The identification and assessment of plant stress using wetland satellite images is a major task in remote sensing. In this study, one of the largest constructed wetlands (CWs) in the world, located in the Sultanate of Oman, was examined, assessed, and evaluated using remote sensor data from Sentinel-2. This CW system treats produced water generated during oil exploration activities in a desert environment; thus, CW vegetation is subjected to stress induced by oil hydrocarbons and water salinity. This study examined the plant stress and detected changes between the years of 2017 and 2019. Sentinel satellite images were evaluated for vegetation status extraction. The Normalized Difference Vegetation Index (NDVI), Modified Soil-Adjusted Vegetation Index (MSAVI), and Normalized Difference Salinity Index (NDSI) were used to evaluate the vegetation change. The results showed a comprehensive mapping identification of the plant stress and water flow parameter factors including oil in water contamination (OIW), dissolved oxygen (DO), water temperature (WT), and water conductivity (COND). Among the three indices, it was found that the NDVI showed a very good correlation with all parameters in both years with average R² = 0.78, 0.67, 0.75, and 0.60 for OIW, DO, WT, and COND, respectively. The same trend was found for MSAVI but with R² = 0.59, 0.48, 0.55, and 0.56 for OIW, DO, WT, and COND, respectively. This shows that the NDVI performed better than the MSAVI in evaluating the water flow parameters. On the other hand, the NDSI showed a strong correlation with one flow parameter, that is, water conductivity, especially at the outlet cells of the CW with R² = 0.86 and 0.82 for winter time and summer time, respectively. The synchronization and correlation between the water flow parameters and remote sensing vegetation indices in this study lead to a new approach to large-scale landscape wetland monitoring that improves and helps predict any degradation or stress on vegetation growth. Furthermore, the results of this work can help decision makers potentially modify the wetland design and water flow path to improve future expansion phases. The mapping of such a critical and massive industrial CW should consider the use of high spatial resolution sensors where identifications and classifications are further improved. In summary, this research demonstrates that it is feasible to estimate vegetation stress within the constructed wetland using remote sensing techniques across extensive regions when an ample dataset comprising field data, satellite imagery, and supporting information is accessible.

This study emphasizes the role of Prosopis cineraria (Druce) in promoting sustainability through its contribution to carbon sequestration and climate change mitigation. The accurate quantification of the aboveground biomass (AGB) of Druce trees is essential for assessing their potential in reducing carbon emissions, yet remains a significant challenge. To address this, the study aimed to (1) estimate the AGB using destructive sampling; (2) analyze variability in existing allometric biomass equations; (3) evaluate remote sensing and machine learning techniques for estimating AGB and carbon sequestration; and (4) develop and validate new allometric equations based on field and remote sensing data. The Druce trees, with diameters at breast height ranging from 20.7 to 28.97 cm, exhibited an AGB of 208.3 kg per tree, which corresponds with a carbon sequestration stock of 97.89 kg C/tree. This translates to an annual carbon dioxide sequestration potential of 0.36 t C/tree. The newly developed allometric model (Model-2) was found to demonstrate superior accuracy, with performance metrics including a mean absolute percentage error (MAPE) of 2.6%, relative bias of 5.3%, R² of 0.906, mean absolute error (MAE) of 0.151, and root mean square error (RMSE) of 0.189. These improvements highlight the significant role of remote sensing technologies in advancing sustainable carbon monitoring and offer a more precise tool for enhancing global carbon sequestration models. By integrating field-based measurements and advanced technologies, this study strengthens our ability to assess the carbon sequestration potential of trees, contributing to more sustainable management and climate resilience strategies.

The effective management of produced water (PW), a by-product of oil extraction in Oman, is essential for sustainable water use and environmental protection. PW contains petroleum residues, heavy metals, and salts, which require treatment before safe reuse. In the Nimr oil field, PW undergoes partial treatment in constructed wetlands vegetated with buffelgrass (Cenchrus ciliaris). This study investigated the reuse potential of treated PW for irrigation through two parallel field experiments conducted at Sultan Qaboos University (SQU) and the Nimr wetlands site. At the SQU site, native halophytic plants were irrigated with three water sources: treated municipal wastewater, underground water (from an on-site well), and treated produced water. At the Nimr site, irrigation was conducted using underground water and treated PW. Two soil types were used: well-draining control soil and Nimr soil from southern Oman. The treatments included: (i) PW + control soil, (ii) PW + Nimr soil, (iii) PW + gypsum (3.5 g/kg soil), (iv) PW + biochar (10 g/kg soil), (v) underground water + control soil, and (vi) treated municipal wastewater + control soil. Biochar, produced from locally sourced buffelgrass via low-temperature pyrolysis (300 °C for 3 h), and gypsum (46.57% acid-extractable sulfate) were mixed into the soil before sowing. The impact of each treatment was assessed in terms of soil quality (salinity, boron, major cations), plant physiological responses, and mineral accumulation. PW irrigation (TDS ~ 6500–7000 mg/L) led to a sixfold increase in soil sodium and raised boron levels in plant tissues to over 200 mg/kg, exceeding livestock feed safety limits. Copper remained within acceptable thresholds (≤9.5 mg/kg). Biochar reduced boron uptake, but gypsum showed limited benefit. Neither amendment improved plant growth under PW irrigation. These findings highlight the need for regulated PW reuse, emphasizing the importance of soil management strategies and alternating water sources to mitigate salinity stress.

