Search Results (49)

This study explores machine learning for estimating soil moisture at multiple depths (0–5 cm, 0–10 cm, 0–20 cm, 0–50 cm, and 0–100 cm) across the coterminous United States. A framework is developed that integrates soil moisture from Soil Moisture Active Passive (SMAP), precipitation from the Global Precipitation Measurement (GPM), evapotranspiration from the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), vegetation data from the Moderate Resolution Imaging Spectroradiometer (MODIS), soil properties from gridded National Soil Survey Geographic (gNATSGO), and land cover information from the National Land Cover Database (NLCD). Five machine learning algorithms are evaluated including the feed-forward artificial neural network, random forest, extreme gradient boosting (XGBoost), Categorical Boosting, and Light Gradient Boosting Machine. The methods are tested by comparing to in situ soil moisture observations from several national and regional networks. XGBoost exhibits the best performance for estimating soil moisture, achieving higher correlation coefficients (ranging from 0.76 at 0–5 cm depth to 0.86 at 0–100 cm depth), lower root mean squared errors (from 0.024 cm³/cm³ at 0–100 cm depth to 0.039 cm³/cm³ at 0–5 cm depth), higher Nash–Sutcliffe Efficiencies (from 0.551 at 0–5 cm depth to 0.694 at 0–100 cm depth), and higher Kling–Gupta Efficiencies (0.511 at 0–5 cm depth to 0.696 at 0–100 cm depth). Additionally, XGBoost outperforms the SMAP Level 4 product in representing the time series of soil moisture for the networks. Key factors influencing the soil moisture estimation are elevation, clay content, aridity index, and antecedent soil moisture derived from SMAP. Full article

Monitoring slow-moving landslides is a crucial task for socioeconomic risk prevention and/or mitigation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced remote sensing method for monitoring ground deformation. In this research, PS-InSAR time series derived from COSMO-SkyMed (descending orbit) and Sentinel-1 (ascending orbit) are analyzed for a region in Central Apennines in Italy. The sequential turning point detection method (STPD) is implemented to detect the trend turning dates and their directions in the PS-InSAR time series within areas of interest susceptible to landslides. The monthly maps of significant turning points and their directions for years 2018, 2019, 2020, and 2021 are produced and classified for four Italian administrative regions, namely, Marche, Umbria, Abruzzo, and Lazio. Monthly global precipitation measurement (GPM) images at $0.1^{\circ }\times 0.1^{\circ }$ spatial resolution and four local precipitation time series are also analyzed by STPD to investigate when the precipitation rate has changed and how they might have reactivated slow-moving landslides. Generally, a strong correlation ($r\ge 0.7$) is observed between GPM (satellite-based) and local precipitation (station-based) with similar STPD results. Marche and Abruzzo (the coastal regions) have an insignificant precipitation rate while Umbria and Lazio have a significant increase in precipitation from 2017 to 2023. The coastal regions also exhibit relatively lower precipitation amounts. The results indicate a strong correlation between the trend turning dates of the accumulated precipitation and displacement time series, especially for Lazio during summer and fall 2020, where relatively more significant precipitation rate of change is observed. The findings of this study may guide stakeholders and responsible authorities for risk management and mitigating damage to infrastructures. Full article

With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological data, the dynamic changes in the prediction targets relative to their background information often exhibit more significant dynamic characteristics. Previous prediction methods did not specifically analyze and study the dynamic change information of prediction targets at spatiotemporal multi-scale. Therefore, this paper proposes a neural prediction network based on perceptual multi-scale spatiotemporal dynamic changes (PMSTD-Net). By designing Multi-Scale Space Motion Change Attention Unit (MCAU) to perceive the local situation and spatial displacement dynamic features of prediction targets at different scales, attention is ensured on capturing the dynamic information in their spatial dimensions adequately. On this basis, this paper proposes Multi-Scale Spatiotemporal Evolution Attention (MSEA) unit, which further integrates the spatial change features perceived by MCAU units in higher channel dimensions, and learns the spatiotemporal evolution characteristics at different scales, effectively predicting the dynamic characteristics and regularities of targets in sensor information.Through experiments on spatiotemporal prediction standard datasets such as Moving MNIST, video prediction dataset KTH, and Human3.6m, PMSTD-Net demonstrates prediction performance surpassing previous methods. We construct the GPM satellite remote sensing precipitation dataset, demonstrating the network’s advantages in perceiving multi-scale spatiotemporal dynamic changes in remote sensing meteorological data. Finally, through extensive ablation experiments, the performance of each module in PMSTD-Net is thoroughly validated. Full article

