Please wait a minute...
img

官方微信

遥感技术与应用  2015, Vol. 30 Issue (6): 1122-1128    DOI: 10.11873/j.issn.1004-0323.2015.6.1122
模型与反演     
主被动遥感数据协同估算干旱区草原植被生物量
行敏锋,何彬彬
(电子科技大学资源与环境学院,四川 成都611731)
Estimation of Vegetation Biomass in Arid Region with Active and Passive Remote Sensing Data
Xing Minfeng,He Binbin
(School of Resources and Environment,University of Electronic Science and
Technology of China,Chengdu 611731,China)
 全文: PDF(2078 KB)  
摘要:

结合主动微波遥感和被动光学遥感反映地表植被的各自优势,发展了一种主被动遥感协同估算干旱区草原植被生物量的模型。该模型将植被覆盖度作为水云模型的附加参数,将总体散射分为植被覆盖区散射和裸土区散射两部分,将水云模型应用到了植被覆盖稀疏区域。利用改进的水云模型和双极化ASAR数据,通过建立方程组估算植被生物量。将该方法用于乌图美仁草原植被生物量的估算,验证了该方法的有效性。结果表明:该主被动遥感协同估算模型能够成功地估算干旱区草原植被生物量,并且取得了较好的估算精度(R2=0.8562,RMSE=0.1813 kg/m2)。最后,分析了该方法估算植被生物量的误差来源。

关键词: 生物量水云模型协同估算干旱区草原    
Abstract:

Integrating the respective advantages of optical and microwave data for the vegetation,an optical and microwave synergistic method for the Above Ground Biomass (AGB) in the prairie of arid regions was developed in this paper.Vegetation coverage was combined in water cloud model as additional information.The total backscattering was divided into the amount attributed to areas covered with vegetation and attributed to areas of bare soil.Thus,the water cloud model can be applied in the sparse vegetation cover area.Using the modified water cloud model and dual-polarization ASAR data,the vegetation biomass was estimated by the established equations.The method was applied to estimate the AGB of Wutumeiren prairie.The results indicated that the method of active and passive remote sensing synergy was of the operational potential in AGB.And the better accuracy of the biomass retrieval was achieved(R2=0.8562,RMSE=0.1813 kg/m2).Finally,the error of biomass estimation using this method was analyzed.

Key words: Biomass    Water Cloud Model    Synergistic estimation    Prairie of arid regions
收稿日期: 2014-08-12 出版日期: 2016-01-25
:  TP 79  
基金资助:

国家863计划项目(2013AA12A302),中央高校基本业务费重点培育项目(ZYGX2012Z005)。

通讯作者: 何彬彬(1972-),男,湖南邵阳人,教授,博导,主要从事定量遥感和数据挖掘研究。Email:binbinhe@uestc.edu.cn。    
作者简介: 行敏锋(1982-),男,陕西渭南人,博士研究生,主要从事定量遥感研究。Email:xingminfeng@163.com。
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
行敏锋
何彬彬

引用本文:

行敏锋,何彬彬. 主被动遥感数据协同估算干旱区草原植被生物量[J]. 遥感技术与应用, 2015, 30(6): 1122-1128.

Xing Minfeng,He Binbin. Estimation of Vegetation Biomass in Arid Region with Active and Passive Remote Sensing Data. Remote Sensing Technology and Application, 2015, 30(6): 1122-1128.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.6.1122        http://www.rsta.ac.cn/CN/Y2015/V30/I6/1122

[1]Liu J,Pattey E,Miller J R,et al.Estimating Crop Stresses,aboveground Dry Biomass and Yield of Corn Using Multi-Temporal Optical Data Combined with a Radiation Use Efficiency Model[J].Remote Sensing of Environment,2010,114(6):1167-1177.

[2]Muukkonen P,Heiskanen J.Estimating Biomass for Boreal Forests Using ASTER Satellite Data Combined with Standwise Forest Inventory Data[J].Remote Sensing of Environment,2005,99(4):434-447.

[3]Zheng D,Rademacher J,Chen J,et al.Estimating Aboveground Biomass Using Landsat 7 ETM+ Data across a Managed Landscape in Northern Wisconsin,USA[J].Remote Sensing of Environment,2004,93(3):402-411.

[4]Han Ying,Pei Liang,Du Jia.Remote Sensing Inversion of Aboveground Biomass over the Honghe Wetland[J].Remote Sensing Technology and Application,2014,29(2):224-231.[韩颖,裴亮,杜嘉.洪河湿地植被地上生物量遥感反演研究[J].遥感技术与应用,2014,29(2):224-231.]

