陆表物候监测的遥感指数多维度评估

孙莉昕; 朱文泉; 谢志英; 詹培; 李雪莹

doi:10.11834/jrs.20221135

林业与农业 | 浏览量 : 0 下载量: 451 CSCD: 0 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

陆表物候监测的遥感指数多维度评估
Multi-dimension evaluation of remote sensing indices for land surface phenology monitoring
2023年27卷第11期页码：2653-2669
纸质出版日期： 2023-11-07 ，
DOI： 10.11834/jrs.20221135

扫描看全文

孙莉昕，朱文泉，谢志英，詹培，李雪莹.2023.陆表物候监测的遥感指数多维度评估.遥感学报，27（11）： 2653-2669

Sun L X， Zhu W Q， Xie Z Y， Zhan P and Li X Y. 2023. Multi-dimension evaluation of remote sensing indices for land surface phenology monitoring. National Remote Sensing Bulletin， 27（11）：2653-2669
孙莉昕，朱文泉，谢志英，詹培，李雪莹.2023.陆表物候监测的遥感指数多维度评估.遥感学报，27（11）： 2653-2669 DOI： 10.11834/jrs.20221135.

Sun L X， Zhu W Q， Xie Z Y， Zhan P and Li X Y. 2023. Multi-dimension evaluation of remote sensing indices for land surface phenology monitoring. National Remote Sensing Bulletin， 27（11）：2653-2669 DOI： 10.11834/jrs.20221135.

摘要

针对陆表植被物候监测已发展了很多遥感指数，但不同遥感指数表征陆表植被季节性变化的能力存在差异。目前，有关陆表植被物候遥感指数的评估大多在不同标准下开展，导致研究结果间的可比性较差，致使无法根据不同区域选择出最佳的遥感指数，从而影响大尺度（如半球乃至全球）的陆表物候监测精度。本文在北半球中高纬度地区，以75个碳通量塔站点的406条记录和129个物候相机站点的482条记录为参考标准，对10种遥感指数应用于陆表物候监测的能力进行了系统性评估，并从两个精度评估视角（物候提取准确度、物候变化趋势一致性）、4个维度（植被类型、地理环境、物候类型、物候事件）对比分析了各种情况下的最佳遥感指数及其精度。虽然部分遥感指数在多数情况下均表现最佳，但不同植被类型、地理环境、物候类型（功能物候、结构物候）、物候事件（春季、秋季）组合情况下的最佳遥感指数并不聚焦于少数几种，而是散布于各类遥感指数之中；即使是采用了最佳遥感指数，但在某些情况下，其用于陆表物候监测的误差仍较大。从不同的精度评估视角来看，物候提取准确度高的遥感指数并不一定与物候变化趋势一致性高的遥感指数相对应，说明应根据关注视角来选择最佳遥感指数。本文研究结果可为不同情况下的陆表植被物候监测提供最佳遥感指数选择依据，从而有利于提高大尺度的陆表植被物候监测精度以及评估其不确定性。

Abstract

Many remote sensing indices have been developed for land surface phenology monitoring

but the ability of different remote sensing indices to represent the seasonal changes of land surface vegetation differs. At present

the evaluations of remote sensing indices for land surface phenology monitoring are mostly conducted under different standards

which results in poor comparability among research results. Thus

the best remote sensing indices cannot be selected according to different regions based on the aforementioned research results

which would affect the large-scale (e.g.

hemispheric and even global) land surface phenology monitoring. Taking 406 records from 75 carbon flux tower stations and 482 records from 129 phenological camera stations as the reference standard

this study systematically evaluated the application of 10 remote sensing indexes in monitoring land surface phenology in the middle and high latitudes of the northern hemisphere. In addition

the best remote sensing indices and their accuracy under different conditions were compared and analyzed from two evaluation perspectives (including phenological extraction accuracy and phenological change trend consistency) and four dimensions (including vegetation type

geographical environment

phenological type

and phenological event).

