## 4 Discussion  
With the development of remote sensing, multi-source data provide an opportunity to directly reflect the real situation of cropland productivity, especially for the continuous grain crop planting region. The light use efficiency (LUE) models have been widely used to estimate regional vegetation productivity due to their simple form and the relatively long period of data availability [39]. At present, the VPM model is widely used to estimate the productivity of farmland ecosystem and shows good simulation ability [39], such as for the winter wheat and summer maize rotation farmland at Yucheng flux station [37] and the VPM simulated productivity of Tongyu farmland (corn) station [40]. In addition, the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sampling Region** & **Construction Year** & **Before (kg/mu)** & **After (kg/mu)** \\ \hline Project A & 95.9 & 1.8 & 94.1 \\ Project B & 84.9 & 3.2 & 81.7 \\ Project C & 100 & 10.2 & 89.8 \\ Project D & 97.6 & 16.2 & 81.4 \\ Project E & 96.1 & 15.4 & 80.7 \\ Project F & 100 & 8.9 & 91.1 \\ Project G & 100 & 23.1 & 76.9 \\ Project H & 100 & 20.5 & 79.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical data on the proportion of middle–low productive cropland before and after the sampling project regions. Figure 8: Distribution of middle–low productive cropland before and after the HSF construction (Projects A and B are examples). Yingke farmland (corn) station [41] can capture more than 80% of the observed interannual variation in productivity, and the accuracy can be improved by about 50% compared to MODIS17 products [42] (Figure 9). In this study, we use VPM NPP data that combines MODIS and Landsat8-OLI data to evaluate the effectiveness of HSF construction in Henan Province. The NPP calculated using MODIS-OLI fusion data more accurately detects differences in cropland productivity due to the improvement in the spatial resolution of the data. Simple MODIS data are limited by low spatial resolution and cannot express spatial details, while high spatial resolution NPP data calculated using MODIS-OLI fusion data can clearly reflect the difference between high, medium and low productivity within the field. In addition, the MODIS-OLI fusion data achieves dynamic and high temporal resolution monitoring in 8-day steps, which can more accurately describe the impact of drought and floods on vegetation growth during the crop growth period, thus reflecting changes in DRA of HSF. The obtained results show that the changes in cropland productivity can be effectively detected, as well as disaster resistance ability and homogeneous yield degree before and after the construction of HSF. To confirm the ability of the above methods to detect changes in cropland productivity, we analyzed the difference in cropland productivity of the three NPP products before and after the completion of the HSF project region. This was performed by comparing the NPP with a spatial resolution of 30 m calculated by the VPM after the integration of MODIS-OLI, the NPP with a spatial resolution of 500 m (VPM-MODIS) calculated by the VPM and the MODIS NPP standard products. The results of the analysis show that the ability to detect NPP products obtained by MODIS-OLI fusion is 50% higher than before the fusion, while MODIS NPP products can hardly detect changes in cropland productivity before and after HSF construction (Figure 10a). Comparing the 8-day time steps of MODIS-OLI NPP and VPM-MODIS NPP, it is shown that in the region where the cropland area accounts for more than 30% of the grid area, two sets of NPP data products maintain good consistency. However, in the region where the cropland area accounts for less than 30% of the grid area, the NPP calculated by MODIS-OLI fusion data is significantly higher than that calculated directly using MODIS data.