Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau

Abstract

This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness parameter based on the hypothesis that the roughness is invariant among SAR acquisitions. Afterward, the vegetation water content (VWC) in the WCM is calculated from the daily MODIS NDVI data obtained by temporal interpolation. To improve the performance of the model, the parameters A, B, and α of the WCM are analyzed and optimized using randomly selected half of the sampled dataset. Then, the soil moisture is retrieved by minimizing a cost function between the simulated and measured backscattering coefficients. The comparison of the retrieved soil moisture with the ground measurements shows the determination coefficient R2 and the Root Mean Square Error (RMSE) are 0.46 and 0.08 m3/m3, respectively. These results demonstrate the capability and reliability of Sentinel-1 SAR data for estimating the soil moisture over the Tibetan Plateau.

Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau

“…Referring to the B value in Table 1, we experimented with 0.01 as the interval between 0.03 and 0.14. When B was 0.05, the soil moisture inversion results were closest to the measured soil moisture value [53]. 2.…”

“…Then we used the bare soil backscatter coefficient as the input data for the DBN to establish the relationship with soil moisture, which enabled the neural network characteristics to perform better and significantly improved the backscatter coefficient inversion soil moisture accuracy. In the Naqu area of the Tibetan Plateau, many scholars have used different models for soil moisture inversion research, for example, Yang combined a vegetation water cloud model and cost distance function to estimate soil moisture [53]; the R 2 was 0.46 and RMSE was 0.08 in the accuracy analysis. Wang used the semiempirical Oh model to estimate the surface roughness parameters to improve the water cloud model before soil moisture inversion [51] and obtained a higher accuracy of soil moisture inversion results (R = 0.89 and RMSE = 0.058).…”

Soil Moisture Inversion in Grassland Ecosystem Using Remote Sensing Considering Different Grazing Intensities and Growing Seasons

“…To date, most studies have primarily examined the influence of various vegetation types on SM inversion [30][31][32][33][34][35]. For example, Lei et al [35] developed an enhanced WCM to retrieve the soil moisture in areas covered by greenwood, deciduous forest, mixed forest, composite shrub grass, and grassland.…”

Retrieving Soil Moisture in the Permafrost Environment by Sentinel-1/2 Temporal Data on the Qinghai–Tibet Plateau

“…In 2017, Bai et al first estimated SM in the alpine steppe region of Magu using Sentinel-1 (S1) data with the WCM [35]. In 2021, Yang et al coupled the improved Oh model in 2004 and WCM to estimate SM in the Nagqu region based on S1 data and MODIS optical data with the assumption of constant surface roughness [36]. Despite the significant advance in scattering modeling, SM inversion from these microwave scattering models are commonly ill-posed and complicated [37,38].…”