Narrow-waveband spectral indices for prediction of yield loss in frost-damaged winter wheat during stem elongation

Abstract Frost during stem elongation is one of the most destructive disasters in China, which has a significant impact on the production of winter wheat. Automatic monitoring of frost injury to canopy is of vital importance to the early prediction of yield loss. This study investigated the potential of hyperspectral techniques in predicting the Percent Yield Difference (PYD) of frost-damaged winter wheat. Three artificial frost experiments were conducted to obtain hyperspectral reflectance and grain yield for winter wheat subjected to sub-freezing temperature treatments. The PYD was used to evaluate the level of frost damage. Nine new indices based on the best combination of wavelengths were selected through the contour mapping approach. All new and published indices were used to establish the linear regression models with PYD. The results showed that as the value of PYD increased, the NIR reflectance within 760–1140 nm decreased, whereas the red and SWIR reflectance increased. The most significant change was found in the NIR region, where the water absorption bands within 930–970 nm almost disappeared. The cross-validation results indicated that the three water-sensitive spectral indices NWI-2, RDSI ( ( R 958 - R 545 ) / ( R 826 - R 545 ) ) , and NDSI ( ( R 962 - R 829 ) / ( R 962 + R 829 ) ) demonstrated the best prediction accuracy and outperformed the partial least square regression (PLSR) and support vector regression (SVR) models. NWI-2 and NDSI represented a relatively simple waveband combination similar to NDVI, which could be referenced for developing satellite multispectral products to predict PYD at a large spatial scale. GS and PYD range had a significant impact on the spectral indices. The prediction accuracy of PYD for a single GS improved as development advanced before heading. When the PYD value was above 10 %, no significant differences between the subfreezing treatments and the unfrosted controls was detected until the PYD value exceeded 30–40%. It was difficult to predict the relatively low PYD level due to the hybrid response of the spectral reflectance to frost damage.

[1]  J. H. Spink,et al.  Frost damage to winter wheat in the UK: the effect of plant population density , 2004 .

[2]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[3]  W. Single,et al.  Frost Injury to Wheat Stems and Grain Production , 1976 .

[4]  G. A. Blackburn,et al.  Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves , 1998 .

[5]  J. Huang,et al.  Changes in Frost Resistance of Wheat Young Ears with Development During Jointing Stage , 2008 .

[6]  Elizabeth Pattey,et al.  Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance , 2002 .

[7]  Haikuan Feng,et al.  Estimating leaf SPAD values of freeze-damaged winter wheat using continuous wavelet analysis. , 2016, Plant physiology and biochemistry : PPB.

[8]  Prasanna H. Gowda,et al.  Long-term spatial and temporal trends in frost indices in Kansas, USA , 2013, Climatic Change.

[9]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[10]  Kaiyu Guan,et al.  Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms , 2019, Front. Plant Sci..

[11]  Tilden Meyers,et al.  The 2007 Eastern US Spring Freeze: Increased Cold Damage in a Warming World , 2008 .

[12]  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.

[13]  Jin Wu,et al.  High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity , 2019, Remote sensing of environment.

[14]  M. Pumphrey,et al.  Spectral Reflectance for Indirect Selection and Genome‐Wide Association Analyses of Grain Yield and Drought Tolerance in North American Spring Wheat , 2018, Crop Science.

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

[16]  W. Raun,et al.  Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices , 2007 .

[17]  Tauqueer Ahmad,et al.  Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing , 2019, Agricultural Water Management.

[18]  L. Plümer,et al.  Development of spectral indices for detecting and identifying plant diseases , 2013 .

[19]  H. Marcellos,et al.  Frost Injury in Wheat Ears After Ear Emergence , 1984 .

[20]  W. Cao,et al.  Individual and combined effects of jointing and booting low-temperature stress on wheat yield , 2020 .

[21]  M. Trnka,et al.  Adverse weather conditions for UK wheat production under climate change , 2020, Agricultural and forest meteorology.

[22]  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 .

