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The growth status of winter wheat in irrigated field and rainfed field are obviously different and the field types may have an effect on the predictive accuracy of hyperspectral model. The objectives of the present study were to understand the difference of spectral sensitive wavelengths for leaf area index (LAI) in two field types and realize its hyperspectral prediction. In study, a total of 31 and 28 sample sites in irrigated fields and rainfed fields respectively were selected from Wenxi County, and the LAI and canopy spectra were also collected at the main grow stage of winter wheat. The method of successive projections algorithm (SPA) was applied by selecting the important wavelengths, and the multiple linear regression (MLR) and partial least squares regression (PLSR) were used to construct the predictive model based on the important wavelengths and full wavelengths, respectively. Moreover, the parameters of variable importance project (VIP) and B-coefficient derived from PLSR analysis were implemented to validate the evaluated wavelengths using the SPA method. The sensitive wavelengths of LAI for irrigated field and rainfed field were 404, 407, 413, 417, 450, 677, 715, 735, 816, 1127 and 404, 406, 432, 501, 540, 679, 727, 779, 1120, 1290 nm, respectively, and these wavelengths proved to be highly correlated with LAI. Compared with the model performance based on the SPA-MLR and PLSR methods, the method of SPA-MLR was proved to be better (rainfed field: R2 = 0.736, RMSE = 1.169, RPD = 1.6245; irrigated field: R2 = 0.716, RMSE = 1.059, RPD = 1.538). Moreover, the predictive model of LAI in rainfed fields had a better accuracy than the model in irrigated fields. The results from this study indicated that it was necessary to classify the field type while monitoring the winter wheat using the remote sensing technology. This study also demonstrated that the multivariate method of SPA-MLR could accurately evaluate the sensitive wavelengths and construct the predictive model of LAI. [ABSTRACT FROM AUTHOR]

To extract the sensitive bands for estimating the winter wheat growth status and yields, field experiments were conducted. The crop variables including aboveground biomass (AGB), soil and plant analyzer development (SPAD) value, yield, and canopy spectra were determined. Statistical methods of correlation analysis, partial least squares (PLS), and stepwise multiple linear regression (SMLR) were used to extract sensitive bands and estimate the crop variables with calibration set. The predictive model based on the selected bands was tested with validation set. The results showed that the crop variables were significantly correlated with spectral reflectance. The major spectral regions were selected with the B-coefficient and variable importance on projection (VIP) parameter derived from the PLS analysis. The calibrated SMLR model based on the selected wavelengths demonstrated an excellent performance as the R2, TC, and RMSE were 0.634, 0.055, and 843.392 for yield; 0.671, 0.017, and 1.798 for SPAD; and 0.760, 0.081, and 1.164 for AGB. These models also performed accurately and robustly by using the field validation data set. It indicated that these wavelengths retained in models were important. The determined wavelengths for yield, SPAD, and AGB were 350, 410, 730, 1015, 1185 and 1245 nm; 355, 400, 515, 705, 935, 1090, and 1365 nm; and 470, 570, 895, 1170, 1285, and 1355 nm, respectively. This study illustrated that it was feasible to predict the crop variables by using the multivariate method. The step-by-step procedure to select the significant bands and optimize the prediction model of crop variables may serve as a valuable approach. The findings of this study may provide a theoretical and practical reference for rapidly and accurately monitoring the crop growth status and predicting the yield of winter wheat. [ABSTRACT FROM AUTHOR]

For rapid estimation of soybean chlorophyll by hyperspectral technology, 76 soybean varieties were investigated by simulation of Landsat-8 satellite bands through spectral resampling technology and vegetation indices. Four different modeling methods viz. Support Vector Machine (SVM), Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR), and Back Propagation Neural Network (BPNN), were combined to analyze the response characteristics of resampled spectra to predict soybean chlorophyll and a prediction model based on vegetation indices was constructed. The results revealed that chlorophyll concentration and the corresponding spectral reflectance were inversely related. Also, Normalized Differential Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Transformed Vegetation Index (TVI) and Ratio Vegetation Index (RVI) were significantly correlated with chlorophyll and the GNDVI exhibited significant correlation with the whole growth period (r = 0.70). Furthermore, the BPNN model was the best in predicting the chlorophyll contents and vegetation index of the soybean at seed-filling stage (R v 2 = 0.846, RMSE v = 0.384, RPD v = 2.413). Based on the results, we propose that after spectral resampling, the soybean leaf chlorophyll content can be effectively predicted by BPNN with a combination of vegetation indices. [ABSTRACT FROM AUTHOR]

• This study explored the monitoring performance of vegetation index from the perspective of algebraic operation. • The performance of three-band index is better than two-band index. • Optimal band distribution and monitoring performance of vegetation index is greatly influenced by calculation formula. The vegetation indices (VIs) derived from the different band combinations can be used for monitoring crop quality traits. We conducted field experiments over two years time to investigate critical growth stages across four varieties and by using different nitrogen (N) application rates. In order to explore and evaluate the performance of different VIs on estimation of the canopy chlorophyll content (CCC) of winter wheat, the published and modified indices were optimized by using the random band combination through original spectrum (OS) and first-order differential (FD) treatment. The results showed that the first derivative processing improved the correlation between the red edge band and winter wheat CCC. The three-band VI can break the restriction of the number of bands on the extraction of target information, relieved the saturation problem of the dual-band VI, and improved the monitoring accuracy of winter wheat CCC. The index 2 × R1-R2-R3 was found to be the best VI for assessing the CCC of winter wheat based on the original and first-order differential spectrum (calibration R2 > 0.733), R2 and RMSE of validation set were 0.688, 0.755 and 1.515, 1.336, respectively. In addition, the index expression formula R1/(R2 × R3) was recommended as a favorable choice for monitoring the agronomic traits of crop. Moreover, the VI is suggested to use at the red edge position band to monitor crop growth indicators. In conclusion, the use of VI can better monitor winter wheat CCC which could provide a theoretical basis for precision agriculture. [ABSTRACT FROM AUTHOR]