定量遥感尺度转换方法研究进展

doi:10.13249/j.cnki.sgs.2019.03.002

地理科学 ›› 2019, Vol. 39 ›› Issue (3): 367-376.doi: 10.13249/j.cnki.sgs.2019.03.002

定量遥感尺度转换方法研究进展

姚远^1,^3,⁴(), 陈曦^1,⁴, 钱静^1,²()

1.中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室,新疆乌鲁木齐 830011
2. 中国科学院深圳先进技术研究院,广东深圳 518055
3. 中国科学院大学,北京 100049
4. 中国科学院中亚生态与环境研究中心,新疆乌鲁木齐 830011

收稿日期:2018-03-05 修回日期:2018-07-18 出版日期:2019-03-10 发布日期:2019-03-10
作者简介:
作者简介：姚远（1987-）,男,新疆乌鲁木齐人,博士研究生,主要从事干旱区资源遥感研究。E-mail: xinjiangyaoyuan@sina.com
基金资助:
中国科学院A类战略性先导科技专项（XDA20060303）、中国科学院“西部之光”人才培养引进计划青年学者A类项目（2016-QNXZ-A-5）、国家自然科学基金项目（41761144079）、深圳国际合作研究项目（GJHZ20160229194322570）和广东省省级科技计划项目（2017A050501027）资助

A Review on the Methodology of Scale Issues in Quantitative Remote Sensing

Yuan Yao^1,^3,⁴(), Xi Chen^1,⁴, Jing Qian^1,²()

1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
3. University of Chinese Academy of Sciences, Beijing 100049, Beijing, China
4. Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China

Received:2018-03-05 Revised:2018-07-18 Online:2019-03-10 Published:2019-03-10
Supported by:
The Strategic Priority Research Program of Chinese Academy of Sciencess (XDA20060303), Chinese Academy of Sciences “Light of West China” Program (2016-QNXZ-A-5), National Natural Science Foundation of China (41761144079) , Shenzhen International S&T Cooperation Project (GJHZ20160229194322570) and Science and Technology Planning Project of Guangdong Province (2017A050501027)

摘要/Abstract

摘要：

首先对遥感科学和气象学、水文学、生态学、地理学和的尺度概念及其转换方法进行了区分。其次对遥感数据空间尺度转换方法的国内外研究进展情况进行了系统梳理,重点比对了目前空间尺度转换常用6种转换方法：统计转换法、分类转换法、数据融合转换方法、分形分析法、基于局域动态模型的转换方法和基于物理意义尺度转换方法及其各自所属方法的优点和缺点。再次以遥感时间尺度转换应用最为广泛的地表蒸散发和农业旱情监测等2个领域为例,对遥感时间尺度转换方法进行了总结。最后预测了今后定量遥感尺度转换研究可能的研究重点,以期为今后更好地开展尺度效应和尺度转换研究工作提供参考。

关键词: 定量遥感, 尺度效应, 尺度转换, 时间尺度, 空间尺度

Abstract:

The advance of remote sensing technology provides an effective way for disaster prediction, environmental monitoring, resource surveys. Remote sensing technology has become a effective tools to get geo-information comprehensively, accurately, and quickly. Undoubtedly, remote sensing technology in the 21th century has entered an era of quantitative analysis. Thus, the scale issues have always attracted increasing the attention in quantitative remote sensing owing to scale effects limite the accuracy of retrieval the development of its applications. It is a significant problem in remote sensing research, and the methods of scale transformation have been proved is useful to resolve this problem. In ordert to study the scale issues of remote sensing, the advances in methodology of scale issues in quantitative remote sensing must be discussed. Firstly, as we know, the concept of scale is now suffering from a discrepancy in different research area, such as remote sensing, meteorology, hydrology, ecology and geography. Therefore, the definition of scale and relevant methodology in above mentioned research area are introduced in the first of this paper. Secondly, in reviewing the scale issues in remote sensing, the approaches for spatial and temporal scale transformation are reviewed. We summarize the advanges and disadvantages of the mainly spatial scale transformation methods, such as statistical transformation method, classification transformation method, data fusion transformation method, fractal analysis method, local dynamic model based scaling approach and scale transformation method based on physical meaning. Thirdly, the application of measured vapotranspiration and agricultural drought are restricted by remotely sensed data owing to the direct result are instantaneous value estimated at the passing time of satellite. Consequently, different time scales values have practiacal significance. We have summarized many remote sensing data time scale extrapolation methods. The results obtained show that each method has its own advantages and disadvantages. Finally, the future research directions were equally proposed in order to provide a reference for future researchers interested in quantitative remote sensing.

