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.

The long-term satellite products including MODIS AOD, GOME-2 NO2and OMI O3were collected and processed respectively, and were verified by AErosol RObotic NETwork (AERONET) ground measurements. Then the time series data in eastern China were plotted and analyzed. The results show that MODIS AOD and OMI O3have high correlation with AERONET measurements, the correlated coefficients are 0.81 and 0.94 respectively. The GOME-2 NO2inversion algorithm has lower accuracy which the correlated coefficient (R) is 0.71. All the datasets have seasonal cycles, the maximum values of AOD and trop O3appear in summer, while the NO2appears in winter. After 2013, the value of all satellite products is in a downward trend, which may be closely related to the implementation of the “Ten Atmospheric Regulations” in 2013, and human activities and economic development level will affect the distribution of AOD and trop NO2.

Aerosols have been proven to be associated with the Coronavirus Disease (COVID-19). Satellite-derived aerosol optical depth (AOD) at a medium or high resolution e.g. 20 m may provide a new perspective for public health study, especially controlling the spread of COVID-19. Based on the Sentinel-2 MSI data, this paper uses the structure function method and the 6SV model to perform aerosol retrieval, and obtains the AOD retrieval result with a resolution of 20m in the study area. The results show that the distribution of surface features has important influence and correlation on the retrieval results, and the pixel interval, aerosol model and atmospheric model have obvious influence on aerosol retrieval. Moreover, the AOD inversion value is relatively higher and the error is larger, so the more data need to be collected later to improve and verify the algorithm.

Based on Himawari-8 hourly AOD product, meteorological variables, auxiliary data and PM2.5 observations, this study established a random forest (RF) model to estimate the PM2.5 concentration in China in 2018. The results showed that the estimated PM2.5 has high consistency with the ground observations. Ten-fold cross-validation (CV) result showed that the model has high accuracy, with R2 of 0.801, RMSE of$15.677\mu\mathrm{g}\cdot\mathrm{m}^{-3}$In areas with sparse sites, there are fewer ground observation data, resulting in fewer training samples and poor model accuracy. During the day, the RF model performed best around noon due to the meteorological observation conditions. Seasonally, the RF model has the highest fit goodness in autumn and the lowest in summer. In addition, the model has the lowest RMSE in summer. Result of the model prediction showed that the spatial distribution of PM2.5 prediction and PM2.5 observation are relatively consistent. The high values in PM2.5 are mainly concentrated in Jiangsu, Shandong, Henan and Anhui, southeast of Beijing-Tianjin-Hebei and east of Sichuan Province.

In this study, ChinaHighPM2.5 data sets were used for trend analysis of PM2.5 concentration in China and its five typical regions after the issue of “Air Pollution Prevention and Control Action Plan”. The result shows that the average PM2.5 in the five selected regions from 2014 to 2019 was higher than that of China. China's PM2.5 showed a significant downward trend from 2014 to 2019, with a year on year growth rate of -43.57%. Beijing-Tianjin-Hebei and Chengdu-Chongqing City Group had the most significant decline in PM2.5 concentration, with that of -46.63% and - 53.05% respectively, both higher than the national average. Except for Taklimakan Desert Region, the PM2.5 in other regions showed a clear downward trend and was significant negatively correlated with the year. In addition, the regional difference in PM2.5 concentration decreased with the year, as well as concentration interval.

Due to the Coronavirus Disease (COVID-19) pandemic, the human activities in China and even in the world were reduced in 2020, which also caused the variation of the atmospheric environment, especially atmospheric aerosol emissions. In this paper, the MODIS level-3 gridded atmosphere monthly global joint product in 2019 and 2020 were collected and processed. After preliminary analysis, we found that MODIS annual aerosol optical depth (AOD) over China in 2020 is generally lower than in 2019. In some regions such as Beijing-Tianjin-Hebei and Yangtze River Delta, AOD values dropped the most in February. However, in some months and regions, AOD in 2020 is even higher than in 2019. More studies are still ongoing.

