垂直与倾斜相机观测对遥感物候参数验证影响的对比研究

许丽娜; 屈永华; 孙晨曦; 万华伟; 阿斯娅·曼力克; 刘绍民

doi:10.11834/jrs.20232488

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垂直与倾斜相机观测对遥感物候参数验证影响的对比研究
Comparison of vertical and inclined camera observation on the validation results of remote sensing phenological parameters
2023年页码：1-17
网络出版日期： 2023-04-27 ，
DOI： 10.11834/jrs.20232488

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许丽娜，屈永华，孙晨曦，万华伟，阿斯娅·曼力克，刘绍民.XXXX.垂直与倾斜相机观测对遥感物候参数验证影响的对比研究.遥感学报，XX（XX）： 1-17

XU Lina，QU Yonghua，SUN Chenxi，WAN Huawei，ASIYA Manlike，LIU Shaomin. XXXX. Comparison of vertical and inclined camera observation on the validation results of remote sensing phenological parameters. National Remote Sensing Bulletin， XX（XX）：1-17
许丽娜，屈永华，孙晨曦，万华伟，阿斯娅·曼力克，刘绍民.XXXX.垂直与倾斜相机观测对遥感物候参数验证影响的对比研究.遥感学报，XX（XX）： 1-17 DOI： 10.11834/jrs.20232488.

XU Lina，QU Yonghua，SUN Chenxi，WAN Huawei，ASIYA Manlike，LIU Shaomin. XXXX. Comparison of vertical and inclined camera observation on the validation results of remote sensing phenological parameters. National Remote Sensing Bulletin， XX（XX）：1-17 DOI： 10.11834/jrs.20232488.

摘要

基于卫星遥感的大尺度植被物候监测对于农业生产管理、气候变化响应等领域具有重要意义。卫星遥感提取的物候参数真实性检验常以近地面数字相机观测为数据源。以往的验证研究中，大多关注观测尺度的差异造成的影响，忽略了地面相机与卫星观测天顶角的不一致性。本文获取了垂直（PhotoNet）与倾斜（PhenoCam）物候相机在相近纬度、同种植被类型的观测站点数据，分别与Sentinel-2卫星提取的物候参数进行对比，系统性地评估两种地面相机观测角度对卫星物候期验证结果的影响。结果表明，相机的观测角度是卫星遥感物候验证研究中的不确定性原因之一。在多数情况下，卫星遥感与垂直观测相机提取的物候期表现出更好一致性，平均相差9天，而与倾斜观测相机平均相差可达19天。然而，偏差天数受植被类型和生长阶段的影响。在玉米凋落期至休眠期，垂直观测偏差反而高于倾斜观测。植被冠层方向反射特性和相机视场范围内目标组分差异是引起这种现象的主要原因。本研究证实了地面相机观测角度是卫星物候真实性检验中不可忽视的影响因素，相机野外布设时应充分考虑角度效应带来的验证误差，从而为卫星遥感物候监测提供更可靠的验证数据。

Abstract

In the fields of agricultural production management and climate change research

it has been well recognized that monitoring large-scale plant phenology with satellite-based remote sensing is of great significance to reveal the process of the interaction of biology and nature environment. In the activities of validation on remotely sensed phenology information

near-surface digital cameras are often employed as main data sources. However

more efforts were focused on the scale difference between ground and remotely sensed data

and rarely on the difference of sensors viewing zenith

i.e.

the satellites mainly adopted the near- nadir observation while cameras were mostly inclined arrangement. In order to systematically assess the effects of camera observation angles on the satellite phenological verification results

vertical (PhotoNet) and inclined (PhenoCam) camera observations were acquired at the similar latitude for the same vegetation type

then we compared them with phenological parameters extracted from Sentinel-2 data. For sixteen locations we compared greenness chromatic coordinate (GCC) series derived from digital cameras and Sentinel-2. A double hyperbolic tangent model was fitted for each series. The threshold method was applied to the annual complete modelled data

and the curvature extremum method was used for incomplete data to estimate onset of greenup

maturity of the green canopy

peak of season

end of greenness and dormancy of the green vegetation (OG/MG/PS90/EG/DG). The results showed that the viewing zenith of cameras is one of the uncertain sources to validate phenology information from satellite imagery. In most cases

the vertically observed camera showed better agreement with the phenological parameters extracted by satellite-based method

with an average bias of 9 days

while a larger bias of 19 days was observed for inclined camera observation. Therefore

the two camera observation methods result in the verification deviations of up to 10 days on average. However

the deviations might be vegetation type and growth stage dependently. It was found that the bias of vertical observation was significantly higher than that of inclined observation during the end to dormant period for maize. The different result of the vertical and inclined camera on the similar vegetation can be partly explained from the directional reflection characteristics of vegetation canopy and the difference of target components (e.g.

