Plant functional traits and trial-error method have been used successfully in suitable species screening for vegetation restoration on tropical coral island ecosystems. However, there remains controversy about which functional traits should be used as key indicators for plant screening, and the feasibility of classifying plant types through functional traits. Here we determined 25 structural and physiological traits of a total of 56 plant species in a tropical mainland nursery and a tropical coral island, comparing differences of each trait between the two habitats. We attempted to study plant adaptations based on restoration needs and life forms, and then selected suitable species based on key functional traits related with survival performance. We found that leaf thickness, plant height, palisade and spongy tissue thickness, were four key traits in future tropical coral island restoration species screening, and listed nine suitable species from our species pool. Contrary to nursery plants employing the acquisitive strategy under sufficient resources, island plants featured more resilient leaf structure and higher antioxidant capacity, exhibiting a tolerance strategy to better cope with stresses. Meanwhile, plant functional traits rather than restoration needs or life forms are more fundamental in studying species adaptation. Plants adjusted traits from growth to tolerance strategy in the harsh island habitat, and using key functional traits is an accurate and efficient way for screening suitable species in the revegetation of degraded tropical coral islands.

The widespread existence of time series data in information systems poses significant challenges to data cleaning due to its quality issues, particularly the complex interdependencies among attributes and the persistence of errors. Existing semantic constraints, such as conditional regression rules and speed constraints, though helpful, remain insufficient for this task. This paper introduces two novel online cleaning methods: MTSClean and MTSClean-soft, designed to improve cleaning efficiency and robustness. By combining row and column constraints, we significantly accelerate the cleaning process, reducing the time complexity of the exact solution MTSClean from 𝑂(︁(𝑁𝑀)3.5|Σ|)︁ to 𝑂(︁𝑁𝑀3.5|Σ|)︁. Meanwhile, MTSClean-softachieves𝑂(︁𝑁𝑀2)︁andmorepreciserepairsthrough optimized search for key cells and a novel repair cost function. Comparative experiments against nine benchmark methods highlight our approach’s superiority in multiple metrics, completing cleaning tasks faster and performing better than state-of-the-art methods. This demonstrates the practicality and advantage of the proposed methods in cleaning multidimensional time series data.

Accurate evapotranspiration (ET) estimation is vital for hydrological modeling, yet remotely sensed ET (RS-ET) products are often limited by algorithmic uncertainties and sensor biases. To mitigate error propagation and better capture spatial patterns, this study introduces the Composite Efficiency of Absolute ET and Spatial Autocorrelation (CEASA) -a dual-constraint framework that integrates absolute ET magnitude and spatial autocorrelation to enhance simulation accuracy, which marks a pivotal shift by moving beyond traditional individual-value-based calibration to incorporate spatially explicit pattern constraints. Using four RS-ET products in China's Meichuan Basin (three high-bias: MOD16, GLASS, SSEBop; one low-bias: PMLV2), CEASA demonstrated: (1) Dual-constraint superiority: CEASA outperformed single-constraint methods. Compared to the absolute-value-only scheme (M1), it reduced PBIAS by 18-33 % and improved KGE from 0.47 to 0.51 to 0.76-0.77 under high-bias datasets, meanwhile improving KGE to 0.84 and reducing PBIAS to 9.4 % under low-bias PMLV2. It also surpassed spatial-pattern-only approaches by 11 % in KGE under low-bias data. Notably, CEASA achieved comparable streamflow accuracy to streamflow-based calibration (M0) while improving ET simulation. (2) Quality adaptivity: CEASA's weighted dual-criteria architecture dynamically adapted to RS-ET quality-achieving peak performance for PMLV2 and maintaining stable accuracy for high-bias datasets by emphasizing spatial neighborhood information. (3) Spatial dominance: Entropy analysis showed spatial autocorrelation contributed >70 % of the optimization signal, with higher information content than absolute ET magnitude (2.85-3.42 vs. 0.39-1.22). CEASA redefines RS-ET application by emphasizing spatial patterns, offering a bias-resilient solution for ungauged basins. Future work should explore scale-sensitive metrics and intelligent weighting schemes for broader applicability.

