Timely crop mapping is crucial for field management, policy formulation, phenological monitoring, and yield forecasting. However, acquiring sufficient labeled samples in the current year presents a formidable challenge for in-season mapping. Previously proposed solutions mainly include classifier transfer and sample transfer strategies. The classifier transfer strategy trains classifiers with historical samples associated with historical-year features and then transfers the trained historical sample classifiers (HSC) to classify remote sensing data in the current year; the sample transfer strategy generates trusted samples associated with current-year remote sensing features by predicting labels of the current-year sample based on some prior knowledge (e.g., crop rotation pattern) and then trains trusted sample classifiers (TSC) for current-year classification. However, the performance of the classifier-transfer strategy may degrade when there is large interannual feature variation, while the performance of the sample-transfer strategy depends on the reliability of the generated trusted samples. This study proposes a novel approach that integrates the above two strategies for in-season mapping through a sample weighting technique. Firstly, two sample sets, trusted samples and classified samples associated with current-year features, are generated by crop rotation prediction and HSC, respectively. Subsequently, based on an independent assumption between the rotational prediction errors and the current-year remote sensing features, the optimal weights of these two sample sets are derived based on the Bayesian principle. Finally, an optimal weighted sample classifier (OWSC) is trained using the weighted samples for in-season classification. To illustrate the robustness of the proposed OWSC, we compared it with different methods combined with various classification models across four regions with different interannual feature variation and crop rotation stability. Results demonstrated that OWSC maintained its advantages across various regions and different available lengths of historical crop-type sequences. Owing to its independence from specific classifiers, the proposed sample weighting method can be seamlessly applied to any classification model and thus continues to benefit from advances in classification algorithms. Additionally, sensitivity experiments regarding the uncertainty in trusted samples and historical crop-type sequences showed that OWSC performed stably across different scenarios. Therefore, OWSC provides a promising solution for in-season crop mapping without current-year samples.

Maize, as one of the most widely cultivated crops worldwide, is crucial for food security and livelihoods. Accurate dynamic maize mapping is essential for production forecasting and preharvest decision-making. However, current approaches remain limited by incomplete seasonal representation and poor model transferability across growth stages, highlighting the need for an automated and dynamic maize mapping method throughout the growing season. In this study, we first explored the spectral bands that maximally differentiate maize from other crops in terms of water content, chlorophyll levels, and canopy leaf structure during the growing season and proposed a novel normalized difference composite index (NDCI). Based on this index, an automated and dynamic maize identification method that does not rely on crop labels was constructed using a multitemporal Gaussian Mixture Model (GMM), referred to as NDCI-mGMM. The framework was evaluated in five representative maize-growing regions across China, the United States, and France, and further validated in 2021 at two United States sites (Iowa and Georgia) to assess its temporal transferability. Across these regions and years, NDCI-mGMM achieved overall accuracies of approximately 85%, demonstrating stable performance under varying phenological and environmental conditions. Compared with commonly used vegetation indices (Datt99, REP, LSWI, and CIgreen), the NDCI achieved higher F1-scores (by 4-49%) and improved maize separability. Moreover, NDCI-mGMM enabled early-season maize mapping during the tasseling stage-up to two months before harvest-with satisfactory accuracy (F1 >= 79%). As the method operates independently of crop labels, it provides a scalable and temporally transferable framework for in-season maize mapping and early-season area estimation in data-limited regions, thereby supporting timely crop monitoring for production forecasting and preharvest decision-making.

