## 3 Results and Discussion
### Sensitive Wavebands Selection  
In this paper, the competitive adaptive reweighted squares (CARS) algorithm was used to select the characteristic variables of NIR spectra. Meanwhile, the BIPLS algorithm was used for selecting the characteristic intervals. The selection process of characteristic variables and characteristic intervals after 40 \({}^{\circ}\)C soil drying is taken as an example in the following. Figure 4a displays the selection process of characteristic variables carried out by CARS. In the CARS algorithm, the sampling times were set as 500. The variables were selected by attenuation functions during each iteration, but only the wavelength variables with large absolute values of PLS regression coefficients were preserved. In this experiment, the CARS algorithm was operated for 10 times for each temperature, and characteristic variables that occurred more than 6 times were selected as the final characteristic variables. The specific steps of the algorithm can be found in the reference [20]. Figure 4b shows the selection process of the characteristic intervals of soil spectra by BIPLS and Figure 4c presents the best principle numbers determined by leave-one-out cross validation. Table 2 lists the variable selection results of BIPLS when the spectra with 400 points are divided into 20 segments. As the spectral interval is gradually removed, the RMSECV value of the model changes continuously, and the number of remaining intervals and variables in the model decreases at the same time. As can be seen in Table 2, when the spectral interval numbered 4 is eliminated, the RMSECV of the model reaches a minimum 0.166. The remaining three intervals in the model are the range of the final participation. The selected interval numbers are 3, 20, and 19. Figure 4: The sensitive waveband selection process: **(a)** The characteristic variables selected by competitive adaptive reweighted squares (CARS); **(b)** the number of PLS components and the corresponding the root mean square error of cross validation (RMSECV) values while running Backward Interval Partial Least Squares (BIPLS); **(c)** the characteristic intervals determined by BIPLS. Table 3 presents the characteristic variables and characteristic intervals, respectively. On the one hand, it can be seen that the characteristic variables selected by CARS are not same at different drying temperatures. The characteristic variables are mainly concentrated in the range of 1610-1650 nm and 1660-1670 nm when the soil was detected at 25 \({}^{\circ}\)C, and the characteristic variables changes when the soil was dried after 40 \({}^{\circ}\)C (1638-1650 nm, 1670-1680 nm). Compared with the characteristic variables, however, the characteristic intervals in BIPLS are mainly distributed at 991-1034 nm, 1036-1078 nm, and 1633-1666 nm. Besides this, when the drying temperatures rose to 60 \({}^{\circ}\)C, 80 \({}^{\circ}\)C, and 105 \({}^{\circ}\)C, the characteristic variables are mainly distributed in 930-950 nm and 1600-1670 nm, while the interval of the characteristic variables is mainly distributed in 991-1022 nm and 1524-1666 nm. According to the Beer-Lambert law, the NIR spectra vary a lot because of the variation of sample components and structure [37]. Therefore, the drying temperature does affect the soil nitrogen characteristic variables based on the soil properties, and the greater the temperature differs, the greater the differences among characteristic variables. Moreover, although the selected characteristic variables are different to some extent due to the different algorithm principles of CARS and BIPLS [30; 31; 32], the overlap characteristic variables are mainly distributed at 1630-1680 nm and around 930 nm. When the characteristic variables and characteristic intervals were selected, the linear correlation analysis was performed for each characteristic variable and each characteristic interval. The characteristic variables are sorted by correlation coefficients from the highest to the lowest.