Soil moisture (SM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often hindered by the vegetation layer and soil roughness. Most SM inversion algorithms require in situ SM data for a calibration to eliminate these two disturbing factors, while collecting in situ data is a project that consumes a lot of manpower and resources. This paper aims to tentatively develop an inversion algorithm for retrieving SM in the absence of in situ SM in areas covered by winter wheat vegetation. Based on the analysis of the data set simulated by the Michigan Microwave Canopy Scattering (MIMICS) model, an improved ratio model is proposed to remove the effect of the vegetation layer. Through the parameterization of the advanced integral equation model (AIEM), the effect of the soil roughness on the inversion of soil moisture is eliminated. The spatial distribution of SM in winter wheat fields is obtained using the Sentinel-1 SAR and Sentinel-2 images. The comparison results between the inverted SM and the in situ measured data reveal a good correlation (R = 0.85, RMSE = 0.032 cm³·cm⁻³), and the result confirms that the algorithm developed only based on theoretical models can also effectively monitor the spatial changes of SM over winter wheat fields.

While the size of the four-bar linkage is the basis of kinematic performance analysis in a beam pumping unit, there is still a lack of effective and direct measurement of it. Since the motor input power and the polished rod position are commonly used production data, a size identification algorithm of the four-bar linkage based on the motor input power and the polished rod position is proposed in this paper. Firstly, the kinematic model of a beam pumping unit, the speed model of a motor, the initial value, and the state space model are established. Secondly, a Cubature Kalman filter with nonlinear constraints is designed and the size identification algorithm is worked out. Lastly, the model and the size identification algorithm are validated based on the simulated and measured data, and the characteristics of identification with different measurement covariance are studied. The results demonstrate that both the model and the algorithm are feasible. The maximum relative error between the identified and the real size increases from 0.23% to 9.37% when the covariance increases from 10⁻⁶ to 10⁻¹. With the measured covariance of the polished rod velocity, the maximum relative error is 7.09%. A comparison of several current identification methods demonstrates that the proposed algorithm is more accurate.

Fault dynamometer cards are the basis of the diagnosis technique for sucker rod pumping systems. Predicting fault cards with a pumping condition model is an economical and effective method. The usual model is described by a mixed function of the pump displacement and pump load, and it is difficult to use in the prediction method based on the analytical solution of the sucker rod string wave equation. In this paper, a normal pumping condition model described by a function of polished rod velocity is proposed. For the analytical solution of the sucker rod wave equation, an iterative prediction algorithm with pumping condition models is proposed, its convergence is analyzed, and then it is validated by classical finite difference method simulated cards and measured surface dynamometer cards. The results show that the proposed algorithm is accurate. The algorithm has a maximum relative error of 0.10% for the classical method simulated card area and 1.45% for the measured card area. The research of this paper provides an effective scheme for the design, prediction, and fault diagnosis of a sucker rod pumping system with an analytical solution.

Simulating surface conditions by solving the wave equation of a sucker-rod string is the theoretical basis of a sucker-rod pumping system. To overcome the shortcomings of the conventional finite difference method and analytical solution, this work describes a novel hybrid method that combines the analytical solution with the finite difference method. In this method, an analytical solution of the tapered rod wave equation with a recursive matrix form based on the Fourier series is proposed, a unified pumping condition model is established, a modified finite difference method is given, a hybrid strategy is established, and a convergence calculation method is proposed. Based on two different types of oil wells, the analytical solutions are verified by comparing different methods. The hybrid method is verified by using the finite difference method simulated data and measured oil data. The pumping speed sensitivity and convergence of the hybrid method are studied. The results show that the proposed analytical solution has high accuracy, with a maximum relative error relative to that of the classical finite difference method of 0.062%. The proposed hybrid method has a high simulation accuracy, with a maximum relative area error relative to that of the finite difference method of 0.09% and a maximum relative area error relative to measured data of 1.89%. Even at higher pumping speeds, the hybrid method still has accuracy. The hybrid method in this paper is convergent. The introduction of the finite difference method allows the hybrid method to more easily converge. The novelty of this work is that it combines the advantages of the finite difference method and the analytical solution, and it provides a convergence calculation method to provide guidance for its application. The hybrid method presented in this paper provides an alternative scheme for predicting the behavior of sucker-rod pumping systems and a new approach for solving wave equations with complex boundary conditions.

Soil Moisture (SM) plays a key role in the energy exchange between the atmosphere and the land surface. Most of the SM products retrieved from satellite remote sensing data are not suitable for drought monitoring and irrigation management in smart agriculture applications due to their coarse spatial resolution. We propose an SM downscaling method named Water Cloud Change Detection (WCCD) that effectively combines the Water-Cloud Model (WCM) and the Change Detection Method (CDM) to downscale the China Land Data Assimilation System soil moisture (CLDAS_SM, 6000-m resolution) product. The WCM is used to retrieve the soil backscattering at a fine spatial resolution by deducting the canopy backscattering from the surface total backscattering, and the linear regression relationship between soil backscattering and CLDAS_SM is established for each pixel at the coarse scale under the assumption that the surface roughness does not change for dozens of days. The performance of the algorithm is tested in an agricultural crop region in Hebei province of China with Sentinel-1 and Sentinel-2 images. The validation results show that the downscaled SM at different spatial resolutions are in good agreement with the in-situ measurements with the correlation coefficient (R) higher than 0.71 and the Root Mean Squared Error (RMSE) lower than 0.042 cm³×cm⁻³.