Search Results (385)

South America (SA) features diverse land cover types and varied climate conditions, both of which significantly influence the variability of soil moisture (SMO). Obtaining ground-truth measurements for SMO is often costly and labor-intensive, and the limited number of ground SMO stations in SA further complicates the evaluation of satellite-derived SMO products. In this work, we proposed an approach that integrates some statistical methods to assess the reliability of Soil Moisture Active Passive (SMAP), the H113 dataset from the Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite-derived SMO products in SA from 14 May 2015 to 31 December 2016. The integrated methods are error metrics (correlation (R), bias, and ubiased root mean square error (ubRMSE)), Triple Collocation Method (TCM), and Hovmöller diagrams. ERA5 and GLDAS-Noah SM products were used as references for validation. The quality of SMO products was assessed by considering environmental variables, including land cover, vegetation density, and precipitation, within the different climate zones of SA. The results presented that SMAP overall outperforms SMOS and ASCAT, with the highest average correlation (0.55 with GLDAS and 0.61 with ERA5), slight average bias (−0.058 with GLDAS and −0.014 with ERA5), and lowest average ubRMSE (0.045 with GLDAS and 0.041 with ERA5). In arid, semi-arid, and moderate vegetation regions, the SMAP satellite outperforms SMOS and ASCAT, achieving better statistics values with GLDAS and ERA5 datasets, and achieving low error variance and high S/N in the TCM analysis. While the ASCAT H113 product showed good performance, which makes it a good alternative to SMAP, it still has limitations in more dense vegetation regions. SMOS showed the lowest performance across SA, especially in the Amazon basin. The Amazon basin emerges as a critical region where all SMO products displayed a significant SMO variability; however, SMAP showed slightly better results than ASCAT and SMOS. In the absence of ground truths, the proposed approach provides a better evaluation of satellite SMO products. Meanwhile, it provides new spatiotemporal statistical insights into satellite SMO retrieval performance evaluation within diverse climate zones of SA. This research provides valuable guidance for improving SMO monitoring and agricultural management in tropical and semi-arid ecosystems. Full article

This study investigates the impact of mechanical loading on the electromagnetic performance of conformal load-bearing antenna structures (CLASs), focusing on the resonant frequency response. Using 6-ply [0/90] GFRP as the CLAS substrate, the research evaluated the effects of two mechanical loading scenarios: the quasi-static uniaxial tensile test and cyclic fatigue. The quasi-static tests explore the response of CLASs to significant elongation, while the cyclic fatigue tests simulate localised damage propagation under operational loads. The results from the quasi-static tests demonstrated that the dominant effect under uniaxial tensile loading is the increase in substrate permittivity due to damage, causing a decrease in resonant frequency. The cyclic fatigue tests employed two configurations: removeable antenna patch (RAP), which isolates the antenna from mechanical loading to focus on substrate damage; and surface-mounted antenna patch (SMAP), which examines the combined effects of substrate damage and antenna elongation. The RAP results showed a consistent correlation between substrate damage and resonant frequency decrease, while SMAP demonstrated complex frequency behaviour due to competing effects of substrate damage and antenna elongation. A comparison with [±45]₆ GFRP results showed that the resonant behaviour remained consistent regardless of ply configuration during the initial damage accumulation induced by cyclic fatigue. However, with significant elongation in quasi-static tests, resonant frequency behaviour was affected by the specimen’s ply configuration, with substrate permittivity changes due to mechanical loading being the dominant factor. These findings provide valuable insights into the relationship between damage sustained by the CLAS system and resonant frequency shifts, providing critical information for predicting CLAS’s reliability and service life. Full article

