## 4 Conclusions  
The inconsistent data quality in RS images, especially HJ-1 CCD data, is mainly influenced by environmental factors, and creates problems in the application of these data. In this paper, the mountain valley with farmland and forestland in North China is selected as the experimental area, and an improved method is presented to obtain the HJ-1 inversion NBAR data using linear matching of the RTLSR model, and then to predict reflectivity using the SARIMA model. The HJ-1 data and MODIS data are fused to obtain ample available data for inversion. The fusion data have advantages of high spatial and temporal resolution, as well as ensuring the requirements of high quality and quantity of small-scale regional data in the case study.  
Using these data, the prediction of HJ-1 NBAR is found to be more similar with observed values than MODIS. Compared with MODIS NBAR products, the HJ-1 NBAR data reduce the error due to mixed pixels, and are suitable for small-scale RS surface monitoring. This provides a reference for dynamic information acquisition and application of small satellite data, and contributes to the improvement in RS estimation of surface environment variables. Moreover, the NBAR time series is a special sequence of surface observations, which contains important information about the four seasons and solar radiation. Thus, the predicted HJ-1 NBAR in the case study can contribute to the early warning of pests and phonological changes, and it can also be used to calculate other surface parameters and analyze the variation in different time scales awaiting further study, through detailed understanding and the application of inverse methods.  
Conceptualization, Z.H. and H.L.; methodology, H.L. and H.W.; formal analysis and investigation, H.L. and Z.H.; validation, H.W.; data curation, H.L.; Writing--original draft, H.L. and Z.H.; writing--review and editing, H.L., Z.H. and H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
The study was supported by National Natural Science Foundation of China (Grant No. 42006153), Young Elite Scientists Sponsorship Program by CAST (Grant No. 2021 QNRC001), the Research and Innovation Fund of Tianjin Research Institute for Water Transport Engineering, M.O.T., China (Grant No. TKS20220203).  
Not applicable.  
We owe great appreciation to the anonymous reviewers for their critical, helpful, and constructive comments and suggestions.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Band**} & **RMSE** & **MAPE** & **AAD** & \(R\) & \(R^{2}\) \\ \hline \multirow{2}{*}{R-forest} & HJ-1 & 0.007 & 11.8\% & 0.005 & 0.862 & 0.743 \\ \cline{2-7}  & MODIS & 0.037 & 20.55\% & 0.021 & 0.676 & 0.457 \\ \hline \multirow{2}{*}{NIR-forest} & HJ-1 & 0.029 & 18.9\% & 0.022 & 0.943 & 0.889 \\ \cline{2-7}  & MODIS & 0.040 & 23.69\% & 0.038 & 0.873 & 0.762 \\ \hline \multirow{2}{*}{R-farmland} & HJ-1 & 0.018 & 14\% & 0.013 & 0.946 & 0.895 \\ \cline{2-7}  & MODIS & 0.056 & 23.51\% & 0.034 & 0.695 & 0.483 \\ \hline \multirow{2}{*}{NIR-farmland} & HJ-1 & 0.024 & 8.5\% & 0.017 & 0.948 & 0.899 \\ \cline{2-7}  & MODIS & 0.038 & 13.75\% & 0.032 & 0.906 & 0.821 \\ \hline \hline \end{tabular}
\end{table}
Table 6: HJ-1 and MODIS NBAR prediction result accuracy test.