# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN
## 4 Discussion  
The CLQ implies the carrying capacity of land productivity and is critical for food supply and security. However, CLQ often changes dramatically due to human activity induced disturbances and environmental changes [4]. Thus, it is necessary to realize real-time monitoring and evaluation of CLQ in agricultural regions, especially vulnerable or urban fringe areas [48; 49]. Previous studies [4; 15; 16] on remote sensing-based evaluation of CLQ mainly focused on retrieving spectral indicators in both the traditional evaluation system and the PSR framework system. However, it is impossible to acquire accurate CLQ data using the previous evaluation methods due to ignoring spectral relationships of spectral indicators from crop growth stages with CLQ. This study is the first attempt to propose the  
Figure 6: Spatial distributions of the predicted CLQ in 2013 using the GA-BPNN model for the study area: (**a**) Aotou Town and (**b**) Zhongxin Town. Figure 7: Measured and estimated CLQ in 2013 using the GA-BPNN model with the 120 validation sample plots for mapping: (**a**) Aotou Town and (**b**) Zhongxin Town. spectral models relating CLQ to GPP spectral indicators obtained from four growth stages of late rice phenology for evaluating CLQ. It is expected that this study can enhance the literature in this field. Based on the comparison of the evaluation results from three kinds of models, SVR (average R\({}^{2}\) = 0.64 and NRMSE = 9.78%) and GA-BPNN (average R\({}^{2}\) = 0.69 and NRMSE = 8.59%) models performed better than the PLSR model (average R\({}^{2}\) = 0.38 and NRMSE = 11.55%), implying that there is obvious non-linear correlation of CLQ with GPP spectral indicator. This conclusion is consistent with the findings of previous studies [16], indicating that the non-linear models are appropriate. It was also found that the GA-BPNN models provided more accurate predictions of CLQ than the SVR and PLSR models, which was mainly attributed to the integration of BPNN with GA which has the ability of optimizing the BPNN weights and thresholds. For the SVR models, however, the kernel function and penalty factor used only referenced expert experiences and they were limited in the accuracy of CLQ evaluation [50; 51; 52]. Based on the field measured GPP values, moreover, the accuracy of the 30 m MODIS GPP generated by the EBK interpolation, with 7.43 of RMSE and 1.59% of RRMSE, was higher than those of the 500 m MODIS GPP, with 33.43 of RMSE and 7.18% of RRMSE, showing the improvements of 26% in RMSE and 5.59% in NRMSE, respectively. These results show that the downscaled 30 m MODIS GPPs were more accurate than the original 500 m spatial resolution products. Although previous studies have shown that machine learning algorithms provide potential on CLQ evaluation, with R\({}^{2}\) of 0.59 and NRMSE of 11.19% [16], the GA-BPNN model proposed in this study shows stronger ability for CLQ evaluation with R\({}^{2}\) = 0.69 and NRMSE = 8.59%, implying that the GPP spectral indicator provides a direct and effective means for estimating CLQ. The further application of the GA-BPNN model to mapping CLQ for Aotou Town and Zhongxin Town resulted in NRMSE values of 10.47% and 17.75% based on 120 validation samples. This indicated that the GA-BPNN model proposed in this study had great potential to map CLQ at a large scale. It should be noticed that in this study the experiment was conducted only for paddy fields in which cultivated lands often have good and excellent quality. We are currently unable to verify whether the GA-BPNN model based on the relationship of CLQ with GPP can perform well in other types of cultivated lands. Therefore, in the future, we will expand the study to other kinds of cultivated lands with different grades of CLQ. Moreover, in order to further validate the GA-BPNN model for CLQ evaluations, larger sample sizes should be employed. In addition, a limited accuracy assessment using MODIS GPPs with 500 m spatial resolution was undertaken in this study.