Remote Sensing Based Soil Moisture Detection
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Transcript of Remote Sensing Based Soil Moisture Detection
Remote Sensing Based Soil Moisture Detection
Sanaz Shafian, Stephan J. MaasDepartment of Plant and Soil
Science Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Introduction Soil moisture influences
Monitoring of plant water requirements Water resources and irrigation
management Surface energy partitioning between the
sensible and latent heat flux
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Introduction Challenges of directly soil moisture
measurement Expensive Necessity of using surface meteorological
observations Not readily available over large areas Produce point type measurements Restricted to specific locations
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Statement of problem Satellite remote sensing offers a means of
measuring soil moisture Across a wide area Continuously
Key variables in soil moisture estimation Vegetation cover Surface temperature
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Statement of problem Most current soil moisture estimation methods require
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
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Objectives 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 GC\TIR 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
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Literature review The Concept of using data from TIR band
to monitor canopy water stress was originally proposed by Jackson(1977)
Carlson (1989) studied the Ts\VI feature space properties and discovered that changes in soil moisture could be described within the Ts\VI ‘triangle’
Moran et al. (1994) introduced a concept termed the ‘vegetation index–temperature (VIT) trapezoid’ for the estimation of LE fluxes using the Ts\VI domain in areas of partial vegetation cover
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
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
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
GC\TIR Space Observed properties of the GC\TIR 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
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
GC\TIR Space Observed properties of the GC\TIR 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
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
GC\TIR Space Observed properties of the GC\TIR Space
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
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Description of the PSMI Method Modeling the trapezoid \ triangle
Image processing Produce ground cover images by using PVI
method • Red and NIR bands
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
𝑮𝑪=𝑷𝑽𝑰𝒂𝒏𝒚 𝒑𝒊𝒙𝒆𝒍 /𝑷𝑽𝑰 𝑭𝒖𝒍𝒍
Description of the PSMI method Modeling the trapezoid \ triangle
Image processing Produce GC\TIR scatter plot for each image Normalizing TIR between 0 and 1 Produce Normalized GC\TIR scatter plot
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Description of the PSMI method 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
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Description of the PSMI method Modeling the trapezoid \ triangle
Consider the line that passes through the origin as the reference of soil moisture
GC = 0 TIR = 0 Slope = - 45°
Calculate perpendicular distance from each pixel from this line
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Description of the PSMI method Modeling the trapezoid \ triangle
Normalizing the distance between 0 and 1
Considering the effect of GC
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
(𝐷
(1+( 𝐺𝐶 )3 )2)×
1
√2
𝐷 /√2
Description of the PSMI method Calculate PSMI
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
𝑃𝑆𝑀𝐼=1−[(𝐷
(1+( 𝐺𝐶 )3 )2)×
1
√2]
So, as PSMI goes from 0 to 1, you go from low to high soil moisture.
Materials Study area
Measuring soil moisture using TDR probe in 19 different fields
Satellite Imagery 6 images from Landsat 7(ETM+)( 2012 and
2013 growing season) 4 images from Landsat 8(LCDM)( 2013
growing season)
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Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Results GC/TIR space is well defined in all cases
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Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Results Comparison between measured and
estimated soil moisture
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Results Comparison between measured and
estimated soil moisture
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Results Creating soil moisture map
Spatial variation of soil moisture using PSMI
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Conclusions GC\TIR space can be used instead VI\Ts
space to estimate soil moisture GC\TIR space is well defined in all cases PSMI is always between 0 and 1 PSMI describes distribution of soil
moisture in GC\Normalized TIR space PSMI is closely related to measured soil
moisture PSMI and measured soil moisture have
similar spatial pattern
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Future work 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
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing
Acknowledgment This project was funded by Texas Alliance
Water Conservation (TAWC) We would like to thank John Deere
Company for sharing soil moisture data
Texas Tech University
Beyond Diagnostics: Insights and Recommendations from Remote Sensing