different fraction of soil and vegetation

photosynthetic and non-photosynthetic components of vegetation) within the camera field of view. Our results demonstrate that the viewing zenith angle of the near-surface cameras is an important factor in validation of satellite phenological parameters. In addition

the analysis found that the uncertainty of verification results caused by phenological period extraction method

illumination and satellite observation geometry is limited

which is not the main factor affecting the verification of satellite phenology parameters. As a result

it is suggested that to provide more reliable verification data for satellite remote sensing monitoring

the verification error introduced by angle effect should be fully considered while near surface cameras are deployed in the field.

关键词

数字相机观测角度卫星物候近地物候

Keywords

digital cameraobservation angleremotely sensed phenologynear-surface phenology

references

Beurs K 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]

Bolton D K, Gray J M, Melaas E K, Moon M, Eklundh L and Friedl M A. 2020. Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery. Remote Sensing of Environment, 240(C): 111685-111685 [DOI: 10.1016/j.rse.2020.111685http://dx.doi.org/10.1016/j.rse.2020.111685]

Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B and Eklundh L. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sensing of Environment, 91(3-4): 332-344 [DOI: 10.1016/j.rse.2004.03.014http://dx.doi.org/10.1016/j.rse.2004.03.014]

Chen X Q and Wang L H. 2009. Progress in remote sensing phenological research. Progress in Geography, 28(01): 33-40

陈效逑, 王林海. 2009. 遥感物候学研究进展. 地理科学进展, 28(01): 33-40 [DOI: 10.11820/dlkxjz.2009.01.005http://dx.doi.org/10.11820/dlkxjz.2009.01.005]

Cheng Y, Vrieling A, Fava F, Meroni M, Marshall M and Gachoki S. 2020. Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2. Remote Sensing of Environment, 248 [DOI: 10.1016/j.rse.2020.112004http://dx.doi.org/10.1016/j.rse.2020.112004]

Cui K, Meng J H and Zuo T Y. 2012. Monitoring of crop phenology with remote sensing. Journal of Anhui Agricultural Sciences, 40(10): 6279-6281

崔凯, 蒙继华, 左廷英. 2012. 遥感作物物候监测方法研究. 安徽农业科学, 40(10): 6279-6281 [DOI: 10.13989/j.cnki.0517-6611.2012.10.135http://dx.doi.org/10.13989/j.cnki.0517-6611.2012.10.135]

Fan D Q, Zhao X S, Zhu W Q and Zheng Z T. 2016. Review of influencing factors of plant phenology monitoring based on remote sensing data. Progress in Geography, 35(03): 304-319

范德芹, 赵学胜, 朱文泉, 郑周涛. 2016. 植物物候遥感监测精度影响因素研究综述. 地理科学进展, 35(03): 304-319 [DOI: 10.18306/dlkxjz.2016.03.005http://dx.doi.org/10.18306/dlkxjz.2016.03.005]

Fisher J I and Mustard J F. 2007. Cross-scalar satellite phenology from ground, Landsat, and MODIS data. Remote Sensing of Environment, 109(3): 261-273 [DOI: 10.1016/j.rse.2007.01.004http://dx.doi.org/10.1016/j.rse.2007.01.004]

Gianluca F, Edoardo C, Mirco M, Marta G, Oliver S, Elyn H, Koen H, Youngryel R, Joseph V, Umberto M C and Andrew D. R. 2018. NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types. Agricultural and Forest Meteorology, 249: 275-285 [DOI: 10.1016/j.agrformet.2017.11.003http://dx.doi.org/10.1016/j.agrformet.2017.11.003]

Hufkens K, Friedl M, Sonnentag O, Braswell B H, Milliman T and Richardson A D. 2012. Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sensing of Environment, 117: 307-321 [DOI: 10.1016/j.rse.2011.10.006http://dx.doi.org/10.1016/j.rse.2011.10.006]

Klosterman S T, Hufkens K, Gray J M, Melaas E, Sonnentag O, Lavine I, Mitchell L, Norman R, Friedl M A and Richardson A D. 2014. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences, 11(16): 4305-4320 [DOI: 10.5194/bg-11-4305-2014http://dx.doi.org/10.5194/bg-11-4305-2014]

