Comparison of In-Field Measurements and INSAR Estimates of Soil Moisture: Inversion Strategies of Interferometric Data

Conde, Vasco; Nico, Giovanni; Catalão, João

doi:10.1109/igarss.2019.8899855

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…In their study, conducted in two different regions, they found that the behavior of the short-lived signal varies significantly in different land cover types, and the model proposed by [8], based on soil moisture variation, was not sufficient to explain the observed phase inconsistencies. The same result was also obtained by [26]. Therefore, they propose an alternative model for soil moisture variation that considers the effect of vegetation biomass growth [25].…”

Section: Introductionsupporting

confidence: 68%

On the Mitigation of Phase Bias in SAR Interferometry Applications: A New Model Based on NDWI

Mira,

Catalão,

Nico

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This work presents a study of the relationship between decorrelation phase in SAR interferogram, soil moisture and water content in vegetation with the aim of mitigating the contribution of decorrelation phase in SAR interferometry (InSAR) estimates of terrain displacements. A methodology for the mitigation of the phase bias based on the temporal variation of the vegetation water content is presented. Decorrelation phases are computed using timeseries of Sentinel-1 images and compared to in-situ measurements of soil moisture. It is shown that soil moisture can partially explain the observed values of decorrelation phases pointing out the role of vegetation water content. A new model is proposed to compute the contribution of vegetation to the decorrelation phase based on the NDWI (Normalized Difference Water Index) index. The methodology is applied to all short temporal baseline interferograms obtained from the timeseries of Sentinel-1 SAR images, using the NDWI maps generated from Sentinel-2 multispectral images. The cumulative displacement is computed by integrating the short temporal baseline interferograms, corrected for the land cover and soil moisture changes. It is shown that the proposed methodology can reduce the variance of estimated cumulative displacement in areas covered by vegetation.

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Section: Introductionsupporting

confidence: 68%

On the Mitigation of Phase Bias in SAR Interferometry Applications: A New Model Based on NDWI

Mira,

Catalão,

Nico

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Previous semi-empirical studies have considered single polarization to build a relationship between soil moisture and a backscatter model at 10 cm depth [9] and estimated ϑ v with a root mean square error (RMSE) of 3-6% [10][11][12] using C-band data. There have also been studies that have used the SAR interferometry technique and Sentinel-1 data to estimate soil moisture and compare them with in situ measurements [13]. Even though SAR interferometry is less frequently used in the remote sensing community to estimate soil moisture, its advantage lies in its ability to disentangle moisture and terrain roughness contributions.…”

Section: Introductionmentioning

confidence: 99%

“…Even though SAR interferometry is less frequently used in the remote sensing community to estimate soil moisture, its advantage lies in its ability to disentangle moisture and terrain roughness contributions. Most SAR-based soil moisture estimation studies have covered small areas limited to a few hundred square kilometers [11][12][13][14][15][16][17]. Estimating soil moisture over a wider area and at a higher resolution using SAR imagery will provide information on managing water resources and irrigation scheduling that can benefit a large number of farmers [14].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Observing Soil Moisture Change Using C-Band Interferometry using Machine Learning Regression

Mira

Catalão

Nico

2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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Comparison of In-Field Measurements and INSAR Estimates of Soil Moisture: Inversion Strategies of Interferometric Data

Cited by 4 publications

References 3 publications

On the Mitigation of Phase Bias in SAR Interferometry Applications: A New Model Based on NDWI

On the Mitigation of Phase Bias in SAR Interferometry Applications: A New Model Based on NDWI

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

Observing Soil Moisture Change Using C-Band Interferometry using Machine Learning Regression

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