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.

High-spatiotemporal resolution soil moisture (SM) plays an essential role in optimized irrigation, agricultural droughts, and hydrometeorological model simulations. However, producing high-spatiotemporal seamless soil moisture products is challenging due to the inability of optical bands to penetrate clouds and the coarse spatiotemporal resolution of microwave and reanalysis products. To address these issues, this study proposed a framework for multi-source data merging based on the triple collocation (TC) method with an explicit physical mechanism, which was dedicated to generating seamless 1 km daily soil moisture products. Current merging techniques based on the TC method often lack seamless daily optical data input. To remedy this deficiency, our study performed a spatiotemporal reconstruction on MODIS LST and NDVI, and retrieved seamless daily optical soil moisture products. Then, the optical-derived sm₁, microwave-retrieved sm₂ (ESA CCI combined), and reanalysis sm₃ (CLDAS) were matched by the cumulative distribution function (CDF) method to eliminate bias, and their weights were determined by the TC method. Finally, the least squares algorithm and the significance judgment were adopted to complete the merging. Although the CLDAS soil moisture presented anomalies over several stations, our proposed method can detect and reduce this impact by minimizing its weight, which shows the robustness of the method. This framework was implemented in the Naqu region, and the results showed that the merged products captured the temporal variability of the SM and depicted spatial information in detail; the validation with the in situ measurement obtained an average ubRMSE of 0.046 m³/m³. Additionally, this framework is transferrable to any area with measured sites for better agricultural and hydrological applications.

Integrating satellite observations into hydrologic models has the potential to enhance soil moisture (SM) retrievals at high spatial and temporal resolutions. The hydrological model SWAT can produce spatiotemporally continuous soil moisture product with high resolution. This study proposed to physically couple the SWAT and Tau-omega radiative transfer model to retrieve SM with a spatial resolution of 30 m. First, the SWAT model integrated a physically-based SM module was used to simulate hourly SM, from which the brightness temperature ( TB ) at 30 m was obtained by forwardly simulating the Tau-omega model. Then, the simulated TB at fine resolution and SMAP TB at coarse resolution were merged by Kalman filter into a disaggregated TB. Finally, SM at 30m resolution was retrieved from the disaggregated TB . The proposed methods were evaluated over the Lanjiang River Basin. The results show that the retrieved SM is highly correlated with two official SMAP SM products and that it has a better accuracy than five SMAP SM products. Particularly, due to the high resolution, the retrieved product captured the heterogeneity of SM within and among fields, compared to the individual use of the hydrological model or satellite observation. Our proposed approach can aid the decision-making in agricultural management at the field scale.

The Cyclone Global Navigation Satellite System (CYGNSS) has emerged as a key focus in soil moisture (SM) retrieval research due to its high temporal resolution and quasi-global coverage. The Normalized Difference Vegetation Index (NDVI) is widely used for large-scale SM retrieval due to its ability to characterize vegetation's physiological status and growth. In comparison, Sun-Induced Fluorescence (SIF) demonstrates greater sensitivity to SM variations. However, there has been limited research exploring the application of SIF in SM retrieval based on CYGNSS, and existing studies have largely overlooked the potential lag effect of SIF on SM (LESOS). To enhance the accuracy of CYGNSS-based SM retrieval, this study proposed a novel SM retrieval scheme considering the LESOS. The results indicated that there was a lag effect in the response of SIF to SM, and this effect varied significantly across different regions. After accounting for LESOS, the mean root mean square error (RMSE) of the retrieved SM across the entire study area decreased from 0.0360 cm³$\cdot$cm^-3 to 0.0355 cm³$\cdot$cm^-3, while the Pearson correlation coefficient (PCC) increased from 0.5217 to 0.5358. In regions where the lag effect was entirely positive, both metrics showed more substantial improvements: RMSE decreased by 5.7%, and PCC improved by 10.8%. In Hunan Province, where positive lag effects predominated, retrieval accuracy was further enhanced. Moreover, the retrieved SM in the positive delay region showed better consistency with in-situ SM. The proposed method offers a novel solution for high-precision SM retrieval using SIF based on CYGNSS.

Soil moisture (SM) plays a vital role in pan-tropical water and energy cycles, meteorology, climate regulation, and agricultural applications. The Cyclone Global Navigation Satellite System (CYGNSS) offers unique potential for high-temporal-resolution SM monitoring through delay Doppler map (DDM) observables. The DDM captures both coherent and incoherent scattering power, reflecting complex surface characteristics influenced by land surface SM. However, the choice of both retrieval methodology and DDM-based reflectivity significantly influences retrieval accuracy. This study conducts a comprehensive pan-tropical evaluation of SM retrieval using surface reflectivity (SR) estimates derived from signal-to-noise ratio (SNR), power analog, and bistatic radar cross section (BRCS), within three distinct modeling frameworks: (i) pan-tropical unified reflectivity–vegetation–roughness (RVR) model with a single set of parameters applied across all grids integrating reflectivity, vegetation optical depth (VOD), and roughness coefficient (RC), (ii) pixel-by-pixel model using only SR, and (iii) pixel-by-pixel multi-parameter reflectivity-vegetation-temperature (RVT) model integrating SR, VOD, and land surface temperature. The CYGNSS L1 Version 3.2 data of the years 2020 and 2021 are used for calibration and validation, respectively. The results consistently show that the pixel-by-pixel RVT model achieves the highest overall accuracy, with Γ _σ yielding a RMSE of 0.0441 m ³ m ⁻³ , substantially better than the pixel-by-pixel R-only model (RMSE ≈ 0.0561–0.0600 m ³ m ⁻³ ) and the unified-grid RVR model (RMSE ≈ 0.0780–0.1157 m ³ m ⁻³ ). Moreover, BRCS-derived reflectivity (Γ _σ ) outperforms both power analog (Γ _peak ) and SNR (Γ _snr ) across all modeling strategies, confirming its superior sensitivity to SM. These findings underscore three key principles for advancing CYGNSS-based SM retrieval: (i) the necessity of spatially adaptive, grid-specific calibration to account for land surface heterogeneity, (ii) the advantage of using physically robust reflectivity estimate BRCS, and (iii) land cover types, RC, and VOD significantly affect retrieval accuracy. The results provide actionable guidance for the design of next-generation operational CYGNSS SM algorithms and products.