Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China

ISPRS International Journal of Geo-Information

Quanying Zhao³  Victoria I.S. Lenz-Wiedemann³  Fei Yuan²  Rongfeng Jiang¹  Yuxin Miao²  Fusuo Zhang¹  Georg Bareth³ 

[1] College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China; E-Mails:;International Center for Agro-Informatics and Sustainable Development (ICASD), Cologne 50923, Germany; E-Mail:;Institute of Geography, University of Cologne, Cologne 50923, Germany; E-Mails:

关键词: rice;    FORMOSAT-2;    agronomic variable;    expert classification;    multi-data-approach (MDA);    within-field variability;    Sanjiang Plain;    Northeast China;   

DOI  :  10.3390/ijgi4010236

来源: mdpi

【 摘 要 】

Rice is a primary staple food for the world population and there is a strong need to map its cultivation area and monitor its crop status on regional scales. This study was conducted in the Qixing Farm County of the Sanjiang Plain, Northeast China. First, the rice cultivation areas were identified by integrating the remote sensing (RS) classification maps from three dates and the Geographic Information System (GIS) data obtained from a local agency. Specifically, three FORMOSAT-2 (FS-2) images captured during the growing season in 2009 and a GIS topographic map were combined using a knowledge-based classification method. A highly accurate classification map (overall accuracy = 91.6%) was generated based on this Multi-Data-Approach (MDA). Secondly, measured agronomic variables that include biomass, leaf area index (LAI), plant nitrogen (N) concentration and plant N uptake were correlated with the date-specific FS-2 image spectra using stepwise multiple linear regression models. The best model validation results with a relative error (RE) of 8.9% were found in the biomass regression model at the phenological stage of heading. The best index of agreement (IA) value of 0.85 with an RE of 13.6% was found in the LAI model, also at the heading stage. For plant N uptake estimation, the most accurate model was again achieved at the heading stage with an RE of 11% and an IA value of 0.77; however, for plant N concentration estimation, the model performance was best at the booting stage. Finally, the regression models were applied to the identified rice areas to map the within-field variability of the four agronomic variables at different growth stages for the Qixing Farm County. The results provide detailed spatial information on the within-field variability on a regional scale, which is critical for effective field management in precision agriculture.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.