Phosphorus input into surface water is a global concern due to its role in eutrophication, which is especially critical in semi-arid regions with their challenging climatic conditions. This study evaluated the best model for estimating the phosphorus decay coefficient (k) in semi-arid lakes, using flows from the Soil Moisture Accounting Procedure (SMAP), model of Génie Rural à 4 paramètres Journalier (GR4J), and reverse water balance hydrological models. Conducted at the Orós reservoir with 37 sampling campaigns from 2008 to 2017, it compared decay rates for temperate, tropical, and semi-arid climates. Some analyses also used phosphorus concentrations measured at the reservoir inlet. Model efficiency was assessed with bias, mean relative error, mean squared error, root mean squared error, and standard deviation. from the best models, water quality classes were classified based on phosphorus concentrations with the use of a confusion matrix to calculate accuracy, precision, recall, and F1 score. The findings demonstrated that the decay rate tailored for semi-arid regions, when combined with GR4J flow data, offered the highest accuracy in estimating phosphorus concentrations (bias = 0.0012, RMSE = 0.0326, EMR = 60.6134, STD = 0.0312). In contrast, the decay rate calibrated for tropical conditions with SMAP-derived flows proved superior for classifying water quality categories (classes defined by CONAMA Resolution 357/05). Therefore, the GR4J model for semi-arid conditions stands out for concentration estimation, while the tropical decay rate with SMAP flows is preferable for effective classification of water quality status. Full article

Soil moisture (SM) is crucial for agricultural production and ecological restoration, and high-resolution SM data can be efficiently acquired through downscaling remote sensing products. Although deep learning is widely used for downscaling, its mechanisms often lack interpretability. In this study, a downscaling model based on the architecture of the Kolmogorov–Arnold Network (KAN) was constructed using multi-source remote sensing data to generate SM data at 500 m resolution in the Tibetan Plateau. In addition, the interpretability of the model was effectively improved by fitting mathematical equations between SM and simplified variables. The results showed that (1) the downscaled SM of KANs demonstrated superior accuracy compared to ANN, significantly reducing the RMSE and ubRMSE of the SMAP product while closely aligning with station observations in temporal change trends; (2) correlation and SHAP analysis results showed that NDVI was the most important environmental factor affecting SM changes, followed by DEM and ET; (3) the symbolic equations of SM and simplified variables for each month were obtained by fitting the KAN model, whose downscaling accuracy was less than that of the KAN model but better than that of the SMAP product. Overall, this study explored the relationship between SM and environmental factors, and the obtained high-resolution SM data have good accuracy, which can meet the needs of small- or medium-scale research and provide a reference for subsequent downscaling studies. Full article

Off-road vehicle mobility assessments rely on fine-resolution (~10 m) estimates of soil moisture and strength across the region of interest. Such estimates are often produced by downscaling soil moisture from a microwave satellite like SMAP, then using the soil moisture in a soil strength model. Soil moisture downscaling methods typically assume consistent relationships between the moisture and topographic, vegetation, and soil composition characteristics within the microwave satellite grid cells. The objective of this study is to examine whether soil moisture and strength exhibit heterogenous dependencies on topography, vegetation, and soil composition characteristics within a SMAP grid cell. Soil moisture and strength data were collected at four geographically separated regions within a 9 km SMAP grid cell in the Front Range foothills of northern Colorado. Laboratory methods and pedotransfer functions were used to characterize soil attributes, and remote sensing data were used to determine topographic and vegetation attributes. Pearson correlation analyses were used to quantify the direction, strength, and significance of the relationships of both soil moisture and strength with topography, vegetation, and soil composition. Contrary to the common assumption, spatial variations in the slope and correlation of the relationships are observed for both soil moisture and strength. The findings indicate that improved predictions of soil moisture and soil strength may be achievable by soil moisture downscaling procedures that use spatially variable parameters across the downscaling extent. Full article

Accurate knowledge of surface and subsurface soil moisture (SM) is essential for hydrologic modeling, weather forecasting, and agricultural water management. NASA’s Soil Moisture Active Passive (SMAP) satellite (level 3) provides ‘surface’ SM with 2–3 days temporal resolution, hence lacks daily and subsurface SM information. This study developed a convolutional neural network–long short-term memory (ConvLSTM) deep learning model to produce ‘daily’ surface (5 cm) and subsurface (25 cm) SM products (9 km) by integrating SMAP level 3 ancillary data, North American Land Data Assimilation System (NLDAS-2; 12 km) SM, and Soil Landscapes of the United States (SOLUS100) digital maps across the contiguous U.S. Two input scenarios were evaluated: scenario 1 used only SMAP ancillary data, while scenario 2 included both SMAP ancillary data and SOLUS100 soil maps. Model evaluation with in situ SM data showed higher accuracy for scenario 2, indicating the importance of soil properties (texture and bulk density) in SM estimation. Coarse-textured soils showed the highest estimation accuracy, followed by medium- and fine-textured soils. The model also performed in estimating subsurface SM than surface SM for most land-cover types. Incorporating SMAP ancillary data and SOLUS100 digital soil maps into the ConvLSTM improved the spatial and temporal estimation of surface and subsurface SM. The results highlight the potential of deep learning for integrating multi-source multi-scale observations for improving SM estimation at large scale. Full article

