Universität zu Köln

Huang, Shanyu, Miao, Yuxin, Zhao, Guangming, Yuan, Fei ORCID: 0000-0001-6979-0029, Ma, Xiaobo, Tan, Chuanxiang, Yu, Weifeng, Gnyp, Martin L. ORCID: 0000-0002-5702-4914, Lenz-Wiedemann, Victoria I. S., Rascher, Uwe ORCID: 0000-0002-9993-4588 and Bareth, Georg (2015). Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China. Remote Sens., 7 (8). S. 10646 - 10668. BASEL: MDPI. ISSN 2072-4292

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Abstract

Rice farming in Northeast China is crucially important for China's food security and sustainable development. A key challenge is how to optimize nitrogen (N) management to ensure high yield production while improving N use efficiency and protecting the environment. Handheld chlorophyll meter (CM) and active crop canopy sensors have been used to improve rice N management in this region. However, these technologies are still time consuming for large-scale applications. Satellite remote sensing provides a promising technology for large-scale crop growth monitoring and precision management. The objective of this study was to evaluate the potential of using FORMOSAT-2 satellite images to diagnose rice N status for guiding topdressing N application at the stem elongation stage in Northeast China. Five farmers' fields (three in 2011 and two in 2012) were selected from the Qixing Farm in Heilongjiang Province of Northeast China. FORMOSAT-2 satellite images were collected in late June. Simultaneously, 92 field samples were collected and six agronomic variables, including aboveground biomass, leaf area index (LAI), plant N concentration (PNC), plant N uptake (PNU), CM readings and N nutrition index (NNI) defined as the ratio of actual PNC and critical PNC, were determined. Based on the FORMOSAT-2 imagery, a total of 50 vegetation indices (VIs) were computed and correlated with the field-based agronomic variables. Results indicated that 45% of NNI variability could be explained using Ratio Vegetation Index 3 (RVI3) directly across years. A more practical and promising approach was proposed by using satellite remote sensing to estimate aboveground biomass and PNU at the panicle initiation stage and then using these two variables to estimate NNI indirectly (R-2 = 0.52 across years). Further, the difference between the estimated PNU and the critical PNU can be used to guide the topdressing N application rate adjustments.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Huang, Shanyu UNSPECIFIED UNSPECIFIED UNSPECIFIED Miao, Yuxin UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhao, Guangming UNSPECIFIED UNSPECIFIED UNSPECIFIED Yuan, Fei UNSPECIFIED orcid.org/0000-0001-6979-0029 UNSPECIFIED Ma, Xiaobo UNSPECIFIED UNSPECIFIED UNSPECIFIED Tan, Chuanxiang UNSPECIFIED UNSPECIFIED UNSPECIFIED Yu, Weifeng UNSPECIFIED UNSPECIFIED UNSPECIFIED Gnyp, Martin L. UNSPECIFIED orcid.org/0000-0002-5702-4914 UNSPECIFIED Lenz-Wiedemann, Victoria I. S. UNSPECIFIED UNSPECIFIED UNSPECIFIED Rascher, Uwe UNSPECIFIED orcid.org/0000-0002-9993-4588 UNSPECIFIED Bareth, Georg UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-397164
DOI:	10.3390/rs70810646
Journal or Publication Title:	Remote Sens.
Volume:	7
Number:	8
Page Range:	S. 10646 - 10668
Date:	2015
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	2072-4292
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 REDUCING ENVIRONMENTAL RISK; CROP CHLOROPHYLL CONTENT; NUTRITION INDEX; VEGETATION INDEXES; SPECTRAL REFLECTANCE; GROWTH-RATE; PADDY RICE; MANAGEMENT; LEAF; PLANT Multiple languages Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/39716