[5]Meng Jihua,Du Xin,Zhang Miao,et al.Integrating Crop Phenophase Infromation in Large-area Crop Condition Evaluation with Remote Sensing[J].Remote Sensing Technology and Application,2014,29(2):278-285.[蒙继华,杜鑫,张淼,等.物候信息在大范围作物长势遥感监测中的应用[J].遥感技术与应用,2014,29(2):278-285.]

[6]Englhart S,Keuck V,Siegert F.Aboveground Biomass Retrieval in Tropical Forests—the Potential of Combined X- and L-Band SAR Data Use[J].Remote Sensing of Environment,2011,115(5):1260-1271.

[7]Tsolmon R,Tateishi R,Tetuko J.A Method to Estimate Forest Biomass and Its Application to Monitor Mongolian Taiga Using JERS-1 SAR Data[J].International Journal of Remote Sensing,2002,23(22):4971-4978.

[8]Wang H,Ouchi K.Accuracy of the K-distribution Regression Model for Forest Biomass Estimation by High-resolution Polarimetric SAR:Comparison of Model Estimation and Field Data[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(4):1058-1064.

[9]Shen Guozhuang,Liao Jingjuan,Guo Huadong,et al.Retrieval of Biomass in Poyang Lake Wetland from Envisat ASAR Data[J].Chinese High Technology Letters,2009,19(6):644-649.[沈国状,廖静娟,郭华东,等.基于Envisat ASAR数据的鄱阳湖湿地生物量反演研究[J].高技术通讯,2009,19 (6):644-649.]

[10]Amini J,Sumantyo J T S.Employing a Method on SAR and Optical Images for Forest Biomass Estimation[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(12):4020-4026.

[11]Chen W,Blain D,Li J,et al.Biomass Measurements and Relationships with Landsat-7/ETM+ and JERS-1/SAR Data Over Canada's Western Sub-Arctic and Low Arctic[J].International Journal of Remote Sensing,2009,30(9):2355-2376.

[12]Wang C,Qi J.Biophysical Estimation in Tropical Forests Using JERS-1 SAR and VNIR Imagery.II.aboveground Woody Biomass[J].International Journal of Remote Sensing,2008,29(23):6827-6849.

[13]Wang Qing,Liao Jingjuan.Estimation of Wetland Vegetation Biomass in the Poyang Lake Area Using Landsat TM and Envisat ASAR Data[J].Journal of Geo-information Science,2010,12(2):282-291.[王庆,廖静娟.基于Landsat TM和ENVISAT ASAR数据的鄱阳湖湿地植被生物量的反演[J].地球信息科学学报,2010,12(2):282-291.]

[14]Svoray T,Shoshany M.SAR-based Estimation of Areal aboveground Biomass (AAB) of Herbaceous Vegetation in the Semi-Arid Zone:A Modification of the Water Cloud Model[J].International Journal of Remote Sensing,2002,23(19):4089-4100.

[15]Li Yanli,Yang Taibao,Zeng Biao.Land Cover Change of Southern Fringe Oasis in the Qaidam Basin from 2000 to 2009 based on MODIS Data[J].Journal of Desert Research,2011,30(1):34-42.[李艳丽,杨太保,曾彪.基于MODIS数据的柴达木盆地南缘绿洲土地覆盖动态变化研究[J].中国沙漠,2011,31(1):34-42.]

[16]Vermote E F,Tanré D,Deuze J L,et al.Second Simulation of the Satellite Signal in the Solar Spectrum,6S:an Overview[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(3):675-686.

[17]Lopes A,Touzi R,Nezry E.Adaptive Speckle Filters and Scene Heterogeneity[J].IEEE Transactions on Geoscience and Remote Sensing,1990,28(6):992-1000.

[18]Attema E,Ulaby F T.Vegetation Modeled as a Water Cloud[J].Radio Science,1978,13(2):357-364.

[19]Moran M S,Vidal A,Troufleau D,et al.Ku- and C-Band SAR for Discriminating Agricultural Crop and Soil Conditions[J].IEEE Transactions on Geoscience and Remote Sensing,1998,36(1):265-272.

[20]Prévot L,Champion I,Guyot G.Estimating Surface Soil Moisture and Leaf Area Index of a Wheat Canopy Using a Dual-Frequency (C and X Bands) Scatterometer[J].Remote Sensing of Environment,1993,46(3):331-339.

