Journal Paper Digests 2026 #2
- Scale dependence of genome-derived microbial functional diversity informing soil functions
- AgriFM: A multi-source temporal remote sensing foundation model for Agriculture mapping
- A radiometrically and spatially consistent super-resolution framework for Sentinel-2
- Multi-sensor (since 1997) global soil moisture mapping with enhanced Spatio-temporal coverage through machine learning framework fusion
- A Century of Drought Research (1900–2023): Scientific Developments, Methodological Innovations, and Emerging Frontiers
- Multiple Global Change Stressors Boost Soil Greenhouse Gas Emissions Worldwide
- Effects of microplastics on farmland soils and plants: a review
Effects of microplastics on farmland soils and plants: a review
Microplastics (MPs) are plastic particles smaller than 5 mm in size, which are widely present and have become one of the major pollutants in the natural environment, and are increasingly recognised as emerging pollutants in agricultural ecosystems. Due to their small size and high mobility, MPs can easily migrate into farmland soils and attach to plant surfaces, thereby altering the physical, chemical and microbial properties of the soil. These changes may affect seed germination, plant growth, and physiological and biochemical functions. This review systematically synthesises current research on the impact of MPs on agricultural soil, focusing on their effects on soil structure, chemical properties and microbial diversity. The positive and negative effects of MPs on plant seed germination, growth, and physiological and biochemical processes are critically analysed. Furthermore, the potential ecological risks of MPs to soil and plant health are discussed. Mitigation strategies and future research priorities are proposed to address MPs contamination in agricultural systems. This study aims to provide both theoretical insights and practical references to support the prevention and control of MPs pollution in farmland soils, thereby contributing to sustainable agricultural development and soil ecosystem resilience.
Multiple Global Change Stressors Boost Soil Greenhouse Gas Emissions Worldwide
Soil carbon greenhouse gas (GHG) emissions are integral to climate security worldwide. Global change is known to impact soil GHG emissions; yet, the contribution of an increasing number of global change factors (GCFs) to the rates of carbon GHG emissions remains virtually unknown, challenging our capacity to forecast the trajectory of climate change. Here, we synthesize 1803 observations on soil CO2 and CH4 fluxes across 21 types of GCFs spanning a wide range of ecosystems (i.e., forests, grasslands, farmland, wetlands, tundras, and deserts) and found that an increasing number of GCFs will result in significant increases in CO2 and CH4 emissions. The impacts of GCFs on GHG emissions were largely explained by climate, biome types, and GCF-induced changes in soil moisture, providing potential tools for managing global change. Our work provides critical insights, emphasizing that the number of global change stressors needs to be immediately reduced to help minimize the negative impacts of carbon greenhouse gas emissions on climate change.
A Century of Drought Research (1900–2023): Scientific Developments, Methodological Innovations, and Emerging Frontiers
Drought significantly affects water resources, agriculture, energy, and ecosystems, revealing enduring socio-economic vulnerabilities over the centuries. This review synthesizes a century of development and recent advances in drought research (1900–2023), drawing on a bibliometric analysis of over 152,000 peer-reviewed publications. The review begins by exploring ancient and historical droughts, their climatic drivers, and societal impacts, then examines the evolving disciplinary landscape, shifting research priorities, and the progression of drought research over the past century. Key methodological advances are discussed, including statistical and probabilistic modeling, machine learning, and deep learning. Technical milestones such as satellite remote sensing, hydrological and land surface modeling, and global climate modeling have greatly expanded both the scope and precision of drought studies. Research on climate change has deepened understanding of drought processes by examining changes in climate variability and teleconnections, attributing events to human influence, and projecting future risks. Simultaneously, there has been a notable shift from reactive approaches to resilience-oriented management, enhancing preparedness. In the past decade, increasing attention has focused on emerging societal challenges such as environmental degradation, public health risks, social inequities, and resource conflicts. Despite significant progress, critical gaps remain, including the need for stakeholder-informed indicators, improved flash drought detection, a deeper understanding of cascading processes, integration of human-driven factors, enhanced interpretability of AI models, next-generation satellite monitoring, and comprehensive risk management for drought-related compound hazards. This synthesis consolidates a century of progress and presents a forward-looking framework aimed at strengthening resilience and guiding actionable drought risk governance.
Multi-sensor (since 1997) global soil moisture mapping with enhanced Spatio-temporal coverage through machine learning framework fusion
The successful deployment of multiple satellites equipped with passive microwave sensors has been pivotal for monitoring global soil moisture. Despite their importance, limitations including varying service durations, orbital scanning gaps, and differences in retrieval algorithms result in poor spatio-temporal consistency and coverage. This study introduces a two-stage paradigm to overcome the inconsistency of multi-sensors: Firstly, high-precision soil moisture is generated from SMAP L-band observations through the multi-channel collaborative algorithm (MCCA) as the physically anchored training target. Then, a long short-term memory (LSTM) network specifically designed for global gridded soil moisture dynamics is trained based on cross-calibrated brightness temperature observations (inclined orbit satellite sensors (TMI and GMI) and polar orbit satellite sensors (AMSR-E and AMSR2)) to obtain the high-quality retrieval accuracy of MCCA SMAP. Finally, the daily global soil moisture product (25 km resolution, 1997–2023) is provided by fusing the instantaneous soil moisture data of the four sensors from the model output. The study performed extensive validation with ground measurements and cross-validation with other datasets for both temporal and spatial consistency. The results indicate that the spatial distribution and seasonal variation patterns of MCCA-ML closely match those of MCCA SMAP, reflecting global climatic and geographic features. Verified by 24 dense global observation networks, the global r value of MCCA-ML SM is 0.76, the RMSE is 0.068 m3/m3, and the ubRMSE is 0.059 m3/m3, which well inherits the excellent performance of SMAP. During the service period of two or more satellites, the daily global land coverage of MCCA-ML SM usually exceeds 80 %, and it has a good ability to detect soil moisture.