Key words: quantitative remote sensing, scale effect, scale transformation, spatial scale, temporal scale

中图分类号:

P237

姚远, 陈曦, 钱静. 定量遥感尺度转换方法研究进展[J]. 地理科学, 2019, 39(3): 367-376.

Yuan Yao, Xi Chen, Jing Qian. A Review on the Methodology of Scale Issues in Quantitative Remote Sensing[J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(3): 367-376.

图/表 4

图1

表1

基于统计的遥感尺度转换方法"

方法名称	定义	优点	缺点
（1）局部平均法^[23]	将高分辨率遥感影像中一定大小窗口内的像元合并,以其平均值作为合并后像元的值,通过计算所有窗口来获取转换后的较低分辨率影像	够较好地保持影像均值信息,且运算简单	易丢失部分细节信息
（2）中心像元法^[24]	将高分辨率遥感影像中一定大小窗口内中心像元的值作为新像元的值,通过计算所有窗口来获取转换后的较低分辨率影像	方法简单易行,能够在一定程度上保持原影像的纹理特征	转换后的图像质量较差,且在窗口较大的情况下接近于随机图像
（3）中值采样法^[25]	将高分辨率遥感影像中一定大小窗口内所有像元值的中位数值作为新像元的值,通过计算所有窗口来获取转换后的较低分辨率影像	该方法运算量小,能够较好地保持原图像的均值	尺度转换过程中信息损失量大,像元值不连续的问题
（4）重采样法^[26] ① 最近邻法	将高分辨率遥感影像中一定大小窗口内离采样点最近的像元值赋予转换后低分辨率影像所对应的像元值	算法简单、易实现	没有考虑除最近像元点以外的其它点,因而图像质量易受损失,像元不连续,且转换后的图像人为加工痕迹较明显
② 双线性内插法	将高分辨率遥感影像中2个正交方向上的像元值,按照先水平方向后垂直方向的顺序进行一阶线性插值,通过加权平均作为转换后较低分辨率影像中对应点的新像元值。	能够克服最近邻法的不足,避免转换后图像像元的不连续	运算量较大,转换精度不高、图像边缘信息易丢失
③ 无重叠平均法	将高分辨率遥感影像中无重叠方形窗口内的像元平均值赋予转换后低分辨率影像所对应的像元值	Hay等^[27]认为该方法的转换效果较好,而最近邻法、双线性内插法和立方卷积内插法在转换尺度超过5个像元时,不适用于遥感影像的尺度上推,转换可信度低	当影像中涉及大量复杂地物时,尺度转换精度不高
④ 立方卷积内插法	与双线性内插法原理相似,将高分辨率影像中的某像元周边的4个像元值改为16个进行加权平均,通过转换得到较低分辨率影像中对应点的新像元值	计算精度较高,考虑了相邻像元间的灰度值和其间的变化率,影像的边缘信息和纹理得到较好地保留	计算量大