[23]  H. Marcellos,et al.  Temperatures in wheat during radiation frost , 1975 .

[24]  Scott D. Noble,et al.  Image-Based Rapid Estimation of Frost Damage in Canola (Brassica napus L.) , 2018 .

[25]  Pol Coppin,et al.  Determining the water status of Satsuma mandarin trees [Citrus Unshiu Marcovitch] using spectral indices and by combining hyperspectral and physiological data , 2010 .

[26]  J. Wang,et al.  Hyperspectral characteristics of winter wheat under freezing injury stress and LWC inversion model , 2012, 2012 First International Conference on Agro- Geoinformatics (Agro-Geoinformatics).

[27]  Armando Apan,et al.  Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery , 2004 .

[28]  Josep Peñuelas,et al.  Visible and Near‐Infrared Reflectance Assessment of Salinity Effects on Barley , 1997 .

[29]  A. Limin,et al.  Developmental Regulation of Low-temperature Tolerance in Winter Wheat , 2001 .

[30]  S. Ollinger Sources of variability in canopy reflectance and the convergent properties of plants. , 2011, The New phytologist.

[31]  A. Viña,et al.  Remote estimation of canopy chlorophyll content in crops , 2005 .

[32]  W. Cao,et al.  Spring Freeze Effect on Wheat Yield is Modulated by Winter Temperature Fluctuations: Evidence from Meta‐Analysis and Simulating Experiment , 2015 .

[33]  Yu Huang,et al.  Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration , 2015, Remote. Sens..

[34]  C. Giardino,et al.  Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling , 2008 .

[35]  B. Mackey,et al.  Freezing tolerance of winter wheat plants frozen in saturated soil. , 2009 .

[36]  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.

[37]  Davoud Ashourloo,et al.  Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina) , 2014, Remote. Sens..

[38]  R. Sahoo,et al.  Comparison of different uni- and multi-variate techniques for monitoring leaf water status as an indicator of water-deficit stress in wheat through spectroscopy , 2017 .

[39]  John Nikolaus Callow,et al.  Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing , 2020, Remote. Sens..

[40]  Christopher B. Field,et al.  Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance , 1993 .

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

[42]  W. Cao,et al.  Winter wheat photosynthesis and grain yield responses to spring freeze , 2015 .

[43]  George Alan Blackburn,et al.  Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves , 2017 .

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

[45]  Matthew P. Reynolds,et al.  Effect of leaf and spike morphological traits on the relationship between spectral reflectance indices and yield in wheat , 2015 .

[46]  Song Xiaoyan,et al.  Monitoring and evaluation in freeze stress of winter wheat (Triticum aestivum L.) through canopy hyperspectrum reflectance and multiple statistical analysis , 2018 .

[47]  Matthew P. Reynolds,et al.  Association of water spectral indices with plant and soil water relations in contrasting wheat genotypes , 2010, Journal of experimental botany.

[48]  R. Boer,et al.  Characteristics of frost in a major wheat-growing region of Australia , 1993 .

[49]  M. Fuller,et al.  Frost Hardiness Expression and Characterisation in Wheat at Ear Emergence , 2013 .

[50]  Paul E. Gessler,et al.  Sensitivity of Ground‐Based Remote Sensing Estimates of Wheat Chlorophyll Content to Variation in Soil Reflectance , 2009 .

[51]  G. Fitzgerald,et al.  In-field methods for rapid detection of frost damage in Australian dryland wheat during the reproductive and grain-filling phase , 2017, Crop and Pasture Science.

[52]  N. Elliott,et al.  Original papers: Differentiating stress induced by greenbugs and Russian wheat aphids in wheat using remote sensing , 2009 .

[53]  U. Baumann,et al.  Varietal and chromosome 2H locus-specific frost tolerance in reproductive tissues of barley (Hordeum vulgare L.) detected using a frost simulation chamber , 2009, Theoretical and Applied Genetics.

[54]  M. Fuller,et al.  The freezing characteristics of wheat at ear emergence , 2007 .

