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Optical remote sensing imagery can serve as a crucial data source in numerous fields. However, the susceptibility to weather conditions (like clouds and rain) can render the cloud contamination, leading to the significant influence on optical images. The image quality can be required to restore the spectral information in cloud-contaminated regions. In this study, the spectral information was reconstructed in the cloud-contaminated regions using an object-oriented approach. Synthetic aperture radar (SAR) data was integrated with the radar signals and land cover information, in order to serve as the constraints for the reconstruction. The distance matrix was calculated on the SAR images, and then transplant onto the optical images for the replacement. The pixel-level matching accuracy was enhanced to maximize the retention of spectral information from the original images. A solid foundation was provided for the accurate extraction of subsequent land surface information. The performance was also evaluated to verify the improved model. The comparative experiments were conducted with the traditional pixel replacement, weighted linear regression, and curvature-driven. Visual inspection was carried out over various scenes. The results revealed that the superior quality was achieved in the imagery reconstruction in the thick cloud areas and cloud shadows. Particularly, the consistency was maintained among various land cover pixels in the reconstructed areas by the object-oriented method. The boundaries and areas were accurately delineated in the various types of fine-grained land cover. Quantitative analysis was conducted to validate using some metrics, such as correlation coefficient, root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The spectral characteristic curves of the pixels were reconstructed to closely match those of the reference images, with the correlation coefficients exceeding 0.99. Notably, significant improvement was observed in the spectral curve recovery of vegetation pixels with the enhanced relative to the rest. The radar vegetation indices were optimized to employ an object-oriented matching mechanism during pixel reconstruction. The traditional pixel replacement model was optimized effectively. Furthermore, the band reflectance was closely resembled that of the reference images. Among the five bands of cloud-contaminated images after reconstruction, the highest correlation coefficients were observed with Band 2 and Band 3 reaching 0.925 and 0.922, respectively, and RMSE values of 0.009 and 0.007, respectively. The reconstructed images shared the minimal quality loss and high similarity to the reference images, with the highest PSNR and SSIM values across all bands. Specifically, the structural similarity values of the reconstructed Band 2 and Band 3 images were 0.902 and 0.910, respectively, with the PSNR values reaching 31.978 and 33.173, respectively. The superior image quality was obtained similar to reference images, which was consistence with spectral characteristics. The experimental results were used to the qualitatively and quantitatively validated the effectiveness of the reconstruction. The object-oriented approach can be expected to reconstruct the spectral information in the cloud-contaminated regions. The remotely sensed images were consistently produced with the higher spectral reconstruction accuracy in the different types of land cover and spectral bands. However, the replacing pixel spectral information was relied primarily on from single-image data. Some limitations was matched the effectiveness between cloudy and cloud-free regions, when reconstructing the large-scale, thick cloud areas in single remote sensing images. Additionally, the global searching can fail to the optimal reconstruction for the locally contaminated cloud pixels. Therefore, further investigation is required to determine the optimal local search range for the pixel matching using different extents of cloud contamination. [ABSTRACT FROM AUTHOR]

Field roads have been the most important transportation ways for commercial grain and agricultural products in hilly and mountainous regions in China. Especially, the youth labor can rapidly migrate from the rural to the urban areas against everincreasing urbanization. Current manual production cannot fully meet the large-scale and precision agriculture in recent years, owing to the protracted yield cycle and low rate of return. Therefore, it is urgent to implement the mechanization and intelligent agriculture for the national food security in hilly and mountainous regions. Among them, the autonomous and safe operation of intelligent machinery can be critical to navigate or circumvent obstacles on field roads. However, the conventional machine vision cannot accurately and rapidly construct the visual guidance lines in such terrains. In this study, the extraction was proposed to enhance the visual guidance and obstacle avoidance using millimeter-wave radar and vision fusion. The state information was also detected from the target objects on the field road. The specific steps were as follow. Preprocessing techniques were employed to filter the portion of the radar object data. A multi-target tracking was utilized to eliminate the interference data for the continuous tracking of dynamic objects. Accurate radar object data was then obtained for subsequent data fusion. A semantic segmentation network was created using Deeplabv3+, and then leveraged a dataset of