Time series of soil moisture (SM) data in the Qinghai–Tibet plateau (QTP) covering a period longer than one decade are important for understanding the dynamics of land surface–atmosphere feedbacks in the global climate system. However, most existing SM products have a relatively short time series or show low performance over the challenging terrain of the QTP. In order to improve the spaceborne monitoring in this area, this study presents a random forest (RF) method to rebuild a high-accuracy SM product over the QTP from 19 June 2002 to 31 March 2015 by adopting the advanced microwave scanning radiometer for earth observing system (AMSR-E), and the advanced microwave scanning radiometer 2 (AMSR2), and tracking brightness temperatures with latitude and longitude using the International Geosphere–Biospheres Programme (IGBP) classification data, the digital elevation model (DEM) and the day of the year (DOY) as spatial predictors. Brightness temperature products (from frequencies 10.7 GHz, 18.7 GHz and 36.5 GHz) of AMSR2 were used to train the random forest model on two years of Soil Moisture Active Passive (SMAP) SM data. The simulated SM values were compared with third year SMAP data and in situ stations. The results show that the RF model has high reliability as compared to SMAP, with a high correlation (R = 0.95) and low values of root mean square error (RMSE = 0.03 m³/m³) and mean absolute percent error (MAPE = 19%). Moreover, the random forest soil moisture (RFSM) results agree well with the data from five in situ networks, with mean values of R = 0.75, RMSE = 0.06 m³/m³, and bias = −0.03 m³/m³ over the whole year and R = 0.70, RMSE = 0.07 m³/m³, and bias = −0.05 m³/m³ during the unfrozen seasons. In order to test its performance throughout the whole region of QTP, the three-cornered hat (TCH) method based on removing common signals from observations and then calculating the uncertainties is applied. The results indicate that RFSM has the smallest relative error in 56% of the region, and it performs best relative to the Japan Aerospace Exploration Agency (JAXA), Global Land Data Assimilation System (GLDAS), and European Space Agency’s Climate Change Initiative (ESA CCI) project. The spatial distribution shows that RFSM has a similar spatial trend as GLDAS and ESA CCI, but RFSM exhibits a more distinct spatial distribution and responds to precipitation more effectively than GLDAS and ESA CCI. Moreover, a trend analysis shows that the temporal variation of RFSM agrees well with precipitation and LST (land surface temperature), with a dry trend in most regions of QTP and a wet trend in few north, southeast and southwest regions of QTP. In conclusion, a spatiotemporally continuous SM product with a high accuracy over the QTP was obtained.

High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms.

Available ground-based observation networks for the validation of soil moisture remote sensing products are commonly sparse; thus, ground truth determinations are difficult at the validated remote sensing pixel scale. Based on the consistency of temporal trends between ground truth and in situ measurements, it is feasible to estimate ground truth by building a linear relationship between temporal sparse ground observations and truth samples. Herein, auxiliary remote sensing data with a moderate spatial resolution can be transformed into truth samples depending on the stronger representation of remote sensing data to spatial heterogeneity in the validated pixel relative to limited sites. When solving weighting coefficients for the relationship model, the underlying correlations among the in situ measurements cause the multicollinearity problem, leading to failed predictions. An upscaling algorithm called ridge regression (RR) addresses this by introducing a regularization parameter. With sparse sites, the RR method is tested in two cases employing six and nine sites, and compared with the ordinary least squares and the arithmetic mean. The upscaling results of the RR method show higher prediction accuracies compared to the other two methods. When the RR method is used, the six-site case has the same estimation accuracy as the nine-site case due to maintaining the diversity of in situ measurements through the analysis of the ridge trace and variance inflation factor (VIF). Thus, the ridge trace and VIF analysis is considered as the optimal selection method for the existing observation networks if the RR method will be used in future validation work. With a different number of sites, the RR method always displays the best estimation accuracy and is not sensitive to the number of sites, which indicates that the RR method can potentially upscale sparse sites. However, if the sites are too few, e.g., one to four, it is difficult to perform the upscaling method.

