Analyzing the Uncertainty of the Multi-source Remote Sensing-based Vegetation Products for Drought Monitoring
- Pages: 1-21(2023)
Published Online： 15 March 2023
DOI： 10.11834/jrs.20232388
扫 描 看 全 文
浏览全部资源
扫码关注微信
- About Journal
- Journal Online
- Special Source
摘要
在全球气候持续变暖的背景下，干旱风险持续增大，对自然生态系统和社会经济系统的可持续发展带来巨大威胁。植被遥感产品能够量化地表植被状况，表征生态系统对受气候及人类活动影响的响应。基于长时序植被遥感产品定义的植被状态指数（Vegetation Condition Index，VCI）被广泛应用于大尺度快速干旱监测。当前植被遥感产品众多，产品之间时空一致性各异，由此导致所表征的地表植被扰动特征的不一致性目前尚不明晰。本文以长江中游地区为例，考虑传感器（MODIS、AVHRR）、生物物理定义（NDVI、EVI、LAI、VOD）和时间跨度（5年、10年、20年）等产品特征因素的影响，定量分析了多源植被遥感产品应用于VCI计算的不确定性，并对产品之间存在的差异进行了归因分析。结果表明：（1）由传感器导致的差异较大，基于不同传感器的NDVI产品计算得到的VCI时间序列，在大部分区域上显示出显著的不一致性，二者计算所得的VCI时间序列之间存在微弱相关性、总体偏差较大；（2）不同的植被参数产品导致的差异远低于不同传感器导致的差异，但在特定区域上的差异仍然非常显著，基于NDVI和EVI产品、NDVI和LAI产品分别计算得到的VCI时间序列之间具有较强的相关性，而基于NDVI和VOD产品计算得到的VCI时间序列在大部分区域均呈现出显著性差异；（3）由时间跨度导致的差异较小，基于不同时间跨度计算所得的VCI时间序列之间具有较好的一致性，产品的时间跨度越大、VCI变化差异越小。总之，在使用植被遥感产品进行干旱监测时，需要全面地分析和评估多源植被遥感产品之间的不确定性特征，对监测结果作出合理性的解释，从而保证旱情评价结果的准确性和有效性。此外，对于短时的遥感干旱监测应用，建议先剔除产品中的长期趋势项再进行干旱指数计算。
Abstract
In the context of continued global warming
the risks of drought have increased significantly
causing tremendous impacts on the sustainability of natural ecosystems and socio-economic systems. Remote sensing-based vegetation products are widely used to capture terrestrial vegetation dynamic and reflect the response of ecosystem to drought events. The Vegetation Condition Index (VCI) derived from long-term vegetation products is one of the most popular indices for drought monitoring.
Objective
2
VCI calculated from multi-source vegetation products had been applied in applications for drought monitoring. However
little attention has been paid to quantify the uncertainty of the drought monitoring result caused by these products. This study aims to explore the uncertainty of drought monitoring using VCI derived from multi-source remote-sensed vegetation products by considering the impact of difference in sensors
physical definition of vegetation parameters
and historical time-span of products on VCI result.Method In this study
the uncertainty of multi-source Remote sensing-based vegetation products applied to drought monitoring was analyzed and evaluated by taking the middle reaches of Yangtze River as an example. Specifically
based on the experimental settings of different sensors (MODIS
AVHRR)
different vegetation parameters (NDVI
EVI
LAI
VOD)
and different time spans (5
10
and 20 years)
the corresponding VCI time series were calculated. Then the correlation coefficient (r) and Root Mean Square Deviation (RMSD) between VCI time series under different experimental settings were calculated to quantify the uncertainty of multi-source vegetation remote sensing products for drought monitoring. The possible explanation for the quantified uncertainty was further attributed in the paper.Result (1) The VCI time series calculated from NDVI products based on different sensors show significant inconsistencies over most of the study area
with weak correlation and large overall deviations. The inconsistent long-term trend pattern between MODIS-NDVI and AVHRR-NDVI over the study area might account for the large uncertainty. (2) The differences due to different vegetation parameter products are much lower than those due to different sensors
but the differences are still significant over specific regions
with strong correlations between the VCI time series calculated based on NDVI and EVI products
NDVI and LAI products
respectively
while the VCI time series calculated based on NDVI and VOD products show significant differences in most regions. The saturation effect of NDVI still effect VCI calculation over highly vegetated area. (3) The VCI time series calculated based on different time spans keep well consistency with each other
and the larger the time span of the products
the smaller the differences in VCI changes.Conclusion In summary
when using vegetation remote sensing products for drought monitoring
the consistency characteristics among multi-source vegetation remote sensing products need to be carefully addressed to ensure the validity of the monitoring results. Similar analysis should be further expanded to global scale and more vegetation products to systematically quantify the uncertainty.
