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

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.

[1]  Penghuan Liu,et al.  A preliminary precision rice management system for increasing both grain yield and nitrogen use efficiency , 2013 .

[2]  Li Ning.  The Comparison on Various Methods for Determing Different Proteins , 2006 .

[3]  Yong-xing Yang,et al.  Effects of agriculture reclamation on the hydrologic characteristics in the Sanjiang Plain, China , 2001 .

[4]  Shanyu Huang,et al.  Active canopy sensor-based precision N management strategy for rice , 2012, Agronomy for Sustainable Development.

[5]  Nicolas Tremblay,et al.  Plant-Based Diagnostic Tools for Evaluating Wheat Nitrogen Status , 2010 .

[6]  J. Chen.  Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications , 1996 .

[7]  B. Datt,et al.  Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves , 1999 .

[8]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[9]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[10]  Jianliang Huang,et al.  Improving Nitrogen Fertilization in Rice by Site-Specific N Management , 2011 .

[11]  Xin-ping Chen,et al.  Reducing environmental risk by improving N management in intensive Chinese agricultural systems , 2009, Proceedings of the National Academy of Sciences.

[12]  Gilles Lemaire,et al.  Growth Rate and % N of Field Grown Crops: Theory and Experiments , 1991 .

[13]  Yuxin Miao,et al.  Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn , 2008, Precision Agriculture.

[14]  Fusuo Zhang,et al.  Integrated soil-crop system management: reducing environmental risk while increasing crop productivity and improving nutrient use efficiency in China. , 2011, Journal of environmental quality.

[15]  Jie Chang,et al.  [Study on the method for the determination of nitric nitrogen, ammoniacal nitrogen and total nitrogen in plant]. , 2004, Guang pu xue yu guang pu fen xi = Guang pu.

[16]  Bruno Mary,et al.  Elaboration of a nitrogen nutrition indicator for winter wheat based on leaf area index and chlorophyll content for making nitrogen recommendations , 2007 .

[17]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[18]  Jianliang Huang,et al.  Improving nitrogen fertilization in rice by sitespecific N management. A review , 2010, Agronomy for Sustainable Development.

[19]  Cheng-Chien Liu,et al.  Processing of FORMOSAT-2 Daily Revisit Imagery for Site Surveillance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[20]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[21]  D. Mulla.  Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[22]  Christoph Hütt,et al.  Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[23]  B. Mistele,et al.  Estimating the nitrogen nutrition index using spectral canopy reflectance measurements , 2008 .

[24]  E. Justes,et al.  Determination of a Critical Nitrogen Dilution Curve for Winter Wheat Crops , 1994 .

[25]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[26]  Jianbo Shen,et al.  Transforming agriculture in China: From solely high yield to both high yield and high resource use efficiency , 2013 .

[27]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[28]  S. Ustin,et al.  Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages , 2014 .

[29]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[30]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[31]  Wenjiang Huang,et al.  Critical Nitrogen Curve and Remote Detection of Nitrogen Nutrition Index for Corn in the Northwestern Plain of Shandong Province, China , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  Susan L. Ustin,et al.  Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices , 2014 .

[33]  M. Jeuffroy,et al.  Replacing the nitrogen nutrition index by the chlorophyll meter to assess wheat N status , 2007, Agronomy for Sustainable Development.

[34]  Claus Buschmann,et al.  In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation , 1993 .

[35]  Bin Liu,et al.  Active canopy sensing of winter wheat nitrogen status: An evaluation of two sensor systems , 2015, Comput. Electron. Agric..

[36]  Stefano Amaducci,et al.  Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery , 2014, Remote. Sens..

[37]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[38]  Deng Wei,et al.  Climate change in the Sanjiang Plain disturbed by large-scale reclamation , 2002 .

[39]  John R. Miller,et al.  Remote Estimation of Crop Chlorophyll Content Using Spectral Indices Derived From Hyperspectral Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Nanjing.  Pedogenetic characteristics of albic soils in the Three River Plain, Heilongjiang, China , 1994 .

[41]  M. Jeuffroy,et al.  Diagnosis tool for plant and crop N status in vegetative stage Theory and practices for crop N management , 2008 .

[42]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[43]  John R. Miller,et al.  Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture , 2002 .

[44]  R. Nagarajan,et al.  Site-specific nutrient management for intensive rice cropping systems in Asia , 2002 .

[45]  Y. Miao,et al.  Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming , 2012, Precision Agriculture.

[46]  Shanyu Huang,et al.  Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor , 2013 .

[47]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[48]  Yukio Kosugi,et al.  Characterization of Rice Paddies by a UAV-Mounted Miniature Hyperspectral Sensor System , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[49]  G. Talwar,et al.  CO2 EXCHANGE, PRIMARY PHOTOCHEMICAL REACTIONS AND CERTAIN ENZYMES OF PHOTOSYNTHETIC CARBON REDUCTION CYCLE OF BRASSICA PODS UNDER REDUCED IRRADIANCE , 1996 .

[50]  Qiang Cao,et al.  In-Season Estimation of Rice Nitrogen Status With an Active Crop Canopy Sensor , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[51]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[52]  Jiyun Jin,et al.  Changes in the efficiency of fertiliser use in China. , 2012, Journal of the science of food and agriculture.

[53]  M. Bauer,et al.  Comparison of petiole nitrate concentrations, SPAD chlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies , 2007 .

[54]  Yubin Lan,et al.  Development and prospect of unmanned aerial vehicle technologies for agricultural production management , 2013 .

[55]  A. Gitelson.  Wide Dynamic Range Vegetation Index for remote quantification of biophysical characteristics of vegetation. , 2004, Journal of plant physiology.

[56]  Cheng-Chien Liu,et al.  Using FORMOSAT-2 Satellite Data to Estimate Leaf Area Index of Rice Crop , 2008 .

[57]  J. Eitel,et al.  Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status , 2007 .

[58]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[59]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[60]  Roshanak Darvishzadeh,et al.  Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[61]  J. J. Neeteson,et al.  Quantitative relationships for the dependence of growth rate of arable crops on their nitrogen content, dry weight and aerial environment , 1986, Plant and Soil.

[62]  Nicolas Tremblay,et al.  Chlorophyll Measurements and Nitrogen Nutrition Index for the Evaluation of Corn Nitrogen Status , 2008 .

[63]  Georg Bareth,et al.  Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain , 2013 .

[64]  D. Dawe,et al.  The contribution of rice research to poverty alleviation , 2000 .

[65]  Weixing Cao,et al.  Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat , 2012 .

[66]  Tim R. McVicar,et al.  Current and potential uses of optical remote sensing in rice-based irrigation systems: a review , 2004 .

[67]  David W. Lamb,et al.  Radiometry of Proximal Active Optical Sensors (AOS) for Agricultural Sensing , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[68]  Jeng-Shing Chern,et al.  Lesson learned from FORMOSAT-2 mission operations , 2006 .

[69]  E. Justes,et al.  Relationship Between the Normalized SPAD Index and the Nitrogen Nutrition Index: Application to Durum Wheat , 2006 .

[70]  Li Shao-kun,et al.  Comparitive Study on the Measure Methods of the Leaf Area , 2005 .