Although some remote sensing indices are the best in most conditions

the best remote sensing indices for different vegetation types

geographical environment

phenological types (functional phenology

structural phenology)

and phenological events (spring and autumn) do not focus on a few of remote sensing indices but are scattered among all kinds of them. Even with the best remote sensing index

the error of land surface phenology monitoring is still large in some conditions. From different evaluation perspectives

the remote sensing indices with a high accuracy of phenology extraction are not exactly the same as those with a high consistency of phenological change trend

which suggests that the best remote sensing index should be selected according to the objects. The results of this study can provide the best remote sensing index selection basis for land surface phenology monitoring under different conditions

which will be helpful to improve the accuracy of large-scale land surface phenology monitoring and evaluate its uncertainty.

关键词

遥感指数陆表植被物候植被类型地理环境结构物候功能物候

Keywords

remote sensing indexland surface phenologyvegetation typegeographical environmentstructural phenologyfunctional phenology

references

Badgley G, Field C B and Berry J A. 2017. Canopy near-infrared reflectance and terrestrial photosynthesis. Science Advances, 3(3): e1602244 [DOI: 10.1126/sciadv.1602244http://dx.doi.org/10.1126/sciadv.1602244]

Beck P S A, Atzberger C, Høgda K A, Johansen B and Skidmore A K. 2006. Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sensing of Environment, 100(3): 321-334 [DOI: 10.1016/j.rse.2005.10.021http://dx.doi.org/10.1016/j.rse.2005.10.021]

Busetto L, Colombo R, Migliavacca M, Cremonese E, Meroni M, Galvagno M, Rossini M, Siniscalco C, Morra Di Cella U and Pari E. 2010. Remote sensing of larch phenological cycle and analysis of relationships with climate in the Alpine region. Global Change Biology, 16(9): 2504-2517 [DOI: 10.1111/j.1365-2486.2010.02189.xhttp://dx.doi.org/10.1111/j.1365-2486.2010.02189.x]

Cao R Y, Chen J, Shen M G and Tang Y H. 2015. An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data. Agricultural and Forest Meteorology, 200: 9-20 [DOI: 10.1016/j.agrformet.2014.09.009http://dx.doi.org/10.1016/j.agrformet.2014.09.009]

Cardot H, Maisongrande P and Faivre R. 2008. Varying-time random effects models for longitudinal data: unmixing and temporal interpolation of remote-sensing data. Journal of Applied Statistics, 35(8): 827-846 [DOI: 10.1080/02664760802061970http://dx.doi.org/10.1080/02664760802061970]

Chandrasekar K, Sesha Sai M V R, Roy P S and Dwevedi R S. 2010. Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS vegetation index product. International Journal of Remote Sensing, 31(15): 3987-4005 [DOI: 10.1080/01431160802575653http://dx.doi.org/10.1080/01431160802575653]

Che M L, Chen B Z, Innes J L, Wang G Y, Dou X M, Zhou T M, Zhang H F, Yan J W, Xu G and Zhao H W. 2014. Spatial and temporal variations in the end date of the vegetation growing season throughout the Qinghai-Tibetan Plateau from 1982 to 2011. Agricultural and Forest Meteorology, 189-190: 81-90 [DOI: 10.1016/j.agrformet.2014.01.004http://dx.doi.org/10.1016/j.agrformet.2014.01.004]

Clerici N, Weissteiner C J and Gerard F. 2012. Exploring the use of MODIS NDVI-based phenology indicators for classifying forest general habitat categories. Remote Sensing, 4(6): 1781-1803 [DOI: 10.3390/rs4061781http://dx.doi.org/10.3390/rs4061781]

de Beurs K M and Henebry G M. 2004. Land surface phenology, climatic variation, and institutional change: analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment, 89(4): 497-509 [DOI: 10.1016/j.rse.2003.11.006http://dx.doi.org/10.1016/j.rse.2003.11.006]

Delbart N, Le Toan T, Kergoat L and Fedotova V. 2006. Remote sensing of spring phenology in boreal regions: a free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004). Remote Sensing of Environment, 101(1): 52-62 [DOI: 10.1016/j.rse.2005.11.012http://dx.doi.org/10.1016/j.rse.2005.11.012]