Between 23 and 25 January 2020, the Metropolitan Region of Belo Horizonte (MRBH) in Brazil experienced 32 natural disasters, which affected 90,000 people, resulted in 13 fatalities, and caused economic damages of approximately USD 250 million. This study aims to describe the synoptic and mesoscale conditions that triggered these natural disasters in the MRBH and the physical properties of the associated clouds and precipitation. To achieve this, we analyzed data from various sources, including natural disaster records from the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), GOES-16 satellite imagery, soil moisture data from the Soil Moisture Active Passive (SMAP) satellite mission, ERA5 reanalysis, reflectivity from weather radar, and lightning data from the Lightning Location System. The South Atlantic Convergence Zone, coupled with a low-pressure system off the southeast coast of Brazil, was the predominant synoptic pattern responsible for creating favorable conditions for precipitation during the studied period. Clouds and precipitating cells, with cloud-top temperatures below −65 °C, over several days contributed to the high precipitation volumes and lightning activity. Prolonged rainfall, with a maximum of 240 mm day⁻¹ and 48 mm h⁻¹, combined with the region’s soil characteristics, enhanced water infiltration and was critical in triggering and intensifying natural disasters. These findings highlight the importance of monitoring atmospheric conditions in conjunction with soil moisture over an extended period to provide additional information for mitigating the impacts of natural disasters. Full article

As extreme forms of tropical cyclones (TCs), typhoons pose significant threats to both human society and the natural environment. To better understand and predict their behavior, scientists have relied on numerical simulations. Current typhoon modeling primarily falls into two categories: (1) complex simulations based on fluid dynamics and thermodynamics, and (2) empirical parameterized models. Most comparative studies on these models have focused on wind speed below 50 m/s, with fewer studies addressing high wind speed (above 50 m/s). In this study, we design and compare four different simulation approaches to model two super typhoons: Typhoon Surigae (2102) and Typhoon Nepartak (1601). These approaches include: (1) The Weather Research and Forecasting (WRF) model simulation driven by NCEP Final Operational Global Analysis data (FNL), (2) WRF simulation driven by the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), (3) the empirical parameterized Holland model, and (4) the empirical parameterized Jelesnianski model. The simulated wind fields were compared with the measured wind data from The Soil Moisture Active Passive (SMAP) platform, and the resulting wind fields were then used as inputs for the Simulating WAves Nearshore (SWAN) model to simulate typhoon-induced waves. Our findings are as follows: (1) for high wind speeds, the performance of the empirical models surpasses that of the WRF simulations; (2) using more accurate driving wind data improves the WRF model’s performance in simulating typhoon wind speeds, and WRF simulations excel in representing wind fields in the outer regions of the typhoon; (3) careful adjustment of the maximum wind speed radius parameter is essential for improving the accuracy of the empirical models. Full article

Soil moisture is a key factor affecting the exchange of heat and water between the land and the atmosphere. Land data assimilation (LDA) methods that leverage the strengths of both models and observations can generate more accurate initial conditions. However, soil moisture exhibits significant spatial heterogeneity, implying strong local characteristics for both observational and background errors. To elucidate the impact of error localization on LDA, we constructed a land data assimilation system (LDAS) suitable for the Common Land Model (CoLM), based on the simplified extended Kalman filter (SEKF) method. Through practical assimilation experiments using soil moisture retrieval products from the Soil Moisture Active Passive (SMAP) and Fenyun-3D (FY3D) satellites, we investigated the influence of spatial static and dynamic observational and background errors on LDA. The results indicate that by incorporating dynamic errors that account for the spatial heterogeneity of soil, LDAS can adaptively absorb observational information, thereby significantly enhancing assimilation impact and subsequent model forecast accuracy. Compared to experiments applying static errors, dynamic errors increased the spatial correlation coefficients by 17.4% and reduced the root mean square error (RMSE) by 11.2%. The results clearly demonstrate that for soil variable assimilation studies with strong spatial heterogeneity, progressively refined dynamic error estimation is a crucial direction for improving land surface assimilation performance. Full article

