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


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



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



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



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



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



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



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




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




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



Description of the PSMI Method Modeling the trapezoid \ triangle

Image processing Produce ground cover images by using PVI

method • Red and NIR bands



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



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




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




Normalizing the distance between 0 and 1

Considering the effect of GC



(𝐷

(1+( 𝐺𝐶 )3 )2)×

1

√2

𝐷 /√2

Description of the PSMI method Calculate PSMI



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)



Results GC/TIR space is well defined in all cases



Results Comparison between measured and

estimated soil moisture



Results Creating soil moisture map

Spatial variation of soil moisture using PSMI



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



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



Acknowledgment This project was funded by Texas Alliance

Water Conservation (TAWC) We would like to thank John Deere

Company for sharing soil moisture data



remote sensing based soil moisture detection

Education

monitoring soil moisture

soil moisture estimation

reference of soil moisture

dry edge minimum soil

decreasebeyond diagnostics

trapezoidbeyond diagnostics

linebeyond diagnostics

trianglebeyond diagnostics