Soil Moisture Retrieval From SMAP: A Validation and Error Analysis Study Using Ground-Based Observations Over the Little Washita Watershed

Chen, Quan; Zeng, Jiangyuan; Cui, Chao; Li, Zhen; Chen, Kun-Shan; Bai, Xiaojing; Xu, Junzeng

doi:10.1109/tgrs.2017.2762462

Cited by 59 publications

(38 citation statements)

References 41 publications

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“…We also investigated the responses of the HH-pol, and VV-pol backscatter of

and

to in-situ soil moisture to explore whether the correlation between the backscattering coefficient and soil moisture had improved following the elimination of the contribution of vegetation, shown in Figure 8 . It is observed that all radar observations generally exhibit correlation with soil moisture, which is consistent with previous findings [ 8 ], demonstrating the good potential of GF-3 observations for estimating soil moisture. As expected,

backscatter correlated better with soil moisture than

for HH-pol and VV-pol ( R > 0.650, Table 4 ).…”

Section: Resultssupporting

confidence: 91%

“…Passive microwave remote sensing can observe soil moisture with higher temporal resolution (e.g., 1~3 days) [ 7 ] than active radar especially the synthetic aperture radar (SAR) (typically the temporal resolution of a single SAR system is several weeks). However, passive radiometers often limits on spatial resolution (e.g., >25 km) while SAR can provide observations with much higher spatial resolution than the passive radiometers that can be used for many practical applications such as agricultural productivity estimation at a local scale [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields

Zhang

Meng

Yao³

et al. 2018

Sensors

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Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed for the GF-3 satellite based on a backscattering coefficient simulation database. We adopted eight optical vegetation indices to determine the relationships between these indices and vegetation water content (VWC) by combining Landsat-8 data and field measurements. A backscattering coefficient database was built using an advanced integral equation model (AIEM). The effects of vegetation on backscattering coefficients were corrected using the water cloud model (WCM) to obtain the bare soil backscattering coefficient (σsoil°). Then, soil moisture retrievals were obtained at HH, VV and HH+VV combination respectively by minimizing the observed bare soil backscattering coefficient (σsoil°) and the AIEM-simulated backscattering coefficient (σitalicsoil-simu°). Finally, the proposed algorithm was validated in agriculture region of wheat and corn in China using ground soil moisture measurements. The results showed that the normalized difference infrared index (NDII) had the best fit with measured VWC values (R = 0.885) among the eight vegetation water indices; thus, it was adopted to correct the effects of vegetation. The proposed algorithm using GF-3 satellite data performed well in soil moisture retrieval, and the scheme combining HH and VV polarization exhibited the highest accuracy, with a root mean square error (RMSE) of 0.044 m3m−3, followed by HH polarization (RMSE = 0.049 m3m−3) and VV polarization (RMSE = 0.053 m3m−3). Therefore, the proposed algorithm has good potential to operationally estimate soil moisture from the new GF-3 satellite data.

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“…We also investigated the responses of the HH-pol, and VV-pol backscatter of

and

backscatter correlated better with soil moisture than

for HH-pol and VV-pol ( R > 0.650, Table 4 ).…”

Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields

Zhang

Meng

Yao³

et al. 2018

Sensors

Self Cite

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“…A sensitivity analysis between the GF-3 total radar backscatter σ°(HH and VV polarizations) and in situ SMC was conducted based on all the eld measurements data to explore whether SMC could be retrieved directly using regression methods, as shown in Figure 5. GF-3 total radar backscatter σ°(HH and VV polarizations) was correlated with SMC, which is consistent with previous ndings [55], demonstrating the potential of GF-3 satellite data for SMC retrieval. However, R 2 between σ°and in situ SMC, both with HH and VV polarizations, was lower than 0.146 (Table 3), thus indicating that simple regression methods cannot achieve high-precision inversion of SMC.…”

Section: Correlations Between In Situ Smc and Correspondingsupporting

confidence: 91%

Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

Meng

Zhang

Xie

et al. 2018

Advances in Meteorology

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Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

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“…SMAP soil moisture estimates tend to dry-down faster than estimated soil moisture of the hillslopes which is consistent with the study on the SMAP moisture dry-down rates (Shellito et al, 2016) and an overall assessment of SMAP soil moisture products (Chan et al, 2016). Chen et al (2018) speculated that underestimation in auxiliary surface temperature of SMAP could be one of the contributors to dry-bias of SMAP soil moisture estimates.…”

Section: Basin-scale Soil Moisturesupporting

confidence: 87%

Data-driven stochastic model for basin and sub-grid variability of SMAP satellite soil moisture

Jadidoleslam¹,

Mantilla²,

Krajewski³

et al. 2019

Preprint

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This study focused on the utility of coarse surface soil moisture observations for applications that require high resolution surface soil moisture information. This was accomplished by quantifying the information content of average soil moisture for three different spatial scales of 81 km2, 790 km2, and 4,400 km2. In situ point observations of soil moisture from 31 stations in Iowa were used to develop a spatial stochastic model that assumes hillslope-scale model parameters are independent. Soil moisture dry-downs and wetting regimes were analyzed using rain gauge and soil moisture sensor data. The statistical nature of dry-downs were parameterized using a power-law decay, and soil moisture increases due to rainfall were parameterized using a non-dimensional logistic curve that is a function of soil moisture deficit. The resulting stochastic model is used to quantify the magnitude of the standard deviation, σ(θ), and skewness G(θ) as a function of the areal average. We show that the greatest information content (small spatial standard deviation) of average observation corresponded to values near the minimum or the maximum soil moisture with σ(θ) < 5%, while average observations for intermediate soil moisture values had the lowest information content with σ(θ) > 20%. The differences in information content as a function of the areal average were consistent with the statistical nature of soil moisture that can be interpreted as small range bounded variable. However, this study provides quantitative estimates for the magnitude of the sub-grid and basin scale variability, documenting the utility for applications that require high resolution information. These results form the basis for the investigation of spatial runoff production in response to rainfall and to inform plot scale agriculture applications.

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Soil Moisture Retrieval From SMAP: A Validation and Error Analysis Study Using Ground-Based Observations Over the Little Washita Watershed

Cited by 59 publications

References 41 publications

Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields

Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields

Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

Data-driven stochastic model for basin and sub-grid variability of SMAP satellite soil moisture

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