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Faced with increasing global soil degradation, spatially explicit data on cropland soil organic matter (SOM) provides crucial data for soil carbon pool accounting, cropland quality assessment and the formulation of effective management policies. As a spatial information prediction technique, digital soil mapping (DSM) has been widely used to spatially map soil information at different scales. However, the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance. To overcome this limitation, this study systematically assessed a framework of “information extraction-feature selection-model averaging” for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou, China in 2021. The results showed that using the framework of dynamic information extraction, feature selection and model averaging could efficiently improve the accuracy of the final predictions (R2: 0.48 to 0.53) without having obviously negative impacts on uncertainty. Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM, which improved the R2 of random forest from 0.44 to 0.48 and the R2 of extreme gradient boosting from 0.37 to 0.43. Forward recursive feature selection (FRFS) is recommended when there are relatively few environmental covariates (500). The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty. When the structures of initial prediction models are similar, increasing in the number of averaging models did not have significantly positive effects on the final predictions. Given the advantages of these selected strategies over information extraction, feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales, so this approach can provide more reliable references for soil conservation policy-making.

Understanding the spatial distribution and mechanisms driving soil organic carbon (SOC) is crucial for assessing soil carbon stocks and implementing effective carbon sequestration strategies in agricultural landscapes. The linear and nonlinear relationships between environmental variables and SOC have been extensively documented, but the threshold and interaction effects among multiple covariates on SOC remain underexplored. This study focused on farmland within the Qilu Lake watershed in Yunnan Province, China, which is characterized by complex surface conditions shaped by both climate change and anthropogenic activities. Utilizing 216 soil samples from the watershed, this research aimed to investigate the threshold and interaction effects of environmental variables on SOC. To achieve this, gradient boosted decision tree (GBDT) combined with partial dependence analysis were employed to elucidate the spatial distribution of SOC and the intricate relationships between environmental factors and SOC. In order to enhance the accuracy of SOC prediction, we employed the landscape metrics as environmental variables, thereby facilitating a more comprehensive description of the landscape. The results indicated that GBDT (R2 = 0.47) outperformed random forest (R2 = 0.38), achieving higher accuracy and lower uncertainty, indicated by a narrower 90% prediction interval. The SOC distribution was predominantly influenced by soil moisture, elevation, and the contagion index (CONTAG), with threshold effects observed at relatively high soil moisture levels (>50%), CONTAG levels (>85%), and relatively low elevations (

Soil organic carbon (SOC) mapping delivers invaluable information to the global carbon budget and climate change mitigation endeavour. Environmental variables at sample locations are frequently used as explanatory variables for simulating the spatial distribution of soil properties. However, these may not fully capture the spatial information generated by soil-forming processes. We applied multi-scale landscape metrics that can comprehensively characterize the surrounding landscape information of sample points. The metrics were extracted at two levels (landscape level and class level) and include the diversity, shape and area, fragmentation and connectivity with a buffer distance from 500 m to 5,000 m. We then investigated its effectiveness as environmental variables via recursive feature elimination, random forests, and quantile regression forests. The Jianghan Plain, China, was selected as the study area, where over 19,000 topsoil samples were collected. Results indicated that multi-scale landscape metrics enhanced the predictability of SOC mapping, with R2 increased by 43 %. Specifically, Shannon’s diversity index, the percentage of landscape index, interspersion and juxtaposition index, and patch cohesion index outperformed environmental variables that were extracted at the sample location. In addition, the relationships between SOC and landscape metrics were found to be scale-dependent. Landscape metrics demonstrated significant explanatory capacity for SOC across various spatial scales. Notably, as the scale surpassed 3,000 m, there was a discernible improvement in the explanatory effectiveness of the landscape metrics for SOC. Our findings highlight that landscape metrics are effective in characterising the soil-landscape relationship that is generated by multi-scale natural and anthropogenic soil-forming processes. Meanwhile, knowledge of the intricate relationship between landscape characteristics and SOC is crucial for informing land management decisions aimed at enhancing carbon sequestration, mitigating climate change, and maintaining soil fertility.

Accurate delineation of agricultural parcels is crucial for ensuring food security and implementing precision agriculture practices. However, one of the primary challenges in parcel delineation revolves around the accurate extraction of parcel edges due to the diverse morphologies of these parcels. Current deep learning models for agricultural parcel delineation often produce unclosed or incomplete edges, highlighting the need to enhance the edge-awareness of the model. In this study, we proposed a refined edge-aware U-Net (REAUNet) to extract accurate agricultural parcel boundaries from Sentinel-2 images and GF-2 images. REAUNet integrates four innovative components for edge enhancement, i.e., the deep supervision, the edge detection block, the dual attention block and the refine module. The edge detection block and the dual attention block strengthen the ability of capturing spatial contextual information, and the deep supervision constrains parcel delineation across multiple scales, and the refine module further refines the parcel edges. Together, these components enhance the edge-awareness of the model, resulting in more accurate agricultural parcel delineation. From the ablation experiment, REAUNet with all four components reached the higher accuracy than the scenarios that only three out of four components were retained, which suggested that the inclusion of all four components was essential. The comparative evaluation showed that our proposed model surpassed the representative deep learning models for both thematic and geomantic accuracy. REAUNet demonstrated an increase of 2.0 % in F1-score and a decrease of 3.4 % in GTC (Global Total-Classification) compared to the SEANet, and exhibited a 0.8 % increase in IoU and a 2 % decrease in GTC relative to the MPSPNet. We also assessed the capability of the REAUNet model transferred for a new geographic region, and reported that a pre-train REAUNet model with only a small number of local samples could achieve a satisfactory accuracy. To conclude, the newly proposed REAUNet model reaches a high mapping accuracy for delineating agricultural parcels from the high-resolution images. With the powerful edge-aware capability, REAUNet can effectively utilize limited training samples to transfer a deep learning model for a new region, facilitating the generation of agricultural parcel products and a large-scale agricultural parcel mapping in practical applications.

Understanding and managing soil organic carbon stocks (SOCS) are integral to ensuring environmental sustainability and the health of terrestrial ecosystems. The information of soil bulk density (BD) is important in accurately determining SOCS while it is often missing in the soil database. Using 3,504 soil profiles (14,170 soil samples) that represented diverse regions across China, we investigated the effectiveness of various pedotransfer functions (PTFs), including traditional PTFs, machine learning (ML), and ensemble model (EM), in predicting BD. The results showed that refitting the parameter(s) in traditional PTFs was essential for BD prediction (coefficient of determination (R2) of 0.299–0.432, root mean squared error (RMSE) of 0.156–0.162 g cm−3, Lin’s concordance coefficient (LCCC) of 0.428–0.605). Compared to traditional PTFs, ML can greatly improve the model performance for BD prediction with R2 of 0.425–0.616, RMSE of 0.129–0.158 g cm−3 and LCCC of 0.622–0.765. Our results also showed that EM can further improve BD prediction by ensembling four ML models (R2 = 0.630, RMSE = 0.126 g cm−3, LCCC = 0.775). Using the EM model, we filled the missing BD (1207 soil profiles with 3,112 soil samples) in our database and built the SOC stock database (4,275 soil profiles with 17,282 soil samples). This study can be a good reference for gap-filling the missing BD depending on the data availability, thus contribute to a deeper understanding in soil C related climate change mitigation, ecological balance preservation and environmental sustainability promotion.

