# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 1 Introduction  
Food is the basis of human survival and development. However, in recent years, with the increase in population and human activity, the global ecosystem has been threatened. Air pollution and the loss of cultivated land have had significant impacts on grain crops, creating a major worldwide challenge in the form of food shortages and food security [1; 2; 3]. Accurate and timely monitoring and evaluation of regional grain yield is vital to the scientific formulation of agricultural import and export plans, the regulation of the grain market, and the guidance and adjustment of planting structures [4; 5; 6; 7]. At present, with the rapid development of remote sensing technology, the traditional time-consuming methods for estimating grain yield, such as statistical surveying, agronomy forecasting, and agrometeorology, have gradually integrated the use of remote sensing images to more rapidly estimate grain yield and other factors over large areas [8; 9; 10; 11]. In recent years, research has progressed in the field of estimating crop yield based on remote sensing images [12; 13; 14; 15; 16]. In particular, research has focused on methods to calculate the net primary production (NPP) of crops based on remote sensing data to estimate the mass of crop dry matter quality and the associated crop yield [17]. The conventional models used to estimate models crop yield from remote sensing data include statistical, crop-growth, and parameter models [18]. Examples of the statistical model include the work of [PERSON] et al. [19], who used Landsat5 TM remote sensing images as input into an iteration algorithm of weighted optimal combination (WOC) to assign the optimal weight to barley yield over multiple time periods. [PERSON] et al. [20] then established a more accurate statistical model to estimate yield by using the maximum Normalized Difference Vegetation Index (NDVI) during the crop-growing season as the yield-evaluation index. [PERSON] et al. [21] used a time-series NDVI curve to extract numerous characteristic parameters and constructed a model based on multiple linear regression analysis of yield to estimate the winter wheat yield. However, these statistical models for estimating yield are not based on crop physiology, which causes problems such as low stability and poor spatial and temporal expansion. In contrast, the high-precision crop-growth models: Crop Environment Resource Synthesis (CERES) [22], Soil Water Atmosphere Plant (SWAP) [23], and World Food Study (WOFOST) [24; 25] take full account of the physiological process of crop formation. Unfortunately, these models are too complex and involve a large number of input parameters, so they are only suitable for small areas [8; 26]. Parameter model combines the characteristics of empirical and mechanistic models and therefore offers the advantages of both types of models, such as simple structure, less complicated parameters, easy access, and strong applicability. It not only meets the requirements of national and even global large-scale NPP estimates, but can also be used on regional scales. It is a good model for estimating yield by simulating crop NPP [27; 28; 29]. A typical parameter model is the Carnegie-Ames-Stanford approach (CASA) model, which was originally developed from the model proposed by [PERSON] in 1972. The CASA model estimates the NPP by focusing on the driving role of the absorbed photosynthetically active radiation (APAR) and the light use efficiency (\(\varepsilon\)) absorbed by vegetation [30]. Since the development of the CASA model, it has been widely used to estimate the NPP [31; 32; 33; 34]. However, the original CASA model is established based on estimates of the NPP of various types of vegetation in North America, and numerous deficiencies remain in the calculation and processing of some of its parameters. For example, the original CASA model used the maximum light use efficiency (\(\varepsilon^{*}\)) = 0.389 g C M]\({}^{-1}\) as the maximum light use efficiency and did not distinguish between vegetation types.