Acta Geographica Sinica ›› 2021, Vol. 76 ›› Issue (4): 888-902.

• Agricultural and Rural Development •

### Monthly calibration and optimization of Ångström-Prescott equation coefficients for agricultural comprehensive area in China

XIA Xingsheng1,2(), PAN Yaozhong1,2, ZHU Xiufang1,3(), ZHANG Jinshui1,3

1. 1. State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
2. Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810008, China
3. Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
• Received:2019-11-08 Revised:2020-10-03 Online:2021-04-25 Published:2021-06-25
• Contact: ZHU Xiufang E-mail:xiayuan1104@163.com;zhuxiufang@bnu.edu.cn
• Supported by:
National High Resolution Earth Observation System Technology Projects of China;Local Scientific & Technological Development Projects of Qinghai Guided by Central Government of China;Disaster Research Foundation of PICC P&C(2017D24-03)

Abstract:

?ngstr?m-Prescott equation is the recommended algorithm for calculating the radiation coefficients for the Penman-Monteith formula, which is the standard method for reference crop evapotranspiration recommended by the Food and Agriculture Organization (FAO) of the United Nations. The calibration and optimization of as and bs coefficients in the equation is the key to accurately calculate the surface solar radiation. This study aims at obtaining the ?ngstr?m-Prescott equation coefficients as and bs, which are optimized for China's comprehensive agricultural areas. The monthly average solar radiation (Rs) (from Dataset of Monthly Values of Radiation Data from Chinese Surface Stations) and srelative sunshine duration data (from Dataset of Monthly Values of Climate Data from Chinese Surface Stations) at 121 stations during 1957-2016 were collected. Using the data from 1957 to 2010, we calculated the monthly as and bs coefficients for each area through the least squares regression. Then, taking the observation values of Rs from 2011 to 2016 as the true values, we estimated and compared the relative accuracy of Rs calculated by regression values of coefficients as and bs and that calculated by FAO suggested coefficients as and bs. The results showed that the monthly average coefficients as and bs of each area are significantly different from the FAO recommended coefficients both temporally and spatially. There are some differences between regions and within regions, and the relative value of as and bs shows the opposite state. The relative error range (0-54%) of solar radiation calculated by the regression as and bs coefficients is small, while the relative error range (0-77%) of solar radiation calculated by the FAO recommended value is large. So, overall, the relative accuracy of Rs calculated by regression values of as and bs coefficients is better than that calculated by the FAO suggested coefficients. The relative error was reduced by 1% to 6%, and the relative error decreases more in winter and spring than in summer and autumn. However, regression values of as and bs coefficients perform worse in some months and some agricultural areas for verification in application. It is said that the regression values of as and bs are not entirely reliable. For each month and each agricultural area, the best scheme is to combine the regression values of as and bs coefficients with the FAO recommended values. Therefore, we chose the as and bs coefficients with the minimum Rs estimation error as the final coefficients and made a coefficient recommendation table for 38 agricultural production and management areas in the Chinese mainland. This study further illustrates the necessity of localization modification of ?ngstr?m-Prescott equation coefficients in application, and enriches the case study of coefficient calibration of ?ngstr?m-Prescott equation in China, which is helpful for improving the accuracy of calculation of surface solar radiation and reference crop evapotranspiration based on existing data.