Assessment of an Automated Calibration of the SEBAL Algorithm to Estimate Dry-Season Surface-Energy Partitioning in a Forest-Savanna Transition in Brazil

[1]  C. Santos,et al.  Analysis of the evaporative fraction using eddy covariance and remote sensing techniques , 2010 .

[2]  J. Foley,et al.  Trends in the hydrologic cycle of the Amazon basin , 1999 .

[3]  S. Shang,et al.  Remote sensing temporal and spatial patterns of evapotranspiration and the responses to water management in a large irrigation district of North China , 2012 .

[4]  C. Rennó,et al.  Atmosphere and hydrological controls of the evapotranspiration over a floodplain forest in the Bananal Island region, Amazonia , 2009 .

[5]  K.M.P.S Bandara,et al.  Monitoring irrigation performance in Sri Lanka with high-frequency satellite measurements during the dry season , 2003 .

[6]  Marcos Heil Costa,et al.  Effects of Amazon and Central Brazil deforestation scenarios on the duration of the dry season in the arc of deforestation , 2010 .

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

[8]  Matthew F. McCabe,et al.  The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms , 2015 .

[9]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[10]  Lars Ribbe,et al.  Satellite-based evapotranspiration over Gezira Irrigation Scheme, Sudan: A comparative study , 2016 .

[11]  Yonggwan Lee,et al.  The Modified SEBAL for Mapping Daily Spatial Evapotranspiration of South Korea Using Three Flux Towers and Terra MODIS Data , 2016, Remote. Sens..

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

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

[14]  Jungho Im,et al.  A new optimized algorithm for automating endmember pixel selection in the SEBAL and METRIC models , 2017 .

[15]  Justin L. Huntington,et al.  Assessing Calibration Uncertainty and Automation for Estimating Evapotranspiration from Agricultural Areas Using METRIC , 2013 .

[16]  Nathaniel A. Brunsell,et al.  Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia , 2016, Sensors.

[17]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[18]  José A. Sobrino,et al.  Intercomparison of remote-sensing based evapotranspiration algorithms over amazonian forests , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[19]  Yadvinder Malhi,et al.  Energy and water dynamics of a central Amazonian rain forest , 2002 .

[20]  Reginaldo Moura Brasil Neto,et al.  Estimation of evapotranspiration for different land covers in a Brazilian semi-arid region: A case study of the Brígida River basin, Brazil , 2017 .

[21]  Delphine Clara Zemp,et al.  Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks , 2017, Nature Communications.

[22]  M. V. Folegatti,et al.  Spatial variability of coffee plant water consumption based on the SEBAL algorithm , 2019, Scientia Agricola.

[23]  Minha Choi,et al.  Assessment of satellite- and reanalysis-based evapotranspiration products with two blending approaches over the complex landscapes and climates of Australia , 2018, Agricultural and Forest Meteorology.

[24]  W. Bastiaanssen,et al.  A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan , 2003 .

[25]  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..

[26]  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 .

[27]  Jonathon S. Wright,et al.  Rainforest-initiated wet season onset over the southern Amazon , 2017, Proceedings of the National Academy of Sciences.

[28]  Martha C. Anderson,et al.  Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery , 2010 .

[29]  Atul K. Jain,et al.  Overview of the large-scale biosphere–atmosphere experiment in Amazonia data model intercomparison project (LBA-DMIP) , 2013 .

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

[31]  K. Davis,et al.  Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data , 2010 .

[32]  G. Wohlfahrt,et al.  Can an energy balance model provide additional constraints on how to close the energy imbalance? , 2013, Agricultural and forest meteorology.

[33]  Jungho Im,et al.  Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[34]  Dawei Han,et al.  Estimating reference evapotranspiration using numerical weather modelling , 2010 .

[35]  Y. Shimabukuro,et al.  Effects of land‐cover changes on the partitioning of surface energy and water fluxes in Amazonia using high‐resolution satellite imagery , 2019, Ecohydrology.

[36]  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 .

[37]  P. Gowda,et al.  Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum , 2017 .

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

[39]  Kul Khand,et al.  Evaluation of Landsat-Based METRIC Modeling to Provide High-Spatial Resolution Evapotranspiration Estimates for Amazonian Forests , 2017, Remote. Sens..