High-resolution (HR) crop maps are the foundation data for conducting precise agricultural monitoring and land use change analysis. However, HR crop mapping remains hindered by costly manual annotations, label noise from low resolution (LR) products, convolutional neural network (CNN) architectural limitations, and spatialspectral constraints of single source remote sensing imagery. LR crop classification products serve as costeffective weak supervision but introduce geometric and semantic label noise. Conventional CNNs struggle with preserving fine spatial details and modeling long-range contexts, while data-level multi-source fusion blurs parcel boundaries. To address these issues, we propose a multisource weakly supervised crop classification framework (MSWCF) that utilizes LR products to guide HR mapping. The MSWCF features dual parallel branches: the HR branch extracts parcel boundaries, while the LR branch captures semantic information. Each branch integrates spatial resolution preserving and Vision Transformer modules to concurrently extract local details and global contexts. A cross-attention module (CAM) adaptively fuses these features, while a noise label removal module suppresses boundary and semantic errors in LR labels. Experiments were conducted in Heilongjiang Province, China. Results show that MSWCF achieves superior performance (Overall Accuracy = 93.8 %) surpassing state-of-the-art baselines. Cross-spatiotemporal application indicates that it has generalization capabilities. Ablation studies confirm that the dual-branch structure and CAM fusion effectively utilize spatial-spectral information, preventing boundary smoothing. Feature visualization verifies that SRP focuses on boundaries while ViT captures global objects. MSWCF enables accurate, fine-grained crop mapping without manual annotations, validating its practicality for largescale HR crop mapping.

Accurate and timely estimation of corn yields in the U.S. (the world's leading corn producer) is crucial for commodity trading and global food security. Most researchers have explored the use of multi-source data, including remote sensing data and climate data, in combination with machine learning models such as Least Absolute Shrinkage and Selection Operator, Support Vector Regression, Random Forest, and Long Short-Term Memory Network (LSTM) for corn yield estimation. In recent years, there have been studies including phenology information to define growth phases (GPs) as the time scale to calculate model variables rater than aggregate time-series data on a fixed time scale to obtain the variables. Although some progress has been made in this yield estimation method, its effectiveness remains to be further comprehensively studied. This study defines GPs to calculate phenology, remote sensing, and climate variables from 2008 to 2022, which were combined with machine learning models to estimate county-level corn yields in the U.S. The results show that: (1) RMSE of all models built by the different combinations of the phenology, remote sensing, and climate parameters range from 740.10 to 1051.31 kg/ha, and these models can achieve ideal yield prediction about two months before harvest; (2) The accuracy of the models constructed with the variables at the GP time scale (predicted R2 values: 0.78-0.83) outperforms that of the models built with the variables constructed on a monthly time scale (predicted R2 values: 0.74-0.80); (3) The combination of phenology, 2-band enhanced vegetation index (EVI2), solarinduced chlorophyll fluorescence (SIF), killing degree days (KDD), and minimum vapor pressure deficit (VPDmin) with LSTM achieved the optimal yield estimation (RMSE = 740.10 kg/ha). Our findings demonstrated a scalable and effective method for predicting and estimating corn yield over a large area, which can potentially be applied to other crops in different geographical contexts.

Accurately estimating large-scale crop yields amid climate change is increasingly critical for crop management, marketing, and storage. Remote-sensing data-especially from moderate-resolution imaging spectroradiometer (MODIS)-are integral to such estimations, yet MODISs coarse spatial resolution causes notable mixed-pixel issues. Crop-specific masks can improve accuracy by filtering out nonagricultural pixels to obtain pure crop pixels, but the optimal mask percentage remains unclear. We evaluated six crop-mask thresholds (50%-100%) to define pure corn pixels for multiple MODIS-derived vegetation indices (VIs) [two-band enhanced vegetation index (EVI2)/normalized difference vegetation index (NDVI) from MOD09Q1 at 250 m/8-day; and enhanced vegetation index/NDVI from MOD09A1, MOD13Q1, and MOD13A1 at 500 m/8-day, 250 m/16-day, and 500 m/16-day], and integrated these VIs with regression and machine/deep-learning algorithms for county-level corn yield estimation across the U.S. Corn Belt. Results showed that the seasonal trajectories and interannual variabilities of VI-yield correlations derived from different mask thresholds were similar in both pattern and intensity. Importantly, mask threshold selection had a clear influence on corn yield estimation performance and was primarily governed by spatial resolution: for 250 m VIs, higher thresholds (80%-100%) generally achieved lower errors, whereas for 500 m VIs, lower thresholds (50%-60%) were more robust. In the extreme drought year (2012), 250 m VIs benefitted from higher thresholds and 500 m VIs achieved optimal performance at intermediate thresholds (70%-80%). These findings provide practical guidance for selecting crop-mask thresholds to improve MODIS-based large-scale corn yield estimation.