[21]Taconet O,Benallegue M,Vidal-Madjar D,et al.Estimation of Soil and Crop Parameters for Wheat from Airborne Radar Backscattering Data in C and X Bands[J].Remote Sensing of Environment,1994,50(3):287-294.

[22]Gutman G,Ignatov A.The Derivation of the Green Vegetation Fraction from NOAA/AVHRR Data for Use in Numerical Weather Prediction Models[J].International Journal of Remote Sensing,1998,19(8):1533-1543.

[23]Singh D.Scatterometer Performance with Polarization Discrimination Ratio Approach to Retrieve Crop Soybean Parameter at X-Band[J].International Journal of Remote Sensing,2006,27(19):4101-4115.

[24]Prasad R.Retrieval of Crop Variables with Field-based X-Band Microwave Remote Sensing of Ladyfinger[J].Advances in Space Research,2009,43(9):1356-1363.

[25]Svoray T,Shoshany M,Curran P,et al.Relationship between Green Leaf Biomass Volumetric Density and ERS-2 SAR Backscatter of Four Vegetation Formations in the Semi-Arid Zone of Israel[J].International Journal of Remote Sensing,2001,22(8):1601-1607.

[26]Imhoff M L.Radar Backscatter and Biomass Saturation:Ramifications for Global Biomass Inventory[J].IEEE Transactions on Geoscience and Remote Sensing,1995,33(2):511-518.

[1] 廖凯涛,齐述华,王成,王点. 结合GLAS和TM卫星数据的江西省森林高度和生物量制图[J]. 遥感技术与应用, 2018, 33(4): 713-720.
[2] 林利斌,鲍艳松,左泉,房世波. 基于Sentinel-1与FY-3C数据反演植被覆盖地表土壤水分[J]. 遥感技术与应用, 2018, 33(4): 750-758.
[3] 张雅,尹小君,王伟强. 基于Landsat 8 OLI遥感影像的天山北坡草地地上生物量估算[J]. 遥感技术与应用, 2017, 32(6): 1012-1021.
[4] 李伟娜,韦玮,张怀清,刘华. 基于多角度高光谱数据的高寒沼泽湿地植被生物量估算[J]. 遥感技术与应用, 2017, 32(5): 809-817.
[5] 于惠,吴玉锋,金毅,张峰. 基于MODIS SWIR数据的干旱区草地地上生物量反演及时空变化研究[J]. 遥感技术与应用, 2017, 32(3): 524-530.
[6] 李兰,陈尔学,李增元,冯琦,赵磊. 合成孔径雷达森林树高和地上生物量估测研究进展[J]. 遥感技术与应用, 2016, 31(4): 625-633.
[7] 王渊博,冯德俊,李淑娟,武文娟,任红艳. 基于遥感信息的农作物生物量估算研究进展[J]. 遥感技术与应用, 2016, 31(3): 468-475.
[8] 张正健,李爱农,边金虎,赵伟,南希,靳华安,谭剑波. 基于无人机影像可见光植被指数的若尔盖草地地上生物量估算研究[J]. 遥感技术与应用, 2016, 31(1): 51-62.
[9] 穆喜云,张秋良,刘清旺,庞勇,胡凯龙. 基于激光雷达的大兴安岭典型森林生物量制图技术研究[J]. 遥感技术与应用, 2015, 30(2): 220-225.
[10] 徐婷,曹林,佘光辉. 基于Landsat 8 OLI的特征变量优化提取及森林生物量反演[J]. 遥感技术与应用, 2015, 30(2): 226-234.
[11] 韩颖,裴亮,杜嘉. 洪河湿地植被地上生物量遥感反演研究[J]. 遥感技术与应用, 2014, 29(2): 224-231.
[12] 蒙继华,杜鑫,张淼,游行至,吴炳方. 物候信息在大范围作物长势遥感监测中的应用[J]. 遥感技术与应用, 2014, 29(2): 278-285.
[13] 唐爽,陈蜀江. 基于CBERS-2卫星数据的艾比湖浮游植物生物量的反演研究[J]. 遥感技术与应用, 2013, 28(3): 543-548.
[14] 何媛,文军,张堂堂,田辉,刘蓉,吕少宁,赖欣. 卫星微波遥感结合可见光遥感估算黄河源区土壤湿度研究[J]. 遥感技术与应用, 2013, 28(2): 300-308.
[15] 程鹏飞,王金亮,徐 申,程 峰,王小花. 区域森林生物量遥感信息模型构建研究[J]. 遥感技术与应用, 2012, 27(5): 722-727.