Multi-LUTs method for canopy nitrogen density estimation in winter wheat by field and UAV hyperspectral

Abstract Unmanned aerial vehicle (UAV) based hyperspectral images linked to a radiative transfer model can provide a promising approach for high throughput monitoring of plant nitrogen (N) status. In this study, multiple lookup tables (Multi-LUTs), each LUT corresponding to one growth stage, were constructed based on the N-PROSAIL model, a radiative transfer model, and LUT size was optimized for improving computing efficiency. The objective is to use the constructed Multi-LUTs for estimating canopy N density (CND) in winter wheat. Results suggest that Multi-LUTs of leaf area index, leaf N density and two spectral indices (MSR and MCARI/MTVI2) in winter wheat demonstrate good performance of CND estimation; and LUTs with the optimal size of 6000 rows can yield good accuracy. The R2 and nRMSE values of the regression relationship between estimated and measured CND were 0.83 and 0.23 from field hyperspectral data, and 0.69 and 0.27 from UAV based hyperspectral imagery during the 2014–2015 growing season. CND by Multi-LUTs method was also accurately estimated from field hyperspectral data during the 2013–2014 growing season, with R2 and nRMSE values of 0.74 and 0.26. The estimation accuracy of CND based UAV data was a slightly lower than based field data. The resultant thematic CND map accurately exhibits CND variability at varying spatial and temporal scales. Results from this study confirmed the potential of combining UAV based hyperspectral imagery and physical optics approach for estimating CND in winter wheat.

[1]  Xingang Xu,et al.  Comparison of Four Chemometric Techniques for Estimating Leaf Nitrogen Concentrations in Winter Wheat (Triticum Aestivum) Based on Hyperspectral Features , 2016 .

[2]  Jihua Wang,et al.  Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation , 2015, Remote. Sens..

[3]  Ronggao Liu,et al.  Nitrogen Availability Dampens the Positive Impacts of CO2 Fertilization on Terrestrial Ecosystem Carbon and Water Cycles , 2017 .

[4]  L. Kumar,et al.  A review of data assimilation of remote sensing and crop models , 2018 .

[5]  Mark P. Wachowiak,et al.  Relationship between Hyperspectral Measurements and Mangrove Leaf Nitrogen Concentrations , 2013, Remote. Sens..

[6]  Hao Yang,et al.  Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives , 2017, Front. Plant Sci..

[7]  J. Zadoks.  A decimal code for the growth stages of cereals , 1974 .

[8]  Francesco Montemurro,et al.  Precision nitrogen management of wheat. A review , 2012, Agronomy for Sustainable Development.

[9]  Yanjie Wang,et al.  Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models , 2017, Remote. Sens..

[10]  Brian Krienke,et al.  Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor , 2017, Precision Agriculture.

[11]  Edward M. Barnes,et al.  Remote Sensing of Cotton Nitrogen Status Using the Canopy Chlorophyll Content Index (CCCI) , 2008 .

[12]  Jihua Wang,et al.  Estimating winter wheat (Triticum aestivum) LAI and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the PROSAIL model , 2015 .

[13]  Ruiliang Pu,et al.  Leaf nitrogen spectral reflectance model of winter wheat (Triticum aestivum) based on PROSPECT: simulation and inversion , 2015 .

[14]  Pierre Roumet,et al.  Assessing leaf nitrogen content and leaf mass per unit area of wheat in the field throughout plant cycle with a portable spectrometer , 2013 .

[15]  Huanhuan Yuan,et al.  The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager , 2017, Remote. Sens..

[16]  Michael E. Schaepman,et al.  A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Frédéric Baret,et al.  Estimating leaf chlorophyll content in sugar beet canopies using millimeter- to centimeter-scale reflectance imagery , 2017 .

[18]  Shuang-Li Qi,et al.  Remote detection of canopy leaf nitrogen concentration in winter wheat by using water resistance vegetation indices from in-situ hyperspectral data , 2016 .

[19]  Hao Yang,et al.  Remote Sensing of Leaf and Canopy Nitrogen Status in Winter Wheat (Triticum aestivum L.) Based on N-PROSAIL Model , 2018, Remote. Sens..

[20]  Li Geng.  Comparison of Root Characteristics and Nitrogen Uptake and Use Efficiency in Different Corn Genotypes , 2011 .

[21]  Ni Wang,et al.  Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery , 2017, Remote. Sens..

[22]  Xin-gang Xu,et al.  Using optimal combination method and in situ hyperspectral measurements to estimate leaf nitrogen concentration in barley , 2013, Precision Agriculture.

[23]  Yuri Knyazikhin,et al.  Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem , 2003 .

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

[25]  A. Skidmore,et al.  Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland , 2008 .

[26]  F. Baret,et al.  Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. , 2017 .

[27]  Andreas Burkart,et al.  Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance , 2015 .

[28]  John Smith,et al.  Assessment of In-Season Cotton Nitrogen Status and Lint Yield Prediction from Unmanned Aerial System Imagery , 2017, Remote. Sens..

[29]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[30]  Wenjiang Huang,et al.  Relationships of leaf nitrogen concentration and canopy nitrogen density with spectral features parameters and narrow-band spectral indices calculated from field winter wheat (Triticum aestivum L.) spectra , 2012 .

[31]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .

[32]  Jan G. P. W. Clevers,et al.  Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[33]  Gero Barmeier,et al.  High-Throughput Field Phenotyping of Leaves, Leaf Sheaths, Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness , 2017, Front. Plant Sci..

[34]  J. Peñuelas,et al.  Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: Decomposing biochemical from structural signals , 2002 .

[35]  D. Haboudane,et al.  New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat , 2010 .

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

[37]  Xu Tongyu,et al.  Radiative transfer models (RTMs) for field phenotyping inversion of rice based on UAV hyperspectral remote sensing , 2017 .