###### Abstract
Unlike CGMs, the key factor, leaf area index (LAI), for simulating photosynthesis rate RS-based models is remotely sensed rather than simulated by the model [23], which makes RS-based models more applicable to regional applications. An RS-based land process model has long been used to simulate ecosystem productivity and is also useful for simulating crop yield [23; 24]. SDT, which is also an essential input of RS-based models to map crop yield, can be obtained by analyzing the RS vegetation indices (VI) time series [25; 26; 27]. Hence, we can use RS data to study the contribution of suboptimum SDT to Yg over a broad region. Vegetation parameters retrieved from RS data reflect the actual growing condition of crops and seem to be useful for simulating only Ya. However, spatiotemporal variations in pixel-level Ya predicted using an RS method are potentially useful for quantifying Yb [28] and understanding the contribution of different factors to Yg [2; 29]. Assuming that potential yield is realized on a local scale, pixel-level Yb could be computed as the high percentile (95 th or 99 th) of yield distribution of surrounding pixels [28; 30]. To avoid this assumption, [PERSON] [28] suggested using a hybrid method that estimates the real Yb by fitting the Yb derived from the above RS-based method to the estimate of a calibrated CGM. However, the hybrid approach may in turn be restricted by CGMs' high input data requirement over a broad region. In addition, such a method may fail when Yg and factors contributing to Yg varied significantly and irregularly in space. Therefore, an RS-based method to quantify regional-scale Yb, Yg, and the effect of SDT on crop yield, without the need for CGMs, is needed. Multiple types of satellite data are available for modeling crop yield and Ygs. High-spatial-resolution data, such as Landsat and SPOT, that have relatively long revisit times have been generally used to develop empirical relationships between crop yield and spectral indices at a specific developmental stage of the crop [29; 31; 32; 33]. These empirical methods are limited to quantify Yb and Yg for regions with lower levels of field management [28]. Besides, these methods do not explicitly consider the effect of SDT on crop yield, being not able to quantify the SDT to Yg. The two Sentinel-2 satellites (Sentinel-2A and B) can provide 10 to 20 m resolution multispectral data with a revisit period of 4-5 days and are useful for driving a process-based model to estimate crop yield, which requires time-continuous inputs [34]. However, Sentinel-2B data were only available from 2018 when the satellite was launched; hence, using process-based methods with Sentinel-2 data to reproduce Yg and Yb from earlier years is impossible. Although the widely used MODIS data have a coarser spatial resolution than Landsat, SPOT, and Sentinel-2, it can provide time-continuous images with more available pixels in time and are available from 2000 to present. The data have also been used to estimate crop yield with process-based models [23; 35]. In this study, we aim to develop a new RS approach that uses MODIS data to quantify Yb and the contribution of suboptimum SDT to Yg of summer maize over the North China Plain (NCP), with three main objectives: (1) To develop a novel RS-based method driven by MODIS data to simulate Yb and Yg at a large spatial scale; (2) to assess the reliability of the developed RS-based method in simulating Ya and Yb over the NCP; and (3) to quantify Yg and the contribution of suboptimum SDT to the Yg of summer maize over the NCP in the period 2010 to 2015.