[40]  T. A. Black,et al.  Rain-fed and irrigated cropland-atmosphere water fluxes and their implications for agricultural production in Southern Amazonia , 2018, Agricultural and Forest Meteorology.

[41]  J. Stape,et al.  Köppen's climate classification map for Brazil , 2013 .

[42]  R. Koster,et al.  Land Surface Precipitation in MERRA-2 , 2017 .

[43]  Di Long,et al.  A modified surface energy balance algorithm for land (M‐SEBAL) based on a trapezoidal framework , 2012 .

[44]  Michael T. Coe,et al.  The effects of deforestation and climate variability on the streamflow of the Araguaia River, Brazil , 2011 .

[45]  Yann H. Chemin,et al.  Mapping Maize Evapotranspiration at Field Scale Using SEBAL: A Comparison with the FAO Method and Soil-Plant Model Simulations , 2018, Remote. Sens..

[46]  Di Long,et al.  Assessing the impact of end‐member selection on the accuracy of satellite‐based spatial variability models for actual evapotranspiration estimation , 2013 .

[47]  Gabriel B. Senay,et al.  Satellite-based water use dynamics using historical Landsat data (1984-2014) in the southwestern United States , 2017 .

[48]  David R. Miller,et al.  Sensitivity Analysis of the Surface Energy Balance Algorithm for Land (SEBAL) , 2009 .

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

[50]  Chengquan Huang,et al.  Forest carbon emissions from cropland expansion in the Brazilian Cerrado biome , 2017 .

[51]  L. Aragão.  Environmental science: The rainforest's water pump , 2012, Nature.

[52]  M. Keller,et al.  The Large‐Scale Biosphere‐Atmosphere Experiment in Amazonia: Analyzing Regional Land Use Change Effects , 2013 .

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

[54]  Michael E. Barber,et al.  Estimating Calibration Variability in Evapotranspiration Derived from a Satellite-Based Energy Balance Model , 2018, Remote. Sens..

[55]  Bin Zhao,et al.  The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). , 2017, Journal of climate.

[56]  J. Tollefson.  Deforestation ticks up in Brazil's savannah , 2018, Nature.

[57]  B. Wilcox,et al.  Changes to the energy budget and evapotranspiration following conversion of tropical savannas to agricultural lands in São Paulo State, Brazil , 2015 .

[58]  C. Adam Schlosser,et al.  Assessing Evapotranspiration Estimates from the Second Global Soil Wetness Project (GSWP-2) Simulations , 2010 .

[59]  Wendy S. Parker,et al.  Reanalyses and observations : what's the difference? , 2016 .

[60]  Atul K. Jain,et al.  Mechanisms of water supply and vegetation demand govern the seasonality and magnitude of evapotranspiration in Amazonia and Cerrado , 2014 .

[61]  Kul Khand,et al.  Dry Season Evapotranspiration Dynamics over Human-Impacted Landscapes in the Southern Amazon Using the Landsat-Based METRIC Model , 2017, Remote. Sens..

[62]  P. van der Zaag,et al.  Mapping evapotranspiration trends using MODIS and SEBAL model in a data scarce and heterogeneous landscape in Eastern Africa , 2013 .

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

[64]  W. Oechel,et al.  Energy balance closure at FLUXNET sites , 2002 .

[65]  Ge Sun,et al.  Response of evapotranspiration to changes in land use and land cover and climate in China during 2001-2013. , 2017, The Science of the total environment.

[66]  Randal D. Koster,et al.  Assessment of MERRA-2 Land Surface Energy Flux Estimates , 2018 .

[67]  Bergson G. Bezerra,et al.  Comparative analyzes and use of evapotranspiration obtained through remote sensing to identify deforested areas in the Amazon , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[68]  Laura S. Borma,et al.  Patterns of water and heat flux across a biome gradient from tropical forest to savanna in Brazil , 2009 .

[69]  Wim G.M. Bastiaanssen,et al.  Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low - Middle Sao Francisco River basin, Brazil : part A : calibration and validation , 2009 .

