Universität zu Köln

Zhao, Quanying, Lenz-Wiedemann, Victoria I. S., Yuan, Fei ORCID: 0000-0001-6979-0029, Jiang, Rongfeng, Miao, Yuxin, Zhang, Fusuo and Bareth, Georg (2015). Investigating Within-Field Variability of Rice from High Resolution Satellite Imagery in Qixing Farm County, Northeast China. ISPRS Int. Geo-Inf., 4 (1). S. 236 - 262. BASEL: MDPI. ISSN 2220-9964

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Abstract

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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Zhao, Quanying UNSPECIFIED UNSPECIFIED UNSPECIFIED Lenz-Wiedemann, Victoria I. S. UNSPECIFIED UNSPECIFIED UNSPECIFIED Yuan, Fei UNSPECIFIED orcid.org/0000-0001-6979-0029 UNSPECIFIED Jiang, Rongfeng UNSPECIFIED UNSPECIFIED UNSPECIFIED Miao, Yuxin UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhang, Fusuo UNSPECIFIED UNSPECIFIED UNSPECIFIED Bareth, Georg UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-410260
DOI:	10.3390/ijgi4010236
Journal or Publication Title:	ISPRS Int. Geo-Inf.
Volume:	4
Number:	1
Page Range:	S. 236 - 262
Date:	2015
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	2220-9964
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Geosciences > Geographisches Institut
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language PADDY RICE; VEGETATION INDEXES; NITROGEN STATUS; LANDSAT-TM; NOAA AVHRR; REFLECTANCE; WHEAT; GROWTH; AGRICULTURE; LAI Multiple languages Computer Science, Information Systems; Geography, Physical; Remote Sensing Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/41026