Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Abstract Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76–0.90 μm wavelength) and band 5 (1.55–1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R 2 of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation.

[1]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - Theoretical approach , 2002 .

[2]  S. Rambal,et al.  An examination of the interaction between climate, soil and leaf area index in a Quercus ilex ecosystem , 2003 .

[3]  Steven E. Franklin,et al.  Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production , 1997 .

[4]  B. Markham,et al.  Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges , 2003, IEEE Trans. Geosci. Remote. Sens..

[5]  T. Jackson,et al.  Temporal persistence and stability of surface soil moisture in a semi-arid watershed , 2008 .

[6]  Frédéric Baret,et al.  Estimation of leaf water content and specific leaf weight from reflectance and transmittance measurements , 1997 .

[7]  Dudley A. Williams,et al.  Optical properties of water in the near infrared. , 1974 .

[8]  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 .

[9]  E. Ben-Dor Quantitative remote sensing of soil properties , 2002 .

[10]  Susan L. Ustin,et al.  Investigating the Relationship Between Liquid Water and Leaf Area in Clonal Populus , 1998 .

[11]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[12]  Thomas J. Jackson,et al.  Comparison of ground-based and remotely-sensed surface soil moisture estimates over complex terrain during SMEX04 , 2008 .

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

[14]  D. Sims,et al.  Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features , 2003 .

[15]  D. Roberts,et al.  Spectral and Structural Measures of Northwest Forest Vegetation at Leaf to Landscape Scales , 2004, Ecosystems.

[16]  D. H. Knight,et al.  Aims and Methods of Vegetation Ecology , 1974 .

[17]  Bisun Datt,et al.  Remote Sensing of Water Content in Eucalyptus Leaves , 1999 .

[18]  Thomas J. Jackson,et al.  Crop condition and yield simulations using Landsat and MODIS , 2004 .

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

[20]  T. Jackson Soil Moisture Experiments 2003 (SMEX03) , 2002 .

[21]  P. C. Doraiswamya,et al.  Crop condition and yield simulations using Landsat and MODIS , 2004 .

[22]  S. Adler-Golden,et al.  Atmospheric Correction for Short-wave Spectral Imagery Based on MODTRAN 4 , 2000 .

[23]  Ramakrishna R. Nemani,et al.  A remote sensing based vegetation classification logic for global land cover analysis , 1995 .

[24]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications , 2002 .

[25]  D. Roberts,et al.  Use of Normalized Difference Water Index for monitoring live fuel moisture , 2005 .

[26]  Gail P. Anderson,et al.  Atmospheric correction for shortwave spectral imagery based on MODTRAN4 , 1999, Optics & Photonics.

[27]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[28]  Paul A. Keddy,et al.  North American Terrestrial Vegetation , 1988 .

[29]  Jing M. Chen,et al.  Determining digital hemispherical photograph exposure for leaf area index estimation , 2005 .

[30]  J. Norman,et al.  Instrument for Indirect Measurement of Canopy Architecture , 1991 .

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

[32]  D. Lloyd,et al.  A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery , 1990 .

[33]  R. Fensholt,et al.  Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semiarid environment , 2003 .

[34]  ATMOSPHERIC CORRECTION ALGORITHMS FOR ADEOS/OCTS , 1998 .

[35]  Susan L. Ustin,et al.  Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Ju , 2005 .

[36]  D. Muchoney,et al.  Regional vegetation mapping and direct land surface parameterization from remotely sensed and site data , 2002 .

[37]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .

[38]  M. Hardisky The Influence of Soil Salinity, Growth Form, and Leaf Moisture on-the Spectral Radiance of Spartina alterniflora Canopies , 2008 .

[39]  C. Justice,et al.  Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation , 1997 .

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

[41]  F. M. Danson,et al.  High-spectral resolution data for determining leaf water content , 1992 .

[42]  C. Tucker Remote sensing of leaf water content in the near infrared , 1980 .

[43]  D. Riaño,et al.  Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment , 2002 .

[44]  C. Brodley,et al.  Decision tree classification of land cover from remotely sensed data , 1997 .

[45]  C. M. U. Nealeb,et al.  Upscaling ground observations of vegetation water content , canopy height , and leaf area index during SMEX 02 using aircraft and Landsat imagery , 2004 .

[46]  T. M. Lillesand,et al.  Rule-based classification models: flexible integration of satellite imagery and thematic spatial data , 1992 .

[47]  E. Raymond Hunt,et al.  Airborne remote sensing of canopy water thickness scaled from leaf spectrometer data , 1991 .

[48]  Shusen Wang,et al.  Remote sensing of grassland–shrubland vegetation water content in the shortwave domain , 2006 .

[49]  S. Running,et al.  Relationship of thematic mapper simulator data to leaf area index , 1987 .

[50]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[51]  Martha C. Anderson,et al.  Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery , 2004 .

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

[53]  A. S. Belward,et al.  A comparison of supervised maximum likelihood and decision tree classification for crop cover estimation from multitemporal LANDSAT MSS data , 1987 .

[54]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[55]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

[56]  Ramakrishna R. Nemani,et al.  Testing a theoretical climate-soil-leaf area hydrologic equilibrium of forests using satellite data and ecosystem simulation , 1989 .