Spectroscopic Estimation of Biomass in Canopy Components of Paddy Rice Using Dry Matter and Chlorophyll Indices

Crop biomass is a critical variable for characterizing crop growth development, understanding dry matter partitioning, and predicting grain yield. Previous studies on the spectroscopic estimation of crop biomass focused on the use of various spectral indices based on chlorophyll absorption features and found that they often became saturated at high biomass levels. Given that crop biomass is commonly expressed as the dry weight of canopy components per unit ground area, it may be better estimated using the spectral indices that directly characterize dry matter absorption. This study aims to evaluate a group of four dry matter indices (DMIs) by comparison with a group of four chlorophyll indices (CIs) for estimating the biomass of individual components (e.g., leaves, stems) and their combinations with the field data collected from a two-year rice cultivation experiment. The Red-edge Chlorophyll Index (CIRed-edge) of the CI group exhibited the best relationship with leaf biomass (R2 = 0.82) for the whole growing season and with total biomass (R2 = 0.81), but only for the growth stages before heading. However, the Normalized Difference Index for Leaf Mass per Area (NDLMA) of the DMI group showed the best relationships with both stem biomass (R2 = 0.81) and total biomass (R2 = 0.81) for the whole season. This research demonstrated the suitability of dry matter indices and provided physical explanations for the superior performance of dry matter indices over chlorophyll indices for the estimation of whole-season total biomass.

[1]  Simon Bennertz,et al.  Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..

[2]  P. Thenkabail,et al.  Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation , 2015 .

[3]  C. François,et al.  Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements , 2004 .

[4]  K. Barry,et al.  Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling , 2011 .

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

[6]  Guijun Yang,et al.  Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements , 2014 .

[7]  Elizabeth Pattey,et al.  Using Leaf Area Index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops , 2012 .

[8]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

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

[10]  G. A. Blackburn,et al.  Hyperspectral remote sensing of plant pigments. , 2006, Journal of experimental botany.

[11]  Weixing Cao,et al.  Development of critical nitrogen dilution curve in rice based on leaf dry matter , 2014 .

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

[13]  R. Kokaly,et al.  Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies , 2009 .

[14]  Weixing Cao,et al.  Development of critical nitrogen dilution curve of Japonica rice in Yangtze River Reaches , 2013 .

[15]  M. Boschetti,et al.  Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry , 2009 .

[16]  Fei Li,et al.  Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[17]  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.

[18]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

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

[20]  Xinkai Zhu,et al.  Estimation of biomass in wheat using random forest regression algorithm and remote sensing data , 2016 .

[21]  K. Swain,et al.  Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. , 2010 .

[22]  Georg Bareth,et al.  ESTIMATING WINTER WHEAT BIOMASS AND NITROGEN STATUS USING AN ACTIVE CROP SENSOR , 2010 .

[23]  J. Peñuelas,et al.  Remote sensing of biomass and yield of winter wheat under different nitrogen supplies , 2000 .

[24]  S. B. Phillips,et al.  Estimating Rice Grain Yield Potential Using Normalized Difference Vegetation Index , 2011 .

[25]  Patrick Hostert,et al.  The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation , 2015, Remote. Sens..

[26]  D. Harrell,et al.  Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields , 2016, Precision Agriculture.

[27]  R. Green,et al.  An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities , 2015 .

[28]  M. Cho,et al.  A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method , 2006 .

[29]  Yubin Lan,et al.  Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data , 2015, Remote. Sens..

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

[31]  Heather McNairn,et al.  International Journal of Applied Earth Observation and Geoinformation , 2014 .

[32]  Xianjun Hao,et al.  Estimating dry matter content from spectral reflectance for green leaves of different species , 2011 .

[33]  T. Reeves,et al.  The Cereal of the World's Poor Takes Center Stage , 2002, Science.

[34]  J. Dash,et al.  Evaluation of the MERIS terrestrial chlorophyll index , 2004 .

[35]  William R. Raun,et al.  Spectral Reflectance to Estimate Genetic Variation for In-Season Biomass, Leaf Chlorophyll, and Canopy Temperature in Wheat , 2006 .

[36]  Anatoly A. Gitelson,et al.  Application of chlorophyll-related vegetation indices for remote estimation of maize productivity , 2011 .

[37]  Lalit Kumar,et al.  Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data , 2016, Remote. Sens..

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

[39]  Shi Qiu,et al.  Estimating the Aboveground Dry Biomass of Grass by Assimilation of Retrieved LAI Into a Crop Growth Model , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[41]  John R. Miller,et al.  Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model , 2010 .

[42]  Pengfei Chen,et al.  Deriving Maximum Light Use Efficiency From Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[43]  P. Thenkabail,et al.  Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .

[44]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

[45]  J. Goudriaan,et al.  Monitoring rice reflectance at field level for estimating biomass and LAI , 1998 .

[46]  Romeo M. Visperas,et al.  Grain and dry matter yields and partitioning of assimilates in japonica/indica hybrid rice , 2002 .

[47]  A. Gitelson,et al.  Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation. , 2016, Plant, cell & environment.

[48]  K. Moffett,et al.  Remote Sens , 2015 .

[49]  W. Dean Hively,et al.  Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[50]  K. Soudani,et al.  Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass , 2008 .

[51]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[52]  J. Eitel,et al.  LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status , 2014 .

[53]  Heather McNairn,et al.  RADARSAT-2 Polarimetric SAR Response to Crop Biomass for Agricultural Production Monitoring , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[54]  S. Freden,et al.  Third Earth Resources Technology Satellite-1 Symposium- Volume I: Technical Presentations. NASA SP-351 , 1974 .