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Background and aims Abandonment of agricultural land is a common type of land-use change worldwide. Nevertheless, there is currently no consensus on how soil aggregates and aggregate-associated organic carbon (C) vary with agricultural land abandonment on a global scale. Methods We synthesized the global responses and controlling factors of distribution, stability, and associated organic C concentration of soil water-stable aggregates under the influence of agricultural land abandonment using meta-analysis. Results On average, agricultural land abandonment significantly enhanced the mass proportion of large macroaggregates (LMA) and mean weight diameter (MWD) by 89.9% and 51.1%, respectively, while leading to a significant reduction in the proportion of silt-clay particles (SC) (−26.6%). By contrast, the proportions of both small macroaggregates (SMA) and microaggregates (MIA) showed no response to agricultural land abandonment. Overall, agricultural land abandonment significantly increased the aggregate-associated organic C concentrations by 23.3–24.8%, and the highest increase was observed for LMA. In most cases, the responses of soil aggregates to agricultural land abandonment did not differ significantly between subgroups of mean annual temperature, mean annual precipitation, soil texture, and abandonment duration (AD). We found that the dynamics of MWD and associated organic C concentrations were positively related to AD according to redundancy analysis. Conclusion Our findings suggested that the formation and C accrual of LMA, which could be improved with the increase of AD due to a more favorable environment for plant and microbial growth, played crucial roles in both soil structural rehabilitation and soil C sequestration during agricultural land abandonment.

Determination of the dominant factors which affect soil moisture (SM) predictions for drought analysis is an essential step to assess the reliability of the prediction results. However, artificial intelligence (AI) based drought modelling only provides prediction results without the physical interpretation of the models. Here, we propose an explainable AI (XAI) framework to reveal the modelling of SM drought events. Random forest based site-specific SM prediction models were developed using the data from 30 sites, covering 8 vegetation types. The unity of multiply XAI tools was applied to interpret the site-models both globally (generally) and locally. Globally, the models were interpreted using two methods: permutation importance and accumulated local effect (ALE). On the other hand, for each drought event, the models were interpreted locally via Shapley additive explanations (SHAP), local interpretable model-agnostic explanation (LIME) and individual conditional expectation (ICE) methods. Globally, the dominant features for SM predictions were identified as soil temperature, atmospheric aridity, time variables and latent heat flux. But through local interpretations of the drought events, SM showed a greater reliance on soil temperature, atmospheric aridity and latent heat flux at grass sites, with higher correlation to the time-dependent parameters at the sites located in forests. The temporal variation of the feature which effects the drought events was also demonstrated. The interpretation could shed light on how predictions are made and could promote the application of AI techniques in drought prediction, which may be useful for irrigation and water resource management.

Water and salt stress significantly impact the accumulation of crop biomass (TB); however, the relative contributions of photosynthetic, physiological, and morphological factors remain poorly understood. This study aims to comprehensively investigate the effects of water and salt stress on crop growth physiology and identify the primary factors influencing biomass accumulation. We examined four quinoa varieties (Qingli No.1, Qingli No.8, Gongza No.4, and Black quinoa) under four salinity levels (s0: 0 mmol/L, s1: 100 mmol/L, s2: 200 mmol/L, and s3: 300 mmol/L) and two moisture levels (w1: 30% field capacity (FC), w2: 80% FC). Using principal component analysis (PCA) and correlation analysis, we constructed a random forest model (RF) and a partial least-squares path modeling (PLS-PM) framework to elucidate the effects of water and salt stress on quinoa growth physiology and clarify the adaptive mechanisms of quinoa under varying salinity conditions. The results indicate that (1) salinity has a more substantial regulatory effect on the accumulation of proline (Pro) and sodium ions (Na+) than water availability. Under conditions of adequate moisture (w2), the activity of antioxidant enzymes increased in response to mild salinity stress (s1). However, with escalating salinity levels, a significant decrease in enzyme activity was observed (p < 0.05). (2) PCA identified salinity as a key factor significantly influencing physiological changes in quinoa growth. The RF model indicated that, under severe salinity conditions (s3), intrinsic water-use efficiency (iWUE) emerged as a critical driver affecting biomass (TB) accumulation. (3) The PLS-PM model quantified the relative contribution rates of various factors to total biomass (TB). It revealed that, as salinity increased, the path coefficients of photosynthetic factors also rose, but their relative contribution diminished due to a corresponding reduction in the contribution of morphological factors. These findings offer a theoretical foundation and decision-making support for the integrated management of water–salt conditions in saline–alkali agricultural fields, as well as for the cultivation of salt-tolerant crops.