Study region The Meichuan Basin, China Study focus Soil water processes are critical in hydrological modeling, yet most studies focus on surface moisture due to data limitations, which hampers accurate simulations of root zone soil moisture dynamics. To address this gap, we developed three calibration schemes: M1 and M2, two benchmarks that rely solely on traditional streamflow data and incorporate both streamflow data and top-layer soil moisture data, respectively. In contrast, M3 integrates both streamflow data and multi-layer soil moisture information from SMCI 1.0. These schemes aim to assess the added value of integrating multi-layer soil moisture data to enhance hydrological modeling performance. New hydrological insights for the region The M3 scheme yielded the most accurate simulation of the spatial and temporal distribution of multi-layer soil moisture compared to M1 and M2 benchmarks. In this subtropical humid basin, the M3 model effectively captured the pronounced fluctuations in soil moisture driven by frequent and intense precipitation events, as well as the seasonal variability between wet and dry periods. M3 also improved the accuracy of evapotranspiration simulations across all subbasins, while maintaining acceptable streamflow simulations at gauge stations. These findings underscore the importance of using advanced multi-layer soil moisture data in models to regulate hydrological processes and control water distribution within the hydrological cycle.

Introduction A critical issue in tropical forests is that anthropogenic deforestation (i.e., mining) degrades the integrity of its ecosystem. Reforestation with appropriate native plant species helps to alleviate these detrimental impacts. A protocol to select appropriate plant species for this purpose currently lacks efficacy and timeliness.Methods We provided a trait-based protocol to quickly and effectively select native plant species for mining reforestation. A 0.2-km2 area of Baopoling (BPL) at Hainan Island, China, was used as a study site, which has been severely degraded by 20 years of limestone mining for cement production. First, we identified the tree species in nearby undisturbed tropical forests, followed by evaluating the similarities in functional traits of the most dominant one (target species) and 60 local candidate native plant species (candidate species) whose saplings can be purchased from a local market.Results and discussion This dataset was used in our trait-based protocol, and only within 1 month, we successfully selected eight plant species which are very similar to target species from the 60 candidate species. We also quantified whether the eight selected plant species were indeed suitable for sustained reforestation by testing their effects on landscape and also their survival rate and recruitment ability after using them to perform reforestation in BPL from 2016 to 2023. Finally, these eight plant species are indeed suitable for reforestation due to their huge influences on a significant shift from originally degraded landscape (comprising only barren rocks) to a forest landscape totally and also their high survival rate (90%-97%) and ability for natural recruitment after 7 years' reforestation in BPL. Thus, we anticipate that this protocol would be integral to species selection during reforestation of tropical mining areas.

Modern hydrological modeling frequently incorporates global remote sensing or reanalysis products for multivariate calibration. Although these datasets significantly contribute to model accuracy, the inherent uncertainties in the datasets and multivariate calibration present challenges in the modeling process. To address this issue, this study introduces an adaptive, process-wise fitting framework for the iterative multivariate calibration of hydrological models using global remote sensing and reanalysis products. A distinctive feature is the “kinship” concept, which defines the relationship between model parameters and hydrological processes, highlighting their impacts and connectivity within a directed graph. The framework subsequently develops an enhanced particle swarm optimization (PSO) algorithm for stepwise calibration of hydrological processes. This algorithm introduces a learning rate that reflects the parameter’s kinship to the calibrated hydrological process, facilitating efficient exploration in search of suitable parameter values. This approach maximizes the performance of the calibrated process while ensuring a balance with other processes. To ease the impact of inherent uncertainties in the datasets, the Extended Triple Collocation (ETC) method, operating independently of ground truth data, is integrated into the framework to assess the simulation of the calibrated process using remote sensing products with inherent data uncertainty. This proposed approach was implemented with the SWAT model in both arid and humid basins. Five calibration schemes were designed and evaluated through a comprehensive comparison of their performance in three repeated experiments. The results highlight that this approach not only improved the accuracy of ET simulation across sub-basins but also enhanced the precision of streamflow at gauge stations, concurrently reducing parameter uncertainty. This approach significantly advances our understanding of hydrological processes, demonstrating the potential for both theoretical and practical applications in hydrology.