表1

表2

表3

参考文献 68

[1]	胡云峰, 徐芝英, 刘越, 等. 地理空间数据的尺度转换[J]. 地球科学进展,2013,28(3):294-304. doi: 10.11867/j.issn.1001-8166.2013.03.0297
	[Hu Yunfeng, Xu Zhiying, Liu Yue et al. A review of the scaling issues of geospatial data. Advances in Earth Science, 2013,28(3):294-304.] doi: 10.11867/j.issn.1001-8166.2013.03.0297
[2]	Stoter J, Visser T, Van Oosterom P et al. A semantic-rich multi-scale information model for topography[J]. International Journal of Geographical Information Science, 2011, 25(5):739-763. doi: 10.1080/13658816.2010.490218
[3]	Abdulle A, Weinan E, Engquist B et al. The heterogeneous multiscale metho[J]. Acta Numerica, 2012, 21:1-87. doi: 10.1017/S0962492912000025
[4]	明冬萍, 王群, 杨建宇. 遥感影像空间尺度特征与最佳空间分辨率选择[J]. 遥感学报, 2008,12(4):529-537. doi: 10.3321/j.issn:1007-4619.2008.04.001
	[Ming Dongping, Wang Qun, Yang Jianyu.Spatial scale of remote sensing image and selection of optimal spatial resolution. Journal of Remote Sensing, 2008, 12(4):529-537.] doi: 10.3321/j.issn:1007-4619.2008.04.001
[5]	Estes J E, Jensen J R, Simonett D S. Impacts of remote sensing on U.S. geography[J]. Remote Sensing of Environment, 1980, 10: 43-80. doi: 10.1016/0034-4257(80)90098-x
[6]	李小文, 王祎婷. 定量遥感尺度效应刍议[J]. 遥感学报, 2013, 68(9):1163-1169. doi: 10.11821/dlxb201309001
	[Li Xiaowen, Wang Yiting.Prospects on future developments of quantitative remote sensing. Journal of Remote Sensing, 2013, 68(9):1163-1169.] doi: 10.11821/dlxb201309001
[7]	Silvestri S, Marani M, Settle J et al. Salt marsh vegetation radiometry: Data analysis and scaling[J]. Remote Sensing of Environment, 2002, 80(3):473-482. doi: 10.1016/S0034-4257(01)00325-X
[8]	Robinson W S.Ecological correlations and the behavior of individuals[J]. American Sociological Review, 1950, 15(3):351-357. doi: 10.1093/ije/dyn357 pmid: 19179346
[9]	Boville B A.Sensitivity of simulated climate to model resolution[J]. Journal of Climate, 1991, 4:469-485. doi: 10.1175/1520-0442(1991)0042.0.CO;2
[10]	Boyle J S.Sensitivity of dynamical quantities to horizontal resolution for a climate simulation using the ECMWF(Cycle33) Model[J]. Journal of Climate, 1993, 6:796-815. doi: 10.1175/1520-0442(1993)0062.0.CO;2
[11]	Kioutsioukis I, Melas D, Zanis P.Statistical downscaling of daily precipitation over Greece[J]. International Journal of Climatology, 2008, 28(5):679-691. doi: 10.1002/joc.1557
[12]	Blöschl G, Sivapalan M.Scale issues in hydrological modelling: a review[J]. Hydrological Processes, 1995, 9(3-4):251-290. doi: 10.1002/hyp.3360090305
[13]	钟晔, 金昌杰, 裴铁璠. 水文尺度转换探讨[J]. 应用生态学报, 2005, 16(8):1537-1540.
	[Zhong Ye, Jin Changjie, Pei Tiefan.Discussion on hydrological scaling. Chinese Journal of Applied Ecology, 2005, 16(8):1537-1540.]
[14]	Wu J G, Jones K B, Li H et al. Scaling and uncertainty analysis in ecology[M]. Dordrecht: Springer, 2006.
[15]	张娜. 生态学中的尺度问题[J]. 生态学报, 2007, 27(10):4252-4266.
	[Zhang Na.Scale issues in ecology: Upscaling. Acta Ecologica Sinica, 2007, 27(10):4252-4266.]
[16]	McCarthy Albert J P. The Irish national electrification scheme[J]. Geographical Review, 1957, 47(4):539-554. doi: 10.2307/211864
[17]	Openshaw S.Ecological fallacies and the analysis of the areal census data[J]. Environment and Planning A, 1984, 16(1):17-31. doi: 10.1068/a160017 pmid: 12265900
[18]	Cao C Y, Lam N.Understanding the scale and resolution effects in remote sensing and GIS[M]. New York: Lewis Publishers, 1997.
[19]	Bierkens M F P, Finke P A, De Willigen P. Upscaling and downscaling methods for environmental research[M]. Dordrecht: Kluwer Academic Publishers, 2000.
[20]	Woodcock C E, Strahler A H.The factor of scale in remote sensing[J]. Remote Sensing of Environment, 1987, 21(3):311-332. doi: 10.1016/0034-4257(87)90015-0
[21]	彭晓鹃, 邓儒儒, 刘小平. 遥感尺度转换研究进展[J]. 地理与地理信息科学, 2004, 20(5):6-14. doi: 10.3969/j.issn.1672-0504.2004.05.002
	[Peng Xiaojuan, Deng Ruru, Liu Xiaoping.A review of transformation in remote sensing. Geography and Geo-Information Science, Geography and Geo- Information Science, 2004, 20(5):6-14. doi: 10.3969/j.issn.1672-0504.2004.05.002
[22]	Quattrochi D A, Goodchild M F.Scale in remote sensing and GIS[M]. New York: Lewis Publishers, 1997:100-118.
[23]	周觅, 张杰林. 遥感影像尺度转换及最优尺度选择探讨[J]. 世界核地质科学, 2011, 28(2):94-98. doi: 10.3969/j.issn.1672-0636.2011.02.006
	[Zhou Mi, Zhang Jielin.Review on scale transformation for remote sensing image and selection of optimal spatial resolution. World Nuclear Geoscience, 2011, 28(2):94-98.] doi: 10.3969/j.issn.1672-0636.2011.02.006
[24]	秦承志, 呼雪梅. 栅格数字地形分析中的尺度问题研究方法[J]. 地理研究, 2014, 33(2):270-283. doi: 10.11821/dlyj201402007