The microwave polarization index (PI), defined as the difference between vertically polarized (V-pol) and horizontally polarized (H-pol) brightness temperature divided by their average, is independent of land surface temperature. Since soil emission is stronger at V-pol than H-pol and vegetation attenuates this polarized soil signal primarily because of liquid water stored in vegetation tissue, a lower PI will be indicative of more water in vegetation if vegetation emits a mostly unpolarized signal and changes in soil moisture within the emitting depth are small (like during periods of drought) or accommodated by averaging over long periods. We hypothesize that the L-band PI will reveal diurnal changes in vegetation water related to whether plants have adequate soil water. We compare 6 a.m. and 6 p.m. L-band PI from NASA’s Soil Moisture Active Passive (SMAP) satellite to the evaporative stress index (ESI) in the U.S. Corn Belt during the growing season. When $\mathrm{ESI}<0$ (there is not adequate plant-available water, also called plant water stress), the L-band PI is not significantly different at 6 a.m. vs. 6 p.m. On the other hand, when $\mathrm{ESI}\ge 0$ (no plant water stress), the L-band PI is greater in the evening than in the morning. This diurnal behavior can be explained by transpiration outpacing root water uptake during daylight hours (resulting in a decrease in vegetation water from 6 a.m. to 6 p.m.) and continued root water uptake overnight (that recharges vegetation water) only when plants have adequate soil water. Consequently, it may be possible to use L-band PI to identify plant water stress in the Corn Belt. Full article

This paper deals with the topics of modeling joint distributions on a generalized probability space. An algebraic structure known as quantum logic is taken as the basic model. There is a brief summary of some earlier published findings concerning a function s-map, which is a mathematical tool suitable for constructing virtual joint probabilities of even non-compatible propositions. The paper completes conclusions published in 2020 and extends the results for three or more random variables if the marginal distributions are known. The existence of a $(n+1)$-variate joint distribution is shown in special cases when the quantum logic consists of at most n blocks of Boolean algebras. Full article

Multivariate time series anomaly detection is a crucial technology to prevent unexpected errors from causing critical impacts. Effective anomaly detection in such data requires accurately capturing temporal patterns and ensuring the availability of adequate data. This study proposes a patch-wise framework for anomaly detection. The proposed approach comprises four key components: (i) maintaining continuous features through patching, (ii) incorporating various temporal information by learning channel dependencies and adding relative positional bias, (iii) achieving feature representation learning through self-supervised learning, and (iv) supervised learning based on anomaly augmentation for downstream tasks. The proposed method demonstrates strong anomaly detection performance by leveraging patching to maintain temporal continuity while effectively learning data representations and handling downstream tasks. Additionally, it mitigates the issue of insufficient anomaly data by supporting the learning of diverse types of anomalies. The experimental results show that our model achieved a 23% to 205% improvement in the F1 score compared to existing methods on datasets such as MSL, which has a relatively small amount of training data. Furthermore, the model also delivered a competitive performance on the SMAP dataset. By systematically learning both local and global dependencies, the proposed method strikes an effective balance between feature representation and anomaly detection accuracy, making it a valuable tool for real-world multivariate time series applications. Full article

