As one of the most important application for remote sensing monitoring, land cover mapping has witnessed notable advancements in data acquisition, algorithmic diversity, and classification accuracy. Despite the instrumental role data-driven algorithms have played in the development of global land cover products, their inherent limitations as “black box” methods often fall short of meeting end-users’ specific requirements. In this study, built upon the foundation of the earlier land cover monitoring platform [FROM-GLC plus(FGP)], a data and knowledge dual-driven framework (FGP 2.0) was developed as a user-adaptive framework for intelligent remote sensing land cover mapping. By incorporating ontology-based semantic descriptions with advanced data-driven algorithms, FGP 2.0 provides the capacity for both traditional annual mapping and emerging dynamic mapping. Our results illustrate that FGP 2.0 significantly improves the overall accuracy of annual maps by ~5%, and dynamic maps by ~20% compared to FGP. Moreover, an operational dynamic mapping tool has been developed on the Google Earth engine (GEE), enabling the generation of near-real-time land cover maps for any given place. With an extensible and flexible mapping framework, FGP 2.0 demonstrates the potential of customized land cover monitoring results to suit different application scenarios. This innovative approach not only meets the current demand for reliable annual and dynamic land cover maps but also sets a new benchmark for the integration of geoscientific expertise with machine learning techniques in remote sensing monitoring.

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.

Deep semantic segmentation networks perform better in cloud detection of satellite imagery than traditional methods due to their ability to extract high-level features over a large receptive field. However, a large receptive field often leads to loss of spatial details and blurring of boundaries. Therefore, it is crucial to understand the role of the receptive field on the segmentation results, which has rarely been investigated for cloud detection tasks. This study, for the first time, explored the relationship between the receptive field size and the performance of a cloud detection network. Six typical networks commonly used for cloud detection and nine modified UNet variants with different depths, dilated convolutions, and skip connections were evaluated based on the Landsat 8 Biome (L8 Biome) dataset. The theoretical receptive field (TRF) and the effective receptive field (ERF) were introduced to measure the receptive field sizes of different networks. The results revealed a negative correlation between the ERF size and cloud segmentation accuracies for different cloud distributions and a relatively weak negative correlation between the TRF size and segmentation accuracies. Furthermore, ERFs were considerably smaller than the corresponding TRFs for most networks, implying that large-scale contextual information was not learned after training. This result indicates the importance of using networks with a small receptive field for cloud detection of Landsat 8 OLI imagery. Moreover, as the boundary accuracies are significantly lower than the region accuracies, future efforts should be devoted to addressing inaccurate boundary localization rather than exploring the contextual information over a large receptive field.

Crop phenology extraction using remote sensing is influenced by satellite data and retrieval method. We conducted a comprehensive evaluation of the accuracy for winter wheat green-up date (GUD) extraction with four VIs (NDVI, EVI, EVI2 and NDPI) and six methods (CCRmax, βmax, RCmax, RT10, RT20 and RT50), comparing with ground-observed GUD in China. We evaluated the performance with correlation coefficient (R), root mean square error (RMSE), and observations (BIAS). The results showed that GUDs extracted from NDPI time series presented the overall highest accuracy. Based on NDPI time series, we also found that GUDs extracted with CCRmaxmethod had the highest consistency with ground-observed GUDs. Our finding suggested caution on the uncertainties in phenology extraction with remote sensing methods.

In recent years, nighttime light (NTL) data has been widely used in the urban associated researches thanks to its ability in characterizing human's activity. NTL data has an advantage on detecting urban areas and reflecting economic conditions. However, NTL data has two main problems: saturation and blooming effect. These two problems will reduce the accuracy of urban detection and social-economic indicators estimations. There have been increasing attentions on saturation effect and correction although new version of VIIRS data has significantly alleviated the saturation effect. While for blooming effect, few researches is existing. Most researches about blooming effect are still qualitative rather than quantitative. This paper takes China as study area to measure blooming distance of NTL data and explore the contributions of three main factors on blooming effect.

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.

Cloud removal is significantly needed for enhancing the further utilization of Landsat imagery, since such optical remote sensing satellite images are inevitably contaminated by clouds. Clouds dynamically affect the signal transmission due to their different shapes, heights, and distribution. Generally, pixel replacement is the only and common method used to remove thick opaque clouds, and radiometric correction techniques has been widely adopted to remove the thin clouds. However, no methods can remove both thick and thin clouds at the same time. In this paper, a new method is proposed based on fitting “trajectory” of cloudy pixels with the help of IHOT spatially charactering clouds for pixel correction, which considers signal transmission including not only the additive reflectance from the clouds but also the energy attenuation when solar radiation passes through them. The experimental results show that the proposed approach performs effective removal for thick and thin clouds, and possesses the highest accuracy with the reference image, which can restore land cover information accurately.