	[Qin Chengzhi, Hu Xuemei.Review on scale-related researches in grid-based digital terrain analysis. Geographical Research, 2014, 33(2):270-283.] doi: 10.11821/dlyj201402007
[25]	Bian L, Butler R.Comparing effects of aggregation methods on statistical and spatial properties of simulated spatial data[J]. Photogrammetric Engineering and Remote Sensing, 1999, 65(1):73-84.
[26]	Liang S L.Quantitative remote sensing of land surfaces[M]. Hoboken: John Wiley and Sons, 2005.
[27]	Hay G J, Niernann K O, Goodenough D G.Spatial thresholds, image-objects, and upscaling: A multiscale evaluation[J]. Remote Sensing of Environment, 1997, 62(1):1-19. doi: 10.1016/S0034-4257(97)81622-7
[28]	Jin Z, Tian Q, Chen J M et al. Spatial scaling between leaf area index maps of different resolutions[J]. Journal of Environmental Management, 2007, 85(3):628-637. doi: 10.1016/j.jenvman.2006.08.016 pmid: 17123700
[29]	梁顺林, 李小文, 王锦地, 等. 定量遥感:理念与算法[M]. 北京:科学出版社, 2013.
	[Liang Shunlin, Li Xiaowen, Wang Jindi et al. Quantitative remote sensing: Idea and algorithm. Beijing: Science Press, 2013.]
[30]	Brice M, Michael A.An approach using Dempster-Shafer Theory to fuse spatial data and satellite image derived crown metrics for estimation of forest stand leading species[J]. Information Fusion, 2012, 121(4):122-134. doi: 10.1016/j.inffus.2012.05.004
[31]	Al-Ani A, Deriche M.A new technique for combining multiple classifiers using the Dempster-Shafer Theory of evidence[J]. Journal of Artificial Intelligence Research, 2002, 17:333-361. doi: 10.1613/jair.1026
[32]	Zhang Y, Hong G.An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural color IKONOS and QuickBird images[J]. Information Fusion, 2005, 6(3):225-234. doi: 10.1016/j.inffus.2004.06.009
[33]	Da Cunha A L, Zhou J, Do M N. The nonsubsampled contourlet transform: theory, design, and applications[J]. IEEE Transactions on Image Processing, 2006, 15(10):3089-3101. doi: 10.1109/TIP.2006.877507 pmid: 17022272
[34]	卢炎. 多源遥感影像融合方法研究[J]. 吉林大学学报, 2007, 37: 176-179.
	[Lu Yan.The research on multi-source remote sensing images fusion technology. Journal of Jilin University, 2007, 37: 176-179.]
[35]	Arivazhagan S, Ganesan L, Kumar T G S. A modified statistical approach for image fusion using wavelet transform[J]. Signal Image and Video Processing, 2009, 3(2):137-144. doi: 10.1007/s11760-008-0065-4
[36]	Amolins K, Zhang Y, Dare P.Wavelet based image fusion techniques-An introduction, review and comparison[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2007, 62(4):249-263. doi: 10.1016/j.isprsjprs.2007.05.009
[37]	Pohl C, Genderen J L V. Multisensor image fusion in remote sensing: Concepts, methods and applications[J]. International Journal of Remote Sensing, 1998, 19(5):823-854. doi: 10.1080/014311698215748
[38]	高永刚. 多尺度多平台遥感影像在城市遥感中的应用研究[D]. 福州:福州大学, 2013.
	[Gao Yonggang.Study on the application of multi-source and multi-scale images in the urban remote sensing. Fuzhou: Fuzhou University, 2013.]
[39]	庞振平. 遥感影像融合技术理论与方法研究[D]. 长春: 吉林大学, 2008.
	[Pang Zhenping.Research on the theory and method of remote sensing image fusion. Changchun: Jilin University, 2008.]
[40]	刘宇琼. 基于像素级多源遥感影像融合技术的研究[D]. 西安: 长安大学, 2007.
	[Liu Yuqiong.Study on pixel-level multi-source remote sensing data fusion technique. Xi’an: Chang’an University, 2007.]
[41]	Xia Z G, Clarke K C.Approaches to scaling of geo-spatial data[M]. Boca Raton:CRC Lewis Publishers, 1997.
[42]	徐希孺. 遥感物理[M]. 北京: 北京大学出版社, 2005.
	[Xu Xiru.Physical principles of remote sensing. Beijing: Peking University Press, 2005.]
[43]	徐建华. 现代地理学中的数学方法[M]. 北京:高等教育出版社, 2002.
	[Xu Jianhua.Mathematical methods in contemporary geography. Beijing:Higher Education Press, 2002.]
[44]	张娜. 生态学中的尺度问题[J]. 生态学报, 2007, 27(10):4252-4266.
	[Zhang Na.Scale issues in ecology: Upscaling. Acta Ecology Sinica, 2007, 27(10):4252-4266.]
[45]	Turner M G, Gardner R H.Quantitative methods in landscape ecology, ecological studies[M]. New York: Springer, 1991.
[46]	Li X W, Wang J D, Alan S.Scale effects and scaling-up by geo-metric-optical model[J]. Science in China (Series E), 2000, 43(Suppl.):17-22.
[47]	Li X W, Wan Z M.Comments on reciprocity in the BRDF Modeling[J]. Progress in Natural Science, 1999, 8(3):354-358.
[48]	Albert B J, Strahler A H, Li X W et al. Radiometric measurements of gap probability in conifer tree canopies[J]. Remote Sensing of Environment, 1990, 34:179-192. doi: 10.1016/0034-4257(90)90067-V
[49]	Becker F, Li Z L.Surface temperature and emissivity at various scales: Definition, measurement, and related problems[J]. Remote Sensing Reviews, 1995, 12:225-253. doi: 10.1080/02757259509532286
