# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 5 Discussion
[57], where temperature and precipitation can be used as input data to calculate parameters: the net radiation (\(R_{n}\)) and the local potential evapotranspiration (\(E_{p0}\)). The value of \(\varepsilon^{*}\) has a great influence on the simulation of NPP, and its size is controversial [55]. Therefore, designing experiment is the good way to determine the value of \(\varepsilon^{*}\). [PERSON] et al. [74] applied Particle Swarm Optimizer (PSO) to search for the optimization of LUE retrievals through the CASA model combined with time-series NDVI and NPP ground measurements, which not only proved the significant difference of among various vegetation types, but also determined the specific value of \(\varepsilon^{*}\) in grassland. However, the objective of this study was to estimate NPP of winter wheat based on the improved CASA model, so no specific experiments were designed to determine the value of \(\varepsilon^{*}\). Upon combining this with the growth characteristics of winter wheat in the study area, the range of maximum light use efficiency (\(\varepsilon^{*}\)) was determined. Then, the average of the range was calculated and rounded to get \(\varepsilon^{*}\) of 1.7 g C MJ\({}^{-1}\). This study develops the improved CASA model and combines it with the NPP to estimate winter wheat yield from time-series satellite remote sensing images. The comparison of the estimated results with the ground-measured-yield data shows that the estimation accuracy satisfies the requirements for estimating regional winter wheat. Compared with the study by [PERSON] et al. [65], both methods have good results, but this model has obvious advantages in particular, widely used remote sensing images, NPP distribution with high temporal and spatial resolution, and simpler input parameters. It is still an important objective to improve the accuracy of the estimated yield of crop, which can be considered from two aspects. One is the optimization of data sources. The estimation of crop yield depends partly on the quality and quantity of the input data. In this study, the mature integrated GPP product (MOD17A2H) was applied, which existed objectively and was widely used. Reference [73] creates an optimization model to compensate for missing data, which can improve the precision of crop yield estimation combined with Monteith model (CASA). Although the study object is not winter wheat, it is still worth paying attention to this optimization method. The other is the essential parameters in the model for estimating winter wheat yield based on remote sensing images: the \(\varepsilon^{*}\) and the HI, which, based on previous research, were assigned constant values in the present study. However, these two parameters are affected by other factors, so variable processing must be done in addition to relevant experimentation, which will be the subject of future research. A total of 29 ground-measured winter wheat yield data were used in this study and winter wheat yield were concentrated in the relatively small range of 3-8 t ha\({}^{-1}\). An ideal result was obtained by using 29 sampling points in this study area, but if the study area scale is continuously expanded, further research is required to verify the accuracy and applicability of the proposed model.