Timely and accurate estimation of crop area is fundamental for agricultural policy formulation, production forecasting, and economic assessment. Probability-sampling-based area estimation provides statistically rigorous inference with quantifiable uncertainty, but its operational implementation remains challenging. Two barriers are particularly important for probability-sampling-based in-season crop area estimation. First, collecting spatially dispersed probability samples in field is highly costly, especially in regions under complex traffic transportation conditions. Second, although progressively updated in-season crop maps provide valuable auxiliary information for improving estimation precision, standard post-stratified estimation is often impractical when samples were originally selected using an earlier stratification map (the updated post-strata may strongly overlap with the original sampling strata, leaving some substrata with insufficient samples). To address these two challenges, we present a practical framework for progressive in-season crop area estimation by introducing two complementary techniques. First, a vehicle–unmanned aerial vehicle (UAV) cooperative response design assigns sample units to multiple park-and-launch sites and minimizes the overall time cost of the vehicle tour and UAV sorties using ant colony optimization, thereby improving the efficiency of the probability samples collection. Second, a poststratified combined ratio estimator, a design-consistent estimation method that does not require sufficient samples within each substratum, is applied to progressively refine crop area estimates by leveraging the updated in-season crop maps in later season. In a field experiment conducted for rapeseed area estimation in Yanting County, 263 probability sample units selected under the initial stratification (the first in-season crop map) were collected in 31 hours using the proposed UAV cooperative sampling method. The poststratified combined ratio estimator was then introduced to update the crop area estimate using later-season crop maps, which reduced the standard error of the area estimate from 1.34% to 1.06% (≈21% relative reduction). Overall, the proposed framework provides a statistically robust rigorous and operationally feasible solution for progressive in-season crop area estimation.

With global climate change intensifying wildfire frequency, precise monitoring of burned area dynamics is critical for assessing forest carbon cycles and informing ecological restoration efforts. However, conventional methods often struggle to accurately delineate small-scale burned areas or those with subtle spectral differences from unburned vegetation. To address this gap, we introduce the novel Red-Edge Burned Area Index (REBAI) using Sentinel-2 data, leveraging differential spectral responses in red-edge, red, and short-wave infrared (SWIR) bands between burned and unburned forest. We validated REBAI against 10 spectral bands and 13 common indices using two separability metrics and feature importance analysis. Its detection accuracy was then assessed by applying 16 adaptive thresholding algorithms. Comprehensive validation across 12 diverse global forest study areas confirmed REBAI's superior performance, particularly for small-scale and subtle spectral burns, achieving a Jeffries-Matusita (JM) distance > 1.9 and a mean Common Isolation Values (CIV) proportion of 0.001, ranking highest in feature importance. Detection accuracy is high and stable, with metrics tightly clustered around their mean overall accuracy (96.60 % +/- 3.58 %), precision (92.97 % +/- 11.27 %), recall (87.12 % +/- 8.07 %), and F1 score (0.8917 +/- 0.0702), significantly outperforming common indices (e.g., NDVI, NBR). Furthermore, preliminary evidence suggests that REBAI may possess broad applicability across various vegetation types, with an exploratory analysis on grassland burns yielding an average F1 score of 0.91. Its accuracy could potentially be enhanced through future adaptations such as bi-temporal analysis or machine learning integration. In summary, we propose REBAI, a robust, physically-based burned area detection index, offering strong support for post-fire damage assessment and ecosystem monitoring.