[50]	栾海军, 田庆久, 余涛, 等. 定量遥感升尺度转换研究综述[J]. 地球科学进展, 2013, 28(6):657-664. doi: 10.11867/j.issn.1001-8166.2013.06.0657
	[Luan Haijun, Tian Qingjiu, Yu Tao et al. Review of up-scaling of quantitative remote sensing. Advances in Earth Science, 2013, 28(6):657-664.] doi: 10.11867/j.issn.1001-8166.2013.06.0657
[51]	Long D, Singh V P, Li Z L.How sensitive is SEBAL to changes in input variables, domain size and satellite sensor[J]. Journal of Geophysical Research Atmospheres, 2011, 116:D21107. doi: 10.1029/2011JD016542
[52]	Kalma J D, Mcvicar T R, Mccabe M F.Estimating Land Surface Evaporation: A review of methods using remotely sensed surface temperature data[J]. Surveys in Geophysics, 2008, 29(5):421-469. doi: 10.1007/s10712-008-9037-z
[53]	Gentine P, Entekhabi D, Chehbouni A et al. Analysis of evaporative fraction diurnal behaviour[J]. Agricultural and Forest Meteorology, 2007, 143(1):13-29. doi: 10.1016/j.agrformet.2006.11.002
[54]	Su Z.The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes[J]. Hydrology and Earth System Sciences, 2002, 6(1):85-99. doi: 10.5194/hess-6-85-2002
[55]	Allen R G, Smith M, Wright J L et al. FAO-56 dual crop coefficient method for estimating evaporation from soil and application extensions[J]. Journal of Irrigation and Drainage Engineering, 2005, 131(1):2-13. doi: 10.1061/(ASCE)0733-9437(2005)131:1(2)
[56]	Wright J L, Allen R G, Tasumi M et al. Satellite-based energy balance to assess within population variance of crop coefficient curves[J]. Journal of Irrigation and Drainage Engineering, 2005, 131(1):94-109. doi: 10.1061/(ASCE)0733-9437(2005)131:1(94)
[57]	Liu G, Liu Y, Xu D.Comparison of evapotranspiration temporal scaling methods based on lysimeter measurements[J]. Journal of Remote Sensing, 2011, 15(2):270-280.
[58]	Colaizzi P D, Evett S R, Howell T A et al. Comparison of five models to scale daily evapotranspiration from one time of day measurements[J]. Transactions of the ASABE, 2006, 49(5):1409-1417. doi: 10.13031/2013.22056
[59]	Jackson R D, Hatfield J L, Reginato R J et al. Estimation of daily evapotranspiration from one time-of-day measurements[J]. Agricultural Water Management, 1983, 7(1/3):351-362. doi: 10.1016/0378-3774(83)90095-1
[60]	刘素华, 田静, 米素娟. 遥感估算蒸散发量的日尺度扩展方法综述[J]. 国土资源遥感, 2016, 28(4):10-17. doi: 10.6046/gtzyyg.2016.04.02
	[Liu Suhua, Tian Jing, Mi Sujuan.Review of methods on estimation of daily evapotranspiration from one time of day remotely sensed transient evapotranspiration. Remote Sensing for Land and Resources, 2016, 28(4):10-17.] doi: 10.6046/gtzyyg.2016.04.02
[61]	陈素华. 干旱对内蒙古粮食产量的影响及其评估方法的建立[J]. 华北农学报, 2004, 19(S1):81-85. doi: 10.3321/j.issn:1000-7091.2004.z1.019
	[Chen Suhua.Effect on the grain yield for the drought and establish the model in Inner Mongolia. Acta Agriculturae Boreali-Sinica, 2004, 19(S1):81-85.] doi: 10.3321/j.issn:1000-7091.2004.z1.019
[62]	Bayarjargal Y, Karnieli A, Bayasgalan M et al. A comparative study of NOAA-AVHRR derived drought indices using change vector analysis[J]. Remote Sensing of Environment, 2006, 105(1):9-22. doi: 10.1016/j.rse.2006.06.003
[63]	Tian M, Wang P, Khan J.Drought forecasting with vegetation temperature condition index using ARIMA Models in the Guanzhong Plain[J]. Remote Sensing, 2016, 8(9): 690. doi: 10.3390/rs8090690
[64]	王蕾. 基于条件植被温度指数的冬小麦主要生育期干旱影响评估研究[D]. 北京: 中国农业大学, 2015.
	[Wang Lei.Estimation of drought impact on the main growth stage of winter wheat based on vegetation temperature condition index. Beijing: China Agricultural University, 2015.]
[65]	Xu Z S.On consistency of the weighted geometric mean complex judgement matrix in AHP[J]. European Journal of Operational Research, 2000, 126(3):683-687. doi: 10.1016/S0377-2217(99)00082-X
[66]	白雪娇. 条件植被温度指数的时空尺度上推方法研究[D]. 北京: 中国农业大学, 2017.
	[Bai Xuejiao.Temporal and spatial up-scaling methods for vegetation temperature condition index. Beijing: China Agricultural University, 2017.]
[67]	白雪娇, 王鹏新, 张树誉, 等. 基于遗传算法的条件植被温度指数的时间尺度转换方法[J]. 水土保持研究, 2018, 25(1): 190-196.
	[Bai Xuejiao, Wang Pengxin, Zhang Shuyu et al. Time-scale transform of multi-temporal vegetation temperature condition index based on the genetic algorithm. Research of Soil and Water Conservation, 2018, 25(1): 190-196.]
[68]	李新, 晋锐, 刘绍民, 等. 黑河遥感实验中尺度上推研究的进展与前瞻[J]. 遥感学报, 2016, 20(5):921-932. doi: 10.11834/jrs.20166241
	[Li Xin, Jin Rui, Liu Shaomin et al. Upscaling research in Hiwater: Progress and prospects. Journal of Remote Sensing, 2016, 20(5):921-932.] doi: 10.11834/jrs.20166241

