Typical Region Research Articles

Multi-objectives optimization can contribute to conservation and restoration priority setting in dryland region

The ecological conservation and restoration of drylands is a global challenge, which is always under resources constraints and in competition with land developments. Here, we aim to identify ecological conservation and restoration priority areas based on the objectives of maximizing ecological values and minimizing opportunity costs with zonation as a heuristic spatial optimization tool. We take the Inner Mongolia (IM) of China as a typical dryland region. Results reveal that the efficiency of multi-objectives for ecological restoration is often better than a single objective. IM should focus on ecological conservation supplemented by restoration. Conserving 36.9% and restoring 13.1% of IM can provide an average of 72% of ecosystem functions while consuming 20% of opportunity costs. Our framework aligns with the need for nature-based solutions and rationalizes the allocation of resources for ecological conservation and restoration projects in complex dryland regions.

River–Spring Connectivity and Hydrogeochemical Processes in a Karst Water System of Northern China: A Case Study of Jinan Spring Catchment

Frequent surface water–groundwater interactions and prevalent anthropogenic inputs make karst water systems vulnerable to human disturbance. As a typical karst region in North China, the Jinan Spring Catchment has become increasingly threatened due to rapid population growth and urban expansion. In this study, the local river–spring interaction and its interference with the hydrogeochemical evolution of groundwater are evaluated based on water stable isotopes and hydrochemistry. Twenty-two karst groundwater, eleven Quaternary pore water, sixteen spring water, and thirty-two surface water samples were collected during low- and high-flow conditions over the course of a year. The isotopic signatures of four different water types display significant differences, reflecting the recharge–discharge relationship of the karst water system. Mountainous springs feature lighter isotopes, whereas urban springs have significantly heavier isotopes. The result of end-member mixing analysis shows that the surface–groundwater interaction varies spatially and temporally within the spring catchment. Urban springs receive considerable replenishment from the surface water, especially after rainy episodes (up to 50%), while mountainous springs show little hydraulic dependence on surface water leakage (4~6%). Local mineral dissolution (including calcite, dolomite, gypsum, and halite), CO2 dissolution/exsolution, and cation exchange are the main hydrogeochemical processes constraining water chemistry in the spring catchment. The deterioration of water quality can be attributed to anthropogenic influences involving the discharge of domestic effluents, agricultural activities, and irrigation return flow. The findings of this work can improve our understanding of the complex karst water system and serve as a reference for sustainable groundwater management in other karst areas of northern China.

Design of locally arranged sensor arrays in wearable OPM-MEG based on sensor volume constraints

Optically pumped magnetometers-based magnetoencephalography (OPM-MEG) has a more flexible sensor configuration. This implies that OPM-MEG can be customized as locally arranged sensor arrays (LASAs) when the number of sensors is limited. Currently, various methods are proposed to optimize LASA for accurately estimating source currents. However, the sensor volume, as a practical constraint in the construction of an OPM-MEG system, was not taken into account during the design of sensor arrays. In this study, we proposed a sensor-array design method for LASAs that considers the physical size of OPM sensors, with the array sensitivity metrics serving as the optimization objective. To illustrate the effectiveness of the proposed method, several LASAs covering typical brain functional regions were designed and tested in both simulations and practical OPM-MEG experiments. The results indicated that the designed LASAs under sensor volume constraints demonstrated reduced source localization errors and focused localization range for the target source area.

The Influence of Vegetation on Climate Elements in Northwestern China

Vegetation plays a crucial role in maintaining the balance between nature, water and soil resources. However, understanding its impact mechanisms in arid and semi-arid areas remains limited. This study aims to analyze the spatial–temporal characteristics of the vegetation leaf area index (LAI) and climate elements in typical regions of northwest China and the correlations between LAI and climate elements; it also aims to explore the influence of regional vegetation growth on climate change. The results reveal significant correlations between LAI and various climate elements. Specifically, within the same region, surface temperature, precipitation, vegetation transpiration, and total evaporation show positive correlations with the LAI, whereas surface albedo shows a negative correlation. Vegetation may affect climate through both heat and water exchange between the land and atmosphere. Increased vegetation leads to the enhanced absorption of solar radiation by the land surface, elevating surface temperature. Increased levels of vegetation also increase vegetation transpiration and total evaporation, increasing the water vapor content in the atmosphere and thus leading to increased surface precipitation. Therefore, vegetation distribution plays a role in climate change, and ecological restoration projects in the northwest region hold significant potential for addressing ecological challenges in its arid and semi-arid areas.