Dong Q, Chen X H, Chen J, Zhang C S, Liu L C, Cao X, Zang Y Z, Zhu X F and Cui X H. 2020. Mapping winter wheat in North China using sentinel 2A/B data: a method based on phenology-time weighted dynamic time warping. Remote Sensing, 12(8): 1274 [DOI: 10.3390/rs12081274http://dx.doi.org/10.3390/rs12081274]

Duchemin B, Goubier J and Courrier G. 1999. Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data. Remote Sensing of Environment, 67(1): 68-82 [DOI: 10.1016/S0034-4257(98)00067-4http://dx.doi.org/10.1016/S0034-4257(98)00067-4]

Dye D G, Middleton B R, Vogel J M, Wu Z T and Velasco M. 2016. Exploiting differential vegetation phenology for satellite-based mapping of semiarid grass vegetation in the southwestern United States and Northern Mexico. Remote Sensing, 8(11): 889 [DOI: 10.3390/rs8110889http://dx.doi.org/10.3390/rs8110889]

Fan D Q, Zhao X S, Zhu W Q and Zheng Z T. 2016. Review of influencing factors of accuracy of plant phenology monitoring based on remote sensing data. Progress in Geography, 35(3): 304-319

Fan D Q, Zhu W Q, Pan Y Z, Jiang N and Zheng Z T. 2014. Identifying an optimal method for estimating green-up date of Kobresia pygmaea alpine meadow in Qinghai-Tibetan Plateau. Journal of Remote Sensing, 18(5): 1117-1127

Fu Y Z. 2010. Study on Vegetation Index of Remote Sensing and Its Aplications. Fuzhou: Fuzhou University

Gao B C. 1996. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257-266 [DOI: 10.1016/S0034-4257(96)00067-3http://dx.doi.org/10.1016/S0034-4257(96)00067-3]

Gonsamo A, Chen J M, Price D T, Kurz W A and Wu C Y. 2012. Land surface phenology from optical satellite measurement and CO2 eddy covariance technique. Journal of Geophysical Research: Biogeosciences, 117(G3): G03032 [DOI: 10.1029/2012jg002070http://dx.doi.org/10.1029/2012jg002070]

Guyon D, Guillot M, Vitasse Y, Cardot H, Hagolle O, Delzon S and Wigneron J P. 2011. Monitoring elevation variations in leaf phenology of deciduous broadleaf forests from SPOT/VEGETATION time-series. Remote Sensing of Environment, 115(2): 615-627 [DOI: 10.1016/j.rse.2010.10.006http://dx.doi.org/10.1016/j.rse.2010.10.006]

Heumann B W, Seaquist J W, Eklundh L and Jönsson P. 2007. AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982-2005. Remote Sensing of Environment, 108(4): 385-392 [DOI: 10.1016/j.rse.2006.11.025http://dx.doi.org/10.1016/j.rse.2006.11.025]

Hird J N and McDermid G J. 2009. Noise reduction of NDVI time series: an empirical comparison of selected techniques. Remote Sensing of Environment, 113(1): 248-258 [DOI: 10.1016/j.rse.2008.09.003http://dx.doi.org/10.1016/j.rse.2008.09.003]

Hudson I L and Keatley M R. 2010. Phenological Research: Methods for Environmental and Climate Change Analysis. Dordrecht: Springer.

Huete A, Didan K, Miura T, Rodriguez E P, Gao X and Ferreira L G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1/2): 195-213 [DOI: 10.1016/S0034-4257(02)00096-2http://dx.doi.org/10.1016/S0034-4257(02)00096-2]

Jiang Z Y, Huete A R, Didan K and Miura T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10): 3833-3845 [DOI: 10.1016/j.rse.2008.06.006http://dx.doi.org/10.1016/j.rse.2008.06.006]

Jin H X and Eklundh L. 2014. A physically based vegetation index for improved monitoring of plant phenology. Remote Sensing of Environment, 152: 512-525 [DOI: 10.1016/j.rse.2014.07.010http://dx.doi.org/10.1016/j.rse.2014.07.010]