The Tibetan Plateau vortex (TPV), as an α-scale mesoscale weather system, often brings severe weather conditions like torrential rain and severe convective storms. Based on the detections from the Global Precipitation Measurement (GPM) Core Observatory’s Dual-frequency Precipitation Radar (DPR) and the FY-4A satellite’s Advanced Geostationary Radiation Imager (AGRI), combined with ERA5 reanalysis data, the precipitation characteristics of a TPV moving eastward during 8–13 July 2021 at different developmental stages are explored in this study. It was clear that the near-surface precipitation rate of the TPV during the initial stage at the eastern Tibetan Plateau (TP) was below 1 mm·h⁻¹, implying overall weak precipitation dominated by stratiform clouds. After moving out of the TP, the radar reflectivity factor (Ze), precipitation rate, and normalized intercept parameter (dBNw) significantly increased, while the proportion of convective clouds gradually rose. Following the TPV movement, the distribution range and vertical thickness of Ze, mass-weighted mean diameter (Dm), and dBNw tended to increase. The high-frequency region of Ze appeared at 15–20 dBZ, while Dm and dBNw occurred at around 1 mm and 33 mm⁻¹·m⁻³, respectively. Near the melting layer, Ze was characterized by a significant increase due to the aggregation and melting of ice crystals. The precipitation rate of convective clouds was generally greater than that of stratiform clouds, whilst both of them increased during the movement of the TPV. Particularly, at 01:00 on 12 July, there was a significant increase in the precipitation rate and Dm of convective clouds, while dBNw noticeably decreased. These findings could provide valuable insights into the three-dimensional structure and microphysical characteristics of the precipitation during the movement of the TPV, contributing to a better understanding of cloud precipitation mechanisms. Full article

Limited availability of hydrometeorological data and lack of data sharing practices have added to the challenge of hydrological modelling of large and transboundary catchments. This research evaluates the suitability of latest near real-time global precipitation measurement (GPM)-era satellite precipitation products (SPPs), IMERG-Early, IMERG-Late and GSMaP-NRT, for hydrological and hydrodynamic modelling of the Brahmaputra Basin. The HEC-HMS modelling system was used for the hydrological modelling of the Brahmaputra Basin, using IMERG-Early, IMERG-Late, and GSMaP-NRT. The findings showed good results using GPM SPPs for hydrological modelling of large basins like Brahmaputra, with Nash–Sutcliffe efficiency (NSE) and R² values in the range of 0.75–0.85, and root mean square error (RMSE) between 7000 and 9000 m³ s⁻¹, and the average discharge was 20611 m³ s⁻¹. Output of the GPM-based hydrological models was then used as input to a 1D hydrodynamic model to assess suitability for flood inundation mapping of the Brahmaputra River. Simulated flood extents were compared with Landsat satellite-captured images of flood extents. In critical areas along the river, the probability of detection (POD) and critical success index (CSI) values were above 0.70 with all the SPPs used in this study. The accuracy of the models was found to increase when simulated using SPPs corrected with ground-based precipitation datasets. It was also found that IMERG-Late performed better than the other two precipitation products as far as hydrological modelling was concerned. However, for flood inundation mapping, all of the three selected products showed equally good results. The conclusion is reached that for sparsely gauged large basins, particularly for trans-boundary ones, GPM-era SPPs can be used for discharge simulation and flood inundation mapping. Full article