the adjacent field roads. The millimeter-wave radar and vision data were synchronized in both time and space via the timestamp alignment and least squares-based coordinate transformation. Extraction approach was then established for the visual guidance lines, particularly for the scenarios where dynamic target state was both available and unavailable. A series of experiments were carried out to validate the extraction of visual guide line. The average errors of detection were ranged from 1.60 to 9.20 pixels at the real road midpoints in the scenes without dynamic targets. Moreover, the visual guidance lines were successfully extracted for the obstacle avoidance proactively when encountering dynamic targets. Evidently, the integrated approach was effectively overcome the constraints of traditional machine vision, thereby enhancing the safety and reliability of machinery operation in hilly and mountainous terrains. Meanwhile, the efficacy was depended mainly on the precise projection of millimeter-wave radar data onto the visual plane. Simultaneously, the variables were markedly introduced the positional discrepancies, such as the posture of platform (including pitch and roll). Furthermore, the driveability of the operational area was effectively evaluated in the forthcoming regions. At the same time, the real-world conditions were more complex in the frequent presence of common elements like livestock, vehicles, and tricycles. The resilience of approach can be expected to improve the radar data processing for the detection of static objects. Moreover, positional deviations can also be mitigated from mechanical movements. As such, the precise visual guidance lines were established in the hilly and mountainous terrains. Therefore, the radar data processing can be focused on the dataset expanding and position deviation that induced by various factors. The intelligent level of agricultural machinery can be improved in hilly and mountainous area. The finding is of great significance to consolidate national food security. [ABSTRACT FROM AUTHOR]

Non-destructive testing of tree roots has the vast application potential to the root system evaluation of fruit and ancient trees for the better management of plant health. Among them, ground-penetrating radar (GPR) can be expected to nondestructively detect the tree roots, due to the portable and low-cost. However, the interpretation of GPR data is dependent mainly on the manual analysis, leading to the reduced automation and accuracy. Moreover, the clutters and noise that caused by soil heterogeneity can severely affect the accuracy of root detection and identification. In this study, a detection method was proposed for the clutter suppression and root parameter prediction of ground penetrating radar. The data collection was divided into two parts: the dataset and field test. The datasets consisted of simulated and synthesized real data. The roots with the radius ranging from 5 to 30 mm were randomly distributed within 300 mm underground. The field experiment was conducted in three sand pits. The soil heterogeneity was simulated to create the different moisture levels, when adding water into different parts in two of the dry sand pits. The rest sandpit was a condition of higher water content after heavy rainfall. At the same time, the roots were excavated to verify the model under real soil conditions. Four test pits contained the roots of different tree species, the different soil environments, as well as the different radii and depths. The attention mechanism was integrated into the U-net model, in order to enhance the capability in the clutter suppression and root target reflection restoration. A clutter suppression network was constructed to test the effectiveness of the model. The encoding-decoding structure of U-net was separated the target root hyperbolic reflection from the overall background. The attention mechanism enabled the network to better adaptively focus on the hyperbolic reflection of the target root system without identifying the clutter as the target reflection. The clutter suppression was realized for the original B-scan image to remove the adverse effects, due to the soil heterogeneity and radar antenna coupling. Furthermore, the network of root parameter prediction was constructed using residual blocks and inception. The multi-scale receptive field of inception was used to extract the global and local features, and simultaneously to predict the root radius and depth. The predicted results were obtained by parallel inputting the clutter suppressed image and the original B-scan image into the network. The effectiveness of the model was evaluated using datasets and field experiments. The test results of clutter suppression were evaluated using peak signal to noise ratio (PSNR) and structural similarity (SSIM), which were 39.42 dB and 0.991, respectively, better than before. The test results on datasets showed the mean absolute error (MAE) of 1.7 mm, and the coefficient of determination R² value of 0.914 for the root radius prediction, while the mean absolute error (MAE) of 6.3 mm and the R² value of 0.989 for the root depth prediction. The clutter suppression was also achieved in the better performance than the rest on the field data. The maximum prediction errors for the root radius and depth were 1.85, and 13.6 mm, respectively, where the total average relative error was 6.55%. Influencing factors were determined for the prediction effectiveness of ground-penetrating radar. The future research directions were pointed out as well. The findings can be applied to the actual prediction of root depth and radius of tree roots, particularly for the health administration of fruit trees and the protection of ancient trees.protection of ancient trees.protection of ancient trees. [ABSTRACT FROM AUTHOR]

