The diameter of roots is crucial to study the geometric characteristics of subsurface root structure. Due to the invisibility of the underground root system, its diameter parameters are also difficult to obtain directly. Therefore, estimating the diameter based on ground-penetrating radar B-Scan images is challenging. In this study, CycleGAN-guided multi-objective integration neural network (CMI-Net) was constructed to simultaneously extract root diameter and location. The CMI-Net includes two sub-networks: CycleGAN and YOLOv4-Hyperbolic Position and Diameter (YOLOv4-HPD). The former ensures that the YOLOv4-HPD model, trained on the simulated datasets, can be used in the field environment. The latter can accurately identify the root objects and estimate the root diameter. The performance of the model was evaluated using simulated test dataset and field control experimental dataset. The model’s availability in estimating root diameter was demonstrated by the experimental results.

The spatial distribution and biomass of roots are the key parameters to reflect plant growth and development. The object of this study is to develop a simple method to estimate the spatial distribution and biomass of roots based on GPR data. First, we measured 58 individual full-root systems of Caragana microphylla Lam. by ground-penetrating radar (GPR). Second, we examined the root distribution pattern in horizontal direction using the logistic model. Three critical root traits (i.e., parameters of the logistic model) including total root biomass, active root accretion zone and root growth rate are extracted from the fitted logistic model. Finally, the relationship between canopy area and root traits was tested, which can be further utilized to estimate the root biomass and characterize the root spatial distribution in a large region. GPR is a high-throughput phenotyping tool that allows for the detection of the root spatial distribution and quantitative characteristics.

Nighttime light (NTL) products have become emerging instruments for studying human activity patterns in various applications, including demarcating urban areas, estimating the residential population, and monitoring the economic livelihood of cities. However, the deployment of such products, particularly the Defense Meteorological Satellite Program (DMSP)/Operational Line Scan (OLS) stable NTL data, is also subject to issues of data quality and methodological biases, including interannual inconsistency, saturation and blooming effect, and, in particular, the saturation problem, posing a challenge for further applications. This study proposed a novel saturation correction method based on regression model and radiance-calibrated NTL data (SARMRC) to correct the saturation effect in annual stable NTL data. The proposed method was applied to mainland China. The results show that SARMRC can effectively recover the light intensity distribution within the saturated areas of the stable NTL data. The corrected images were proven to be more spatially consistent with the reference National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data and can better reflect the temporal development of cities compared to existing saturation correction methods (e.g., the index-based vegetation adjusted NTL urban index (VANUI) method and the interpolation method). The superior performance of SARMRC can be attributed to: 1) better utilization of both radiance-calibrated and stable NTL products and 2) the employment of training pixel selection and logarithmic model as well as double-year adjustment. The new method is expected to improve the data quality of DMSP/OLS stable NTL data for analyzing both local and regional socioeconomic activities.

In recent years, ground penetrating radar (GPR) has become increasingly important as a nondestructive way to explore plant roots. Automatic recognition and localization of root objects from GPR images presents a significant challenge. GPR images for the root system contain complicated hyperbolic signals that appear deformation depending on root size, orientation, aggregation degree and soil background. This paper presents a new deep learning approach, YOLOv4-hyperbola, that provides fully automatic recognition and localization of root objects from GPR images. YOLOv4-hyperbola improves the YOLOv4 (You Only Look Once v4) architecture by introducing keypoints detection branch in order to accurately locate roots while identifying them. The YOLOv4-hyperbola model was trained by combining field datasets and simulated datasets to simultaneously identify and locate hyperbolic features representing potential root objects across GPR images, and evaluated on datasets of root detection from two experiments in the field. Compared with Randomized Hough transform (RHT) method, the proposed approach demonstrated higher accuracy and efficiency in root object detection on GPR image. YOLOv4-hyperbola was able to accurately recognize and locate abnormal hyperbolic signals caused by the complexity of root system in nature. The validation on the two independent datasets showed that the proposed approach had good generalization and great application potential for real-time detection and location of roots over large areas in the field.

Root water content (RWC) is a vital component in water flux in soil–plant–atmosphere continuum. Knowledge of RWC helps to better understand the root function and the soil–root interaction and improves water cycle modeling. However, due to the lack of appropriate methods, field monitoring of RWC is seriously constrained. In this study, we used ground-penetrating radar (GPR), a common geophysical technique, to characterize RWC of coarse roots noninvasively. An automatic GPR data processing framework was proposed to (1) identify hyperbolic root reflections and locate roots in GPR images and (2) extract waveform parameters from the reflected wave of identified roots. These waveform parameters were then used to establish an empirical model and a semiempirical model to determine RWC. We validated the developed models using GPR root data at three antenna center frequencies (500 MHz, 900 MHz, and 2 GHz) that were produced from simulation experiments (with RWC ranging from 70% to 150%) and field experiments in sandy soils (with RWC ranging from 66% to 144%). Our results show that both the empirical and the semiempirical models achieved a good performance in estimating RWC with similar accuracy, i.e., the prediction error [root-mean-square error (RMSE)] was less than 8% for the simulation data and 12% for the field data. For both models, the accuracy of RWC estimation was the highest when applied to 2-GHz data. This study renders a new opportunity to determine RWC under field conditions that enhances the application of GPR for root study and the understanding and modeling of ecohydrology in the rhizosphere.

