Passive microwave remote sensing has witnessed unprecedented progress in soil moisture (SM) estimation over the decades. However, it is challenging to estimate SM accurately due to the insufficient understanding of microwave emission mechanisms. A ground-based radiometry experiment is undertaken over an agricultural field, toward the reexamination and improvement of the microwave emission models and retrieval algorithms of SM and vegetation water content (VWC). This article reports the preliminary analysis of the experimental data and the calibration of the$\tau $–$\omega $model against the collected measurements. First, the collected multifrequency dual-polarized brightness temperature (TB) reflects the temporal variation of surface SM and a significantly negative correlation between them is observed, with the coefficient of determination ($R^{2}$) and slope (S) of a linear fitting line ranging from 0.036 to 0.367 and from −16.7 to −81.5, respectively. Surface roughness and VWC impact the relationship between TB and SM, with variable$R^{2}$and S observed. Second, the calibrated parameters have improved the model performance, with$R^{2}$greater than 0.65 and root mean standard error (RMSE) less than 4.7 K at all frequencies and polarizations. The parameter values are frequency- and polarization-dependent, and the best performance of the model simulation is observed at V-polarization of L- and Ku-bands, with$R^{2} =0.80$and RMSE =4.69 K at the L-band and$R^{2} =0.74$and RMSE =2.9 K at the Ku-band. Overall, the experiment has provided valuable datasets for calibrating forward models and the calibrated model will facilitate the improvement of surface parameters (e.g., SM and VWC) retrieval.

The effective use of observational constraints to mitigate the impacts of surface roughness and vegetation cover and the quantitative estimation of soil moisture from nonstationary microwave signals present critical challenges. Integrating the temporal constraints imposed by observations can provide additional information for achieving improved soil moisture estimation accuracy. Considering the time-dimensional autocorrelation of time-series passive microwave brightness temperatures, blind source separation (BSS) was introduced for soil moisture estimation. The single-channel BSS was used to decompose the brightness temperature into several intrinsic mode functions (IMFs). The Akaike information criterion and average silhouette width were used to recognize the number of blind sources based on the IMFs for multidimensional signal reconstruction. The multichannel BSS was carried out to decompose the reconstructed multidimensional signals to determine the trends of their blind sources to estimate soil moisture. An experiment conducted in Naqu demonstrated that the BSS-based method effectively estimated soil moisture, with an RMSE of 0.027 cm3/cm3. Through 1000 experiments, an error-bound analysis indicated that the method is robust, maintaining an average soil moisture estimation error of 0.032 cm3/cm3[range 0–0.074 cm3/cm3]. Additionally, decomposing the SMAP brightness temperature for soil moisture estimation in Tianjun, Maqu, and Pali yielded good performance, with RMSEs of 0.037 cm3/cm3, 0.047 cm3/cm3, and 0.041 cm3/cm3, representing a significant improvement over the traditional inversion algorithms. Moreover, the proposed method bypasses the challenges of remote sensing-based soil moisture estimation, which is affected by variables such as surface roughness and vegetation cover. This changes the limitations of traditional soil moisture estimation methods that rely on microwave models.

Improving the accuracy of remotely sensed soil moisture (SM) is a challenging and popular topic. Quantifying the uncertainty in the SM inversion process and enhancing the confidence of SM retrieval are promising ways to address this challenge but have received little attention. We present a Bayesian probabilistic inversion algorithm that can simultaneously retrieve SM, surface roughness, and vegetation optical depth and quantify the uncertainty in the inversion. The proposed algorithm is evaluated using airborne polarimetric L-band multibeam radiometer (PLMR) observations. We use three combinations, 3-angular observations at V-polarization (3CV), 3-angular observations at H-polarization (3CH), and 6-channel observations (6CA), to identify the optimal configuration for SM retrieval by taking advantage of PLMR’s dual-polarization and multiple angles. Uncertainties are quantified by introducing multiple uncertainty quantification metrics into Bayesian posterior distributions of SM retrievals. The estimates are validated against multiscale ground-based measurements, including manual measurements and wireless sensor network (WSN) measurements, and the spatial representativeness of the ground-based reference regarding the validation of pixel-scale SM retrievals is discussed. The 6CA attempt yields the best SM estimates (correlation coefficient (R)$\ge0.864$, root mean square error (RMSE)$\le0.04~\text{m}^{3}/\text{m}^{3}$, and unbiased RMSE (ubRMSE)$\le0.035~\text{m}^{3}/\text{m}^{3}$), while the 3CH attempt yields the lowest uncertainty. In addition, dense manual measurements are more representative than sparsely distributed WSN measurements. Overall, combining dual-polarized observations yields the best SM estimates but introduces additional uncertainty. This study highlights uncertainties quantification in SM inversion and thus provides confidence in SM inversion, facilitating improved SM retrieval algorithms.