All pineapples are harvested manually at present in China. But manual harvesting cannot fully meet the large-scale production against the ever-increasing greying workforce. Fortunately, automatic navigation can be expected to develop the pineapple harvester. In this study, a path planning algorithm was proposed as the navigation scheme to improve the mechanization and automation level of pineapple harvesting. An improved RRT* algorithm was also used for global path planning. Firstly, the self-heuristic idea was used to constrain the generation range of sampling points. Then, the bias probability pbias was introduced to generate the random sampling points. Specifically, the sampling points were randomly generated with the probability p in the space, when p>pbias. Otherwise, the target point was used as the sampling point, in order to decrease the blindness of sampling point generation. Thirdly, the gravitational field of the artificial potential field, and the concept of direction weight were introduced in the new node expansion. The weights wg and wk were assigned to the directions of the sampling and target point, respectively, where the direction was constrained in the expansion of the new node. Fourthly, the bidirectional expansion was used to speed up the iteration speed in the double-tree expansion. Finally, the greedy algorithm was applied to prune the redundant nodes of the path. The Cantmull-Rom interpolation function was also used to smooth the path corners. Three environments (including multiple obstacles, mazes, and narrow passages) were created to simulate the path planning process, in order to evaluate the performance among the improved navigation path planning, RRT* and bidirectional RRT* algorithm. Planning time, node number, and path length were selected as the indicators. Each algorithm experimented with 30 times in every single environment. The average, maximum, minimum, and standard deviation were calculated for the simulation data of the three indicators, respectively. The simulation results showed that the average planning time of the path planning algorithm in the three environments was 17.97%, and 46.12% higher than that of the RRT* and bidirectional RRT* algorithms, respectively, while the average programming speed was 4.7, and 2.2 times as rapid as that of the RRT*, and bidirectional RRT* algorithm, respectively. Furthermore, the average node number was 87.22% and 52.52% less than that of the RRT* and bidirectional RRT* algorithms, respectively. The average path length was 3.81% and 6.08% less than the RRT* and bidirectional RRT* algorithms, respectively. The field test showed that the planning time was only 14.12% and 20.34% of the RRT* and bidirectional RRT*, respectively. The iteration number was 80.89% and 69.70% less than that of the RRT* and bidirectional RRT*, respectively. In addition, the rotation angles larger than 60° on the path planned by RRT* and bidirectional RRT* algorithms were 1.56 and 2.06 times as much as that of the improved, respectively, and the rotation angles larger than 100° on the path were 1.55 and 2.18 times. The improved RRT* algorithm can fully meet the path navigation requirements of agricultural machinery in the field. The pineapple harvester can run along the planned path to the target point as moving with the speed of 0.2, 0.4, and 0.6 m/s, but the position and heading deviation increase with the moving speed. This finding can provide a sound reference for the navigation development in agricultural machines. [ABSTRACT FROM AUTHOR]

An accurate and rapid extraction of fruit tree canopy has been one of the most important steps to determine the precise application of pesticides in modern agriculture. However, the current detection cannot fully meet the harsh requirements in the complex environment of an orchard in recent years. In this study, a novel extraction system was proposed to identify the fruit tree canopy in an orchard using millimeter-wave radar. Firstly, the region of interest (ROI) was set to remove a large number of environmental point clouds, and then the random sampling was used to fit and segment the ground point clouds. Secondly, a density-based clustering was selected to integrate the resolution of millimeter-wave radar and the distance parameter of the point cloud. An adaptive density clustering was then established to deal with the different densities of the point cloud at different distances when using the millimeter-wave radar. As such, the neighborhood radius was adaptively adjusted in different directions, according to the distance from the point cloud to the radar. The point cloud density was then used to identify the fruit tree canopy, further improving the recognition performance of the point cloud. Finally, an Alpha-shape was selected to reconstruct the three-dimensional surface of the fruit tree. The optimal parameters of the three-dimensional reconstruction were achieved to evaluate the volume from Alpha values and the geometric measurement. The random sampling was also used to extract the structural parameters. The parameters of the mathematical model were estimated from a data set with a large number of outliers using multiple iterations. A curve model was then obtained closer to the actual situation. In addition, a field test was conducted to verify the feasibility of the model in the Loquat Plant Resource Garden (113.367 789°E, 23.164 129°N) of South China Agricultural University on April 2th, 2021. 