# Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data
## 3 Results
### Regional-Scale CLQ Estimation Based on HJ-1A Data  
Given the optimal spectral variables and the band range (459-956 nm) of the HJ-1A HSI data, we finally chose four optimal spectral variables (X1:FD477, X2:FD870, X5:SD503, and X6:SD612) to estimate CLQ. According to Equation (11), the four optimal spectral variables obtained from the HJ-1A HSI data were transformed into the dry soil spectral variables, as shown in Figure 7.  
Based on the four dry soil spectral variables transformed above and CLQ in the Conghua district, the BPNN model was reconstructed to estimate CLQ. We used the value of CLQ from 200 training sample points (black plots in Figure 2c) as the dependent variable and the corresponding dry soil spectral variables from the HJ-1A HSI data as independent variables. Figure 8a presents the modeling accuracy of the estimation model, with R\({}^{2}\) and NRMSE values of 0.69 and 11.82%, respectively. We used 100 test samples (blue plots in Figure 2c) to validate the model. The R\({}^{2}\) of the model was 0.66, and the NRMSE was 12.20%; the majority of the points in the scatterplot were close to the 1:1 line (Figure 8b), indicating the reliability of the model. Figure 8: Scatterplots of measured versus estimated values of CLQ using the (**a**) training dataset and (**b**) testing dataset. Figure 7: The optimal spectral variables for regional-scale CLQ estimation: (**a–d**) the spectral variables obtained from HJ-1A HSI data; (**e–h**) the dry soil spectral variables transformed from HJ-1A HSI data. The result of the regional-scale CLQ estimation is presented in Figure 9a. Most of the CLQ values in the study area are between 2700 and 3100. The CLQ values are greater in the north of the study area and lower in the south, which is similar to the trend of the CLQ map obtained using the traditional CLQ assessment method (Figure 9b). 100 sample points (pink point in Figure 2c) were used to calculate the accuracy metrics (R\({}^{2}\) and NRMSE), as shown in Figure 10. The results showed that the R\({}^{2}\) of the regional-scale CLQ estimation was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional CLQ assessment method. Moreover, we compared CLQ estimates using original HJ-1A HSI spectral variables to dry soil spectral variables from HJ-1A HSI data, and the results (Figure 10) showed  
The optimal spectral variables (FD478, FD872, FD966, FD1796, SD502, SD614, and SD1007) were selected using the GBDT algorithm and VIF. It was found that some optimal variables (e.g., FD478, FD872, and FD966) were consistent with those of soil nutrients (e.g., FD405-FD483, FD840, FD890, and FD967-FD1031), reflecting CLQ in previous studies [37; 38] and indicating that the optimal variables of the dry soil were reliable. In this study, a linear algorithm (PLSR) and a nonlinear algorithm (BPNN) were used to establish the CLQ estimation model. The BPNN model performed better than the PLSR model, and its R\({}^{2}\) was 0.33 higher than that of the PLSR model. This result suggested a nonlinear relationship between the CLQ and the spectral variables, which agrees with previous studies [7]. Previous studies mostly used remote sensing images (e.g., MODIS images) to construct the CLQ estimation model [13]. We developed a CLQ estimation model based on measured dry soil spectral data using an AvaField portable spectrometer. However, the spectral variables from the HJ-1A HSI data were different from the spectral variables from the measured dry soil spectral data. Thus, a linkage model was used to transform the spectral variables obtained from the HJ-1A HSI data into spectral variables corresponding to dry soil to estimate CLQ at the regional scale using the HJ-1A HSI image. The accuracy of the CLQ estimation was higher when using the dry soil spectral variable than the original image data, indicating the effectiveness of the linkage model.