Dr. Babasaheb Ambedkar Marathwada University, Aurangabad
Title of the proposed research
“ Soil Dielectric Constant Estimation Under Vegetation Canopy
Using Microwave Active SAR Polarimetry”
Presented by,
Miss. Khole Monika Sudhakar
Under the Guidance of
Dr. Sanjay K. Tupe
(Assistant Professor)
KALIKA DEVI ARTS, COMMERCE AND SCIENCE COLLEGE, SHIRUR (KASAR),BEED
Outlines
 Introduction
 Statement of the proposed problem
 Objectives
 Hypothesis
 Methodology
- Integral Equation Model
- Dubois Model
 Expected outcomes
 References
INTRODUCTION
 Remote Sensing
 Active and Passive Microwave Remote Sensing
 SAR
 Polarimetry
PROBLEM STATEMENT
The research problem stated in this study is a number of models that
simulate soil moisture based on synthesis aperture radar (SAR) data
have been developed for bare soil. But these models cannot be
applied directly in vegetated areas due to the scattering of
vegetation. Vegetation canopies complicate the retrieval of moisture
in the underlying soil because canopies contain moisture of their
own.
OBJECTIVES
To estimate the soil dielectric constant under
vegetation canopy.
To get soil moisture retrieval using different
polarization modes of SAR data.
To study effect of soil surface roughness for
estimating dielectric constant from SAR
polarimetry.
HYPOTHESIS
The dielectric constant determines the response of the soil to an
incident electromagnetic wave. In a non-homogeneous medium such as
soils, the dielectric properties have a strong impact on its microwave
emission. However, the relationship between the soil dielectric constant
and the soil physical properties is not straightforward.
Methodology
Integral Equation Model
 Backscattering Coefficient :
𝑘2
°
𝜎𝑝𝑝 =
2
2 𝑘 2 cos2 𝜃
2
−4𝑟𝑚𝑠
𝑓𝑝𝑝 𝑒
𝑘2
𝑅𝑒
2
∗ 𝐹
𝑓𝑝𝑝
𝑝𝑝
𝑘2
8
2 −2𝑟𝑚𝑠 2 𝑘 2 cos2 𝜃
𝑓𝑝𝑝 𝑒
2 𝑘 2 cos2 𝜃
−3𝑟𝑚𝑠
𝑒
2 2
2
+∞ (4𝑟𝑚𝑠 𝑘 cos 𝜃)^𝑛
𝑛
𝑊
𝑛=1
𝑛!
2 2
2
+∞ (4𝑟𝑚𝑠 𝑘 cos 𝜃)^𝑛
𝑊𝑛
𝑛=1
𝑛!
2 2
2
+∞ (𝑟𝑚𝑠 𝑘 cos 𝜃)^𝑛
𝑛
𝑊
𝑛=1
𝑛!
2𝑘𝑠𝑖𝑛𝜃, 0 +
2𝑘𝑠𝑖𝑛𝜃, 0 +
2𝑘𝑠𝑖𝑛𝜃, 0
Where,
𝑓ℎℎ =
−2𝑅ℎ
𝑐𝑜𝑠𝜃
𝑓𝑣𝑣 =
2𝑅𝑣
𝑐𝑜𝑠𝜃
sin2 𝜃
1
𝑓ℎℎ = 2
4𝑅ℎ − 1 −
𝑐𝑜𝑠𝜃
𝜀𝑟
sin2 𝜃
𝑓ℎℎ = 2
𝑐𝑜𝑠𝜃
𝑅ℎ =
𝑊 𝑛 𝑎, 𝑏 =
1
2𝜋
1 + 𝑅ℎ 2
𝜀𝑟 cos2 𝜃
1−
𝜇𝑟 𝜀𝑟 − sin2 𝜃
1 − 𝑅ℎ 2 − 1 −
1
𝜀𝑟
1 + 𝑅𝑣 2
𝜇𝑟 𝑐𝑜𝑠𝜃− 𝜇𝑟 𝜀𝑟 −sin2 𝜃
: Fresnel coefficient at horizontal polarization
𝜇𝑟 𝑐𝑜𝑠𝜃+ 𝜇𝑟 𝜀𝑟 −sin2 𝜃
𝜀 𝑐𝑜𝑠𝜃− 𝜇 𝜀 −sin2 𝜃
𝑟 𝑟
𝑅𝑣 = 𝑟
: Fresnel coefficient at vertical polarization
𝜀𝑟 𝑐𝑜𝑠𝜃+
𝜇𝑟 𝜀𝑟 −sin2 𝜃
𝑛
−𝑖(𝑎𝑥+𝑏𝑦)
𝜌 (𝑥, 𝑦)𝑒
𝑑𝑥𝑑𝑦
Dielectric constant: (using Modified Dubois Model)
°
𝜀 = ((𝑙𝑜𝑔(𝜎ℎℎ
)) − log (AC)))/B
Where,
10−2.75 𝐶𝑜𝑠𝜃 1.5
𝐴=
𝑆𝑖𝑛𝜃 1.5
𝐵 = 0.028𝑡𝑎𝑛𝜃,
𝐶 = 𝑘𝑠 𝑠𝑖𝑛𝜃 1.4 λ 0.7
,
Expected Outcome
Soil moisture plays an important role in the water cycle since it controls
the proportion of rainfall that percolates, runs off, or evaporates from the
land, influences plants growth and transpiration, and is related to the
precipitation variability within a region. Although soil moisture dependent
dielectric constant is one of the main parameters used in climate models,
because of the large temporal and spatial variations of soil moisture sparse
in-situ measurements are inadequate to be of much use in these models.
Remote sensing with sufficient accuracy would provide meaningful soil
moisture data over large regions.
References
 Thanabalan, P, Vidhya, R, “Derivation of Soil Moisture using Modified Dubois Model with
field assisted surface roughness on RISAT-1 data”. Earth Sciences Research Journal. 2018,
22 (1), 13-18;
 N. Baghdadi, I. Gherboudj, M. Zribi, M. Sahebi, C. King & F. Bonn (2004), “Semiempirical calibration of the IEM backscattering model using radar images and moisture and
roughness field measurements,” International Journal of Remote Sensing, 25:18, 35933623, DOI: 10.1080/01431160310001654392;
 “Fundamentals of Remote sensing”. A Canada Centre for Remote Sensing.
https://www.nrcan.gc.ca/sites/www.nrcan.gc.ca/files/earthsciences/pdf/resource/tutor/funda
m/pdf/fundamentals_e.pdf.
 Tsenchieh Chiu, Kamal Sarabandi, “Electromagnetic Scattering from Short Branching
Vegetation”. IEEE Transactions on Geoscience and Remote Sensing.2000, 38 (2), 911-925;
 Jing Yuan, Xin Wang, Chang-xiang Yan, Shu-rong Wang, Xue-ping Ju and Yi Li, “Soil
Moisture Retrieval Model for Remote Sensing using Reflected Hyperspectral Information”.
Remote Sens.2019, 11(3), 366;
 Thomas Jagdhuber, Irena Hajnsek, Konstantinos P. Papathanassiou, Axel Bronstert “Soil
moisture retrieval under agricultural vegetation using fully polarimetric SAR”. 2012 IEEE
International Geoscience and Remote Sensing Symposium, 2012, 1481-1484;