40 yellow-bark fruit trees were also scanned in the test. The results showed that a higher performance was achieved to effectively identify and extract the point clouds of a single tree canopy, where the F1 score was 93.7% for the recognition accuracy of fruit trees. Furthermore, the average relative errors of the plant height and crown width in the fruit trees were 8.7% and 8.1%, respectively, while the coefficients of determination were 0.84 and 0.92, respectively, and the root mean square errors were 16.39 and 7.82, respectively, compared with the manual measurement. In addition, the average volume of fruit trees was 5.6 m³ using Alpha-shape, increasing by 59.4% in the accuracy of volume, compared with the traditional. Nevertheless, two recommendations can be addressed during this time: 1) To identify the fruit trees with the overlapping canopies using a relatively higher resolution of millimeter-wave radar; 2) To quantitatively extract the volume of the fruit tree in the harsh sense. Anyway, the millimeter-wave radar can be widely expected to accurately extract the canopy information in an orchard. The finding can provide a new promising technology to extract the canopy information for the data collection and automatic operation in modern agriculture. [ABSTRACT FROM AUTHOR]

Cohesive soil is a common soil medium in land resource evaluation and farmland land consolidation. The dielectric properties, physical, chemical properties and mechanical properties of cohesive soils are closely related to the soil water content. Ground penetrating radar (GPR), as a fast and non-destructive detection tool, can provide information about electromagnetic wave propagation in the ground, so as to realize the detection of underground media. Especially in soil water content detection, ground penetrating radar (GPR) has been widely used. However, due to the complex composition of cohesive soil and the fast attenuation of electromagnetic waves in cohesive, GPR is rarely used in the detection of water content in cohesive soil, which greatly restricts the application and development of GPR technology. The early signal of radar wave is the signal generated by the superposition of air wave and ground wave due to the small distance between the transmitting and receiving antenna, and it can also reflect the physical properties of the surrounding medium. The results show that there is a certain correlation between the early signal amplitude, waveform and soil water content. For cohesive soils, most of the radar wave energy has been lost near the surface due to the high conductivity, so ground waves are more useful waveform signals than reflected waves. In order to verify the accuracy of GPR, AEA (average amplitude envelope) method in detecting the water content of cohesive soil, this study used GPR to detect the soil water content of field farmland (<0.3 m) before and after rainfall, and TDR was used for comparison. The results showed that there was a certain correlation between soil water content and clay content. In a large area (1 000 m long survey line), the average soil water content detected by TDR was 14.16, 16.91 cm³ /cm3, respectively. The average soil water content detected by AEA method was 14.62, 17.88 cm³ /cm3, respectively, and the difference between them and the measured water content by TDR was 0.46, 0.97 cm3 /cm3, respectively. The water content detected by the two methods had extremely significant correlation (P<0.01), and the correlation coefficients were 0.825 and 0.814, respectively (P<0.01). Within a small range (40 m×40 m), the water content of cohesive soil detected by TDR before and after rainfall was 14.11, 16.77 cm³ /cm³, respectively. The average soil water content detected by AEA method was 14.86, 17.46 cm³ /cm³, respectively, which were 0.74, 0.69 cm3 /cm³ higher than that measured by TDR. The correlation coefficients of water content detected by the two methods were 0.701 and 0.827, respectively (P<0.01). Analysis of the water cut plane distribution of the two detection methods showed that the soil water content detected by AEA method was similar to TDR method. The results showed that ground penetrating radar (GPR) AEA model could accurately detect the water content of cohesive soil. The conventional GPR common mid point method and fixed offset method were used to detect the water content of cohesive soil in the study area. The results showed that neither of the two methods could effectively detect the moisture content of cohesive soil. [ABSTRACT FROM AUTHOR]

