Estimation of Surface Soil Moisture from Thermal Infrared Remote Sensing Using an Improved Trapezoid Method

Surface soil moisture (SM) plays a fundamental role in energy and water partitioning in the soil–plant–atmosphere continuum. A reliable and operational algorithm is much needed to retrieve regional surface SM at high spatial and temporal resolutions. Here, we provide an operational framework of estimating surface SM at fine spatial resolutions (using visible/thermal infrared images and concurrent meteorological data) based on a trapezoidal space defined by remotely sensed vegetation cover (Fc) and land surface temperature (LST). Theoretical solutions of the wet and dry edges were derived to achieve a more accurate and effective determination of the Fc/LST space. Subjectivity and uncertainty arising from visual examination of extreme boundaries can consequently be largely reduced. In addition, theoretical derivation of the extreme boundaries allows a per-pixel determination of the VI/LST space such that the assumption of uniform atmospheric forcing over the entire domain is no longer required. The developed approach was tested at the Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN) site in central Tibet, China, from August 2010 to August 2011 using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra images. Results indicate that the developed trapezoid model reproduced the spatial and temporal patterns of observed surface SM reasonably well, with showing a root-mean-square error of 0.06 m3·m−3 at the site level and 0.03 m3·m−3 at the regional scale. In addition, a case study on 2 September 2010 highlighted the importance of the theoretically calculated wet and dry edges, as they can effectively obviate subjectivity and uncertainties in determining the Fc/LST space arising from visual interpretation of satellite images. Compared with Land Surface Models (LSMs) in Global Land Data Assimilation System-1, the remote sensing-based trapezoid approach gave generally better surface SM estimates, whereas the LSMs showed systematic underestimation. Sensitivity analyses suggested that the trapezoid method is most sensitive to field capacity and temperature but less sensitive to other meteorological observations and parameters.

[1]  Yuting Yang,et al.  Modeling evapotranspiration and its partitioning over a semiarid shrub ecosystem from satellite imagery: a multiple validation , 2013 .

[2]  T. Carlson.  An Overview of the “Triangle Method” for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery , 2007, Sensors (Basel, Switzerland).

[3]  Eric F. Wood,et al.  Effects of soil moisture aggregation on surface evaporative fluxes , 1997 .

[4]  S. Liang.  Narrowband to broadband conversions of land surface albedo I Algorithms , 2001 .

[5]  Y. Kerr,et al.  Soil moisture active and passive microwave products : intercomparison and evaluation over a Sahelian site , 2009 .

[6]  Yaoming Ma,et al.  Method Development for Estimating Sensible Heat Flux over the Tibetan Plateau from CMA Data , 2009 .

[7]  Jun Qin,et al.  Parameterizing soil organic carbon’s impacts on soil porosity and thermal parameters for Eastern Tibet grasslands , 2012, Science China Earth Sciences.

[8]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

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

[10]  Harry Vereecken,et al.  Spatiotemporal analysis of soil moisture observations within a Tibetan mesoscale area and its implication to regional soil moisture measurements , 2013 .

[11]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

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

[13]  D. Long,et al.  Comparison of three dual‐source remote sensing evapotranspiration models during the MUSOEXE‐12 campaign: Revisit of model physics , 2015 .

[14]  B. Scanlon,et al.  Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites , 2014 .

[15]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation , 2007 .

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

[17]  M. Schaap,et al.  Neural network analysis for hierarchical prediction of soil hydraulic properties , 1998 .

[18]  T. Carlson,et al.  Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models , 1995 .

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

[20]  D. Hodáňová.  An introduction to environmental biophysics , 1979, Biologia Plantarum.

[21]  A. Al Bitar,et al.  An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data , 2010 .

[22]  R. Dickinson,et al.  The Common Land Model , 2003 .

[23]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[24]  Hongjie Xie,et al.  Analysis and optimization of NDVI definitions and areal fraction models in remote sensing of vegetation , 2009 .

[25]  Li Chao.  Correcting the smoothing effect of ordinary Kriging estimates in soil moisture interpolation , 2010 .

[26]  H. R. Haise,et al.  Soil Moisture Studies of Some Great Plains Soils: II. Field Capacity as Related to 1/3‐Atmosphere Percentage, and “Minimum Point” as Related to 15‐ and 26‐Atmosphere Percentages , 1955 .

[27]  Randal D. Koster,et al.  The components of a 'SVAT' scheme and their effects on a GCM's hydrological cycle , 1994 .

[28]  J. Qin,et al.  Evaluation of AMSR‐E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau , 2013 .

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

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

[31]  William P. Kustas,et al.  Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations , 2008 .

[32]  Lazhu,et al.  A MULTISCALE SOIL MOISTURE AND FREEZE-THAW MONITORING NETWORK ON THE THIRD POLE , 2013 .

[33]  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 .

[34]  Wolfgang Wagner,et al.  Inter-comparison of microwave satellite soil moisture retrievals over the Murrumbidgee Basin, southeast Australia , 2013 .

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

[36]  Dan Zaslavsky,et al.  Soil water dynamics , 1960 .

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

[38]  T. Carlson,et al.  A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover , 1994 .

[39]  Yann Kerr,et al.  Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission , 2001, IEEE Trans. Geosci. Remote. Sens..

[40]  Rasmus Fensholt,et al.  Combining the triangle method with thermal inertia to estimate regional evapotranspiration — Applied to MSG-SEVIRI data in the Senegal River basin , 2008 .

[41]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[42]  S. Seneviratne,et al.  Investigating soil moisture-climate interactions in a changing climate: A review , 2010 .

[43]  N. Lu,et al.  Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia , 2013 .

[44]  T. McVicar,et al.  Impact of CO2 fertilization on maximum foliage cover across the globe's warm, arid environments , 2013 .

[45]  Y. Kerr,et al.  Operational readiness of microwave remote sensing of soil moisture for hydrologic applications , 2007 .

[46]  B. Scanlon,et al.  GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas , 2013 .

[47]  Thomas A. Hennig,et al.  The Shuttle Radar Topography Mission , 2001, Digital Earth Moving.

[48]  N. U. Ahmed,et al.  Relations between evaporation coefficients and vegetation indices studied by model simulations , 1994 .

[49]  W. Brutsaert.  On a derivable formula for long-wave radiation from clear skies , 1975 .

[50]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[51]  Youshouzhai Gu.  Echo , 1980, The Craft of Poetry.

[52]  M. Mccabe,et al.  Evaluation of AMSR-E-Derived Soil Moisture Retrievals Using Ground-Based and PSR Airborne Data during SMEX02 , 2005 .

[53]  B. Scanlon,et al.  Global analysis of approaches for deriving total water storage changes from GRACE satellites , 2015 .

[54]  Venkat Lakshmi,et al.  Remote Sensing of Soil Moisture , 2013 .