## 2 Materials and Methods
### Methodology  
3.1 Satellite Monitoring Method of General Survey and Sampling Survey to Evaluate the Effectiveness of HSF Construction  
Satellite monitoring of the effectiveness of HSF construction projects (including the MLPF transformation project regions) is carried out by a combination of a general survey and a sampling survey. The general survey can evaluate all projects, fully understand the overall improvement in cropland productivity after the construction of HSF and master regional differences in the improvement level. The sampling survey helps to deeply analyze the construction effectiveness of the project, not only to identify improvements in cropland productivity levels in each typical agri-ecological region but also to measure disaster resistance ability and homogeneous yield degree.  
1. General survey  
With the reform of agricultural supply, the planting structure in the HSF project region is changing. Part of it is changing from traditional planting of grain crops to economic crops, such as vegetables, and facility agriculture, such as greenhouses. In addition, part of it is changing from continuous agricultural planting to fallow rotation farming. To accurately understand the improvement capacity of HSF construction, it is necessary to select a project region with a stable planting structure for comparative analysis before and after project construction. In order to avoid the impact of mixed pixels obtained from remote sensing on the evaluation results, the project region participating in the evaluation should have a concentrated and contiguous distribution of cultivated land.  
Based on the above considerations, our team screened 630 project regions in Henan Province using Landsat-OLI data with a spatial resolution of 30 m and a Google Earth High-Definition image. A project region where cultivated land is intensively and continuously distributed and maintained as a grain crop planting was taken as the project region of  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Data Name** & **Source** & **Time Range** & **Resolution** & **Usage** \\ \hline Google Earth & & & per quarter year, & Used for selecting the \\ High-Definition image & Google Earth Engine & 2011–2016 & 0.5 m & general survey \\  & & & & project region \\ \hline Enhanced Vegetation & & & & \\ Index (EVI) & & & & \\ products (MOD9A1) & & & & \\ \hline Land Surface Water & & & & \\ Index (LSWI) & & & & \\ products (MOD9A1) & & & & \\ \hline Meteorological data 1 & Meteorological stations & 2008–2016 & daily & Used for running VPM and \\  & & & & & production of NPP data \\ \hline Radiation data 2 & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline Grain yield & China Statistical Yearbook & 2008–2016 & yearly, county & Use for estimate cropland \\  & & & & productivity from NPP \\ \hline Agrometeorological & & & & \\ disasters data & & & & \\  & & & & \\  & & & & \\ \hline \end{tabular}  
* Based on 1098 ground meteorological stations, the gridded temperature data was generated by using ANUSPLIN interpolation computer software [32]. The gridded temperature data have a spatial resolution of 500 m and a time step of one day. 2 To optimize the model parameters, net radiation data from 53 sites were used, and then the net radiation data from 699 weather sites were calculated. The gridded net radiation data were obtained using ANUSPLIN interpolation computer software [33]. The gridded net radiation data have a spatial resolution of 500 m and a time step of one day.  
\end{table}
Table 1: \(Cont.\)