Affected by the Coronavirus Disease (COVID-19) pandemic, almost all students in China have to study online at home from February to June, 2020. In this paper, we discussed the forms of online courses and took Jiangsu Normal University as an example to introduce the online courses of remote sensing in China. The results of the satisfaction survey show that more than 90% of the respondents agree with online courses and believe that online courses can at least meet basic learning needs in the age of COVID-19, and more than 60% of respondents claimed that they had met or exceeded their learning expectations. The major advantages of online course include reducing the gathering of people and thus the risk of infection. However, there are still some problems with online courses, and we hope that these problems can be solved well in the future.

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.

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.

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 Cosmic-ray Soil Moisture Observing System is a promising soil moisture measurement network. It can measure soil moisture at an intermediate spatial scale with a single sensor. In this letter, the measured cosmic-ray neutron counts during the Heihe Watershed Allied Telemetry Experimental Research were used to evaluate the capabilities of the cosmic-ray probe in soil moisture retrieval at a heterogeneous farmland. The Cosmic-ray Soil Moisture Interaction Code model was utilized to model the interaction between the measured neutron counts and the soil water content. Soil moisture at the footprint scale of the cosmic-ray probe obtained using a wireless sensor network (SoilNET) was used as the calibration and validation data. The results show that the cosmic-ray probe is capable of monitoring the hourly heterogeneous soil moisture dynamics at the intermediate spatial scale in a noninvasive way. Moreover, the informative measurement depth of the cosmic-ray probe can also be derived and is consistent with the soil moisture results.

The Heihe Watershed Allied Telemetry Experimental Research (WATER) (HiWATER) experiment is currently being conducted in the Heihe River Basin in China. Its predecessor experiment, the WATER project, was conducted in the same river basin from 2007 to 2010. As an interdisciplinary, intensive, coordinated, and multiscale experiment, WATER produced data that ranged from remote sensing observations to field campaign measurements. These data, which comprise a total of 2.1 TB contained in 255 data sets, were released on the WATER data Web site in July 2010. This letter provides an overview of the WATER data repository, the major data sets, and some data examples. The WATER Information System (WIS), which enables the user community to explore and to order the data in a simple manner, was designed. The data acquisition records indicate that the WATER data sets have received extensive attention and demonstrate that WIS works effectively. Numerous scientific applications have been achieved with the aid of WATER data collection, and some of these applications have revealed that the availability of complete high-quality data produced from a large-scale experiment can provide substantial support to studies of ecohydrological processes and remote sensing and also aid the development of watershed sciences. HiWATER will use the data information system developed during WATER to make it freely available to international scientific communities.

In this paper, a mixed controller for solving the trajectory tracking and point stabilization problems of a mobile robot is presented, applying the integration of backstepping technique and neural dynamics. By introducing a virtual target point, the whole motion process is divided into two parts. The first one is employed to realize tracking control and the other one is adopted to implement point stabilization. Each part produces a feedback control law by using backstepping technique. Moreover, to solve the speed and torque jump problems and make the controller generate smooth and continuous signal when controllers switch, the neural dynamics model is integrated into the backstepping. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, simulation results are given to illustrate the effectiveness of the proposed control scheme.

Radar remote sensing has shown its potential for retrieving soil moisture from soil surfaces. Since the backscattering process is also influenced by soil roughness, the characterization of this roughness is crucial for an accurate estimation. The algorithm proposed in this investigation aiming to obtain the roughness parameters for every SAR pixel which could facilitate the derivation of soil moisture in virtue of multi-angle ASAR images, and combined with a semi-empirical calibration for the correlation length. An application of the means was performed in the middle reaches of the Heihe river basin and achieved satisfactory results (RMSE less than 6 vol %).

According to the characteristics of airfield and harbor from remote sensing images, a method of large target recognition based on the combination of target region and shape features is presented. First, edge detection and improved Hough transform are used to select line segments, the region including regular-array line segments in image is considered as region of interesting (ROI). ROI detection is the base for recognition. Target geometry shape is extracted from ROI using optimum threshold segmentation, which removes location effect and improves efficiency. As calculating shape principal orientations, all shapes are rotated to the same horizontally right to avoid rotation effect. The features extracted from shape implement multi-levels representation with moment features, normalized moment of inertia, length-width ratio and compact ration. Finally, feature vectors are normalized to measure similarity between target and template. Experiments show that target regions can be located accurately using ROI detection and it is effective for target recognition. Besides, the extracted features have good invariability with respect to rotation, translation and scaling, and they comprise local and overall consistency of the target, therefore, the recognition results meet expectations well.