方法名称	定义	优点	缺点
简单聚合法	通过同时增加不同遥感影像的空间分辨率,将较高分辨率影像的参数或变量的平均值转换为较低分辨率影像	方法简单,容易实现	忽略了变量或参数在遥感影像尺度变化时发生的非线性变化,误差较大
显示积分法	要求高分辨率影像模型在空间上能够积分,是空间显示的数学函数,通过显示积分进行尺度转换,已连续函数表示空间异质性	计算复杂度较期望外推法和直接外推法有所降低,且在理论上是一种有效和精确的方法	以连续函数形式表达空间异质性的难度较大,方法尚待进一步完善,因而限制了该方法的推广应用
期望外推法	通过高分辨率影像模型按照地物类别的不同进行模拟,并根据结果计算较低空间分辨率影像的空间结构特征期望值与影像面积相乘,以此实现尺度转换	是当前较为通用的一种方法。通过蒙特卡罗模拟法估算得出的期望值,经得起不确定性分析	转换结果的准确性易受类别现象描述的准确性或空间变异性的影响
直接外推法	将局部分析模型从高分辨率影像应用到适合该模型的所有区域,通过计算不同地物的像素输出综合,实现尺度转换	计算方法简单直接,概念明晰,应用推广度最好	尺度转换的准确性易受到影像尺度转换过程中的误差积累影响