Endmember variability associated with impervious layer has been a serious problem in spectral mixture analysis (SMA). A reliable spectral library which ideally models the endmember variability is required for precise SMA. Even though many endmember bundles extraction algorithms have been proposed, there are still some problems in these methods which blur the threshold and endmember numbers. In this paper, an iterative classification extraction endmember bundles algorithm (ICEEA) is proposed. Impervious and pervious training sample are provided with GlobeLand30 product, and Maximum Likelihood Classifier (MLC) is used to conduct iteratively classification. After each classification, the artificial layer pixels which are misclassified as pervious class are excluded from artificial cover, and the impervious sample is selected again in new artificial cover. It stops when there are none misclassified pixels existing in the artificial layer. According to the results of simulated 30m data and real TM data, ICEEA has two advantages over PPI: (1) producing more reliable impervious endmember bundles which can model the endmember variability well; (2) having none threshold setting problem; (2) running much faster than PPI.

Most previous haze/cloud detection methods for Landsat imagery, e.g., haze optimized transformation (HOT), cannot adequately suppress land surface information and, in particular, often overestimate haze thickness over bright surfaces. This paper proposes an iterative HOT (IHOT) for improving haze detection with the help of a corresponding clear image. With an iterative procedure of regressions among HOT, the reflectance difference at the top of atmosphere (TOA) between hazy and clear images, and TOA reflectances of hazy and clear images, the land surface information can be removed, and the iterative HOT (IHOT) result is derived to spatially characterize the haze contamination in the Landsat images. A group of Landsat images that were acquired in different landscapes and seasons were used to test IHOT. Visual comparisons indicate that IHOT performed better than previous haze detection methods for images that were acquired in diverse landscapes and also performed robustly for hazy images that were acquired at different seasons when using the same reference clear image. Additionally, two indirect quantitative validations were used to illustrate that IHOT can provide the best transformation for accurately determining haze information. Therefore, it is expected that the proposed IHOT method will be used for automatic cloud/haze detection for large numbers of Landsat images if data sets of clear Landsat imagery are available.

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.

Bilinear Model (BM) is one of widely used nonlinear spectral unmixing methods, which are developed to deal with nonlinearity resulted from the multiple scattering within mixed pixels such as plant-soil mixtures. In the BM, products of endmember spectra are used to represent multiple scattering effect, and this approximation needs a validation. This study applies a Monte Carlo ray-tracing (MCRT) model to test this approximation by exploring the correlations between multiple scattering reflectances and endmember products. The correlations are found linear, proving the rationality of this approximation. Besides, the correlations between multiple scattering coefficients in the BM and vegetation characters are analyzed. Scattering coefficients are found to have quadratic relationships with vegetation coverage and linear relationships with crown height. The correlations can be used as constraints when solving the BM to retrieve the abundance of each component, to relieve the collinearity problem which impacts the solution precision.

Fractional vegetation cover (FVC) is one of the fundamental parameters for characterizing terrestrial ecosystems, with wide uses in various environmental and climate-related modeling applications. The remote sensing technique provides a unique opportunity for estimating FVC over large geographical areas by employing spectral mixture analysis (SMA). The effectiveness of SMA depends largely on the accurate extraction of representative and pure endmembers. However, in arid and semiarid environments that have sparse vegetation distributions, most current SMA models may produce large biases due to difficulties in obtaining pure vegetation spectra from the satellite images. This letter developed a new approach to estimate FVC from satellite observations by integrating an endmember spectrum purification procedure into a nonlinear SMA model. The proposed method is capable of extracting pure endmember spectra even though pure vegetation endmember is not present in target images in arid and semiarid environments, which improves the accuracy of FVC retrievals. Validation experiments conducted in the Xilingol grassland, Inner Mongolia, China, demonstrate that the proposed method produces more accurate FVC estimates (RMSE <; 0.13, AD <; 0.06) than do current algorithms. The better performance of the proposed method can be attributed to the purified vegetation spectra that more closely resemble the real pure vegetation spectra.

Multiple scattering within a mixed pixel results in a nonlinear effect on the measured spectra in remotely sensed imagery. This study provides a quantitative assessment of multiple scattering in the reflectance of semiarid shrublands and explores its relationship to the characteristics of shrubs (density and height) and imaging parameters (wavelength and viewing angles). Field measurements were conducted at the southern fringe of the Otindag Sandy Land in China. A Monte Carlo ray tracing model, the Forest LIGHT interaction model (FLIGHT), was applied to simulate the multiple scattering results. FLIGHT simulation results were first evaluated against field measurements and then compared with a Landsat-8 OLI image. Results show that: 1) the contribution of multiple scattering to the spectra of a scene increases linearly with the fractional cover of vegetation and crown height; 2) in general, multiple scattering has a stronger effect on the near-infrared (NIR) domain than on the visible bands; 3) shadows significantly strengthen the multiple scattering effect, specifically within the visible bands; and 4) 80 to 100% of the total multiple scattering is caused by the second-order scattering within the visible bands and 60% to 90% within the NIR band. This study helps to improve our understanding of the multiple scattering effect and to select between linear and nonlinear spectral unmixing models to solve the abundances of shrubs and soil in mixed pixels.