Despite efforts made to model and predict COVID-19 transmission, large predictive uncertainty remains. Failure to understand the dynamics of the nonlinear pandemic prediction model is an important reason. To this end, local and multiple global sensitivity analysis approaches are synthetically applied to analyze the sensitivities of parameters and initial state variables and community size (N) in susceptible-infected-recovered (SIR) and its variant susceptible-exposed-infected-recovered (SEIR) models and basic reproduction number (R0), aiming to provide prior information for parameter estimation and suggestions for COVID-19 prevention and control measures. We found that N influences both the maximum number of actively infected cases and the date on which the maximum number of actively infected cases is reached. The high effect of N on maximum actively infected cases and peak date suggests the necessity of isolating the infected cases in a small community. The protection rate and average quarantined time are most sensitive to the infected populations, with a summation of their first-order sensitivity indices greater than 0.585, and their interactions are also substantial, being 0.389 and 0.334, respectively. The high sensitivities and interaction between the protection rate and average quarantined time suggest that protection and isolation measures should always be implemented in conjunction and started as early as possible. These findings provide insights into the predictability of the pandemic models by estimating influential parameters and suggest how to effectively prevent and control epidemic transmission.

Microwave backscattering models play key roles in surface scattering modeling and soil moisture inversion in active microwave remote sensing. However, numerous evaluations indicate that significant discrepancies between the model simulations and radar observations remain, and these discrepancies are regarded to be attributed to inaccuracies in the models. What do such discrepancies originate from is unclear and has not been comprehensively analyzed. To this end, this paper presents an uncertainty analysis to explore the intrinsic reason for the discrepancies between the backscattering model simulations and radar observations. The probability distribution function and the corresponding statistical characteristics are introduced to describe the uncertainty in the model outputs. We find that the scale dependence of the key model inputs leads to significant uncertainties in the model inputs, and the uncertainties are transferred into the model outputs. Thus, the discrepancies between the model simulations and radar observations are intrinsically caused by the spatial scaling and related uncertainties of key model inputs. In short, the scale mismatch between the model inputs and remote sensing pixels is an intrinsic factor that causes the discrepancies between the model simulations and radar observations. This finding suggests that the scaling effect of model inputs should be carefully considered when using the backscattering models at the pixel scale, and equivalent inputs matched at the corresponding scales should be developed for remote sensing applications. Thus, this analysis insights into the scale dependence of inputs for backscattering models and suggests to provide scale-matched inputs where the models are applied at different scales.

The soil dielectric constant, the basis of the microwave remote sensing for soil moisture and salinity estimations, is one of the main parameters of microwave remote sensing research. It is very important to select the high precision soil water and salt dielectric model to improve the precision of soil moisture and salinity inversion. This paper presents a comparison and analysis of permittivity models base on the simulation values of Dobson model considering the salt effect on the free water (Dobson-S model) and WYR model and the measured values under the same condition at L-, C- and X-bands. The complex permittivity of soil samples with different texture, water and salinity contents by microwave vector network analyzer (VNA). Research result indicates that Dobson-S model and WYR model can well simulate the real part of the dielectric constant of saline soil. However, associating to the imaginary part of the saline soil dielectric constant, the results show: 1) Dobson-S model better than WYR model under the condition of soil volumetric water content (MV) equaling to 0.1(cm3/cm3). 2) WYR model better than Dobson-S model when MV equals to 0.2 and 0.3(cm3/cm3). 3) WYR model better than Dobson-S model at L- and C-bands, while two models performs unsatisfactorily at X-band under the condition of MV equaling to 0.4 (cm3/cm3). The most significant contribution of this analysis is providing an insight for the future joint inversion of soil moisture and salinity based on microwave remote sensing.