Existing validation of mean wind speed estimates via reflectometry from global navigation systems of satellites (GNSS-R)—has been largely limited in spatial coverage to equatorial buoys or tropical cyclone events near continental United States. Two alternative validation techniques are presented for the Cyclone GNSS (CYGNSS) mission using surface-based observations along coasts and coral reefs instead of buoys, and triple collocation analysis (TCA) instead of a 1:1 gridded comparison for tropical cyclone (TC) events. For the surface-based analysis, Fully Developed Seas (FDS) v3.2 and NOAA v1.2 were compared to anemometer data provided by the Australian Bureau of Meteorology across the Australia and Pacific regions. Overall, the products performed similarly to previous studies with NOAA having higher correlations and lower errors than FDS, though FDS performed better than NOAA over the Australian dataset for high wind speed events. TCA was used to validate NOAA v1.2 and Merged v3.2 datasets with other satellite remotely sensed products from the Soil Moisture Active Passive (SMAP) mission and Synthetic Aperture Radar (SAR). Both additive and multiplicative error models for TCA were applied. The performance overall was similar between the two products, with NOAA producing higher errors. NOAA performed better than Merged for mean winds above 17 m/s as the large temporal averaging reduced sensitivity to high winds. For SMAP winds above 17 m/s, NOAA’s average bias (−2.1 m/s) was significantly smaller than the average bias in Merged (−4.4 m/s). Future ideas for rapid intensification detection and constellation design are discussed. Full article

A novel Portable L-band radiometer (PoLRa), compatible with tower-, vehicle- and drone-based platforms, can provide gridded soil moisture estimations from a few meters to several hundred meters yet its retrieval accuracy has rarely been examined. This study aims to provide an initial assessment of the performance of PoLRa-derived soil moisture at a spatial resolution of approximately 0.7 m × 0.7 m at a set of sampling pixels in central Illinois, USA. This preliminary evaluation focuses on (1) the consistency of PoLRa-measured brightness temperatures from different viewing directions over the same area and (2) whether PoLRa-derived soil moisture retrievals are within an acceptable accuracy range. As PoLRa shares many aspects of the L-band radiometer onboard NASA’s Soil Moisture Active Passive (SMAP) mission, two SMAP operational algorithms and the conventional dual-channel algorithm (DCA) were applied to calculate volumetric soil moisture from the measured brightness temperatures. The vertically polarized brightness temperatures from the PoLRa are typically more stable than their horizontally polarized counterparts across all four directions. In each test period, the standard deviations of observed dual-polarization brightness temperatures are generally less than 5 K. By comparing PoLRa-based soil moisture retrievals against the simultaneous moisture values obtained by a handheld capacitance probe, the unbiased root mean square error (ubRMSE) and the Pearson correlation coefficient (R) are mostly below 0.05 m³/m³ and above 0.7 for various algorithms adopted here. While SMAP models and the DCA algorithm can derive soil moisture from PoLRa observations, no single algorithm consistently outperforms the others. These findings highlight the significant potential of ground- or drone-based PoLRa measurements as a standalone reference for the calibration and validation of spaceborne L-band synthetic aperture radars and radiometers. The accuracy of PoLRa-yielded high-resolution soil moisture can be further improved via standardized operational procedures and appropriate tau-omega parameters. Full article

Surface soil moisture (SSM) is crucial for understanding terrestrial hydrological processes. Despite its widespread use since 2015, the Soil Moisture Active and Passive (SMAP) SSM dataset faces challenges due to its inherent low spatial resolution and data gaps. This study addresses these limitations through a deep learning approach aimed at interpolating missing values and downscaling soil moisture data. The result is a seamless, daily 1 km resolution SSM dataset for China, spanning from 1 January 2016 to 31 December 2022. For the original 9 km daily SMAP products, a convolutional neural network (CNN) with residual connections was employed to achieve the spatially seamless 9 km SSM data, integrating multi-source remote sensing data. Subsequently, auxiliary data including land cover, land surface temperatures, vegetation indices, vegetation temperature drought indices, elevation, and soil texture were integrated into the CNN-based downscaling model to generate the spatially seamless 1 km SSM. Comparative analysis of the spatially seamless 9 km and 1 km SSM datasets with ground observations yielded unbiased root mean square error values of 0.09 cm³/cm³ for both, demonstrating the effectiveness of the downscaling method. This approach provides a promising solution for generating high-resolution, spatially seamless soil moisture data to meet the needs of hydrological, meteorological, and agricultural applications. Full article