Evapotranspiration Estimation with UAVs in Agriculture: A Review

Estimating evapotranspiration (ET) has been one of the most important research in agriculture recently because of water scarcity, growing population, and climate change. ET is the sum of evaporation from the soil and transpiration from the crops to the atmosphere. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, people use weighing lysimeters, Bowen ratio, eddy covariance and many other methods to estimate ET. However, these ET methods are points or location-specific measurements and cannot be extended to a large scale of ET estimation. With the advent of satellites technology, remote sensing images can provide spatially distributed measurements. The satellites multispectral images spatial resolution, however, is in the range of meters, which is often not enough for crops with clumped canopy structure such as trees and vines. And, the timing or frequency of satellites overpass is not always enough to meet the research or water management needs. The Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, can help solve these spatial and temporal challenges. Lightweight cameras and sensors can be mounted on drones and take high-resolution images on a large scale of field. Compared with satellites images, the spatial resolution of UAVs’ images can be as high as 1 cm per pixel. And, people can fly a drone at any time if the weather condition is good. Cloud cover is less of a concern than satellite remote sensing. Both temporal and spatial resolution is highly improved by drones. In this paper, a review of different UAVs based approaches of ET estimations are presented. Different modified models used by UAVs, such as Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), Two-source energy balance (TSEB) model, etc, are also discussed.

[1]  E. Fereres,et al.  Improving the precision of irrigation in a pistachio farm using an unmanned airborne thermal system , 2014, Irrigation Science.

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

[3]  B. Séguin,et al.  Using midday surface temperature to estimate daily evaporation from satellite thermal IR data , 1983 .

[4]  K. Swain,et al.  Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. , 2010 .

[5]  Albert Olioso,et al.  Mapping surface fluxes using airborne visible, near infrared, thermal infrared remote sensing data and a spatialized surface energy balance model , 2002 .

[6]  Thomas J. Schmugge,et al.  An interpretation of methodologies for indirect measurement of soil water content , 1995 .

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

[8]  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.

[9]  Frank Veroustraete,et al.  Assessment of Evapotranspiration and Soil Moisture Content Across Different Scales of Observation , 2008, Sensors.

[10]  Arko Lucieer,et al.  An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds , 2012, Remote. Sens..

[11]  YangQuan Chen,et al.  Low-cost UAV-based thermal infrared remote sensing: Platform, calibration and applications , 2010, Proceedings of 2010 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications.

[12]  Brandon Stark,et al.  A detailed field study of direct correlations between ground truth crop water stress and normalized difference vegetation index (NDVI) from small unmanned aerial system (sUAS) , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[13]  Yongqiang Zhang,et al.  Determination of daily evaporation and evapotranspiration of winter wheat and maize by large-scale weighing lysimeter and micro-lysimeter , 2002 .

[14]  Suyoung Park.  Estimating plant water stress and evapotranspiration using very-high-resolution (VHR) UAV imagery , 2018 .

[15]  S. Myint,et al.  Quantifying Outdoor Water Consumption of Urban Land Use/Land Cover: Sensitivity to Drought , 2014, Environmental Management.

[16]  William P. Kustas,et al.  A two‐source approach for estimating turbulent fluxes using multiple angle thermal infrared observations , 1997 .

[17]  Manal Elarab,et al.  The application of unmanned aerial vehicle to precision agriculture: Chlorophyll, nitrogen, and evapotranspiration estimation , 2015 .

[18]  YangQuan Chen,et al.  A detailed study on accuracy of uncooled thermal cameras by exploring the data collection workflow , 2018, Commercial + Scientific Sensing and Imaging.

[19]  M. S. Moran,et al.  Assessing the Spatial Distribution of Evapotranspiration Using Remotely Sensed Inputs , 1991 .

[20]  G. Campbell,et al.  An Introduction to Environmental Biophysics , 1977 .

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

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

[23]  Vijay P. Singh,et al.  Evaluation of three complementary relationship evapotranspiration models by water balance approach to estimate actual regional evapotranspiration in different climatic regions , 2005 .

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

[25]  Pamela L. Nagler,et al.  Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers , 2005 .

[26]  Tiebiao Zhao,et al.  Quantifying Almond Water Stress Using Unmanned Aerial Vehicles (UAVs): Correlation of Stem Water Potential and Higher Order Moments of Non-Normalized Canopy Distribution , 2017 .

[27]  J. Norman,et al.  A Two-Source Energy Balance Approach Using Directional Radiometric Temperature Observations for Sparse Canopy Covered Surfaces , 2000 .

[28]  Arko Lucieer,et al.  HyperUAS—Imaging Spectroscopy from a Multirotor Unmanned Aircraft System , 2014, J. Field Robotics.

[29]  C. M. U. Nealeb,et al.  Upscaling ground observations of vegetation water content , canopy height , and leaf area index during SMEX 02 using aircraft and Landsat imagery , 2004 .

[30]  R D Jackson,et al.  Estimating Evaporation: A Technique Adaptable to Remote Sensing , 1975, Science.

[31]  Arko Lucieer,et al.  Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery , 2012, Remote. Sens..

[32]  Paul D. Colaizzi,et al.  Radiation Model for Row Crops: I. Geometric View Factors and Parameter Optimization , 2012 .

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

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

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

[36]  James L. Wright,et al.  New Evapotranspiration Crop Coefficients , 1982 .

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

[38]  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 .

[39]  Luiz Eduardo Oliveira E. Cruz de Aragão,et al.  A MODIS-Based Energy Balance to Estimate Evapotranspiration for Clear-Sky Days in Brazilian Tropical Savannas , 2012, Remote. Sens..

