Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system

ABSTRACT In this study, high-resolution thermal imagery acquired with a small unmanned aerial vehicle (UAV) is used to map evapotranspiration (ET) at a grassland site in Luxembourg. The land surface temperature (LST) information from the thermal imagery is the key input to a one-source and two-source energy balance model. While the one-source model treats the surface as a single uniform layer, the two-source model partitions the surface temperature and fluxes into soil and vegetation components. It thus explicitly accounts for the different contributions of both components to surface temperature as well as turbulent flux exchange with the atmosphere. Contrary to the two-source model, the one-source model requires an empirical adjustment parameter in order to account for the effect of the two components. Turbulent heat flux estimates of both modelling approaches are compared to eddy covariance (EC) measurements using the high-resolution input imagery UAVs provide. In this comparison, the effect of different methods for energy balance closure of the EC data on the agreement between modelled and measured fluxes is also analysed. Additionally, the sensitivity of the one-source model to the derivation of the empirical adjustment parameter is tested. Due to the very dry and hot conditions during the experiment, pronounced thermal patterns developed over the grassland site. These patterns result in spatially variable turbulent heat fluxes. The model comparison indicates that both models are able to derive ET estimates that compare well with EC measurements under these conditions. However, the two-source model, with a more complex treatment of the energy and surface temperature partitioning between the soil and vegetation, outperformed the simpler one-source model in estimating sensible and latent heat fluxes. This is consistent with findings from prior studies. For the one-source model, a time-variant expression of the adjustment parameter (to account for the difference between aerodynamic and radiometric temperature) that depends on the surface-to-air temperature gradient yielded the best agreement with EC measurements. This study showed that the applied UAV system equipped with a dual-camera set-up allows for the acquisition of thermal imagery with high spatial and temporal resolution that illustrates the small-scale heterogeneity of thermal surface properties. The UAV-based thermal imagery therefore provides the means for analysing patterns of LST and other surface properties with a high level of detail that cannot be obtained by traditional remote sensing methods.

[1]  J. M. Norman,et al.  Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery , 2011 .

[2]  Pablo J. Zarco-Tejada,et al.  Estimating evaporation with thermal UAV data and two-source energy balance models , 2016 .

[3]  Jean-Paul Lhomme,et al.  Sensible Heat Flux-Radiometric Surface Temperature Relationship Over Sparse Vegetation: Parameterizing B-1 , 2000, Boundary-Layer Meteorology.

[4]  T. Foken.  The energy balance closure problem: an overview. , 2008, Ecological applications : a publication of the Ecological Society of America.

[5]  M. S. Moran,et al.  Determination of sensible heat flux over sparse canopy using thermal infrared data , 1989 .

[6]  Marco Dubbini,et al.  Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..

[7]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[8]  A. S. Thom,et al.  Momentum, mass and heat exchange of vegetation , 1972 .

[9]  J. Norman,et al.  Correcting eddy-covariance flux underestimates over a grassland , 2000 .

[10]  Ronglin Tang,et al.  Evaluating one- and two-source energy balance models in estimating surface evapotranspiration from Landsat-derived surface temperature and field measurements , 2013 .

[11]  S. Running,et al.  Regional evaporation estimates from flux tower and MODIS satellite data , 2007 .

[12]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation , 2007 .

[13]  Wilfried Brutsaert,et al.  Hydrology: An Introduction , 2005 .

[14]  Di Long,et al.  Intercomparison of remote sensing‐based models for estimation of evapotranspiration and accuracy assessment based on SWAT , 2008 .

[15]  J. Norman,et al.  Terminology in thermal infrared remote sensing of natural surfaces , 1995 .

[16]  Dai Matsushima,et al.  Relations between Aerodynamic Parameters of Heat Transfer and Thermal-Infrared Thermometry in the Bulk Surface Formulation , 2005 .

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

[18]  S. Christensen,et al.  Colour and shape analysis techniques for weed detection in cereal fields , 2000 .

[19]  F. Beyrich,et al.  Energy balance closure for the LITFASS-2003 experiment , 2010 .

[20]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[21]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[22]  S. Cosentino,et al.  Dry matter and qualitative characteristics of alfalfa as affected by harvest times and soil water content , 2011 .

[23]  Martha C. Anderson,et al.  A comparison of operational remote sensing-based models for estimating crop evapotranspiration , 2009 .

[24]  George E. Meyer,et al.  Plant species identification, size, and enumeration using machine vision techniques on near-binary images , 1993, Other Conferences.

[25]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology , 2007 .

[26]  Kelly R. Thorp,et al.  Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models , 2015 .

[27]  J. Norman,et al.  Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover , 1999 .

