Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. By leveraging its advantages in image processing, deep learning technology has significantly enhanced the accuracy of plant disease and pest detection and identification. This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. It begins by outlining the limitations of traditional methods in this domain, followed by a systematic discussion of the latest developments in applying various deep learning techniques—including image classification, object detection, semantic segmentation, and change detection—to plant disease and pest identification. Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. Key challenges, such as enhancing model generalization, addressing small lesion detection, and ensuring the availability of high-quality, diverse training datasets, are critically examined. Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. Deep learning technology, with its powerful capabilities in data processing and pattern recognition, has become a pivotal tool for promoting sustainable agricultural practices, enhancing productivity, and advancing precision agriculture.

Surface solar radiation is essential for regional climate and environmental research. The widely used European Centre for Medium-Range Weather Forecasts ERA5 reanalysis products provide long-term spatial estimates of the total and direct solar radiation on an hourly scale; however, its accuracy has not been systematically evaluated for China. In this letter, in situ measurements from 98 sites are used to analyze the deviations in the hourly radiation from ERA5 and to compare the daily variations. The results reveal that estimates from the reanalysis correlate well with ground observations and completely reflect the regional characteristics and daily variations at individual sites. Overall, ERA5 overestimates the hourly total solar radiation and direct radiation by approximately 30.87 and 73.95 W/m², respectively, whereas it underestimates the diffuse radiation by approximately 43.08 W/m². Spatiotemporal differences are found by comparing the errors and time series of different sites. The fact that the accuracy in areas with rainy and cloudy weather is obviously lower indicates that the ERA5 analysis poorly performs when retrieving cloud properties. These conclusions can contribute to guiding the rational use of ERA5 radiation products.

Accelerating energy transition towards renewables is central to net-zero emissions. However, building a global power system dominated by solar and wind energy presents immense challenges. Here, we demonstrate the potential of a globally interconnected solar-wind system to meet future electricity demands. We estimate that such a system could generate ~3.1 times the projected 2050 global electricity demand. By optimizing solar-wind deployment, storage capacity, and trans-regional transmission, the solar-wind penetration could be achieved using only 29.4% of the highest potential, with a 15.6% reduction in initial investment compared to a strategy without interconnection. Global interconnection improves energy efficiency, mitigates the variability of renewable energy, promotes energy availability, and eases the economic burden of decarbonization. Importantly, this interconnected system shows remarkable resilience to climate extremes, generation outages, transmission disruptions, and geopolitical conflicts. Our findings underscore the potential of global interconnection in enabling high renewable penetration and guiding sustainable energy transitions.

Recently, surface diffuse solar radiation (R_dif) has been attracting a growing interest in view of its function in improving plant productivity, thus promoting global carbon uptake, and its impacts on solar energy utilization. To date, very few radiation products provide estimates of R_dif, and systematic validation and evaluation are even more scare. In this study, R_dif estimates from Reanalysis Fifth Generation (ERA5) of European Center for Medium-Range Weather Forecasts and satellite-based retrieval (called JiEA) are evaluated over East Asia using ground measurements at 39 stations from World Radiation Data Center (WRDC) and China Meteorological Administration (CMA). The results show that JiEA agrees well with measurements, while ERA5 underestimates R_dif significantly. Both datasets perform better at monthly mean scale than at daily mean and hourly scale. The mean bias error and root-mean-square error of daily mean estimates are −1.21 W/m² and 20.06 W/m² for JiEA and −17.18 W/m² and 32.42 W/m² for ERA5, respectively. Regardless of over- or underestimation, correlations of estimated time series of ERA5 and JiEA show high similarity. JiEA reveals a slight decreasing trend at regional scale, but ERA5 shows no significant trend, and neither of them reproduces temporal variability of ground measurements. Data accuracy of ERA5 is more robust than JiEA in time but less in space. Latitudinal dependency is noted for ERA5 while not for JiEA. In addition, spatial distributions of R_dif from ERA5 and JiEA show pronounced discrepancy. Neglect of adjacency effects caused by horizontal photon transport is the main cause for R_dif underestimation of ERA5. Spatial analysis calls for improvements to the representation of clouds, aerosols and water vapor for reproducing fine spatial distribution and seasonal variations of R_dif.

The rapid development of remote sensing technology has promoted the generation of different vegetation index products, resulting in substantive accomplishment in comprehensive economic development and monitoring of natural environmental changes. The results of scientific experiments based on various vegetation index products are also different with the variation of time and space. In this work, the consistency characteristics among three global normalized difference vegetation index (NDVI) products, namely, GIMMS3g NDVI, MOD13A3 NDVI, and SPOT-VGT NDVI, are intercompared and validated based on Landsat 8 NDVI at biome and regional scale over the Mongolian Plateau (MP) from 2000 to 2014 by decomposing time series datasets. The agreement coefficient (AC) and statistical scores such as Pearson correlation coefficient, root mean square error (RMSE), mean bias error (MBE), and standard deviation (STD) are used to evaluate the consistency between three NDVI datasets. Intercomparison results reveal that GIMMS3g NDVI has the highest values basically over the MP, while SPOT-VGT NDVI has the lowest values. The spatial distribution of AC values between various NDVI products indicates that the three NDVI datasets are highly consistent with each other in the northern regions of the MP, and MOD13A3 NDVI and SPOT-VGT NDVI have better consistency in expressing vegetation cover and change trends due to the highest proportions of pixels with AC values greater than 0.6. However, the trend components of decomposed NDVI sequences show that SPOT-VGT NDVI values are about 0.02 lower than the other two datasets in the whole variation periods. The zonal characteristics show that GIMMS3g NDVI in January 2013 is significantly higher than those of the other two datasets. However, in July 2013, the three datasets are remarkably consistent because of the greater vegetation coverage. Consistency validation results show that values of SPOT-VGT NDVI agree more with Landsat 8 NDVI than GIMMS3g NDVI and MOD13A3 NDVI, and the consistencies in the northeast of the MP are higher than northwest regions.

Change detection (CD) based on high-resolution remote sensing images can be used to monitor land cover changes, which is an important and challenging topic in the remote sensing field. In recent years, with the development of deep learning, CD methods based on deep learning have achieved good results in the field of CD. However, most current CD methods use single-stream or dual-stream networks to extract change features, which is insufficient to extract and learn bitemporal change information thoroughly. This paper proposes a triple-stream network (TSNet) with cross-stage feature fusion for CD in high-resolution bitemporal remote sensing images. First, to obtain highly representative deep features in the original image, we perform feature extraction on bitemporal remote sensing images and their concatenated image with a dual-stream encoder and a single-stream encoder, respectively. Then, the bitemporal multiscale features extracted by the dual-stream encoder are input into a multistage bidirectional convolutional gated recurrent unit (MSBC_GRU) feature fusion module, allowing the network to learn the change information in a cross-stage manner. In addition, we use a dual-channel attention module to fuse the features extracted by dual-stream and single-stream encoders, improving the network’s ability to discriminate changed features. The effectiveness of TSNet is demonstrated with three publicly available CD datasets. The extensive experimental results demonstrate that the proposed method achieves state-of-the-art CD performance on the above three datasets.