The effects of sowing date on maize: Phenology, morphology, and yield formation in a hot subtropical monsoon region

Context or problemWith abundant thermal resources, there is a long-time span for potential maize sowing in the hot subtropical monsoon region. However, with different sowing dates, maize may be exposed to elevated temperatures at different growth stages. ObjectiveThis study aims to understand the impact of sowing date on maize growth and development and yield formation as temperatures increase. MethodsA 3-year field experiment was carried out in the typical subtropical monsoon region in China, with 11 sowing dates ranging from mid-March to late June with approximately 10-day intervals (named S1 to S11), and three main commercial varieties were included to validate the impact of sowing date. ResultsFollowing delayed sowing, maize plants were exposed to increased temperatures during the emergence (VE) to silking (R1) stages. Maize plants of the three varieties exhibited similar responses to delayed sowing dates in terms of growth, development, and yield performance. Vegetative growth was accelerated, while the anthesis-silking interval (ASI) and the vegetative-to-reproductive transition, that is, the interval of V12 (twelfth leaf) to R1 stages, were prolonged. The grain yield gradually decreased and reached a minimum at approximately S8-S10 (varied among varieties), which was in accordance with the decreased grain number. Further analyses revealed that the decreased grain number and yield were associated with a prolonged V12-R1 interval with delayed sowing. The duration of V12-R1 was significantly positively correlated with the maximum and minimum temperatures within a day, suggesting that the V12-R1 interval could be a parameter for evaluation of heat’s impact on yield formation. Grain weight decreased as the temperature of the R1-R6 stage rose before S8 but increased as the temperature dropped after S8. Inconsistently, biomass accumulation, leaf area, and plant and ear height first increased and then decreased before and after S3-S6, respectively. Conclusions and significanceDelayed sowing in hot subtropical monsoon regions exposes maize plants to elevated temperatures, leading to accelerated vegetative and reproductive growth but a prolonged vegetative-to-reproductive transition, and suppresses yield by reducing grain number and/or weight, resulting the yield decreased with the delayed sowing. By demonstrating the impacts of high temperature with different sowing dates on the phenology, morphology, and yield formation of maize, this study provides a basis for improving maize yield in practical production by optimizing the sowing date.

Statistical analysis of the differentiation of regions of the Russian Federation by tax revenues of consolidated budgets using the R language

The subject area of this article is the application of descriptive statistics and multidimensional classification methods to describe the regional features of the formation of tax components of revenues of consolidated budgets of the Russian Federation. The aim of the work is to demonstrate the simplicity and effectiveness of using mathematical and statistical methods and the functionality of the open source language R to solve problems of structural analysis of tax revenues, identify regional specifics, and comparative analysis of regions from the point of view of tax revenues. The apparatus of mathematical statistics implemented in the R language, in particular, opens up wide opportunities for classifying tax subjects, including multidimensional ones, significantly facilitating the procedures of analysis, ranking and planning. The functionality described in the article can be used in the process of forming and adjusting tax policy at different levels. The possibilities of the mathematical statistics apparatus in combination with the instrumental methods of the R language are revealed by the example of the classification analysis of the regions of the Russian Federation. At the same time, the absolute and relative values of tax revenues in the revenues of regional budgets are selected as classification features. The classification by belonging to the federal district and the study of the "natural" stratification by the method of cluster analysis are considered. The apparatus of mathematical statistics and, especially, the tools of the R language are used unreasonably rarely in research of this kind, despite the ease of use and the absence of the need for special training, these circumstances determine the relevance of this article. Aggregation by federal districts made it possible to identify: the Ural Federal District as the leader in terms of the average regional share of tax revenues in the revenue part of the budget and the North Caucasus Federal District, characterized by the lowest average regional contributions of tax payments to regional budgets. The analysis of the natural stratification of the regions of the Russian Federation by their relative tax contributions to consolidated budgets made it possible to identify groups: the most typical regions, subsidized regions, donor regions and regions in which the most expensive assets of enterprises of the Russian Federation are concentrated

