# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 5 Discussion  
Accurate and timely monitoring and evaluation of regional grain crop yield is a research priority of numerous countries, and remote sensing technology is widely used in a wide range of techniques to estimate crop yield [68; 69; 70]. MODIS remote sensing images are often used as a data source in many studies. These images may be downloaded for free and offer multiple data bands and a fast update frequency [71; 72]. However, the choice of spatial resolution for MODIS images is only 250, 500, and 1000 m. Thus, single MODIS remote sensing images with high temporal resolution and low spatial resolution are often applicable for large areas. The Chinese domestic satellite HJ-1A/B, however, offers a relatively high spatial resolution of 30 m, but the return-time period is inferior to that of MODIS. Therefore, to study the Tongzhou and Shunyi districts of Beijing, the improved CASA model was developed to estimate winter wheat NPP based on HJ-1A/B images with high temporal and medium spatial resolution. At the same time, MOD17A2H images were incorporated to fully exploit the advantages of each remote sensing data source and obtain the spatial distribution of winter wheat NPP with a high spatial and temporal resolution (5-day intervals over the entire winter wheat growing season). The results provide support for monitoring and evaluation of winter wheat yield. Thus, by using the improved CASA model and the NPP-yield conversion model, the winter wheat yield in the study area was estimated based on remote sensing images. However, the parameters used in the two models were often affected by factors such as vegetation type, geographical conditions, climate, and environment. Therefore, based on previous studies, some of the model parameters were optimized based on the actual environmental conditions in the study area during the winter wheat growing season. The most influential parameters for the improved CASA model were fPAR and the light use efficiency (\(\varepsilon\)). The original CASA model only establishes a linear relationship between fPAR and SR. In this study, the two vegetation indices NDVI and SR are used to calculate fPAR\({}_{\text{NDVI}}\) and fPAR\({}_{\text{SR}}\), respectively, with each weighted the same, to estimate fPAR. In addition, although most studies calculate the maximum and minimum NDVI by using the 95% lower side value (NDVI\({}_{\text{max}}\)) and the 5% lower side value (NDVI\({}_{\text{min}}\)) of the NDVI maximum probability distribution extracted from remote sensing images [56], respectively a single constant was assigned to the NDVI maximum and minimum monthly because of the long growing season of winter wheat and the large number of remote sensing images involved in the calculations. In view of the actual situation of winter wheat over the entire  
Figure 9: Predicted winter wheat yield as a function of measured winter wheat yield for the entire study area for the 2008–2009 growing season. growing season, this approach was justified because it reduced the parameter error and improves the estimation accuracy. The original CASA model used a soil-moisture model to calculate the water stress factor that appeared in the light use efficiency \((\varepsilon)\). The maximum light use efficiency (\(\varepsilon^{*}\)) was assigned a value of 0.389 g C MJ\({}^{-1}\). In addition, many studies have been done to optimize the parameters of the model Reference [73], which showed that remote sensing and optimization techniques can be used to improve the precision of crop yield estimation, where the water stress factor depends on energy balance equation and \(\varepsilon^{*}\) is equal to 0.29 g C/MJ for C3 crops. However, both the structure of the soil-moisture model and energy balance equation are complex and the parameters are not easy to determine, so \(\varepsilon\) in this study was calculated by using the actual regional evapotranspiration model proposed by [PERSON] et al.