Search Results (40)

Land surface temperature (LST) is a key variable in the physics of land surface processes on both regional and global scales. This paper addresses the all-sky (clear-sky and cloudy-sky) LSTs retrieval from the data acquired by the Medium-Resolution Spectral Imager II on Fengyun 3D (FY-3D) satellite. First, an improved split-window algorithm to retrieve clear-sky LSTs is developed using numerical radiative transfer modeling experiments. Then, clear-sky LSTs are retrieved from MERSI-II/FY-3D data in January and July 2022 over an Asian area (70°E~130°E, 10°N~50°N), and cross-validated against MODIS/Aqua LST/emissivity (LST/E) Daily version 6 (MYD11C1 V6) product. Next, a hybrid method combining the eXtreme Gradient Boosting (XGBoost) model and the surface energy balance theory is developed to estimate cloudy-sky LSTs. After that, cloudy-sky LSTs are estimated from the MERSI-II data and validated with the China Meteorological Administration Land Data Assimilation System Version 2 (CLDAS V2) dataset. Against the MYD11C1 LSTs, the root mean square error (RMSE), bias and coefficient of determination (R²) of the retrieved clear-sky LSTs are 1.15 K, 0.01 ± 1.14 K, and 0.99, respectively. Against the CLDAS LSTs, the RMSE, bias and R² of the estimated hypothetical clear-sky LSTs are 4.05 K, 0.75 ± 3.98 K and 0.91, respectively, while they are 3.69 K, 0.36 ± 3.67 K, and 0.92 for the retrieved cloudy-sky LSTs, respectively, which indicates that the retrieval accuracy of cloudy-sky LSTs is improved after the cloud radiation effect correction. The all-sky LSTs retrieved in this study are accurate and consistent with the results in previous studies. Full article

Satellite remote sensing-based soil moisture (SM) retrieval quantifies the spatial and temporal distributions of SM to support Earth system modeling. However, existing SM products, including satellite remote sensing, model-simulated, and land data assimilation products, are plagued by large measurement errors. The Triple Collocation (TC) method can systematically quantify these errors and generate spatially and temporally continuous SM. In this study, we analyzed SM over the Qinghai-Tibetan Plateau (QTP) using three mainstream products: The European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-interim) SM, the National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) SM, and the China Meteorological Administration Land Data Assimilation System Version 1.0 (CLDAS-V1.0) SM. Results show that the ERA-interim contributes the largest weight to the TC-blended SM over the QTP, followed by CFSv2, while CLDAS-V1.0 makes the minimum contribution. The three products yield consistent results in the eastern and southern QTP but show significant discrepancies in the northwestern region. The TC-blended SM performs well across most land cover categories in the QTP, except Alpine swamp meadow areas. Our findings confirm that this SM blending technique effectively improves the accuracy of existing SM products, with wide application potential in future research. Full article

To address the challenge of obtaining high-spatiotemporal-resolution and high-precision temperature grids for agricultural meteorological monitoring, this research focuses on rice heat stress monitoring with the China Meteorological Administration Land Data Assimilation System (CLDAS) and develops a temperature correction model that synergizes physical mechanisms with a data-driven strategy by introducing a GeoAI framework. Ensemble learning methods (XGBoost, LightGBM, and Random Forest) were utilized to process a comprehensive set of predictors, integrating dynamic surface features derived from FY-4 satellite’s high-frequency observation data. The data comprised surface thermal regime metrics, specifically the daily maximum land surface temperature (LST_max) and its diurnal range (LST_{max_min}), along with vegetation indices including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Further, topographic attributes derived from a digital elevation model (DEM) were incorporated, such as slope, aspect, the terrain ruggedness index (TRI), and the topographic position index (TPI). The approach uniquely capitalized on the temporal resolution of geostationary data to capture the diurnal land surface dynamics crucial for bias correction. The proposed models not only enhanced temperature data quality but also achieved impressive accuracy. Across China, the root mean square error (RMSE) was reduced to 1.04 °C, mean absolute error (MAE) to 0.53 °C, and accuracy (ACC) to 0.97. Additionally, the most notable improvement was that the RMSE decreased by nearly 50% (from 2.17 °C to 1.11 °C), MAE dropped from 1.48 °C to 0.80 °C, and ACC increased from 0.72 to 0.96 in the southwestern region of China. The corrected rice heat stress data (2020–2023) indicated that significant negative correlations exist between yield loss and various heat stress metrics in the severely affected middle and lower Yangtze River region. The research confirms that embedding geostationary meteorological satellites within a GeoAI framework can effectively enhance the precision of agricultural weather monitoring and related impact assessments. Full article