Soil organic carbon (SOC) is crucial for soil health and quality, and its sequestration has been suggested as a natural solution to climate change. Accurate and cost-efficient determination of SOC and its functional fractions is essential for effective SOC management. Visible near-infrared spectroscopy (vis-NIR) has emerged as a cost-efficient approach. However, its ability to predict whole-profile SOC content and its fractions has rarely been assessed. Here, we measured SOC and its two functional fractions, particulate (POC) and mineral-associated organic carbon (MAOC), down to a depth of 200 ​cm in seven sequential layers across 183 dryland cropping fields in northwest, southwest, and south China. Then, vis-NIR spectra of the soil samples were collected to train a machine learning model (partial least squares regression) to predict SOC, POC, MAOC, and the ratio of MAOC to SOC (MAOC/SOC – an index of carbon vulnerability). We found that the accuracy of the model indicated by the determination coefficient of validation (Rval2) is 0.39, 0.30, 0.49, and 0.48 for SOC, POC, MAOC, and MAOC/SOC, respectively. Incorporating mean annual temperature improved model performance, and Rval2 was increased to 0.64, 0.31, 0.63, and 0.51 for the four carbon variables, respectively. Further incorporating SOC into the model increased Rval2 to 0.82, 0.64, and 0.59, respectively. These results suggest that combining vis-NIR spectroscopy with readily-available climate data and total SOC measurements enables fast and accurate estimation of whole-profile POC and MAOC across diverse environmental conditions, facilitating reliable prediction of whole-profile SOC dynamics over large spatial extents.

According to the convergence confinement theory, it is an effective measure to control the large deformation of high ground stress in fractured soft rock tunnels by using yielding support. The yielding support can be classified as either radial or circumferential yielding support. Circumferential yielding support is achieved by transforming radial displacement into circumferential tangential closure without compromising the support capacity of the primary lining support structure. Based on this, and inspired by the design principle of dampers, a yielding support structure system with spring damping elements as its core was developed, based on the connection characteristics of steel arches in highway tunnel, which can provide increasing support resistance in the yielding deformation section. Then the mechanical properties of spring damping elements were obtained through indoor axial pressure and flexural tests. In addition, according to these results with numerical calculations, the yielding support structure system with embedded spring damping elements can reduce the internal force of the support structure by approximately 10% and increase the area of the plastic zone of the surrounding rock by 11.23%, which can fully utilize the self-bearing capacity of surrounding rock and verify the effectiveness of circumferential yielding support. Finally, the spring damping support structure system was designed with reference to the construction process of the tunnel excavated by drilling and blasting method, and the transformation of the spring damping element to spring damping support structure was achieved. Based on field test results, surrounding ground pressure for the yielding support optimization scheme was reduced by 40% and more evenly distributed, resulting in the successful application and a reduction in the construction cost of large deformation tunnels in soft rock.

Abstract The diffusion model is used to calculate both the time-averaged flow of particles in stochastic media and the propagation of waves averaged over ensembles of disordered static configurations. For classical waves exciting static disordered samples, such as a layer of paint or a tissue sample, the flux transmitted through the sample may be dramatically enhanced or suppressed relative to predictions of diffusion theory when the sample is excited by a waveform corresponding to a transmission eigenchannel. Even so, it is widely assumed that the velocity of waves is irretrievably randomized in scattering media. Here we demonstrate in microwave measurements and numerical simulations that the statistics of velocity of different transmission eigenchannels are distinct and remains so on all length scales and are identical on the incident and output surfaces. The interplay between eigenchannel velocities and transmission eigenvalues determines the energy density within the medium, the diffusion coefficient, and the dynamics of propagation. The diffusion coefficient and all scattering parameters, including the scattering mean free path, oscillate with the width of the sample as the number and shape of the propagating channels in the medium change.

The current advanced neural network models are expanding in size and complexity to achieve improved detection accuracy. This study designs a lightweight fabric defect detection algorithm based on YOLOv7-tiny, called YOLOv7-tiny-MGCK. Its objectives are to improve the performance of fabric defect detection against complex backgrounds and to find a balance between the algorithm’s lightweight nature and its accuracy. The algorithm utilizes the Mish activation function, known for its superior nonlinear performance capability and smoother curve, enabling the neural network to manage more complex challenges. The Ghost convolution module is also incorporated to reduce computation and model parameters. The lightweight upsampling technique CARAFE facilitates the flexible extraction of deep features, coupled with their integration with shallow features. In addition, an improved K-Means clustering algorithm, KMMP, is employed to select appropriate anchor box for fabric defects. The experimental results show: a reduction in the number of parameters by 45.5% and computational volume by 41.0%, along with increases in precision by 3.9%, recall by 7.0%, and mAP by 3.0%. These results indicated that the improved algorithm achieves a more effective balance between detection performance and the requirement for a lightweight solution.

ABSTRACTLong-term, high-resolution soil moisture (SM) is a vital variable for understanding the water-energy cycle and the impacts of climate change on the Qinghai-Tibet Plateau (QTP). However, most existing satellite SM data are only available at coarse scale (~25 km) and suffer a lot from data gaps due to satellite orbit coverage and snow cover, especially on the QTP. Although substantial efforts have been devoted to downscale SM utilizing multiple soil moisture indices (SMIs) or diverse machine learning (ML) methods, the potentials of different SMIs and ML approaches in SM downscaling on the complex plateau remain unclear, and there is still a necessity to obtain an accurate, long-term, high-resolution and seamless SM data over the QTP. To address this issue, this study generated the long-term, high-accuracy and seamless soil moisture dataset (LHS-SM) over the QTP during 2001–2020 using a two-step downscaling method (first downscaling then merging). Firstly, the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1 km utilizing five ML approaches. Then, a dynamic data merging method that considers spatiotemporal nonstationary error was applied to derive the final LHS-SM data. The performance of fifteen SMIs was also assessed and the optimal indexes for downscaling were identified. Results indicated that the shortwave infrared band-based indices had better performance than the near infrared band-based and energy-based indices. The generated LHS-SM data exhibited satisfying accuracy (mean R = 0.52, ubRMSE = 0.047 m3/m3) and certain improvement to the ESA CCI SM data both at station and network scales. Compared with existing 1 km SM datasets, the LHS-SM data also showed the best performance (mean R = 0.62, ubRMSE = 0.047 m3/m3), while existing datasets either failed to fully characterize the spatial details or had some data gaps and unreasonable distributions. Strong spatial heterogeneity was observed in the SM dynamics during 2001–2020 with the southwest and northeast showing a “dry gets wetter” scheme and the southeast presenting a “wet gets drier” trend. Overall, the LHS-SM dataset gained its added values by compensating the drawbacks of existing 1 km SM products over the QTP and was much valuable for many regional applications.

Polycyclic aromatic hydrocarbons (PAHs) and carbon dioxide primarily originate from the combustion of fossil fuels and biomass. The implementation of the Chinese “double carbon strategy” is expected to impact the distribution of PAH emissions, consequently influencing the spatial distribution trend of PAHs in surface soil. Therefore, it is crucial to quantitatively evaluate the effectiveness of the Chinese “double carbon strategy” on soil PAH pollution for the purpose of “the reduction of pollution and carbon emissions”. This study utilized 15,088 individual PAH concentration data from 943 soil samples collected between 2003 and 2020 in China, in conjunction with PAH emissions at a 10 km resolution, for meta-analysis. The calculated PAH emissions in this study are in line with the global PAH emission inventory (PKU-PAH-2007), with a relative standard deviation at the provincial level of less than 25 %. Subsequently, a novel method was developed using emission density and Kow of PAHs to predict PAH concentrations in surface soil based on a least-squares regression model. Compared to other environmental models, the method established in this study significantly reduced the percent sample deviation to less than 70 %. Furthermore, energy consumption data for China were simulated based on the implementation plan of the “double carbon strategy” to project PAH emissions and soil PAH levels for the years 2030 and 2060. The predicted PAH emissions in China were estimated to decrease to 41,300 t in 2030 and 10,406.5 t in 2060 from 78,815 t in 2020. Moreover, the heavily contaminated areas of soil PAHs (i.e., total PAH concentrations in soil exceeding 1000 μg kg−1) were projected to decrease by 45 % and 82 % in 2030 and 2060, respectively, compared to levels in 2020. These findings suggest that the implementation of the “double carbon strategy” can fundamentally reduce the pollution of PAHs in surface soil of China.