Kaduk J D and Heimann M. 1996. A prognostic phenology model for global terrestrial carbon cycle models. Climate Research, 6(1): 1-19 [DOI: 10.3354/cr006001http://dx.doi.org/10.3354/cr006001]

Karkauskaite P, Tagesson T and Fensholt R. 2017. Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for start-of-season trend analysis of the northern Hemisphere boreal zone. Remote Sensing, 9(5): 485 [DOI: 10.3390/rs9050485http://dx.doi.org/10.3390/rs9050485]

Li H, Chen Z X, Jiang Z W, Wu W B, Ren J Q, Liu B and Tuya H. 2017. Comparative analysis of GF-1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat. Journal of Integrative Agriculture, 16(2): 266-285 [DOI: 10.1016/s2095-3119(15)61293-xhttp://dx.doi.org/10.1016/s2095-3119(15)61293-x]

Lin Z H and Mo X G. 2006. Phenologies from harmonics analysis of AVHRR NDVI time series. Transactions of the Chinese Society of Agricultural Engineering, 22(12): 138-144

Liu X L. 2019. Remote Sensing of Vegetation Phenology in Snow-covered Area of Northern Hemisphere. Chengdu: University of Electronic Science and Technology of China (刘喜龙. 2019. 北半球积雪地区遥感植被物候研究. 成都: 电子科技大学)

Liu Y, Hill M J, Zhang X Y, Wang Z S, Richardson A D, Hufkens K, Filippa G, Baldocchi D D, Ma S Y, Verfaillie J and Schaaf C B. 2017. Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales. Agricultural and Forest Meteorology, 237-238: 311-325 [DOI: 10.1016/j.agrformet.2017.02.026http://dx.doi.org/10.1016/j.agrformet.2017.02.026]

Liu Y X, Wu C Y, Peng D L, Xu S G, Gonsamo A, Jassal R S, Altaf Arain M, Lu L L, Fang B and Chen J M. 2016. Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America. Remote Sensing of Environment, 176: 152-162 [DOI: 10.1016/j.rse.2016.01.021http://dx.doi.org/10.1016/j.rse.2016.01.021]

Matsushita B, Yang W, Chen J, Onda Y and Qiu G Y. 2007. Sensitivity of the Enhanced Vegetation Index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors, 7(11): 2636-2651 [DOI: 10.3390/s7112636http://dx.doi.org/10.3390/s7112636]

Mou M J, Zhu W Q, Wang L L, Xu Y J and Liu J H. 2012. Evaluation of remote sensing extraction methods for vegetation phenology based on flux tower net ecosystem carbon exchange data. Chinese Journal of Applied Ecology, 23(2): 319-327

Pan Y Z, Li L, Zhang J S, Liang S L, Zhu X F and Sulla-Menashe D. 2012. Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index. Remote Sensing of Environment, 119: 232-242 [DOI: 10.1016/j.rse.2011.10.011http://dx.doi.org/10.1016/j.rse.2011.10.011]

Pastick N J, Dahal D, Wylie B K, Parajuli S, Boyte S P and Wu Z T. 2020. Characterizing land surface phenology and exotic annual grasses in Dryland ecosystems using Landsat and sentinel-2 data in Harmony. Remote Sensing, 12(4): 725 [DOI: 10.3390/rs12040725http://dx.doi.org/10.3390/rs12040725]

Peng D L, Zhang X Y, Wu C Y, Huang W J, Gonsamo A, Huete A R, Didan K, Tan B, Liu X J and Zhang B. 2017. Intercomparison and evaluation of spring phenology products using National Phenology Network and AmeriFlux observations in the contiguous United States. Agricultural and Forest Meteorology, 242: 33-46 [DOI: 10.1016/j.agrformet.2017.04.009http://dx.doi.org/10.1016/j.agrformet.2017.04.009]