方法名称	定义	优点	缺点
蒸发比法^[53,54]	在云量保持恒定或晴朗的白天,由于正午瞬时蒸发比近似日均蒸发比,以此建立基于蒸发比的尺度转换模型,实现区域ET的估算	方法简易,且较为常用	在实际应用中误差较大,需要进一步改进,并校正偏差,提高精度
作物系数法^[55,56]	由于实际ET与作物ET比值（作物系数）在日内变化较小,因而主要由联合国粮农组织提供的基于作物系数的时间尺度扩展方法,将遥感影像所获取的瞬时作物系数代替日作物系数,实现由瞬时 ET 到日的时间尺度扩展	在地表植被均匀的情况下,所得到的结果精度较高	在作物生育中后期,该方法所确定的作物系数与实测值有较大误差,必须进行参数调校,才能提高精度
冠层阻力法^[57,58]	以蒸散发估算最为有效,应用最为广泛的Penman-Monteith模型为基础,采用遥感瞬时数据估算模型中的下垫面、通量数据等相关参数,进而推导出卫星过境时刻的冠层阻力。据此,估算不同时间尺度区域ET值	大量研究结果表明,该方法可以有效实现大区域尺度,长时间序列ET数据的扩展	易受遥感数据及模型局限性的限制,估算结果的变异性较大
正弦关系法^[59]	太阳辐射在日尺度范围内呈现周期性变化。在晴朗的天气条件下,卫星过境时获取的瞬时辐射与日内辐射间存在正弦曲线关系,其比值可用于瞬时ET与日ET比值的替换,以此实现ET的时间尺度扩展	简单易用,便于理解,在天气晴朗的条件下,具有较好的可行性,结果精度较高	扩展后的日ET数值易受复杂天气和地面条件的影响,与实测数值日ET数值相比有所偏离

定量遥感尺度转换方法研究进展

A Review on the Methodology of Scale Issues in Quantitative Remote Sensing

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