Field Crops Research 135 (2012) 24–29 Contents lists available at SciVerse ScienceDirect Field Crops Research jou rnal h om epage: www.elsevier.com/locate/fcr Comparison of two methods for estimation of leaf total chlorophyll content using remote sensing in wheat Xiu-liang Jin a,b , Ke-ru Wang a,c , Chun-hua Xiao c , Wan-ying Diao c , Fang-yong Wang d , Bing Chen d , Shao-kun Li a,c,∗ a Institute of Crop Science, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Production Ministry of Agriculture, Beijing 100081, China b Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Yangzhou University, Yangzhou 225009, China c Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops, Shihezi 832003, China d Institute of Cotton, Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, China a r t i c l e i n f o Article history: Received 6 March 2012 Received in revised form 26 June 2012 Accepted 27 June 2012 Keywords: Leaf total chlorophyll content Stepwise regression methods Spectral parameters Biomass dry weight a b s t r a c t Leaf total chlorophyll content (LTCC) is an important indicator for assessment of crop health and predic- tion of crop yield. The objective of this study was to develop a precise agricultural practice that could estimate wheat LTCC. In this study, we compared two methods of LTCC estimation: one method used the products of spectral parameters and biomass dry weight (BDW), and the other method used stepwise regression methods (SRM). We selected the highest determination coefficient (R 2 ) simulation model to improve prediction accuracy. The results showed that for the mND705 × BDW index, the R 2 was 0.9639 and the root mean square error (RMSE) was 0.202 g/m 2 . For the 3.575Red edge model-1.118PSSRb index, the R 2 was 0.868 and RMSE was 0.384 g/m 2 . The mND705 × BDW index accounted for 96.39% of LTCC, while the 3.575Red edge model-1.118PSSRb accounted for 86.8% of LTCC. Further, the RMSE of mND705 × BDW was lower than that of 3.575Red edge model-1.118PSSRb for predicting LTCC. The results indicated that the spectral parameters × BDW methods, in which spectral parameters defection was improved, was superior to SRM. © 2012 Elsevier B.V. All rights reserved. 1. Introduction Chlorophyll is essential to convert light energy into stored chemical energy, so crop growth and yield are directly affected by chlorophyll (Chl) content. Some studies have shown a positive cor- relation between leaf nitrogen content and chlorophyll content. So, quantifying Chl content may provide an indirect measurement of nitrogen status (Filella et al., 1995; Moran et al., 2000). The develop- ment of remote sensing has provided new opportunities to predict Chl content at the crop growth stages. Many studies have pro- vided a great deal of information about the relationships between Chl content and spectral parameters (Buschmann and Nagel, 1993; Gitelson and Merzlyak, 1994a,b; Markwell et al., 1995; Gamon and Surfusm, 1999; Gitelson et al., 2001, 2002). Many studies developed new indices that were well-correlated with Chl (Curran et al., 1995; Gitelson and Merzlyak, 1996, 1997; Gitelson et al., 1996; Blackburn, 1998a,b; Datt, 1998, 1999; Adams et al., 1999). Buschmann and Nagel (1993) found that a nonlinear relationship ∗ Corresponding author at: Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Production Ministry of Agriculture, Beijing 100081, China. Tel.: +86 13910325766. E-mail address: lishk@mail.caas.net.cn (S.-k. Li). existed between spectral reflectance in the visible range and leaf Chl. Gamon and Surfusm (1999) demonstrated that the relationship between total Chl content and the normalized difference vege- tation index (NDVI) was markedly different for the coniferous Pseudotsuga menziesii and the herbaceous Helianthus annuus. Sims and Gamon (2002) proposed that vegetation indices of R 750 /R 700 and (R 750 - R 705 )/(R 750 + R 705 ) according to spectral reflectance at around 700 nm were the most sensitive indicators for Chl content. Recently, Merzlyak et al. (1999) found an anthocyanin absorption maxima between 537 and 542 nm. Munden et al. (1994) studied the relationship between red edge and Chl concentration in win- ter wheat, and the results demonstrated that the red edge could be used to estimate Chl concentration. Van den Berg and Perkins (2005) suggested that the ratio of the green band (530 nm) to the near-infrared (NIR) band (940 nm) could be used for predicting anthocyanin, and this index was named the anthocyanin content index (ACI). This method was modified by Steele et al. (2009) and called the modified ACI (mACI). Jiang et al. (2008) suggested that the enhanced vegetation index 2 (EVI2) had a good correlation with Chl content. Gitelson et al. (2005) proved that there was a positive cor- relation between (R NIR /R red egde ) - 1 and (R NIR /R green ) - 1 and Chl content, and estimation of gross primary production (GPP) based on the relationship between the Chl content and GPP (Gitelson et al., 2006; Peng et al., 2011). 0378-4290/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.fcr.2012.06.017