Pidgorodetska L V and Zyelyk Y I. 2015. Detection of winter crops by satellite data on the basis of soil-adaptive perpendicular vegetation index. Astronomical School’s Report, 11(1): 91-98 [DOI: 10.18372/2411-6602.11.1091http://dx.doi.org/10.18372/2411-6602.11.1091]

Reed B C, Brown J F, VanderZee D, Loveland T R, Merchant J W and Ohlen D O. 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science, 5(5): 703-714 [DOI: 10.2307/3235884http://dx.doi.org/10.2307/3235884]

Richardson A D, Hufkens K, Milliman T, Aubrecht D M, Chen M, Gray J M, Johnston M R, Keenan T F, Klosterman S T, Kosmala M, Melaas E K, Friedl M A and Frolking S. 2018. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Scientific Data, 5(1): 180028 [DOI: 10.1038/sdata.2018.28http://dx.doi.org/10.1038/sdata.2018.28]

Richardson A J and Wiegand C L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12): 1541-1552

Rouse J W, Haas R H, Schell J A and Deering D W. 1973. Monitoring vegetation systems in the Great Plains with ERTS//Goddard Space Flight Center 3d ERTS-1 Symp. Washington: NASA

Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N and Ohno H. 2005. A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, 96(3/4): 366-374 [DOI: 10.1016/j.rse.2005.03.008http://dx.doi.org/10.1016/j.rse.2005.03.008]

Schwartz M D. 2013. Phenology: An Integrative Environmental Science. 2nd ed. Dordrecht: Springer [DOI: 10.1007/978-94-007-6925-0http://dx.doi.org/10.1007/978-94-007-6925-0]

Schwartz M D, Reed B C and White M A. 2002. Assessing satellite-derived start-of-season measures in the conterminous USA. International Journal of Climatology, 22(14): 1793-1805 [DOI: 10.1002/joc.819http://dx.doi.org/10.1002/joc.819]

Shen M and Piao S. 2013. Increasing altitudinal spring phenology gradient of vegetation over the last decade in Qinghai-Tibetan Plateau//Paper Presented at the AGU Fall Meeting, GC33B-1117

Tan B, Morisette J T, Wolfe R E, Gao F, Ederer G A, Nightingale J and Pedelty J A. 2011. An enhanced TIMESAT algorithm for estimating vegetation phenology metrics from MODIS data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2): 361-371 [DOI: 10.1109/JSTARS.2010.2075916http://dx.doi.org/10.1109/JSTARS.2010.2075916]

Tong X Y, Tian F, Brandt M, Liu Y, Zhang W M and Fensholt R. 2019. Trends of land surface phenology derived from passive microwave and optical remote sensing systems and associated drivers across the dry tropics 1992—2012. Remote Sensing of Environment, 232: 111307 [DOI: 10.1016/j.rse.2019.111307http://dx.doi.org/10.1016/j.rse.2019.111307]

Wang C, Chen J, Wu J, Tang Y H, Shi P J, Andrew Black T and Zhu K. 2017. A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sensing of Environment, 196: 1-12 [DOI: 10.1016/j.rse.2017.04.031http://dx.doi.org/10.1016/j.rse.2017.04.031]

Wang J M and Zhang X Y. 2020. Investigation of wildfire impacts on land surface phenology from MODIS time series in the western US forests. ISPRS Journal of Photogrammetry and Remote Sensing, 159: 281-295 [DOI: 10.1016/j.isprsjprs.2019.11.027http://dx.doi.org/10.1016/j.isprsjprs.2019.11.027]

Wang M, Tao F L and Shi W J. 2014. Corn yield forecasting in Northeast China using remotely sensed spectral indices and crop phenology metrics. Journal of Integrative Agriculture, 13(7): 1538-1545 [DOI: 10.1016/S2095-3119(14)60817-0http://dx.doi.org/10.1016/S2095-3119(14)60817-0]

Wang X H, Piao S L, Ciais P, Li J S, Friedlingstein P, Koven C and Chen A P. 2011. Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006. Proceedings of the National Academy of Sciences of the United States of America, 108(4): 1240-1245 [DOI: 10.1073/pnas.1014425108http://dx.doi.org/10.1073/pnas.1014425108]

Wardlow B D, Egbert S L and Kastens J H. 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sensing of Environment, 108(3): 290-310 [DOI: 10.1016/j.rse.2006.11.021http://dx.doi.org/10.1016/j.rse.2006.11.021]

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 C Y, Gonsamo A, Chen J M, Kurz W A, Price D T, Lafleur P M, Jassal R S, Dragoni D, Bohrer G, Gough C M, Verma S B, Suyker A E and Munger J W. 2012. Interannual and spatial impacts of phenological transitions, growing season length, and spring and autumn temperatures on carbon sequestration: a North America flux data synthesis. Global and Planetary Change, 92-93: 179-190 [DOI: 10.1016/j.gloplacha.2012.05.021http://dx.doi.org/10.1016/j.gloplacha.2012.05.021]

Wu C Y, Gonsamo A, Gough C M, Chen J M and Xu S G. 2014. Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS. Remote Sensing of Environment, 147: 79-88 [DOI: 10.1016/j.rse.2014.03.001http://dx.doi.org/10.1016/j.rse.2014.03.001]

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

Xu D D and Li W L. 2009. Vegetation index controlling soil background[EB/OL]. China Scientific Papers Online. [2009-06-12]. http://www.paper.edu.cn/releasepaper/content/200906-376http://www.paper.edu.cn/releasepaper/content/200906-376

Xu Y Y, Zhang J H and Yang L M. 2012. Detecting major phenological stages of rice using MODIS-EVI data and Symlet11 wavelet in Northeast China. Acta Ecologica Sinica, 32(7): 2091-2098

Yang W, Kobayashi H, Wang C, Shen M G, Chen J, Matsushita B, Tang Y H, Kim Y, Bret-Harte M S, Zona D, Oechel W and Kondoh A. 2019. A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems. Remote Sensing of Environment, 228: 31-44 [DOI: 10.1016/j.rse.2019.03.028http://dx.doi.org/10.1016/j.rse.2019.03.028]

Yuan H H, Wu C Y, Lu L L and Wang X Y. 2018. A new algorithm predicting the end of growth at five evergreen conifer forests based on nighttime temperature and the enhanced vegetation index. ISPRS Journal of Photogrammetry and Remote Sensing, 144: 390-399 [DOI: 10.1016/j.isprsjprs.2018.08.013http://dx.doi.org/10.1016/j.isprsjprs.2018.08.013]

Zeng L L, Wardlow B D, Xiang D X, Hu S and Li D R. 2020. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sensing of Environment, 237: 111511 [DOI: 10.1016/j.rse.2019.111511http://dx.doi.org/10.1016/j.rse.2019.111511]

Zhang F, Wu B F, Liu C L and Luo Z M. 2004. Methods of monitoring crop phonological stages using time series of vegetation indicator. Transactions of the Chinese Society of Agricultural Engineering, 20(1): 155-159

Zhang X Y, Friedl M A, Schaaf C B, Strahler A H, Hodges J C F, Gao F, Reed B C and Huete A. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3): 471-475 [DOI: 10.1016/S0034-4257(02)00135-9http://dx.doi.org/10.1016/S0034-4257(02)00135-9]

Zhang X Y, Tan B and Yu Y Y. 2014. Interannual variations and trends in global land surface phenology derived from enhanced vegetation index during 1982—2010. International Journal of Biometeorology, 58(4): 547-564 [DOI: 10.1007/s00484-014-0802-zhttp://dx.doi.org/10.1007/s00484-014-0802-z]

Zhu Y X, Zhang Y J, Zu J X, Wang Z P, Huang K, Cong N and Tang Z. 2019. Effects of data temporal resolution on phenology extractions from the alpine grasslands of the Tibetan Plateau. Ecological Indicators, 104: 365-377 [DOI: 10.1016/j.ecolind.2019.05.004http://dx.doi.org/10.1016/j.ecolind.2019.05.004]

文章被引用时，请邮件提醒。

提交

暂无数据