Timely and accurate detection and assessment of forest disturbances are crucial for early warning and risk prevention of forest disasters. This study presents an automated forest disturbance monitoring algorithm with three main steps: (1) extracting features that capture the remote sensing response of forest fires and deforestation and creating threshold maps of features; (2) using the threshold maps to extract potential forest disturbance pixels as training samples to train a one-class support vector machine (OC-SVM) and detect forest disturbance regions; and (3) applying diagnostic rules to diagnose the disturbance type as either burned or deforested areas. We validated the method using 10 global cases (five forest fires and five deforestation events). The results show that Sentinel-2 bands 6, 7, 8 and 8A, the mean textures of bands 6, 7, 8 and 8A and the difference vegetation index (DVI) are effective for disturbance detection. The overall detection accuracy of the 10 cases is 98.46% on average, with an average F1 score of 0.923 and a type diagnosis accuracy of 100%. This approach enables high-precision, automated detection and classification of forest disturbances and can be extended to monitor other disturbance types, offering a valuable tool for forest disaster response and management.

Large-scale agricultural remote sensing monitoring is challenged by pronounced spatial heterogeneity arising from fragmented terrain, complex climatic backgrounds, and diverse cropping structures. However, existing agricultural zoning schemes generally lack an integrated consideration of remote sensing imaging mechanisms and key variable conditions such as atmospheric interference and crop phenology, limiting their direct utility in guiding region-specific sensor selection and classification algorithm calibration. To address this limitation, this study integrates multi-source earth observation data and agricultural statistical information to construct an Agricultural Remote-sensing Classification Difficulty Index (ARCDI) from multiple dimensions, including image availability, cropping structure, cropland fragmentation, and topographic environment. On this basis, a graph theory-based spatially constrained Skater clustering algorithm is introduced to establish a two-tier “cropland–major cereal crops” zoning framework oriented toward remote sensing applications. The results indicate that the proposed framework delineates five distinct first-tier cropland classification difficulty zones across China. This zoning scheme effectively quantifies the regional heterogeneities in monitoring challenges. Building upon this first-tier zoning, the framework is further refined into 50 second-tier major cereal crop classification difficulty zones, including 13 winter wheat zones, 21 maize zones, and 16 rice zones. Statistical tests and spatial analyses demonstrate that the proposed zoning scheme significantly outperforms conventional clustering approaches in balancing within-zone homogeneity and spatial continuity. This advantage is quantitatively reflected by consistently lower residual spatial autocorrelation (residual Moran’s I ≈ 0.10–0.11) and an approximately 20% reduction in within-zone variance compared with other spatially constrained methods. Extensive field-sample validation provides preliminary evidence of an inverse relationship between crop-type classification difficulty and accuracy. These results confirm the framework’s reliability in identifying regional difficulty and its decision-support value for selecting remote sensing strategies. Overall, this study systematically elucidates the spatial differentiation patterns of remote sensing classification difficulty for cropland and major cereal crops across China. The proposed framework provides robust scientific support for data selection, algorithm optimization, and differentiated strategy formulation in national-scale agricultural monitoring, thereby facilitating the operationalization of regional agricultural remote sensing applications.

Satellites strive to strike a delicate balance between temporal and spatial resolution, thereby rendering the achievement of high resolution in both aspects challenging. Spatiotemporal fusion algorithms have emerged as a promising solution to tackle this challenge. However, with changes in spatiotemporal conditions, existing spatiotemporal fusion methods, particularly those based on deep learning, face challenges such as decreased prediction accuracy and poor reconstruction accuracy in areas of abrupt changes. This presents significant challenges for the fusion of multi-source remote sensing data to generate cloud-free remote sensing images on a daily scale. In this context, the study proposes a multiscale Attention-Guided deep optimization network for Spatiotemporal Data Fusion (AGSDF) method. The algorithm is designed to generate daily fine images using coarse image, based on historical reference fine images. Specifically, it firstly attempts to use a physical attention mechanism to mitigate the effects of climate change in time-series images. Implementing a continuous spatiotemporal fusion process across multiple scales significantly enhances the model's robustness. The performance of AGSDF was evaluated and compared to nine methods at six sites worldwide. The experimental results indicate that AGSDF achieved a top score in the assessment. Consequently, AGSDF holds high potential to produce accurate remote sensing products with high temporal and spatial resolution across extensive regions.