To meet the requirements of accuracy and reliability of plant protection robot in ridge identification and route planning, also improve the working conditions of farmers, and achieve an unmanned operation purpose, an algorithm based on multi-support-vector proportioning weight of SVM (support vector machine) to identify the ridge line of vineyards, and the path planning of plant protection robots were proposed. The strategy first uses Kalman filter to pre-process coarse orchard data information obtained by Lidar scanning. According to the principle that Kalman filter complies with Gaussian distribution, the prior point between 2 adjacent points was taken as the prior state, and the latter point was used as the observed point to obtain posteriori state estimation, so as to realize data integration and analysis. With its good system state estimation characteristics, the collected data can be used to judge the trend of the ridge line, so as to correct the noise outliers in the data and improve the readability of the data. Then according to the characteristics of the vineyard branch ridge and the characteristics of ridges line with clearly separable spacing, and corresponding to the situation that the ridge line on both sides can be completely separated, the method was combined with SVM linear classification. With the unique advantages of the classification and due to that SVM can search the unique segmentation hyperplane, the maximum interval and segmentation hyperplane, classification margins in the ridge environment could be gotten. The split hyperplane obtained at this time would be between ridge lines. However, there was a big deviation from the angle of the direction of the ridge line and the horizontal distance. It could not meet the precise operation requirements of plant protection robots. It needed further data processing and analysis. In order to obtain accurate position of the center of ridge line, finally, the relative weights were assigned to the sample points of each ridge based on the geometric interval relationship between the sample points on both sides of the ridge and the corresponding SVM classification marginal line. The classification marginal line was reformed according to the number of sample points and the relative weights. According to the condition of the product of the interval relationship between each sample point and the classification margin, their relative weight must be consistent with the quality value of classification margin. The random sampling consistency iteration method (RANSAC) would avoid the error of cost estimate, and could estimate the parameters of the mathematical model from a group of observed data with outliers, so as to obtain the predicted safety location of the ridge. Although the pre-estimated security location of ridge line was not necessarily consistent with the actual location of the vineyard ridge, navigation line could be obtained indirectly by the security ridge line on both sides and the principle of angle bisector which could meet the requirements of precision operation of plant protection robot. Operation guidance line for plant protection robot could be acquired. After a number of actual samples were tested, the average angular deviation between the fitted navigation line and the actual ridge centerline was 0.72°, and the average distance deviation of the relative plant protection robot was 4.22 mm. Experimental results showed that this algorithm could effectively identify and locate the navigation route needed by the plant protection robot. The fitted navigation line could meet the requirements of accurate operation of the plant protection robot in the vineyard. However, the redundancy of the algorithm was relatively large, and the time required to process data in a single time was about 2.05 s. With the accelerated calculation speed of the processor in the future, the algorithm provided in the article can provide a reference solution for such a problem. [ABSTRACT FROM AUTHOR]

The expanding trend of soil salinization has become more and more severe especially in arid and semi-arid areas in the northwest China. Therefore it is specially important to dynamically monitor the soil salinization in the arid and semi-arid areas scientifically, accurately and rapidly. Microwave remote sensing technique has become a promising method to detect and monitor the soil salinity due to its many advantages. The aim of this study was to investigate the capability of C-band RADARSAT-2 SAR (synthetic aperture radar) data in soil salinity estimation over agricultural fields. In this study, Jiefangzha zone of Hetao irrigation district, Inner Mongolia, China was selected as the study area. Based on the back-scatter coefficient value and soil salt content, this paper used 3 kinds of methods including the multiple linear regression (MLR), geographically weighted regression (GWR) and back propagation artificial neural network (BP ANN) to establish the quantitative inversion models of soil salt content. Soil salinity information was extracted from the RADARSAT-2 SAR data, which had a kind of fine four-polarization SLC (single look complex) format and were bought in 2013, and covered an area of 25 km × 25 km with 8 m ground resolution. Taking the spatial unevenness distribution of the saline soil into account, 69 sampling points were designed in the study area, and field digging depth of soil was 10 cm. Hand-held GPS (global position system) receiver was used to record the coordinates of sampling points, and the soil total soluble salt content was measured in the indoor. Mainly use the SAR Scape module of ENVI software to perform the radar image processing, including radiometric calibration, geometric correction, slant range turning and filtering. The four-polarization back-scatter coefficient values corresponding to the sampling points were extracted based on the previous results by the spatial analysis module of ArcGIS. Total salt content was took as dependent variable, four-polarization back-scatter coefficient values and soil moisture as independent variables, and the MLR and GWR salinity prediction models were established. Use the MLR module of SPSS and the GWR module of GWR4 software to deal with data respectively. The correlation of GWR model was higher than the MLR, but these 2 statistical models