Ecosystem mean residence times of carbon, nitrogen and phosphorus (τ, τ and τ, respectively) are important ecosystem properties. By analyzing the dependence of the observation-based estimates of τ, τ and τ of 127 mature forests on climate, vegetation, soil and terrain-related variables, we found that climate, particularly mean annual minimum temperature ( T ), had the greatest influence on τ, τ and τ. Different from previous studies, we found that both τ and τ increased with T when T > 0 ℃. Despite a decreasing phosphorus input with T , vegetation adapted to low phosphorus environment by increasing internal recycling and phosphorus use efficiency, together with the increasing soil clay content with T , not the direct response to T , explained why both τ and τ increased with T when T > 0 ℃. Our results highlight the importance of factors beyond climate in regulating residence times, especially in subtropical forests.

Abstract Ground penetrating radar technology is an effective underground physical detection method. In order to better study the ground penetrating radar echo characteristics of complex soil media rough interfaces, a soil rough surface electromagnetic scattering model based on fractal rough surface geometry modeling and soil media dielectric properties modeling is established. The mixed soil characteristics including sand content, water content, clay content, and sand particle density are analyzed, and the radar echo image characteristic of soil rough media interfaces under different fractal dimensions is discussed.

In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.

Biochar (BC) application to croplands aims to sequester carbon and improve soil quality, but its impact on soil organic carbon (SOC) dynamics is not represented in most land models used for assessing land-based climate change mitigation; therefore, we are unable to quantify the effects of biochar application under different climate or land management conditions. Here, to fill this gap, we implement a submodel to represent biochar in a microbial decomposition model named MIMICS (MIcrobial-MIneral Carbon Stabilization). We first calibrate and validate MIMICS with new representations of the density-dependent microbial turnover rate, adsorption of available organic carbon on mineral soil particles, and soil moisture effects on decomposition using global field-measured cropland SOC at 285 sites. We further integrate biochar in MIMICS by accounting for its effect on microbial decomposition and SOC sorption/desorption and optimize two biochar-related parameters in these processes using 134 paired SOC measurements with and without biochar addition. The MIMICS-biochar version can generally reproduce the short-term (≤ 6 years) and long-term (8 years) SOC changes after adding (mean addition rate of 25.6 t ha−1) biochar (R2= 0.79 and 0.97, respectively) with a low root-mean-square error (RMSE = 3.73 and 6.08 g kg−1, respectively). Our study incorporates sorption and soil moisture processes into MIMICS and extends its capacity to simulate biochar decomposition, providing a useful tool to couple with dynamic land models to evaluate the effectiveness of biochar application with respect to removing CO2 from the atmosphere.

Rapid urban expansion and economic development lead to the deterioration of ecosystems, which not only aggravates regional ecological risks but also leads to the degradation of ecosystem functions. It is of great significance to rationally divide regions and provide targeted management strategies for realizing the sustainability of regional economic development and ecological maintenance. Taking southwest China (Sichuan, Yunnan, Guizhou and Chongqing) as an example, land use data from 2000, 2010 and 2020 were used to evaluate the value of landscape ecological risk (LER) and ecosystem services, and comprehensive zoning was divided according to their spatial correlation. The socio-economic development characteristics of each zone were analyzed, and differentiated and targeted sustainable development paths were proposed. The results showed that the overall LER level of southwest China increased, and the gap of internal LER narrowed gradually. The ecosystem service value (ESV) per unit area showed an increasing trend, but the core metropolitan areas and northwest Sichuan had little change. According to the differences in population, industrial structure and land use, the low-ESV zone was densely populated, while the high-ESV zone was sparsely populated, and the population from the high-LER zone gradually migrated to the low-LER zone. The economic development of the low-ESV zone was better than that of the high-ESV zone, and secondary industry was an important driving force of regional economic development. Large-scale forestland can alleviate the LER, but the increase in cultivated land and grassland further aggravated the LER. According to the social and economic characteristics of each zone, this study put forward a differentiated development strategy for southwest China and also provided reference for the coordinated development of ecological protection and social economy in other key ecological regions.

