Winter wheat biomass estimation using high temporal and spatial resolution satellite data combined with a light use efficiency model

被引:22
作者
Du, Xin [1 ,2 ]
Li, Qiangzi [1 ,2 ]
Dong, Taifeng [3 ,4 ]
Jia, Kun [5 ]
机构
[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China
[2] Natl Engn Res Ctr Geoinformat, Beijing, Peoples R China
[3] Agr & Agri Food Canada, Eastern Cereal & Oilseed Res Ctr, Ottawa, ON, Canada
[4] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
[5] Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
biomass; winter wheat; HJ CCD; MODIS; PHOTOSYNTHETICALLY ACTIVE RADIATION; NET PRIMARY PRODUCTIVITY; YIELD; LANDSAT; CHINA; REFLECTANCE; SIMULATION; CLIMATE; WATER; NDVI;
D O I
10.1080/10106049.2014.937467
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Winter wheat biomass was estimated using HJ CCD and MODIS data, combined with a radiation use efficiency model. Results were validated with ground measurement data. Winter wheat biomass estimated with HJ CCD data correlated well with observed biomass in different experiments (coefficients of determination R-2 of 0.507, 0.556 and 0.499; n = 48). In addition, R-2 values between MODIS estimated and observed biomass are 0.420, 0.502 and 0.633. Even if we downscaled biomass estimated using HJ CCD data to MODIS pixel size (9 x 9 HJ CCD pixels to approximate that MODIS pixel), R-2 values between estimated and observed biomass were still higher than those from MODIS. We conclude that estimation with remote sensing data, such as the HJ CCD data with high spatial resolution and shorter revisit cycle, can show more detail in spatial pattern and improve the application of remote sensing on a local scale. There is also potential for applying the approach to many other studies, including agricultural production estimation, crop growth monitoring and agricultural ecosystem carbon cycle studies.
引用
收藏
页码:258 / 269
页数:12
相关论文
共 30 条
[1]  
Allen R.G., 1998, Crop EvapotranspirationGuidelines for Computing Crop Water Requirements
[2]  
Asseng S, 2013, NAT CLIM CHANGE, V3, P827, DOI [10.1038/nclimate1916, 10.1038/NCLIMATE1916]
[3]   A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan [J].
Bastiaanssen, WGM ;
Ali, S .
AGRICULTURE ECOSYSTEMS & ENVIRONMENT, 2003, 94 (03) :321-340
[4]   Remotely sensed interannual variations and trends in terestrial net primary productivity 1981-2000 [J].
Cao, MK ;
Prince, SD ;
Small, J ;
Goetz, SJ .
ECOSYSTEMS, 2004, 7 (03) :233-242
[5]   Spatialising crop models [J].
Faivre, R ;
Leenhardt, D ;
Voltz, M ;
Benoît, M ;
Papy, F ;
Dedieu, G ;
Wallach, D .
AGRONOMIE, 2004, 24 (04) :205-217
[6]   Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing [J].
Feng, X. ;
Liu, G. ;
Chen, J. M. ;
Chen, M. ;
Liu, J. ;
Ju, W. M. ;
Sun, R. ;
Zhou, W. .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2007, 85 (03) :563-573
[7]   GLOBAL NET PRIMARY PRODUCTION - COMBINING ECOLOGY AND REMOTE-SENSING [J].
FIELD, CB ;
RANDERSON, JT ;
MALMSTROM, CM .
REMOTE SENSING OF ENVIRONMENT, 1995, 51 (01) :74-88
[8]   On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance [J].
Gao, Feng ;
Masek, Jeff ;
Schwaller, Matt ;
Hall, Forrest .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2006, 44 (08) :2207-2218
[9]   NDVI - CROP MONITORING AND EARLY YIELD ASSESSMENT OF BURKINA-FASO [J].
GROTEN, SME .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 1993, 14 (08) :1495-1515
[10]   Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model [J].
Hilker, Thomas ;
Wulder, Michael A. ;
Coops, Nicholas C. ;
Seitz, Nicole ;
White, Joanne C. ;
Gao, Feng ;
Masek, Jeffrey G. ;
Stenhouse, Gordon .
REMOTE SENSING OF ENVIRONMENT, 2009, 113 (09) :1988-1999