关键词
植被状态指数（VCI）干旱监测不确定性分析植被遥感遥感产品
Keywords
Vegetation Condition Index (VCI)Drought monitoringUncertainty analysisRemote sensing of VegetationRemote sensing products
references
Afshar M H, Al-Yaari A, Yilmaz M T. 2021. Comparative Evaluation of Microwave L-Band VOD and Optical NDVI for Agriculture Drought Detection over Central Europe. Remote Sensing 13(7):1251. [Doi: 10.3390/rs13071251http://dx.doi.org/10.3390/rs13071251]
AghaKouchak A, Farahmand A, Melton F S, Teixeira J, Anderson M C, Wardlow B D and Hain C R. 2015. Remote sensing of drought: Progress, challenges and opportunities. Reviews of Geophysics 53 (2):452-80. [Doi: 10.1002/2014RG000456http://dx.doi.org/10.1002/2014RG000456]
Chen C, Park Taejin, Wang X H, Piao S L, Xu B D, Chaturvedi Rajiv, Fuchs Richard, et al. 2019. China and India lead in greening of the world through land-use management. Nature Sustainability 2:122-9. [Doi: 10.1038/s41893-019-0220-7http://dx.doi.org/10.1038/s41893-019-0220-7]
Cui Y K, Chen X, Gao J Y, Yan B Y, Tang G Q and Hong Y. 2018. Global water cycle and remote sensing big data: overview, challenge, and opportunities. Big Earth Data 2 (3):282-97. [Doi: 10.1080/20964471.2018.1548052http://dx.doi.org/10.1080/20964471.2018.1548052]
Dai A G. 2011. Drought under global warming: a review. WIREs Climate Change 2 (1):45-65. [Doi: 10.1002/wcc.81http://dx.doi.org/10.1002/wcc.81]
Dai A G, Trenberth K E. and Qian T T. 2004. A Global Dataset of Palmer Drought Severity Index for 1870–2002: Relationship with Soil Moisture and Effects of Surface Warming. Journal of Hydrometeorology 5 (6):1117-30. [Doi: 10.1175/jhm-386.1http://dx.doi.org/10.1175/jhm-386.1]
Didan and Kamel. 2015. MOD13A2 MODIS/Terra Vegetation Indices 16-Day L3 Global 1km SIN Grid V006. NASA EOSDIS Land Processes DAAC. https:// ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD13A2#data-availabilityhttps://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD13A2#data-availability
Didan K, Munoz A B, Solano R and Huete A. 2015. MODIS Vegetation Index User’s Guide (MOD13 Series). University of Arizona: Vegetation Index and Phenology Lab. https://documents.pub/document/ modis-vegetation-index-user-s-guide-mod13-series-vegetation-index-users-guide.html?page=2https://documents.pub/document/modis-vegetation-index-user-s-guide-mod13-series-vegetation-index-users-guide.html?page=2
Dutta D, Kundu A, Patel N R, Saha S K and Siddiqui A R. 2015. Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). The Egyptian Journal of Remote Sensing and Space Science 18 (1):53-63. [Doi: 10.1016/j.ejrs.2015.03.006http://dx.doi.org/10.1016/j.ejrs.2015.03.006]
Fang H L, Baret F, Plummer S and Schaepman-Strub G. 2019. An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications. Reviews of Geophysics 57 (3):739-99. [Doi: 10.1029/2018RG000608http://dx.doi.org/10.1029/2018RG000608]
Feng Q, Tian G L and Liu Q H. 2003. Research on the Operational System of Drought Monitoring by Remote Sensing in China. Journal of Remote Sensing (1): 14-18
冯强, 田国良, 柳钦火. 2003. 全国干旱遥感监测运行系统的研制. 遥感学报, (1): 14-18. [Doi: 10.11834/jrs.20030103http://dx.doi.org/10.11834/jrs.20030103]
Fensholt R and Proud S R. 2012. Evaluation of Earth Observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series. Remote Sensing of Environment 119:131-47. [Doi: 10.1016/j.rse.2011.12.015http://dx.doi.org/10.1016/j.rse.2011.12.015]
Fuentes I, Padarian J and Vervoort R W. 2022. Spatial and Temporal Global Patterns of Drought Propagation. Frontiers in Environmental Science 10. [Doi:10.3389/fenvs.2022.788248http://dx.doi.org/10.3389/fenvs.2022.788248]