Information relating to errors in evapotranspiration (ET) products, including satellite-derived ET products, is critical to their application but often challenging to obtain, with a limited number of flux towers available for the sufficient validation of measurements. Triple collocation (TC) methods can assess the inherent uncertainties of the above ET products using just three independent variables as a triplet input. However, both the severity with which the variables in the triplet violate the assumptions of zero error correlations and the corresponding impact on the error estimation are unknown. This study proposed a cross-correlation analysis approach to discover the optimal triplet of satellite-derived ET products with regard to providing the most reliable error estimation. All possible triple collocation solutions for the same product were first evaluated by the extended triple collocation (ETC), among which the optimum was selected based on the correlation between ETC-based and in-situ-based error metrics, and correspondingly, a statistic experiment based on ranked triplets demonstrated how the optimal triplet was valid for all pixels of the product. Six popular products (MOD16, PML_V2, GLASS, SSEBop, ERA5, and GLEAM) that were produced between 2003 to 2018 and which cover China’s mainland were chosen for the experiment, in which the error estimates were compared with measurements from 23 in-situ flux towers. The findings suggest that (1) there exists an optimal triplet in which a product as an input of TC with other collocating inputs together violate TC assumptions the least; (2) the error characteristics of the six ET products varied significantly across China, with GLASS performing the best (median error: 0.1 mm/day), followed by GLEAM, ERA5, and MOD16 (median errors below 0.2 mm/day), while PML_V2 and SSEBop had slightly higher median errors (0.24 mm/day and 0.27 mm/day, respectively); and (3) removing seasonal variations in ET signals has a substantial impact on enhancing the accuracy of error estimations.

Abstract. Biochar application in croplands aims to sequester carbon and improve soil quality, but its impact on soil organic carbon (SOC) dynamics is not represented in most land models used for assessing land-based climate mitigation, therefore we are unable to quantify the effect of biochar applications under different climate conditions or land management. To fill this gap, here we implemented a submodel to represent biochar into a microbial decomposition model named MIMICS (MIcrobial-MIneral Carbon Stabilization). We first calibrate MIMICS with new representations of density-dependent microbial turnover rate, adsorption of available organic carbon on mineral soil particles, and soil moisture effects on decomposition using global field measured cropland SOC at 58 sites. The calibration of MIMICS leads to an increase in explained spatial variation of SOC from 38 % in the default version to 47 %–52 % in the updated model with new representations. We further integrate biochar in MIMICS resolving its effect on microbial decomposition and SOC sorption/desorption and optimize two biochar-related parameters in these processes using 134 paired SOC measurements with and without biochar addition. The MIMICS-biochar version can generally reproduce the short-term (≤ 6 yr) and long-term (8 yr) SOC changes after adding biochar (mean addition rate: 25.6 t ha-1) (R2 = 0.65 and 0.84) with a low root mean square error (RMSE = 3.61 and 3.31 g kg-1). Our study incorporates sorption and soil moisture processes into MIMICS and extends its capacity to simulate biochar decomposition, providing a useful tool to couple with dynamic land models to evaluate the effectiveness of biochar applications on removing CO2 from the atmosphere.

In the 1980s, China began to recognize the gravity of the problem of non-point agricultural source pollution and conduct research on it. Agricultural non-point source pollution in China, on the other hand, differs from foreign agricultural non-point source pollution and industrial point source pollution. Because the features of agricultural non-point source pollution are complicated, it is critical to investigate a whole-chain management policy system appropriate for China’s agricultural pattern. Based on the current situation of agricultural non-point source pollution in China, this study summarizes the four stages of agricultural non-point source pollution prevention and control policies, namely the discovery stage with macro policies as the main focus, the exploration stage with single research indicators, the initial systematic strengthening stage, and the focused stage with targeted characteristics. Simultaneously, it examined the technological approaches that are suitable for China’s national circumstances and have been investigated by relying on international experience in present-day Chinese management. However, there are still some problems and challenges in agricultural non-point source pollution management policies, such as a lack of non-point source information support, a lack of coordination between different departments, a lack of support in measurement and retroactive calculation and treatment, a lack of an in-depth concept of zoning and classification, a lack of policy, an institutional system, and insufficient capital investment. Based on these problems and combining them with Green Agriculture, Beautiful China, and other goals, this paper puts forward suggestions to strengthen the policy data support of the agricultural non-point source pollution management system, enhance the research and development of the law of pollutant migration and transformation, encourage the innovation of low-cost and high-benefit treatment technology, improve the construction of the management system, strengthen the collaboration of departments, increase the investment of funds, and make other suggestions so as to promote the treatment of agricultural non-point source pollution with high quality and efficiency.