A radiometrically and spatially consistent super-resolution framework for Sentinel-2
Deep learning-based super-resolution (SR) models offer a promising approach to enhancing the effective spatial resolution of optical satellite images. However, existing SR implementations have shown that, while these models can reconstruct fine-scale details, they often introduce undesirable artifacts, such as nonexistent local structures, reflectance distortions, and geometric misalignment. To mitigate these issues, fully synthetic data approaches have been explored for training, as they provide complete control over the degradation process and allow precise supervision and ground-truth availability. However, challenges in domain transfer have limited their effectiveness when applied to real satellite images. In this work, we propose SEN2SR, a new deep learning framework trained to super-resolve Sentinel-2 images while preserving spectral and spatial alignment consistency. Our approach harmonizes synthetic training data to match the spectral and spatial characteristics of Sentinel-2, ensuring realistic and artifact-free enhancements. SEN2SR generates 2.5-meter resolution images for Sentinel-2, upsampling the 10-meter RGB and NIR bands and the 20-meter Red Edge and SWIR bands. To ensure that SR models focus exclusively on enhancing spatial resolution, we introduce a low-frequency hard constraint layer at the final stage of SR networks that always enforces spectral consistency by preserving the original low-frequency content. We evaluate a range of deep learning architectures, including Convolutional Neural Networks, Mamba, and Swin Transformers, within a comprehensive assessment framework that integrates Explainable AI (xAI) techniques. Quantitatively, our framework achieves superior PSNR while maintaining near-zero reflectance deviation and spatial misalignment, outperforming state-of-the-art SR frameworks. Moreover, we demonstrate maintained radiometric fidelity in downstream tasks that demand high-fidelity spectral information and reveal a significant correlation between model performance and pixel-level model activation. Qualitative results show that SR networks effectively handle diverse land cover scenarios without introducing spurious high-frequency details in out-of-distribution cases. Overall, this research underscores the potential of SR techniques in Earth observation, paving the way for more precise monitoring of the Earth’s surface. Models, code, and examples are publicly available at https://github.com/ESAOpenSR/SEN2SR.
AgriFM: A multi-source temporal remote sensing foundation model for Agriculture mapping
Climate change and population growth intensify the demand for precise agriculture mapping to enhance food security. Such mapping tasks require robust modeling of multi-scale spatiotemporal patterns from fine field textures to landscape context, and from short-term phenology to full growing-season dynamics. Existing methods often process spatial and temporal features separately, limiting their ability to capture essential agricultural dynamics. While transformer-based remote sensing foundation models (RSFMs) offer unified spatiotemporal modeling ability, most of them remain suboptimal: they either use fixed windows that ignore multi-scale crop characteristics or neglect temporal information entirely. To address these gaps, we propose AgriFM, a multi-source, multi-temporal foundation model for agriculture mapping. AgriFM introduces a synchronized spatiotemporal downsampling strategy within a Video Swin Transformer backbone, enabling efficient handling of long and variable-length satellite time series while preserving multi-scale spatial and phenological information. It is pre-trained on a globally representative dataset comprising over 25 million samples from MODIS, Landsat-8/9, and Sentinel-2 with land cover fractions as pre-training supervision. AgriFM further integrates a versatile decoder specifically designed to dynamically fuse multi-source features from different stages of backbone and accommodate varying temporal lengths, thereby supporting consistent and scalable agriculture mapping across diverse satellite sources and task requirements. It supports diverse tasks including agricultural land mapping, field boundary delineation, agricultural land use/land cover mapping, and specific crop mapping (e.g., winter wheat and paddy rice) with different data sources. Comprehensive evaluations show that AgriFM consistently outperforms existing deep learning models and general-purpose RSFMs across multiple agriculture mapping tasks. Codes and models are available at https://github.com/flyakon/AgriFM and https://glass.hku.hk
Scale dependence of genome-derived microbial functional diversity informing soil functions
The relationship between soil multifunctionality and microbial diversity is well established, and using genomic data to link microbial diversity with soil functions is increasingly recognized as a reliable approach, despite challenges such as horizontal gene transfer, functional redundancy, and transcriptional uncertainty. Here, we investigated how microbial taxonomic and functional diversities derived from metagenomic data explain soil multifunctionality across soil profiles. We conducted analyses across four seasons and two contrasting hydrological habitats: wetland and cropland. We found that microbial functional diversity captured soil functions more effectively than taxonomic diversity, and its explanatory power depended on scale, strongest at broader classification levels (phylum/module) and higher data hierarchies (cosmopolitan). Microbial functional diversity explained 95 % and 79 % of individual soil functions in wetland and cropland, respectively, and showed a closer association with overall soil multifunctionality. The relationship remained consistent across spatial (0–100 cm soil profiles), temporal (four seasons), and hydrological (wetland and cropland) gradients, demonstrating greater stability than taxonomic diversity. By linking microbial diversity to soil functions across space and time, our findings show that genome-derived microbial functional diversity provides a robust and reliable framework for explaining soil functions, reinforcing the potential of genome-based microbial modeling.