Incorporating the spatial correlation information into the land data assimilation system provides the opportunities to improve the analysis quality of the assimilation system. In order to make use of the spatial autocorrelation information effectively in the land data assimilation system, we used the geostatistics to explore and describe the spatial variations of the microwave brightness temperature data. The corresponding experimental semivariogram model, covariance model, nugget effect, sill value and range were calculated. Then these results were applied to the localization of the microwave remote sensing observation error covariance successfully, and played an important role regarding the enhanced performance of the direct radiance assimilation system.

In order to offer a method for the rehabilitation of injured fingers and a means of quantitative detection and evaluation, an exoskeleton based continuous passive motion (CPM) machine is presented in this paper. Corresponding to each finger of human hand, the CPM machine has 4 degrees of freedom (DOF) driven by two DC motors. The joint force and position sensors are all integrated into the machine. The device can be easily attached and also be adjusted to fit different hand sizes. During the injured fingerpsilas flexion and extension motion the machine can always exert perpendicular forces on the finger phalanges, meanwhile it can achieve the precise control of scope, force and speed of the moving fingers. In order to control the CPM machine, we have also designed an embedded control system based on S3C2410 (a kind of 32-bit RISC microprocessor). The whole system is open-ended for new functions and applications. The function modularization method provides a new thinking of design for the control system.

In order to control the continuous passive motion (CPM) machine for injured fingers, we develop an embedded control platform. We first bring forward the philosophy of function modularization design for control platforms. Then we actually begin to develop an embedded control platform of the CPM machine by using the method of function modularization. The core of the control platform consists of two main parts: the data acquisition function module and the motor control function module, both are based on the serial peripheral interface (SPI) network. The whole control platform is open-ended for new functions and applications. It can be easily expanded if we add new modules to the SPI network. Primary experiments have proved that the control platform works well and the design method of function modularization provides a new method for the design of control platforms

A curvature optical fiber sensor is reported in this paper. The curvature measurement sensitivity is improved using bend enhanced method. The operation principle of this intensity modulate macro-bend curvature optical fiber sensor is proposed based on light scattering theory: the bend of sensitive zone brings about mode coupling and leads to the variation of surface scattering loss. The mathematic model of relationship among light loss, bending curvature, surface roughness and parameters of the fiber's configuration is also presented

In this paper, a sensor-based motion planning method for robot arm manipulators operating among unknown obstacles of arbitrary shape is presented. It can be applied to on-line collision avoidance with no prior knowledge of the obstacles. The sensitive skin is used to build a description of the robot’s surroundings. This approach is based on the configuration space but the construction of the C-obstacle surface is avoided. The motion planning algorithm consists of three phases. In each phase, the point automation moves along a specified plane, on which the mapping of the obstacles into configuration space can be simplified. Hence, the computation time is reduced and the algorithm can work in real time. The effectiveness of the proposed method is verified by a series of simulations.

This paper deals with sensor-based motion planning method for a robot arm manipulator operating among unknown obstacles of arbitrary shape. It can be applied to online collision avoidance with no prior knowledge of the obstacles. The sensitive skin is used to build a description of the robot's surroundings. This approach is based on the configuration space but the construction of the C-obstacle surface is avoided. The point automation is confined on some planes with square grids in the C-space. A path searching algorithm based on the square grids is used to guide the automation maneuvering around the C-obstacles on the selected planes. To avoid the construction of the C-obstacle surface, the robot geometry model is expanded, and the static collision detection method is used. Hence, the computation time is reduced and the algorithm can work in real time. The effectiveness of the proposed method is verified by a series of simulations.