Accurate quantification of regional ET is essential for agricultural water management. Upscaling methods based on flux tower observations have been widely applied in large-scale ET estimation. However, the coarse spatial resolution of existing upscaling approaches limits their utility in field-scale management. Therefore, this study proposes an integrated upscaling framework that combines data fusion and machine learning, enabling spatiotemporally continuous ET estimation at the field scale (30 m × 30 m). First, daily 30 m resolution land surface temperature (LST) and vegetation indices were generated by fusing MODIS, Landsat, and China Land Data Assimilation System (CLDAS) datasets. These variables, along with meteorological data and the footprint model, were used as inputs for machine learning. The upscaled ET was evaluated under varying surface heterogeneity using optical-microwave scintillometers (OMS). The results show that a one-dimensional convolutional neural network (1D CNN) using both remote sensing and meteorological data performed best in relatively homogeneous croplands, achieving a correlation coefficient (R) of 0.90, a bias of −0.14 mm/d, a mean absolute error (MAE) of 0.46 mm/d, and a root mean square error (RMSE) of 0.66 mm/d. In contrast, for heterogeneous urban-agricultural landscapes, the 1D CNN using only remote sensing data outperformed other models, with R, bias, MAE, and RMSE of 0.93, −0.14 mm/d, 0.66 mm/d, and 0.88 mm/d, respectively. Furthermore, SHapley Additive exPlanations (SHAP) revealed that LST and the two-band enhanced vegetation index (EVI2) were the most influential drivers in the models. The framework successfully enables ET modeling and spatial extrapolation in heterogeneous regions, providing a foundation for precision water resource management. Full article

Various data such as power grid sensors and manual observed icing, CMA (China Meteorological Administration) Land Surface Data Assimilation System (CLDAS) products, and the Fifth Generation Atmospheric Reanalysis of the Global Climate from Europe Center of Middle Range Weather Forecast (ERA5) are adopted to diagnose an icing process under a cold surge during 16–23 December 2023 in northeastern Yunnan Province. The results show that: (1) in the early stage of the process, mainly the freezing types, such as GG (temperature > 0 °C, relative humidity ≥ 75%) and DG (temperature < 0 °C, relative humidity ≥ 75%), occur. At the end of the process, an increase in icing type as GD (temperature > 0 °C, relative humidity < 75%) appears. (2) Significant differences exist in the elements during different stages of icing, and the atmospheric thermal, dynamic, and water vapor conditions are conducive to the occurrence of freezing rain during ice accretion. The main impact weather systems of this process include a strong high ridge in the mid to high latitudes of East Asia, transverse troughs in front of the high ridge south to Lake Baikal, low altitude troughs, and ground fronts. The transverse trough in front of the high ridge can cause cold air to accumulate and then move eastward and southward. The southerly flows, surface fronts, and other low-pressure systems can provide powerful thermodynamic and moisture conditions for ice accumulation. Full article

A reliable precipitation dataset with high spatial resolution is essential for climate research in the Tarim Basin. This study evaluated the performances of four models, namely a random forest (RF), a long short-term memory network (LSTM), a support vector machine (SVM), and a feedforward neural network (FNN). FNN, which was found to be superior to the other models, was used to integrate eight precipitation datasets spanning from 1990 to 2022 across the Tarim Basin, resulting in a new monthly high-resolution (0.1°) precipitation dataset named MoHiPr-TB. This dataset was subsequently bias-corrected by the China Land Data Assimilation System version 2.0 (CLDAS2.0). Validation results indicate that the corrected MoHiPr-TB not only accurately reflects the spatial distribution of precipitation but also effectively simulates its intensity and interannual and seasonal variations. Moreover, MoHiPr-TB is capable of detecting the precipitation–elevation relationship in the Pamir Plateau, where precipitation initially increases and then decreases with elevation, as well as the synchronous variation of precipitation and elevation in the Tianshan region. Collectively, this study delivers a high-accuracy precipitation dataset for the Tarim Basin, which is anticipated to have extensive applications in meteorological, hydrological, and ecological research. Full article

