Satellite Detection of Water Stress Effects on Terrestrial Latent Heat Flux With MODIS Shortwave Infrared Reflectance Data

The MODerate‐resolution Imaging Spectroradiometer (MODIS) provides spatially contiguous measurements of terrestrial biophysical variables, which can be used to estimate the terrestrial latent heat flux (LE). MODIS‐derived shortwave infrared reflectance (SWIR) metrics (SWIRs) are sensitive to the soil moisture and vegetation water stress. In this study, we used the MODIS‐derived SWIRs with eddy covariance flux measurements obtained from 25 flux tower sites representing 10 different land cover types within China to evaluate the sensitivity of SWIRs to ground‐measured evaporation fraction and LE. The water constraint metrics determined using the MODIS‐derived SWIR generally corresponded better with the ground‐measured evaporation fraction values than those obtained without using SWIR. The MODIS‐derived SWIRs were used as proxies for the soil and vegetation water supply constraints in a revised Priestley‐Taylor algorithm to estimate the terrestrial LE. The estimated LE using the MODIS‐derived SWIRs generally corresponded well with the ground‐measured LE (0.56 ≤ R2 ≤ 0.97) for most of the flux tower sites. Regional algorithm sensitivity analysis using the MODIS‐derived SWIRs as water supply proxies demonstrated that water limitations reduce LE by more than 53% over China, and the atmospheric vapor pressure deficit and relative humidity are not sufficient to characterize both the atmosphere demand and water supply for LE estimation. Our results demonstrate the potential of using MODIS‐derived SWIRs to characterize soil and vegetation water supply factors for determining LE, where the relatively high spatial and temporal resolutions (500 m and daily) are closer to the scale of the eddy covariance ground measurements.

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

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

[3]  R. Granger,et al.  Evaporation from natural nonsaturated surfaces , 1989 .

[4]  H. Schmid.  Source areas for scalars and scalar fluxes , 1994 .

[5]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

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

[7]  Wenjun He,et al.  A Rational Parameterization of Evaporation from Dry, Bare Soil , 1998 .

[8]  Alan H. Strahler,et al.  An algorithm for the retrieval of albedo from space using semiempirical BRDF models , 2000, IEEE Trans. Geosci. Remote. Sens..

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

[10]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .

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

[12]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[13]  P. North,et al.  Remote sensing of canopy light use efficiency using the photochemical reflectance index , 2001 .

[14]  L. S. Pereira,et al.  Revised FAO Procedures for Calculating Evapotranspiration: Irrigation and Drainage Paper No. 56 with Testing in Idaho , 2001 .

[15]  Changsheng Li,et al.  Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data , 2002 .

[16]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[17]  J. Tenhunen,et al.  Severe drought effects on ecosystem CO2 and H2O fluxes at three Mediterranean evergreen sites: revision of current hypotheses? , 2002 .

[18]  S. Swenson,et al.  Methods for inferring regional surface‐mass anomalies from Gravity Recovery and Climate Experiment (GRACE) measurements of time‐variable gravity , 2002 .

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

[20]  S. Ustin,et al.  Water content estimation in vegetation with MODIS reflectance data and model inversion methods , 2003 .

[21]  M. Tamura,et al.  Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data , 2004 .

[22]  Martha C. Anderson,et al.  Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans , 2004 .

[23]  E. Davidson,et al.  Satellite-based modeling of gross primary production in an evergreen needleleaf forest , 2004 .

[24]  P. Dirmeyer,et al.  Comparison, Validation, and Transferability of Eight Multiyear Global Soil Wetness Products , 2004 .

[25]  Sun Xiaomin,et al.  An operational two-layer remote sensing model to estimate surface flux in regional scale: Physical background , 2005 .

[26]  T. Jackson,et al.  Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands , 2005 .

[27]  D. Hollinger,et al.  MODELING GROSS PRIMARY PRODUCTION OF AN EVERGREEN NEEDLELEAF FOREST USING MODIS AND CLIMATE DATA , 2005 .

[28]  Guofu Yuan,et al.  The impact of averaging period on eddy fluxes observed at ChinaFLUX sites , 2006 .

[29]  X. Lee,et al.  Overview of ChinaFLUX and evaluation of its eddy covariance measurement , 2006 .

[30]  Xiaomin Sun,et al.  CO2 fluxes over an old, temperate mixed forest in northeastern China , 2006 .

[31]  Xiaomin Sun,et al.  Soil moisture effect on the temperature dependence of ecosystem respiration in a subtropical Pinus plantation of southeastern China , 2006 .

[32]  Liang Zhao,et al.  Depression of net ecosystem CO2 exchange in semi-arid Leymus chinensis steppe and alpine shrub , 2006 .

[33]  M. Hodnett,et al.  Control of Dry Season Evapotranspiration over the Amazonian Forest as Inferred from Observations at a Southern Amazon Forest Site , 2007 .

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

[35]  Zhanqing Li,et al.  A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature , 2007 .

[36]  H. Kondo,et al.  Carbon fluxes and their response to environmental variables in a Dahurian larch forest ecosystem in northeast China , 2008, Journal of Forestry Research.

[37]  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.

[38]  Jiquan Chen,et al.  Closing the carbon budget of estuarine wetlands with tower‐based measurements and MODIS time series , 2008 .

[39]  T. Foken.  The energy balance closure problem: an overview. , 2008, Ecological applications : a publication of the Ecological Society of America.

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

[41]  D. Baldocchi.  ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems , 2008 .

[42]  T. Jackson,et al.  Remote sensing of vegetation water content from equivalent water thickness using satellite imagery , 2008 .

[43]  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 .

[44]  T. Holmes,et al.  Global land-surface evaporation , 2010 .

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

[46]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

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

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

[49]  L. Mahrt.  Computing turbulent fluxes near the surface: Needed improvements , 2010 .

[50]  Zhuguo Ma,et al.  Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling , 2010 .

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

[52]  W. Oechel,et al.  A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data , 2010, Remote Sensing of Environment.

[53]  Chaoyang Wu,et al.  Gross primary production estimation from MODIS data with vegetation index and photosynthetically active radiation in maize , 2010 .

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

[55]  S. Seneviratne,et al.  Energy balance closure of eddy-covariance data: a multisite analysis for European FLUXNET stations. , 2010 .

[56]  Xiaoliang Lu,et al.  Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data , 2010 .

[57]  Jie He,et al.  On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau , 2010 .

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

[59]  Xiaodong Liu,et al.  Quantifying the hydrological responses to climate change in an intact forested small watershed in Southern China , 2011 .

[60]  S. Liang,et al.  Improving Predictions of Water and Heat Fluxes by Assimilating MODIS Land Surface Temperature Products into the Common Land Model , 2011 .

[61]  J. Randerson,et al.  Continental-scale net radiation and evapotranspiration estimated using MODIS satellite observations , 2011 .

[62]  Shaomin Liu,et al.  A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem , 2011 .

[63]  Jie He,et al.  Improving land surface temperature modeling for dry land of China , 2011 .

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

[65]  A. Noormets,et al.  Carbon budget and its response to environmental factors in young and mature poplar plantations along the middle and lower reaches of the Yangtze River, China , 2011 .

[66]  C. Schmullius,et al.  Remote sensing of ecosystem light use efficiency with MODIS-based PRI , 2011 .

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

[68]  G. D. Jenerette,et al.  High efficiency in water use and carbon gain in a wet year for a desert halophyte community , 2012 .

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

[70]  Frank Veroustraete,et al.  Vegetation primary production estimation at maize and alpine meadow over the Heihe River Basin, China , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[71]  C. Beijing.  Source Area in-FLUX Measurements by FSAM Model over the Populus deltoides Plantation in Yueyang , 2012 .

[72]  Shaomin Liu,et al.  Validation of remotely sensed evapotranspiration over the Hai River Basin, China , 2012 .

[73]  S. M. Jong,et al.  Observation uncertainty of satellite soil moisture products determined with physically-based modeling , 2012 .

[74]  Guo JianwenLi Jianyuan Wu Adan.  The Application of Web-based Visualization System for a Wireless Sensor Network in Heihe Watershed Allied Telemetry Experimental Research , 2013 .

[75]  S. Liang,et al.  MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley-Taylor algorithm , 2013 .

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

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

[78]  Qing Xiao,et al.  Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design , 2013 .

[79]  Jiemin Wang,et al.  Intercomparison of surface energy flux measurement systems used during the HiWATER‐MUSOEXE , 2013 .

