Soil, the largest terrestrial carbon reservoir, is central to climate change and relevant feedback to environmental health. Minerals are the essential components that contribute to over 60% of soil carbon storage. However, how the interactions between minerals and organic carbon shape the carbon transformation and stability remains poorly understood. Herein, we critically review the primary interactions between organic carbon and soil minerals and the relevant mechanisms, including sorption, redox reaction, co-precipitation, dissolution, polymerization, and catalytic reaction. These interactions, highly complex with the combination of multiple processes, greatly affect the stability of organic carbon through the following processes: (1) formation or deconstruction of the mineral-organic carbon association; (2) oxidative transformation of the organic carbon with minerals; (3) catalytic polymerization of organic carbon with minerals; and (4) varying association stability of organic carbon according to the mineral transformation. Several pieces of evidence related to the carbon turnover and stability during the interaction with soil minerals in the real eco-environment are then demonstrated. We also highlight the current research gaps and outline research priorities, which may map future directions for a deeper mechanisms-based understanding of the soil carbon storage capacity considering its interactions with minerals. 

Mineral-mediated stability of organic carbon in soil and relevant interaction mechanisms

We develop an integrated neural network landslide susceptibility assessment (LSA) method that integrates temporal dynamic features of interferometry synthetic aperture radar (InSAR) deformation data and the spatial features of landslide influencing factors. We construct a time-distributed convolutional neural network (TD-CNN) and bidirectional gated recurrent unit (Bi-GRU) to better understand the temporal dynamic features of InSAR cumulative deformation, and construct a multi-scale convolutional neural network (MSCNN) to determine the spatial features of landslide influencing factors, and construct a parallel unified deep learning network model to fuse these temporal and spatial features for LSA. Compared with the traditional MSCNN method, the accuracy of the proposed model is improved by 1.20%. The performance of the proposed model is preferable to MSCNN. The area under the receiver operating characteristic curve (AUC) of the testing set reaches 0.91. Our LSA results show that the proposed model clearly depicts areas with very high susceptibility landslides. Further, only 10.18% of the study area accurately covers 84.79% of historical landslide areas. Subjective consequences and objective indicators show that the proposed model that is integrated time-series InSAR deformation dynamic features can make full use of landslide characteristics and effectively improve the reliability of LSA. 

An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features

Construction activities of accelerated urbanization in Shenzhen have increased the landslide risk area, which has intensified the potential threat to human and natural environment. However, the risk of landslides in Shenzhen is poorly evaluated. In this paper, a landslide risk evaluation (LRE) model is constructed using landslide susceptibility map (LSM) and landslide vulnerability. In the experiment, a stacking ensemble learning (SEL) model is constructed based on convolutional neural network (CNN), multilayer perceptron (MLP), gated recurrent unit (GRU) and support vector machine regression (SVR) to generate LSM by using topography, geology, human engineering activities, time-series precipitation and time-series normalized difference vegetation index (NDVI). Road network, building distribution density and annual average precipitation data are used to evaluate landslide vulnerability based on entropy weight method. In this study, multiple statistical indicators are used to evaluate the performance of the LSM model, and Interferometric Synthetic Aperture Radar (InSAR) deformation data are utilized to verify the LRE results in Shenzhen. The results show that the SEL method has more refined results for LSM, with a best overall evaluation accuracy, especially in the receiver operating characteristic curve (ROC), where the accuracy is improved by nearly 8%. In LRE of Shenzhen, very high, high, moderate, low and very low risk areas account for 0.283%, 0.451%, 0.859%, 36.890% and 61.517%, respectively. In most of very high risk area, InSAR deformation results show a clear concentrated deformation trend with a large deformation rate. Research results can provide technical and data support for landslide disaster prevention in Shenzhen. 

https://ieeexplore.ieee.org/ielx7/4609443/4609444/10171365.pdf

Landslide risk evaluation in Shenzhen based on stacking ensemble learning and InSAR

Accurate prediction of the trend of Normalized Difference Vegetation Index (NDVI) time series in the Yellow River Basin (YRB) is crucial for the assessment of the hydrological and ecological environment in this region. Currently, the NDVI time series prediction model is primarily based on traditional models and single-variable neural network models. Nevertheless, these models present challenges in considering the limitations of multiple factors, causing the NDVI time series prediction results to lack reliability. To predict NDVI time-series in the YRB of China, this study constructed a multilayer multivariate Long-Short Term Memory (LSTM) neural network model including climatic components. The initial important climatic elements in this region were identified using GeoDetector. Then, the relationship between NDVI and climatic factors in the YRB of China is established. Finally, numerical scale data are used to train and predict a multilayer multivariate LSTM model with climatic components. According to the results, the three-layer multivariate LSTM neural network NDVI time series prediction model developed in this study has the best performance among the evaluated indices. When compared to existing time series prediction models, the proposed model in this study takes into account the common constraint effect of various climate factors on NDVI. This leads to a significantly improved prediction accuracy, presenting new opportunities for enhancing the prediction model. By analyzing the NDVI time series prediction outcomes for the YRB, it has been determined that the ecological environment of the area will continuously improve in the future. This study offers significant technological and theoretical backing for assessing the hydrological and ecological environment of the YRB and comparable ecologically vulnerable regions in China. 

LSTM time series NDVI prediction method incorporating climate elements: A case study of Yellow River Basin, China

A novel oxalated zero-valent iron nanoparticle for Pb(II) removal from aqueous solution: Performance and synergistic mechanisms

Accurate landslide extraction is significant for landslide disaster prevention and control. Remote sensing images have been widely used in landslide investigation, and landslide extraction methods based on deep learning combined with remote sensing images (such as U-Net) have received a lot of attention. However, because of the variable shape and texture features of landslides in remote sensing images, the rich spectral features, and the complexity of their surrounding features, landslide extraction using U-Net can lead to problems such as false detection and missed detection. Therefore, this study introduces the channel attention mechanism called the squeeze-and-excitation network (SENet) in the feature fusion part of U-Net; the study also constructs an attention U-Net landside extraction model combining SENet and U-Net, and uses Sentinel-2A remote sensing images for model training and validation. The extraction results are evaluated through different evaluation metrics and compared with those of two models: U-Net and U-Net Backbone (U-Net Without Skip Connection). The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%, which is about 2% and 3% higher than U-Net and U-Net Backbone, respectively, with less false detection and more accurate extraction results. 

A landslide extraction method of channel attention mechanism U-Net network based on Sentinel-2A remote sensing images

Oxalate, a typical natural ligand, plays a significant role in promoting iron dissolution from iron (oxyhydr)oxide minerals to improve iron bioavailability in nature. However, the mechanism of oxalate promoted iron...

Oxalate Promoted Iron Dissolution of Hematite via Proton Coupled Electron Transfer

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A landslide extraction method of channel attention mechanism U-Net network based on Sentinel-2A remote sensing images

Oxalate Promoted Iron Dissolution of Hematite via Proton Coupled Electron Transfer