Estimating Chlorophyll Content of Apple Leaves Based on Different Scales in Differential Window
Affiliation(s)
1 College of Resources and Environment, Shandong Agricultural University, Tai’an, China. 2 Key Laboratory of Agricultural Ecology Environment of Shandong Agricultural University, Tai’an, China.
1 College of Resources and Environment, Shandong Agricultural University, Tai’an, China. 2 Key Laboratory of Agricultural Ecology Environment of Shandong Agricultural University, Tai’an, China.
ABSTRACT
The aims of this study are to explore the effect of different scales in the high spectral data on the estimation of chlorophyll content of apple leaves, to find out the optimal differential window scale and to establish a model for estimating the chlorophyll content of apple leaves. Taking the apple leaves as the research object, the actual spectral reflectance of the leaves was determined by the ASD Field Spec 3 spectrometer and the chlorophyll contents of the leaves were measured in the laboratory. Firstly, the differential transformations from 1 to 30 window scales were done for actual spectral data respectively, and correlation analyses were done between apple leaf chlorophyll content and differential data, then two sensitive wavelengths were chosen under each window. Secondly, taking five consecutive differential windows as a group, the best differential window was selected in each group. Lastly, after the conversion of two sensitive wavelengths in six differential windows, relationship analyses between chlorophyll content of apple leaves and two sensitive wavelengths were done, then two new parameters with the largest correlation coefficient were chosen to establish estimation model. Results showed that with increasing differential window, the determination coefficient (R2) of estimation model decreased after an initial increase, the tipping point was at the 13th differential window scale. Testing the partial least squares (PLS) model and the stepwise regression (SR) model established under differential window scale of the 13th, the R2 of the SR model was higher than that of the PLS model. The RMSE and RE% of the SR model were lower than that of the PLS model, which showed that SR model was more suitable to estimate chlorophyll content.
The aims of this study are to explore the effect of different scales in the high spectral data on the estimation of chlorophyll content of apple leaves, to find out the optimal differential window scale and to establish a model for estimating the chlorophyll content of apple leaves. Taking the apple leaves as the research object, the actual spectral reflectance of the leaves was determined by the ASD Field Spec 3 spectrometer and the chlorophyll contents of the leaves were measured in the laboratory. Firstly, the differential transformations from 1 to 30 window scales were done for actual spectral data respectively, and correlation analyses were done between apple leaf chlorophyll content and differential data, then two sensitive wavelengths were chosen under each window. Secondly, taking five consecutive differential windows as a group, the best differential window was selected in each group. Lastly, after the conversion of two sensitive wavelengths in six differential windows, relationship analyses between chlorophyll content of apple leaves and two sensitive wavelengths were done, then two new parameters with the largest correlation coefficient were chosen to establish estimation model. Results showed that with increasing differential window, the determination coefficient (R2) of estimation model decreased after an initial increase, the tipping point was at the 13th differential window scale. Testing the partial least squares (PLS) model and the stepwise regression (SR) model established under differential window scale of the 13th, the R2 of the SR model was higher than that of the PLS model. The RMSE and RE% of the SR model were lower than that of the PLS model, which showed that SR model was more suitable to estimate chlorophyll content.
Cite this paper
Han, Z. , Zhu, X. , Wang, Z. , Zhao, G. and Wang, L. (2015) Estimating Chlorophyll Content of Apple Leaves Based on Different Scales in Differential Window. Agricultural Sciences, 6, 1106-1114. doi: 10.4236/as.2015.69106.
Han, Z. , Zhu, X. , Wang, Z. , Zhao, G. and Wang, L. (2015) Estimating Chlorophyll Content of Apple Leaves Based on Different Scales in Differential Window. Agricultural Sciences, 6, 1106-1114. doi: 10.4236/as.2015.69106.