Garcia Asuero A, Sayago A and González G. 2006. The Correlation Coefficient: An Overview. Critical Reviews in Analytical Chemistry - CRIT REV ANAL CHEM 36:41-59. [Doi: 10.1080/10408340500526766http://dx.doi.org/10.1080/10408340500526766]
Gocic M and Trajkovic S. 2013. Analysis of changes in meteorological variables using Mann-Kendall and Sen's slope estimator statistical tests in Serbia. Global and Planetary Change 100:172-82. [Doi: 10.1016/j.gloplacha.2012.10.014http://dx.doi.org/10.1016/j.gloplacha.2012.10.014]
Guan Y H. 2015. Extreme Climate Change And Its Trend Prediction In The YANGTZE River Basin. Doctor, Northwest A & F University
关颖慧. 2015. 长江流域极端气候变化及其未来趋势预测. 博士, 西北农林科技大学
Guo N. 2003. Vegetation Index and Its Advances. Journal of Arid Meteorology 21(04):71-5
郭铌. 2003. 植被指数及其研究进展. 干旱气象, 21(04): 71-5.
Jiang Z Y, Huete A R, K Didan and Miura T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112 (10):3833-45. [Doi: 10.1016/j.rse.2008.06.006http://dx.doi.org/10.1016/j.rse.2008.06.006]
Sanchez H D and Luisa A. 2018. Vegetation Optical Depth: Its Potential as an Agricultural Drought Indicator: A Case Study on Pepper Fields in Indonesia. Master thesis, Delft University of Technology. http://resolver.tudelft.nl/uuid:29d144ff-8116-4162-bc89-d8acbaf28423http://resolver.tudelft.nl/uuid:29d144ff-8116-4162-bc89-d8acbaf28423.
Jiao W Z, Wang L X and McCabe M F. 2021. Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future. Remote Sensing of Environment 256:112313. [Doi: 10.1016/j.rse.2021.112313http://dx.doi.org/10.1016/j.rse.2021.112313]
Karnieli A, Agam N, Pinker R, Anderson M, Imhoff M, Gutman G, Panov N and Goldberg A. 2010. Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations. Journal of Climate - J CLIMATE 23:618-33. [Doi: 10.1175/2009JCLI2900.1http://dx.doi.org/10.1175/2009JCLI2900.1]
Kogan F N. 1995. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research 15 (11):91-100. [Doi: 10.1016/0273-1177(95)00079-Thttp://dx.doi.org/10.1016/0273-1177(95)00079-T]
Kogan F. N. 1990. Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing 11 (8):1405-19. [Doi: 10.1080/01431169008955102http://dx.doi.org/10.1080/01431169008955102]
Leeuwen W J D, Orr B J, Marsh S Eet al. 2006. Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications. Remote Sensing of Environment 100 (1):67-81. [Doi: 10.1016/j.rse.2005.10.002http://dx.doi.org/10.1016/j.rse.2005.10.002]
Li X. 2018. Research on Drought Monitoring and Terrestrial Ecosystem Response Based on Multi-Source Remote Sensing Data. doctor, University Of Electronic Science And Technology Of China (李星. 2018. 基于多源遥感数据的干旱监测方法及生态系统响应研究. 博士, 电子科技大学)
Liu W T and Kogan F N. 1996. Monitoring regional drought using the Vegetation Condition Index. International Journal of Remote Sensing 17 (14):2761-82. [Doi: 10.1080/01431169608949106http://dx.doi.org/10.1080/01431169608949106]
Liu Y Y, de Jeu Richard A M, McCabe Matthew F, Evans Jason P and van Dijk Albert I J M. 2011. Global long-term passive microwave satellite-based retrievals of vegetation optical depth. Geophysical Research Letters 38 (18). [Doi: 10.1029/2011GL048684http://dx.doi.org/10.1029/2011GL048684]
Massimo M and Jia L. 2016. Observing the response of the land surface to climate variability by timeseries analysis of satellite observation. Journal of Remote Sensing 20(5): 946-957
