A comparison of two methods for estimating surface soil moisture based on the triangle model using optical/thermal infrared remote sensing over the source area of the Yellow River

ABSTRACT As an important surface parameter, surface soil moisture (SSM) plays a significant role in water resources management, crop growth, land degradation, and vegetation coverage as well as global climate change studies. In particular, the triangle model based on the spatial relationship between the land surface temperature (LST) and vegetation index from optical/thermal infrared remote sensing data has been widely used for the estimation of SSM. In the present study, Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data, including the MOD11A2 LST and MOD13A2 normalised differential vegetation index (NDVI) from July to October, 2008 to 2010, over the source area of the Yellow River (SAYR) in the alpine vegetation region of the Tibetan Plateau, are selected to construct the LST/NDVI triangle space. Two methods based on the triangle space, namely, the temperature vegetation drought index (TVDI) method and second order polynomial method, is used to estimate the SSM at the regional scale. Finally, an analysis between the estimated SSM and ground-measured SSM is carried out to explore not only the quality of these two methods in triangle space but also the applicability of these two methods in the alpine vegetation region of the Tibetan Plateau. In addition, the spatial distribution of SSM in the study area is investigated. The results of the validation of the estimated SSM results using ground-measured data show that the accuracy of the second order polynomial method is significantly higher than that of the TVDI method. The TVDI method root mean square error (RMSE) is 0.072 m3 m−3, coefficient of determination R2 is 0.461, and bias is −0.022 m3 m−3, while the second order polynomial method RMSE is 0.075 m3 m−3, R2 is 0.519, and bias is 0.007 m3 m−3. Moreover, the SSM decreases from west to the east; this distribution can be obtained with the estimated results by using either of the two methods. In addition, the SSM estimations by using the two methods are in good correlation with the Climate Change Initiative (CCI) soil moisture product. The present study indicates that the second order polynomial method can simulate the change in SSM in the triangle space more realistically and effectively and that the second order polynomial method is more suitable for the estimation of SSM over the alpine vegetation region of the Tibetan Plateau.

[1]  Catherine Champagne,et al.  Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies , 2013 .

[2]  Guoqing Zhou,et al.  Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review , 2016, Sensors.

[3]  Mercedes Salvia,et al.  Land-atmosphere interaction patterns in southeastern South America using satellite products and climate models , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Ainong Li,et al.  Triangle Space-Based Surface Soil Moisture Estimation by the Synergistic Use of $In\ Situ$ Measurements and Optical/Thermal Infrared Remote Sensing: An Alternative to Conventional Validations , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Qiming Qin,et al.  Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China , 2008 .

[6]  Niu Zheng,et al.  Evaluating Soil Moisture Status in China Using the Temperature/Vegetation Dryness Index（TVDI） , 2003, National Remote Sensing Bulletin.

[7]  Kaniska Mallick,et al.  Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI , 2009 .

[8]  N. Baghdadi,et al.  Soil moisture retrieval over irrigated grassland using X-band SAR data , 2016 .

[9]  Thomas J. Schmugge,et al.  An interpretation of methodologies for indirect measurement of soil water content , 1995 .

[10]  W. Kustas,et al.  A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e , 1997 .

[11]  Z. Wan,et al.  Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA , 2004 .

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

[13]  R. Gillies.  A verification of the 'triangle' method for obtaining surface water content and energy fluxes from remote measurements of Normalized Difference Vegetation Index (NDVI) and surface radiant temperature , 1997 .

[14]  Zhao-Liang Li,et al.  Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images , 2017 .

[15]  Bhaskar J. Choudhury,et al.  Analysis of normalized difference and surface temperature observations over southeastern Australia , 1991 .

[16]  S. Seneviratne,et al.  Seasonal Variations in Terrestrial Water Storage for Major Midlatitude River Basins , 2006 .

[17]  Yangwen Jia.  Ananlyses on MODIS-NDVI Index Saturation in Northwest China , 2008 .

[18]  D. Jupp,et al.  The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review , 1998 .

[19]  Z. Li,et al.  Temperature-independent spectral indices in thermal infrared bands , 1990 .

[20]  T. Clarke.  An Empirical Approach for Detecting Crop Water Stress Using Multispectral Airborne Sensors , 1997 .

[21]  G. Fu,et al.  A modified soil water deficit index (MSWDI) for agricultural drought monitoring: Case study of Songnen Plain, China , 2017 .

[22]  X. Hao,et al.  Soil moisture estimation using MODIS and ground measurements in eastern China , 2007 .

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

[24]  I. Sandholt,et al.  A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .

[25]  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 .

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

[27]  Zhao-Liang Li,et al.  A practical approach for deriving all-weather soil moisture content using combined satellite and meteorological data , 2017 .

[28]  S. Running,et al.  Developing Satellite-derived Estimates of Surface Moisture Status , 1993 .

[29]  David M. Le Vine,et al.  Aquarius Active/Passive RFI Environment at L-Band , 2014, IEEE Geoscience and Remote Sensing Letters.

[30]  S. Miller,et al.  Spaceborne soil moisture estimation at high resolution: a microwave-optical/IR synergistic approach , 2003 .

[31]  Eric F. Lambin,et al.  The surface temperature-vegetation index space for land cover and land-cover change analysis , 1996 .

[32]  Ramakrishna R. Nemani,et al.  LAND COVER CHARACTERIZATION USING MULTITEMPORAL RED, NEAR‐IR, AND THERMAL‐IR DATA FROM NOAA/AVHRR , 1997 .

[33]  Samuel N. Goward,et al.  Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model , 2002 .

[34]  Zheng Niu,et al.  Evaluating soil moisture status in China using the temperature–vegetation dryness index (TVDI) , 2004 .

[35]  K. Omasa,et al.  Comparative evaluation of the Vegetation Dryness Index (VDI), the Temperature Vegetation Dryness Index (TVDI) and the improved TVDI (iTVDI) for water stress detection in semi-arid regions of Iran , 2012 .