References
[1] Li, R.H., Guo, P.G., Michael, B., Stefania, G. and Salvatore, C. (2006) Evaluation of Chlorophyll Content and Fluorescence Parameters as Indicators of Drought Tolerance in Barley. Agricultural Sciences in China, 10, 751-757.
http://dx.doi.org/10.1016/S1671-2927(06)60120-X [2] Zlatev, Z., Berova, M., Stoeva, N. and Vassilev, A. (2003) Use of Physiological Parameters as Stress Indicators. Journal of Environmental Protection and Ecology, 4, 841-849. [3] Yi, Q.X., Huang J.F., Wang X.Z. and Qian, Y. (2007) Hyperspectral Remote Sensing Estimation Models for Chlorophyll Concentration in Corn. Bulletin of Science and Technology, 23, 83-87. (In Chinese) [4] Daughtry, C.S.T., Walthall, C.L., Kim, M.S., de Colstount, E.B. and McMurtrey III, J.E. (2000) Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment, 74, 229-239.
http://dx.doi.org/10.1016/S0034-4257(00)00113-9 [5] Feng, W., Yao, X., Tian, Y.C., Cao, W.X. and Zhu, Y. (2008) Monitoring Leaf Pigment Status with Hyperspectral Remote Sensing in Wheat. Australian Journal of Agricultural Research, 59, 748-760.
http://dx.doi.org/10.1071/AR07282 [6] Hansen, P.M. and Schjoerring, J.K. (2003) Reflectance Measurement of Canopy Biomass and Nitrogen Status in Wheat Crops Using Normalized Difference Vegetation Indices and Partial Least Squares Regression. Remote Sensing of Environment, 86, 542-553.
http://dx.doi.org/10.1016/S0034-4257(03)00131-7 [7] Chen, Y.J., Yan, G.J., Lv, L., Zhang, K. and Li, K. (2012) Hyperspectral Reflectance Model to Estimate Chlorophyll Content in Soybean Leaves. Journal of Beijing Normal University (Natural Science), 1, 60-65. (In Chinese) [8] Song, K.S., Zhang, B., Wang, C.M. Liu, H.J. and Duan, H.T. (2006) Inverse Model for Estimating Soybean Chlorophyll Concentration Using In-Situ Collected Canopy Hyperspectraldata. Transactions of the CSAE, 22, 16-21. (In Chinese) [9] Yuan, J., Wang, D.W., Huang, C.Y., Qi, Y.Q., Chen, Y., Ma, Q.J. and Wang, H.R. ( 2007) Estimating Chlorophyll Density of Cotton Canopy in North of Xinjiang by Using Hyperspectral Date. Agricultural Research in the Arid Areas, 25, 89-93. (In Chinese) [10] Wang, D.W., Li, S.K., Tian, Q.J., Huang, C.Y., Cao, L.P., Xiao, C.H., Ma, Y.Q. and Yang, X.J. (2003) Estimating of Main Cultivation Physiology Parameters of Cotton by Using Hyperspectral Remote Sensing. Scientia Agricultura Sinica, 36, 770-774. (In Chinese) [11] Huang, C.Y., Wang, D.W., Yan, J., Zhang, H.X., Cao, L.P. and Cheng, C. (2007) Monitoring of Cotton Canopy Chlorophyll Density and Leaf Nitrogen Accumulation Status by Using Hyperspectral Data. Acta Agronomica Sinica, 33, 931-936. (In Chinese) [12] Wang, D.W., Huang, C.Y., Zhang, W., Ma, Q.J. and Zhao, P.J. (2008) Relationships Analysis between Cotton Chlorophyll Content, Chlorophyll Density and Hyperspectral Data. Cotton Science, 36, 368-371. (In Chinese) [13] Yang, J., Tian, Y.C., Yao, X., Cao, W.X., Zhang, Y.C. and Zhu, Y. (2009) Hyperspectral Estimation Model for Chlorophyll Concentrations in Top Leaves of Rice. Acta Ecologica Sinica, 29, 6561-6571. (In Chinese) [14] Chen, S.Y., Tian, Q.J. and