Multiple soil moisture products have been generated from data acquired by satellite. However, these satellite soil moisture products are not spatially or temporally complete, primarily due to track changes, radio-frequency interference, dense vegetation, and frozen soil. These deficiencies limit the application of soil moisture in land surface process simulation, climatic modeling, and global change research. To fill the gaps and generate spatially and temporally complete soil moisture data, a data assimilation algorithm is proposed in this study. A soil moisture model is used to simulate soil moisture over time, and the shuffled complex evolution optimization method, developed at the University of Arizona, is used to estimate the control variables of the soil moisture model from good-quality satellite soil moisture data covering one year, so that the temporal behavior of the modeled soil moisture reaches the best agreement with the good-quality satellite soil moisture data. Soil moisture time series were then reconstructed by the soil moisture model according to the optimal values of the control variables. To analyze its performance, the data assimilation algorithm was applied to a daily soil moisture product derived from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Microwave Radiometer Imager (MWRI), and the Advanced Microwave Scanning Radiometer 2 (AMSR2). Preliminary analysis using soil moisture data simulated by the Global Land Data Assimilation System (GLDAS) Noah model and soil moisture measurements at a multi-scale Soil Moisture and Temperature Monitoring Network on the central Tibetan Plateau (CTP-SMTMN) was performed to validate this method. The results show that the data assimilation algorithm can efficiently reconstruct spatially and temporally complete soil moisture time series. The reconstructed soil moisture data are consistent with the spatial precipitation distribution and have strong positive correlations with the values simulated by the GLDAS Noah model over large areas of the region. Compared to the soil moisture measurements at the medium and large networks, the reconstructed soil moisture data have almost the same accuracy as the soil moisture product derived from AMSR-E/MWRI/AMSR2 for ascending and descending orbits.

Successfully applied in the carbon research area, sun-induced chlorophyll fluorescence (SIF) has raised the interest of researchers from the water research domain. However, current works focused on the empirical relationship between SIF and plant transpiration (T), while the mechanistic linkage between them has not been fully explored. Two mechanism methods were developed to estimate T via SIF, namely the water-use efficiency (WUE) method and conductance method based on the carbon–water coupling framework. The T estimated by these two methods was compared with T partitioned from eddy covariance instrument measured evapotranspiration at four different sites. Both methods showed good performance at the hourly (R² = 0.57 for the WUE method and 0.67 for the conductance method) and daily scales (R² = 0.67 for the WUE method and 0.78 for the conductance method). The developed mechanism methods provide theoretical support and have a great potential basis for deriving ecosystem T by satellite SIF observations.

Soil moisture (SM) is an important land-surface parameter. Although microwave remote sensing is recognized as one of the most appropriate methods for retrieving SM, such retrievals often cannot meet the requirements of specific applications because of their coarse spatial resolution and spatiotemporal data gaps. A range of general models (GMs) for SM analysis topics (e.g., gap-filling, forecasting, and downscaling) have been introduced to address these shortcomings. This work presents a novel strategy (i.e., optimized wavelet-coupled fitting method (OWCM)) to enhance the fitting accuracy of GMs by introducing a wavelet transform (WT) technique. Four separate GMs are selected, i.e., elastic network regression, area-to-area regression kriging, random forest regression, and neural network regression. The fitting procedures are then tested within a downscaling analysis implemented between aggregated Global Land Surface Satellite products (i.e., LAI, FVC, albedo), Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST, and Random Forest Soil Moisture (RFSM) datasets in both the WT space and the regular space. Then, eight fine-resolution SM datasets mapped from the trained GMs and OWCMs are analyzed using direct comparisons with in situ SM measurements and indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. The results demonstrate that OWCM-derived SM products are generally closer to the in situ SM observations, and better capture in situ SM dynamics during the unfrozen season, compared to the corresponding GM-derived SM product, which shows fewer time changes and more stable trends. Moreover, OWCM-derived SM products represent a significant improvement over corresponding GM-derived SM products in terms of their ability to spatially and temporally match RFSM. Although spatial heterogeneity still substantially impacts the fitting accuracies of both GM and OWCM SM products, the improvements of OWCMs over GMs are significant. This improvement can likely be attributed to the fitting procedure of OWCMs implemented in the WT space, which better captures high- and low-frequency image features than in the regular space.