were not significant, and the inversion results were difficult to reflect the distribution of severe salinity and saline-alkali soil. Therefore the emphasis was focused on the building of the 8:140:1 structure of three-layer BP ANN model. Training set contained the data of 49 sampling points in the test area, the input layer was made up of sampling point coordinates, soil moisture, four-polarization back-scattering coefficients and their combined value, and the number of net neurons was 8; the output layer was a neuron corresponding to the total salt content of sample point. After a lot of tentatively computation, the optimal number of neurons in the hidden layer was selected as 140. The hidden layer of the three-layer BP ANN model used the hyperbolic tangent sigmoid activation function, the output layer utilized the linear activation function, the traingda function was selected to perform network learning and training, and the fitting target error was set as 0.1 mg/g. Then the BP model was applied to the RADARSAT-2 remote sensing images to achieve the high-precision quantitative inversion of soil salinity when the BP model inversion accuracy met the requirements of salinity prediction. Whole calculation process was implemented by the MATLAB neural network toolbox. It was found that the correlation of the MLR and GWR models was weak and their standard error (SE) was 0.55 and 0.47 mg/g respectively, but the SE of internal and external inspection using the ANN (BP) model was only 0.24 and 0.33 mg/g respectively, better than the other 2 models, whose salinity area accounted for about 65. 4%, and was basically consistent with ground validation. The artificial intelligence inversion model of soil salt considered the moisture’s influence, was directly based on the polarization radar scattering coefficients and their composition, and had no complex dielectric constant model. So the ANN (BP) model can reduce the smoothing effect compared with the 2 traditional models and improve the accuracy and reliability of model predictions, which meets the needs of soil salinity monitoring to a certain extent, and can promote and develop the application of microwave remote sensing in the soil salinity monitoring. [ABSTRACT FROM AUTHOR]

Soil water content measurement is an important and tedious task, and the ground penetrating radar (GPR) technique has been used for soil water content distribution estimation. The GPR is a fast and nondestructive tool, capable of providing information of electromagnetic wave propagation. The conventional methods of GPR for detecting soil water content mainly include the air direct wave method, the surface direct wave method, the reflected wave method and the borehole GPR method. Because the electromagnetic wave attenuation in aqueous medium is fast, the air direct wave method and the surface direct wave method can only detect the soil water content near the surface, and these models are vulnerable to interference, and difficult to determine the zero take-off time, so the detection results are always not accurate. Reflection wave method is widely used at present, but it requires clear reflector in the scope of radar wave propagation, and it is difficult to apply in complex underground area. The borehole GPR is mainly suitable for the detection of soil moisture content in small scale. The radar wave average envelope amplitude (AEA) method is to find out the radar average amplitude envelope signal value having the best correlation with the shallow strata dielectric constant by analyzing and establishing the relationship between the radar early-time average amplitude envelope signal value and shallow strata dielectric constant. The instantaneous amplitude has been computed by considering the amplitude of the Hilbert transform of signals simulated by a typical common-offset configuration, using the value of average envelope amplitude calculated in different early-time windows to back calculation the dielectric constant, then the Topp formula is used to calculate the soil water content. In order to assess the accuracy of GPR in detecting the surface soil moisture content, early-time ground penetrating radar signal AEA method is used to detect the sandy soil with water contents of 0.05, 0.15 and 0.25 cm3/cm3 in laboratory. Comparing the water contents derived by AEA and Time domain reflectometry it shows that the root mean square errors are 0.026, 0.015 and 0.01 cm3/cm3, and the effective detection depths of AEA method are 0.9, 0.6 and 0.3 m, respectively, and with the increase of water content, the detection accuracy of AEA method increases, but the detection depth decreases sharply; when water moisture content is small, the detection depth of AEA method increases, while the detection error also increases accordingly. Field detection is conducted at the same time. Comparing the water content derived by TDR and drilling sampling and AEA, it indicates that, within a certain accuracy requirement, AEA method can get the similar soil water content with TDR and drilling sampling, and is able to get soil water content and distribution state for large area fast, accurately and continuously. It is feasible to detect the shallow stratum soil moisture content by using the method of early-time ground penetrating radar signal AEA. [ABSTRACT FROM AUTHOR]