With advancements in remote sensing technology and the increasing availability of remote sensing platforms, the capacity to monitor droughts using multiple satellite remote sensing observations has significantly improved. This enhanced capability facilitates a comprehensive understanding of drought conditions and early warnings for extreme drought events. In this study, multiple satellite datasets, including Gravity Recovery and Climate Experiment (GRACE), the Global Precipitation Measurement (GPM) precipitation dataset, and the Global Land the Data Assimilation System (GLDAS) dataset, were used to conduct an innovative in-depth characteristic analysis and identification of the extreme drought event in the Poyang Lake Basin (PLB) in 2022. Furthermore, the drought characteristics were also supplemented by processing the synthetic aperture radar (SAR) image data to obtain lake water area changes and integrating in situ water level data as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index dataset, which provided additional instances of utilizing multi-source remote sensing satellite data for feature analysis on extreme drought events. The extreme drought event in 2022 was identified by the detection of non-seasonal negative anomalies in terrestrial water storage derived from the GRACE and GLDAS datasets. The Mann–Kendall (M-K) test results for water levels indicated a significant abrupt decrease around July 2022, passing a significance test with a 95% confidence level, which further validated the reliability of our finding. The minimum area of Poyang Lake estimated by SAR data, corresponding to 814 km², matched well with the observed drought characteristics. Additionally, the evident lower vegetation index compared to other years also demonstrated the severity of the drought event. The utilization of these diverse datasets and their validation in this study can contribute to achieving a multi-dimensional monitoring of drought characteristics and the establishment of more robust drought models. Full article

El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon on Earth due to its ability to change the global atmospheric circulation which influences temperature and precipitation across the globe. In this study, we investigate the responses of climatic and vegetation parameters due to two strong ENSO phases, i.e., La Niña (2010/2011) and El Niño (2015/2016) in South Africa. The study aims to understand the influence of strong seasonal ENSO events on climatic and vegetation parameters over South Africa. Remote sensing data from the Global Precipitation Measurement (GPM), Moderate Resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and Atmospheric Infrared Sounder (AIRS) was used. The relationship between precipitation, temperature, and Normalized Difference Vegetation Index (NDVI) were studied using Pearson’s correlation. Comparison between the La Niña, neutral year, and El Niño periods showed two interesting results: (1) higher precipitation from the south coast to the east coast of South Africa, with some low precipitation in the interior during the La Niña and El Niño periods, and (2) a drop in precipitation by ~46.6% was observed in the southwestern parts of South Africa during the La Niña and El Niño events. The study further showed that wind speed and wind direction were not impacted by strong ENSO events during the MAM, JJA and SON seasons, but the DJF season showed varying wind speeds, especially on the west coast, during both ENSO events. Overall, the Pearson’s correlation results clearly showed that the relationship between climatic parameters such as precipitation, temperature, and vegetation parameters such a NDVI is highly correlated while other parameters, such as wind speed and direction, are not. This study has provided new insights into the relationship between temperature, precipitation, and NDVI in South Africa; however, future work will include other climatic and vegetation parameters such as relative humidity and net longwave radiation. Full article

Accurate estimation of precipitation is critically important for a variety of fields, such as climatology, meteorology, and water resources. However, the availability of precipitation measurements has proved to be spatially inadequate for many applications. In this study, to acquire high-quality precipitation fields with enhanced accuracy and a fine-scale spatial resolution of 1 km $\times$ 1 km, we developed a new data fusion method by establishing an energy function model using the downscaled Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) precipitation product and high-density station observation in mainland China. Our merging approach was inspired by the interdisciplinary research framework integrating the methods in the fields of image processing, earth science, and machine learning. Cross-validation analyses were performed for the monthly precipitation over the period 2009–2018. It was found that the results of the newly developed method were more accurate than the original IMERG products in terms of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and Kling–Gupta efficiency (KGE). The merging precipitation results exhibit consistent spatial patterns with the original IMERG products, yet have good agreement with station observations. The gauge observations were the major source of the prediction skill of precipitation for the proposed method, and the downscaled-IMERG precipitation products added additional spatial details in the final merging results. Results indicate that the proposed merging method can reproduce the spatial details of the precipitation fields as well as enhance their accuracy. In addition, the time evolution of the error index indicates that the improvement in the merged result was stable over time, with KGE improving by 14% on average. The developed approach provides a promising way of estimating precipitation with high spatial resolution and high accuracy, which will benefit hydrological and climatological studies. Full article