The volume of research on fast radio bursts (FRBs) observation have been seeing a dramatic growth. To facilitate the systematic analysis of the FRB population, we established a database platform, Blinkverse, as a central inventory of FRBs from various observatories and with published properties, particularly dynamic spectra from FAST, CHIME, GBT, Arecibo, etc. Blinkverse thus not only forms a superset of FRBCAT, TNS, and CHIME/FRB, but also provides convenient access to thousands of FRB dynamic spectra from FAST, some of which were not available before. Blinkverse is regularly maintained and will be updated by external users in the future. Data entries of FRBs can be retrieved through parameter searches through FRB location, fluence, etc., and their logical combinations. Interactive visualization was built into the platform. We analyzed the energy distribution, period analysis, and classification of FRBs based on data downloaded from Blinkverse. The energy distributions of repeaters and non-repeaters are found to be distinct from one another.

As the only route formed in the inner Qinghai-Tibet plateau, the Tang-Tibet Ancient Road promoted the extension of the Overland Silk Roads to the inner Qinghai-Tibet plateau. Considering the Complex geographical and environmental factors of inner Qinghai-Tibet Plateau, we constructed a weighted trade route network based on geographical integration factors, and then adopted the principle of minimum cost and the shortest path on the network to simulate the ancient Tang-Tibet Ancient Road. We then compared the locations of known key points documented in the literature, and found a significant correspondence in the Qinghai section. However, there was a certain deviation between the key points recorded in Tibetan section and the simulated route; we found that the reason is the relative oxygen content (ROC) became a limited factor of the choice of the Tibetan section road. Moreover, we argue that the warm and humid climate and the human migration to the hinterland of the Qinghai-Tibet plateau were the fundamental driving forces for the formation of the Tang-Tibet Ancient Road.

Soil moisture (SM) has significant impacts on the Earth’s energy and water cycle system. Remote sensing, such as the Soil Moisture Active Passive (SMAP) mission, has delivered valuable estimations of global surface soil moisture. However, it has a 2~3 days revisit time leading to gaps between SMAP areas. To achieve accurate and comprehensive real-time forecast of SM, we propose a spatial–temporal deep learning model based on the Convolutional Gated Recursive Units with Data Integration (DI_ConvGRU) to capture the spatial and temporal variation in SM simultaneously by modeling the influence of adjacent SM values in space and time. Experiments show that the DI_ConvGRU outperforms the ConvGRU with Linear Interpolation (interp_ConvGRU) and the Long Short-Term Memory with Data Integration (DI_LSTM). The best performance (Bias = 0.0132 m3/m3, ubRMSE = 0.022 m3/m3, R = 0.977) has been achieved through the use of spatial–temporal deep learning model and Data Integration term. In comparison with interp_ConvGRU and DI_LSTM, DI_ConvGRU has improved the model performance in 74.88% and 68.99% of the regions according to RMSE, respectively. The predictability of SM depends highly on SM memory characteristics. DI_ConvGRU can provide accurate spatial–temporal forecast for SM with missing data, making them potentially useful for applications such as filling observational gaps in satellite data.