Various land use and land cover (LULC) products have been produced over the past decade with the development of remote sensing technology. Despite the differences in LULC classification schemes, there is a lack of research on assessing the accuracy of their application to croplands in a unified framework. Thus, this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products (i.e., MCD12Q1 V6, GlobCover2009, FROM-GLC and GlobeLand30) based on the harmonised FAO criterion, and quantified the relationships between four factors (i.e., slope, elevation, field size and crop system) and cropland classification agreement. The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency, with overall accuracies of 94.90 and 93.52%, respectively. The FROM-GLC showed the worst performance, with an overall accuracy of 83.17%. Overlaying the cropland generated by the four global LULC products, we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification, respectively. High consistency was mainly observed in the Northeast China Plain, the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain, China. In contrast, low consistency was detected primarily on the eastern edge of the northern and semiarid region, the Yunnan-Guizhou Plateau and southern China. Field size was the most important factor for mapping cropland. For area accuracy, compared with China Statistical Yearbook data at the provincial scale, the accuracies of different products in descending order were: GlobeLand30, FROM-GLC, MCD12Q1, and GlobCover2009. The cropland classification schemes mainly caused large area deviations among the four products, and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products. Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction, which is essential for food security and crop management, so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.

Selecting the appropriate unmanned aerial vehicle flight height is beneficial for increasing the monitoring efficiency. We firstly used an unmanned aerial vehicle to explore the scale effect on monitoring rice aboveground biomass. The results confirmed the feasibility of using vegetation indices and textures from hyperspectral images to improve the estimations at different spatial resolutions. The monitoring accuracy of combining vegetation indices and textures was the highest, and exhibited a decreasing trend as the spatial resolution decreased with the greatest accuracy appearing at 13 cm. Two new concepts were proposed: “appropriate monitoring scale domain” to define the range of spatial resolution where the monitoring accuracy was less affected by scale effect, and “appropriate monitoring scale threshold” to define the spatial resolution where accuracy dropped noticeably. The appropriate monitoring scale domains varied at different growth stages and the appropriate monitoring scale thresholds of using vegetation indices and textures were lower than those using textures: 39 cm, 52 cm, and 65 cm at the pre-heading, post-heading, and entire growth stages, respectively when using textures, and 52 cm, 65 cm, and 78 cm at the corresponding growth stages when combining vegetation indices and textures. In terms of aboveground biomass level, growth stage and error value, the relatively lower aboveground biomass levels, earlier growth stages of the multi-temporal models, and overestimations were more likely to yield notable accuracy changes when the spatial resolution converted to lower level on both sides of appropriate monitoring scale threshold. Vegetation indices containing red-edge or near-infrared bands were effective for estimation. Yellow/green band textures and vegetation indices containing green bands with near-infrared/red-edge bands also obtained inspiring performances. MEA was indispensable in estimation while more diverse textures were incorporated into the models of the entire growth stages and models established at lower spatial resolutions. These findings are essential for understanding the scale effect in estimating rice aboveground biomass, facilitating efficient monitoring at field scale.

Soil pH is one of the critical indicators of soil quality. A fine resolution map of soil pH is urgently required to address practical issues of agricultural production, environmental protection, and ecosystem functioning, which often fall short of meeting the demands for local applications. To fill this gap, we used data from an extensive survey of 13,424 surface soil samples (0–0.2 m) across the cropland of Jiangxi Province in Southern China. Using digital soil mapping techniques with 46 covariates, we produced a 30 m resolution soil map of topsoil pH in cropland of Southern China. We integrate different variable selection algorithms and machine learning methods. Our results indicate the Random Forest with covariates selected by recursive feature selection had the best performance for mapping soil pH with r of 0.583 and RMSE of 0.41. The prediction interval coverage probability for our prediction was 0.92, indicating a low estimated prediction uncertainty. Climate was identified as the most critical variable for predicting soil pH with a contribution of 37.42 %, followed by soil properties (29.09 %), soil management (21.86 %), parent material (6.22 %), and biota (5.39 %) factors. The mean topsoil pH in the cropland of Jiangxi Province was 5.21, indicating a great pressure of soil acidification in this region. The high soil pH values were mainly distributed in the Northern, Western, and Eastern parts of the survey region while low soil pH values were majorly located in the central part. Compared with past surveys in 1980 s, there was no significant soil pH change in the surveyed region. Our soil pH map can provide important implications and guidance decisions on soil heavy metal pollution remediation, precision agriculture, and prevention of soil acidification.

Abstract Climate and land management changes are altering carbon inputs to soil. The consequence of such input changes on long‐term soil organic carbon (SOC) balance depends on the transit behavior of carbon inputs. Using observational carbon input and radiocarbon data in global soil profiles, we reveal that on average nearly 25% of new entering carbon leave soil in 1 year irrespective of entering depth, and the remained fraction after 30 years is only ∼13%. Nevertheless, the majority of SOC is older than 30 years in all soil depths. Together, these results demonstrate low transfer efficiency of carbon inputs to aged SOC which is the meaningful carbon component for long‐term SOC sequestration. Additionally, we reveal that SOC aging and carbon input transiting are two distinct processes, which should be simultaneously, but mechanistical‐separately, considered to predict and manage SOC dynamics in response to carbon input changes under climate and land management changes.

The positioning error of the GPS method at close distances is relatively large, rendering it incapable of accurately guiding unmanned surface vehicles (USVs) back to the mother ship. Therefore, this study proposes a near-distance recovery method for USVs based on monocular vision. By deploying a monocular camera on the USV to identify artificial markers on the mother ship and subsequently leveraging the geometric relationships among these markers, precise distance and angle information can be extracted. This enables effective guidance for the USVs to return to the mother ship. The experimental results validate the effectiveness of this approach, with positioning distance errors of less than 40 mm within a 10 m range and positioning angle errors of less than 5 degrees within a range of ±60 degrees.

Functional gait disorder (FGD) is a common subtype of functional movement disorder (FMD), mainly characterized by instability, lameness and Parkinson's gait. The clinical manifestations of FGD are the same as those of other types of gait disorder, with the possibility of misdiagnosis. However, there are significant differences in the treatment principles between them, so clear diagnosis and accurate treatment are crucial to improve patients' symptoms and prognosis. This paper aims to systematically review the epidemiological characteristics, clinical manifestations, auxiliary examination, diagnosis and differential diagnosis, treatment and prognosis of FGD, in order to improve the understanding of clinicians.

Salinization is a threat to global agricultural and soil resource allocation. Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets (>250 m) and semiqualitative classification rules (five ranks). Based on these two limitations, we proposed a framework to quantitatively estimate global soil salt content in five climate regions at 10 m by integrating Sentinel-1/2 remotely sensed images, climate, parent material, terrain data, and machine learning. In hyper-arid and arid region, models established using Sentinel-2 and other geospatial data showed the highest accuracy with R2 of 0.85 and 0.62, respectively. In semi-arid, dry sub-humid, and humid regions, models performed best using Sentinel-1, Sentinel-2, and other geospatial data with R2 of 0.87, 0.80, and 0.87, respectively. The accuracy of the global models is considerable with field validation in Iran and Xinjiang, and compared with digitized salinity maps in California, Brazil, Turkey, South Africa, and Shandong. The proportion of extremely saline soils in Europe is 10.21%, followed by South America (5.91%), Oceania (5.80%), North America (4.05%), Asia (1.19%), and Africa (1.11%). Climatic conditions, groundwater, and salinity index are key covariates in global soil salinity estimation. Use of radar data improves estimation accuracy in wet regions. The map of global soil salinity at 10 m provides a detailed, high-precision basis for soil property investigation and resource management.