Improving the accuracy of precipitation estimates in a typical inland arid region of China using a dynamic Bayesian model averaging approach

Improving the accuracy of precipitation estimates in a typical inland arid region of China using a dynamic Bayesian model averaging approach

Solar Energetic Particle Charge States and Abundances with Nonthermal Electrons

An important aspect of solar energetic particle (SEP) events is their source populations. Elemental abundance enhancements of impulsive SEP events, originating in presumed coronal reconnection episodes, can be fitted to steep power laws of A/Q, where A and Q are the atomic mass and ionic charge. Since thermal electron energies are enhanced and nonthermal electron distributions arise in the reconnection process, we might expect that ionic charge states Q would be increased through ionization interactions with those electron populations during the acceleration process. The temperature estimated from the SEPs corresponds to the charge state during the acceleration process, while the actual charge state measured in situ may be modified as the SEPs pass through the corona. We examine whether the temperature estimation from the A/Q would differ with various κ values in a κ function representing high-energy tail deviating from a Maxwellian velocity distribution. We find that the differences in the A/Q between a Maxwellian and an extreme κ distribution are about 10%–30%. We fit power-law enhancement of element abundances as a function of their A/Q with various κ values. Then, we find that the derived source region temperature is not significantly affected by whether or not the electron velocity distribution deviates from a Maxwellian, i.e., thermal, distribution. Assuming that electrons are heated in the acceleration region, the agreement of the SEP charge state during acceleration with typical active region temperatures suggests that SEPs are accelerated and leave the acceleration region in a shorter time than the ionization timescale.

Weak transport of atmospheric water-insoluble particulate carbon from South Asia to the inner Tibetan Plateau in the monsoon season

Carbonaceous particles play a crucial role in atmospheric radiative forcing. However, our understanding of the behavior and sources of carbonaceous particles in remote regions remains limited. The Tibetan Plateau (TP) is a typical remote region that receives long-range transport of carbonaceous particles from severely polluted areas such as South Asia. Based on carbon isotopic compositions (Δ14C/δ13C) of water-insoluble particulate carbon (IPC) in total suspended particle (TSP), PM2.5, and precipitation samples collected during 2020–22 at the Nam Co Station, a remote site in the inner TP, the following results were achieved: First, fossil fuel contributions (ffossil) to IPC in TSP samples (28.60 ± 9.52 %) were higher than that of precipitation samples (23.11 ± 8.60 %), and it is estimated that the scavenging ratio of IPC from non-fossil fuel sources was around 2 times that from fossil fuel combustion during the monsoon season. The ffossil of IPC in both TSP and PM2.5 samples peaked during the monsoon season. Because heavy precipitation during the monsoon season scavenges large amounts of long-range transported carbonaceous particles, the contribution of local emissions from the TP largely outweighs that from South Asia during this season. The results of the IPC source apportionment based on Δ14C and δ13C in PM2.5 samples showed that the highest contribution of liquid fossil fuel combustion also occurred in the monsoon season, reflecting increased human activities (e.g., tourism) on the TP during this period. The results of this study highlight the longer lifetime of fossil fuel-sourced IPC in the atmosphere than that of non-fossil fuel sources in the inner TP and the importance of local emissions from the TP during the monsoon season. The findings provide new knowledge for model improvement and mitigation of carbonaceous particles.