The SWAT model is a widely used hydrological model that offers a range of simulation capabilities.However, it is well-established that the accuracy of model simulations is heavily dependent on the proper specification of SWAT model parameters. While the official SWAT-CUP software is widely used for parameter uncertainty quantification of SWAT model, it has several limitations. For example, it relies on simple sensitivity analysis methods, lacks flexibility in terms of additional options, and its parameter optimization methods are computationally inefficient. Furthermore, as a closed-source software, SWAT-CUP can only be used on the Windows platform, which hampers the applicability of the SWAT model and may compromise simulation results.To overcome these issues, the Uncertainty Quantification Python Laboratory（UQ-PyL） platform, which offers a comprehensive toolset for parameter uncertainty analysis. In addition, a new module has been developed to couple UQ-PyL with the SWAT model, providing a user-friendly and efficient way to perform parameter uncertainty analysis using various algorithms offered by UQ-PyL.To assess the efficacy of UQ-PyL in analyzing parameter uncertainty of SWAT models, four distinct SWAT models across different watersheds in China were constructed, each subjected to varying climatic conditions. The results of parameter uncertainty analysis were comprehensively evaluated by comparing UQ-PyL with SWAT-CUP.In terms of sensitivity analysis, four different methods（Morris, MARS, DT, and Sobol’） in UQ-PyL, and qualitative sensitivity analysis in SWAT-CUP were employed to analyze model parameters. The selection of sensitive parameters between UQ-PyL and SWAT-CUP was compared in terms of rationality, by the Sobol’ method as a reference to test the validity of the results from the four qualitative methods of sensitivity analysis. Additionally, the SCE-UA algorithm was used to optimize the sensitive parameter groups selected by UQ-PyL and SWAT-CUP separately, and the final converged objective function values was compared, thereby indirectly validating the appropriateness of the selected sensitive parameters by both software tools. Regarding optimization effectiveness,the sensitive parameters using ASMO, SCE-UA of UQ-PyL, and SUFI-2, which is the most widely used algorithm in SWAT-CUP. The computational efficiency and accuracy of different optimization algorithms were compared by evaluating the number of runs required for the final objective function to converge, and the value of the objective function when it converged. Moreover, the applicability of UQ-PyL in watersheds with different climate zones was further validated.The findings reveal that, among the four sensitivity analysis techniques, MARS exhibits the strongest performance, followed by Morris, DT and the SWAT-CUP sensitivity analysis method. Moreover, when utilizing the SCE-UA optimization algorithm to optimize the sensitive parameters identified by UQ-PyL and SWAT-CUP,the optimization outcomes of the UQ-PyL parameter group are relatively superior to those of the SWAT-CUP parameter group across the four watersheds. In terms of parameter optimization, the ASMO optimization algorithm in UQ-PyL demonstrates a higher level of computing efficiency, while the SCE-UA optimization algorithm yields greater accuracy compared to the SUFI-2 algorithm. Additionally, when optimizing independent processes, UQ-PyL solutions offer higher efficiency and accuracy compared to SWAT-CUP solutions. Moreover, UQ-PyL outperformed SWAT-CUP in terms of overall performance across the four watersheds, indicating its robustness.In summary, compared to the single sensitivity analysis method in SWAT-CUP, UQ-PyL offers both quantitative sensitivity analysis using the Sobol’ algorithm, as well as qualitative sensitivity analysis using the MARS, Morris, and DT algorithms. This enables a more comprehensive and reasonable screening of sensitive parameters. In terms of parameter optimization, UQ-PyL outperforms the SUFI-2 algorithm in SWAT-CUP by providing two optimization algorithms with better computational efficiency（ASMO） and higher accuracy（SCEUA）. In the four watersheds, UQ-PyL demonstrated superior performance to SWAT-CUP, with the best results observed in humid watersheds and slightly lower performance in drier watersheds.

Accurately predicting global drought-induced tree mortality remains a major challenge facing plant science and ecology. Stem hydraulic safety margin (HSM, the difference between water potential at the minimum value and the value that causes xylem vulnerability to embolism) performs as one of the best hydraulic traits in predicting global drought-induced tree mortality, however, HSM is time-consuming and very difficult to measure. We proposed to use leaf turgor loss point (TLP, the water potential at which leaves start to wilt) as a proxy for HSM because HSM may be highly correlated to TLP, as both of them are tightly linked with water potential changes after stomatal closure. Since TLP is more easy and rapid to measure than HSM, if we find strong HSM-TLP relationships at the global scale, TLP can be used in predicting global drought-induced tree mortality. However, no study has quantified the relationships between HSM and TLP at the global scale. Here we draw together published data on HSM and TLP for 1,773 species from 370 sites worldwide to check whether HSM and TLP are highly associated. We found that HSMs and TLPs are merely highly related in tropical forests, thus TLP can be a reliable surrogate of HSM to predict drought-induced tree mortality in tropical forest. Here we are certainly not advocating for the use of TLP instead of HSM to predict drought-induced tree mortality in tropical forests, but simply for predicting drought-induced tree mortality in tropical forests in supplementary of HSM in the future.

