Estimation of transpiration and canopy cover of winter wheat under different fertilization levels using thermal infrared and visible imagery

Abstract Food security has always been one of the most important goals for government at all levels in China. Although more fertilization is an effective solution, many other problems appeared with the overuse of fertilization, such as water pollution, soil compaction and so on. Thus, in order to provide suitable advice on fertilization, we used the combination of high-resolution thermal and visible images to evaluate the transpiration rate and canopy cover and identify the effects of fertilization on crop growth. Three different fertilization levels of: (A) 900 kg/ha, (B) 675 kg/ha, and (C) 450 kg/ha were designed for winter wheat. Spatial-temporal distribution of transpiration rate and canopy coverage under different growth stages were depicted based on three temperature model and canopy software. Results showed that: (1) the daily variation of canopy temperature (Tc) and transpiration rate were both unimodal curve, and both of them reached their daily peak values at 14:00. Moreover, the spatial heterogeneity of land surface temperature (LST) and transpiration rate was quite obvious and common for all growth stages, and this spatial heterogeneity presented variation from pixel to pixel. (2) Fertilization had a significant effect on the growth of winter wheat. With the higher fertilization amount, the growth of winter wheat became more vigorous, the canopy cover and transpiration rate turned higher, and the Tc was lower. For example during recovering stage, the canopy cover under treatment A、B and C were 78.7%, 52.5%, and 42.7%, respectively, as well as the peak values of transpiration rate for treatment A, B, and C were 0.45 mm/h, 0.29 mm/h, and 0.12 mm/h, respectively. The transpiration rate had a positive correlation with canopy cover, and the coefficients of determination R2 = 0.78. (3) The advancement in the crop growth led to the increase of canopy cover and transpiration rate for winter wheat under different fertilization levels. However, the difference of transpiration rate and canopy cover under different fertilization levels decreased gradually with crop growth. Our research accurately detected the effects of fertilization on crop growth based on high-resolution thermal and visible images, which will provide feasible assistance for rational fertilization and it is possible to foresee the broad application prospects in the future.

[1]  T. Ochsner,et al.  Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover , 2015, Agronomy Journal.

[2]  Fernando André Silva Santos,et al.  Gas exchange, leaf and root dry mass in Bell Pepper under fertigation with nitrogen and potassium , 2018 .

[3]  Pablo J. Zarco-Tejada,et al.  Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery , 2009 .

[4]  Yang Wang,et al.  Dynamical effects of plastic mulch on evapotranspiration partitioning in a mulched agriculture ecosystem: Measurement with numerical modeling , 2019, Agricultural and Forest Meteorology.

[5]  Uttam Kumar Mandal,et al.  Green manuring: its effect on soil properties and crop growth under rice–wheat cropping system , 2003 .

[6]  Z. Zhu,et al.  Nitrogen losses from fertilizers applied to maize, wheat and rice in the North China Plain , 2002, Nutrient Cycling in Agroecosystems.

[7]  Z. Su.  The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes , 2002 .

[8]  Andreas Marx,et al.  Uncertainty analysis for satellite derived sensible heat fluxes and scintillometer measurements over Savannah environment and comparison to mesoscale meteorological simulation results , 2008 .

[9]  Christopher J. Still,et al.  Canopy skin temperature variations in relation to climate, soil temperature, and carbon flux at a ponderosa pine forest in central Oregon , 2016 .

[10]  Lu Zhang,et al.  Estimating Crop Transpiration of Soybean under Different Irrigation Treatments Using Thermal Infrared Remote Sensing Imagery , 2018, Agronomy.

[11]  L. S. Pereira,et al.  Evapotranspiration information reporting: I. Factors governing measurement accuracy , 2011 .

[12]  Nicola Cooley,et al.  Automated canopy temperature estimation via infrared thermography: A first step towards automated plant water stress monitoring , 2010 .

[13]  Mathieu Sebilo,et al.  Long-term fate of nitrate fertilizer in agricultural soils , 2013, Proceedings of the National Academy of Sciences.

[14]  Maj-Liz Nordberg,et al.  Monitoring Change in Mountainous Dry-heath Vegetation at a Regional ScaleUsing Multitemporal Landsat TM Data , 2003, Ambio.

[15]  C. Körner,et al.  Tree species diversity affects canopy leaf temperatures in a mature temperate forest , 2007 .

[16]  Seishi Ninomiya,et al.  Characterization of peach tree crown by using high-resolution images from an unmanned aerial vehicle , 2018, Horticulture Research.

[17]  Paul D. Colaizzi,et al.  Evaluation of Sensible Heat Flux and Evapotranspiration Estimates Using a Surface Layer Scintillometer and a Large Weighing Lysimeter , 2017, Sensors.

[18]  Guo Yu Qiu,et al.  Remotely monitoring evaporation rate and soil water status using thermal imaging and "three-temperatures model (3T Model)" under field-scale conditions. , 2010, Journal of Environmental Monitoring.

[19]  Lei Zhang,et al.  A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China , 2009 .

