Optical-based and thermal-based surface conductance and actual evapotranspiration estimation, an evaluation study in the North China Plain

Abstract Accurate estimation of surface conductance (G s ) and evapotranspiration (ET) from remote sensing data has received increasing interest, but the data interpretation method requires further development. The objective of this study is to evaluate the capability of optical and thermal information to quantify G s and ET in the frame of the Penman-Monteith model. We evaluated the three remote sensing data-based retrievals of daily G s and ET using Moderate Resolution Imaging Spectroradiometer (MODIS) data and eddy covariance measurements at three sites in the North China Plain. The G s models were established on the basis of (1) single vegetation index (VI), including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), (2) temperature vegetation dryness index (TVDI), and (3) combination of VI and TVDI. The results demonstrated that the combination of NDVI and theoretical TVDI achieved the best accuracy of quantifying G s and ET. The single VI-based model also performed well. The empirical TVDI-based model failed to estimate G s and ET since there existed significant uncertainties in the calculation of the dry and wet edge. In contrast, the theoretical TVDI with an apparent seasonal pattern was of more value to acquire G s and ET due to its explicit physical mechanism. From this study, the combination of VI and TVDI, as well as single VI, were recommended to build alternative approaches to acquiring ET. These G s models highly rely on remote sensing data and thus show promising potential in regional-scale application.

[1]  D. Baldocchi,et al.  Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites , 2008 .

[2]  Toshio Koike,et al.  Surface Flux Parameterization in the Tibetan Plateau , 2003 .

[3]  A. Chehbouni,et al.  Modified Penman–Monteith equation for monitoring evapotranspiration of wheat crop: Relationship between the surface resistance and remotely sensed stress index , 2017 .

[4]  Shaozhong Kang,et al.  Comparison of several surface resistance models for estimating crop evapotranspiration over the entire growing season in arid regions , 2015 .

[5]  S. Seneviratne,et al.  Recent decline in the global land evapotranspiration trend due to limited moisture supply , 2010, Nature.

[6]  Lydie Guilioni,et al.  On the relationships between stomatal resistance and leaf temperatures in thermography , 2008 .

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

[8]  S. Shang,et al.  A hybrid dual‐source scheme and trapezoid framework–based evapotranspiration model (HTEM) using satellite images: Algorithm and model test , 2013 .

[9]  I Leinonen,et al.  Estimating stomatal conductance with thermal imagery. , 2006, Plant, cell & environment.

[10]  W. Oechel,et al.  Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model , 2012 .

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

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

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

[14]  Bo-Hui Tang,et al.  An application of the Ts–VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: Implementation and validation , 2010 .

[15]  Antonio Motisi,et al.  A time domain triangle method approach to estimate actual evapotranspiration: Application in a Mediterranean region using MODIS and MSG-SEVIRI products , 2016 .

[16]  S. Liang,et al.  Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. , 2017 .

[17]  Alfredo Huete,et al.  Passive microwave and optical index approaches for estimating surface conductance and evapotranspiration in forest ecosystems , 2015 .

[18]  S. Kanae,et al.  Global Hydrological Cycles and World Water Resources , 2006, Science.

[19]  J. Monteith,et al.  Principles of Environmental Physics , 2014 .

[20]  E. Schulze,et al.  Relationships among Maximum Stomatal Conductance, Ecosystem Surface Conductance, Carbon Assimilation Rate, and Plant Nitrogen Nutrition: A Global Ecology Scaling Exercise , 1994 .

[21]  Amélie Rajaud,et al.  A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman‐Monteith equation , 2008 .

[22]  Alfred Stein,et al.  Validation of ETWatch using field measurements at diverse landscapes: A case study in Hai Basin of China , 2012 .

[23]  Shaomin Liu,et al.  Measurements of evapotranspiration from eddy-covariance systems and large aperture scintillometers in the Hai River Basin, China , 2013 .

[24]  Fengmei Yao,et al.  Using precipitation, vertical root distribution, and satellite‐retrieved vegetation information to parameterize water stress in a Penman‐Monteith approach to evapotranspiration modeling under Mediterranean climate , 2017 .