Super-resolution (SR) of remote sensing images is essential to compensate for missing information in the original high-resolution (HR) images. Single-image super-resolution (SISR) technique aims to recover high-resolution images from low-resolution (LR) images. However, traditional SISR methods often result in blurred and unclear images due to the loss of high-frequency details in LR images at high magnifications. In this paper, a super-segmental reconstruction model STGAN for remote sensing images is proposed, which fuses the Generative Adversarial Networks (GANs) and self-attention mechanism based on the Reference Super Resolution method (RefSR). The core module of the model consists of multiple CNN-Swin Transformer blocks (MCST), each of which consists of a CNN layer and a specific modified Swin Transformer, constituting the feature extraction channel. In image hypersegmentation reconstruction, the optimized and improved correlation attention block (RAM-V) uses feature maps and gradient maps to improve the robustness of the model under different scenarios (such as land cover change). The experimental results show that the STGAN model proposed in this paper exhibits the best image data perception quality results with the best performance of LPIPS and PI metrics in the test set under RRSSRD public datasets. In the experimental test set, the PSNR reaches 31.4151, the SSIM is 0.8408, and the performance on the RMSE and SAM metrics is excellent, which demonstrate the model’s superior image reconstruction details in super-resolution reconstruction and highlighting the great potential of RefSR’s application to the task of super-scalar processing of remotely sensed images.

Soil temperature (ST) and soil moisture (SM) are two fundamental land surface variables that directly or indirectly affect the processes and functions of alpine ecosystems. To clarify dynamics and interactions of SM and ST during degradation and restoration of alpine swamp meadow, four successional stages of alpine swamp meadow (non-degraded, NG; Kobresia humilis-dominated degraded, DG1; bare soil/weed-type degraded, DG2; artificially restored, RE) were selected to measure SM and ST at 10, 20 and 30 cm depths with 30-minute time interval in 2021 and 2022. Results showed that: (1) With the degradation and restoration of alpine swamp meadow, SM at 10 cm depth decreased at first, and then increased significantly (p < 0.05), which was attributed to the role of vegetation coverage and soil organic carbon in soil evaporation and water holding capacity, respectively; (2) ST at various depths did not respond to diverse degradation and restoration stages of alpine swamp meadow (p > 0.05); (3) The relationships between ST and SM varied with seasons, with positive and negative linear correlation in spring and summer, and positive exponential correlation in autumn and winter (p < 0.01). The study of SM and ST at different degradation and restoration stages of alpine swamp meadow will provide theoretical support for the research of related ecological processes and functions of such ecosystem.

Soil moisture (SM) is a key variable in Earth system science that affects various hydrological and agricultural processes. Convolutional long short-term memory (Conv-LSTM) networks are widely used deep learning models for spatio-temporal SM prediction, but they are often regarded as black boxes that lack interpretability and transparency. This study aims to interpret Conv-LSTM for spatio-temporal SM prediction in China, using the permutation importance and smooth gradient methods for global and local interpretation, respectively. The trained Conv-LSTM model achieved a high R2 of 0.92. The global interpretation revealed that precipitation and soil properties are the most important factors affecting SM prediction. Furthermore, the local interpretation showed that the seasonality of variables was more evident in the high-latitude regions, but their effects were stronger in low-latitude regions. Overall, this study provides a novel approach to enhance the trust-building for Conv-LSTM models and to demonstrate the potential of artificial intelligence-assisted Earth system modeling and understanding element prediction in the future.

Land use changes modify soil properties, including aggregate structure, and thus, profoundly affect soil quality and health. However, the effects of land use changes originating from alpine grassland on soil aggregates and aggregate-associated organic carbon have received little attention. Soil aggregate fraction, aggregate-associated organic carbon and relevant influencing factors were determined at 0–20, 20–40 cm soil layers for alpine grassland, cropland and abandoned land in the eastern Qinghai–Tibet Plateau (QTP), and their relationships were analyzed by partial least square regression (PLSR). Results showed the following: (1) conversion from alpine grassland to cropland resulted in a significant decline macroaggregate fraction (R0.25), mean weight diameter (MWD), mean weight diameter (GMD), soil organic carbon (SOC), and microaggregate-associated SOC; (2) almost all aggregate stability indexes, SOC, and aggregate-associated SOCs were significantly positively correlated with silt and glomalin, suggesting that the binding of fine particles (silt) with the organic cementing agent (glomalin) was probably a key mechanism of SOC formation and aggregate stability in the studied region; (3) compared with biotic factors such as SOC, glomalin and root biomass, abiotic factors including silt and sand can better predict aggregate stability and SOC fraction using the PLSR model. The above results indicated that the conversion of alpine grassland to other land use types in high altitude areas would destroy soil structure and decrease soil organic carbon content, and then reduce soil quality.