[70]  Wenzhi Zhao,et al.  Satellite‐based actual evapotranspiration estimation in the middle reach of the Heihe River Basin using the SEBAL method , 2010 .

[71]  Alexandro Medeiros Silva,et al.  Automated surface energy balance algorithm for land (ASEBAL) based on automating endmember pixel selection for evapotranspiration calculation in MODIS orbital images , 2019, Int. J. Appl. Earth Obs. Geoinformation.

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

[73]  Markus Reichstein,et al.  Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis , 2013 .

[74]  Zhao-Liang Li,et al.  How sensitive is SEBAL to changes in input variables, domain size and satellite sensor? , 2011 .

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

[76]  C. J. Moore,et al.  Estimating heat storage in Amazonian tropical forest , 1986 .

[77]  N. Verhoest,et al.  GLEAM v3: satellite-based land evaporation and root-zone soil moisture , 2016 .

[78]  S. Wofsy,et al.  What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network , 2013 .

[79]  H. Jaafar,et al.  Time series trends of Landsat-based ET using automated calibration in METRIC and SEBAL: The Bekaa Valley, Lebanon , 2020 .

[80]  Christopher M. U. Neale,et al.  Comparison of the NLDAS Weather Forcing Model to Agrometeorological Measurements in the western United States , 2014 .

[81]  C. Taylor,et al.  Observations of increased tropical rainfall preceded by air passage over forests , 2012, Nature.

[82]  M. Coe,et al.  Land‐use change affects water recycling in Brazil's last agricultural frontier , 2016, Global change biology.

[83]  Baharin Bin Ahmad,et al.  SENSITIVITY ANALYSIS OF METRIC–BASED EVAPOTRANSPIRATION ALGORITHM , 2013 .

[84]  M. Coe,et al.  Deforestation offsets water balance changes due to climate variability in the Xingu River in eastern Amazonia , 2015 .

[85]  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 .

[86]  Gonzalo E. Espinoza-Dávalos,et al.  The spatial variability of actual evapotranspiration across the Amazon River Basin based on remote sensing products validated with flux towers , 2019, Ecological Processes.

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

[88]  Trent W. Biggs,et al.  Mapping daily and seasonal evapotranspiration from irrigated crops using global climate grids and satellite imagery: Automation and methods comparison , 2016 .

[89]  Prasanna H. Gowda,et al.  Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach , 2013 .

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

[91]  Wolfram Mauser,et al.  The Impact of Land Cover Change on Surface Energy and Water Balance in Mato Grosso, Brazil , 2006 .

[92]  Xiaotong Zhang,et al.  Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis , 2018 .

[93]  R. Caldwell,et al.  Using remote sensing to characterize and compare evapotranspiration from different irrigation regimes in the Smith River Watershed of central Montana , 2017 .

[94]  Pierre Gentine,et al.  Deep learning to represent subgrid processes in climate models , 2018, Proceedings of the National Academy of Sciences.

[95]  Ronglin Tang,et al.  Spatial-scale effect on the SEBAL model for evapotranspiration estimation using remote sensing data , 2013 .

[96]  W. J. Kramber,et al.  Automated Calibration of the METRIC‐Landsat Evapotranspiration Process , 2013 .

[97]  Meha Jain,et al.  An automated multi-model evapotranspiration mapping framework using remotely sensed and reanalysis data , 2019, Remote Sensing of Environment.

[98]  M. Coe,et al.  Effects of land cover change on evapotranspiration and streamflow of small catchments in the Upper Xingu River Basin, Central Brazil , 2015 .

[99]  Randal D. Koster,et al.  Assessment of MERRA-2 Land Surface Hydrology Estimates , 2017 .

[100]  Sensible and Latent Heat Storage Fluxes within the Canopy Air-Space in the Amazon Rainforest , 2012 .

[101]  Kyle R. Douglas-Mankin,et al.  Long-Term (1986-2015) Crop Water Use Characterization over the Upper Rio Grande Basin of United States and Mexico Using Landsat-Based Evapotranspiration , 2019, Remote. Sens..

[102]  R. Allen,et al.  At-Surface Reflectance and Albedo from Satellite for Operational Calculation of Land Surface Energy Balance , 2008 .