Talha Mahmood

Soil moisture (SM) is a key parameter for irrigation monitoring, scheduling, and supporting precision agriculture. In this study, we used Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data to estimate SM at high spatial resolution (20 m) in the Lower Chenab Canal Command (LCC) area of Punjab, Pakistan. To achieve this, we applied a semiempirical water cloud model (WCM) using both the VV and VH polarizations from SAR data. Additionally, two widely used machine learning (ML) models, random forest (RF) and support vector machines (SVM), were employed to estimate SM at the field scale. To assess model reliability and transferability, reference data from two field sites were split using two approaches: (1) stratified random sampling, with 50% of data from each site used for training and 50% for validation, and (2) spatial splitting, where one site was used solely for training and the other for validation. Results showed that with random splitting, ML models, particularly SVM, outperformed the WCM, with a coefficient of determination (R²) of 0.58, root mean square error (RMSE) of 6.78 Vol.%, and mean absolute error (MAE) of 5.71 Vol.%. Moreover, VV outperformed VH with an RMSE (R ² ) of 7.37 Vol.% (0.51), compared with 7.69 Vol.% (0.46) when a stratified random split was used. In contrast, the spatial split yielded lower accuracy, as WCM with VH polarization performed best, achieving an R ² of 0.45 and RMSE (MAE) of 7.82 (6.23) Vol.%. The high-resolution SM estimates offer practical value for optimizing irrigation management, particularly in agricultural areas challenged by limited water availability.

Accurate estimation of soil organic carbon (SOC) is crucial for soil health, agricultural productivity, and climate change mitigation. Digital SOC mapping often lacks multi-sensor integration, improved bare soil compositing, and robust uncertainty assessment. We used 6-year multi-temporal Synthetic Aperture Radar (SAR) from Sentinel-1 and optical data from Sentinel-2. This study also utilized confidence interval (CI)–based bare soil compositing for SOC prediction in an agricultural landscape in northeast Germany to enhance SOC estimation.Four Random Forest models were developed to isolate and compare the independent and combined contributions of optical and SAR data. Local soil samples collected between 2013 and 2022 were divided into training and testing datasets. Independent validation was conducted using samples collected in 2024. Pixel-wise uncertainty was quantified through 100 repeated model runs with different training and testing splits, resulting in a spatially explicit SOC uncertainty map.Combining SAR and optical data improved in model calibration, while CI-based compositing further enhanced prediction accuracy. Using important features, the model achieved a coefficient of determination (R²) of 0.79 and a ratio of performance to deviation (RPD) of 2.23 in independent validation. The models incorporating SAR data showed higher uncertainty due to its sensitivity to soil conditions; however, standalone SAR data still yielded acceptable SOC mapping performance (R² = 0.57, RPD = 1.54). These results show that combining multi-temporal optical and SAR data with explicit uncertainty assessment enhances the robustness and reliability of SOC mapping across agricultural landscapes.

Accurate spatio-temporal information on the soil water balance is critical for an efficient and sustainable irrigation. Recent irrigation scheduling approaches are often limited to a representation of (i) the local or point scale soil water balance by in-situ measurements, (ii) solely surface soil water contents at a coarse spatial resolution by microwave remote sensing technologies, or (iii) only selected components of the soil water balance by simple crop evapotranspiration models. To reconcile the need for accurate estimates of different components of the soil water balance with feasible effort, this study proposes the application of physically-based one-dimensional soil water balance models in a spatially-distributed manner.The HYDRUS-1D software environment is applied at 70 m spatial resolution across a 1,600 ha study farm in Mecklenburg-Western Pomerania, Germany, with heterogeneous soil textures and different crops. Depth-specific (0 cm to 60 cm, in 10 cm increments) soil water balance simulations were conducted from 1st April to 30th September 2021 and 2022 to estimate the soil water content, plant available water content, infiltration, crop evapotranspiration, root water uptake, and deep percolation, at daily intervals. Simulated soil water contents were validated against in-situ measurements and two microwave remote sensing surface soil water content datasets (“Soil Moisture Active Passive”, SMAP; Sentinel-1, S1-SWC). Spatially distributed irrigation demands and irrigation timings at daily intervals, crop-specific irrigation efficiencies and potential farm-scale water savings are estimated using the simulated soil water balance to explore the contribution of this simulation framework for precision irrigation.The average simulation performance metrices were Root Mean Square Error (RMSE) = 0.020 m3 m-3, Mean Absolute Error (MAE) = 0.017 m3 m-3, coefficient of determination (R²) = 0.676, and bias = -0.008 m3 m-3, showing a good accuracy of spatially-distributed HYDRUS-1D simulations. The agreement with remotely-sensed data was moderate to weak (RMSEmean = 0.059 (0.150) m3 m-3, MAEmean = 0.049 (0.123) m3 m-3, R2mean = 0.208 (0.141), mean bias = 0.021 (0.108) m3 m-3 for SMAP (S1-SWC)). Average crop specific irrigation efficiencies were 65.0% (potato), 47.3% (wheat), 40.5% (rye), and 58.2% (sugar beet). Potential water savings amounted to 87,006.9 m³ (11.2 % of the applied irrigation water; 2021) and 71,396.6 m³ (10.4 %; 2022).The proposed simulation framework offers an easy-to-adopt and physically-based foundation for the estimation of crop-specific irrigation demands and irrigation timings at high spatial resolution. Further accuracy improvements by using depth-specific remote-sensing derived soil water contents (“Soil Water Index”) for model calibration are under ongoing investigation.