The lack of ground-truth samples greatly limits the acquisition of spatiotemporally explicit crop distribution information essential for precision management, food security, and sustainable agricultural development. To address this limitation, various phenological knowledge-driven automated sample generation frameworks have been proposed. However, most studies prioritize the quantity and label accuracy of training samples while overlooking their spectral representativeness, which is crucial for balancing model accuracy and generalization performance. To tackle this challenge, this study proposed a simple index-based sample generation strategy that transformed crop mapping indices (i.e., phenology indices, PIs) into confidence indicators that characterize sample label accuracy, spectral representativeness, and spatial location, thereby guiding crop sampling and mapping. This approach satisfies the dual requirements of sample quantity and quality for model training while providing a practical solution to the limitations of index-based crop mapping in transfer applications. In this study, three well-validated PIs—Rice Phenology Index (RPI), Variation of the Vegetation-Pigment index (VVP), and Greenness and Water Content Composite Index (GWCCI)—were introduced to guide the sampling and mapping of rice, maize, and soybean in Northeast China from 2017 to 2025, with 2019 serving as the primary experimental year. First, a piecewise linear normalization procedure was developed based on threshold-based classification rules to scale the original PI values to a uniform scale of [-1, 1]. Subsequently, the relationships between the normalized PIs and the label accuracy, spectral properties, and within-field spatial locations of the corresponding samples were comprehensively evaluated. Then, crop sample subsets from different confidence levels were combined and fed into a Random Forest classifier for crop mapping to identify the optimal combination that balances model accuracy and generalization performance. Finally, the cross-temporal transferability of the proposed sampling strategy was further evaluated over extended time series. The results demonstrated that normalized PIs can serve as confidence indicators for guiding sample acquisition, allowing for an explicit assessment of label correctness and the distribution of spectral information in feature space. Classification results suggested that combining medium- and high-confidence samples ([0.25, 1]) yielded more satisfactory model accuracy (OA>90%) and performance (R2>0.91). Conversely, the results confirmed that relying solely on a single classification threshold is not advisable, as even a small proportion of non-random noises can substantially impair model training; similarly, relying only on super-high-confidence samples is also inadvisable. Consequently, by mitigating the detrimental effects of samples near the classification threshold, the proposed strategy effectively avoids direct classification errors when applied across spatiotemporal domains in index-based mapping methods, thereby enabling large-scale, long-term crop sampling and mapping. Ultimately, the study generated a large set of approximately 596,000 samples along with corresponding spatial distribution maps for rice, maize, and soybean across Northeast China from 2017 to 2025, achieving OA of 90.07%-98.60% and R2 of 0.8990-0.9877 (2019-2023). This study provides a general paradigm that utilizes emerging PIs to facilitate transfe

Efficient fire detection is crucial for minimizing wildfire potential impact. In this study, a new index, Temporal and Spatial Anomaly Index (TSAI), is proposed for fire detection. For a given pixel, it is calculated in terms of the difference in Sentinel-2 B12 reflectance of the pixel between, during, and before a fire, as well as the difference in Sentinel-2 B12 reflectance between the pixel and the surrounding nonfire pixels. The fire detection method based on the TSAI threshold was developed and tested in 19 study areas across different continents worldwide, encompassing various climatic and vegetation types. The results show TSAI exhibited remarkable accuracy in detecting fire, achieving an average commission error ratio of 11.43% and an average omission error ratio of 5.47% across all study areas. The TSAI-based detection method has a high degree of automation and shows great potential for large-scale fire monitoring. In addition, the TSAI construction method in this study can also serve as a reference for the development of other new disaster detection indices.

