## 3 Results and Analysis
### NBAR Time Series Analysis  
Using the HJ-1 NBAR data, we can see the time series line graph, as shown in Figure 11. The reflectivity change in forestland is in accordance with the change in most of the vegetation reflectivity. At around the beginning of March each year, the vegetation starts to grow, and the NIR reflectivity significantly increases, remaining at around 0.25. Upon \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Band** & **RMSE** & **MAPE** & **AAD** & **R** & \(\mathbf{R}^{2}\) \\ \hline R-forest & 0.003 & 16.5\% & 0.002 & 1 & 1 \\ NIR-forest & 0.010 & 14.3\% & 0.009 & 0.999 & 0.998 \\ R-farmland & 0.014 & 19.6\% & 0.012 & 1 & 1 \\ NIR- & 0.014 & 16.8\% & 0.012 & 0.999 & 0.998 \\ farmland & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Inversion result accuracy test. Figure 10: Four scatter plots of inversion reflectivity and observed reflectivity (x, observed MODIS NBAR; y, predicted HJ-1 NBAR): (**a**) the distribution of the red band points of forest; (**b**) the distribution of the NIR band points of forest; (**c**) the distribution of the red band points of farmland; (**d**) the distribution of the NIR band points of farmland. Figure 9: Comparison between HJ-1 and MODIS NBAR time series: (**a**) forest; (**b**) farmland. Black, red, blue, and green curves in the subfigures correspond to MODIS red band, HJ-1 red band, MODIS NIR band, and HJ-1 NIR band NBAR, respectively. the arrival of winter, the vegetation withers and the NIR reflectivity decreases rapidly. The red band reflectivity is significantly lower than the NIR reflectivity, at around 0.05, and the fluctuation is small, increasing before spring and decreasing slowly during spring. As in farmland, the difference from most of the vegetation is due to human influence, as the surface is covered by bare soil before the growth period. The reflectivity changes in farmland are relatively stable. In the early stages of growth, the NIR band reflectivity increases, then decreases during the growth period, whereas the red band reflectivity decreases at the beginning of the growing season, and then increases during the growing season. In sum, the seasonal difference of forest and farmland albedo is mainly affected by the seasonal difference in plant growth. This time series provided comparable dynamic data, avoiding the angular impact, to reflect the dynamic variation patterns of vegetated target areas. Therefore, we used the data to do the forecast analysis for disaster prediction, environmental control, etc. We used the HJ-1 NBAR data and MODIS NBAR data of interest areas 1 and 2 as the raw data. For the HJ-1 NBAR data in the regions of interest, according to the average, we set a threshold of 5% and selected the pixels within the threshold to the new average, excluding mixed pixel effects. We implemented the SARIMA method using Eviews, which is freely available for data analysis, regression analysis, and forecasting. Observing the ACF and PACF figures, we selected the intervals of coefficients. After the seasonal difference and AIC screening, the model with the smallest AIC value was selected as the best, and the resulting model was determined in Table 5.  
The white noise test of the model was effective. The residual sequences are purely random and the autocorrelation coefficients fall into the random interval, which indicates that the models are feasible and the prediction results are meaningful. The Shapiro normality test for the residuals produced \(p\)-values of 0.372 and 0.386, showing that the original hypothesis of following the normal distribution is rational, that is, the residuals exhibit a normal distribution. For each land cover type and band, the predicted time series and the corresponding measured NBAR data are displayed in Figure 11. It can be seen from the figures that the HJ-1 predicted values and the measured values are very close. The calculated root mean square error is 8.36%, 7.58%, 8.87%, and 9.05%, indicating that the match is good.