Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

Abstract

Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data.

A Convolutional Neural Network Algorithm for Soil Moisture Prediction from Sentinel-1 SAR Images

“…Accordingly, two new architectures were presented, called CNN architecture (C II ) and CNN architecture (C III ), as shown in Figure 5. We can now say with high confidence that: (1) VV polarization is more sensitive than VH polarization for soil moisture retrieval; similar results have been found by [19][20][21][22][23]. (2) The combination of VV and VH polarization has resulted in improvement in the prediction of soil moisture from Sentinel-1 images, our result agree with [24][25][26].…”

“…We will attempt to demonstrate and prove this in future research. We can now say with high confidence that: (1) VV polarization is more sensitive than VH polarization for soil moisture retrieval; similar results have been found by [19][20][21][22][23].…”

“…There is a significant reduction in performance metrics as a result of the use of the CNN architecture ( C III ): from ( 𝑅 = 0.8664, 𝑀𝐴𝐸 = 0.0144, 𝑅𝑀𝑆𝐸 = 0.0274 ), ( 𝑅 = 0.8554, 𝑀𝐴𝐸 = 0.0159, 𝑅𝑀𝑆𝐸 = 0.0285 ), and ( 𝑅 = 0.8527, 𝑀𝐴𝐸 = 0.017, 𝑅𝑀𝑆𝐸 = 0.0288 ) to ( 𝑅 = 0.8037, 𝑀𝐴𝐸 = 0.0205, 𝑅𝑀𝑆𝐸 = 0.0333 ), ( 𝑅 = 0.7995, 𝑀𝐴𝐸 = 0.0214, 𝑅𝑀𝑆𝐸 = 0.0336), and (𝑅 = 0.7952, 𝑀𝐴𝐸 = 0.022, 𝑅𝑀𝑆𝐸 = 0.034) for Sigma0_VH and Sigma0_VV, Gamma0_VH and Gamma0_VV, and Beta0_VH and Beta0_VV, respectively. Due to the usage of the CNN architecture (C II ), plunged further to about (𝑅 = 0.5465, 𝑀𝐴𝐸 = 0.0392, 𝑅𝑀𝑆𝐸 = 0.0506 ), ( 𝑅 = 0.5384, 𝑀𝐴𝐸 = 0.0394, 𝑅𝑀𝑆𝐸 = 0.051 ), and ( 𝑅 = 0.5307, 𝑀𝐴𝐸 = 0.0396, 𝑅𝑀𝑆𝐸 = 0.0514 ) for Sigma0_VH and Sigma0_VV, Gamma0_VH and Gamma0_VV, and Beta0_VH and Beta0_VV, respectively.We can now say with high confidence that: (1) VV polarization is more sensitive than VH polarization for soil moisture retrieval; similar results have been found by[19][20][21][22][23]. (2) The combination of VV and VH polarization has resulted in improvement in the prediction of soil moisture from Sentinel-1 images, our result agree with[24][25][26] (3).…”

“…The Sentinel-1 (S1) satellite characteristics include high spatial resolution and high revisit time; hence, Sentinel-1 is suitable for mapping and monitoring soil moisture over agricultural areas [19]. Sentinel-1 images can be used to retrieve soil moisture over wheat-covered areas [20]. VV polarization was more sensitive for soil moisture retrieval from Sentinel-1 data as compared to VH polarization [21][22][23].…”

Mapping Phragmites australis Aboveground Biomass in the Momoge Wetland Ramsar Site Based on Sentinel-1/2 Images

“…Radar images may be unaffected by atmospheric illumination and clouds, and can be used together with Sentinel-2 to obtain information on different types of wetland vegetation [23,24]. Therefore, compared with using a single image source, combining the red-edge and radar features of both Sentinel sensors could improve the accuracy of wetland vegetation identification [25,26].…”