Since flood inundation hampers human life and the economy, flood inundation retrieval with high temporal resolution and accuracy is essential for the projection of the environmental impact. In this study, a novel cyclone global navigation satellite system (CYGNSS)-based index, named the annual threshold flood inundation index (ATFII) for flood inundation retrieval, is proposed, and the grades of flood inundation are quantified. First, the CYGNSS surface reflectivity with land surface properties (i.e., vegetation and surface roughness) calibration is derived based on the zeroth-order radiative transfer model. Then, an index named ATFII is proposed to achieve inundation retrieval, and the inundation grades are classified. The results are validated with the Visible Infrared Imaging Radiometer Suite (VIIRS) flood product and GPM precipitation data. The validation results between ATFII and GPM precipitation indicate that the ATFII enables flood inundation retrieval at rapid timescales and quantifies the inundation variation grades. Likewise, for monthly results, the R value between the VIIRS flood product and ATFII varies from 0.51 to 0.64, with an acceptable significance level (p < 0.05). The study makes contributions in two aspects: (1) it provides an index-based method for mapping daily flood inundation on a large scale, with the advantages of fast speed and convenience, and (2) it provides a new way to derive inundation grade variations, which can help in studying the behavior of inundation in response to environmental impacts directly. Full article

The present work is a comparative study of the effects of mechanical shear, temperature, and concentration of a chemical agent on the devulcanization process of post-industrial ethylene propylene diene (EPDM) rubber waste. Devulcanization was carried out in a heating press (no shear), an internal mixer (low shear), and a co-rotating twin screw extruder (high shear) at temperatures ranging from 100 to 200 °C. The efficiency of pure dibenzamido diphenyl disulfide (DBD) and a commercial devulcanizing agent, Struktol A89^®, containing DBD were studied. Based on the results, the devulcanization process was upscaled from 40 g per batch to a continuous process with a capacity of 270 g/h. The parameters were fine-tuned regarding flow rate, screw speed, and temperature. Blends of virgin rubber (VR) and 25, 50, and 75 wt% recyclates were compared with blends of VR and 25, 50, and 75 wt% of untreated RWP. The quality of the recyclate was determined by rheometer tests, SEM images, TGA, and mechanical properties. The best results were obtained with 2 wt% DBD in the extruder with a temperature profile of 120 to 80 °C, 50 rpm, and 4.5 g per minute (gpm). The tensile strength and strain at break of the recyclate already met the requirements of DIN EN 681-1:2006 for the production of sealing systems. The compression set and Shore A hardness were restored by mixing recyclate with 25 wt% VR. Full article

Drought is an extremely dangerous natural hazard that causes water crises, crop yield reduction, and ecosystem fires. Researchers have developed many drought indices based on ground-based climate data and various remote sensing data. Ground-based drought indices are more accurate but limited in coverage; while the remote sensing drought indices cover larger areas but have poor accuracy. Applying data-driven models to fuse multi-source remote sensing data for reproducing composite drought index may help fill this gap and better monitor drought in terms of spatial resolution. Machine learning methods can effectively analyze the hierarchical and non-linear relationships between the independent and dependent variables, resulting in better performance compared with traditional linear regression models. In this study, seven drought impact factors from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor, Global Precipitation Measurement Mission (GPM), and Global Land Data Assimilation System (GLDAS) were used to reproduce the standard precipitation evapotranspiration index (SPEI) for Shandong province, China, from 2002 to 2020. Three machine learning methods, namely bias-corrected random forest (BRF), extreme gradient boosting (XGBoost), and support vector machines (SVM) were applied as regression models. Then, the best model was used to construct the spatial distribution of SPEI. The results show that the BRF outperforms XGBoost and SVM in SPEI estimation. The BRF model can effectively monitor drought conditions in areas without ground observation data. The BRF model provides comprehensive drought information by producing a spatial distribution of SPEI, which provides reliability for the BRF model to be applied in drought monitoring. Full article

The statistical characteristics of precipitation microphysics in lightning clouds are not yet fully understood, as a result of the limitations of traditional observational methods. Using the latest observations from the dual-frequency radar and microwave imager onboard the Global Precipitation Mission (GPM) and ground-based lightning observations, the precipitation microphysics of precipitation features with and without lightning (LPFs and NLPFs) was investigated across four typical regions of China in summer during the time period of 2014–2021. The statistical results show that the LPFs are characterized by smaller concentration and larger mass-weighted mean diameter (D_m) for rain and ice hydrometeors than those of NLPFs. Below the melting layer, the radar reflectivity (Z_e) for both the LPFs and NLPFs generally decreases toward the surface, indicating the evaporation or strong break-up of rain hydrometeors. Above the melting layer, the Z_e values mainly increase as the altitudes decrease for both LPFs and NLPFs, indicating the rimming, aggregation, or deposition processes. However, the change in slope is much smaller for the LPFs than for the NLPFs, which suggests a more uniform distribution of large ice hydrometeors at high altitudes, probably as a result of the stronger updrafts within the LPFs. The microphysical structures of the LPFs show great regional differences among the four regions of China, which is characterized by the low concentration of large-sized rain hydrometeors over Northeast China, and a high concentration of small-sized rain hydrometeors near the surface over the Yangtze-Huaihe River basin. Full article

