: 10.1038/NCLIMATE2253http://dx.doi.org/10.1038/NCLIMATE2253]
Lawrence D M, Oleson K W, Flanner M G, Thornton P E, Swenson S C, Lawrence P J, Zeng X B, Yang Z L, Levis S, Sakaguchi K, Bonan G B and Slater A G. 2011. Parameterization improvements and functional and structural advances in version 4 of the community land model. Journal of Advances in Modeling Earth Systems, 3(3): M03001 [DOI: 10.1029/2011MS000045http://dx.doi.org/10.1029/2011MS000045]
Li W, MacBean N, Ciais P, Defourny, P, Lamarche C, Bontemps S, Houghton R A and Peng S. 2018. Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992—2015). Earth System Science Data, 10, 219-234 [DOI: 10.5194/essd-10-219-2018http://dx.doi.org/10.5194/essd-10-219-2018]
Liu H L, Ge Q S, Zhou Y, Dai J H, Hao Z X and Yan J H. 2021. Peony-appreciating dates recorded in historical documents around Kaifeng in Henan Province and their indications to climate variations during 981—1040 A.D.[J]. Quaternary Sciences, 41(3): 864-876
刘浩龙, 葛全胜, 周宇, 戴君虎, 郝志新, 闫军辉. 2021. 981—1040年开封牡丹观赏记录及其对北宋气候冷暖变化的指示[J]. 第四纪研究, 41(3): 864-876 [DOI: 10.11928/j.issn.1001-7410.2021.03.20http://dx.doi.org/10.11928/j.issn.1001-7410.2021.03.20]
Liu H Q and Huete A. 1995. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geoscience and Remote Sensing, 33(2): 457-465 [DOI: 10.1109/36.377946http://dx.doi.org/10.1109/36.377946]
Matsumoto K, Ohta T, Irasawa M and Nakamura T. 2003. Climate change and extension of the Ginkgo biloba L. growing season in Japan. Global Change Biology, 9(11): 1634-1642 [DOI: 10.1046/j.1365-2486.2003.00688.xhttp://dx.doi.org/10.1046/j.1365-2486.2003.00688.x]
Menzel A. 2002. Phenology: its importance to the global change community. Climatic Change, 54(4): 379-385 [DOI: 10.1023/A:1016125215496http://dx.doi.org/10.1023/A:1016125215496]
Menzel A, Sparks T H, Estrella N, Koch E, Aasa A, Ahas R, Alm-Kübler K, Bissolli P, Braslavská O, Briede A, Chmielewski F M, Crepinsek Z, Curnel Y, Dahl Å, Defila C, Donnelly A, Filella Y, Jatczak K, Måge F, Mestre A, Nordli Ø, Peñuelas J, Pirinen P, Remišová V, Scheifinger H, Striz M, Susnik A, Van Vliet A J H, Wielgolaski F E, Zach S and Zust A. 2006. European phenological response to climate change matches the warming pattern. Global Change Biology, 12(10): 1969-1976 [DOI: 10.1111/j.1365-2486.2006.01193.xhttp://dx.doi.org/10.1111/j.1365-2486.2006.01193.x]
Nowosad J, Stepinski T F and Netzel P. 2019. Global assessment and mapping of changes in mesoscale landscapes: 1992-2015. International Journal of Applied Earth Observation and Geoinformation, 78: 332-340 [DOI: 10.1016/j.jag.2018.09.013http://dx.doi.org/10.1016/j.jag.2018.09.013]
Piao S L, Fang J Y, Zhou L M, Ciais P and Zhu B. 2006. Variations in satellite-derived phenology in China’s temperate vegetation. Global Change Biology, 12(4): 672-685 [DOI: 10.1111/j.1365-2486.2006.01123.xhttp://dx.doi.org/10.1111/j.1365-2486.2006.01123.x]
Piao S L, Tan J G, Chen A P, Fu Y H, Ciais P, Liu Q, Janssens I A, Vicca S, Zeng Z Z, Jeong S J, Li Y, Myneni R B, Peng S S, Shen M G and Peñuelas J. 2015. Leaf onset in the northern hemisphere triggered by daytime temperature. Nature Communications, 6: 6911 [DOI: 10.1038/ncomms7911http://dx.doi.org/10.1038/ncomms7911]
Richardson A D, Black T A, Ciais P, Delbart N, Friedl M A, Gobron N, Hollinger D Y, Kutsch W L, Longdoz B, Luyssaert S, Migliavacca M, Montagnani L, Munger J W, Moors E, Piao S L, Rebmann C, Reichstein M, Saigusa N, Tomelleri E, Vargas R and Varlagin A. 2010. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1555): 3227-3246 [DOI: 10.1098/rstb.2010.0102http://dx.doi.org/10.1098/rstb.2010.0102]
