Evaluating cellulose absorption index (CAI) for non-photosynthetic biomass estimation in the desert steppe of Inner Mongolia
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- 作者：HongRui Ren (1) (2)
GuangSheng Zhou (1) (3)
Feng Zhang (1)
XinShi Zhang (1) - 关键词：non ; photosynthetic vegetation ; biomass ; MODIS ; TM ; Hyperion ; cellulose absorption index
- 刊名：Chinese Science Bulletin
- 出版年：2012
- 出版时间：May 2012
- 年：2012
- 卷：57
- 期：14
- 页码：1716-1722
- 全文大小：595KB
- 参考文献：1. Gao Z Q, Li J Y. Simulation study of China鈥檚 net primary production. Chin Sci Bull, 2008, 53: 434鈥?43 CrossRef
2. Ma W H, Fang J Y, Yang Y H, et al. Biomass carbon stocks and their changes in northern China鈥檚 grasslands during 1982鈥?006. Sci China Ser C-Life Sci, 2010, 53: 841鈥?50 CrossRef
3. Scurlock J M O, Hall D O. The global carbon sink: A grassland perspective. Glob Change Biol, 1998, 4: 229鈥?33 CrossRef
4. Scurlock J M O, Johnson K, Olson R J. Estimating net primary productivity from grassland biomass dynamics measurements. Glob Change Biol, 2002, 8: 736鈥?53 CrossRef
5. Aase J K, Tanaka D L. Reflectance from four wheat residue cover densities as influenced by three soil backgrounds. Agron J, 1991, 83: 753鈥?57 CrossRef
6. Asner G P, Lobell D B. A biogeophysical approach for automated SWIR unmixing of soils and vegetation. Remote Sens Environ, 2000, 74: 99鈥?12 CrossRef
7. Biard F, Bannari A, Bonn F. SACRI (Soil adjusted corn residue index): An index using near and mid-infrared for the detection of residues of maize (in French). In: 17th Canadian Symposium on Remote Sensing, 1995. 417鈥?23
8. Baird F, Baret F. Crop residue estimation using multiband reflectance. Remote Sens Environ, 1997, 59: 530鈥?36 CrossRef
9. Daughtry C S T, McMurtrey III J E, Nagler P L, et al. Spectral reflectance of soils and crop residues. In: Davies A M C, Williams P, eds. Near Infrared Spectroscopy: The Future Waves. Chichester: NIR Publication, 1996. 505鈥?10
10. Daughtry C S T. Discriminating crop residues from soil by shortwave infrared reflectance. Agron J, 2001, 93: 125鈥?31 CrossRef
11. Daughtry C S T, Hunt Jr E R, McMurtrey III J E. Assessing crop residue cover using shortwave infrared reflectance. Remote Sens Environ, 2004, 90: 126鈥?34 CrossRef
12. Daughtry C S T, Hunt Jr E R, Doraiswamy P C, et al. Remote sensing the spatial distribution of crop residue. Agron J, 2005, 97: 864鈥?74 CrossRef
13. Daughtry C S T, Doraiswamy P C, Hunt Jr E R, et al. Remote sensing of crop residue cover and soil tillage intensity. Soil Tillage Res, 2006, 91: 101鈥?08 CrossRef
14. McNairn H, Protz R. Mapping corn residues cover on agricultural fields in Oxford country, Ontario, using Thematic Mapper. Can J Remote Sens, 1993, 19: 152鈥?59
15. Nagler P L, Inoue Y, Clenn E P, et al. Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes. Remote Sens Environ, 2003, 87: 310鈥?25 CrossRef
16. Smith A M, Bullock M S, Ivle C V. Estimating wheat residue cover using broad and narrow band visible-infrared reflectance. Can J Remote Sens, 2000, 26: 241鈥?52
17. Streck N A, Rundquist D, Connot J. Estimating residue wheat dry matter from remote sensing measurements. Photogr Eng Remote Sens, 2002, 68: 1193鈥?201
18. Elvidge C D. Visible and near infrared reflectance characteristics of dry plant material. Int J Remote Sens, 1990, 11: 1775鈥?795 CrossRef
19. Robert D A, Smith M O, Adams J B. Green vegetation, non-photosynthetic vegetation, and soil in AVIRIS data. Remote Sens Environ, 1993, 44: 255鈥?69 CrossRef
20. Cao X, Chen J, Matsushita B, et al. Developing a MODIS-based index to discriminate dead fuel from photosynthetic vegetation and soil background in the Asian steppe area. Int J Remote Sens, 2010, 31: 1589鈥?604 CrossRef
21. Bannari A, Haboudane D, Bonn F. Mid infrared interest for culture residues cartography. Can J Remote Sens, 2000, 26: 384鈥?93
22. Gong Z T, Chen Z C, Shi X Z. Chinese Soil Taxonomy: Theory, Method, Practice (in Chinese). Beijing: Science Press, 1999. 1鈥?03
23. Murphy R J. The effects of surficial vegetation cover on mineral absorption feature parameters. Int J Remote Sens, 1995, 16: 2153鈥?164 CrossRef - 作者单位：HongRui Ren (1) (2)
GuangSheng Zhou (1) (3)
Feng Zhang (1)
XinShi Zhang (1)
1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
2. Graduate University of Chinese Academy of Sciences, Beijing, 100049, China
3. Chinese Academy of Meteorological Sciences, Beijing, 100081, China - ISSN：1861-9541
文摘
Accurate estimation of non-photosynthetic biomass is critical for modeling carbon dynamics within grassland ecosystems. We evaluated the cellulose absorption index (CAI), widely used for monitoring non-photosynthetic vegetation coverage, for non-photosynthetic biomass estimation. Our analysis was based on in situ hyperspectral measurements, during the growing seasons of 2009 and 2010, in the desert steppe of Inner Mongolia. ASD (Analytical Spectral Device)-derived and Hyperion-derived CAI were found to be effective for non-photosynthetic biomass estimation, yielding relative error (RE) values of 26.4% and 26.6%, respectively. The combination of MODIS (Moderate Resolution Imaging Spectroradiometer)-derived (MODIS2-MODIS5)/(MODIS2+MODIS5) and (MODIS6-MODIS7)/(MODIS6+MODIS7) showed a high multiple correlation (multiple correlation coefficient, r= 0.884) with ASD-derived CAI. A predictive model involving the two MODIS indices gave greater accuracy (RE=28.9%) than the TM (Landsat Thematic Mapper)-derived indices. The latter were the normalized difference index (NDI), the soil adjusted corn residue index (SACRI), and the modified soil adjusted crop residue index (MSACRI). These indices yielded RE values of more than 42%. Our conclusions have great significance for the estimation of regional non-photosynthetic biomass in grasslands, based on remotely sensed data.