文献类型： 外文期刊

作者： Jin, X. ¹ ; Diao, W. ³ ; Xiao, C. ³ ; Wang, F. ⁴ ; Chen, B. ⁴ ; Wang, K. ¹ ; Li, S. ¹ ;

作者机构： 1.Chinese Acad Agr Sci, Minist Agr, Inst Crop Sci, Key Lab Crop Physiol & Prod, Beijing 100193, Peoples R China

2.Yangzhou Univ, Key Lab Crop Physiol, Key Lab Crop Genet & Physiol Jiangsu Prov, Yangzhou, Peoples R China

3.Key Lab Oasis Ecol Agr Xinjiang Construct Crops, Shihezi, Xinjiang, Peoples R China

4.Xinjiang Acad Agr Reclamat Sci, Inst Cotton, Shihezi, Xinjiang, Peoples R China

关键词： leaf total chlorophyll content;stepwise regression methods;vegetation index;partial least squares regression;wheat

期刊名称：NEW ZEALAND JOURNAL OF CROP AND HORTICULTURAL SCIENCE （ 影响因子：1.154； 五年影响因子：1.424 ）

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收录情况： SCI

摘要： Leaf total chlorophyll content (LTCC) provides valuable information about the physiological status of crops. LTCC could potentially be rapidly and non-destructively estimated via remote sensing. The objective of this experiment was to develop precise agricultural practices for predicting the LTCC of wheat. In this study, we compared certain spectral indices using the determination coefficient (R-2), and then combined these indices using stepwise regression methods (SRM) or partial least squares (PLS). We obtained a new index that was more effective at predicting LTCC by SRM than the most effective individual indices were: 3.575Red edge Model-1.118PSSRb. Results showed that for LTCC = 3.575Red edge Model-1.118PSSRb, the R-2 value was 0.87, and the corresponding root mean square error (RMSE) was 0.38 g/m(2). We used the PLS to estimate LTCC, and gained an R-2 value of 0.92 and RMSE of 0.31 g/m(2). The results showed that PLS was better than SRM; these results indicated two methods could be used to improve the estimation accuracy of LTCC.