Remote sensing –Beyond images Mexico 14-15 December 2013 The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

1. Remote Sensing Based Soil Moisture
Detection
Sanaz Shafian, Stephan J. Maas
Department of Plant and Soil Science
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

2. Texas Tech University
Introduction

Soil moisture influences
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
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Monitoring of plant water requirements
Water resources and irrigation management
Surface energy partitioning between the sensible and
latent heat flux
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Introduction
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Challenges of directly soil moisture measurement
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
Expensive
Necessity of using surface meteorological observations



Not readily available over large areas
Produce point type measurements
Restricted to specific locations
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Statement of problem
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Satellite remote sensing offers a means of measuring soil
moisture


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Across a wide area
Continuously
Key variables in soil moisture estimation


Vegetation cover
Surface temperature
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Statement of problem
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Most current soil moisture estimation methods require


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Additional ancillary data
Precise calibration of the surface temperature
 Expensive
 Time consuming
Using NDVI in soil moisture estimation

NDVI is a greenness index does not have physical
interpretation
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Objectives
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To demonstrate how Landsat and other similar data may
be used to estimate temporal and spatial patterns of soil
moisture status
To investigate the potentials of using a combination of
multiple GCTIR spectral signatures to estimate soil
moisture from space and to find the algorithm that will
be best-suited for monitoring soil moisture
To compare the results with soil moisture from direct
measurements
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Literature review
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The Concept of using data from TIR band to monitor
canopy water stress was originally proposed by
Jackson(1977)
Carlson (1989) studied the TsVI feature space
properties and discovered that changes in soil moisture
could be described within the TsVI ‘triangle’
Moran et al. (1994) introduced a concept termed the
‘vegetation index–temperature (VIT) trapezoid’ for the
estimation of LE fluxes using the TsVI domain in
areas of partial vegetation cover
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Literature review


Gillies and Carlson (1995) introduced a method for the
retrieval of spatially distributed maps of soil moisture
availability (Mo), which they termed the ‘triangle’
method
Sandholt et al. (2002) suggested a temperature
vegetation dryness index (TVDI) for each pixel in
trapezoid based on defining slope of dry edge
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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GCTIR Space

Observed properties of the GCTIR Space

There is a relationship between ground cover (GC) and
surface thermal emittance (TIR) of a given region

Shape of the relationship is a truncated triangle or a
trapezoid
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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GCTIR Space

Observed properties of the GCTIR Space

GC increases along the y-axis


Bare soil signal is gradually masked by vegetation
contribution
For a given GC, when TIR increases soil moisture will
decrease
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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GCTIR Space
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Observed properties of the GCTIR Space
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Minimum TIR value at the wet edge (maximum soil moisture)
Maximum TIR value at the dry edge (Minimum soil moisture)
The relative value of soil moisture at each pixel can be defined
in terms of its position within the trapezoid /or triangle
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Description of the PSMI Method
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Modeling the trapezoid triangle

Image processing
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Produce ground cover images by using PVI method
• Red and NIR bands
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Description of the PSMI method
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Modeling the trapezoid triangle
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Image processing
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Produce GCTIR scatter plot for each image
Normalizing TIR between 0 and 1
Produce Normalized GCTIR scatter plot
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Description of the PSMI method
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Decrease atmospheric effect
Normalized TIR can be compared with normalized
surface temperature
Different scatter plots in
different times can be
compared
GC and TIR are in the
same range
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Description of the PSMI method

Modeling the trapezoid triangle

Consider the line that passes through the origin as the
reference of soil moisture
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GC = 0
TIR = 0
Slope = - 45°
Calculate perpendicular
distance from each
pixel from this line
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Description of the PSMI method

Modeling the trapezoid triangle

Normalizing the distance between 0 and 1

Considering the effect of GC
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Description of the PSMI method

Calculate PSMI
So, as PSMI goes from
0 to 1, you go from low
to high soil moisture.
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Materials

Study area


Measuring soil moisture using TDR probe in 19 different
fields
Satellite Imagery
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6 images from Landsat 7(ETM+)( 2012 and 2013
growing season)
4 images from Landsat 8(LCDM)( 2013 growing season)
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Results
 GC/TIR space is well defined in all cases
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Results

Comparison between measured and estimated soil
moisture
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

21. Texas Tech University
Results

Comparison between measured and estimated soil
moisture
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Results

Creating soil moisture map

Spatial variation of soil moisture using PSMI
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

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Conclusions

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GCTIR space can be used instead VITs space to
estimate soil moisture
GCTIR space is well defined in all cases
PSMI is always between 0 and 1
PSMI describes distribution of soil moisture in
GCNormalized TIR space
PSMI is closely related to measured soil moisture
PSMI and measured soil moisture have similar spatial
pattern
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Future work
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Using more data to test the robustness of the method
over large areas
Using different sets of satellite imagery (e.g. AVHRR) to
derive PSMI
Use of PSMI for driving, updating, and validating
hydrological models
Beyond Diagnostics: Insights and Recommendations from Remote Sensing

25. Texas Tech University
Acknowledgment
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
This project was funded by Texas Alliance Water
Conservation (TAWC)
We would like to thank John Deere Company for
sharing soil moisture data
Beyond Diagnostics: Insights and Recommendations from Remote Sensing