Random Forest (RF) is a widely used machine learning algorithm for crop type mapping. RF’s variable importance aids in dimension reduction and identifying relevant multisource hyperspectral data. In this study, we examined spatial effects in a sequential backward feature elimination setting using RF variable importance in the example of a large-scale irrigation system in Punjab, Pakistan. We generated a reference classification with RF applied to 122 SAR and optical features from time series data of Sentinel‑1 and Sentinel‑2, respectively. We ranked features based on variable importance and iteratively repeated the classification by excluding the least important feature, assessing its agreement with the reference classification. McNemar’s test identified the critical point where feature reduction significantly affected the RF model’s predictions. Additionally, spatial assessment metrics were monitored at the pixel level, including spatial confidence (number of classifications agreeing with the reference map) and spatial instability (number of classes occurring during feature reduction). This process was repeated 10 times with ten distinct stratified random sampling splits, which showed similar variable rankings and critical points. In particular, VH SAR data was selected when cloud-free optical observations were unavailable. Omitting 80% of the features resulted in an insignificant loss of only 2% overall accuracy, while spatial confidence decreased by 5%. Moreover, the crop map at the critical point exhibited an increase in spatial instability from a single crop to 1.28. McNemar’s test and the spatial assessment metrics are recommended for optimized feature reduction benchmarks and identifying areas requiring additional ground data to improve the results.

Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone depth optimization (T_opt) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany, T_opt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40–60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy (R ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean R between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest R achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.

In this case report, we describe two unusual presentations of varicella-zoster virus (VZV) reactivation without rash, a condition known as Zoster Sine Herpete (ZSH). In Case 1, a 58-year-old woman presented with severe right-sided chest pain under her breast that radiated to the ipsilateral back. After the initial workup ruled out cardiac and musculoskeletal etiologies, the characteristic dermatomal distribution of pain made us suspect VZV reactivation. A diagnosis of ZSH was made with positive VZV IgG and IgM serologies and symptomatic relief after famciclovir treatment. In Case 2, a 43-year-old woman presented with a severe headache and resolved sharp right flank pain. She was diagnosed with varicella meningitis after cerebrospinal fluid showed positive VZV DNA. Intravenous acyclovir treatment resulted in symptom resolution. The most common presentation of VZV reactivation is Herpes Zoster, or shingles, making ZSH a frequently missed diagnosis. High clinical suspicion is warranted to prevent life-threatening complications of ZSH.

Water crises are becoming severe in recent times, further fueled by population increase and climate change. They result in complex and unsustainable water management. Spatial estimation of consumptive water use is vital for performance assessment of the irrigation system using Remote Sensing (RS). For this study, its estimation is done using the Soil Energy Balance Algorithm for Land (SEBAL) approach. Performance indicators including equity, adequacy, and reliability were worked out at various spatiotemporal scales. Moreover, optimization and sustainable use of water resources are not possible without knowing the factors mainly influencing consumptive water use of major crops. For that purpose, random forest regression modelling was employed using various sets of factors for site-specific, proximity, and cropping system. The results show that the system is underperforming both for Kharif (i.e., summer) and Rabi (i.e., winter) seasons. Performance indicators highlight poor water distribution in the system, a shortage of water supply, and unreliability. The results are relatively good for Rabi as compared to Kharif, with an overall poor situation for both seasons. Factors importance varies for different crops. Overall, distance from canal, road density, canal density, and farm approachability are the most important factors for explaining consumptive water use. Auditing of consumptive water use shows the potential for resource optimization through on-farm water management by the targeted approach. The results are based on the present situation without considering future changes in canal water supply and consumptive water use under climate change.

Land use and land cover (LULC) change is one of the key driving elements responsible for altering the hydrology of a watershed. In this study, we investigated the spatio-temporal LULC changes between 2001 and 2018 and their impacts on the water balance of the Jhelum River Basin. The Soil and Water Assessment Tool (SWAT) was used to analyze the impacts on water yield (WY) and evapotranspiration (ET). The model was calibrated and validated with discharge data between 1995 and 2005 and then simulated with different land use. The increase was observed in forest, settlement and water areas during the study period. At the catchment scale, we found that afforestation has reduced the WY and surface runoff, while enhanced the ET. Moreover, this change was more pronounced at the sub-basin scale. Some sub-basins, especially in the northern part of the study area, exhibited an increase in WY due to an increase in the snow cover area. Similarly, extremes land use scenarios also showed significant impact on water balance components. The basin WY has decreased by 38 mm/year and ET has increased about 36 mm/year. The findings of this study could guide the watershed manager in the development of sustainable LULC planning and water resources management.