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

[81]  P. Ceccato,et al.  Actual evapotranspiration in drylands derived from in-situ and satellite data: Assessing biophysical constraints , 2013 .

[82]  R. Leuning,et al.  Improving evapotranspiration estimates in Mediterranean drylands: The role of soil evaporation , 2013 .

[83]  W. Wagner,et al.  Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing , 2013 .

[84]  Jonas Ardö,et al.  Relation between Seasonally Detrended Shortwave Infrared Reflectance Data and Land Surface Moisture in Semi-Arid Sahel , 2013, Remote. Sens..

[85]  Jinwei Dong,et al.  Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought , 2014 .

[86]  L. Xuemei,et al.  Using Short Wave Visible–Near Infrared Reflectance Spectroscopy to Predict Soil Properties and Content , 2014 .

[87]  Girolimetto Daniela,et al.  Evapotranspiration and Water Stress Estimation from TIR and SWIR Bands , 2014 .

[88]  Yaoming Ma,et al.  A 3-year dataset of sensible and latent heat fluxes from the Tibetan Plateau, derived using eddy covariance measurements , 2015, Theoretical and Applied Climatology.

[89]  Zhuguo Ma,et al.  Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China , 2014 .

[90]  Zhuguo Ma,et al.  Estimation of evapotranspiration over the terrestrial ecosystems in China , 2014 .

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

[92]  Yaoming Ma,et al.  Combining MODIS, AVHRR and in situ data for evapotranspiration estimation over heterogeneous landscape of the Tibetan Plateau , 2014 .

[93]  S. Liang,et al.  A satellite-based hybrid algorithm to determine the Priestley–Taylor parameter for global terrestrial latent heat flux estimation across multiple biomes , 2015 .

[94]  Chunlin Huang,et al.  Improving Estimation of Evapotranspiration under Water-Limited Conditions Based on SEBS and MODIS Data in Arid Regions , 2015, Remote. Sens..

[95]  Rémy Fieuzal,et al.  Improvement of Soil Moisture Retrieval from Hyperspectral VNIR-SWIR Data Using Clay Content Information: From Laboratory to Field Experiments , 2015, Remote. Sens..

[96]  R. Fensholt,et al.  Evaluating EO-based canopy water stress from seasonally detrended NDVI and SIWSI with modeled evapotranspiration in the Senegal River Basin , 2015 .

[97]  Roemi Fernández,et al.  VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity , 2015, Sensors.

[98]  S. Jones,et al.  A linear physically-based model for remote sensing of soil moisture using short wave infrared bands , 2015 .

[99]  P. Cox,et al.  Observing terrestrial ecosystems and the carbon cycle from space , 2015, Global change biology.

[100]  Q. Zhang,et al.  A new index for land surface inhomogeneity and its impact on sensible heat flux measured by Large Aperture Scintillometer (LAS) over eastern Gansu of Loess Plateau , 2016 .

[101]  S. Running,et al.  Satellite detection of soil moisture related water stress impacts on ecosystem productivity using the MODIS-based photochemical reflectance index , 2016 .

[102]  Lisheng Song,et al.  Partitioning Evapotranspiration into Soil Evaporation and Canopy Transpiration via a Two-Source Variational Data Assimilation System , 2016 .

[103]  S. Jones,et al.  The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations , 2017 .

[104]  Guangsheng Zhou,et al.  Deriving a light use efficiency estimation algorithm using in situ hyperspectral and eddy covariance measurements for a maize canopy in Northeast China , 2017, Ecology and evolution.

[105]  Martha C. Anderson,et al.  Quantifying agricultural drought in tallgrass prairie region in the U.S. Southern Great Plains through analysis of a water-related vegetation index from MODIS images , 2017 .

[106]  Zhongli Zhu,et al.  Upscaling of sparse in situ soil moisture observations by integrating auxiliary information from remote sensing , 2017 .

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

[108]  S. Liang,et al.  A simple temperature domain two‐source model for estimating agricultural field surface energy fluxes from Landsat images , 2017 .

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

[110]  C. Schaaf,et al.  Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products , 2018 .

[111]  Jiefang Dong,et al.  A sub-pixel method for estimating planting fraction of paddy rice in Northeast China , 2018 .