## 3 Results  
### Accuracy Verification of the CLQ  
The results of CLQ based on field measurements (measured) and the remote sensing evaluation (evaluated) are shown in Figure 2. In the Conghua district, there was a good spatial consistency between the measured results and the evaluation results. High-quality cultivated land was mainly concentrated in the central and western regions, and medium- and low-quality cultivated land was widely distributed throughout the entire region. In the Zengcheng district, there is a relatively good spatial consistency between the measured results and the evaluation results. High-quality cultivated land was concentrated in the southeastern and northern regions, medium-quality cultivated land was widely distributed, and low-quality cultivated land occupied a relatively small proportion. In the Nansha district, there is a slightly larger spatial difference between the measured results and the evaluation results in 2010. However, in 2015 and 2018, the evaluation results were in good agreement with the measured results. Overall, the verification results can largely indicate that the resultant CLQ based on remote sensing evaluation was reliable. The grade difference between the remote sensing evaluation results and the measured results of CLQ is shown in Figure 3. From 2010 to 2018, in the Conghua district, the average 49% of CLQ had a grade difference of 0, the average 40% of CLQ had a grade difference of 1 (including 1 and \(-1\)), and the average 11% of CLQ had a grade difference of 1 (including 2 and \(-2\)). In the Zengcheng district, the average 48% of CLQ had a grade difference of 0, the average 40% of CLQ had a grade difference of 1 (including 1 and \(-1\)), and the average 12% of CLQ had a grade difference of 1 (including 2 and \(-2\)). In the Nansha district, about  
Figure 2: Spatial distribution of the cultivated land quality from 2010 to 2018 based on field measurements (measured) and the remote sensing evaluation (evaluated). CH: Conghua district; ZC: Zengcheng district; NS: Nansha district. 40% of CLQ had a grade difference of 0 in 2010, which was relatively low. In 2015 and 2018, the average 47% of CLQ had a grade difference of 0, the average 39% of CLQ had a grade difference of 1 (including 1 and \(-\)1), and the average 14% of CLQ had a grade difference of 2 (including 2 and \(-\)2). This indicated that the remote sensing evaluation results of CLQ were in good agreement with the measured results. The comparison of the CLQ results for the well-facilitated farmland and the non-well-facilitated farmland is shown in Table 3. In 2015 and 2018, the CLQ index of the well-facilitated farmland construction area was significantly greater than that of the non-well-facilitated farmland construction area. Moreover, the CLQ of the well-facilitated farmland construction areas was mainly high quality (about 50%), while the CLQ in the non-well-facilitated farmland construction area was mainly medium quality (about 40%) in these two years. These results are consistent with the reasonable hypothesis made in this study, indicating that the resultant CLQ evaluation using integrated remote sensing data is reliable. Figure 3: Grade difference (Evaluated grade minus Measured grade) between the remote sensing evaluation results and the measured results of cultivated land quality (CLQ) from 2010 to 2018. CH: Conghua district; ZC: Zengcheng district; NS: Nansha district. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Results** & **Well-Facilitated Farmland** & **Non-Well-Facilitated Farmland** \\ \hline \multirow{4}{*}{2015} & CLQ index & 2.30 & 1.90 \\  & High quality & 45.77\% & 25.69\% \\  & Medium quality & 40.18\% & 41.73\% \\  & Low quality & 14.05\% & 32.58\% \\  & CLQ index & 2.33 & 2.01 \\
2018 & High quality & 52.09\% & 28.38\% \\  & Medium quality & 28.51\% & 44.09\% \\  & Low quality & 19.40\% & 27.53\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the cultivated land quality (CLQ) of the well-facilitated farmland and non-well-facilitated farmland.