Shi, R.H. (2005) Study on Simulation of Rice Leaf’s Chlorophyll Concentration via the Spectrum. Remote Sensing Information, 6, 12-16. (In Chinese) [15] Zhu, X.C., Zhao, G.X., Wang, R.Y., Dong, F., Wang, L. and Lei, T. (2010) Hyperspectral Characteristics of Apple Leaves and Their Pigment Contents Monitoring. Scientia Agricultura Sinica, 43, 1189-1197. (In Chinese) [16] Li, M.X., Zhang, L.S., Li, B.Z., Zhang, H.Y. and Guo, W. (2010) Relationship between Spectral Reflectance Feature and Their Chlorophyll Concentrations and SPAD Value of Apple Leaves. Journal of Northwest Forestry University, 25, 35-39. (In Chinese) [17] Liang, S., Zhao, G.X. and Zhu, X.C. (2012) Hyperspectral Estimation Models of Chlorophyll Content in Apple Leave. Spectroscopy and Spectral Analysis, 32, 1367-1370. (In Chinese) [18] Fang, X.Y., Zhu, X.C., Wang, L. and Zhao, G.X. (2013) Hyperspectral Monitoring of the Canopy Chlorophyll Content at Apple Tree Prosperous Fruit Stage. Scientia Agricultura Sinica, 46, 3504-3513. (In Chinese) [19] Pan, B., Zhao, G.X., Zhu, X.C., Liu, H.T., Liang, S. and Tian, D.D. (2013) Estimation of Chlorophyll Content in Apple Tree Canopy Based on Hyperspectral Parameters. Spectroscopy and Spectral Analysis, 33, 2203-2206. (In Chinese) [20] Zhang, Q.L., Chen, W.H., Zhang, Y.H., Guo, X.C., Zhu, W.D. and Xu, H.M. (2011) Estimation Models of Chlorophyll Contents in Leaves of Acacia Confusa Based on the Red Edge Position. Journal of Subtropical Resources and Environment, 6, 9-17. (In Chinese) [21] Lu, X. and Peng, H.C. (2015) Predicting Cherry Leaf Chlorophyll Concentrations Based on Foliar Reflectance Spectra Variables. Indian Society of Remote Sensing, 43, 109-120.
http://dx.doi.org/10.1007/s12524-014-0397-1
[1] Li, R.H., Guo, P.G., Michael, B., Stefania, G. and Salvatore, C. (2006) Evaluation of Chlorophyll Content and Fluorescence Parameters as Indicators of Drought Tolerance in Barley. Agricultural Sciences in China, 10, 751-757.
http://dx.doi.org/10.1016/S1671-2927(06)60120-X [2] Zlatev, Z., Berova, M., Stoeva, N. and Vassilev, A. (2003) Use of Physiological Parameters as Stress Indicators. Journal of Environmental Protection and Ecology, 4, 841-849. [3] Yi, Q.X., Huang J.F., Wang X.Z. and Qian, Y. (2007) Hyperspectral Remote Sensing Estimation Models for Chlorophyll Concentration in Corn. Bulletin of Science and Technology, 23, 83-87. (In Chinese) [4] Daughtry, C.S.T., Walthall, C.L., Kim, M.S., de Colstount, E.B. and McMurtrey III, J.E. (2000) Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment, 74, 229-239.
http://dx.doi.org/10.1016/S0034-4257(00)00113-9 [5] Feng, W., Yao, X., Tian, Y.C., Cao, W.X. and Zhu, Y. (2008) Monitoring Leaf Pigment Status with Hyperspectral Remote Sensing in Wheat. Australian Journal of Agricultural Research, 59, 748-760.
http://dx.doi.org/10.1071/AR07282 [6] Hansen, P.M. and Schjoerring, J.K. (2003) Reflectance Measurement of Canopy Biomass and Nitrogen Status in Wheat Crops Using Normalized Difference Vegetation Indices and Partial Least Squares Regression. Remote Sensing of Environment, 86, 542-553.