In land reclamation project of western China, the sandy loam is very fragile for erosion in its ecosystem. The main purposes of the sandy loam soil remediation are the anti-desertification and the improvement of vegetation growth ability. Since water content of sandy loam soil and degree of compacting are two important physical parameters, which have great influences on the vegetation growth, a fast and accurate detection method is needed to determine these parameters. In the land quality evaluation, the sampling method is important. The remote sensing method is based on big surface elements, which is not suitable for the micro examinations. The method of taking soil samples in the field and then oven-drying in the laboratory can yield good precision in the tests, but very inefficient and destructive to soil ecosystem. The other methods as Time Domain Reflectometry (TDR), γ-ray, et al. have the common disadvantages of discontinuity and inaccuracy. The ground penetrating radar (GPR) technique with fast, convenient, precise and nondestructive characteristics is widely used in quality test of engineering projects. The GPR transmits and receives high-frequency electromagnetic waves through the antenna for the accomplishment of detections. This study focused on sandy loam soil collected from a research area in Inner Mongolia where the land consolidation project had just finished. The research started with sandy loam soil samples gathering from the research area. Then a wooden container was built, which was filled with sandy loam soil samples as a physical model. After calibrating water contents and degrees of compactness of the sample in the model, a GPR system with 900MHz antenna was then used for determining soil water content and compaction. The GPR hardware in this study was the 'GR' radar system which was designed and built by China University of Coal Mining technology, Beijing. The Cadzow analysis was used in the Auto-Regressive and Moving Average (ARMA) power spectral recognition to reduce the number of parameters which can cause complex calculations. Then, the original data gathered by the GPR from the physical model was processed by the optimized ARMA power spectrum recognition in order to get the power spectrum data in frequency domain. To find out the relation between power spectrum and water contents and degrees of compactness of sandy loam soil, the distribution of radar power spectral energy in frequency domain needed to be analyzed. The envelopes of power spectrum distributed in different ranges and center frequencies, which represented the sandy loam soil with different water contents and degrees of compactness of different samples in the physical model. The distribution of high and low frequency envelopes showed primarily that the variation of different frequency spectrum energy was related to water contents and degrees of compacting in soil samples. Therefore, to define a cut-off point that can divide power spectrum data in frequency of sandy loam soil was very critical. By analyzed the spectrum data of the tests, two cut-points have been found according to maximum correlations of power spectrum and water contents and degrees of compactness, respectively. It was found that the low frequency part of the power spectrum energy had maximum correlation (r = 0.952) with the water content when the cut-point was at 670 MHz. Similarly, there was another cut-point at 1 000 MHz which had the maximum correlation (r = 0.947) with the degrees of compactness of the sandy loam soils. Two correlation models have been built to describe the water contents and degree of compactness with radar ARMA power spectrum energy data, respectively. The correlation models have been used in the actual tests of water contents and degrees of compactness of sandy loam soils in the research area in Inner Mongolia of China. The results showed that the values inverted by the models were well related to the real values obtained from the conventional test methods for soil water content and degree of soil compaction. . Based on experimental and actual tests and analysis, it was possible to use GPR technique to detect water contents and degrees of compactness of sandy loam soil in similar conditions as the research area. Also, the speed and the precision of physical properties detection can be improved by using ARMA power spectrum estimation method. So, it has significant meaning for quality tests of land consolidation projects. However there still some insufficiencies in this research. The ranges of application of the relation models were limited to the sandy loam soils. And the soil moisture contents and degrees of compactness were the average values from the ground surface to the effective detection depth. Future research was needed to solve these issues. [ABSTRACT FROM AUTHOR]

Grassland is one of the most widely distributed terrestrial ecosystems on Earth. It is critical to accurately estimate grassland biomass of the desert steppe, and to understand its dynamics changes in order to study the regional carbon cycle and the sustainable use of grassland resource. The integration of multisensory data provides the opportunity to explore the benefits of grassland biomass effective estimation via multiple data sources. Based on the field survey data, quadrature-polarization (qual-pol) RADARSAT-2 Synthetic Aperture Radar (SAR) C-band data was utilized to develop a biomass regression model and estimate the aboveground biomass (AGB) of the Caragana microphylla shrubbery in the desert steppe region in the northwest of China. The research area was located at Yangzhaizi Village in Ningxia Autonomous region. Grassland inventory was carried out in 45 randomly distributed plots (30 m × 30 m), and the data was