Prediction of ecological security network in Northeast China based on landscape ecological risk

The expansion of human activities has significantly increased ecological risks, which threaten the security of ecosystems. This paper takes Northeast China, a national typical ecological barrier region, as a case study to explore the evolution of the ecological environment under various future development scenarios. Considering the complexity of ecological assessments, a comprehensive approach integrating various methods is employed. This includes the Patch-based Land Use Simulation (PLUS) model, Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, a landscape ecological risk assessment index system, the Space Minimum Cumulative Resistance (MCR) model, among others. These methods are used to dynamically assess changes in land use, landscape ecological risk (LER), and the layout of ecological networks. An integrated framework is proposed for identifying, constructing, and optimizing the ecological security network (ESN). The results show that the LER in Northeast China presents a “high in the middle, low around” distribution characteristic. High risk areas are mainly distributed in water bodies and construction lands with high landscape fragmentation and isolation, while low risk areas are primarily in the Northeast forest belt and Inner Mongolia grassland areas with lower landscape vulnerability and separation. From 2000 to 2020, the LER in the Northeast China has increased, primarily manifested in the Greater and Lesser Khingan Range areas in the northern part of the study region. Forecast results indicate significant differences in the evolution of LERs and the layout of the ESN under different socio-economic development scenarios. Under the ecological protection (EP) scenario, the average LER in Northeast China is 0.2215. The area of ecological source lands is 302,405.56 km2, the length of ecological corridors is 179,024.71 km, with 339 ecological nodes and 410 ecological breakpoints identified. Compared with the natural development (ND) and urban construction (UC) scenarios, the EP scenario in Northeast China shows the lowest LER and the most ideal layout of the ESN. This study provides a reference for future land resource optimization and ecological restoration in Northeast China.

Identification of two MEF2s and their role in inhibiting the transcription of the mstn2a gene in the yellowfin seabream, Acanthopagrus latus (Hottuyn, 1782)

Myocyte-specific enhancer binding factor 2 (MEF2), which belongs to the MADS superfamily, is a pivotal and conserved transcription factor that combines with the E-box motif to control the expression of muscle genes. Myostatin (mstn), a muscle growth inhibitor, is a vital member of the TGF-β superfamily. Currently, an understanding of the mechanisms of A. latus mstn (Almstn) transcriptional regulation mediated by MEF2 in fish muscle development is lacking. In the present study, two AlMEF2s (AlMEF2A and AlMEF2B) and Almstn2a were characterized from Acanthopagrus latus. AlMEF2A and AlMEF2B had 456 and 315 amino acid (aa) residues, respectively. Two typical regions, a MADS-box, MEF2, and transcriptionally activated (TAD) domains, are present in both AlMEF2s. The expression profiles of the two AlMEF2 genes were similar. The AlMEF2 genes were mainly expressed in the brain, white muscle, and liver, while Almstn2a expression was higher in the brain than in other tissues. Moreover, the expression trends of AlMEF2s and Almstn2a were significantly changed after starvation and refeeding in the five groups. Additionally, truncation experiments showed that −987 to +168 and −105 to +168 were core promoters of Almstn2a that responded to AlMEF2A and AlMEF2B, respectively. The point mutation experiment confirmed that Almstn2a transcription relies on the mutation binding sites 1 or 5 (M1/5) and mutation binding sites 4 or 5 (M4/5) for AlMEF2A and AlMEF2B regulation, respectively. The electrophoretic mobile shift assay (EMSA) further verified that M1 (−527 to −512) was a pivotal site where AlMEF2A acted on the Almstn2a gene. Furthermore, a siRNA interference gene expression experiment showed that reduced levels of AlMEF2A or AlMEF2B could prominently increase Almstn2a transcription. These results provide new information about the regulation of Almstn2a transcriptional activity by AlMEF2s and a theoretical basis for the regulatory mechanisms involved in muscle development in fish.

Irrigation expansion has kept pace with the CO2 fertilization effect on vegetation growth in a typical arid region