The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored. In this study, various drought hazard factors were integrated based on remote sensing data, including from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM), as multisource remote sensing data. Based on the RF, a comprehensive grassland drought monitoring model was constructed and tested in Inner Mongolia, China, as an example. The critical issue addressed is the construction of a grassland drought disaster monitoring model based on meteorological data and multisource remote sensing data by using an RF model, and the verification of the accuracy and reliability of its monitoring results. The results show that the grassland drought monitoring model could quantitatively monitor the drought situation in Inner Mongolia grasslands. There was a significantly positive correlation between the drought indicators output by the model and the standardized precipitation evapotranspiration index (SPEI) measured in the field. The correlation coefficients (R) between the drought degree were 0.9706 and 0.6387 for the training set and test set, respectively. The consistent rate between the model drought index and the SPEI reached 87.90%. Drought events in Inner Mongolia were monitored from April to September in wet years, normal years, and dry years using the constructed model. The monitoring results of the model constructed in this study were in accordance with the actual drought conditions, reflecting the development and spatial evolution of drought conditions. This study provides a new application method for the comprehensive assessment of grassland drought. Full article

This paper introduces the Method of Microwave Rainfall Normalization (MMN) for the Global Satellite Mapping of Precipitation (GSMaP) algorithm in its latest version (V05, algorithm version 8), released in December 2021. The method aims to mitigate the discrepancy of GSMaP rainfall estimates among passive microwave (PMW) imagers/sounders (MWIs/MWSs) due to differences in sensor specifications and retrieval algorithms. The basic idea of the MMN module is to calibrate target PMW sensors with reference sensors (the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)) using the cumulative distribution function (CDF) of the rain rate. Differences between the CDF and normalization table for MWSs are greater than MWIs due to different rain retrieval algorithms. More (less) MWS rainfall is detected over the ocean (land) than GMI rainfall. Matchup rainfall data between GMI and a target PMW sensor are compared to evaluate MMN performance. The monthly mean rainfall and mean bias error were improved for almost all PMW sensors. This study leaves open the possibility for further inter-calibration and improvement of rain detection and heavy rainfall retrievals. Full article

To generate high-quality spatial precipitation estimates, merging rain gauges with a single-satellite precipitation product (SPP) is a common approach. However, a single SPP cannot capture the spatial pattern of precipitation well, and its resolution is also too low. This study proposed an integrated framework for merging multisatellite and gauge precipitation. The framework integrates the geographically weighted regression (GWR) for improving the spatial resolution of precipitation estimations and the long short-term memory (LSTM) network for improving the precipitation estimation accuracy by exploiting the spatiotemporal correlation pattern between multisatellite precipitation products and rain gauges. Specifically, the integrated framework was applied to the Han River Basin of China for generating daily precipitation estimates from the data of both rain gauges and four SPPs (TRMM_3B42, CMORPH, PERSIANN-CDR, and GPM-IMAGE) during the period of 2007–2018. The results show that the GWR-LSTM framework significantly improves the spatial resolution and accuracy of precipitation estimates (resolution of $0.05°,$ correlation coefficient of 0.86, and Kling–Gupta efficiency of 0.6) over original SPPs (resolution of $0.25°$ or $0.1°$, correlation coefficient of 0.36–0.54, Kling–Gupta efficiency of 0.30–0.52). Compared with other methods, the correlation coefficient for the whole basin is improved by approximately 4%. Especially in the lower reaches of the Han River, the correlation coefficient is improved by 15%. In addition, this study demonstrates that merging multiple-satellite and gauge precipitation is much better than merging partial products of multiple satellite with gauge observations. Full article