Soil moisture (SM) inversions based on active microwave remote sensing have shown promising progress but do not easily meet expected application requirements because a number of inversion algorithms can only produce point estimates of SM and cannot quantify the uncertainty of SM inversions. Although previous studies have reported Bayesian maximum posterior estimations that are capable of retrieving SM within a probabilistic framework, they have primarily focused on the optimal estimators of SM and have typically ignored the uncertainty of SM inversions. This paper presents an SM probabilistic inversion (PI) algorithm based on Bayes' theorem and the Markov Chain Monte Carlo technique and capable of revealing the uncertainty of SM inversions and obtaining highly accurate SM estimates via maximum likelihood estimations (MLEs). The algorithm is implemented based on the advanced integral equation model, water cloud model simulations, and dual-polarized TerraSAR-X observations. The ground SM and vegetation water content (VWC) measurements from the Heihe watershed allied telemetry experimental research experiments are applied for validation. The results show that: 1) uncertainties in SM inversions, defined with respect to the measures of dispersion of SM posterior probability distribution, are approximately 0.1-0.12 m3/m3and 2) an acceptable inversion accuracy is obtained via MLEs, which present an SM Root Mean Square Error (RMSE) of 0.045 and 0.047 m3/m3for bare and vegetated soils, respectively, and a VWC RMSE of 0.45 kg/m2. The presented PI can quantify the uncertainty in SM inversions; therefore, it should be useful for improving active microwave remote sensing estimations of SM.

Passive microwave remote sensing has experienced significant success for soil moisture (SM) inversion. However, quantifying the uncertainties caused by soil parameter sensitivities has not attracted sufficient attention. Although local sensitivity analysis (SA) has been used to describe parameter sensitivity in the past, it fails to quantify parameter sensitivities, especially interactions, for nonlinear microwave emission models. This paper presents a comprehensive evaluation that combines physically based emission models and various global SA algorithms to evaluate parameter sensitivity. All the algorithms exhibit highly consistent sensitivity measures, which means a reliable SA result is obtained. The results indicate that the sums of the main sensitivity indices of SM and surface roughness parameters-root-mean-square height (RMSH) and correlation length-are greater than 0.92 and 0.95 for emissivity and brightness temperature (TB), respectively. Furthermore, we find that: 1) the parameter probability distributions have little effect on the sensitivity measures; 2) the SM sensitivity decreases and the RMSH sensitivity increases as the frequency increases and the incidence angle decreases; and 3) the SM is more sensitive on V-polarized than on H-polarized emissivity and TB, while the RMSH is much more sensitive on the polarization index. The presented global SA quantitatively explains the optimal frequency, incidence angle, and polarization for SM inversion and extends the parameter SA for microwave emission models to a more general framework, as well as provides an implication for bare soil emission modeling and SM inversion.

Development and validation of hydrological cycle elements derived from remote sensing observations are of utmost importance for the study of hydrology at different scales, especially at watershed scale. This paper presents the progress we have made in developing and validating watershed scale hydrological cycle products, mainly including precipitation, snow cover area (SCA), soil moisture (SM), evapotranspiration (ET) and groundwater variation. Corresponding high quality remote sensing products (RSPs) have been produced. In addition, to validate the RSPs of water cycle variables, we established several ground observation networks which can provide extensive and high quality validation dataset. Our efforts significantly improve our understanding in watershed water cycle variables, and the developed water cycle products and validation data products have been widely used in several research domains, providing supporting for several key research projects. Based on these efforts, the developed and validated RSPs having been merged into hydrological and land surface models with the aid of land data assimilation method, to allow us to close the water cycle at the basin scale, and further improve our knowledge on terrestrial water study.