Dynamics of vegetation in arid areas have drawn worldwide attention. The expansion of irrigated cropland (ICE) in arid regions contributes to increased food security and impacts on the extent and development of regional vegetation. However, the quantitative attribution of vegetation growth variation from ICE and biogeochemical factors (e.g., atmospheric CO2 concentration, climatic factors) is still lacking. Here, we assessed key drivers of vegetation growth in the inland arid region of Northwest China (IANC) from 1982 to 2018, including ICE, increased nitrogen rates, elevated atmospheric CO2 concentration (eCO2) and climate drivers, using normalized difference vegetation index (NDVI) and ecosystem gross primary productivity (GPP) as measures. These variables were quantified through trend decomposition, machine learning algorithms, and a satellite-based model. The results show that vegetation growth was increased in IANC mainly due to eCO2 and ICE. After 1995, as the regional climatic aridity intensified, the CO2 fertilization effect on vegetation growth decreased, as the atmospheric CO2 concentration continued to increase. Meanwhile, irrigated cropland area increased sharply, and ICE-driven GPP variation exceeded that driven by eCO2 in the whole region, while the ICE-driven NDVI variation exceeded that due to eCO2 when the ICE reached 6.38%. The ICE effect on regional vegetation growth rather than the CO2 fertilization effect has mitigated the slowdown of the rate of vegetation growth caused by climate changes. Although the ICE is conducive to food security and continuous greening of arid areas, further reclamation will exacerbate water scarcity. Our results provide research base for identifying the scale of sustainable agricultural development.

Characteristics of Natural Ti-Bearing Nanoparticles in Groundwater within Karst Areas of Northern China

Karst areas are widespread in China and can be divided into southern karst and northern karst based on the geographical boundary of Qinling Mountains and Huaihe River. In northern karst regions, karst springs are the predominant landform. Previous studies on karst springs have predominantly focused on macroscopic perspectives, such as water chemistry characteristics, with less attention given to the microscopic characteristics of springs. Therefore, this study focused on the Jinan Baotu Spring area, representative of a typical northern karst region, and investigated the natural nanoparticles present in different aquifers at various depths from a microscopic point of view. Through the observation of nanoparticle tracking analyzer (NTA), numerous nanoparticles were identified in the groundwater samples. The particle size range of the particles contained in groundwater is mainly concentrated in the range of 150–500 nm, and the particle concentration is mainly concentrated in the range of 1.5–5.0 × 105 Particles/L. The microstructure, chemical composition, and element distribution of these nanoparticles were analyzed using TEM-EDS techniques. The results unveiled the presence of Ti-bearing nanoparticles in various groundwater layers, including both crystalline and amorphous states, as well as nanoparticles exhibiting the coexistence of crystal and amorphous structures. By comparing the measured lattice spacing with PDF cards, the crystalline Ti-bearing nanoparticles were identified as rutile, brookite, anatase, ilmenite, pseudorutile, and ulvospinel. Furthermore, the main components of the amorphous Ti-bearing nanoparticles predominantly consisted of Ti or a mixture of Ti and Fe. EDS analysis further indicated that the Ti-bearing nanoparticles carried additional metal elements, such as Zn, Ca, Mn, Mo, Cr, and Ni, suggesting their potential role as carriers of metal elements during groundwater transportation. This discovery provided new insights into the migration of metal elements in groundwater and underscores the capacity of nanoparticles to enhance the mobility of inorganic substances within the water environment. Notably, brookite was detected in three different areas, including the direct discharge area, indirect recharge area, and discharge area, which may indicate that some special natural nanoparticles could serve as natural mineral tracer particles in the process of groundwater migration.

Efficiency of the Sprinkling Irrigation Method for Irrigation Of Soybean And Cauliflower

In order to reduce the population's need for food and rational use of water resources in the conditions of typical irrigated gray soils Tashkent region, the sprinkling irrigation method was used for soybean crops "Nafis" and cauliflower "Raskot", while taking into account the volumetric mass of the soil, soil porosity, soil permeability. This is explained by the fact that the timing of irrigation, given to crops, irrigation rates, seasonal irrigation rates, the impact on growth, the development and productivity of plants are described

The greenhouse gas emissions from meat sheep production contribute double of household consumption in a Eurasian meadow steppe