Lettuce is a kind of nutritious leafy vegetable. The lettuce storage time has a significant impact on its nutrition and taste. Therefore, to classify lettuce samples with different storage times accurately and non-destructively, this study built classification models by combining several feature extraction methods and categorical boosting (CatBoost). Firstly, the near-infrared (NIR) spectral data of lettuce samples were collected using a NIR spectrometer, and then they were preprocessed using six preprocessing methods. Next, feature extraction was carried out on the spectral data using approximate linear discriminant analysis (ALDA), common-vector linear discriminant analysis (CLDA), maximum-uncertainty linear discriminant analysis (MLDA), and null-space linear discriminant analysis (NLDA). These four feature extraction methods can solve the problem of small sample sizes. Finally, the classification was achieved using classification and regression trees (CARTs) and CatBoost, respectively. The experimental results showed that the classification accuracy of NLDA combined with CatBoost could reach 97.67%. Therefore, the combination of feature extraction methods (NLDA) and CatBoost using NIR spectroscopy is an effective way to classify lettuce storage time. Full article

Accurate soil moisture (SM) estimates with high spatial resolution are highly desirable for agricultural, hydrological, and environmental applications. This study developed a two-step reconstruction approach to obtain a high-quality and high-spatial-resolution (0.05°) SM dataset from microwave and model-based SM products, combining Bayesian three-cornered hat (BTCH) merging and machine/deep learning downscaling algorithms. Firstly, a three-cornered hat (TCH) method was used to analyze the uncertainty of seven SM products on four main land cover types in the Pearl River Basin (PRB). On this basis, the SM products with low uncertainty were merged using the BTCH method. Secondly, two machine/deep learning algorithms (random forest, RF, and long short-term memory, LSTM) were applied to downscale the merged SM data from 0.25° to 0.05° based on the relationship between SM and auxiliary variables. The overall performance of RF and LSTM downscaling models with/without antecedent precipitation were compared. The merged and downscaled SM results were validated against in situ observations and the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) SM data. The results indicated the following: (1) The BTCH-based SM estimate outperformed the parent products and the AVE-based SM estimate (the arithmetic average), indicating that BTCH is a fusion approach that can effectively reduce data uncertainties and optimize weights. (2) The optimal time scale for the cumulative effect of precipitation on SM was 35 days during 2015–2020 in the PRB. SM estimations using RF and LSTM downscaling algorithms both had substantial improvement by considering the antecedent precipitation variable, both at the 0.25° and 0.05° spatial scales. Feature importance assessment also revealed the most important role of antecedent precipitation (30.01%). Moreover, the LSTM model with antecedent precipitation performed slightly better than the RF model with antecedent precipitation. (3) The downscaled SM results all mitigated the overestimation inherent in the original SM data, though they were inevitably limited by the performance of the original SM data and difficult to surpass. The developed two-step reconstruction approach was effective in generating an accurate SM dataset at a finer spatial scale for wide regional applications. Full article

We report the topochemical solid-state polymerization of different series of symmetrical diacetylenes (DAs) and asymmetrical chlorodiacetylenes (ClDAs), whose members differ in their alkyl spacing lengths of one to four methylene units (n = 1, 2, 3, 4) between the diyne and carbamate functionalities. Structure determination by single-crystal X-Ray diffraction (SCXRD) confirms that in each of these series, at least 50% of the analyses show monomers with a particular stacking pattern presenting two potential directions of polymerization simultaneously. An organization of a crystalline polydiacetylene (PDA) with an oblique chain orientation with respect to the network of cooperatives hydrogen bonds is rather rare in the literature (only two cases), and here we have obtained two more examples of this type of structural motif (supported by SCXRD analysis of the polymer). Orientation control is essential to optimize the performance of conjugated polymers, and a spacer length modification strategy presents a potential way to achieve this in the case of PDA. Full article

Red jujube is a nutritious food, known as the “king of all fruits”. The quality of Chinese red jujube is closely associated with its place of origin. To classify Chinese red jujube more correctly, based on the combination of adaptive boosting (Adaboost) and common vectors linear discriminant analysis (CLDA), Adaboost-CLDA was proposed to classify the near-infrared (NIR) spectra of red jujube samples. In the study, the NIR-M-R2 spectrometer was employed to scan red jujube from four different origins to acquire their NIR spectra. Savitzky–Golay filtering was used to preprocess the spectra. CLDA can effectively address the “small sample size” problem, and Adaboost-CLDA can achieve an extremely high classification accuracy rate; thus, Adaboost-CLDA was performed for feature extraction from the NIR spectra. Finally, K-nearest neighbor (KNN) and Bayes served as the classifiers for the identification of red jujube samples. Experiments indicated that Adaboost-CLDA achieved the highest identification accuracy in this identification system for red jujube compared with other feature extraction algorithms. This demonstrates that the combination of Adaboost-CLDA and NIR spectroscopy significantly enhances the classification accuracy, providing an effective method for identifying the geographical origin of Chinese red jujube. Full article