Li Y, Jiao Z T, Zhao K G, Dong Y D, Zhou Y Y, Zeng Y L, Xu H Q, Zhang X N, Hu T X and Cui L. 2021. Influence of Varying Solar Zenith Angles on Land Surface Phenology Derived from Vegetation Indices: A Case Study in the Harvard Forest. Remote Sensing, 13(20): 4126 [DOI: 10.3390/RS13204126http://dx.doi.org/10.3390/RS13204126]

Liu L C, Cao R Y, Shen M G, Chen J, Wang J M and Zhang X Y. 2019. How Does Scale Effect Influence Spring Vegetation Phenology Estimated from Satellite-Derived Vegetation Indexes? Remote Sensing, 11(18): 2137-2137 [DOI: 10.3390/rs11182137http://dx.doi.org/10.3390/rs11182137]

Liu S M, Li X, Xu Z W, Che T, Xiao Q, Ma M G, Liu Q H, Jin R, Guo J W, Wang L X, Wang W Z, Qi Y, Li H Y, Xu T R, Ran Y H, Hu X L, Shi S J, Zhu Z L, Tan J L, Zhang Y and Ren Z G. 2018. The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China. Vadose Zone Journal, 17(1): 1-21 [DOI: 10.2136/vzj2018.04.0072http://dx.doi.org/10.2136/vzj2018.04.0072]

Liu S M, Qu Y H, Che T, Xu Z W, Tan J L, Ren Z G. 2022a. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Daman Superstation-2021). National Tibetan Plateau Data Center

刘绍民, 屈永华, 车涛, 徐自为, 谭俊磊, 任志国. 2022a. 祁连山综合观测网: 黑河流域地表过程综合观测网(大满超级站物候相机观测数据集-2021).国家青藏高原科学数据中心 [DOI: 10.11888/Terre.tpdc.272624http://dx.doi.org/10.11888/Terre.tpdc.272624]

Liu S M, Qu Y H, Che T, Xu Z W, Zhang Y. 2022b. Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of A’rou Superstation-2021). National Tibetan Plateau Data Center

刘绍民, 屈永华, 车涛, 徐自为, 张阳. 2022b. 祁连山综合观测网: 黑河流域地表过程综合观测网(阿柔超级站物候相机观测数据集-2021). 国家青藏高原科学数据中心 [DOI: 10.11888/Terre.tpdc.272626http://dx.doi.org/10.11888/Terre.tpdc.272626]

Liu Y, Hill M J, Zhang X, Wang Z, Richardson A D, Hufkens K, Filippa G, Baldocchi D D, Ma S, 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]

Ma X L, Huete A, Tran N N, Bi J, Gao S and Zeng Y L. 2020. Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8. Remote Sensing, 12(8):1339 [DOI: 10.3390/rs12081339http://dx.doi.org/10.3390/rs12081339]

Menzel A, Helm R and Zang C. 2015. Patterns of late spring frost leaf damage and recovery in a European beech (Fagus sylvatica L.) stand in south-eastern Germany based on repeated digital photographs. Frontiers in plant science, 6: 110 [DOI: 10.3389/fpls.2015.00110http://dx.doi.org/10.3389/fpls.2015.00110]

Meroni M, Verstraete M M, Rembold F, Urbano F and Kayitakire F. 2014. A phenology-based method to derive biomass production anomalies for food security monitoring in the Horn of Africa. International Journal of Remote Sensing, 35(7): 2472-2492 [DOI: 10.1080/01431161.2014.883090http://dx.doi.org/10.1080/01431161.2014.883090]

Moon M, Richardson A D and Friedl M A. 2021. Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery. Remote Sensing of Environment, 266 [DOI: 10.1016/j.rse.2021.112716http://dx.doi.org/10.1016/j.rse.2021.112716]

Mou M J. 2012. Effects of remote sensing methods for retrieving vegetation phenology on the monitoring of vegetation phenology shifts. Beijing: Beijing Normal University

牟敏杰. 2012. 植被物候遥感识别方法对植被物候变化监测的影响. 北京: 北京师范大学

Nasahara K N and Nagai S. 2015. Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN). Ecological Research, 30(2): 211-223 [DOI: 10.1007/s11284-014-1239-xhttp://dx.doi.org/10.1007/s11284-014-1239-x]

Norris J R and Walker J J. 2020. Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States. Remote Sensing of Environment, 249(2): 112013 [DOI: 10.1016/J.RSE.2020.112013http://dx.doi.org/10.1016/J.RSE.2020.112013]

Pan Z K, Huang J F, Zhou Q B, Wang L M, Cheng Y X, Zhang H K, Blackburn G A, Yan J and Liu J H. 2015. Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data. International Journal of Applied Earth Observation and Geoinformation, 34: 188-197 [DOI: 10.1016/j.jag.2014.08.011http://dx.doi.org/10.1016/j.jag.2014.08.011]