Soil microbes drive soil organic carbon (SOC) mineralization. Because microbial groups differ in metabolic efficiency and respond differently to temperature variation, it is reasonable to expect a close association of SOC mineralization and its temperature sensitivity (Q10 which is defined as the factor of the change of soil carbon mineralization induced by 10 °C temperature increase) with microbial community diversity and composition. However, these relations have rarely been tested. Here, we conducted an incubation experiment to assess the temperature responses of microbial α diversity and the relative abundance of microbial r- and K-strategists in soils from a wide range of ecosystems across a climate gradient in the southeast Tibet. The results indicated that the instantaneous α diversity and the relative abundance of r- and K-strategists are significantly (P

Soil organic carbon (SOC) plays a crucial role in soil health and global carbon cycling, therefore accurate estimates of its spatial distribution are important for managing soil health and mitigating global climate change. Digital soil mapping shows its potential to provide accurate and high-resolution spatial distribution of SOC across scales. To convert SOC content to SOC density (SOCD), two inference trajectories exist for predicting SOCD in digital soil mapping: the direct approach (calculate-then-model) and indirect approach (model-then-calculate). However, there is a lack of comprehensive exploration regarding the differences in their performance in SOCD estimates, particularly in regions characterized by diverse pedoclimatic conditions. To bridge this knowledge gap, we evaluated the two approaches based on model performance of SOCD in France. Using 916 topsoils (0−20 ​cm) from the LUCAS Soil 2018 and 24 environmental covariates, random forest model and forward recursive feature selection were used to build the spatial predictive models of SOCD using direct and indirect approaches. The results show that, using random forest model and full covariates, both approaches show moderate performance (R2 ​= ​0.28−0.32). By utilizing forward recursive feature selection model, the number of predictors was reduced from 24 to 9, enhancing model performance for direct approach (R2 of 0.35), with no improvement for indirect approach (R2 of 0.28). The mean SOCD of the French topsoil was 5.29 and 6.14 ​kg ​m−2 by direct and indirect approaches, resulting in SOC stock of 2.8 and 3.3 ​Pg, respectively. We found that the direct approach clearly underestimated the high SOCD (>9 ​kg ​m−2), while the indirect approach performed much better for high SOCD. Our findings serve as a valuable reference for SOCD mapping, thereby providing a scientific basis for maintaining soil health.

Soil salinization and the shortage of water resources have become important factors affecting the sustainable development of agriculture in the southern Xinjiang. Agricultural flooding in winter or spring is the main means to reduce the damage of secondary soil salinization in southern Xinjiang. However, the spatial heterogeneity of soil salinization is not considered in previous irrigation strategy. Excessive irrigation always leads to a serious waste of agricultural water resources. Irrigation zones delineation based on the degree of soil salinization is beneficial for the management of agricultural salinized land and the improvement of water use efficiency in arid areas. This study used 1599 sets of apparent electrical conductivity (ECa) data obtained before winter irrigation and after crop harvest to characterize soil salinity directly. And the spatial heterogeneity of soil salinity in the study area was analyzed directly using ECa values and statistical as well as geostatistical methods. Three main variables obtained from apparent electrical conductivity measurements (EC0.375, EC0.75, EC1.5), vegetation information (NDVI, GNDVI, SAVI), which indirectly reflected the degree of salinity, and environmental factors (Clay, Sand, Silt, DEM), which affecting the distribution of salinity, were used as auxiliary variables. Four zoning models including soil information, soil + vegetation information, soil information + environmental factors and soil + vegetation information + environmental factors were constructed. A multi-scale segmentation algorithm was used to delineate for four zone patterns and evaluate the results of management zones based on the different information. The results showed that there was a strong spatial heterogeneity of soil salinity in the study area, with the coefficients of variation of ECa all higher than 74 %. But the mean of coefficient of variation intra-zone in each zone after delineation was less than 24 %, which was significantly lower than the value before delineation. The zoning result based on only soil information was more fragmented than other zoning results, and the homogeneity intra-zone and heterogeneity among zones were both lower. This result was not desirable to the development and effective implementation of water resource allocation. Management zones delineation with additional vegetation information or environmental factors showed a better zoning result. Delineation using multisource data of soil, vegetation information and environmental factors not only had the highest homogeneity intra-zone and heterogeneity among zones, but also facilitated the implementation of variable-rate irrigation. Consequently, salinity management zoning, which integrates soil, vegetation information and environmental factors, will facilitate the implementation of efficient irrigation with variable irrigating rate.

Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (Hdsm) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R2, root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the Hdsm, are 0.58, 4528.23 kg/hm2, and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm2) is slightly smaller than the measured mean AGB (16,960.23 kg/hm2). (2) The R2, RMSE, and RPD of the improved AGB estimation model, based on AGB/Hdsm, are 0.88, 2291.90 kg/hm2, and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm2) is more similar to the measured mean AGB (17,222.59 kg/hm2). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the Hdsm. Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios.

Soil health has gained increasing attention under the rapid development of industrialization and the requirement for green agriculture. Therefore, up-to-date soil information related to soil health is urgently needed to ensure food security and biodiversity protection. Previous studies have shown the potential of proximal soil sensing in measuring soil information, while it remains challenging to get cost-efficient and robust estimates of multiple soil health indicators simultaneously via sensor fusion. In this study, we investigated the potential of visible near-infrared (vis-NIR), and mid-infrared (MIR) spectroscopy as well as three model averaging methods in predicting three soil health properties, including soil organic matter (SOM), pH, cation exchange capacity (CEC). The model averaging methods are not only used for model fusion but also for high-level sensor fusion, which include Granger-Ramanathan (GR), Bayesian Model Averaging and Spectral-Guided Ensemble Modelling (S-GEM). Here, S-GEM is a recently proposed algorithm that can improve soil spectroscopic prediction by including spectral information in ensemble modelling. Four widely used prediction models were evaluated, including partial least square regression, Cubist, memory based learning and convolutional neural network. For SOM, sensor fusion based on model averaging algorithms was comparable to that of Sensorsingle + Modelmultiple (MIR singly based on S-GEM) with R2 of 0.86. However, MIR only with S-GEM performed the best among all methods (LCCC of 0.92, RMSE of 3.66 g kg−1 and RPIQ of 3.68). The 10-fold cross-validation results indicated that Sensorsingle + Modelmultiple (MIR singly based on S-GEM) performed best among all methods for pH, with R2 of 0.84, LCCC of 0.90, RMSE of 0.45 and RPIQ of 3.65. For CEC, Sensormultiple + Modelmultiple based on GR performed best with R2 of 0.66, LCCC of 0.80, RMSE of 3.48 cmol + kg−1 and RPIQ was 2.22. Our results also showed that sensor fusion failed to improve spectral prediction of soil information when the performance among sensors differed a lot (△R2 > 0.2), and the use of a single best sensor is therefore suggested in this case. When the sensors have a close model performance (△R2

Growth monitoring of rice is of great significance to food security of human society. Rice flowering is an important growing stage for grain formation, and flower intensity is the dominant factor in determining rice yield. The estimation of flower intensity helps us to know the rice yield in advance. This research proposed a series of the flower index (FI) to monitor status of rice flowers by developing flower intensity estimation models using stepwise multiple linear regression (SMLR) and random forest (RF), and their performance was compared to the models developed by vegetation indices (VI) of some key growth stages. The FI that in types of normalization (NDFI), ratio (RFI) and differences (DFI) were tested. The involved FIs in the FI-based models were those in type of difference (DFI) that obtained by difference of reflectance before and after flowering (DR) and their first derivative (DR’). The FIs of ten consecutive days during the flowering were obtained and their correlations with flower intensity showed that FIs of the late flowering (the 8th day, 9th day and 10th day) were more significantly correlated to flower intensity than those at the early or mid-flowering, with the maximum correlation coefficient of 0.702 given by FIs in difference type formed by DR’. The accuracy assessment of flower intensity estimation models showed that FI-based models had the equivalent accuracies to VI-based models, especially for the SMLR model that based on four FIs, which had R2 = 0.707, MAPE = 10.54%, rRMSE = 11.39%, was comparable to the model that developed by three VIs of the booting, heading and jointing stages (R2 = 0.751, MAPE = 8.99%, rRMSE = 10.31%). The promising results of FIs in estimating flower intensity make the simple data acquisition possible and provide an alternative way to get information about rice yield.

