Advances in the Remote Sensing of Terrestrial Evaporation

Characterizing the terrestrial carbon, water, and energy cycles depends strongly on a capacity to accurately reproduce the spatial and temporal dynamics of land surface evaporation. For this, and many other reasons, monitoring terrestrial evaporation across multiple space and time scales has been an area of focused research for a number of decades. Much of this activity has been supported by developments in satellite remote sensing, which have been leveraged to deliver new process insights, model development and methodological improvements. In this Special Issue, published contributions explored a range of research topics directed towards the enhanced estimation of terrestrial evaporation. Here we summarize these cutting-edge efforts and provide an overview of some of the state-of-the-art approaches for retrieving this key variable. Some perspectives on outstanding challenges, issues, and opportunities are also presented.

[1]  Filipe Aires,et al.  Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence. , 2017, Biogeosciences.

[2]  Hideki Kobayashi,et al.  Integration of MODIS land and atmosphere products with a coupled‐process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales , 2011 .

[3]  Matthew F. McCabe,et al.  The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets , 2015 .

[4]  Pierre Gentine,et al.  Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates , 2019, Remote. Sens..

[5]  Jie Cheng,et al.  Using Very High Resolution Thermal Infrared Imagery for More Accurate Determination of the Impact of Land Cover Differences on Evapotranspiration in an Irrigated Agricultural Area , 2019, Remote. Sens..

[6]  Pierre Gentine,et al.  Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges , 2018, Annals of the New York Academy of Sciences.

[7]  Niko E. C. Verhoest,et al.  Towards Estimating Land Evaporation at Field Scales Using GLEAM , 2018, Remote. Sens..

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

[9]  Markus Reichstein,et al.  Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product , 2018, Earth System Science Data.

[10]  Bruno Aragon,et al.  CubeSats Enable High Spatiotemporal Retrievals of Crop-Water Use for Precision Agriculture , 2018, Remote. Sens..

[11]  C. Frankenberg,et al.  New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity , 2011, Geophysical Research Letters.

[12]  T. McVicar,et al.  Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017 , 2019, Remote Sensing of Environment.

[13]  Kuolin Hsu,et al.  HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community , 2018, Hydrology and Earth System Sciences.

[14]  Martha C. Anderson,et al.  Microwave implementation of two-source energy balance approach for estimating evapotranspiration. , 2017, Hydrology and earth system sciences.

[15]  G. Rasul,et al.  The nexus approach to water–energy–food security: an option for adaptation to climate change , 2016 .

[16]  Bruno Aragon,et al.  CubeSats in Hydrology: Ultrahigh‐Resolution Insights Into Vegetation Dynamics and Terrestrial Evaporation , 2017 .

[17]  Yi Y. Liu,et al.  Multi-decadal trends in global terrestrial evapotranspiration and its components , 2016, Scientific Reports.

[18]  Matthew F. McCabe,et al.  Partitioning of evapotranspiration in remote sensing-based models , 2018, Agricultural and Forest Meteorology.

[19]  Giorgos Mallinis,et al.  On the Use of Unmanned Aerial Systems for Environmental Monitoring , 2018, Remote. Sens..

[20]  Joshua B. Fisher,et al.  Vegetation Water Use Based on a Thermal and Optical Remote Sensing Model in the Mediterranean Region of Doñana , 2018, Remote. Sens..

[21]  Sandra C. Freitas,et al.  The Satellite Application Facility for Land Surface Analysis , 2011 .

[22]  Martha C. Anderson,et al.  The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields. , 2016 .

[23]  L. Guanter,et al.  Modeling canopy conductance and transpiration from solar-induced chlorophyll fluorescence , 2019, Agricultural and Forest Meteorology.

[24]  Jiancheng Shi,et al.  The Future of Earth Observation in Hydrology. , 2017, Hydrology and earth system sciences.

[25]  Andreas Colliander,et al.  SMAP soil moisture improves global evapotranspiration , 2018, Remote Sensing of Environment.

[26]  Simon J. Hook,et al.  Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty - A Sensitivity Study Using AmeriFlux Data , 2019, Remote. Sens..

[27]  Matthew F. McCabe,et al.  Impact of model structure and parameterization on Penman-Monteith type evaporation models , 2015 .

[28]  Wilfried Brutsaert,et al.  Regional surface fluxes from satellite-derived surface temperatures (AVHRR) and radiosonde profiles , 1992 .

[29]  Philip N. Slater,et al.  Mapping surface energy balance components by combining landsat thematic mapper and ground-based meteorological data , 1989 .

[30]  Simona Consoli,et al.  Combining Electrical Resistivity Tomography and Satellite Images for Improving Evapotranspiration Estimates of Citrus Orchards , 2019, Remote. Sens..

[31]  Y. Xue,et al.  Satellite Chlorophyll Fluorescence and Soil Moisture Observations Lead to Advances in the Predictive Understanding of Global Terrestrial Coupled Carbon‐Water Cycles , 2018 .

[32]  A. Arneth,et al.  Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations , 2011 .

[33]  Simon J. Hook,et al.  ECOSTRESS, A NASA Earth-Ventures Instrument for studying links between the water cycle and plant health over the diurnal cycle , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[34]  Alicia M. Kinoshita,et al.  Estimating Evapotranspiration in a Post-Fire Environment Using Remote Sensing and Machine Learning , 2018, Remote. Sens..

[35]  Matthew F. McCabe,et al.  Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data , 2018, Remote. Sens..

[36]  R. Dickinson,et al.  A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability , 2011 .

[37]  M. Qureshi,et al.  Global water crisis and future food security in an era of climate change , 2010 .

[38]  Christopher Small,et al.  Spectral Mixture Analysis as a Unified Framework for the Remote Sensing of Evapotranspiration , 2018, Remote. Sens..

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

[40]  T. Holmes,et al.  Global land-surface evaporation estimated from satellite-based observations , 2010 .

[41]  Matthew F. McCabe,et al.  The GEWEX LandFlux project: evaluation of model evaporation using tower-based and globally gridded forcing data , 2015 .

[42]  Sujay V. Kumar,et al.  Attribution of Flux Partitioning Variations between Land Surface Models over the Continental U.S , 2018, Remote. Sens..

[43]  Michael E. Schaepman,et al.  Sentinels for science: potential of Sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land , 2012 .

[44]  Yun Yang,et al.  Field-Scale Assessment of Land and Water Use Change over the California Delta Using Remote Sensing , 2018, Remote. Sens..

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

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

[47]  Carlos Jimenez,et al.  Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models , 2018, Remote. Sens..