Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems

ABSTRACT Spatially distributed high-resolution data of land surface temperature (LST) and evapotranspiration (ET) are important information for crop water management and other applications in the agricultural sector. While satellite data can provide LST high-resolution data of 100 m, the current development of unmanned aerial systems (UAS) and affordable low-weight thermal cameras allows LST and subsequent ET to be derived at resolutions down to centimetre scale. In this study, UAS-based images in the thermal infrared (TIR) and visible spectral range were collected over a managed temperate grassland in July 2016 at the Terrestrial Environmental Observatories Networks TERENO preAlpine observatory site at Fendt, Germany. The UAS set-up included a lightweight thermal camera (Optris Pi Lightweight) and a regular digital camera (Sony α 6000) that allowed for the acquisition of thermal and optical images with a ground resolution of 5 cm and 1 cm, respectively. Three TIR-based ET models of different complexity were applied and the resulting ET estimates were compared to the Eddy covariance (EC) observations of turbulent energy fluxes and also to the evaporative fraction. While the Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature (DATTUTDUT) model and the Triangle Method belong to the group of simpler contextual models, the Two-Source Energy Balance (TSEB) model incorporates a more physically based formulation of the surface energy balance. In addition to the comparison of UAS-based estimates of latent heat fluxes to EC observations, the effect of the spatial resolution of the model imagery input on the modelled results was analysed by running the models with imagery from the native resolution of the acquired images to resolutions that were aggregated up to 30 m. The results show that both contextual models are sensitive to the input image resolution and that the agreement with the EC observations improves with increasing image resolution. The TSEB model assumes that LST pixels represent a mixed signal of the soil and canopy components, thus an image resolution coarse enough to ensure this assumption should be chosen. However, with the exception of the native image resolution of 5 cm, we found no effect of image resolution on the spatially weighted mean TSEB estimates. For the studied grassland, the comparison of model estimates with EC observations indicates that all three models are able to reproduce observed energy fluxes with comparable accuracy with mean absolute errors of ET between 20 and 40 W m−2. The TSEB model showed larger deviations from the reference observations under cloudy conditions with rapid fluctuations of LST within the 30 min averaging period for EC. The two contextual models yielded similar results for most of the flights. The good performance of the DATTUTDUT model, which had the lowest input requirements of the three models, is especially promising in view of the application of UAS for routine near-real-time ET monitoring.

[1]  Rasmus Fensholt,et al.  Inter-comparison of energy balance and hydrological models for land surface energy flux estimation over a whole river catchment , 2014 .

[2]  Katja Brinkmann,et al.  Monitoring of crop biomass using true colour aerial photographs taken from a remote controlled hexacopter , 2015 .

[3]  V. Singh,et al.  Deriving theoretical boundaries to address scale dependencies of triangle models for evapotranspiration estimation , 2012 .

[4]  Karsten Schulz,et al.  Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system , 2017, International journal of remote sensing.

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

[6]  H. Nieto,et al.  Crop water stress maps for an entire growing season from visible and thermal UAV imagery , 2016 .

[7]  Feng Gao,et al.  Simple method for retrieving leaf area index from Landsat using MODIS leaf area index products as reference , 2012 .

[8]  C. Daughtry,et al.  Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status , 2005, Precision Agriculture.

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

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

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

[12]  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 .

[13]  Mohammadmehdi Saberioon,et al.  Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale , 2014, Int. J. Appl. Earth Obs. Geoinformation.

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

[15]  Pablo J. Zarco-Tejada,et al.  Using radiometric surface temperature for surface energy flux estimation in Mediterranean drylands from a two-source perspective , 2013 .

[16]  Mona Hess,et al.  Structure from Motion: Photogrammetry , 2017 .

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

[18]  Nathaniel A. Brunsell,et al.  Regional evapotranspiration from an image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous US , 2017 .

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

[20]  T. McMahon,et al.  Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis , 2013 .

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

[22]  Mark A. Fonstad,et al.  Topographic structure from motion: a new development in photogrammetric measurement , 2013 .

[23]  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 .

[24]  Rasmus Fensholt,et al.  Combining the triangle method with thermal inertia to estimate regional evapotranspiration — Applied to MSG-SEVIRI data in the Senegal River basin , 2008 .

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

[26]  J. Monteith Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.

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

[28]  M. Friedl,et al.  Diurnal Covariation in Soil Heat Flux and Net Radiation , 2003 .

[29]  Irena Hajnsek,et al.  A Network of Terrestrial Environmental Observatories in Germany , 2011 .

[30]  M. S. Moran,et al.  Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index , 1994 .

[31]  Inge Sandholt,et al.  Validation and scale dependencies of the triangle method for the evaporative fraction estimation over heterogeneous areas , 2014 .

[32]  Thomas Foken,et al.  Evaluation of six parameterization approaches for the ground heat flux , 2007 .

