Lijun Wang

Characterizing the spatiotemporal differences between meteorological and agricultural drought is important for drought monitoring and risk management in major grain-producing regions. However, coordinated assessment of meteorological anomalies and crop moisture responses using multi-source data remains challenging. In this study, an integrated multi-source framework was developed to jointly characterize meteorological and agricultural drought in winter wheat across six selected growing seasons in Henan Province, China. The standardized precipitation index (SPI) and vegetation water index (VWI) were used to characterize meteorological and agricultural drought, respectively. Furthermore, standard deviational ellipse analysis and center-based spatial statistics were used to characterize drought distribution, orientation, and spatial migration, while Pearson correlation analysis was used to examine statistical associations among SPI-defined drought, VWI-defined drought, and effective irrigated area. Across the selected study periods, meteorological and agricultural drought showed distinct temporal and spatial patterns. In addition, the SPI–VWI association varied with drought severity: light drought showed the strongest positive association (r = 0.64), moderate drought showed a weak negative association (r = −0.20), and severe drought showed a moderate positive association (r = 0.57). The association between total SPI- and VWI-defined drought areas was weak (r = −0.07). At the common 1-km analysis scale, VWI-defined agricultural drought maintained a more stable spatial center and major-axis orientation than SPI-defined meteorological drought, while the difference in ellipse extent was more pronounced in the earlier study periods and narrowed markedly in the later periods. Effective irrigated area showed an exploratory negative association with total VWI-defined agricultural drought area (r = −0.72, p = 0.11; n = 6); although the association did not reach conventional statistical significance, its direction identifies irrigation conditions as relevant contextual information when interpreting variations in agricultural drought. The integrated framework provides a complementary basis for jointly assessing meteorological stress and crop moisture conditions and may support drought-risk assessment and irrigation management in major winter wheat-producing regions.

Accurate multi-year crop mapping is often constrained by the cost of constructing dense labels for every target year. This study evaluated a cross-year crop and land-cover classification framework using Sentinel-2 imagery in Jiyuan City, China. Dense semantic-segmentation labels were constructed for three representative sites in 2020 and 2021 and used to train DeepLab V3+, U-Net, and U-Net++. Two input schemes, spectral bands alone and spectral bands combined with six vegetation indices, were compared, and the trained models were applied to 2019 and 2022–2024 without additional year-specific dense semantic-segmentation labels. U-Net++ with vegetation-index-enhanced inputs achieved the best overall performance among the tested networks, with annual overall accuracy ranging from 87.35% to 93.18%. Winter wheat was consistently identified with high producer’s and user’s accuracies, whereas other crops and urban areas remained more uncertain. The resulting annual maps supported analysis of winter-wheat spatial dynamics across 2019–2024. These results demonstrate the practical applicability of a fixed source-year semantic-segmentation model for multi-year crop mapping within the study area, while broader temporal and spatial transferability require further validation.

Hyperspectral technology for detecting camouflaged targets in complex backgrounds represents a current research hotspot. Hyperspectral data often contain numerous spectral bands, which can lead to data redundancy. Traditional anomaly detection methods typically assume that the background spectrum follows a Gaussian distribution, which limits their adaptability to complex and non-Gaussian backgrounds. Furthermore, most existing anomaly detection methods fail to fully leverage the spatial information in hyperspectral images. To address these challenges, we propose a hyperspectral anomaly detection by spatial-spectral fusion based on extreme value-entropy band selection and Cauchy graph distance optimization (EBS-CGD). First, to effectively reduce the redundant bands in hyperspectral images, we introduce a hybrid band selection algorithm. This algorithm combines spectral extremum detection with information entropy filtering to select the most representative bands by considering multi-dimensional information. Second, to enhance the model's adaptability to non-gaussian backgrounds and maximize spectral information utilization, we propose a Cauchy graph distance-optimized RX anomaly detection approach. This method replaces the traditional covariance matrix with a significance-weighted Cauchy distance graph, reducing the influence of the target on the background and improving the model's robustness in complex environments. Subsequently, a low-rank and sparse representation model (LRASR) is employed to construct background dictionaries, low-rank coefficient matrices for the background, and sparse anomaly matrices, enabling effective spatial information utilization for detecting anomalous targets. Finally, to fully exploit spatial-spectral information, we employ a weighted fusion of the detection results to reduce false positives and retain more true anomalies. We tested the proposed method on four real hyperspectral camouflage net datasets, each containing 126 bands. Experimental results demonstrate that the proposed EBS-CGD algorithm outperforms other methods in anomaly detection performance.

