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Accuracies of Soil Moisture Estimations Using a Semi-Empirical Model over Bare Soil Agricultural Croplands from Sentinel-1 SAR Data

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Relation http://oar.icrisat.org/11513/
https://doi.org/10.3390/rs12101664
doi:10.3390/rs12101664
 
Title Accuracies of Soil Moisture Estimations Using a Semi-Empirical Model over Bare Soil Agricultural Croplands from Sentinel-1 SAR Data
 
Creator Anil Kumar, H
Nico, G
Irshad Ahmed, M
Whitbread, A M
 
Subject Remote Sensing
GIS Techniques/Remote Sensing
Soil Science
 
Description This study describes a semi-empirical model developed to estimate volumetric soil
moisture ( v
ϑ) in bare soils during the dry season (March–May) using C-band (5.42 GHz) synthetic
aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m
spatial resolution. The semi-empirical model was developed using backscatter coefficient (σ° dB)
and in situ soil moisture collected from Siruguppa taluk (sub-district) in the Karnataka state of
India. The backscatter coefficients 0
VV σ and 0
VH σ were extracted from SAR images at 62
geo-referenced locations where ground sampling and volumetric soil moisture were measured at a
10 cm (0–10 cm) depth using a soil core sampler and a standard gravimetric method during the dry
months (March–May) of 2017 and 2018. A linear equation was proposed by combining 0
VV σ and
0
VH σ to estimate soil moisture. Both localized and generalized linear models were derived.
Thirty-nine localized linear models were obtained using the 13 Sentinel-1 images used in this
study, considering each polarimetric channel Co-Polarization (VV) and Cross-Polarization(VH)
separately, and also their linear combination of VV + VH. Furthermore, nine generalized linear
models were derived using all the Sentinel-1 images acquired in 2017 and 2018; three generalized
models were derived by combining the two years (2017 and 2018) for each polarimetric channel;
and three more models were derived for the linear combination of 0
VV σ and 0
VH σ . The above set of
equations were validated and the Root Mean Square Error (RMSE) was 0.030 and 0.030 for 2017 and
2018, respectively, and 0.02 for the combined years of 2017 and 2018. Both localized and
generalized models were compared with in situ data. Both kind of models revealed that the linear
combination of 0
VV σ + 0
VH σ showed a significantly higher R2 than the individual polarimetric
channels.
 
Publisher MDPI
 
Date 2020-05
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Identifier http://oar.icrisat.org/11513/1/remotesensing-12-01664%20%281%29.pdf
Anil Kumar, H and Nico, G and Irshad Ahmed, M and Whitbread, A M (2020) Accuracies of Soil Moisture Estimations Using a Semi-Empirical Model over Bare Soil Agricultural Croplands from Sentinel-1 SAR Data. Remote Sensing (TSI), 12 (10). pp. 1-22. ISSN 2072-4292