[55]  S. Irmak,et al.  U.S. Agro-Climate in 20th Century: Growing Degree Days, First and Last Frost, Growing Season Length, and Impacts on Crop Yields , 2018, Scientific Reports.

[56]  A. Borrell,et al.  Post head-emergence frost resistance of barley genotypes in the northern grain region of Australia , 2011 .

[57]  Y. G. Prasad,et al.  Hyperspectral indices for assessing damage by the solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton , 2013 .

[58]  David Riaño,et al.  Detecting diurnal and seasonal variation in canopy water content of nut tree orchards from airborne imaging spectroscopy data using continuous wavelet analysis , 2014 .

[59]  Y. G. Prasad,et al.  Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae) , 2011 .

[60]  Li He,et al.  Estimating canopy leaf nitrogen concentration in winter wheat based on multi-angular hyperspectral remote sensing , 2016 .

[61]  G. O'Leary,et al.  Frost response in wheat and early detection using proximal sensors , 2018, Journal of Agronomy and Crop Science.

[62]  W. Cao,et al.  Effects of jointing and booting low temperature stresses on grain yield and yield components in wheat , 2017 .

[63]  A. Borrell,et al.  Low temperature adaption of wheat post head-emergence in northern Australia , 2008 .

[64]  G. Carter PRIMARY AND SECONDARY EFFECTS OF WATER CONTENT ON THE SPECTRAL REFLECTANCE OF LEAVES , 1991 .

[65]  Yf Wu,et al.  Frost affects grain yield components in winter wheat , 2014 .

[66]  Georg Bareth,et al.  Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects , 2014 .

[67]  Glenn J. Fitzgerald,et al.  Frost Damage Assessment in Wheat Using Spectral Mixture Analysis , 2019, Remote. Sens..

[68]  Scott C. Chapman,et al.  Recent changes in southern Australian frost occurrence: implications for wheat production risk , 2016, Crop and Pasture Science.

[69]  S. Asseng,et al.  Estimating spring frost and its impact on yield across winter wheat in China , 2018, Agricultural and Forest Meteorology.

[70]  Reimund P. Rötter,et al.  Adverse weather conditions for European wheat production will become more frequent with climate change , 2014 .

[71]  K. Chenu,et al.  Frost trends and their estimated impact on yield in the Australian wheatbelt , 2015, Journal of experimental botany.

[72]  G. Kang,et al.  Proteomic analysis of spring freeze-stress responsive proteins in leaves of bread wheat (Triticum aestivum L.). , 2013, Plant physiology and biochemistry : PPB.

[73]  Susan L. Ustin,et al.  Evaluation of the potential of Hyperion data to estimate wildfire hazard in the Santa Ynez front range, Santa Barbara, California , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[74]  A. Borrell,et al.  Post-head-emergence frost in wheat and barley: defining the problem, assessing the damage, and identifying resistance. , 2015, Journal of experimental botany.

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

[76]  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.

[77]  Ruiliang Pu,et al.  Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses , 2012 .

[78]  T. Jarmer,et al.  Comparison of different regression models and validation techniques for the assessment of wheat leaf area index from hyperspectral data , 2015 .

[79]  V. Singh,et al.  Combined use of in situ hyperspectral vegetation indices for estimating pasture biomass at peak productive period for harvest decision , 2018, Precision Agriculture.

[80]  Graeme L. Hammer,et al.  Frost in Northeast Australia: Trends and Influences of Phases of the Southern Oscillation , 1996 .

[81]  R. Sahoo,et al.  Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices , 2014 .

[82]  J. Dash,et al.  The MERIS terrestrial chlorophyll index , 2004 .

[83]  M. Feng,et al.  Canopy hyperspectral characteristics and yield estimation of winter wheat (Triticum aestivum) under low temperature injury , 2020, Scientific Reports.

[84]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[85]  William R. Raun,et al.  Spectral Reflectance Indices as a Potential Indirect Selection Criteria for Wheat Yield under Irrigation , 2006 .