As an essential climate variable, soil moisture (SM) exerts an indispensable influence on numerous disciplines. However, various degrees of data gaps exist in current microwave SM products. Therefore, this article proposed a spatiotemporal attention-based residual deep network (STARN) to reconstruct gaps of the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) over the Qinghai–Tibet Plateau (QTP) during unfrozen seasons (May to September) from 2001 to 2021. The developed model is an end-to-end residual network embedded with three attention modules to comprehensively consider the potential relationship between SM and surface variables. Evaluation results revealed that the proposed model could well reconstruct SM gaps with an overall median R and unbiased RMSE (ubRMSE) values of 0.52 and 0.054 m3/m3, while the overall median R and ubRMSE values for the ESA CCI SM were 0.41 and 0.058 m3/m3. Besides, comparison with five baseline methods (e.g., the artificial neural network, convolutional neural network, extreme gradient boosting, long-short term memory, and DCT-PLS model) indicated that the STARN model had certain advantages over the five baseline models with higher correlation and more reasonable distribution patterns. The R/ubRMSE values for the five models were 0.38/0.057, 0.34/0.058, 0.40/0.058, 0.41/0.056, and 0.41/0.058, respectively. The pretraining using the ERA5-Land SM data could further improve the accuracy of generated seamless SM data since the ERA5-Land and ESA CCI SM complemented each other to a certain extent on the QTP. In summary, by leveraging the spatiotemporal information and attention modules, the STARN model showed great potentials in SM gap filling.

Abstract Soil microbial community's responses to climate warming alter the global carbon cycle. In temperate ecosystems, soil microbial communities function along seasonal cycles. However, little is known about how the responses of soil microbial communities to warming vary when the season changes. In this study, we investigated the seasonal dynamics of soil bacterial community under experimental warming in a temperate tall‐grass prairie ecosystem. Our results showed that warming significantly (p = 0.001) shifted community structure, such that the differences of microbial communities between warming and control plots increased nonlinearly (R2 = 0.578, p = 0.021) from spring to winter. Also, warming significantly (p

The response of soil organic carbon to climate warming may be soil depth-dependent, but remains unquantified in situ. Here the authors show that warming induces more proportional soil carbon losses in topsoil than in subsoil, particularly from high-latitudinal carbon-rich soils.

For development planning and biodiversity management in China, it is crucial to understand the past and present patterns of biodiversity. Using satellite data has proven to be an effective means of characterizing the spatial distribution of species based on the species energy hypothesis, thereby supporting the conservation of biodiversity. This study’s specific purposes included assessing different surrogates for annual species richness in China using MODIS as input for the Dynamic Habitat Indices (DHIs) and analyzing the trend and triggers of variation in DHI from 2003 to 2018. We evaluated the linear relationships between the species richness and different DHIs (containing DHI Cum, cumulative productivity; DHI Min, minimum productivity; DHI Var, intra-annual variation of productivity) in China. Further, we conducted a least squares linear regression analysis to investigate the long-term trend of the best performed DHI, as well as an analysis of correlation factors. Our results demonstrated that both DHI Cum and DHI Min had strong explanatory power for estimating species richness, while DHI Var had a poor performance. Among all individual DHIs, GPP-DHI Cum performed the best. The trend analysis showed that the species habitat appropriateness in the areas with high species habitat appropriateness tended to decrease over time, while that in the areas with low species habitat appropriateness tended to increase over time. We also found that the correlation factors for the variation of biodiversity varied spatially. The decrease of biodiversity in the south and eastern coast of China was associated with the decrease of the solar radiation, the increases of the temperature and precipitation, and the expansion of human footprint. By comparison, the increase of biodiversity in the Tibetan Plateau was associated with the increases of the temperature while that in the Loess Plateau was associated with the increases of the precipitation. In addition to climatic factors, the implementations of ecological restoration projects have also driven increased diversity. Besides an improved understanding of biodiversity dynamics, these findings may help to promote biodiversity conservation and policy making.

Long-term exposure to high concentrations of fine particles can cause irreversible damage to people’s health. Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long short-term memory neural network and random forest regression model were established for PM2.5 concentration estimation. We finally selected the random forest regression model (R2 = 0.93, RMSE = 4.59 μg m−3) as our prediction model by the model evaluation index. The PM2.5 concentration distribution across China in 2021 was estimated, and then the influence factors of high-value regions were explored. It is clear that PM2.5 concentration is not only related to the local geographical and meteorological conditions, but also closely related to economic and social development.

Water parameter estimation based on remote sensing is one of the common water quality evaluation methods. However, it is difficult to describe the relationship between the reflectance and the concentration of non-optically active substances due to their weak optical characteristics, and machine learning has become a viable solution for this problem. Therefore, based on machine learning methods, this study estimated four non-optically active water quality parameters including the permanganate index (CODMn), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Specifically, four machine learning models including Support Vector Machine Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) were constructed for each parameter and their performances were assessed. The results showed that the optimal models of CODMn, DO, TN, and TP were RF (R2 = 0.52), SVR (R2 = 0.36), XGBoost (R2 = 0.45), and RF (R2 = 0.39), respectively. The seasonal 10 m water quality over the Zhejiang Province was measured using these optimal models based on Sentinel-2 images, and the spatiotemporal distribution was analyzed. The results indicated that the annual mean values of CODMn, DO, TN, and TP in 2022 were 2.3 mg/L, 6.6 mg/L, 1.85 mg/L, and 0.063 mg/L, respectively, and the water quality in the western Zhejiang region was better than that in the northeastern Zhejiang region. The seasonal variations in water quality and possible causes were further discussed with some regions as examples. It was found that DO would decrease and CODMn would increase in summer due to the higher temperature and other factors. The results of this study helped understand the water quality in Zhejiang Province and can also be applied to the integrated management of the water environment. The models constructed in this study can also provide references for related research.

Abstract Background Anthropogenic activities have increased the inputs of atmospheric reactive nitrogen (N) into terrestrial ecosystems, affecting soil carbon stability and microbial communities. Previous studies have primarily examined the effects of nitrogen deposition on microbial taxonomy, enzymatic activities, and functional processes. Here, we examined various functional traits of soil microbial communities and how these traits are interrelated in a Mediterranean-type grassland administrated with 14 years of 7 g m−2 year−1 of N amendment, based on estimated atmospheric N deposition in areas within California, USA, by the end of the twenty-first century. Results Soil microbial communities were significantly altered by N deposition. Consistent with higher aboveground plant biomass and litter, fast-growing bacteria, assessed by abundance-weighted average rRNA operon copy number, were favored in N deposited soils. The relative abundances of genes associated with labile carbon (C) degradation (e.g., amyA and cda) were also increased. In contrast, the relative abundances of functional genes associated with the degradation of more recalcitrant C (e.g., mannanase and chitinase) were either unchanged or decreased. Compared with the ambient control, N deposition significantly reduced network complexity, such as average degree and connectedness. The network for N deposited samples contained only genes associated with C degradation, suggesting that C degradation genes became more intensely connected under N deposition. Conclusions We propose a conceptual model to summarize the mechanisms of how changes in above- and belowground ecosystems by long-term N deposition collectively lead to more soil C accumulation. Video Abstract

Primary hepatocellular carcinoma (HCC) is the sixth most common malignant tumor in the world, and ranks third in tumor-related deaths. The incidence and mortality of HCC in China are among the top five in both men and women, and are increasing year by year, while less than 30% of patients can be surgically resected. With the continuous development of medical technology, interventional therapy has played a key role in the treatment of HCC. This article reviews the latest progress of interventional therapy such as transarterial chemoembolization, transarterial radioembolization, radiofrequency ablation and microwave ablation, as well as interventional therapy combined with molecular targeted therapy and immunotherapy.

Previous studies rarely involved the mechanical properties of anchorage system under the condition of internal absolute displacement of surrounding rock and the solution of important parameters of anchorage system. In this paper, the absolute displacement law of rock mass under the anchorage system is analyzed by the fiber grating (FBG) multi-point displacement meter and multidirectional displacement conversion formula. Based on the coordinated deformation principle for rock bolt and rock mass, the mechanical analysis model for rock bolt-rock mass system is established, and the influence rules of rock bolt length, rock bolt diameter and rock bolt preload on the internal force distribution of rock bolt is studied based on the stress analysis of full-length rock bolt in a tunnel engineering. The results show that: (1) with the increase of rock bolt length, the anchoring effect gradually enhances, but the improvement degree decreases gradually; (2) with the increase of rock bolt diameter, the anchoring effect is enhanced; at the same time, however, the shear stress of rock bolt is larger, and the possibility of rock bolt sliding also increases; and (3) with the increase of preload, the increase of “anchor length” makes the anti-sliding ability of rock bolt increase correspondingly, and the supporting effect of rock bolt increases gradually. Then the full-process analysis for the mechanical mechanism of rock bolt sliding failure and the iterative calculation method for rock bolt displacement are studied. At last, the numerical simulation for pull-out test is carried out through the finite element analysis to verify the calculation results of mechanical model, and according to the iterative operation, the two important parameters such as the interface shear stiffness K and the interface peak shear strength τP are solved. The analysis show that the numerical simulation results are in good agreement with the mechanical model calculation. This study can provide a theoretical basis for rock bolt support technology and support structure design.

Unsustainable human management has negative effects on cropland soil organic carbon (SOC), causing a decrease in soil health and the emission of greenhouse gas. Due to contiguous fields, large-scale mechanized operations are widely used in the Northeast China Plain, which greatly improves production efficiency while decreasing the soil quality, especially for SOC. Therefore, an up-to-date SOC map is needed to estimate soil health after long-term cultivation to inform better land management. Using Quantile Regression Forest, a total of 396 soil samples from 132 sampling sites at three soil depth intervals and 40 environmental covariates (e.g., Landsat 8 spectral indices, and WorldClim 2 and MODIS products) selected by the Boruta feature selection algorithm were used to map the spatial distribution of SOC in the cropland of the Northeast Plain at a 90 m spatial resolution. The results showed that SOC increased overall from the southern area to the northern area, with an average of 17.34 g kg−1 in the plough layer (PL) and 13.92 g kg−1 in the compacted layer (CL). At the vertical scale, SOC decreased, with depths getting deeper. The average decrease in SOC from PL to CL was 3.41 g kg−1. Climate (i.e., average temperature, daytime and nighttime land surface temperature, and mean temperature of driest quarter) was the dominant controlling factor, followed by position (i.e., oblique geographic coordinate at 105°), and organism (i.e., the average and variance of net primary productivity in the non-crop period). The average uncertainty was 1.04 in the PL and 1.07 in the CL. The high uncertainty appeared in the area with relatively scattered fields, high altitudes, and complex landforms. This study updated the 90 m resolution cropland SOC maps at spatial and vertical scales, which clarifies the influence of mechanized operations and provides a reference for soil conservation policy-making.

Faster and more accurate ranging can be achieved by combining the object detection technique based on deep learning with conventional visual ranging. However, changes in scene, uneven lighting, fuzzy object boundaries and other factors may result in a non-fit phenomenon between the detection bounding box and the object. The pixel spacing between the detection bounding box and the object can cause ranging errors. To reduce pixel spacing, increase the degree of fit between the object detection bounding box and the object, and improve ranging accuracy, an object detection bounding box optimization method is proposed. Two evaluation indicators, WOV and HOV, are also proposed to evaluate the results of bounding box optimization. The experimental results show that the pixel width of the bounding box is optimized by 1.19~19.24% and the pixel height is optimized by 0~12.14%. At the same time, the ranging experiments demonstrate that the optimization of the bounding box improves the ranging accuracy. In addition, few practical monocular range measurement techniques can also determine the distance to an object whose size is unknown. Therefore, a similar triangle ranging technique based on height difference is suggested to measure the distance to items of unknown size. A ranging experiment is carried out based on the optimization of the detecting bounding box, and the experimental results reveal that the ranging relative error within 6 m is between 0.7% and 2.47%, allowing for precise distance measurement.

In this study, various structures are designed to improve the bearing capacity of belt-type ultra-high-pressure dies. Via theoretical analysis, numerical simulation, and destructive experiments, the stress distribution, bearing capacity, and failure principle of various dies are analyzed. The results demonstrate that the positive and negative values of the third invariant of the deviatoric stress tensor J3′ determine the deformation mode of the cylinder; when J3′ > 0, the cylinder is in the tensile deformation state, and when J3′ < 0, the cylinder is in the compressive deformation state. The third invariant of the deviatoric stress tensor of the belt-type cylinder is J3′ > 0, which causes tensile failure and rupture due to excessive circumferential stress. The use of a split cylinder can significantly reduce the circumferential stress, thus effectively reducing the maximum shear stress and von Mises stress and improving the pressure capacity of the cavity. However, when J3′ > 0 for the split cylinder, the pressure capacity is affected and the cylinder experiences tensile failure. A tangential split cylinder has a compressive deformation of J3′ < 0, which can fully utilize the properties of hard alloy materials and significantly improve the pressure-bearing capacity of the cylinder. This article provides an effective optimization design theory for belt-type dies, and the effectiveness of this method is proven through experiments.

China implemented a stringent Air Clean Plan (ACP) since 2013 to address environmental and health risks caused by ambient fine particulate matter (PM2.5). However, the policy effectiveness of ACP and co-benefits of carbon mitigation measures to environment and health are still largely unknown. Using satellite-based PM2.5 products produced in our previous study, concentration–response functions, and the logarithmic mean Divisia index (LMDI) method, we analyzed the spatiotemporal dynamics of premature deaths attributable to PM2.5 exposure, and quantitatively estimated the policy benefits of ACP and carbon mitigation measures. We found the annual PM2.5 concentrations in China decreased by 33.65 % (13.41 μg m−3) from 2014 to 2020, accompanied by a decrease in PM2.5-attributable premature deaths of 0.23 million (95 % confidence interval (CI): 0.22–0.27), indicating the huge benefits of China ACP for human health and environment. However, there were still 1.12 million (95 % CI: 0.79–1.56) premature deaths caused by the exposure of PM2.5 in mainland China in 2020. Among all ACP measures, clean production (contributed 55.98 % and 51.14 % to decrease in PM2.5 and premature deaths attributable to PM2.5) and energy consumption control (contributed 32.58 % and 29.54 % to decrease in PM2.5 and premature deaths attributable to PM2.5) made the largest contribution during the past seven years. Nevertheless, the environmental and health benefits of ACP are not fully synergistic in different regions, and the effectiveness of ACP measures reduced from 2018 to 2020. The co-effects of CO2 and PM2.5 has become one of the major drivers for PM2.5 and premature deaths reduction since 2018, confirming the clear environment and health co-benefits of carbon mitigation measures. Our study suggests, with the saturation of clean production and source control, more targeted region-specific strategies and synergistic air pollution-carbon mitigation measures are critical to achieving the WHO’s Air Quality Guideline target and the UN’s Sustainable Development Goal Target in China.

Scientific evaluation of heavy metal pollution is required to understand the status better and formulate policies. However, previous studies primarily focused on a single evaluation of soil pollution or crop pollution, ignoring the collaborative evaluation of soil-crop system pollution from a holistic perspective. In this study, we collected 1815 samples in an industrial city to explore the differences between heavy metal pollution in soil and crops as well as collaborative characteristics of heavy metal pollution in soil-crop system. Our results showed that for the soil where grains and vegetables were grown, approximately half of the study region was comprehensively polluted, concentrated in the downtown. However, grains in most areas were comprehensively polluted, particularly the severe pollution around the downtown (i.e., County C), while the overall crop pollution of vegetables and fruits was much less serious than that of grains. For single soil pollution, the grain was the most polluted crop type in Pb, Hg, Cu, and Zn pollution. In contrast, for single crop pollution, the grain was the most polluted crop type in nearly all heavy metals, especially in Cr, As, and Ni pollution. Therefore, pollution characteristics of the same heavy metal were inconsistent in soil and crops, indicating the demand of an integrated evaluation method for soil-crop system pollution. Our collaborative evaluation results showed that 52.56% of sample points were contaminated and mainly distributed in the middle part. The collaborative pollution grade presented an increasing trend from the periphery to the center, and the west part was more contaminated than the east part. Our study further implies that the grain had the strongest abilities of absorbing and accumulating heavy metals. Cd was the most easily absorbed heavy metal by crops, while Pb was the least easily accumulated heavy metal in soil-crop system.

Surface soil moisture (SSM) estimates from different sources have distinct error characteristics. Multi-source data combination is an efficient way to obtain improved SSM data with reduced uncertainties. Previous data merging studies based on the linear weight averaging scheme rarely considered the impacts of data error covariance (EC) and usually needed a reference dataset, which can lead to suboptimal merging weights. This study applied the quadruple collocation (QC) to estimate EC and combine four SSM datasets simultaneously without the need for a reference. Specifically, two passive microwave satellite datasets (the L3 Soil Moisture Active Passive (SMAP)-V7 and the L3 Soil Moisture and Ocean Salinity -INRA-CESBIO (SMOS-IC)-V2), one active microwave dataset from the Advanced Scatterometer (ASCAT), and one model dataset from the Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA2) were combined. Generally, QC-based data combination reduced SSM data uncertainties with significantly reduced unbiased Root Mean Square Error (ubRMSE) scores against in situ observations and globally decreased fMSE scores. Moreover, in situ evaluation showed that the QC-based fusion products exhibited better skills than the Tripe Collocation (TC)-based products without considering EC. There were statistically significant differences in Pearson correlation coefficients and ubRMSE metric between the QC and TC -based products. Ignoring the EC between SMAPV7 and SMOS-ICV2 caused overestimations in their relative contributions to fusion data and degraded fusion accuracy. Specifically, the QC-based merging weight was reduced averagely by 0.27 (0.28) for SMAP (IC) when their error cross-correlation increased roughly from −0.42 to 0.9. This study can provide guidance for the generation of improved merged datasets at a global scale.

The prediction of soil properties at different depths is an important research topic for promoting the conservation of black soils and the development of precision agriculture. Mid-infrared spectroscopy (MIR, 2500–25000 nm) has shown great potential in predicting soil properties. This study aimed to explore the ability of MIR to predict soil organic matter (OM) and total nitrogen (TN) at five different depths with the calibration from the whole depth (0–100 cm) or the shallow layers (0–40 cm) and compare its performance with visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm). A total of 90 soil samples containing 450 subsamples (0–10 cm, 10–20 cm, 20–40 cm, 40–70 cm, and 70–100 cm depths) and their corresponding MIR and vis-NIR spectra were collected from a field of black soil in Northeast China. Multivariate adaptive regression splines (MARS) were used to build prediction models. The results showed that prediction models based on MIR (OM: RMSEp = 1.07–3.82 g/kg, RPD = 1.10–5.80; TN: RMSEp = 0.11–0.15 g/kg, RPD = 1.70–4.39) outperformed those based on vis-NIR (OM: RMSEp = 1.75–8.95 g/kg, RPD = 0.50–3.61; TN: RMSEp = 0.12–0.27 g/kg; RPD = 1.00–3.11) because of the higher number of characteristic bands. Prediction models based on the whole depth calibration (OM: RMSEp = 1.09–2.97 g/kg, RPD = 2.13–5.80; TN: RMSEp = 0.08–0.19 g/kg, RPD = 1.86–4.39) outperformed those based on the shallow layers (OM: RMSEp = 1.07–8.95 g/kg, RPD = 0.50–3.93; TN: RMSEp = 0.11–0.27 g/kg, RPD = 1.00–2.24) because the soil sample data of the whole depth had a larger and more representative sample size and a wider distribution. However, prediction models based on the whole depth calibration might provide lower accuracy in some shallow layers. Accordingly, it is suggested that the methods pertaining to soil property prediction based on the spectral library should be considered in future studies for an optimal approach to predicting soil properties at specific depths. This study verified the superiority of MIR for soil property prediction at specific depths and confirmed the advantage of modeling with the whole depth calibration, pointing out a possible optimal approach and providing a reference for predicting soil properties at specific depths.

Algal extracellular organic matter (EOM) will cause grievous membrane fouling during the filtration of algae-laden water; hence, boron-doped diamond (BDD) anodizing was selected as the pretreatment process before the ultrafiltration, and the EOM fouling mitigation mechanism and the purification efficiency were systematically investigated. The results showed that BDD oxidation could significantly alleviate the decline of membrane flux and reduce membrane fouling, and the effect was more notable with an increase in oxidation time. Less than 10% flux loss happened when oxidation duration was 100 min. The dominant fouling model was gradually replaced by standard blocking. BDD anodizing preferentially oxidizes hydrophobic organic matter and significantly reduces the DOC concentration in EOM. The effluent DOC was reduced to less than 1 mg/L when 100 min of BDD anodizing was applied. After the pre-oxidation of BDD, the zeta potential and interfacial free energy, including the cohesive and adhesive free energy, were all constantly increasing, which implied that the pollutants would agglomerate and deposit, and the repulsion between foulants and the ultrafiltration membrane was augmented with the extensive oxidation time. This further confirms the control of BDD on membrane fouling. In addition, the BDD anodizing coupled ultrafiltration process also showed excellent performance in removing disinfection by-product precursors.

Nanoplastics have gradually become a concern due to the wide use of plastics. Nanoplastics in aqueous phase can be exposed to users through water supply networks and cannot be efficiently removed by conventional water treatment processes. This work studied the degradation of polystyrene nanoplastics (PS-NP) by two commonly used advanced oxidation systems: UV/NaClO and UV/peroxymonosulfate (PMS). Results showed that almost no turbidity was detected in the PS-NP solution (5.00 mg/L) after treated by both UV/NaClO and UV/PMS for 360 min, suggesting the excellent turbidity removal ability. Yet, scanning electron microscope (SEM) and total organic carbon (TOC) removal tests demonstrated that PS-NP could not be completely degraded by UV/NaClO. The mineralization rate using UV/NaClO was only 7.00% even when the NaClO concentration increased to 5.00 mM, and many PS-NP particles could still be observed in SEM images. By contrast, the mineralization rate reached 63.90% in the UV/PMS system under the identical experimental conditions, and no spherical particles appeared in the SEM results. Density functional theory (DFT) calculations revealed that the different reaction sites and energy barriers of SO4•− and •Cl on PS-NP resulted in the differences in mineralization rates and degradation intermediates. The degradation pathway of PS-NP by UV/NaClO and UV/PMS was proposed accordingly. Additionally, the intermediates toxicity evaluation by a luminescent bacteria test showed that the inhibition rate in the UV/NaClO system (2.97%) was not markedly different from that in the control group without any treatment (1.98%); while that in UV/PMS system increased sharply to 98.19%. This work demonstrated that UV/PMS was more effective in PS-NP degradation than UV/NaClO, and the chemical risks of degradation intermediates were non-negligible.

Fabric defect detection is an important quality inspection process in the textile industry. A fabric defect detection system based on transfer learning and an improved Faster R-CNN is proposed to solve the problems of low detection accuracy, general convergence ability, and poor detection effect for small target defects in existing fabric defect detection algorithms. The pre-trained weights on the big dataset Imagenet are first extracted for transfer learning. Images are then input into the improved Faster R-CNN network, while the ResNet50 and ROI Align are used to replace the original VGG16 feature extraction network structure and a region of interest (ROI) pooling layer to avoid the problems of region mismatch caused by two quantizations from ROI pooling. The region proposal network (RPN) is combined with the multi-scale feature pyramid FPN to generate candidate regions with richer semantic information and project them onto the feature map to obtain the corresponding feature matrix. Cascaded modules are integrated and different IoU thresholds are used for each level to distinguish positive and negative samples. Finally, the softmax classifier is adopted to identify the image and obtain the predictions. The experimental results show that the detection accuracy and convergence ability of the improved Faster R-CNN are greatly enhanced compared with the current mainstream models, which provides a reference for future fabric defect detection methods.

