A Coupling Model for Soil Moisture Retrieval in Sparse Vegetation Covered Areas Based on Microwave and Optical Remote Sensing Data

Kong, Jinling; Yang, Jing; Zhen, Peipei; Li, Jingjing; Yang, Liping

doi:10.1109/tgrs.2018.2849009

Cited by 24 publications

(24 citation statements)

References 44 publications

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“…Specifically, Zhang et al estimated soil moisture over the western Loess Plateau area with a complicated landscape using ENVISAT ASAR data with VV polarization, and the result was strongly influenced by the terrain variation leading to the invalid estimations in the steep inhomogeneous slope of hill region [66]. Kong et al retrieved soil moisture over the blown-sand area, namely Mu Us Desert located in the northern Loess Plateau, and the low soil moisture with major volumetric content between 0 and 15% corroborates our results well [64]. In summary, all the estimated results previously addressed indicated that soil moisture in the Loess Plateau is low.…”

Section: Reliability Evaluation Of Soil Moisture Estimationsupporting

confidence: 83%

“…With respect to the roughness parameters, Zribi and Dechambre previously introduced the original roughness parameter Z s = s 2 /l as a roughness parameter combination [22]. Since the numerical relations are based on sensor parameters and soil conditions in the AIEM, different forms of combined roughness parameter were developed by Yang et al [63] and Kong et al [64] over respective soil conditions. Therefore, the comprehensive roughness proposed in this paper is novel but appropriate for reducing the unknowns in the AIEM in the Loess Plateau region from Sentinel-1A.…”

Section: Reliability Evaluation Of Soil Moisture Estimationmentioning

confidence: 99%

“…Several studies on the soil moisture estimation over the Loess Plateau from SAR have been also reported [64][65][66]. Specifically, Zhang et al estimated soil moisture over the western Loess Plateau area with a complicated landscape using ENVISAT ASAR data with VV polarization, and the result was strongly influenced by the terrain variation leading to the invalid estimations in the steep inhomogeneous slope of hill region [66].…”

Section: Reliability Evaluation Of Soil Moisture Estimationmentioning

confidence: 99%

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An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images

et al. 2019

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As an essential ecological parameter, soil moisture is important for understanding the water exchange between the land surface and the atmosphere, especially in the Loess Plateau (China). Although Synthetic Aperture Radar (SAR) images can be used for soil moisture retrieval, it is still a challenge to mitigate the impacts of complex terrain over hilly areas. Therefore, the objective of this paper is to propose an improved approach for soil moisture estimation in gully fields based on the joint use of the Advanced Integral Equation Model (AIEM) and the Incidence Angle Correction Model (IACM) from Sentinel-1A observations. AIEM is utilized to build a simulation database of microwave backscattering coefficients from various radar parameters and surface parameters, which is the data basis for the retrieval modeling. IACM is proposed to correct the deviation between the local incidence angle at the scatterer and the radar viewing angle. The study area is located in the Loess Plateau of China, where the main land cover is mostly bare land and the terrain is complex. The Sentinel-1A SAR data in C-band with dual polarization acquired on October 19th, 2017 was adopted to extract the VV&VH polarimetric backscattering coefficients. The in situ measurements of soil moisture were collected on the same day of the SAR acquisition, for evaluating the accuracy of the SAR-derived soil moisture. The results showed that, firstly, the estimated soil moisture with volumetric content between 0% and 20% was in the majority. Subsequently, both the RMSE of estimation values (0.963%) and the standard deviation of absolute errors (0.957%) demonstrated a good accuracy of the improved approach. Moreover, the evaluation of IACM confirmed that the improved approach coupling IACM and AIEM was more efficient than employing AIEM solely. In conclusion, the proposed approach has a strong ability to estimate the soil moisture in the gully fields of the Loess Plateau from Sentinel-1A data.