Timely and accurate access to the spatial distribution of crops is critical for precision agriculture. However, large-scale automated in-season crop mapping often faces the challenge of limited training data. In this study, we proposed a dynamic and adaptive in-season winter wheat mapping (DAWM) framework, which leverages temporal-spectral similarity to minimize reliance on ground truth labels. The DAWM framework starts by utilizing historical prior knowledge from target domains to construct a temporal- spectral library for winter wheat, which integrates the phenological curves of winter wheat throughout the entire growing season and the spectral curves of winter wheat at different times. In this part, two modules—confidence learning (CL) and reclustering are used to refine historical crop maps and generate a reference temporal-spectral curve library. Then, a novel dynamic temporal-spectral similarity (DTSS) metric, proposed in this study, is used to produce training data for the random forest (RF) model by dynamically calculating the temporal-spectral similarity of each unlabeled objects to each cluster in the reference library. The proposed DAWM framework was evaluated in three global sites (10 000 km2 each) and compared with two existing phenology-based methods: time-weighted dynamic time warping (twDTW) and phenology-time weighted dynamic time warping (ptDTW). Results demonstrated that DAWM outperforms both methods, achieving F1-scores above 0.90 across all study sites and improving the F1-score by approximately 10% during the heading stage. In several study areas, DAWM also reached stable winter wheat identification about 45 days earlier than the compared methods, demonstrating its strong potential for dynamic in-season crop mapping. Extensive testing in the Huang-Huai-Hai Plain region of China achieved an F1 accuracy of 86.37%, while additional experiments in Heilongjiang Province demonstrated the method’s applicability for mapping maize and rice, with F1 accuracies of 90.35% and 96.31%, respectively. The proposed approach offers a robust solution for dynamic, high-quality crop mapping without relying on local ground labels, providing valuable data support for agricultural production management.

Net solar radiation is an essential parameter that characterizes surface energy exchange and plays a critical role in climate change, solar power generation, and agricultural irrigation. Although the global GLASS surface all-wave daily net radiation (NR) product exhibits high overall accuracy, a comprehensive quality assessment for continental China remains lacking, resulting in unclear regional applicability. Therefore, this study focuses on mainland China. Based on solar net radiation observations from 50 meteorological stations (2000–2016) and 37 ecological stations (2000–2020), four evaluation metrics were used: the correlation coefficient (R), mean bias error (MBE), root mean square error (RMSE), and coefficient of determination (R2). The results indicate that, during the study period, GLASS NR showed relatively small deviations from the observed values across most regions of China, with significant discrepancies observed only in southern Yunnan, Guangdong, Guangxi, and Hainan. Seasonally, GLASS NR performed better in autumn and winter than in spring and summer. Interannually, there was only a slight decline in data quality for a few individual years; however, overall, an upward trend was observed. Regarding land cover types, GLASS NR accuracy was lower for shrublands, forests, and grasslands, whereas it performed better for other land cover types. Overall, the GLASS NR product demonstrates high accuracy and good temporal continuity across mainland China. However, significant regional variations exist, and localized applications require optimization and refinement. This study provides valuable insights for improving net radiation products across multiple spatiotemporal scales.

Accurate mapping of soybean cultivation areas is crucial for agricultural monitoring, resource management, and food security. However, the spectral overlap between soybean and other crops, such as corn, poses significant challenges for remote sensing-based identification. This study proposes a novel soybean identification index (NSII), which is calculated using the second red-edge band (RE2), the first short-wave infrared band (SWIR1), and the Enhanced Vegetation Index (EVI) derived from Sentinel-2 imagery within the optimal time window identified through spectral feature analysis. NSII was implemented in 12 major soybean producing regions in the United States and China over a three-year period (2020-2022). Experimental results from 2020 to 2022 show that the average accuracy of NSII is 0.85, and the average F1 score is 0.80. Compared with the existing Soybean Mapping Composite Index (SMCI), the accuracy increased by 8 percentage points and the F1 score increased by 6 percentage points. NSII also exhibits strong stability and transferability, with consistent performance across diverse climatic and cropping conditions. This study provides a robust and efficient tool for soybean mapping, offering significant potential for precision agriculture and sustainable resource management.