Richardson A D, Hufkens K, Milliman T, Aubrecht D M, Furze M E, Seyednasrollah B, Krassovski M B, Latimer J M, Nettles W R, Heiderman R R, Warren J M and Hanson P J. 2018. Ecosystem warming extends vegetation activity but heightens vulnerability to cold temperatures. Nature, 560(7718): 368-371 [DOI: 10.1038/s41586-018-0399-1http://dx.doi.org/10.1038/s41586-018-0399-1]
Schwartz M D. 1998. Green wave phenology. Nature, 394(6696): 839-840 [DOI: 10.1038/29670http://dx.doi.org/10.1038/29670]
Shen M G, Piao S L, Cong N, Zhang G X and Jassens I A. 2015. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Global Change Biology, 21(10): 3647-3656 [DOI: 10.1111/gcb.12961http://dx.doi.org/10.1111/gcb.12961]
van Vliet A J H, de Groot R S, Bellens Y, Braun P, Bruegger R, Bruns E, Clevers J, Estreguil C, Flechsig M, Jeanneret F, Maggi M, Martens P, Menne B, Menzel A and Sparks T. 2003. The European phenology network. International Journal of Biometeorology, 47(4): 202-212 [DOI: 10.1007/s00484-003-0174-2http://dx.doi.org/10.1007/s00484-003-0174-2]
Wang H J, Dai J H, Zheng J Y and Ge Q S. 2015. Temperature sensitivity of plant phenology in temperate and subtropical regions of China from 1850 to 2009. International Journal of Climatology, 35(6): 913-922 [DOI: 10.1002/joc.4026http://dx.doi.org/10.1002/joc.4026]
Wang M Y, Luo Y, Zhang Z Y, Xie Q Y, Wu X D and Ma X L. 2022. Recent advances in remote sensing of vegetation phenology: Retrieval algorithm and validation strategy. National Remote Sensing Bulletin, 26(3): 431-455
王敏钰, 罗毅, 张正阳, 谢巧云, 吴小丹, 马轩龙. 2022. 植被物候参数遥感提取与验证方法研究进展. 遥感学报, 26(3): 431-455 [DOI: 10.11834/jrs.20211601http://dx.doi.org/10.11834/jrs.20211601]
Wang Z X, Liu C and Huete A. 2003. From AVHRR-NDVI to MODIS-EVI: advances in vegetation index research. Acta Ecologica Sinica, 23(5): 979-987
王正兴, 刘闯, Huete A. 2003. 植被指数研究进展: 从AVHRR-NDVI到MODIS-EVI. 生态学报, 23(5): 979-987 [DOI: 10.3321/j.issn:1000-0933.2003.05.020http://dx.doi.org/10.3321/j.issn:1000-0933.2003.05.020]
White M A, Thornton P E and Running S W. 1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochemical Cycles, 11(2): 217-234 [DOI: 10.1029/97GB00330http://dx.doi.org/10.1029/97GB00330]
Wu Y F, Li M S and Song J Q. 2008. Advance in vegetation phenology monitoring based on remote sensing. Journal of Meteorology and Environment, 24(3): 51-58
武永峰, 李茂松, 宋吉青. 2008. 植物物候遥感监测研究进展. 气象与环境学报, 24(3): 51-58 [DOI: 10.3969/j.issn.1673-503X.2008.03.011http://dx.doi.org/10.3969/j.issn.1673-503X.2008.03.011]
Xia C F, Li J and Liu Q H. 2013. Review of advances in vegetation phenology monitoring by remote sensing. Journal of Remote Sensing, 17(1): 1-16
夏传福, 李静, 柳钦火. 2013. 植被物候遥感监测研究进展. 遥感学报, 17(1): 1-16 [DOI: 10.11834/jrs.20131363http://dx.doi.org/10.11834/jrs.20131363]
Xiao X M, Boles S, Liu J Y, Zhuang D F and Liu M L. 2002. Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data. Remote Sensing of Environment, 82(2/3): 335-348 [DOI: 10.1016/S0034-4257(02)00051-2http://dx.doi.org/10.1016/S0034-4257(02)00051-2]
Zhang X X, Ge Q S and Zheng J Y. 2004. Relationships between climate change and vegetation in Beijing using remote sensed data and phenological data. Acta Phytoecologica Sinica, 28(4): 499-506
张学霞, 葛全胜, 郑景云. 2004. 北京地区气候变化和植被的关系——基于遥感数据和物候资料的分析. 植物生态学报, 28(4): 499-506 [DOI: 10.17521/cjpe.2004.0068http://dx.doi.org/10.17521/cjpe.2004.0068]
Zhu K Z. 1973. A preliminary study on the climatic fluctuations during the last 5,000 years in China. Scientia Sinica, 16(2): 226-256
相关作者
相关机构