caa a22 4500 445864796 CHVBK 20180317145454.0 cr unu---uuuuu 170323e20110401xx s 000 0 eng 10.1007/s11119-010-9166-5 doi (NATIONALLICENCE)springer-10.1007/s11119-010-9166-5 Estimation of vertical distribution of chlorophyll concentration by bi-directional canopy reflectance spectra in winter wheat [Elektronische Daten] [Wenjiang Huang, Zhijie Wang, Linsheng Huang, David Lamb, Zhihong Ma, Jincheng Zhang, Jihua Wang, Chunjiang Zhao] An effective technique to measure foliage chlorophyll concentration (Chl) at a large scale and within a short time could be a powerful tool to determine fertilization amount for crop management. The objective of this study was to investigate the inversion of foliage Chl vertical-layer distribution by bi-directional reflectance difference function (BRDF) data, so as to provide a theoretical basis for monitoring the growth and development of winter wheat and for providing guidance on the application of fertilizer. Remote sensing could provide a powerful tool for large-area estimation of Chl. Because of the vertical distribution of leaves in a wheat stem, Chl vertical distribution characteristics show an obvious decreasing trend from the top of the canopy to the ground surface. The ratio of transformed chlorophyll absorption reflectance index (TCARI) to optimized soil adjusted vegetation index (OSAVI) was called the canopy chlorophyll inversion index (CCII) in this study. The value of CCII at nadir, ±20 and ±30°, at nadir, ±30 and ±40°, and at nadir, ±50 and ±60° view angles were selected and assembled as bottom-layer Chl inversion index (BLCI), middle-layer Chl inversion index (MLCI), and upper-layer Chl inversion index (ULCI), respectively, for the inversion of Chl at the vertical bottom layer, middle layer, and upper layer. The root mean squared error (RMSE) between BLCI-, MLCI-, and ULCI-derived and laboratory-measured Chl were 0.7841, 0.9426, and 1.7398, respectively. The vertical foliage Chl inversion could be used to monitor the crop growth status and to guide fertilizer and irrigation management. The results suggested that vegetation indices derived from bi-directional reflectance spectra (e.g., BLCI, ULCI, and MLCI) were satisfactory for inversion of the Chl vertical distribution. Springer Science+Business Media, LLC, 2010 Winter wheat nationallicence Bi-directional reflectance difference function nationallicence Chlorophyll concentration nationallicence Vertical distribution nationallicence Canopy chlorophyll inversion index nationallicence Chl : Chlorophyll concentration nationallicence BRDF : Bi-directional reflectance difference function nationallicence TCARI : Transformed chlorophyll absorption reflectance index nationallicence CARI : Chlorophyll absorption in reflectance index nationallicence OSAVI : Optimized soil adjusted vegetation index nationallicence CCII : TCARI/OSAVI nationallicence BLCI : Bottom-layer Chl inversion index nationallicence MLCI : Middle-layer Chl inversion index nationallicence ULCI : Upper-layer Chl inversion index nationallicence RMSE : The root mean squared error nationallicence LOV : Leaf orientation value nationallicence LAD : Leaf angle distribution nationallicence VZA : View zenith angles nationallicence Huang Wenjiang National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut Wang Zhijie National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut Huang Linsheng Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, 230039, Hefei, Anhui, China aut Lamb David Precision Agriculture Research Group, School of Science and Technology, University of New England, NSW 2351, Armidale, Australia aut Ma Zhihong National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut Zhang Jincheng National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut Wang Jihua National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut Zhao Chunjiang National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut Precision Agriculture Springer US; http://www.springer-ny.com 12/2(2011-04-01), 165-178 1385-2256 12:2<165 2011 12 11119 https://doi.org/10.1007/s11119-010-9166-5 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11119-010-9166-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Huang Wenjiang National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut NATIONALLICENCE 700 1- Wang Zhijie National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut NATIONALLICENCE 700 1- Huang Linsheng Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, 230039, Hefei, Anhui, China aut NATIONALLICENCE 700 1- Lamb David Precision Agriculture Research Group, School of Science and Technology, University of New England, NSW 2351, Armidale, Australia aut NATIONALLICENCE 700 1- Ma Zhihong National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut NATIONALLICENCE 700 1- Zhang Jincheng National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut NATIONALLICENCE 700 1- Wang Jihua National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut NATIONALLICENCE 700 1- Zhao Chunjiang National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, P.O. Box 2449-26, 100097, Beijing, People's Republic of China aut NATIONALLICENCE 773 0- Precision Agriculture Springer US; http://www.springer-ny.com 12/2(2011-04-01), 165-178 1385-2256 12:2<165 2011 12 11119 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer