Spatial sampling design is essential for accurately assessing land use and land cover (LULC) classification results from remote sensing data. When classification correctness exhibits spatial heterogeneity, spatial stratification can significantly improve spatial sampling efficiency by dividing the study area into heterogeneous strata. Three spatial stratification methods were introduced, respectively focusing on LULC types, the integration of multi-source classification products with different spatial resolutions, and pixel-level uncertainty analysis.First, stratification by LULC types was employed because these categories directly relate to variations in classification accuracy. Second, although LULC products from different sources and resolutions were generated using diverse data and methods, their consistency and inconsistency could indicate potential misclassification. Thus, a stratification method that combined such multi-source products was developed for guiding accuracy assessment sampling. Third, a pixel-based stratification framework was proposed based on uncertainty indices, namely the maximum probability, fuzzy confusion index, and probability entropy.The effectiveness of these methods was tested through a case study of LULC classification in Beijing, China. Results showed that the proposed stratification approaches could effectively distinguish spatial characteristics and improve sample representativeness, thereby optimizing the sampling for classification accuracy evaluation and enhancing its overall reliability.

To address the low application accuracy and poor spreading uniformity of conventional lime spreaders, an electromagnetic vibration-assisted variable-rate lime spreader integrating a shaftless screw metering mechanism was developed. The overall configuration and operating principle are presented. Considering the physicochemical characteristics of lime powder, including fine particle size, strong drift tendency, and poor flowability, a shaftless screw metering unit was designed to improve discharge stability and metering accuracy. To enhance dispersion uniformity, a vertical electromagnetic vibration device was developed, and its key parameters were determined through a theoretical analysis of vibration frequency and amplitude. In addition, the structure and kinematic parameters of the spreading disc were optimized by analyzing particle trajectories and outlet distribution patterns. A closed-loop feedback control strategy was implemented to enable precise variable-rate application. Static bench tests demonstrated a metering accuracy of 96.42%, and the dispersion uniformity was at least 84.14% at an electromagnetic vibration frequency of 10 to 18 Hz. Field evaluations further showed that the coefficient of variation for transverse uniformity was no more than 17.88%, while the maximum coefficient of variation for longitudinal stability was 18.09%. These results indicate that the proposed spreader satisfies the operational requirements for accurate and uniform variable-rate application of lime powder.

Machine learning-based digital soil mapping often struggles with spatial heterogeneity and long-range dependencies. To address these limitations, this study proposes Multi-Attention Convolutional Neural Networks (MACNN). This deep learning algorithm integrates multiple attention mechanisms to improve mapping accuracy. First, environmental covariates are determined from the soil-landscape model. These are then fed as structured input to the Convolutional Neural Network. Next, by incorporating Transformer self-attention and multi-head attention mechanisms, this study effectively models the long-range dependencies between soil types and features. Concurrently, the Convolutional Block Attention Module (CBAM) is introduced. CBAM features both channel and spatial dual attention, enabling adaptive weighting of crucial feature channels and spatial locations. This significantly enhances the algorithm’s sensitivity to discriminative information. To validate its effectiveness, the proposed MACNN algorithm was used for soil type mapping in Heilongjiang Province. Compared to Random Forest, Decision Tree, and One-Dimensional Convolutional Neural Network algorithms, MACNN demonstrated superior classification performance. It achieved an overall classification accuracy of 81.27%. An ablation study was conducted to investigate the importance of individual modules within the proposed algorithm. The findings indicate that progressively integrating Transformer and CBAM modules into the 1D-CNN baseline significantly enhances algorithm performance through synergistic gains. Therefore, this integrated algorithm offers a feasible solution to improve digital soil mapping accuracy, providing significant reference value for future research and applications.

Upright maize straw left in the field during autumn and winter significantly contributes to severe air pollution in agricultural ecosystems due to burning. It is essential to obtain the spatial distribution of upright maize straw quickly and accurately for effective management and environmental protection. However, identifying upright maize straw using remote sensing is difficult because its spectral properties resemble those of other land covers like straw residue, bare soil, and sparse wheat at the same period. This study proposes a novel index for extracting upright maize straw by integrating low-cost unmanned aerial vehicle (UAV) visible to near-infrared spectral bands with digital surface model (DSM) data. First, we analyzed the spectral characteristics of four land cover types: upright maize straw, straw residue, bare soil, and sparse wheat, and proposed the adjusted straw index (ASI) that leverages green, red, and red-edge bands. Next, we combined DSM data with the ASI to develop the adjusted height straw index (AHSI), considering the height of the upright maize straw. Finally, the combination of index-plus-Otsu threshold segmentation and random forest (RF) methods was applied to identify and extract the spatial distribution of upright maize straw. The results showed that our method effectively detected the main regions of upright maize straw. The two proposed straw indices achieved over 87%(ASI) and 96%(AHSI) extraction accuracies across three different study regions. The two new indices not only significantly improve the accuracy of upright maize straw identification but also provide a new approach for low-cost UAV-based identification of non-photosynthetic vegetation (NPV).

The analysis accuracy of energy dispersion X-ray fluorescence spectrometry (XRF) for detecting heavy metal in agricultural soils is severely depending on complex matrix effect, thereby posing a challenge in fast and precise monitoring soil contamination. To calibrate the XRF detection, a Gaussian mixture clustering-multilevel model (GMC-MLM) was proposed to enhance XRF accuracy for Cd in agricultural soils. Compared with other models such as multiple linear regression (MLR), random forest regression (RF), and support vector machine regression (SVMR), the GMC-MLM effectively disentangled the nested distribution of XRF detection errors. The correlation coefficient between the XRF detection results and ICP-MS test results for the corrected samples can reach 0.9085, with 74% of the corrected samples having a relative error of less than 30%. Notably, according to the GMC-MLM correction method, a knowledge base for localizing corrections in XRF detection has been constructed. When the number of knowledge base sample points is 50, the RMSE (Root Mean Squared Error), , and REM (Relative Error of Mean) are 0.7347, 3.7014%, respectively. It can be observed that the model has good extrapolation capability, and with the increase in the number of knowledge base sample points, the correction effect based on the knowledge base gradually stabilizes. This knowledge base-based GMC-MLM calibration method can be embedded into XRF detection instruments to recalibration XRF detection results.

Preventing impending heavy metal pollution in arable soil is crucial for ensuring food security and avoiding challenging remediation. The key to effective prevention strategies lies in proactive identifying currently unpolluted regions that are susceptible to future pollution, which current methods, predicated on the assessment of static pollution status, inadequately characterize the potential accumulative changes in soil heavy metals. In this paper, we proposed a framework for identifying priority areas based on the discrepancy between pollution status and accumulative risk, by considering the specific factors that influence heavy metal accumulation in soil. We applied this framework to a region of Xiangtan County to pinpoint priority areas for preventing impending pollution. The result revealed certain areas exhibited a relatively higher accumulative risk of heavy metal pollution, despite not having reached severe pollution levels for the heavy metals Arsenic (As), Cadmium (Cd), Chromium (Cr), Mercury (Hg), and Lead (Pb), among which the area ratio reached nearly 6 %, 36 %, 1 %, 3 %, 4 %, respectively. The priority areas for preventing Cd pollution were primarily concentrated in the mid-southern, mid-western, and eastern regions, while that of the other four heavy metals were predominantly distributed in the mid-northern regions with varying continuous ranges. Moreover, we prioritized the main pollution risks for comprehensive prevention in the following order: Cd, As, Pb, Hg, and Cr, and investigated the key factors contributing to the pollution of these heavy metals. The insights presented in this study have significant implications for soil environmental quality management, offering valuable guidance for implementing precise measures to prevent heavy metal pollution and efficiently control pollution sources.

The thinning of the arable layer causes soil quality degradation, seriously affecting crop growth and even threatening national food security. This article focuses on the problems and needs of poor timeliness, high cost, and inability to dynamically warn about the thinning of the tillage layer in black soil farmland, and conducts research on corresponding warning methods. Based on daily rainfall data, the percentile method is used to calculate the threshold for erosive rainfall event driven warning. Based on erosive rainfall event driven coupling of external soil erosion stress meteorological elements and influencing processes, as well as the connotation of geographic events, the concept definition of geographic events for thinning warning of arable layer thickness is given. The semantic description and discovery method of geographic events are provided, and geographic event driven rules and the technical process of thinning warning of arable layer thickness are formulated. There is a significant correlation between rainfall and topsoil thickness, with a correlation coefficient of 0.83. A soil thickness thinning warning simulation was conducted based on the daily rainfall data of Hailun City, Heilongjiang Province in 2020. On June 14, 2020, when the daily rainfall exceeded the event threshold, an automatic warning for thinning of the plow layer was triggered, achieving a dynamic warning process for thinning of the plow layer. The research results indicate that this method is helpful in solving the problem of thinning warning of the cultivated layer thickness in black soil farmland, and can provide technical support for the quality construction, protection, and management of black soil farmland.

Aiming at the problem that the existing soil pollution risk assessment methods are not closely related to soil and agricultural products pollution and have poor applicability in different scenarios， a collaborative assessment method of heavy metal pollution risk in cultivated land based on multi-factor fusion is proposed， and the effectiveness of the method has verified by taking the risk assessment of heavy metal pollution in cultivated land soil in a county in southern China as an example. Based on soil environmental quality standards， soil background values， toxicity， and physical and chemical properties of heavy metal elements， the " soil load capacity weight index" was proposed. The heavy metal elements with the greatest ecological hazard in composite pollution were screened out，and the regional soil pollution risk under different scenarios was evaluated by the comprehensive evaluation index， and the risk level was determined by combining the pollution of agricultural products to build a collaborative risk assessment framework．The results showed that the soil pollution risk in the study area is divided into four levels，the negligible risk area accounted for 8．23% of the total cultivated land area，the low risk accounted for 17.45%，the medium risk and high risk areas accounted for 57.18% and 17.14%，respectively，the most significant elements of soil pollution were Cd and Hg， and the medium and high pollution risk areas were mainly distributed in the central and eastern parts of the study area，and agricultural products had strong cadmium enrichment capacity．Compared with the geo-accumulation index method and the potential ecological hazard index method， the comprehensive evaluation index method proposed in this paper can automatically evaluate the risk of multi-element composite pollution， and effectively avoid the problem of unreasonable evaluation results caused by improper setting of the weight of each evaluation index．

Identifying the current status of the heavy metal(loid) input of agricultural soils is vital for the soil ecological environment of agricultural-producing areas. Most previous studies have typically carried been out in small regions with limited sampling sites, which is insufficient to reveal the overall status of China. This study reviewed publications from over the past 20 years and calculated the input fluxes of heavy metal(loid)s in agricultural soil via atmospheric deposition, fertilizer, manure, and irrigation in different regions of China based on spatiotemporal heterogeneity using a meta-analysis, providing more accurate and reliable results. It was found that the heavy metal(loid) input flux of atmospheric deposition in China is large, while that of fertilizer and manure is relatively low compared to Europe. The major sources of As, Cd, Cr, Ni, and Pb entering the soil was atmospheric deposition, which accounted for 12% to 92% of the total input. Manure was responsible for 19% to 75% of the Cu and Zn input. Cd is the element presenting the most significant risk to the environment of agricultural soils in China and its safety limit will be reached within 100 years for most regions. The region we need to be concerned about is Huang-Huai-Hai due to its comprehensive pollution.

Precise mapping based on sampling data is meaningful for efficient soil quality management. The distributions of sampling sites in feature space and geographical space, as well as the distribution of point pairs at different distances, all significantly impact mapping precision, and these three aspects should all be considered in a sampling design. Although optimizing the spread of sampling sites in the above three aspects has been realized and addressed in previous studies, their tradeoff relationship and influence on mapping accuracy have not been comprehensively investigated, partly due to the limitations of weighting-based optimization way. In this article, we proposed a sampling strategy based on Pareto optimality to examine the tradeoff relationship among the three aspects and their influence on interpolation precision. Based on soil organic matter data from Yi’an district, we applied this approach to generate sampling schemes and analyze their distributions and prediction errors. Single-objective optimization of these three aspects was also conducted for comparison. The results revealed that: (1) the sampling strategy that simultaneously optimizes the distributions of sampling sites in the three aspects was able to obtain sampling designs with higher mapping accuracy than the method that only optimizes one aspect. (2) There was an apparent synergistic relationship between the distributions of sampling sites in feature space and geographical space. However, these objectives had antagonistic relationships with the distribution of point pairs at different distances. (3) When considering the tradeoff among the three aspects, the more even the distribution in geographical space was, the higher the mapping accuracy they produced. Furthermore, more even distribution in feature space improved the mapping accuracy, but the benefit faded after a certain degree, whereas the mapping accuracy initially increased as the distance distribution of point pairs in different intervals became more even but subsequently declined beyond a certain point. Attention should be given to the tradeoff relationship and impact on interpolation when sampling for mapping. In similar scenarios like the study case, it is recommended that sampling sites should be distributed in feature space with more than 45% evenness of the optimal state and then distributed in geographical space as evenly as possible, while the distribution of point pairs should not exceed 65% evenness of its optimal state when the sample size range is approximately 50 to 150. This study provides helpful references for scientific sampling with the goal of precise mapping.

Lodging causes severe decreases in crop yield, reduces grain quality, and increases the difficulty of mechanical harvesting. Obtaining the spatial distribution information of maize lodging grades in a timely and accurate manner is essential for yield loss assessment, post-stress management, and insurance claims settlements. The purpose of this study is to explore the ability of unmanned aerial vehicle (UAV) imaging technology to monitor maize lodging stress. With the support of maize lodging control experiments, the canopy chlorophyll density (CCD) of maize populations under stress from different lodging grades was used as the characterization index. The responses between hyperspectral characteristic parameters and CCD with different lodging grade stresses were analyzed. The monitoring model of the maize CCD under lodging stress was constructed using the sensitive characteristic parameters of original canopy spectra (OCS), first-order differential (FOD), wavelet coefficient (WC), and vegetation index (VI). The results showed that the reflectance of the stalk was significantly higher than that of the leaf in hyperspectral imagery, which was the main reason for the change in the original canopy spectra under lodging stress. The original canopy spectral reflectance increased with the severity of lodging stress. The accuracy of the CCD model was VI > WC > FOD > OCS (R2 = 0.63, 0.61, 0.59, 0.57, respectively), in which the accuracy of VI was the highest (R2 = 0.63, RMSE = 0.36 g/m3). This is because CCD considers not only the change in canopy spatial structure after maize lodging, but also the change in physiological activity of maize plants under lodging stress. The maize lodging grades were evaluated according to the CCD model based on the UAV hyperspectral imagery.

Owing to the significant impact of heavy metals in atmospheric deposition on soil, clear knowledge on the present situation and temporal and spatial variation in fluxes of heavy metals in atmospheric deposition all around China is urgently needed. In this study, we collected 99 published papers on deposition fluxes of heavy metals from 2001 to 2021 based on the CNKI and Web of Science database and extracted 718 to 1672 monitoring points from these papers. The Meta-analysis method was used to calculate the weighted average of deposition fluxes of heavy metals, and the spatial-temporal characteristics in different periods from 2000 to 2018 were studied by subgroup analysis, which compared the differences between different types of areas, such as agricultural and rural areas and urban and industrial areas. The results showed that the annual fluxes of heavy metals in atmospheric deposition[mg·(m2·a)-1] in China were as follows:Zn (96.75)>Pb (23.37)>Cu (12.77)>Cr (11.04)>Ni (6.61)>As (2.97)>Cd (0.48)>Hg (0.05). Overall, the estimated value of deposition fluxes in China from 2000 to 2018 was higher than that of rural areas in England from 1995 to 1998. The deposition fluxes in industrial areas and urban areas were much higher than those in the agricultural and rural areas, especially the industrial areas where the heavy metal pollution was more serious. The deposition fluxes of As and Cd in the Changsha-Zhuzhou-Xiangtan area were relatively high, whereas the atmospheric deposition of heavy metals in Northeast China, the Pearl River Delta, and North China Plain was more serious than that in the other areas. In the past 20 years, the annual deposition fluxes of Cd fluctuated around the overall average, without an obviously declining trend, whereas the deposition fluxes of Cd in the urban, agricultural, and rural areas showed a trend of growth. These results suggested that precise and risk control measures of atmospheric emissions should be established based on the characteristics of regional industrial structure, which should cover all levels, all types, and all regions. In addition, more restrictive measures should be taken to solve the current problem caused by the higher deposition flux of Cd in atmospheric deposition.