http://dx.doi.org/10.1016/S0034-4257(03)00131-7 [7] Chen, Y.J., Yan, G.J., Lv, L., Zhang, K. and Li, K. (2012) Hyperspectral Reflectance Model to Estimate Chlorophyll Content in Soybean Leaves. Journal of Beijing Normal University (Natural Science), 1, 60-65. (In Chinese) [8] Song, K.S., Zhang, B., Wang, C.M. Liu, H.J. and Duan, H.T. (2006) Inverse Model for Estimating Soybean Chlorophyll Concentration Using In-Situ Collected Canopy Hyperspectraldata. Transactions of the CSAE, 22, 16-21. (In Chinese) [9] Yuan, J., Wang, D.W., Huang, C.Y., Qi, Y.Q., Chen, Y., Ma, Q.J. and Wang, H.R. ( 2007) Estimating Chlorophyll Density of Cotton Canopy in North of Xinjiang by Using Hyperspectral Date. Agricultural Research in the Arid Areas, 25, 89-93. (In Chinese) [10] Wang, D.W., Li, S.K., Tian, Q.J., Huang, C.Y., Cao, L.P., Xiao, C.H., Ma, Y.Q. and Yang, X.J. (2003) Estimating of Main Cultivation Physiology Parameters of Cotton by Using Hyperspectral Remote Sensing. Scientia Agricultura Sinica, 36, 770-774. (In Chinese) [11] Huang, C.Y., Wang, D.W., Yan, J., Zhang, H.X., Cao, L.P. and Cheng, C. (2007) Monitoring of Cotton Canopy Chlorophyll Density and Leaf Nitrogen Accumulation Status by Using Hyperspectral Data. Acta Agronomica Sinica, 33, 931-936. (In Chinese) [12] Wang, D.W., Huang, C.Y., Zhang, W., Ma, Q.J. and Zhao, P.J. (2008) Relationships Analysis between Cotton Chlorophyll Content, Chlorophyll Density and Hyperspectral Data. Cotton Science, 36, 368-371. (In Chinese) [13] Yang, J., Tian, Y.C., Yao, X., Cao, W.X., Zhang, Y.C. and Zhu, Y. (2009) Hyperspectral Estimation Model for Chlorophyll Concentrations in Top Leaves of Rice. Acta Ecologica Sinica, 29, 6561-6571. (In Chinese) [14] Chen, S.Y., Tian, Q.J. and Shi, R.H. (2005) Study on Simulation of Rice Leaf’s Chlorophyll Concentration via the Spectrum. Remote Sensing Information, 6, 12-16. (In Chinese) [15] Zhu, X.C., Zhao, G.X., Wang, R.Y., Dong, F., Wang, L. and Lei, T. (2010) Hyperspectral Characteristics of Apple Leaves and Their Pigment Contents Monitoring. Scientia Agricultura Sinica, 43, 1189-1197. (In Chinese) [16] Li, M.X., Zhang, L.S., Li, B.Z., Zhang, H.Y. and Guo, W. (2010) Relationship between Spectral Reflectance Feature and Their Chlorophyll Concentrations and SPAD Value of Apple Leaves. Journal of Northwest Forestry University, 25, 35-39. (In Chinese) [17] Liang, S., Zhao, G.X. and Zhu, X.C. (2012) Hyperspectral Estimation Models of Chlorophyll Content in Apple Leave. Spectroscopy and Spectral Analysis, 32, 1367-1370. (In Chinese) [18] Fang, X.Y., Zhu, X.C., Wang, L. and Zhao, G.X. (2013) Hyperspectral Monitoring of the Canopy Chlorophyll Content at Apple Tree Prosperous Fruit Stage. Scientia Agricultura Sinica, 46, 3504-3513. (In Chinese) [19] Pan, B., Zhao, G.X., Zhu, X.C., Liu, H.T., Liang, S. and Tian, D.D. (2013) Estimation of Chlorophyll Content in Apple Tree Canopy Based on Hyperspectral Parameters. Spectroscopy and Spectral Analysis, 33, 2203-2206. (In Chinese) [20] Zhang, Q.L., Chen, W.H., Zhang, Y.H., Guo, X.C., Zhu, W.D. and Xu, H.M. (2011) Estimation Models of Chlorophyll Contents in Leaves of Acacia Confusa Based on the Red Edge Position. Journal of Subtropical Resources and Environment, 6, 9-17. (In Chinese) [21] Lu, X. and Peng, H.C. (2015) Predicting Cherry Leaf Chlorophyll Concentrations Based on Foliar Reflectance Spectra Variables. Indian Society of Remote Sensing, 43, 109-120.
http://dx.doi.org/10.1007/s12524-014-0397-1