Sustainable farmland management is vital for global food security and for mitigating environmental degradation and climate change. While individual practices such as crop rotation and no-tillage are well-documented, this review synthesizes current evidence to illuminate the critical synergistic effects of integrating four key strategies: crop rotation, conservation tillage, organic amendments, and soil microbiome management. Crop rotation enhances nutrient cycling and disrupts pest cycles, while conservation tillage preserves soil structure, reduces erosion, and promotes carbon sequestration. Organic amendments replenish soil organic matter and stimulate biological activity, and a healthy soil microbiome boosts plant resilience to stress and enhances nutrient acquisition through key functional groups like arbuscular mycorrhizal fungi (AMFs). Critically, the integration of these practices yields amplified benefits that far exceed their individual contributions. Integrated management systems not only significantly increase crop yields (by up to 15–30%) and soil organic carbon but also deliver profound global ecosystem services, with a potential to sequester 2.17 billion tons of CO₂ and reduce soil erosion by 2.41 billion tons annually. Despite challenges such as initial yield variability, leveraging these synergies through precision agriculture represents the future direction for the field. This review concludes that a holistic, systems-level approach is essential for building regenerative and climate-resilient agroecosystems.

This review presents a comprehensive overview of recent advances in TiO₂-based photoelectrocatalysis (PEC), with an emphasis on material design strategies to enhance visible-light responsiveness and charge carrier dynamics. Key approaches—including elemental doping, defect engineering, heterojunction construction, and plasmonic enhancement—are systematically discussed in relation to their roles in modulating energy band structures and promoting charge separation. Beyond fundamental mechanisms, the review highlights the broad environmental and energy-related applications of TiO₂-driven PEC systems, encompassing the degradation of persistent organic pollutants, microbial disinfection, heavy metal removal, photoelectrochemical water splitting for hydrogen production, and CO₂ reduction. Recent progress in integrating PEC systems with energy harvesting modules to construct self-powered platforms is critically examined. Current limitations and future directions are also outlined to guide the rational development of next-generation TiO₂-based photoelectrocatalytic systems for sustainable environmental remediation and solar fuel conversion.

Climate change is intensifying the co-occurrence of drought and heat stresses, which substantially constrain global crop yields and threaten food security. Developing climate–resilient crop varieties requires a comprehensive understanding of the physiological and molecular mechanisms underlying combined drought–heat stress tolerance. This review systematically summarizes recent advances in integrating multi-scale remote-sensing phenomics with multi-omics approaches—genomics, transcriptomics, proteomics, and metabolomics—to elucidate stress response pathways and identify adaptive traits. High-throughput phenotyping platforms, including satellites, UAVs, and ground-based sensors, enable non-invasive assessment of key stress indicators such as canopy temperature, vegetation indices, and chlorophyll fluorescence. Concurrently, omics studies have revealed central regulatory networks, including the ABA–SnRK2 signaling cascade, HSF–HSP chaperone systems, and ROS-scavenging pathways. Emerging frameworks integrating genotype × environment × phenotype (G × E × P) interactions, powered by machine learning and deep learning algorithms, are facilitating the discovery of functional genes and predictive phenotypes. This “pixels-to-proteins” paradigm bridges field-scale phenotypes with molecular responses, offering actionable insights for breeding, precision management, and the development of digital twin systems for climate-smart agriculture. We highlight current challenges, including data standardization and cross-platform integration, and propose future research directions to accelerate the deployment of resilient crop varieties.

The rational design of photoanode materials is pivotal for advancing photoelectrochemical (PEC) water splitting toward sustainable hydrogen production. This review highlights recent progress in the machine learning (ML)-assisted development of nanostructured metal oxide photoanodes, focusing on bridging materials discovery and device-level performance optimization. We first delineate the fundamental physicochemical criteria for efficient photoanodes, including suitable band alignment, visible-light absorption, charge carrier mobility, and electrochemical stability. Conventional strategies such as nanostructuring, elemental doping, and surface/interface engineering are critically evaluated. We then discuss the integration of ML techniques—ranging from high-throughput density functional theory (DFT)-based screening to experimental data-driven modeling—for accelerating the identification of promising oxides (e.g., BiVO₄, Fe₂O₃, WO₃) and optimizing key parameters such as dopant selection, morphology, and catalyst interfaces. Particular attention is given to surrogate modeling, Bayesian optimization, convolutional neural networks, and explainable AI approaches that enable closed-loop synthesis-experiment-ML frameworks. ML-assisted performance prediction and tandem device design are also addressed. Finally, current challenges in data standardization, model generalizability, and experimental validation are outlined, and future perspectives are proposed for integrating ML with automated platforms and physics-informed modeling to facilitate scalable PEC material development for clean energy applications.

Forests play a pivotal role in the global carbon cycle, making accurate estimation of forest carbon stocks essential for climate change mitigation efforts. However, the diverse methods available for assessing forest carbon yield varying results and have different limitations. This study provides a comprehensive review of current methods for estimating forest carbon stocks, including field-based measurements, remote sensing techniques, and integrated approaches. We systematically collected and analyzed recent studies (2010–2025) on forest carbon estimation across various ecosystems. Our review indicates that field-based methods, such as forest inventories and allometric equations, offer high accuracy at local scales but are labor-intensive. Remote sensing methods (e.g., LiDAR and satellite imagery) enable large-scale carbon assessment with moderate accuracy and efficiency. Integrated approaches that combine ground measurements with remote sensing data can improve accuracy while expanding spatial coverage. We discuss the strengths and weaknesses of each method category in terms of accuracy, cost, and scalability. Based on the synthesis of findings, we recommend a balanced approach that leverages both ground and remote sensing techniques for reliable forest carbon monitoring. This review also identifies knowledge gaps and suggests directions for future research to enhance the precision and applicability of forest carbon estimation methods.

Geographic Information Systems (GIS) are central to spatial evidence in Environmental Impact Assessment (EIA). In this review, GIS is used in a broad, integrative sense to refer to an ecosystem of geospatial technologies—such as remote sensing (RS) and GPS—where GIS serves as the core platform for managing, analyzing, and communicating spatial data throughout the EIA process. GIS plays a crucial role at each stage of EIA, from baseline data collection to spatial analysis, ecological sensitivity mapping, impact prediction, scenario simulation, and landscape connectivity assessment. These capabilities support alternatives analysis, risk communication, and decision-making in EIA. This paper synthesizes thematic evidence and presents case studies to illustrate the synergies between GIS, remote sensing, GeoAI, and multisource data fusion. It highlights operational workflows and key deliverables for EIA applications, including urban expansion, transport corridors, and protected-area management. We identify persistent challenges in data quality and standardization, interoperability, model uncertainty, and policy gaps. To address them, we propose a minimum geospatial dataset with clear metadata standards, interpretable GeoAI paired with formal sensitivity analysis, IoT–GIS pipelines for real-time monitoring and adaptive management, and the systematic inclusion of cumulative effects and climate scenarios. By linking GIS methods to typical decision points and reporting standards in EIA, this review clarifies where GIS adds value, how to quantify and communicate uncertainty, and how to align analytical outputs with regulatory requirements and stakeholder expectations. The study offers a practical framework and implementation checklist for standardized, transparent, and reproducible EIA processes, contributing to evidence-based ecological governance.