In this study, a comprehensive and systematic analysis of extreme drought events in China from 1961 to 2022 utilizing the standardized precipitation index (SPI) and copula functions based on monthly gridded precipitation data is presented. In this study, drought events and their characteristics are identified using run theory and the 3month SPI. A drought event with a joint exceedance probability of drought severity and duration calculated by the copula function at less than 5 % was subsequently defined as an extreme drought. Under extreme drought conditions, the duration/severity of drought was fixed at a specific value, the corresponding drought severity/ duration was calculated grid by grid, and its spatial heterogeneity and change were analyzed during two time periods (1961-1991 and 1992-2022). The results revealed significant temporal and spatial variations in drought trends, with increased precipitation severity in Northwest China and the Qinghai-Tibet Plateau and more severe drought conditions in Northeast China and South China. The western part of Northwest China (Subregion 1) and the northern Qinghai-Tibet Plateau (Subregion 6) experienced longer and more severe drought events, characterized by average durations of 3.93 months and maximum severities up to 10.52 in Subregion 1, significantly exceeding national averages (3.47 months and 9.26). The duration/severity of extreme drought varied in different regions, with higher durations/severities in drought-prone areas. The frequency, duration, and severity of extreme drought events exhibited significant variations, particularly in central and southern China, where the frequency, duration, and severity of extreme drought events have increased. In subtropical humid regions in Central China and South China (Subregion 5), 47 % of the grids experience an increase in the total number of occurrences, 54 % of the grids experience an increase in the total number of months of occurrence, 64 % of the grids experience an increase in the average severity, and 62 % of the grids experience an increase in the maximum severity. Additionally, the number of extreme droughts caused by both duration and severity was greater than the number of extreme droughts dominated by any one factor alone. This study contributes to a more comprehensive assessment of extreme drought, providing a scientific basis for drought monitoring in China.

CO2 hydrogenation to methanol can reduce CO2 emissions, meanwhile, the prepared methanol can be used as chemical raw material and fuel, and the catalyst Cu-ZnO/Al2O3 exhibits good activity. Nevertheless, the activity and selectivity over the Cu-ZnO/Al2O3 still need improvement for industrialization. In addition, plenty of water will be generated during CO2 hydrogenation, which causes deactivation of the catalyst due to the hydrothermal environment in the reaction. To increase the activity and hydrothermal stability, the Cu-ZnO/AlLaO catalyst was prepared by coprecipitation method in this work, which improved the adsorption of CO2 and inhibited the adsorption of methanol due to the strong surface basicity and the interface structure of Cu and supporter, and then increased the catalytic activity and methanol selectivity. More importantly, various characterization technologies for the structures and surface properties over fresh and hydrothermal treatment catalysts showed that the La2O3 added into the catalyst greatly improved the hydrothermal stability.

Understanding drought responses to global warming can enhance our ability to manage drought risks. In this study, the standardized precipitation evapotranspiration index was computed using 18 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Three drought characteristics (drought frequency, duration, and intensity) were extracted, and the drought hazard index (DHI) was constructed to assess drought hazards in China for the 1995–2014 reference period and future 2 °C, 3 °C and 4 °C temperature rise scenarios. Results revealed that drought frequency in the eastern monsoon region of China was higher in the south, lower in the north, and generally increased with warming. Drought duration and intensity were higher in northwest China and lower in southeast China, markedly increasing with warming. The DHI was relatively high in northwest inland and southeast coastal China. National average DHI values for the reference period and the three temperature rise scenarios were 0.26, 0.31, 0.33, and 0.36, respectively. Under future temperature rise scenarios, the DHI in all regions of China, excluding the southeastern Qinghai-Tibet Plateau, would generally increase compared to the reference period. National average increase values of DHI under the 3 °C and 4 °C scenarios reach 1.5 and 2 times that under the 2 °C scenario.