## 4 Discussion
It reflects the advantages of MODIS-OLI fusion data NPP products in detecting an HYD of cropland due to its high spatial resolution  
Figure 9: Relationship between simulation results of different GPP models and observed values (drawing refers to [33]). (Figure 10b-e). At the same time, the MODIS-OLI fusion data NPP products have a high temporal resolution (8 days) and can detect the DRA of cropland [38]. The accuracy of NPP estimation from LUE-based models significantly relies on the parameter of \(\varepsilon_{max}\)[43; 44]. In most of the current LUE models, the \(\varepsilon_{max}\) is determined by land cover types and is considered as a constant. In our previous study, we developed a spatial dataset of \(\varepsilon_{max}\) by integrating eddy covariance flux measurement, this dynamic modeling method recognizes the spatial and temporal heterogeneity of \(\varepsilon_{max}\), so as to reduce the uncertainties by vegetation spatial heterogeneity [27] and crop rotation [37]. Nevertheless, the fragmentation of cultivated land and the frequent adjustment of agricultural planting structure still limits the accurate estimation of cropland productivity. In recent years, the structural reform on the agricultural supply side in China is powerfully promoting the planting structure change [45; 46]. Some cropland has changed from traditional grain crops to cash crops, such as vegetables, and facility agriculture, such as greenhouses [47]. This study selects high-quality grain fields for continuous grain crop planting through fine interpretation using Landsat data and Google Earth High-Definition images, which can help avoid the uncertainty of the evaluation results caused by differences in crop types before and after project construction due to adjustment of the planting structure. The result of this study showed that only 109 project regions are continuous grain planting regions within the 630 HSF construction project regions in Henan Province. Although we tried to ensure the reliability by filtering the project's area without continuous grain crops through visual interpretation from higher resolution satellite data, the methodology is still worth improving because it is a time-consuming and laborious process. The newly developed cropland variation detection technology based on the Google Earth Engine could improve the efficiency of identifying the continuous grain crops plots [48]. For Henan Province, located in the central part of China, there are about five to seven Landsat images for each year that could be used for data fusion with 8-day MODIS data, which can support the data fusion at each crop rotation season and, therefore, effectively improve the spatial accuracy of NPP estimation. However, for southern regions with more cloudy and rainy weather, where only one or two qualified images in the whole growing season can be used for data fusion [49; 50]. Persistent clouds over agricultural fields can mask key stages of crop growth; the fusional NPP cannot capture the heterology information in different crop rotation seasons, leading to unreliable yield predictions. Therefore, it is necessary to incorporate optical and radar remote sensing to develop new data fusion  
Figure 10: Comparison of the ability to simulate different NPP products on the change of cropland productivity: (**a**) Difference in cropland productivity inside and outside the high-standard farmland area, and (**b–e**) temporal changes in cropland productivity with different cropland coverage (drawing refers to [38]). methods for high tempo-spatial resolution cropland productivity monitoring [51; 52]. Synthetic Aperture Radar (SAR) provides all-weather imagery that can potentially overcome this limitation, but given its high and distinct sensitivity to different surface properties, the fusion of SAR and optical data still remains an open challenge [53; 54].