Peng D L, Zhang X Y, Zhang B, Liu L Y, Liu X J, Huete A R, Huang W J, Wang S Y, Luo S Z, Zhang X and Zhang H L. 2017. Scaling effects on spring phenology detections from MODIS data at multiple spatial resolutions over the contiguous United States. ISPRS Journal of Photogrammetry and Remote Sensing, 132: 185-198 [DOI: 10.1016/j.isprsjprs.2017.09.002http://dx.doi.org/10.1016/j.isprsjprs.2017.09.002]

Petach A R, Toomey M, Aubrecht D M and Richardson A D. 2014. Monitoring vegetation phenology using an infrared-enabled security camera. Agricultural and Forest Meteorology, 195–196: 143–151 [DOI: 10.1016/j.agrformet.2014.05.008http://dx.doi.org/10.1016/j.agrformet.2014.05.008]

Pieter S A, Clement A, Kjell A H, Bernt J and Andrew K S. 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]

Richardson A D, Braswell B H, Hollinger D Y, Jenkins J P and Ollinger S V. 2009. Near surface remote sensing of spatial and temporal variation in canopy phenology. Ecological applications: a publication of the Ecological Society of America, 19(6): 1417-1428 [DOI: 10.1890/08-2022.1http://dx.doi.org/10.1890/08-2022.1]

Seyednasrollah B, Young A M, Hufkens K, Milliman T, Friedl M A, Frolking S and Richardson A D. 2019. Tracking vegetation phenology across diverse biomes using Version 2.0 of the PhenoCam Dataset. Sci Data 6(1): 222 [DOI: 10.1038/s41597-019-0229-9http://dx.doi.org/10.1038/s41597-019-0229-9]

Shuai Y M, Yang J, Wu H, Shao Y C, Xu X C, Liu Y M, Liu T and Liang J. 2021. Variation of multi-angle reflectance collected by UAV over quadrats of paddy-field canopy. Remote Sensing Technology and Application, 36(02): 342-352

帅艳民, 杨健, 吴昊, 邵聪颖, 徐辛超, 刘明岳, 刘涛, 梁继. 2021. 基于无人机观测的水稻冠层样方多角度反射特点分析. 遥感技术与应用, 36(02): 342-352 [DOI: 10.11873/j.issn.1004-0323.2021.2.0342http://dx.doi.org/10.11873/j.issn.1004-0323.2021.2.0342]

Sonnentag O, Hufkens K, Teshera-Sterne C, Young A M, Friedl M, Braswell B H, Milliman T, O’keefe J and Richardson A D. 2012. Digital repeat photography for phenological research in forest ecosystems. Agricultural and Forest Meteorology, 152: 159-177 [DOI: 10.1016/j.agrformet.2011.09.009http://dx.doi.org/10.1016/j.agrformet.2011.09.009]

Thapa S, Garcia M V E and Eklundh L. 2021. Assessing Forest Phenology: A Multi-Scale Comparison of Near-Surface (UAV, Spectral Reflectance Sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) Remote Sensing. Remote Sensing, 13(8): 1597-1597 [DOI: 10.3390/RS13081597http://dx.doi.org/10.3390/RS13081597]

Tian F, Cai Z Z, Jin H X, Hufkens K, Scheifinger H, Tagesson T, Smets B, Van H R, Bonte K, Ivits E, Tong X Y and Ardö J. 2021. Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe. Remote Sensing of Environment, 260 [DOI: 10.1016/j.rse.2021.112456http://dx.doi.org/10.1016/j.rse.2021.112456]

Vrieling A, Meroni M, Darvishzadeh R, Skidmore A K, Wang T, Zurita-Milla R, Oosterbeek K, O'connor B and Paganini M. 2018. Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island. Remote Sensing of Environment, 215: 517-529 [DOI: 10.1016/j.rse.2018.03.014http://dx.doi.org/10.1016/j.rse.2018.03.014]

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 Q. 2016. Effect of solar radiation and observation angle on canopy reflectance spectra for winter wheat. Jiangsu: Nanjing University of Information Science and Technology

王迁. 2016. 太阳辐射与观测角度对冬小麦冠层反射光谱的影响. 江苏: 南京信息工程大学 [DOI:10.7666/d.Y3169815http://dx.doi.org/10.7666/d.Y3169815]