We address the problems of computing form-closure regions as well as computing optimal fingertip locations yielding n-finger form-closure grasps of three-dimensional (3-D) objects. Given grip points of k fingers not in form-closure, we propose a sufficient and necessary condition for the n-k fingers to achieve an n-finger form-closure grasp. Based on the condition, it is demonstrated that the problem of computing form-closure regions can be formulated as an existence problem of a solution for a set of inequalities. A sufficient condition is also proposed to simplify the computation of form-closure regions. As to searching for optimal fingertip locations, we transform the problem to a nonlinear programming problem subject to a set of constraints arising from that sufficient and necessary condition. Furthermore, a performance index is defined by measuring the distance between the center of mass of the grasped object and the center of the contact points. Finally, we have implemented the approach proposed and verified its efficiency by several numerical examples.

Fixtures play an important role in many manufacturing operations such as inspection, machining, and part fabrication. In the development of a fixture, it is desired that the feasible fixturing surfaces and optimal fixturing points on the workpiece be selected automatically. An algorithm is presented to automate the generation of optimal fixturing points, which totally restrains the workpiece in the fixture, i.e., satisfies the form-closure condition, as well as minimizes the workpiece positional errors. First, a heuristics is proposed for searching a set of fixturing surfaces capable of providing form-closure. Then, we formulate the problem of determining optimal fixturing points on the eligible set of fixturing surfaces as a quadratic programming problem with the workpiece positioning accuracy as the performance index and the robust form-closure requirement as the linear constraints. Finally, the implementation for three numerical examples demonstrates the usefulness and efficiency of the proposed algorithm.

We address the problem of computing optimal fingertip locations yielding n-finger form-closure grasps of polyhedral objects. Given grasping points of m fingers not in form-closure, we show that the calculation of optimal grip points for the n-m fingers can be viewed as a nonlinear programming problem. We propose a sufficient and necessary condition for the n-m fingers to achieve an n-finger form-closure grasp with the m fingers given. Based on this condition, we derive a set of nonlinear constraints for the nonlinear programming problem. Furthermore, we give a performance index serving as an objective function which measures the distance between the center of mass of the grasped object and the contact points to be determined. In addition, the algorithm proposed can be applied to the frictionless grasp. We have implemented the algorithm and verified its efficiency by two examples.

Presents a formulation of the synthesis of 3D frictional form-closure grasps of n robot fingers. First, using a recursive reduction technique, we transform the problem in 6D wrench space to one in three dimensions. Then we rewrite the sufficient and necessary condition for form-closure grasps in the equivalent form of the inconsistency of each of the two sets of linear inequalities. Then we propose three conditions to check the inconsistency of the inequality system, which geometrically indicates whether the convex region formed by the inequality system is empty. We have implemented the algorithms and confirmed their real-time efficiency for the synthesis of 3D form-closure grasps.

Assuming that grasp points of n-1 fingers on a polyhedral object are given and the n-1 fingers do not achieve a form-closure grasp, this paper proposes an efficient algorithm for computing all grasp points on the object for the n-th finger to achieve a form-closure grasp with other n-1 fingers. A sufficient and necessary condition is developed for forming the form-closure grasp. Based on the sufficient and necessary condition, we transform the grasp computation to a problem of: 1) computing the convex cone of the primitive contact wrenches of the n-1 fingers; and 2) checking the intersection between a circular region and a convex polygon in a 2D space. We have implemented the algorithm and confirmed its usefulness by an example.

We have developed a garment cutting robot system which is incorporated in the CAD/CAM system, using laser as its cutting tool. The paper systematically introduces the overall hierarchical structure and each of the CAD functional modules. Then the application of laser in cloth cutting is discussed, and a set of cutting system has also been built. We carried out an experiment using the robot platform (CAD/CAM system) with the laser cutting tool, and the results obtained are given.

This paper addresses the problem of computing n-finger form-closure grasps of polyhedral objects. Given grasping points of m fingers not in form-closure, we propose a sufficient and necessary condition for other n-m fingers to achieve an n-finger form-closure grasp with the m fingers. Based on this condition, it is demonstrated that the problem of computing grasping points of the n-m fingers can be formulated as an existence problem of a solution for a set of linear inequalities. Several sufficient conditions are also presented to guarantee the form-closure property of the n-finger grasp.