Menenti Massimo, 贾立. 2016. 遥感定量反演地表参数对于气候变化的响应研究（英文）. 遥感学报, 20(5): 946–957. [Doi: 10.1029/2011GL048684http://dx.doi.org/10.1029/2011GL048684]
Matsushita B, Yang W, Chen Jet al. 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(Basel) 7(11): 2636-2651. [Doi: 10.3390/s7112636http://dx.doi.org/10.3390/s7112636]
Moesinger L, Dorigo W, de Jeu R, van der Schalie R, Scanlon T, Teubner I and Forkel M. 2020. The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth Syst. Sci. Data 12 (1):177-96. [Doi: 10.5194/essd-12-177-2020http://dx.doi.org/10.5194/essd-12-177-2020]
Myneni, Ranga, Knyazikhin, Yuri, Park and Taejin. 2015. MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD15A3H/https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD15A3H/
Nie J, Deng L, Hao X Let al. 2018. Application of GF-4 satellite in drought remote sensing monitoring: A case study of Southeastern Inner Mongolia. Journal of Remote Sensing, 22(3): 400–407
聂娟, 邓磊, 郝向磊等. 2018. 高分四号卫星在干旱遥感监测中的应用. 遥感学报, 22(3): 400–407. [Doi: 10.11834/jrs.20187067http://dx.doi.org/10.11834/jrs.20187067]
Pettorelli N. 2013. The Normalized Difference Vegetation Index. Oxford University Press. [Doi: 10.1093/acprof:osobl/9780199693160.001.0001http://dx.doi.org/10.1093/acprof:osobl/9780199693160.001.0001]
Riebsame W E. 2019. Drought and Natural Resources Management in the United States: Impacts and Implications of the 1987-89 Drought. New York: Routledge. [Doi: 10.4324/9780429045332http://dx.doi.org/10.4324/9780429045332]
Rouse J W. 1973. Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, NASA, Washington, DC., 1973 1:309-17.
Sen P K. 1968. Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association 63:1379-89. [Doi: 10.1080/01621459.1968.10480934http://dx.doi.org/10.1080/01621459.1968.10480934]
Shi J C, Yang D, Du J Y, Jiang L M, Chai L N, Mao K B, Xu P, et al. 2012. Progresses on microwave remote sensing of land surface parameters. Science China Earth Science 55:1052-78. [Doi: 10.1007/s11430-012-4444-xhttp://dx.doi.org/10.1007/s11430-012-4444-x]
Tian F, Fensholt R, Verbesselt J, Grogan K, Horion S and Wang Y J. 2015. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sensing of Environment 163:326-40. [Doi: 10.1016/j.rse.2015.03.031http://dx.doi.org/10.1016/j.rse.2015.03.031]
Tucker C J, Pinzon J E, Brown M Eet al. 2005. An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing 26 (20): 4485-98. [Doi: 10.1080/01431160500168686http://dx.doi.org/10.1080/01431160500168686]
Vogel C, Laing M V and Monnik K. 2000. Drought in South Africa, with special reference to the 1980-94 period: Drought: a global assessment. London: Routledge: 348-366
Wang C Y, Niu Z, Zhang Q Yet al. 1999. Analysis of Vegetation Green Wave Change in China Using NOAA NDVI Data Set. Journal of Image and Graphics 4(11): 976-979
王长耀, 牛铮, 张庆员等. 1999. 利用NOAA NDVI数据集分析中国植被绿波推移规律. 中国图象图形学报, 4(11): 976–979. [Doi: 10.11834/jig.1999011231http://dx.doi.org/10.11834/jig.1999011231]
Wang Q, Adiku S, Tenhunen J and Granier A. 2005. On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment 94 (2):244-55. [Doi: 10.1016/j.rse.2004.10.006http://dx.doi.org/10.1016/j.rse.2004.10.006]
Wang X W, Liu M, Liu L. 2014. Responses of MODIS spectral indices to typical drought events from 2000 to 2012 in southwest Chin. Journal of Remote Sensing, 18(2): 432-452
王先伟, 刘梅, 柳林. 2014. MODIS光谱指数在中国西南干旱监测中的应用. 遥感学报, 18(2): 432-452. [Doi: 10.11834/jrs.20143023http://dx.doi.org/10.11834/jrs.20143023]
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.