White K, Pontius J and Schaberg P. 2014. Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty. Remote Sensing of Environment, 148: 97-107 [DOI: 10.1016/j.rse.2014.03.017http://dx.doi.org/10.1016/j.rse.2014.03.017]

White M A, Thornton P E and Running S W. 1997. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Global Biogeochem. Cycles, 11(2): 217-234 [DOI: 10.1029/97GB00330http://dx.doi.org/10.1029/97GB00330]

Wingate L, Ogée J, Cremonese E, Filippa G, Mizunuma T, Migliavacca M, Moisy C, Wilkinson M, Moureaux C, Wohlfahrt G, Hammerle A, Hörtnagl L, Gimeno C, Porcar-Castell A, Galvagno M, Nakaji T, Morison J, Kolle O, Knohl A, Kutsch W, Kolari P, Nikinmaa E, Ibrom A, Gielen B, Eugster W, Balzarolo M, Papale D, Klumpp K, Köstner B, Grünwald T, Joffre R, Ourcival J M, Hellstrom M, Lindroth A, George C, Longdoz B, Genty B, Levula J, Heinesch B, Sprintsin M, Yakir D, Manise T, Guyon D, Ahrends H, Plaza-Aguilar A, Guan J H and Grace J. 2015. Interpreting canopy development and physiology using a European phenology camera network at flux sites. Biogeosciences, 12(20): 5995-6015 [DOI: 10.5194/bg-12-5995-2015http://dx.doi.org/10.5194/bg-12-5995-2015]

Xiang Y, Xiao Z Q, Liang S L, Wang J D and Song J L. 2014. Validation of Global LAnd Surface Satellite (GLASS) leaf area index product. Journal of Remote Sensing, 18(3):573-596

向阳, 肖志强, 梁顺林, 王锦地, 宋金玲. 2014. GLASS叶面积指数产品验证. 遥感学报, 18(03):573-596 [DOI:10.11834/jrs.20143117http://dx.doi.org/10.11834/jrs.20143117]

Xu X J, Tang Y, Qu Y L, Zhou Z S and Hu J G. 2021. Global Vegetation Photosynthetic Phenology Products Based on MODIS Vegetation Greenness and Temperature: Modeling and Evaluation. Remote Sensing, 13(24): 5080-5080 [DOI: 10.3390/rs13245080http://dx.doi.org/10.3390/rs13245080]

Yang A, Zhong B, Wu S and Liu Q. 2017. Radiometric Cross-Calibration of GF-4 in Multispectral Bands. Remote Sensing, 9(3):232 [DOI: 10.3390/rs9030232http://dx.doi.org/10.3390/rs9030232]

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(C): 111511-111511 [DOI: 10.1016/j.rse.2019.111511http://dx.doi.org/10.1016/j.rse.2019.111511]

Zhang X Y, Friedl M A and Schaaf C B. 2006. Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements. Journal of Geophysical Research: Biogeosciences, 111(G4): G04017-1-G04017-14 [DOI: 10.1029/2006jg000217http://dx.doi.org/10.1029/2006jg000217]

Zhang X Y, Friedl M A, Schaaf C B, Strahler A H, Hodges J, 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]

Zhao J J, Wang Y Q, Hashimoto H, Melton F S, Hiatt S H, Zhang H Y and Nemani R R. 2013. The Variation of Land Surface Phenology From 1982 to 2006 Along the Appalachian Trail. IEEE Transactions on Geoscience and Remote Sensing, 51(4): 2087-2095 [DOI: 10.1109/tgrs.2012.2217149http://dx.doi.org/10.1109/tgrs.2012.2217149]

Zheng J Y, Ge Q S and Zhao H X. 2003. Changes of plant phenological period and its response to climate change for the last 40 years in China. Chinese Journal of Agrometeorology, 24(1): 28-32

郑景云, 葛全胜, 赵会霞. 2003. 近40年中国植物物候对气候变化的响应研究. 中国农业气象, 24(1):28-32 [DOI:10.3969/j.issn.1000-6362.2003.01.009http://dx.doi.org/10.3969/j.issn.1000-6362.2003.01.009]

Zhou Y K. 2018. Comparative Study of vegetation phenology extraction methods based on digital images. Progress in Geography, 37(08): 1031-1044

周玉科. 2018. 基于数码照片的植被物候提取多方法比较研究. 地理科学进展, 37(08): 1031-1044 [DOI: 10.18306/dlkxjzhttp://dx.doi.org/10.18306/dlkxjz]

Zhu K Z, Wan M W. 1999. Phenology. Hunan: Hunan Education Press.

竺可桢, 宛敏渭. 1999. 物候学. 湖南: 湖南教育出版社

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