[20]  Tsutomu Yamanaka,et al.  Application of a two‐source model for partitioning evapotranspiration and assessing its controls in temperate grasslands in central Japan , 2014 .

[21]  Kevan B. Moffett,et al.  A method to calculate heterogeneous evapotranspiration using submeter thermal infrared imagery coupled to a stomatal resistance submodel , 2012 .

[22]  Ajay Sharda,et al.  Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse , 2016, Comput. Electron. Agric..

[23]  Fei Tian,et al.  Comparison of ET estimations by the three-temperature model, SEBAL model and eddy covariance observations , 2014 .

[24]  Yujiu Xiong,et al.  Estimation of evapotranspiration and its partition based on an extended three-temperature model and MODIS products , 2013 .

[25]  P. Zarco-Tejada,et al.  Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle , 2014, Precision Agriculture.

[26]  Francisco L. Santos,et al.  Assessing Olive Evapotranspiration Partitioning from Soil Water Balance and Radiometric Soil and Canopy Temperatures , 2018 .

[27]  Giovanni Mauromicale,et al.  Potato growth, yield and water productivity response to different irrigation and fertilization regimes , 2018 .

[28]  Y. Xiong,et al.  Is scale really a challenge in evapotranspiration estimation? A multi-scale study in the Heihe oasis using thermal remote sensing and the three-temperature model , 2016 .

[29]  M. Matthews,et al.  Water transport properties of cortical cells in roots of nitrogen- and phosphorus-deficient cotton seedlings. , 1989, Plant physiology.

[30]  Yinglong Chen,et al.  Nitrogen fertilization improved water-use efficiency of winter wheat through increasing water use during vegetative rather than grain filling , 2018 .

[31]  Jie Luo,et al.  Nitrogen metabolism of two contrasting poplar species during acclimation to limiting nitrogen availability , 2013, Journal of experimental botany.

[32]  Ling Tong,et al.  Improved application of the Penman–Monteith model using an enhanced Jarvis model that considers the effects of nitrogen fertilization on canopy resistance , 2019, Environmental and Experimental Botany.

[33]  Seishi Ninomiya,et al.  EasyPCC: Benchmark Datasets and Tools for High-Throughput Measurement of the Plant Canopy Coverage Ratio under Field Conditions , 2017, Sensors.

[34]  Klaus Kaiser,et al.  How relevant is recalcitrance for the stabilization of organic matter in soils , 2008 .

[35]  Yuxin Miao,et al.  Soil nitrate-N levels required for high yield maize production in the North China Plain , 2008, Nutrient Cycling in Agroecosystems.

[36]  Stephen R. Waddington,et al.  Soil and fertilizer constraints to wheat and rice production and their alleviation in six intensive cereal-based farming systems of the Indian sub-continent and China , 2014, Food Security.

[37]  N. Ramankutty,et al.  Closing yield gaps through nutrient and water management , 2012, Nature.

[38]  Z. Zhu,et al.  Nitrogen fertilizer use in China – Contributions to food production, impacts on the environment and best management strategies , 2002, Nutrient Cycling in Agroecosystems.

[39]  Li Li,et al.  [Effects of watering and nitrogen fertilization on the growth, grain yield, and water- and nitrogen use efficiency of winter wheat]. , 2013, Ying yong sheng tai xue bao = The journal of applied ecology.

[40]  Yuxin Miao,et al.  Long-term experiments for sustainable nutrient management in China. A review , 2011, Agronomy for Sustainable Development.

[41]  Juan Manuel Sánchez,et al.  Estimating high resolution evapotranspiration from disaggregated thermal images , 2016 .

[42]  Guo Yu Qiu,et al.  An improved methodology to measure evaporation from bare soil based on comparison of surface temperature with a dry soil surface , 1998 .

[43]  Ayse Irmak,et al.  Satellite‐based ET estimation in agriculture using SEBAL and METRIC , 2011 .

[44]  John A. Harrison,et al.  Escalating Worldwide use of Urea – A Global Change Contributing to Coastal Eutrophication , 2006 .

[45]  H. Medrano,et al.  Validation of thermal indices for water status identification in grapevine , 2014 .

[46]  Albert Olioso,et al.  Evaluation of the SPARSE Dual-Source Model for Predicting Water Stress and Evapotranspiration from Thermal Infrared Data over Multiple Crops and Climates , 2018, Remote. Sens..

[47]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

[48]  J. M. Costa,et al.  Canopy and soil thermal patterns to support water and heat stress management in vineyards , 2019, Agricultural Water Management.

[49]  Shaomin Liu,et al.  A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem , 2011 .

[50]  Jeff Baldock,et al.  Field measurements of bare soil evaporation and crop transpiration, and transpiration efficiency, for rainfed grain crops in Australia – A review , 2018, Agricultural Water Management.

[51]  John R. Miller,et al.  Estimating chlorophyll concentration in conifer needles with hyperspectral data: An assessment at the needle and canopy level , 2008 .

[52]  邱 国玉.  A new method for estimation of evapotranspiration , 1996 .