###### Abstract  
Estimating yield potential (Yp) and quantifying the contribution of suboptimum field managements to the yield gap (Yg) of crops are important for improving crop yield effectively. However, achieving this goal on a regional scale remains difficult because of challenges in collecting field management information. In this study, we retrieved crop management information (i.e., emerging stage information and a surrogate of sowing date (SDT)) from a remote sensing (RS) vegetation index time series. Then, we developed a new approach to quantify maize Yp, total Yg, and the suboptimum SDT-induced Yg (Yg0) using a process-based RS-driven crop yield model for maize (PRYM-Maize), which was developed in our previous study. PRYM-Maize and the newly developed method were used over the North China Plain (NCP) to estimate Ya, Yp, Yg, and Yg0 of summer maize. Results showed that PRYM-Maize outputs reasonable estimates for maize yield over the NCP, with correlations and root mean standard deviation of 0.49 \(\pm\) 0.24 and 0.88 \(\pm\) 0.14 \(\mathrm{h}\mathrm{m}^{-2}\), respectively, for modeled annual maize yields versus the reference value for each year over the period 2010 to 2015 on a city level. Yp estimated using our new method can reasonably capture the spatial variations in site-level estimates from crop growth models in previous literature. The mean annual regional Yp of 2010-2015 was estimated to be 11.99 \(\mathrm{t}\mathrm{h}\mathrm{m}^{-2}\), and a Yg value of 5.4 \(\mathrm{t}\mathrm{h}\mathrm{m}^{-2}\) was found between Yp and Ya on a regional scale. An estimated 29-42% of regional Yg in each year (2010-2015) was induced by suboptimum SDT. Results also show that not all Yg0 was persistent over time. Future studies using high spatial-resolution RS images to disaggregate Yg0 into persistent and non-persistent components on a small scale are required to increase maize yield over the NCP. remote sensing; crop souring date; development stage; yield gap; yield potential; process-based model +
Footnote †: journal: Remote Sensing
methods may be available for exploring Yb and quantifying the contribution of this factor to Yg. These methods could be generally categorized into two types: model simulation and field experiments. The latter method provides an estimate of Yb and other levels of yield under controlled experimental conditions, thus it can quantify the effect of SDT on Yg [10]. However, for analysis over a wide spatial or temporal range, such a method is time-costing and uneconomical. In this case, the field experiment generally served as a means of acquiring data for calibrating and validating crop growth models (CGMs) [11; 12; 13]. Model simulation is favored for its low cost and high efficiency [1; 14]. CGMs such as the agricultural production system simulator (APSIM) [15], crop environment resource synthesis (CERES) models [16], and CSM-DIM [17], after being calibrated, can produce reliable estimates of crop Yb and Ya and can also simulate crop yield under controlled conditions [11; 18; 19]. The knowledge of the contribution of management factors, including SDT, to Yg could be revealed by comparing simulated values of crop yield under different management scenarios [6; 20]. However, quantifying the contribution of SDT to Yp is not feasible over a broad region using CGMs owing to the inaccessibility of spatiotemporally continuous field management information and the sparse spatial distribution of accessible meteorological sites at present. Simulations and analyses with CGMs in existing literature were merely performed on meteorological-site levels or within a small region and provided limited information for understanding Yb and Yg in space [6; 20]. The use of remote sensing (RS)-based methods may be an alternative way to address the above issues, as the use of RS data can make a model less dependent on meteorological data and management information and show better performances in regional simulations [21; 22].