With the rapid development of the economy, household activities have emerged as an important source of greenhouse gas (GHG) emissions, making them a crucial focal point for research in the pursuit of sustainable development and carbon emission reduction. Hulunber, as a typical steppe region in eastern Eurasia, is representative of studying the GHG emissions from household ranches, which are the basic production units in this region. In this paper, based on survey data of 2018 and 2019, we quantified and assessed GHG emissions from household ranches by combining life cycle assessment (LCA) and structural equation modeling (SEM) approaches, with LCA to define household ranches system boundary and SEM to determine the key driving factors of emissions. The results showed that GHG emissions of meat sheep live weight was 23.54 kg CO2-eq/kg. The major contributor to household GHG emissions was enteric methane (55.23 %), followed by coal use (20.80 %) and manure management systems (9.16 %), and other contributing factors (14.81 %). The SEM results indicated that the GHG emissions from household ranches were derived primarily by economic level, while the economic level was significantly affected by income. This study also found a significant positive and linear correlation between household GHG emissions and the number of meat sheep (R2 = 0.89, P < 0.001). The GHG emissions from meat sheep production (67.52 %) were double times greater than household livelihood consumption (32.48 %). These findings emphasized the importance of reducing emissions from meat sheep production and adjusting the energy mix of household livelihood, contributing to the establishment of a low-carbon household livelihood operation.

Terrestrial algae: pioneer organisms of carbonate rock solutional weathering in South China karst.

The formation of soil in karst ecosystem has always been a scientific problem of great concern to human beings. Algae can grow on the exposed and non-nutrition carbonate surface, inducing and accelerating weathering of rock substrates, thus promoting soil formation. Yet the actual contribution of algae to solutional weathering intensity remains unclear. In this study, we performed weathering simulation experiment on two algae species (Klebsormidium dissectum (F.Gay) H.Ettl & G.Gärtner and Chlorella vulgaris Beijerinck), which were screened from carbonated rock surfaces from a typical karst region in South China. The results showed: (1) both algae have solutional weathering effect on carbonate rock, (2) there is no difference of solutional intensity observed, yet the solutional modes are different, suggesting different ecological adaptative strategies, (3) algae on carbonate rocks have higher carbonic anhydrase activity (CAA) and secrete more extracellular polysaccharide (EPS), accelerating rock weathering. (4) The absolute dissolution amount of carbonate rock with algae participation is 3 times of that of without algae. These results indicate the significant impact of terrestrial algae on carbonate rock solutional weathering and provides quantitative evidence that terrestrial algae are pioneer species. It also contributes to our further understanding of soil formation in karst ecosystems in South China.

How to Optimize Carbon Sinks and Biodiversity in the Conversion of Norway Spruce to Beech Forests in Austria?

Assessments of synergies and trade-offs between climate change mitigation and forest biodiversity conservation have focused on set-aside areas. We evaluated a more comprehensive portfolio of silvicultural management adaptations to climate change and conservation measures exemplary for managed European beech forests. Based on the available literature, we assessed a range of common silvicultural management and conservation measures for their effects on carbon sequestration in forest and wood products and for substituting more carbon-intensive products. We complemented this review with carbon sequestration simulations for a typical mountainous beech forest region in Austria. We propose three priority actions to enhance the synergies between climate change mitigation and biodiversity. First, actively increase the proportion of European beech in secondary Norway spruce forests, even though beech will not be unaffected by expected water supply limitations. Secondly, optimize the benefits of shelterwood systems and promote uneven-aged forestry, and thirdly, enhance mixed tree species. Targeted conservation measures (deadwood, habitat trees, and old forest patches) increase the total C storage but decrease the annual C sequestration in forests, particularly in wood products. The establishment of a beech wood market with an extended product portfolio to reduce the use of fuelwood is essential for sustainable climate change mitigation. Since there are limitations in the production of saw timber quality beech wood on low fertility sites, C accumulation, and biodiversity can be emphasized in these areas.

Antagonistic Effects, Sources, and Distribution of Selenium and Toxic Metals in Fish from Selenium-Rich and High Cadmium Areas, Dashan Region, China.

