The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Crop yield prediction is a significant component of national food security assessment and food policy making. The data assimilation method which combined crop growth model and multi-source observed data has been proven to be the most effective method to simulate the crop growth process and predict crop yields. Based on the observed LAI, the time-series LAI data sets were obtained by using the smooth...
Leaf area index is an important vegetation canopy structure parameter for vegetation monitoring, climate change, ecological process and data assimilation system. The method of LAI inversion is one of the focuses of quantitative remote sensing research. In this study, the global optimal algorithm SCE-UA (Shuffled Complex Evolution method developed at the University of Arizona) was integrated into canopy...
In recent years, combining spatial and timely remote sensing data and crop growth model is an important way to improve accuracy of crop growth simulation and crop growth monitoring. In this paper, global optimization algorithm SCE-UA (Shuffled Complex Evolution method - University of Arizona) was used to integrate remotely sensed leaf area index (LAI) with EPIC crop growth model to simulate regional...
Crop growth monitoring is critical in yield estimation and prediction. In this paper, the authors investigated several indicators for crop growth monitoring by remote sensing at different scales. The experiments were conducted in a study area in Hebei province in North China Plain. The target crop in this research is winter wheat, which is one of the important grain crops in China. The study at canopy...
In order to acquire more accurate crop yield information, the global optimization algorithm SCE-UA was used to integrate leaf area index derived from remote sensing with crop growth model EPIC to simulate regional summer maize yield and field management information in Huanghuaihai Plain in China. The results showed that the mean relative error of estimated summer maize yield was 4.37% and RMSE was...
Assimilating external data into crop growth model to improve accuracy of crop growth monitoring and yield estimation has been being a research hotspot in recent years. In this paper, the global optimization algorithm SCE-UA (Shuffled Complex Evolution method-University of Arizona) was used to integrate remotely sensed leaf area index (LAI) with crop growth model EPIC to simulate regional yield, sowing...
Assimilating external data into a crop growth model to improve accuracy of crop growth monitoring and yield estimation has been a research focus in recent years. In this paper, the shuffled complex evolution (SCE-UA) global optimization algorithm was used to assimilate field measured LAI into EPIC model to simulate yield, sowing date and nitrogen fertilizer application amount of summer maize in Huanghuaihai...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.