The polarimetric synthetic aperture radar (PolSAR) has been demonstrated a huge application potential in soil moisture (SM) inversion under vegetation. However, this promising approach has seldom been reported applied in an oasis of the northwest arid regions of China. This paper presents an analysis of component decomposition based on Radarsat-2 images. The main purpose of the analysis is to provide a preliminary recognition of the scattering mechanisms. The results of the analysis show that the scattering mechanisms in the vegetated oasis are very complex, which implies that scattering mechanisms must be decomposed before SM inversion in a vegetated surface. The analysis provide basis for the SM inversion under vegetation in an oasis of arid regions.

A profound and comprehensive understanding of the sensitivity of soil parameters related to backscattering coefficient is significant for the use of active microwave algorithms for soil moisture inversion. This paper presents a global sensitivity analysis (SA) based on the Advanced Integral Equation Model for soil moisture retrieval. The analysis involves diverse parameter ranges, sensor frequencies, incidence angles, surface correlation functions, and polarizations across various experiments. The primary objectives are to quantitatively and systematically evaluate the parameter sensitivities and their variations under various conditions, resulting in an improved understanding of microwave scattering and suggesting potential approaches to the improvement of soil moisture retrieval. The performance of this SA leads to the parameter sensitivities being quantified. Sensitive and insensitive parameters are distinguished. The existence of the former informs the direction of model calibration, implying that these parameters can be inverted with high confidence. Setting the latter as constants would be a step toward model simplification. Various conditions are observed to influence the parameter sensitivities, suggesting that it is possible to perform soil moisture or roughness inversions under the most sensitive conditions for the parameters. Finally, an SA of various combinations of dual-polarization, dual-frequency, and dual-incidence-angle backscatter is conducted. The results suggest that certain combinations enhance the sensitivities of certain parameters and allow for better estimation of their values. Ultimately, the presented global SA highlights the quantitative and systematic evaluation of parameter sensitivities, particularly their interactions, leading to a more profound understanding of scattering and an improvement in soil moisture estimation.

The dynamic monitoring and mapping of soil salinization is a practical significance work at present. In this paper, the middle reaches of Heihe River, China, was taken as a study case to discuss the effectiveness of extracting saline land information applying decision tree approach, based on Landsat TM data acquired on Sep.23, 2007. Through visual interpretation and statistical analysis of spectral characteristic associated with field survey and Google Earth image with higher resolution, finally five feature variables: thermal infrared band (TM6), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), the third component of MNF rotation (MNF3) and the wetness of K-T transformation (TC3) were selected to construct decision tree model by setting the proper threshold values. The research suggested that MNF3 is an optimal band to discriminate saline land from other object-grounds on condition of MNF<;-1. The water body and vegetation district can be extracted by NDVI and MNDWI, respectively. Combining MNF3, TC3 and TM6 can well obtain sandy land and farmland information. The overall accuracy of classification results achieves 85.34% and Kappa Coefficient is 0.795, both of which show the effectiveness and feasibility of decision tree approach for monitoring and mapping spatial distribution of soil salinization.

MODIS data and in situ data were used to establish a real thermal inertia model to retrieve soil thermal inertia of Heihe River Basin (HRB) and a soil thermal inertia-soil moisture model to calculate the soil moisture of the basin. Meanwhile, the data of two observation stations in the basin was used to validate the result of retrieval. The correlation coefficients of those between retrieved and observed at two stations were 0.853 and 0.917, respectively. The result shows the method meet the requirement of soil moisture inversion.

The 250m 16-day composite EOS/MODIS products (MOD13Q1) from 2000 to 2006 were chosen for monitoring annual and seasonal variation of Gahai Lake area. And the effects of meteorological factors on Gahai Lake were also discussed. The method of single band with threshold value was applied to lake extraction. The results showed that in 2000~2002 Gahai Lake often dried up, but the lake water appearance time advanced each year. Since 2003, Gahai Lake didn't drought up. The annual average, maximum and minimum area of Gahai Lake increased year after year. Due to some recovery measures, such as the Project “Zhongqu River Water to Gahai Lake” and the Project “Converting Grazing to Grassland”, Gahai Lake area expanded quickly. Its seasonal variation presented peak-valley-peak trend, and the appearance time of peak value and valley value were different each year. In addition, Gahai Lake area was distinctly influenced by meteorological factors.