## 4 Discussion  
The discrepancies for different instruments or platforms greatly influenced the accuracy of LAI inversion. The factors that influenced LAI inversion included the sensor spectral response function, sun-view geometry, and data preprocessing errors [16; 51]. The maximal NDVI difference among ZY-3 MUX, GF-1 WFV, and HJ-1 CCD in theory reached 2.62% due to the different sensor spectral response functions (Figure 10). Because of the similar satellite transit period in the study area, the SZA values of these three sensors were approximately equal, but the VZA difference among these three sensors was approximately 30\({}^{\circ}\). Based on the reflectance in the red and NIR bands in Figure 9, the maximum NDVI difference between ZY-3 MUX, GF-1 WFV, and HJ-1 CCD was 9.74%. The error of geometric correction was less than one pixel for each sensor image in this study. For different sensor resolutions, the one pixel error of geometric correction was 5.8 m for ZY-3 MUX, 16 m for GF-1 WFV and 30 m for HJ-1 CCD. In addition, because of the unstable data quality of HJ-1 CCD, cross-radiometric calibration was performed. In this case, atmospheric correction was achieved based on the method proposed by [PERSON] et al. [57; 59]. After all of the data preprocessing steps, there is still a systematically discrepancies of reflectance (with maximum value of 0.1), both in red and NIR bands when compared with those of Landsat-8 OLI in Figure 11. Therefore, the accuracy of data preprocessing for multiple sensors was the dominant factor that influence the LAI inversion difference among ZY-3 MUX, GF-1 WFV, and HJ-1 CCD.  
NIRv, which combined NIR reflectance with the NDVI, was proposed to accurately estimate the global terrestrial gross primary production (GPP) [78]. NIRv is not easily saturated when compared with the NDVI; therefore, it can be applied to improve the LAI estimation. In this study, NIRv was used to estimate LAI from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD. The performance of LAI inversion  
Figure 11: Density scatter plots of the reflectance difference in the red and NIR bands between Landsat-8 OLI and each of ZY-3 MUX (**a**), GF-1 WFV (**b**), and HJ-1 CCD (**c**) for croplands.  
based on the NIRv-LAI exponential relationship for ZY-3 MUX was best, as shown in Figure 5d. Notably, NIRv depends largely on NIR reflectance. If the NIR reflectance contains noise, the NIRv will deviate more than the NDVI from actual values. NDVI, as a normalized index that can eliminate the fluctuations in the red and NIR bands, and the performance of LAI inversion from the NDVI-LAI relationship was better than that from NIRv-LAI relationship (Figure 5b,c,e,f). In addition, because of the low saturation of NIRv, the superiority of NIRv in LAI inversion will be more apparent when applied for the inversion of high LAI values, such as those of the forests, but not for small values, such as those of the crops as analyzed in this paper.  
Currently, the LAI estimation methods at the regional scale are generally based on a single sensor. The primary restriction for generating regional LAI products with moderate to high spatial resolution is the limited number of sensor observations during a specific period. The multi-sensor data during a specific period can greatly increase the number of observations and improve the accuracy of LAI inversion. The sensors with similar spatial resolution, such as ZY-3 MUX, GF-1 WFV, HJ-1 CCD, Landsat-8 OLI, and Sentinel-2, provide a combined multi-sensor dataset for generating LAI products with moderate to high resolutions. The multi-sensor data have more VZA, and the multi-angular observations from multiple sensors are helpful for improving the accuracy of LAI inversion. However, due to the differences in sensor characteristics, geometric and radiometric normalization between different sensors are necessary.