Abstract Climate warming is known to impact ecosystem composition and functioning. However, it remains largely unclear how soil microbial communities respond to long-term, moderate warming. In this study, we used Illumina sequencing and microarrays (GeoChip 5.0) to analyze taxonomic and functional gene compositions of the soil microbial community after 14 years of warming (at 0.8–1.0 °C for 10 years and then 1.5–2.0 °C for 4 years) in a Californian grassland. Long-term warming had no detectable effect on the taxonomic composition of soil bacterial community, nor on any plant or abiotic soil variables. In contrast, functional gene compositions differed between warming and control for bacterial, archaeal, and fungal communities. Functional genes associated with labile carbon (C) degradation increased in relative abundance in the warming treatment, whereas those associated with recalcitrant C degradation decreased. A number of functional genes associated with nitrogen (N) cycling (e.g., denitrifying genes encoding nitrate-, nitrite-, and nitrous oxidereductases) decreased, whereas nifH gene encoding nitrogenase increased in the warming treatment. These results suggest that microbial functional potentials are more sensitive to long-term moderate warming than the taxonomic composition of microbial community.

As a precious soil resource, black soils in Northeast China are currently facing severe land degradation. Visible and near-infrared spectroscopy (vis-NIR, 350–2500 nm) and mid-infrared spectroscopy (MIR, 2500–25,000 nm) have shown great potential to predict soil properties. However, there is still limited research on using MIR in situ. The aim of this study was to explore the feasibility of in situ MIR for the prediction of soil total nitrogen (TN) and total phosphorus (TP) and to compare its performance with the use of laboratory MIR, as well as the use of in situ and laboratory vis-NIR. A total of 450 samples from 90 soil profiles, along with their in situ and laboratory spectra of MIR and vis-NIR, were collected in a field with ten different tillage and management practices in a typical black soil area of Northeast China. Partial least square regression (PLSR), random forest (RF) and multivariate adaptive regression splines (MARS) were used to generate the calibrations between the spectra and the two properties. The results showed that both MIR and vis-NIR were able to predict the TN whether in laboratory or in situ conditions, but neither of them could predict the TP quantitatively since there was no sensitive band on both spectra regarding the TP. The prediction accuracy of the TN with laboratory spectra was higher than that with in situ spectra, for both vis-NIR and MIR. The optimal prediction accuracy of the TN with laboratory MIR (RMSE = 0.11 g/kg, RPD = 3.12) was higher than that of laboratory vis-NIR (RMSE = 0.14 g/kg, RPD = 2.45). The optimal prediction accuracy of in situ MIR (RMSE = 0.20 g/kg, RPD = 1.80) was lower than that of in situ vis-NIR (RMSE = 0.16 g/kg, RPD = 2.14). The prediction performance of the spectra followed laboratory MIR > laboratory vis-NIR > in situ vis-NIR > in situ MIR. The performance of in situ MIR was relatively poor, mainly due to the fact that MIR was more influenced by soil moisture. This study verified the feasibility of in situ MIR for soil property prediction and provided an approach for obtaining rapid soil information and a reference for soil research and management in black soil areas.

Robust soil organic matter (SOM) mapping is required by farms, but their generation requires a large number of samples to be chemically analyzed, which is cost prohibitive. Recently, research has shown that visible and near-infrared (vis-NIR) reflectance spectroscopy is a fast and accurate technique for estimating SOM in a cost-effective manner. However, few studies have focused on using vis-NIR spectroscopy as a covariate to improve the accuracy of spatial modeling. In this study, our objective was to compare the mapping accuracy from a spatial model using kriging methods with and without the covariate of vis-NIR spectroscopy. We split the 261 samples into a calibration set (104) for building the spectral predictive model, a test set for generating the vis-NIR augmented set from the prediction of the fitted spectral predictive model (131), and a validation set (26) for evaluating map accuracy. We used two datasets (235 samples) for Kriging: a laboratory-based dataset (Ld, observations from calibration and test datasets) and a laboratory-based dataset with vis-NIR augmented predictions (Au.p, observations from calibration and predictions from test dataset), a laboratory-based dataset with vis-NIR spectra as the covariance (Ld.co) and augmented dataset with predictions using vis-NIR with vis-NIR spectra for the covariance (Au.p.co). The first one to seven accumulated principal components of vis-NIR spectra were used as the covariates when we used the measurement of Ld.co and Au.p.co. The map accuracy was evaluated by the validation set for the four datasets using Kriging. The results indicated that adding vis-NIR spectra as covariates had great potential in improving the map accuracy using kriging, and much higher accuracies were observed for Ld.p.co (RMSE of 5.51 g kg−1) and Au.p.co (RMSE of 5.66 g kg−1) than without using vis-NIR spectra as covariates for Ld (RMSE of 7.12 g kg−1) and Au.p (RMSE of 7.69 g kg−1). With a similar model performance to Ld.p.co, Au.p.co can reduce the cost of laboratory analysis for 60% of soil samples, demonstrating its advantage in cost-efficiency for spatial modeling of soil information. Therefore, we conclude that vis-NIR spectra can be used as a cost-effective technique to obtain augmented data to improve fine-resolution spatial mapping of soil information.

Mechanisms and consequences of the acclimation of soil respiration to warming are unclear. Here, the authors combine soil respiration, metagenomics, and functional gene results from a 7-year grassland warming experiment to a microbial-enzyme decomposition model, showing functional gene information to lower uncertainty and improve fit.

Soil, as the largest terrestrial carbon pool, has garnered significant attention concerning its response to global warming. However, accurately estimating the stocks and dynamics of soil organic carbon (SOC) remains challenging due to the complex and unclear influence mechanisms associated with biogeochemical processes in above- and belowground ecosystems, as well as technical limitations. Therefore, it is imperative to facilitate the integration of models and knowledge and promote dialogue between empiricists and modelers. This review provides a concise SOC turnover framework to understand the impact of climate change on SOC dynamics. It covers various factors such as warming, precipitation changes, elevated carbon dioxide, and nitrogen deposition. The review presents impact mechanisms from the perspective of organismal traits (plants, fauna, and microbes), their interactions, and abiotic regulation. Although valuable insights have been gained regarding SOC inputs, decomposition, and stabilization under climate change, there are still knowledge gaps that need to be addressed. In the future, it is essential to conduct systematic and refined research in this field. This includes standardizing the organismal traits most relevant to SOC, studying the standardization of SOC fractions and their resistance to decomposition, and focusing on the interactions and biochemical pathways of biological communities. Through further investigation of biotic and abiotic interactions, a clearer understanding can be attained regarding the physical protection, chemical stability, and biological driving mechanisms of SOC under climate change. This can be achieved by integrating multidisciplinary knowledge, utilizing novel technologies and methodologies, increasing in-situ experiments, and conducting long-term monitoring across multi-scales. By integrating reliable data and elucidating clear mechanisms, the accuracy of models can be enhanced, providing a scientific foundation for mitigating climate change.

Disturbances in vegetated land could dramatically affect the process of vegetation growth and reshape the land cover state. The overall greenup of vegetation on the Tibetan Plateau (TP) has almost served as a consensus to date. However, we still lack consistent acquisitions on the timing, the spatial patterns, and the temporal frequency of vegetation disturbance over the TP, limiting the capacity for planning land management strategies. Therefore, we explored the spatiotemporal pattern and variation of vegetation disturbances across the TP during the past decades and analyzed the disturbance agents. We utilized 37-year Landsat time series images and field observations coupled with a temporal segmentation approach to characterize the spatiotemporal pattern of vegetation disturbances across the TP for the period 1986–2018. The results from this study revealed that 75.71 M ha (accounting for 29.34% of TP’s area) vegetation area underwent at least one disturbance, of which 8.44 M ha area ever experienced large-scale disturbances (disturbance area greater than 0.9 ha and disturbance magnitude (the difference between the spectral value of pre-disturbance and that of post-disturbance) over 0.2). Further, the spatial distributions of these large-scale disturbances varied over time: before 2002, the disturbed sites were evenly distributed over the southeast part of the TP probably induced by overgrazing and unscientific livestock management, while after 2002, most disturbances were concentrated in the south of the Yarlung Tsangpo, mainly caused by anthropogenic activities, such as urban area, roadways, railway, and water control projects. This study presents an effort to characterize vegetation disturbances and their variations over the past decades on the TP, which provides crucial insights toward a complete understanding of vegetation dynamics and its causal relationship with human activities.