Change detection (CD) is an important research direction in the field of remote sensing, which aims to analyze the changes in the same area over different periods and is widely used in urban planning and environmental protection. While supervised learning methods in change detection have demonstrated substantial efficacy, they are often hindered by the rising costs associated with data annotation. Semi-supervised methods have attracted increasing interest, offering promising results with limited data labeling. These approaches typically employ strategies such as consistency regularization, pseudo-labeling, and generative adversarial networks. However, they usually face the problems of insufficient data augmentation and unbalanced quality and quantity of pseudo-labeling. To address the above problems, we propose a semi-supervised change detection method with data augmentation and adaptive threshold updating (DA-AT) for high-resolution remote sensing images. Firstly, a channel-level data augmentation (CLDA) technique is designed to enhance the strong augmentation effect and improve consistency regularization so as to address the problem of insufficient feature representation. Secondly, an adaptive threshold (AT) is proposed to dynamically adjust the threshold during the training process to balance the quality and quantity of pseudo-labeling so as to optimize the self-training process. Finally, an adaptive class weight (ACW) mechanism is proposed to alleviate the impact of the imbalance between the changed classes and the unchanged classes, which effectively enhances the learning ability of the model for the changed classes. We verify the effectiveness and robustness of the proposed method on two high-resolution remote sensing image datasets, WHU-CD and LEVIR-CD. We compare our method to five state-of-the-art change detection methods and show that it achieves better or comparable results. Full article

The Yangtze River Basin experienced a once-in-a-century extreme drought in 2022 due to extreme weather, which had a serious impact on the local agricultural production and ecological environment. In order to investigate the spatial distribution and occurrence of the extreme drought events, this study used multi-source remote sensing data to monitor the extreme drought events in the Yangtze River Basin in 2022. In this study, the gravity satellite data product CSR_Mascon was used to calculate the GRACE Drought Intensity Index (GRACE-DSI), which was analyzed and compared with the commonly used meteorological drought indices, relative soil humidity, and soil water content data. The results show that (1) terrestrial water storage change data can well reflect the change in water storage in the Yangtze River Basin. Throughout the year, the average change in terrestrial water storage in the Yangtze River Basin from January to June is higher than the average value of 33.47 mm, and the average from July to December is lower than the average value of 48.17 mm; (2) the GRACE-DSI responded well to the intensity and spatial distribution of drought events in the Yangtze River Basin region in 2022. From the point of view of drought area, the Yangtze River Basin showed a trend of extreme drought increasing first, and then decreasing in the area of different levels of drought, and the range of drought reached a maximum in September with a drought area of 175.87 km², which accounted for 97.71 per cent of the total area; at the same time, the area of extreme drought was the largest, with an area of 85.69 km²; (3) the spatial and temporal variations of the GRACE-DSI and commonly used meteorological drought indices were well correlated, with correlation coefficients above 0.750, among which the correlation coefficient of the SPEI-3 was higher at 0.937; (4) the soil moisture and soil relative humidity products from the CLDAS, combined with soil moisture products from the GLDAS, reflect the starting and ending times of extreme drought events in the Yangtze River Basin in 2022 well, using the information from the actual stations. In conclusion, gravity satellite data, analyzed in synergy with data from multiple sources, help decision makers to better understand and respond to drought. Full article