[40]  Marvin E. Jensen,et al.  ASCE's standardized reference evapotranspiration equation. , 2001 .

[41]  Arko Lucieer,et al.  Development of a UAV-LiDAR System with Application to Forest Inventory , 2012, Remote. Sens..

[42]  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 .

[43]  Wim G.M. Bastiaanssen,et al.  Satellite surveillance of evaporative depletion across the Indus Basin , 2002 .

[44]  P. Zarco-Tejada,et al.  A PRI-based water stress index combining structural and chlorophyll effects: Assessment using diurnal narrow-band airborne imagery and the CWSI thermal index , 2013 .

[45]  Anthony Morse,et al.  A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning , 2005 .

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

[47]  Martha C. Anderson,et al.  The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources , 2017 .

[48]  YangQuan Chen,et al.  A framework of optimal remote sensing using small unmanned aircraft systems , 2016, 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA).

[49]  P. J. Watts,et al.  Evapotranspiration -- How good is the Bowen ratio method? , 1984 .

[50]  Martha C. Anderson,et al.  Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery , 2004 .

[51]  A. Montibeller.  Estimating energy fluxes and evapotranspiration of corn and soybean with an unmanned aircraft system in Ames, Iowa , 2017 .

[52]  Thomas B. Moeslund,et al.  Thermal cameras and applications: a survey , 2013, Machine Vision and Applications.

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

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

[55]  YangQuan Chen,et al.  Comparing U-Net convolutional network with mask R-CNN in the performances of pomegranate tree canopy segmentation , 2018, Asia-Pacific Remote Sensing.

[56]  Dong Wang,et al.  Tree Canopy Differentiation Using Instance-aware Semantic Segmentation , 2018, 2018 ASABE International Meeting.

[57]  Martha C. Anderson,et al.  A data fusion approach for mapping daily evapotranspiration at field scale , 2013 .

[58]  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 .

[59]  W. Bastiaanssen,et al.  A remote sensing surface energy balance algorithm for land (SEBAL). , 1998 .

[60]  Radoslaw Guzinski,et al.  Remotely sensed land-surface energy fluxes at sub-field scale in heterogeneous agricultural landscape and coniferous plantation , 2014 .

[61]  S. Idso,et al.  Wheat canopy temperature: A practical tool for evaluating water requirements , 1977 .

[62]  Norman J. Rosenberg,et al.  A Resistance Model to Predict Evapotranspiration and Its Application to a Sugar Beet Field1 , 1973 .

[63]  C. Neale,et al.  Use of Remote Sensing to Generate Crop Coefficient and Estimate Actual Crop Evapotranspiration , 2015 .

[64]  Richard L. Snyder,et al.  Pan Evaporation to Reference Evapotranspiration Conversion Methods , 2002 .

[65]  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 .

[66]  D. Quattrochi,et al.  Thermal infrared remote sensing for analysis of landscape ecological processes: methods and applications , 1999, Landscape Ecology.

[67]  W. Bastiaanssen.  SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey , 2000 .

[68]  YangQuan Chen,et al.  Melon yield prediction using small unmanned aerial vehicles , 2017, Commercial + Scientific Sensing and Imaging.

[69]  P. Pinter,et al.  Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index , 2003, Irrigation Science.

[70]  Pablo J. Zarco-Tejada,et al.  High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..

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

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

[73]  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..

[74]  Colm P. O'Donnell,et al.  Applications of thermal imaging in food quality and safety assessment , 2010 .

[75]  M. Tasumi.  Progress in operational estimation of regional evapotranspiration using satellite imagery , 2003 .

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

[77]  Cristina E. Davis,et al.  Advanced methods of plant disease detection. A review , 2014, Agronomy for Sustainable Development.

[78]  P. Wetzel,et al.  Evapotranspiration from Nonuniform Surfaces: A First Approach for Short-Term Numerical Weather Prediction , 1988 .

[79]  L. Fritschen.  ACCURACY OF EVAPOTRANSPIRATION DETERMINATIONS BY THE BOWEN RATIO METHOD , 1965 .

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

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

[82]  David Hernández-López,et al.  Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture , 2017, Sensors.

[83]  Brandon Stark,et al.  More Reliable Crop Water Stress Quantification Using Small Unmanned Aerial Systems (sUAS) , 2016 .

[84]  Aiman Soliman,et al.  Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data , 2013, Remote. Sens..

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

[86]  Gabriel B. Senay,et al.  Comparison of Four Different Energy Balance Models for Estimating Evapotranspiration in the Midwestern United States , 2015 .

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

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

[89]  William P. Kustas,et al.  Use of remote sensing for evapotranspiration monitoring over land surfaces , 1996 .

[90]  H. Jones,et al.  Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. , 2009, Functional plant biology : FPB.

[91]  M. Tasumi,et al.  METRIC: mapping evapotranspiration at high resolution – applications manual for Landsat satellite imagery , 2005 .

[92]  Eija Honkavaara,et al.  Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level , 2015, Remote. Sens..

[93]  Yuei-An Liou,et al.  Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review , 2014 .

[94]  Zheng Liu,et al.  Evapotranspiration estimation based on the SEBAL model in the Nansi Lake Wetland of China , 2011, Math. Comput. Model..

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

[96]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[97]  Z. Su,et al.  Drought assessment by evapotranspiration mapping in Twente , 2012 .

[98]  Ayse Kilic,et al.  Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index , 2013, Remote. Sens..