[28]  Eni G. Njoku,et al.  Examination of the difference between radiative and aerodynamic surface temperatures over sparsely vegetated surfaces , 1996 .

[29]  V. Caselles,et al.  Analysis of thermal infrared data from the Digital Airborne Imaging Spectrometer , 2001 .

[30]  Tiina Markkanen,et al.  Turbulence Statistics Inside and Over Forest: Influence on Footprint Prediction , 2003 .

[31]  J. Norman,et al.  Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature , 1995 .

[32]  J. Norman,et al.  Correcting eddy-covariance flux underestimates over a grassland , 2000 .

[33]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[34]  Martha C. Anderson,et al.  A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing , 1997 .

[35]  S. Islam,et al.  Estimation of surface evaporation map over Southern Great Plains using remote sensing data , 2001 .

[36]  Wade T. Crow,et al.  Cloud tolerance of remote-sensing technologies to measure land surfacetemperature , 2016 .

[37]  W. J. Massman,et al.  A model study of kBH−1 for vegetated surfaces using ‘localized near-field’ Lagrangian theory , 1999 .

[38]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Thomas J. Jackson,et al.  Utility of Remote Sensing–Based Two-Source Energy Balance Model under Low- and High-Vegetation Cover Conditions , 2005 .

[40]  V. Singh,et al.  A Two-source Trapezoid Model for Evapotranspiration (TTME) from satellite imagery , 2012 .

[41]  J. Norman,et al.  Surface flux estimation using radiometric temperature: A dual‐temperature‐difference method to minimize measurement errors , 2000 .

[42]  Anne Verhoef,et al.  Some Practical Notes on the Parameter kB−1 for Sparse Vegetation , 1997 .

[43]  Paul D. Colaizzi,et al.  Comparison of aerodynamic and radiometric surface temperature using precision weighing lysimeters , 2004, SPIE Optics + Photonics.

[44]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[45]  T. Foken,et al.  Update of a Footprint-Based Approach for the Characterisation of Complex Measurement Sites , 2006 .

[46]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[47]  Johanna Link,et al.  Developing and evaluating an aerial sensor platform (ASP) to collect multispectral data for deriving management decisions in precision farming , 2013 .

[48]  Martha C. Anderson,et al.  Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one- and two-source modeling schemes , 2015 .

[49]  Ronglin Tang,et al.  An intercomparison of three remote sensing-based energy balance models using Large Aperture Scintillometer measurements over a wheat–corn production region , 2011 .

[50]  William P. Kustas,et al.  An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, U.S.) during SMACEX , 2009 .

[51]  T. Streck,et al.  On the use of the post-closure methods uncertainty band to evaluate the performance of land surface models against eddy covariance flux data , 2014 .

[52]  Bruno Monteny,et al.  Sensible heat flux and radiometric surface temperature over sparse Sahelian vegetation II. A model for the kB−1 parameter , 1997 .

[53]  Jetse D. Kalma,et al.  Estimating evaporation from pasture using infrared thermometry: evaluation of a one-layer resistance model. , 1990 .

[54]  Frédéric Baret,et al.  Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots , 2008, Sensors.

[55]  Chenghu Zhou,et al.  A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data , 2009, Sensors.

[56]  Thomas Foken,et al.  Eddy-Covariance software TK3 , 2015 .

[57]  Bruno Monteny,et al.  Sensible heat flux and radiometric surface temperature over sparse Sahelian vegetation. I. An experimental analysis of the kB−1 parameter , 1997 .

[58]  J. A. Tolk,et al.  ET mapping for agricultural water management: present status and challenges , 2008, Irrigation Science.

[59]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[60]  E. Noordman,et al.  SEBAL model with remotely sensed data to improve water-resources management under actual field conditions , 2005 .

[61]  William P. Kustas,et al.  An intercomparison of the Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) modeling schemes , 2007 .

[62]  Albert Olioso,et al.  An empirical expression to relate aerodynamic and surface temperatures for use within single-source energy balance models , 2012 .

[63]  B. Hicks,et al.  Momentum, heat and water vapour transfer to and from natural and artificial surfaces , 1973 .

[64]  Eva Rubio,et al.  Thermal–infrared emissivities of natural surfaces: improvements on the experimental set-up and new measurements , 2003 .

[65]  Paul D. Colaizzi,et al.  Two-source energy balance model estimates of evapotranspiration using component and composite surface temperatures☆ , 2012 .

[66]  Sharon A. Robinson,et al.  Spatial Co-Registration of Ultra-High Resolution Visible, Multispectral and Thermal Images Acquired with a Micro-UAV over Antarctic Moss Beds , 2014, Remote. Sens..