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Residence times of ecosystem carbon (ze,C), nitrogen (ze,N) and phosphorus (ze,P) are closely related to efficacies of carbon and nutrients conservation within an ecosystem. However, estimates of ze,C, ze,N and ze,P together are very limited for forest ecosystems, and little is known about the environmental controls. Here we estimated ze,C, ze,N and ze,P of 127 undisturbed forests based on observed carbon, nitrogen and phosphorus stocks and compiled 30 key variables related to climate, vegetation, soil and terrain for the sites. We then performed a variation partitioning analysis to identify dominant controls on ze,C, ze,N and ze,P, and used segmented regression to identify possible thresholds in the dependence of ze,C, ze,N and ze,P on temperature. Climate, particularly average mini-mum temperature of the coldest month of a year (Tmin), was the main driver of ze,C, ze,N and ze,P. In regions with Tmin < 0 degrees C, ze,C and ze,P decreased with increasing Tmin; and in regions with Tmin > 0 degrees C, both ze,C and ze,P increased with increasing Tmin, as a result of a significant increase in total ecosystem carbon pool and a decrease in external phosphorus input, respectively. Our results challenge the use of a single temperature-dependent function of ecosystem carbon or nutrient turnover rate in global land models, and highlight the importance of other factors, such as soil weathering stage, clay content, in influencing the responses of carbon and nutrients cycles in subtropical forests to global warming.

River segmentation based on remote sensing images plays an important role in water conservancy business work, water wading monitoring work, and flood disaster prevention. In actual remote sensing images of rivers, most of the backgrounds are complex, and there is no public remote sensing image dataset specifically for the study of river segmentation. The traditional river segmentation methods have rough edge information and serious noise. To solve the above problems, this paper firstly preprocesses the Gaofen Image Dataset (GID) and Remote Sensing Image Block Segmentation Dataset (BDCI), and creates two datasets for river segmentation in high-resolution remote sensing images respectively (GID-river and BDCI-river) and then proposed a river segmentation method based on U-Net. On the basis of the original U-Net, the ResNet34 and VGG16 structures were combined to strengthen the feature extraction ability of the network, so as to achieve more accurate river edge details. The experimental results shows the mIoU of the ResNet34-UNet network on the GID-river dataset reaches 93.6%, and the mPA of the VGG16-UNet network on the BDCI-river dataset reaches 82.1%.

Accurately predicting the concentration of PM 2.5 (fine particles with a diameter of 2.5 μm or less) is essential for health risk assessment and formulation of air pollution control strategies. At present, there is also a large amount of air pollution data. How to efficiently mine its hidden features to obtain the future concentration of pollutants is very important for the prevention and control of air pollution. Therefore we build a pollutant prediction model based on Lightweight Gradient Boosting Model (LightGBM) shallow machine learning and Long Short-Term Memory (LSTM) neural network. Firstly, the PM 2.5 pollutant concentration data of 34 air quality stations in Beijing and the data of 18 weather stations were matched in time and space to obtain an input data set. Subsequently, the input data set was cleaned and preprocessed, and the training set was obtained by methods such as input feature extraction, input factor normalization, and data outlier processing. The hourly PM 2.5 concentration value prediction was achieved in accordance with experiments conducted with the hourly PM 2.5 data of Beijing from January 1, 2018 to October 1, 2020. Ultimately, the optimal hourly series prediction results were obtained after model comparisons. Through the comparison of these two models, it is found that the RMSE predicted by LSTM model for each pollutant is nearly 50% lower than that of LightGBM, and is more consistent with the fitting curve between the actual observations. The exploration of the input step size of LSTM model found that the accuracy of 3-h input data was higher than that of 12-h input data. It can be used for the management and decision-making of environmental protection departments and the formulation of preventive measures for emergency pollution incidents.