Towards Remote Estimation of Radiation Use Efficiency in Maize Using UAV-Based Low-Cost Camera Imagery

Radiation Use Efficiency (RUE) defines the productivity with which absorbed photosynthetically active radiation (APAR) is converted to plant biomass. Readily used in crop growth models to predict dry matter accumulation, RUE is commonly determined by elaborate static sensor measurements in the field. Different definitions are used, based on total absorbed PAR (RUEtotal) or PAR absorbed by the photosynthetically active leaf tissue only (RUEgreen). Previous studies have shown that the fraction of PAR absorbed (fAPAR), which supports the assessment of RUE, can be reliably estimated via remote sensing (RS), but unfortunately at spatial resolutions too coarse for experimental agriculture. UAV-based RS offers the possibility to cover plant reflectance at very high spatial and temporal resolution, possibly covering several experimental plots in little time. We investigated if (a) UAV-based low-cost camera imagery allowed estimating RUEs in different experimental plots where maize was cultivated in the growing season of 2016, (b) those values were different from the ones previously reported in literature and (c) there was a difference between RUEtotal and RUEgreen. We determined fractional cover and canopy reflectance based on the RS imagery. Our study found that RUEtotal ranges between 4.05 and 4.59, and RUEgreen between 4.11 and 4.65. These values are higher than those published in other research articles, but not outside the range of plausibility. The difference between RUEtotal and RUEgreen was minimal, possibly due to prolonged canopy greenness induced by the stay-green trait of the cultivar grown. The procedure presented here makes time-consuming APAR measurements for determining RUE especially in large experiments superfluous.

[1]  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.

[2]  Kenneth G. Cassman,et al.  Maize Radiation Use Efficiency under Optimal Growth Conditions , 2005 .

[3]  F. Baret,et al.  Green area index from an unmanned aerial system over wheat and rapeseed crops , 2014 .

[4]  C. S. T. Daughtry,et al.  Techniques for Measuring Intercepted and Absorbed Photosynthetically Active Radiation in Corn Canopies1 , 1986 .

[5]  R. Xie,et al.  Changes in the morphological traits of maize genotypes in China between the 1950s and 2000s , 2014 .

[6]  J. Poland,et al.  Application of Geographically Weighted Regression to Improve Grain Yield Prediction from Unmanned Aerial System Imagery , 2017 .

[7]  E. L. Anderson Tillage and N fertilization effects on maize root growth and root:shoot ratio , 1988, Plant and Soil.

[8]  Shaokun Li,et al.  Canopy characteristics of high-yield maize with yield potential of 22.5 Mg ha−1 , 2017 .

[9]  J. Guiamet,et al.  Senescence and yield responses to plant density in stay green and earlier-senescing maize hybrids from Argentina , 2014 .

[10]  D. Reheul,et al.  Stay-green characterization in Belgian forage maize , 2016, The Journal of Agricultural Science.

[11]  Weixing Cao,et al.  Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery , 2017 .

[12]  A. Viña,et al.  New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops , 2005 .

[13]  J. Monteith Climate and the efficiency of crop production in Britain , 1977 .

[14]  C. Birch,et al.  Plant development and leaf area production in contrasting cultivars of maize grown in a cool temperate environment in the field , 2003 .

[15]  E. Milton,et al.  The use of the empirical line method to calibrate remotely sensed data to reflectance , 1999 .

[16]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

[17]  T. Kautz,et al.  Effects of perennial fodder crops on soil structure in agricultural headlands , 2010 .

[18]  Jun Li,et al.  Advanced Spectral Classifiers for Hyperspectral Images: A review , 2017, IEEE Geoscience and Remote Sensing Magazine.

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

[20]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[21]  W. Wilhelm,et al.  Comparison of three leaf area index meters in a corn canopy , 2000 .

[22]  Craig S. T. Daughtry,et al.  Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring , 2010, Remote. Sens..

[23]  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..

[24]  H. Jones,et al.  Remote Sensing of Vegetation: Principles, Techniques, and Applications , 2010 .

[25]  Rachel Gaulton,et al.  ESTIMATION OF THE SPECTRAL SENSITIVITY FUNCTIONS OF UN-MODIFIED AND MODIFIED COMMERCIAL OFF-THE-SHELF DIGITAL CAMERAS TO ENABLE THEIR USE AS A MULTISPECTRAL IMAGING SYSTEM FOR UAVS , 2015 .

[26]  Raziel A. Ordóñez,et al.  Modelling the impact of heat stress on maize yield formation , 2016 .

[27]  M. Sulev,et al.  Sources of errors in measurements of PAR , 2000 .

[28]  Z. Niu,et al.  Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system , 2016 .

[29]  J. Xue,et al.  Morphological Variation of Maize Cultivars in Response to Elevated Plant Densities , 2017 .

[30]  J. Guiamet,et al.  Responses to N Deficiency in Stay Green and Non-Stay Green Argentinean Hybrids of Maize , 2016 .

[31]  A. Gitelson,et al.  The need for a common basis for defining light-use efficiency: Implications for productivity estimation , 2015 .

[32]  F. Baret,et al.  GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops , 2010 .

[33]  D. Timlin,et al.  Plant Density and Leaf Area Index Effects on the Distribution of Light Transmittance to the Soil Surface in Maize , 2014 .

[34]  J. G. D. Silva,et al.  Stay-green: a potentiality in plant breeding. , 2015 .

[35]  M. Otegui,et al.  Plant population density, row spacing and hybrid effects on maize canopy architecture and light attenuation , 2001 .

[36]  R. C. Muchow,et al.  Radiation Use Efficiency , 1999 .

[37]  A. Gitelson,et al.  Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production , 2014 .

[38]  Matthijs Tollenaar,et al.  Radiation Use Efficiency of an Old and a New Maize Hybrid , 1992 .

[39]  Julien Morel,et al.  Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation , 2014 .

[40]  Johannes Pfeifer,et al.  Evidence of improved water uptake from subsoil by spring wheat following lucerne in a temperate humid climate , 2012 .

[41]  N. Breda Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[42]  M. Claverie,et al.  Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data , 2012 .

[43]  J. F. Ortega,et al.  Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle , 2013 .

[44]  A. G. Cirilo,et al.  Yield Responses to Narrow Rows Depend on Increased Radiation Interception , 2002 .

[45]  Ned Horning,et al.  Tools for Remote Sensing Data Analysis , 2015 .

[46]  Jaume Lloveras,et al.  Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service , 2016, Remote. Sens..

[47]  Robert J. Hijmans,et al.  Geographic Data Analysis and Modeling , 2015 .

[48]  Pu Wang,et al.  Influence of plant architecture on maize physiology and yield in the Heilonggang River valley , 2017 .

[49]  Yi Peng,et al.  Productivity, absorbed photosynthetically active radiation, and light use efficiency in crops: implications for remote sensing of crop primary production. , 2015, Journal of plant physiology.

[50]  Arko Lucieer,et al.  Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing , 2012, Remote. Sens..

[51]  C. Stöckle,et al.  Chapter 7 – Crop Radiation Capture and Use Efficiency: A Framework for Crop Growth Analysis , 2009 .

[52]  T. Kraska,et al.  Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution , 2018, Precision Agriculture.

[53]  D. Lobell,et al.  Moisture effects on soil reflectance , 2002 .

[54]  T. Sauer,et al.  Variability of light interception and radiation use efficiency in maize and soybean , 2011 .