Xue Wang

In semi-arid regions, desertification is a critical environmental degradation factor. This study analyzes land degradation and desertification vulnerability dynamics in Ningxia, China (2001–2022) using the MEDALUS framework with a Random Forest classifier. Land-cover change was significant due to irrigation and agricultural expansion, with farmland increasing to 65.29% of the landscape while grassland shrank to 10.13%, intensifying ecological pressure. Bare land followed a U-shaped trend, reaching 9.17% in 2022, indicating increased soil exposure. Soil quality remained moderately stable, with 70–80% of land retaining integrity despite ongoing degradation. Vegetation quality fluctuated considerably, as poor vegetation quality decreased from 42.42% in 2001 to 36.6% in 2022, with scattered local recovery. Management quality improved due to irrigation modernization and policy implementation, while long-term aridity moderately constrained climatic conditions. The Desertification Sensitivity Index (DSI) varied over time: low-desertification areas accounted for 44.47% in 2001, while high-desertification areas comprised 29.06%. By 2011, conservation efforts increased stable areas to 48.26%. However, in 2022, high-desertification areas persisted across 9,552.49 km², primarily in northern and central regions characterized by poor soil and uneven rainfall. This research provides robust scientific evidence to inform land management, vegetation rehabilitation, and climate adaptation strategies in vulnerable drylands.

Land cover (LC) change is reshaping terrestrial ecosystems and profoundly impacting sustainable development in Africa, yet the long-term, continental-scale spatiotemporal dynamics of these shifts remain obscured. To address the above issue, this study systematically explores the spatiotemporal dynamics of LC across Africa from 1985 to 2022 by leveraging the fine-resolution remote-sensing-derived GLC_FCS30D LC dataset within a stratified Intensity Analysis framework. To decompose landscape changes into interval, category, and transition levels across five climatic sub-regions of Africa, we systematically evaluate the temporal consistency of land systems. This hierarchical approach disentangles systematic transition pathways from random fluctuations, thereby revealing the distinct regional regimes governing continental transformation of LC. Our results ultimately show a strong LC change acceleration in Africa after 2010, mainly in Southern, Eastern, and Western Africa, which together made up 80 to 90% of the continent’s LC dynamics. During the whole study period, shrubland and grassland had the highest gross turnover due to their high bidirectional volatility. Intensity-wise, forest remained inactive even though it was a persistent net loser to crop in East Africa (2010–2020), to shrub in Southern Africa (1990–2022), and to wetland in West Africa during the post-2000 intervals. Wetland had a major change in dynamics from historical growth during 1985–1990 to systematic decline in 2015–2022. Cropland increased by systematically targeting shrubland and grassland, mainly in East Africa. Additionally, the Sahel contributed 40% of continental grassland to bare area transitions, despite some recovery of grassland in the region. These findings show that aggregate net-change metrics obscure the volatility in African LC; therefore, distinct regional regimes such as agricultural expansion and forest degradation necessitate spatially differentiated management strategies.

Timely and accurate prediction of crop traits is critical for precision breeding and regional agricultural production. Previous studies have primarily focused on single crop yield traits, neglecting other crop traits and variety-specific analyses. To address this issue, we employed a Meta-Hybrid Regression Ensemble (MHRE) approach by using multiple machine learning (ML) approaches as base learners, integrating regional multi-year, multi-variety crop field trials with satellite remote sensing indices, meteorological and phenological data to predict major crop traits. Results demonstrated MHRE’s optimal performance for rice and cotton, significantly outperforming individual models (RF, XGBoost, CatBoost, and LightGBM). Specifically, for rice crop, MHRE achieved highest accuracy for yield trait (R² = 0.78, RMSE = 0.59 t ha⁻¹) compared to the best individual model (XGBoost: R² = 0.76, RMSE = 0.61 t ha⁻¹); traits like effective spike also showed strong predictability (R² = 0.64, RMSE = 27.81 10,000·spike ha⁻¹). Similarly, for cotton, MHRE substantially improved yield trait prediction (R² = 0.82, RMSE = 0.33 t ha⁻¹) compared to the best individual model (RF: R² = 0.77, RMSE = 0.36 t ha⁻¹); bolls per plant accuracy was highest (R² = 0.93, RMSE = 2.27 bolls plant⁻¹). Moreover, rigorous validation confirmed that crop-specific MHRE models are robust across five rice and three cotton varietal groups and are applicable across six distinct regions in China. Furthermore, we applied the SHAP (SHapley Additive exPlanations) method to analyze the growth stages and key environmental factors affecting major traits. Our study illustrates a practical framework for regional-scale crop traits prediction by fusing multi-source data and ensemble machine learning, offering new insights for precision agriculture and crop management.

Understanding variations in the temporal and spatial distribution of vegetation phenology is essential for adapting to and mitigating future climate change and urbanization. However, there have been limited vegetation phenology studies within small-scale areas such as urban environments over the past decades. Therefore, the present study focuses on Jinan city, Shandong Province, China as the study area and employs a more refined local climate zone (LCZ) approach to investigate spatial and temporal variations in vegetation phenology. The three phenological indicators used in this study from 2007 to 2018, namely, the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS), were provided by MODIS satellite data. The SOS, EOS, and LOS were superimposed on the LCZ and urban–rural gradient to analyze the changes in vegetation phenology, and the applicability of these two analysis methods in the study of urban vegetation phenology was compared by the honest significant difference test. We found that the SOS, EOS, and LOS of vegetation in the study area generally showed an advance, delay, and extension trend, respectively. The means of the SOS and EOS along different LCZ types varied noticeably more than those along urban–rural gradients. In 2016, 77.5%, 80.0%, and 75.8% of LCZ pairs indicated statistically significant differences for SOS, EOS, and LOS, respectively. This study provides a new perspective for the study of urban vegetation phenology which can help in management of urban-scale environments, identification of areas rich in biodiversity, and conservation and restoration of biodiversity in urban areas.

Accurate extraction of crop cultivated area and spatial distribution is essential for food security. Crop classification methods based on machine learning and deep learning and remotely sensed time-series data are widely utilized to detect crop planting area. However, few studies assess the effectiveness of machine learning and deep learning algorithm integrated time-series satellite data for identifying multiple crop type classification over a large-scale region. Hence, this study aims to evaluate the effectiveness of machine learning and deep learning models in crop classification and provide a framework for large-scale multiple crop type classification based on time-series of satellite data. The time-series of the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and NaE (combined NDVI and EVI) were adopted as input features, and four widely used machine learning models, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and their integration (Stacking) were compared to examine the performance of multiple crop types (i.e., wheat, corn, wheat-corn, early rice, and early rice-late rice) classification in the North China Plain. The performance of two types of deep learning classifiers: the One-dimensional Convolutional Neural Network (Conv1D) and Long Short-Term Memory Networks (LSTM), were also tested. The results showed that the NaE feature performed best among three input features, and the Stacking model produced the highest accuracy (77.12%) compared to other algorithms.