[33]  Ramakrishna R. Nemani,et al.  An operational remote sensing algorithm of land surface evaporation , 2003 .

[34]  W. Kustas,et al.  Utility of an Automated Thermal-Based Approach for Monitoring Evapotranspiration , 2015, Acta Geophysica.

[35]  E. Boegh,et al.  Estimating transpiration rates in a Danish agricultural area using landsat thermal mapper data , 2000 .

[36]  Ji Zhou,et al.  Application of remote sensing-based two-source energy balance model for mapping field surface fluxes with composite and component surface temperatures , 2016 .

[37]  W. Kustas,et al.  A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e , 1997 .

[38]  Amaury Tilmant,et al.  A New Temperature-Vegetation Triangle Algorithm with Variable Edges (TAVE) for Satellite-Based Actual Evapotranspiration Estimation , 2016, Remote. Sens..

[39]  W. Kustas,et al.  Impact of Using Different Time-Averaged Inputs for Estimating Sensible Heat Flux of Riparian Vegetation Using Radiometric Surface Temperature , 2002 .

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

[41]  Olivier Merlin,et al.  Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate , 2013 .

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

[43]  L. Jiang,et al.  An intercomparison of regional latent heat flux estimation using remote sensing data , 2003 .

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

[45]  Tristan R. H. Goodbody,et al.  Assessing the status of forest regeneration using digital aerial photogrammetry and unmanned aerial systems , 2018 .

[46]  William P. Kustas,et al.  Using a thermal-based two source energy balance model with time-differencing to estimate surface energy fluxes with day–night MODIS observations , 2013 .

[47]  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.

[48]  F. López-Granados,et al.  Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV , 2014 .

[49]  Mark W. Smith,et al.  Structure from motion photogrammetry in physical geography , 2016 .

[50]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[51]  Paul D. Colaizzi,et al.  Evaluating the two-source energy balance model using local thermal and surface flux observations in a strongly advective irrigated agricultural area , 2012 .

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

[53]  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 .

[54]  T. Murray,et al.  Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements. II. Diurnal shape of soil heat flux , 2007 .

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

[56]  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..

[57]  H. Schmid,et al.  Reduced snow cover affects productivity of upland temperate grasslands , 2017 .

[58]  Stefan Emeis,et al.  Simultaneous multicopter-based air sampling and sensing of meteorological variables , 2017 .

[59]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

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

[61]  T. Carlson,et al.  Uncertainties in latent heat flux measurement and estimation: implications for using a simplified approach with remote sensing data , 2004 .

[62]  W. Kendall Melville,et al.  Development and Testing of Instrumentation for UAV-Based Flux Measurements within Terrestrial and Marine Atmospheric Boundary Layers , 2013 .

[63]  Samuel Ortega-Farías,et al.  Estimation of Energy Balance Components over a Drip-Irrigated Olive Orchard Using Thermal and Multispectral Cameras Placed on a Helicopter-Based Unmanned Aerial Vehicle (UAV) , 2016, Remote. Sens..

[64]  G. Meyer,et al.  Verification of color vegetation indices for automated crop imaging applications , 2008 .

[65]  Noboru Noguchi,et al.  Monitoring of Wheat Growth Status and Mapping of Wheat Yield's within-Field Spatial Variations Using Color Images Acquired from UAV-camera System , 2017, Remote. Sens..

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

[67]  R. Allen,et al.  Estimation of olive evapotranspiration using multispectral and thermal sensors placed aboard an unmanned aerial vehicle , 2017 .

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

[69]  Jon Nielsen,et al.  Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? , 2016 .

[70]  Irena Hajnsek,et al.  The ScaleX campaign: scale-crossing land-surface and boundary layer processes in the TERENO-preAlpine observatory , 2017 .

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

[72]  V. Pampalone,et al.  Measuring rill erosion using structure from motion: A plot experiment , 2017 .

[73]  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 .

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

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

[76]  Laurent Borgniet,et al.  Using UAS optical imagery and SfM photogrammetry to characterize the surface grain size of gravel bars in a braided river (Vénéon River, French Alps) , 2017 .

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

[78]  Samuel C. Zipper,et al.  Using evapotranspiration to assess drought sensitivity on a subfield scale with HRMET, a high resolution surface energy balance model , 2014 .

[79]  J. Wallace,et al.  Evaporation from sparse crops‐an energy combination theory , 2007 .

[80]  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 .

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

[82]  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 .

[83]  T. Carlson,et al.  Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models , 1995 .

[84]  Martha C. Anderson,et al.  Advances in thermal infrared remote sensing for land surface modeling , 2009 .

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

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

[87]  Hans Peter Schmid,et al.  A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements , 2013 .

[88]  Martha C. Anderson,et al.  Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion , 2014 .

[89]  T. Carlson An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery , 2007, Sensors (Basel, Switzerland).