Effective management of produced water (PW), a by-product of oil extraction in Oman, is crucial for sustainable water use and environmental protection. PW contains petroleum residues, heavy met-als, and salts, requiring treatment before reuse. In the Nimr oil field, PW is partially treated through constructed wetlands with reed grass. This study evaluates the feasibility of reusing treated PW for irrigation by assessing its effects on soil quality and mineral accumulation in plants under different irrigation conditions. Field experiments were at Sultan Qaboos University (SQU) and Nimr wet-lands treated PW (TDS ~6,500–7,000 mg/L) along with lower-salinity water sources. Soil amend-ments (biochar and gypsum) were tested for salinity mitigation. Results showed that PW irrigation significantly increased boron and major cations (Na, K, Ca, Mg) in soil and plant tissues, leading to higher soil salinity and potential decline in soil health. While biochar reduced boron levels, neither amendment improved plant growth or nutrient uptake. Plants irrigated with PW exhibited signs of salinity stress, including reduced leaf development and premature senescence. These findings highlight both the potential and challenges of PW reuse for irrigation. Adaptive irrigation strategies and soil management practices are essential to maximize benefits while mitigating long-term soil salinity risks.

Background: Greenhouse cooling is very essential during warm conditions to extend the cropping season and offer year-round production. Although fan-pad systems are widely utilized for greenhouse cooling in arid climates, they have many drawbacks mentioned by researchers. Thus, finding the best cooling system had attracted the attention of many researchers and inventors in such climates. Objective: This paper aims to review the recent patents of greenhouse cooling methods suitable for arid climates. Methods: The most relating patents on greenhouse cooling technologies were discussed and analyzed. Results: A total of 46 patents were reviewed, covering over 21 years, and the majority of them (73.9%) were patented in China, followed by the USA (15.2%), worldwide (4.3%), Japan (2.2%), Russia (2.2%) and Austria (2.2%). The patents were divided into three categories; preventive (8 patents), corrective (33 patents) and combined (5 patents) greenhouse cooling techniques. In the preventive techniques, i.e. avoiding heat load in greenhouses, patents involving shading mechanisms were dominant because it is well known that shading reduces solar radiation penetration as the largest heating source in greenhouses. In the corrective techniques, i.e. removing the accumulated heat, patents on evaporative cooling mechanisms were dominant but their suitability for arid climates is hindered with high humidity and water scarcity. In the combined techniques, i.e. including both preventive and corrective techniques, patents are very comprehensive and thus implementable in hightech commercial greenhouses. Some innovative patents involving refrigerative cooling, desiccation, solar power and multi-systems were also highlighted. Conclusion: Patents implementing preventive cooling techniques alone are insufficient in arid climates due to the extremely high ambient temperatures in the hot months. Similarly, patents involving evaporative cooling methods are constrained by high humidity and water scarcity in arid conditions. Therefore, patents combining preventive and corrective techniques are more energy- and water- efficient. We recommend future work to focus on desiccant-indirect evaporative cooling methods as they can overcome the high humidity limitations in many arid areas.

Climate change mitigation in arid and semi-arid regions requires accurate quantification of carbon sequestration potential in native vegetation, where sparse cover and harsh climatic conditions challenge conventional assessment methods. This study presents an integrated framework combining destructive sampling, remote sensing (RS), Geographic Information Systems (GIS), and machine learning (ML) to estimate above-ground biomass (AGB) and carbon stocks in three representative Omani ecosystems: Prosopis cineraria desert woodlands, Avicennia marina mangroves, and Phragmites australis wetlands. Extensive field campaigns involving over 400 destructive samples were conducted to develop species-specific allometric equations, addressing limitations of generic biomass models in arid environments. High-resolution Sentinel-2 and Landsat-8 imagery were processed to derive vegetation indices (NDVI, MSAVI, SAVI, GRVI), which were integrated with field data to train Multiple Linear Regression and Random Forest models for spatial carbon mapping. Results demonstrate high predictive accuracy for Prosopis cineraria (R² = 0.91) and Avicennia marina (R² = 0.84), while RS-based models significantly outperformed fieldonly methods for Phragmites australis (R² = 0.78). The findings highlight the substantial carbon sequestration potential of native and saline-tolerant species and confirm the effectiveness of machine learning-enhanced remote sensing for scalable carbon monitoring. This framework provides a robust scientific basis for advancing Oman’s national MRV systems, supporting Oman Vision 2040, and informing nature-based solutions for carbon neutrality in arid regions.