Next Article in Journal
Elevation and Climate Effects on Vegetation Greenness in an Arid Mountain-Basin System of Central Asia
Previous Article in Journal
Multi-Parametric Climatological Analysis Reveals the Involvement of Fluids in the Preparation Phase of the 2008 Ms 8.0 Wenchuan and 2013 Ms 7.0 Lushan Earthquakes
Previous Article in Special Issue
To Blend or Not to Blend? A Framework for Nationwide Landsat–MODIS Data Selection for Crop Yield Prediction
Open AccessArticle

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

1
International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502324, India
2
Consiglio Nazionale delle Ricerche, Istituto per le Applicazioni del Calcolo, Via Amendola 122 O, 70126 Bari, Italy
3
Institute of Earth Sciences, Saint Petersburg State University, 199034 Saint Petersburg, Russia
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1664; https://doi.org/10.3390/rs12101664
Received: 24 March 2020 / Revised: 19 May 2020 / Accepted: 21 May 2020 / Published: 22 May 2020
(This article belongs to the Special Issue Remote Sensing in Agriculture: State-of-the-Art)
This study describes a semi-empirical model developed to estimate volumetric soil moisture ( ϑ v ) 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 σ V V 0 and σ V H 0 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 σ V V 0 and σ V H 0 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 σ V V 0 and σ V H 0 . 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 σ V V 0 + σ V H 0 showed a significantly higher R2 than the individual polarimetric channels. View Full-Text
Keywords: volumetric soil moisture; synthetic aperture radar (SAR); Sentinel-1; soil moisture semi-empirical model; soil moisture Karnataka India volumetric soil moisture; synthetic aperture radar (SAR); Sentinel-1; soil moisture semi-empirical model; soil moisture Karnataka India
Show Figures

Graphical abstract

MDPI and ACS Style

Hoskera, A.K.; Nico, G.; Irshad Ahmed, M.; Whitbread, A. Accuracies of Soil Moisture Estimations Using a Semi-Empirical Model over Bare Soil Agricultural Croplands from Sentinel-1 SAR Data. Remote Sens. 2020, 12, 1664.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop