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Remote Sens. 2015, 7(8), 10646-10667; doi:10.3390/rs70810646

Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China

International Center for Agro-Informatics and Sustainable Development, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100083, China
Institute of Geography, University of Cologne, 50923 Cologne, Germany
Department of Geography, Minnesota State University, Mankato, MN 56001, USA
Research Centre Hanninghof, Yara International, 48249 Duelmen, Germany
Forschungszentrum Jülich, Institute of Bio-and Geosciences, IBG-2: Plant Sciences, D-52425 Jülich, Germany
Author to whom correspondence should be addressed.
Academic Editors: Tao Cheng, Zhengwei Yang, Yoshio Inoue, Yan Zhu, Weixing Cao and Prasad S. Thenkabail
Received: 21 April 2015 / Revised: 11 August 2015 / Accepted: 13 August 2015 / Published: 18 August 2015
(This article belongs to the Special Issue Recent Advances in Remote Sensing for Crop Growth Monitoring)
View Full-Text   |   Download PDF [848 KB, uploaded 18 August 2015]   |  


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 (R2 = 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. View Full-Text
Keywords: satellite remote sensing; nitrogen status diagnosis; precision nitrogen management; chlorophyll meter; nitrogen nutrition index; rice; FORMOSAT-2 satellite remote sensing; nitrogen status diagnosis; precision nitrogen management; chlorophyll meter; nitrogen nutrition index; rice; FORMOSAT-2

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Huang, S.; Miao, Y.; Zhao, G.; Yuan, F.; Ma, X.; Tan, C.; Yu, W.; Gnyp, M.L.; Lenz-Wiedemann, V.I.; Rascher, U.; Bareth, G. Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China. Remote Sens. 2015, 7, 10646-10667.

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