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Section: Reliability Evaluation Of Soil Moisture Estimationsupporting

confidence: 83%

Section: Reliability Evaluation Of Soil Moisture Estimationmentioning

confidence: 99%

Section: Reliability Evaluation Of Soil Moisture Estimationmentioning

confidence: 99%

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An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images

et al. 2019

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“…Compared with other vegetation descriptors, NDWI obtained from reflectance data of 1.57-1.65 µm band is the most suitable for removing the influence of vegetation on soil moisture estimation for the wheat areas [30,46,47]. According to the research of Jackson et al [27,48,49], m veg can be obtained by the NDWI through the spectral index method, and the relationship is as follows:…”

Section: Modified Water Cloud Modelmentioning

confidence: 99%

“…Accounted for the limited sampling interval and profile length of the measuring equipment, it is difficult to obtain the surface roughness parameters in practice, and the accuracy of the obtained roughness parameters could not be guaranteed. Aiming at the problem of surface roughness parameterization, new combined roughness parameters were proposed by integrating RMS height and correlation length to characterize the bare soil surface conditions [27][28][29][30]. However, this method cannot avoid the requirement of surface roughness parameters in field measurements.…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data

Zhang

Lang

Zheng

2021

Water

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The objective of this paper is to propose a combined approach for the high-precision mapping of soil moisture during the wheat growth cycle based on synthetic aperture radar (SAR) (Radarsat-2) and optical satellite data (Landsat-8). For this purpose, the influence of vegetation was removed from the total backscatter by using the modified water cloud model (MWCM), which takes the vegetation fraction (fveg) into account. The VV/VH polarization radar backscattering coefficients database was established by a numerical simulation based on the advanced integrated equation model (AIEM) and the cross-polarized ratio of the Oh model. Then the empirical relationship between the bare soil backscattering coefficient and both the soil moisture and the surface roughness was developed by regression analysis. The surface roughness in this paper was described by using the effective roughness parameter and the combined roughness form. The experimental results revealed that using effective roughness as the model input instead of in-situ measured roughness can obtain soil moisture with high accuracy and effectively avoid the uncertainty of roughness measurement. The accuracy of soil moisture inversion could be improved by introducing vegetation fraction on the basis of the water cloud model (WCM). There was a good correlation between the estimated soil moisture and the observed values, with a root mean square error (RMSE) of about 4.14% and the coefficient of determination (R2) about 0.7390.

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Soil moisture influenced the variability of air temperature and oasis effect in a large inland basin of an arid region

Hao

Zhang

2021

Hydrological Processes

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Soil moisture plays a significant role in land‐atmosphere interactions. Changing fractions of latent and sensible heat fluxes caused by soil moisture variations can affect near‐surface air temperature, thus influencing the oasis's cooling effect in arid regions. In this study, the framework for the evaporative fraction (EF) dependence on soil moisture is used to analyse the impacts of soil moisture variation on near‐surface air temperature and the oasis effect. The result showed that soil moisture's contribution rate to EF was significantly higher than that of EF to temperature. Under the interaction of temperature sensitivity to EF and EF to soil moisture, the ∂T/∂ϴ presented a similar tempo‐spatial variation with both of the above. It was most significant in oasis areas during summer (−1.676), while it was weaker in plain desert areas during the autumn (−0.071). In the study region, the effect of soil moisture variation on air temperature can reach 0.018–0.242 K for different land‐cover types in summer. The maximum variation of soil moisture in summer can alter air temperature by up to 0.386 K. The difference in temperature variability between the oasis and desert areas promoted the formation of the oasis effect. For different oasis, the multi‐year average oasis cold effect index (OCI) ranged from −1.36 to −0.26 K. In comparison, the average summer OCI ranged from −1.38 to −0.29 K. The lower bound of the cooling effect of oasis ranged from −4.97 to −1.69 K. The analysis framework and results of this study will provide a new perspective for further research on the evolution process of the oasis effect and water–heat balance in arid areas.

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A Coupling Model for Soil Moisture Retrieval in Sparse Vegetation Covered Areas Based on Microwave and Optical Remote Sensing Data

Cited by 24 publications

References 44 publications

An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images

An Improved Approach for Soil Moisture Estimation in Gully Fields of the Loess Plateau Using Sentinel-1A Radar Images

Soil Moisture Retrieval during the Wheat Growth Cycle Using SAR and Optical Satellite Data

Soil moisture influenced the variability of air temperature and oasis effect in a large inland basin of an arid region

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