used for either model development or validation. An allometric regression model was established to estimate its biomass for every Caragana microphylla shrub with CH (crown width multiple plant height) variable. The local allometric regression equation was applied to calculate AGB per plot. Furthermore, the correlation between the aboveground biomass of Caragana microphylla shrubbery and the radar backscatter coefficient was analyzed. The AGB regression model was developed by integrating field measurements of 25 sample plots with RADARSAT-2 backscatter remotely sensed data. The multiple stepwise regressions algorithm was applied to develop the AGB model and estimate the grassland above-ground biomass from RADARSAT-2 backscatter data. The developed model was validated by using 20 independent sample plots. Simultaneously, RADARSAT-2 images were fused with the optical HJ1B data by using a discrete wavelet transform for the land cover classification. The image classification based on the objects was performed by using the empirical-statistical machine learning techniques, such as a classification and regression trees (CART) algorithm. The overall accuracy and Kappa value of the proposed method was 90.2% and 0.88, respectively. It indicated that the proposed method performed well for the land use and land cover (LULC) classification. An AGB biomass distribution map was produced by RADARSAT-2 backscatter data in combination with the land cover classification image and AGB regression model. As a comparison, the AGB from RADARSAT-2 estimates were compared with the results from the HJ1B normalized difference vegetation index (NDVI) model. The result showed that there was a good quantitative relationship between the AGB from the microphylla shrubbery and the RADARSAT-2 radar backscatter coefficient. A good fit was found between AGB estimated by RADARSAT-2 and ground-measured biomass with a R² (coefficient of determination) and Root Mean-Square Error (RMSE) of 0.71 and 14.2 kg/hm² respectively. Its estimated accuracy was higher than that of the HJ1B NDVI model (R²=0.27, RMSE=20.58 kg/hm²). Consequently, the fusion of optical and radar data for the land cover classification could effectively improve the accuracy of the object recognition for the land cover classification and the estimation accuracy of AGB estimation. Radar remote sensing data could be used for quantitative studies on grassland structural parameters. Moreover, it demonstrated a high potential for monitoring indicators of grassland ecosystem by combining the optical with polarimetric SAR remote sensing imanges. [ABSTRACT FROM AUTHOR]

Wheat lodging is one of the most popular agriculture disaster. It has a great influence on the yield formation of wheat and causes huge loss every year. However, available means for monitoring crop lodging in large area is very limited. The potential capability of radar remote sensing for monitoring wheat lodging was explored in this study. After the backscattering coefficient of wheat field parcels were extracted at different polarization mode, radar backscattering behavior of typical lodging wheat parcels and the one of typical normal wheat parcels was dynamically compared at different growth stages and at different polarization mode, based on 5 multi-temporal Radarsat-2 images that covered an entire wheat growth cycle from sowing to near harvest. Results showed that there were distinct differences between lodging and normal wheat parcels in all polarization channels. When compared to normal wheat, the backscattering coefficient at HH (H, horizontal polarization) polarization decreased remarkably, while the one at VV (V, vertical polarization) polarization increased and the one at HV polarization increased slightly for lodging wheat. It was also found that polarimetric feature from synthetic aperture radar (SAR) data was very sensitive to wheat lodging and the sensibility was caused by unique structural characteristics of wheat vegetation and inherent polarization characteristics of SAR observation. Then a method based on radar polarimetric index, which took the full advantage of this sensibility, was put forward to monitoring wheat lodging. And the method was validated by in-situ data collected in 28 wheat parcels in Shangkuli Farmland in Inner Mongolia, China, at heading and filling stages of spring wheat. The result revealed that the polarimetric index, especially based on the ratio of dual-polarization backscattering coefficient (HH/VV and HH/HV), had excellent performance for distinguishing lodging from normal wheat parcels: all the 11 lodging wheat parcels with different severity were detected successfully from 28 parcels. In addition, the results were compared with the ones by single polarization channel data. It showed that the polarimetric index method had good anti-inference ability such as resisting influence of wheat growth difference, and can better reflect the intrinsic feature of lodging. It should be mentioned that the monitoring result may be influenced by harvest situation and vegetation water content etc. Moreover, while optical remote sensing relied on its spectral features to monitor crop lodging, radar remote sensing utilized polarimetric features to monitor crop lodging since SAR observation had advantage in reflecting the structural variation of lodging. Therefore, radar remote sensing have great potential for crop lodging monitoring, and the study presented a simple and effective method for monitoring wheat lodging in large area. [ABSTRACT FROM AUTHOR]