Existing source apportionment methods for soil heavy metals have failed to establish a credible relationship through global models because the high levels of spatial heterogeneity in soil heavy metals. Bivariate local Moran’s I (B-LISA) provides a local geographic perspective to explore the spatial co-location patterns of driving factors and receptors. The accumulation of heavy metals in soil originates from the high aggregation of driving factors. A factor is considered to be a driving factor if the high aggregation of the factor is spatially co-located with a high accumulation of heavy metals in the soil. Based on this, we propose the use of DHH index to quantify the effect of the potential driving factors on soil heavy metals. The results showed that among the nine factors, the Non-metallic Mineral Product (NMP) industry was the main factor in soil Cd pollution followed by the Chemical (CHM) industry and Non-Metallic Mining and Dressing (NMD). Our analysis revealed that soil Cd contamination was mainly distributed in the economic development zone(YSH town) and the historical coal mining area(TJS town). These two areas show different localized pollution patterns. One is the YSH with multiple industries dominated by NMP, CHM and non-ferrous metals (NMF) with complex interactions. The other is TJS with a single dominant influence of mining industry. This study propose a technical approach to precisely locate and quantify the effects of pollution and source and suggest that source deterrence should be considered based on the spatial co-location patterns of sources and pollution.

Spatial sampling is important for soil surveys and mapping, and the optimization of the sampling design is a hot topic. Most often in soil sampling, multiple purposes are usually involved and corresponding objectives need to be optimized as much as possible. In such cases, balanced optimization is needed to produce the best compromised solutions that reach the maximum common interest, but not to generate a well-spread Pareto front. To solve this problem, the multiple path spatial simulated annealing (MP-SSA) was developed by extending the classic SSA. It can synchronously optimize multiobjective functions of different types and magnitudes by setting one annealing path for each objective, and designing a voting and annealing mechanism. To illustrate the difference and performance for MP-SSA, it was compared with the archived multiobjective simulated annealing (AMOSA) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), both aiming at generating well-spread Pareto front, in two case studies with hypothetical data and actual soil heavy metal data. The results show that the MP-SSA is more efficient in generating the best compromised solutions, and is an efficient and promising tool for balanced multiobjective optimization for spatial sampling design when all objectives need to be optimized as much as possible.

受自然环境因素、人为活动因素综合作用,土壤环境质量在空间上具有明显分层异质性,基于采样点进行环境质量分区是土壤环境质量管理中的关键问题.本文提出一种基于采样点与辅助因素的土壤环境质量分区方法,在采用地理探测器分析土壤环境质量主要影响因素的基础上,运用高斯混合模型对土壤采样点进行属性聚类,结合河流道路等对聚类结果进行边界调整,形成环境质量分区.以北京市顺义区采样数据为例开展方法验证,结果表明:本方法的分区结果优于SOFM聚类、K-means聚类,适用于人为和自然活动综合影响的环境质量分区.

环境质量等级划分是土壤环境调查和监测的一项重要目标.不同于总体估计和空间插值,等级划分只需要准确估计污染物浓度与等级划分阈值之间的大小关系,同时由于土壤环境质量等级具有空间连续性,加密采样时应该将样点布设在等级过渡地带,准确获取等级边界.但是初步调查的样点一般较为稀疏,导致等级估计结果具有较大的不确定性,难以反映真实的等级过渡情况,而且具体应用对两类等级划分错误也有不同的控制要求,构成了土壤环境质量等级划分及加密采样面临的特殊问题.本文分析梳理了国内外土壤环境质量等级划分统计推断与加密采样布局优化方法的研究进展,归纳总结了以等级划分为目的的统计推断方法与加密采样布局优化方法体系,并探讨了下一步研究的方向,为我国目前开展的农用地土壤环境类别划分、工矿企业土壤污染修复区确定等重大需求提供方法选择依据.