## 3 Methodology
### Phenology-GPP Method for Crop Classification  
#### 3.1.1 Remote Sensing Mapping of Cultivated Land  
A cultivated land map in the study area is required to estimate NSP intensity. The accuracy and available years of existing land use products are limited. For cultivated land, the phenomenon of abandonment often occurs, and it happens randomly according to the will of farmers. Therefore, it is necessary to obtain the annual high-precision cultivated land map. We went to the study area and collected data. We found that the main land uses in the study area include water, impervious surface, bare land, cultivated land, forest land, shrubland, and grassland. To obtain the cultivated land map, we needed to distinguish it from other land uses. We collected 30 field samples of each land use (each sample contains 3 \(\times\) 3 pixels) for the average spectral test, as shown in Figure 3. Firstly, we divided land uses into vegetation and non-vegetation and compared the effects of different vegetation indices. In practical application, NDVI and EVI are widely regarded as the best indexes for vegetation identification. We compared them. As shown in Figure 4, NDVI is better than  
Figure 3: Satellite true-color images of each land use and their corresponding Sentinel-2 spectral curves. The black dotted line indicates the reflectance of all bands of sentinel-2 images. The solid red line represents the reflectance of each land use in red, green, blue, and near-red bands. These four bands are the key bands used in this paper. EVI in distinguishing vegetation from building land and water. We have counted the NDVI and EVI values of each land uses sample, which can also prove that NDVI is more effective in extracting vegetation in the study area, as shown in Figure 5. The study area is a semi-arid area with low FVC (fractional vegetation cover). NDVI is sensitive in low FVC area, but it is easy to saturate and is not suitable for high FVC area. EVI is often used in densely vegetated areas. In this experiment, it is clear that NDVI is more suitable. Figure 4: (**a,d**) are sentinel-2 satellite images displayed in true color. (**b,e**) are the results of vegetation identification using NDVI. (**c,f**) are the results of vegetation identification using EVI. Green areas indicate vegetation and white areas indicate non-vegetation. In the red squares are impervious surface and water. Figure 5: Box diagram of NDVI and EVI indices of typical surface features. Horizontal dashed lines indicate thresholds. The red boxes show the NDVI and EVI values of vegetation. The gray boxes show the NDVI and EVI values of other land use types. Vegetation includes shrubland, grassland, forest land, and cultivated land. It is also necessary to separate cultivated land from other vegetation. As shown in Figure 3, the reflectance of forest in red band is generally lower than shrubland, grassland, and cultivated land. The FVC of forest and cultivated land is usually higher, while that of shrubland and grassland is usually lower. Therefore, when distinguishing forest land and cultivated land, it is necessary to use vegetation index sensitive to high FVC areas. Figure 6 shows the sensitivity of each vegetation index in different FVC. The results showed that RVI was more sensitive in high FVC areas, while NDVI was more sensitive in areas of low vegetation coverage. RVI is not only sensitive to high FVC areas but can also magnify the difference of each vegetation in red band, which is more effective in identifying forest. The spectral curves of shrubland and grassland are very similar to that of cultivated land, which cannot be distinguished by general vegetation indices. VIUPD index is more advantageous in reflecting vegetation types by the linear decomposition of the reflectance of soil, water, vegetation, and yellow leaves. As shown in Figure 9c, experiments on samples collected from cultivated land, shrub land, and grassland showed that VIUPD could effectively identify cultivated land.