The Gongxin River is one of the main rivers in the Dashan Region of Anhui Province in China, which is a Se-rich and high-Cd area, but no research has focused on the concentration, source, antagonism, and health risks of Se and TMs in fish. In this study, 120 fish samples (14 species), 24 sediment samples, and 24 water samples were collected to investigate the antagonistic effects, sources, and health risks of TMs and Se in fish from a typical selenium-rich and high cadmium region. Zn and Se in fish from the Gongxin River were higher than in the background, which might be attributed to the different feeding habits of different fish species. The apparent antagonism between Se and Cu, Pb, and Cd was explored, and the results showed that 30%, 75%, and 100% of the Se/Cu, Se/Pb, and Se/Cd ratios were greater than one, indicating that Cu, Pb, and Cd in fish might be combined with or detoxicated by Se. The source analysis of the elements showed that the geological background was the source of TMs and Se in fish samples. The amounts of Se and TMs were all within China's acceptable level guidelines, signifying no harm to citizens from eating fish.

Monitoring Soil Salinity Classes through Remote Sensing-Based Ensemble Learning Concept: Considering Scale Effects

Remote sensing (RS) technology can rapidly obtain spatial distribution information on soil salinization. However, (1) the scale effects resulting from the mismatch between ground-based “point” salinity data and remote sensing pixel-based “spatial” data often limit the accuracy of remote sensing monitoring of soil salinity, and (2) the same salinity RS monitoring model usually provides inconsistent or sometimes conflicting explanations for different data. Therefore, based on Landsat 8 imagery and synchronously collected ground-sampling data of two typical study regions (denoted as N and S, respectively) of the Yichang Irrigation Area in the Hetao Irrigation District for May 2013, this study used geostatistical methods to obtain “relative truth values” of salinity corresponding to the Landsat 8 pixel scale. Additionally, based on Landsat 8 multispectral data, 14 salinity indices were constructed. Subsequently, the Correlation-based Feature Selection (CFS) method was used to select sensitive features, and a strategy similar to the concept of ensemble learning (EL) was adopted to integrate the single-feature-sensitive Bayesian classification (BC) model in order to construct an RS monitoring model for soil salinization (Nonsaline, Slightly saline, Moderately saline, Strongly saline, and Solonchak). The research results indicated that (1) soil salinity exhibits moderate to strong variability within a 30 m scale, and the spatial heterogeneity of soil salinity needs to be considered when developing remote sensing models; (2) the theoretical models of salinity variance functions in the N and S regions conform to the exponential model and the spherical model, with R2 values of 0.817 and 0.967, respectively, indicating a good fit for the variance characteristics of salinity and suitability for Kriging interpolation; and (3) compared to a single-feature BC model, the soil salinization identification model constructed using the concept of EL demonstrated better potential for robustness and effectiveness.

High Spatiotemporal Remote Sensing Images Reveal Spatial Heterogeneity Details of Soil Organic Matter

Soil is the foundation of sustainable agricultural development. Soil organic matter (SOM) is a key indicator for characterizing soil degradation, and remote sensing has been applied in SOM prediction. However, the differences in SOM prediction from different remote sensing data and the ability to combine multi-source and multi-phase remote sensing data for SOM prediction urgently need to be explored. The following research employed Landsat-8, Sentinel-2, and Gaofen-6 satellite data, utilizing a random forest algorithm to establish a SOM prediction model. It aimed to explore the variations in SOM prediction capabilities among these satellites in typical black soil regions. Additionally, the study involved creating multi-phase synthetic images for SOM prediction using Landsat-8 and Sentinel-2 images captured during three years of bare soil periods. Finally, the research examined the ability to combine three satellites to construct high spatiotemporal remote sensing images for SOM prediction. The results showed that (1) using Landsat-8 and Sentinel-2 to extract the principal components of the three-year bare soil period to construct the multi-phase synthetic image for SOM prediction, higher prediction accuracies can be obtained compared with the single-phase images. (2) The highest accuracy can be obtained using multi-phase synthetic images and high spatial resolution images to construct high spatiotemporal remote sensing images and perform SOM prediction (R2 is 0.65, RMSE is 0.67%, MAE is 0.42%). (3) Simultaneously, high spatiotemporal remote sensing images can reach 2 m spatial resolution to reveal the spatial heterogeneity of SOM. The causes of SOM spatial anomalies can be determined after analysis combined with soil degradation information. In subsequent research, SOM prediction should focus more on multi-sensor collaborative prediction.