[25]  Songhao Shang,et al.  A novel algorithm to assess gross primary production for terrestrial ecosystems from MODIS imagery , 2013 .

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

[27]  R. Kormann,et al.  An Analytical Footprint Model For Non-Neutral Stratification , 2001 .

[28]  Toshio Koike,et al.  Turbulent flux transfer over bare-soil surfaces: Characteristics and parameterization , 2008 .

[29]  A. Huete,et al.  Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance , 2013 .

[30]  Mario Minacapilli,et al.  Modelling bulk surface resistance by MODIS data and assessment of MOD16A2 evapotranspiration product in an irrigation district of Southern Italy , 2016 .

[31]  S. Liang,et al.  An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature, and soil moisture , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[32]  Inge Sandholt,et al.  Accuracy of the Temperature-Vegetation Dryness Index using MODIS under water-limited vs. energy-limited evapotranspiration conditions , 2014 .

[33]  J. C. Price.  Using spatial context in satellite data to infer regional scale evapotranspiration , 1990 .

[34]  Hao Sun,et al.  Comparison of Three Theoretical Methods for Determining Dry and Wet Edges of the LST/FVC Space: Revisit of Method Physics , 2017, Remote. Sens..

[35]  S. Idso,et al.  Canopy temperature as a crop water stress indicator , 1981 .

[36]  C. Simmons,et al.  Optimization of canopy conductance models from concurrent measurements of sap flow and stem water potential on Drooping Sheoak in South Australia , 2013 .

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

[38]  T. Vesala,et al.  On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm , 2005 .

[39]  Suat Irmak,et al.  On the dynamics of canopy resistance: Generalized linear estimation and relationships with primary micrometeorological variables , 2010 .

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

[41]  Hamlyn G. Jones,et al.  Use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces , 1999 .

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

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

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

[45]  Maosheng Zhao,et al.  Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .

[46]  A. Prata.  A new long‐wave formula for estimating downward clear‐sky radiation at the surface , 1996 .

[47]  Yuanyuan Zha,et al.  Estimation of actual irrigation amount and its impact on groundwater depletion: A case study in the Hebei Plain, China , 2016 .

[48]  Ray Leuning,et al.  Scaling of potential evapotranspiration with MODIS data reproduces flux observations and catchment water balance observations across Australia , 2009 .

[49]  Robert J. Gurney,et al.  The theoretical relationship between foliage temperature and canopy resistance in sparse crops , 1990 .

[50]  Andrew E. Suyker,et al.  Gap filling strategies for long term energy flux data sets , 2001 .

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

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

[53]  L. Fenstermaker,et al.  On the consequences of the energy imbalance for calculating surface conductance to water vapour. , 2009, Agricultural and forest meteorology.

[54]  P. Jarvis.  The Interpretation of the Variations in Leaf Water Potential and Stomatal Conductance Found in Canopies in the Field , 1976 .

[55]  Xiaotong Zhang,et al.  Using Bayesian model averaging to estimate terrestrial evapotranspiration in China , 2015 .

[56]  S. Running,et al.  A continuous satellite‐derived global record of land surface evapotranspiration from 1983 to 2006 , 2010 .

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

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

[59]  Allen S. Hope,et al.  Estimation of wheat canopy resistance using combined remotely sensed spectral reflectance and thermal observations , 1988 .

[60]  J. Stewart.  Modelling surface conductance of pine forest , 1988 .

[61]  A. Huete,et al.  Estimation of latent heat flux over savannah vegetation across the North Australian Tropical Transect from multiple sensors and global meteorological data , 2017 .

[62]  Hamlyn G. Jones,et al.  Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling , 1999 .

[63]  R. G. Smith,et al.  Inferring stomatal resistance of sparse crops from infrared measurements of foliage temperature , 1988 .

[64]  R. Dickinson,et al.  Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development , 2010 .

[65]  S. Liang,et al.  Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations , 2014 .

[66]  B. Scanlon,et al.  Energy/water budgets and productivity of the typical croplands irrigated with groundwater and surface water in the North China Plain , 2013 .

[67]  Dawen Yang,et al.  Interannual and seasonal variability in evapotranspiration and energy partitioning over an irrigated cropland in the North China Plain , 2010 .