Before 2008, China lacked high-coverage regional surface observation data, making it difficult for the China Meteorological Administration Land Data Assimilation System (CLDAS) to directly backtrack high-resolution, high-quality land assimilation products. To address this issue, this paper proposes a deep learning model named UNET_DCA, based on the UNET architecture, which incorporates a Dual Cross-Attention module (DCA) for multiscale feature fusion by introducing Channel Cross-Attention (CCA) and Spatial Cross-Attention (SCA) mechanisms. This model focuses on the near-surface 10-m wind field and achieves spatial downscaling from 6.25 km to 1 km. We conducted training and validation using data from 2020–2021, tested with data from 2019, and performed ablation experiments to validate the effectiveness of each module. We compared the results with traditional bilinear interpolation methods and the SNCA-CLDASSD model. The experimental results show that the UNET-based model outperforms SNCA-CLDASSD, indicating that the UNET-based model captures richer information in wind field downscaling compared to SNCA-CLDASSD, which relies on sequentially stacked CNN convolution modules. UNET_CCA and UNET_SCA, incorporating cross-attention mechanisms, outperform UNET without attention mechanisms. Furthermore, UNET_DCA, incorporating both Channel Cross-Attention and Spatial Cross-Attention mechanisms, outperforms UNET_CCA and UNET_SCA, which only incorporate one attention mechanism. UNET_DCA performs best on the RMSE, MAE, and COR metrics (0.40 m/s, 0.28 m/s, 0.93), while UNET_DCA_ars, incorporating more auxiliary information, performs best on the PSNR and SSIM metrics (29.006, 0.880). Evaluation across different methods indicates that the optimal model performs best in valleys, followed by mountains, and worst in plains; it performs worse during the day and better at night; and as wind speed levels increase, accuracy decreases. Overall, among various downscaling methods, UNET_DCA and UNET_DCA_ars effectively reconstruct the spatial details of wind fields, providing a deeper exploration for the inversion of high-resolution historical meteorological grid data. Full article

Satellite precipitation products (SPPs) are of great significance for water resource management and utilization in China; however, they suffer from considerable uncertainty. While numerous researchers have evaluated the accuracy of various SPPs, further investigation is needed to assess their performance across China’s nine major water resource regions. This study used the latest precipitation dataset of the China Meteorological Administration’s Land Surface Data Assimilation System (CLDAS-V2.0) as the benchmark and evaluated the performance of six SPPs—GSMaP, PERSIANN, CMORPH, CHIRPS, GPM IMERG, and TRMM—using six indices: correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI), at both daily and hourly scales across China’s nine water resource regions. The conclusions of this study are as follows: (1) The performance of the six SPPs was generally weaker in the west than in the east, with the Continental Basin (CB) exhibiting the poorest performance, followed by the Southwest Basin (SB). (2) At the hourly scale, the performance of the six SPPs was weaker compared to the daily scale, particularly in the high-altitude CB and the high-latitude Songhua and Liaohe River Basin (SLRB), where observing light precipitation and snowfall presents significant challenges. (3) GSMaP, CMORPH, and GPM IMERG demonstrated superior overall performance compared to CHIRPS, PERISANN, and TRMM. (4) CMORPH was found to be better suited for application in drought-prone areas, showcasing optimal performance in the CB and SB. GSMaP excelled in humid regions, displaying the best overall performance in the remaining seven basins. GPM IMERG serves as a complementary precipitation data source for the first two. Full article

Despite the great success of speaker recognition models based on deep neural networks, deploying a pre-trained model in real-world scenarios often leads to significant performance degradation due to the domain mismatch between training and testing conditions. Various adaptation methods have been developed to address this issue by modifying either the front-end embedding network or the back-end scoring model. However, existing methods typically rely on knowledge of the network, scoring model, or even the source data. In this study, we introduce a knowledge-free adaptation approach that only necessitates the unlabeled target data. Our core concept is based on the assumption that domain mismatch primarily stems from distributional distortion in the embedding space, such as shifting, rotation, and scaling while maintaining inter-speaker discrimination for data from unknown domains. Building on this assumption, we propose clustering LDA (C-LDA), a full-rank linear discriminant analysis (LDA) based on agglomerative hierarchical clustering (AHC) to compensate for this distortion. This approach does not need any human labels and does not rely on any knowledge of the model in the source domain, making it suitable for real-world applications. Theoretical analysis indicates that with cosine scoring, C-LDA is capable of eliminating distributional distortion related to global shift and within-speaker covariance rotation and scaling. Surprisingly, our experiments demonstrated that this simple approach can outperform more complex methods that require full or partial knowledge, including front-end approaches such as fine-tuning and distribution alignment, and back-end approaches such as unsupervised probabilistic linear discriminant analysis (PLDA) adaptation. Additional experiments demonstrated that C-LDA is insensitive to hyperparameters and works well in both multi-domain and single-domain adaptation scenarios. Full article