West H, Quinn N and Horswell M. 2019. Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities. Remote Sensing of Environment 232:111291. [Doi: 10.1016/j.rse.2019.111291http://dx.doi.org/10.1016/j.rse.2019.111291]
Wilhite D. A. 2000. Drought as a Natural Hazard: Concepts and Definitions. London:Routledge:3-18.
Willmott C. and Matsuura K. 2005. Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Climate Research 30:79. [Doi: 10.3354/cr030079http://dx.doi.org/10.3354/cr030079]
Xie F and Fan H. 2021. Deriving drought indices from MODIS vegetation indices (NDVI/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary? International Journal of Applied Earth Observation and Geoinformation 101: 102352. [Doi: 10.1016/j.jag.2021.102352http://dx.doi.org/10.1016/j.jag.2021.102352]
Xie Y C, Sha Z Y and Yu M. 2008. Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology 1(1):9-23. [Doi: 10.1093/jpe/rtm005http://dx.doi.org/10.1093/jpe/rtm005]
Xu Z, Cao L, Zhong Set al. 2020. Trends in Global Vegetative Drought From Long-Term Satellite Remote Sensing Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 815-826. [Doi: 10.1109/jstars.2020.2972574http://dx.doi.org/10.1109/jstars.2020.2972574]
Xue J R and Su B F. 2017. Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors 2017: 1-17. [Doi: 10.1155/2017/1353691http://dx.doi.org/10.1155/2017/1353691]
Zhang Q, Zhang L, Cui X C and Zeng J. 2011. Progresses and Challenges in Drought Assessment and Monitoring. Advances in Earth Science 26 (07):16.
张强, 张良, 崔显成 , 曾剑. 2011. 干旱监测与评价技术的发展及其科学挑战. 地球科学进展, 26(07): 16. [DOI: 10.11867/j.issn.1001-8166.2011.07.0763http://dx.doi.org/10.11867/j.issn.1001-8166.2011.07.0763]
Zheng G and Moskal L M. 2009. Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors. Sensors 9(4): 2719-45. [Doi: 10.3390/s90402719http://dx.doi.org/10.3390/s90402719]
Zhou J, Jia L and Massimo M. 2015. Reconstruction of global MODIS NDVI time series: Performance of Harmonic ANalysis of Time Series (HANTS). Remote Sensing of Environment 163:217-28. [Doi: 10.1016/j.rse.2015.03.018http://dx.doi.org/10.1016/j.rse.2015.03.018]
Alert me when the article has been cited
提交
相关文章
相关作者
暂无数据
相关机构
School of Surveying and Land Information Engineering, Henan Polytechnic University
China University of Mining and Technology
Institute of Mineral Resources Research, China Metallurgical Geology Bureau
Zhongke Xingtu Space Technology Co., Ltd
Aerospace Information Research Institute(AIR) Chinese Academy of Sciences(CAS)
选择翻译语言
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0
Bulk Citation
Export to
TOP