Soil moisture is generally considered to be a major limiting factor for root growth of shrubs in the arid and semi-arid regions. However, as a typical shrub species, the correlation between root distribution and soil moisture of Caragana microphylla has not been well investigated. Ground-penetrating radar (GPR) has the advantage of acquiring locations of roots quickly and simply, therefore, we used GPR to acquire locations of roots in the quadrat of 30m*30m, and soil moisture data was acquired by soil drilling method. After processing data, we analyzed the correlation between root density and soil moisture in vertical and horizontal directions. Following conclusions were obtained through vertical and horizontal analysis: In the vertical direction, root density was positively correlated with soil moisture, but the soil moisture was lower in the dense absorbing roots' distribution region, even the preferential flow could enrich soil moisture. Root density and soil moisture also had a positive correlation in horizontal direction, but they were inversely related in the region of dense absorbing roots.

Arid and semiarid grasslands around the world are heavily suffering from shrub encroachment. However, the shrub encroachment process is still poorly understood, especially when historical data field samples are insufficient. High-spatial-resolution (HSR) remotely sensed data provides the only large-scale historical record of grasslands. This study investigated the population structure of Caragana genus plants, a typical shrub in the temperate arid and semiarid grasslands of Inner Mongolia, China. Shrub individuals were identified from HSR images and the age of shrub individuals were estimated, then a caragana population age structure which can calculate mortality and regeneration was established with a negative exponential function.

Due to its in situ and nondestructive nature, ground-penetrating radar (GPR) has recently been applied to the field investigation of plant roots. The discrepancy between the roots and surrounding soils creates a dielectric constant contrast, forming clear hyperbolic reflections on the GPR radargram. The intensity and shape of the reflecting signals from roots are substantially affected by the root orientation as well as the relative geometry between the root in the subsurface and the GPR survey direction on the ground surface. However, no previous study has utilized the information on the intensity and shape of a root's GPR reflection to map its orientation, which is crucial in interpreting radargrams and rebuilding 3-D root system architecture. In this paper, a mathematical formulation of hyperbolic reflection formed by a single root was first deduced based on the principles of electromagnetic wave propagation. Then, using this formulation, curve fitting was conducted on both simulated and field collected data sets by GPR. Information on the horizontal orientation and vertical inclination of a single root was acquired according to the formulation coefficient retrievals. Conditions for this method of application and factors impacting the extraction of root orientation information were analyzed. The results indicated fairly precise root orientation estimations. The proposed method has extended the application of GPR in root investigation, thus advancing the frontier of noninvasive root system architecture mapping.

Positive and unlabeled learning (PUL) algorithm, an one-class classifier which is trained by positive samples and unlabeled samples, has been used in remote sensing classification. However, the effect of training strategy of PUL has not been investigated. This study tested the performances of PUL-SVM on cropland mapping by Landsat TM data using the training samples with different sizes and different purity levels. It is found that the highest accuracy is achieved when the sizes of positive sample and unlabeled sample are comparable if using the random strategy. In contrast, if using the purer positive samples, it is more difficult to find the optimal unlabeled sample size. Therefore, it is recommended the random strategy for the positive samples, and the balanced sizes for positive and unlabeled samples when using PUL-SVM.

Plant self-thinning power law is regarded as an essential regulation which plays an important role in determining population dynamics and community structure. However, little experimental and theoretical studies on the power law have been developed for shrub, and also little has been done from the perspective of below-ground plant parts to reveal the mechanisms underlying the self-thinning process of natural plant especially in field. Taking the shrub, Caragana microphylla, as example, this study revealed the below-ground root biomass-density relationship of Caragana microphylla and explained how and to what extent do below-ground root competition affect the above-ground self-thinning process. The below-ground root systems were surveyed by Ground- penetrating radar (GPR). The root biomass was estimated based on GPR data. Some of the statistical relationships discussed in this study may be useful for predicting root biomass of shrub community in water-limited ecosystems and will also serve as a frame of reference for future studies.

Roots have important functions in the ecosystem. Therefore, establishing root-related parameters such as root size, biomass, and 3-D architecture is necessary. Traditional methods for measuring tree roots are labor intensive and destructive to nature, limiting quantitative and repeated assessments in long-term research. Ground-penetrating radar (GPR) provides a nondestructive method for measuring tree roots. This study investigates the feasibility of a GPR system with 500-MHz, 900-MHz, and 2-GHz measurement frequencies for detecting tree roots and estimating root biomass under controlled experimental conditions in a sandy area. After energy attenuation correction and velocity analysis, not only the individual root in subsurface is able to be located but also the parameters that correlate well with root biomass can be extracted from the processed GPR data. The major findings were as follows. First, both the amplitude and amplitude-area indices were confirmed to be more effective for estimating root biomass after attenuation-effect compensation. This result suggests that the calibration of GPR wave-attenuation effects and velocity changes with depth are helpful in estimating root biomass from GPR parameters. Second, the selection of GPR system frequency was mainly dependent on field conditions, particularly soil water content. Lower frequency was recommended for developing root biomass estimation model under varied soil conditions. Third, the new method based on the metal reflector experiment was effective and easy to perform in situ for attenuation-effect correction.

Postclassification comparison (PCC) and change vector analysis (CVA) have been widely used for land use/cover change detection using remotely sensed data. However, PCC suffers from error cumulation stemmed from an individual image classification error, while a strict requirement of radiometric consistency in remotely sensed data is a bottleneck of CVA. This letter proposes a new method named CVA in posterior probability space (CVAPS), which analyzes the posterior probability by using CVA. The CVAPS approach was applied and validated by a case study of land cover change detection in Shunyi District, Beijing, China, based on multitemporal Landsat Thematic Mapper data. Accuracies of “change/no-change” detection and “from-to” types of change were assessed. The results show that error cumulation in PCC was reduced in CVAPS. Furthermore, the main drawbacks in CVA were also alleviated effectively by using CVAPS. Therefore, CVAPS is potentially useful in land use/cover change detection.