The accurate extraction of cultivated land information is crucial for optimizing regional farmland layouts and enhancing food supply. To address the problem of low accuracy in existing cultivated land products and the poor applicability of cultivated land extraction methods in fragmented, small parcel agricultural landscapes and complex terrain mapping, this study develops an advanced cultivated land extraction model for the western part of Henan Province, China, utilizing Gaofen-2 (GF-2) imagery and an improved U-Net architecture to achieve a 1 m resolution regional mapping in complex terrain. We obtained optimal input data for the U-Net model by fusing spectral features and vegetation index features from remote sensing images. We evaluated and validated the effectiveness of the proposed method from multiple perspectives and conducted a cultivated land change detection and agricultural landscape fragmentation assessment in the study area. The experimental results show that the proposed method achieved an F1 score of 89.55% for the entire study area, with an F1 score ranging from 83.84% to 90.44% in the hilly or transitional zones. Compared to models that solely rely on spectral features, the feature selection-based model demonstrates superior performance in hilly and adjacent mountainous regions, with improvements of 4.5% in Intersection over Union (IoU). Cultivated land mapping results show that 83.84% of the cultivated land parcels are smaller than 0.64 hectares. From 2017 to 2022, the overall cultivated land area decreased by 15.26 km², with the most significant reduction occurring in the adjacent hilly areas, where the land parcels are small and fragmented. This trend highlights the urgent need for effective land management strategies to address fragmentation and prevent further loss of cultivated land in these areas. We anticipate that the findings can contribute to precision agriculture management and agricultural modernization in complex terrains of the world.

Propelled by emerging technologies such as artificial intelligence and deep learning, the essence and scope of cartography have significantly expanded. The rapid progress in neuroscience has raised high expectations for related disciplines, furnishing theoretical support for revealing and deepening the essence of maps. In this study, CiteSpace was used to examine the confluence of cartography and neural networks over the past decade (2013–2023), thus revealing the prevailing research trends and cutting-edge investigations in the field of machine learning and its application in mapping. In addition, this analysis included the systematic categorization of knowledge clusters arising from the fusion of cartography and neural networks, which was followed by the discernment of pivotal clusters in the field of knowledge mapping. Crucially, this study diligently identified the critical studies (milestones) that have made significant contributions to the development of these elucidated clusters. Timeline analysis was used to track these studies’ origins, evolution, and current status. Finally, we constructed collaborative networks among the contributing authors, journals, institutions, and countries. This mapping aids in identifying and visualizing the primary contributing factors of the evolution of knowledge mapping encompassing cartography and neural networks, thus facilitating interdisciplinary and multidisciplinary research and investigations.

Understanding the regional variations and mechanisms of rural hollowing-out in the Yellow River Basin (YRB) is crucial to guiding regional rural revitalization. However, further quantitative evaluation and analysis are essential to address the issue of rural hollowing-out caused by the decrease in rural population and expansion of residential land in the YRB at different spatio-temporal scales. Based on China’s census data and residential areas extracted from remote sensing images, the rural hollowing-out in the YRB is classified into five types: smart development type (SDT), human–-land recession type (HRT), population loss type (PLT), land expansion type (LET), and human–land–vacant waste type (HLW). Then, the influential features shaping the spatial diversity of rural hollowing-out types are examined, and the feature importance values at different spatio-temporal scales are assessed using the XGBoost model. The results of rural hollowing-out in the YBR indicate that (1) the geographical types of rural hollowing-out in the YRB are dominated by the HRT type and show significant heterogeneity and distribution at different spatio-temporal scales. At different time stages, the number of counties dominated by HRT in lower reaches accounts for 57% of the total counties, whereas the number of counties in the middle reaches is only 37%. Compared to the rural hollowing-out results from 2000 to 2010, the number of counties dominated by PLT and HLW from 2010 to 2020 in the middle reaches increased by 19% and 16%, respectively. (2) Precipitation had a positive effect on the variability of the rural hollowing-out distribution results based on the feature importance values, whereas agricultural productivity had a negative effect and exhibited a decreasing trend. In the entire study area, economic non-agriculturalization had a negative impact, but the topographic relief was positively correlated with the rural hollowing-out results of regional areas, and the intensity of its effect showed an increased trend from 2000 to 2020. (3) The evaluation results in the entire study area show that the main functional type and the distance from provincial capitals are the most important factors affecting rural hollowing out. Compared to other factors, the level of non-agricultural economic development has a greater impact on the spatio-temporal differentiation of rural hollowing types in different Yellow River basins. Given the significant effect of non-agricultural economic development in the evaluation of regional rural hollowing-out, it can provide methodological and indicator references for the evaluation of rural hollowing-out in different regions.

Frequent agricultural activities in farmland ecosystems bring challenges to crop information extraction from remote sensing (RS) imagery. The accurate spatiotemporal information of crops serves for regional decision support and ecological assessment, such as disaster monitoring and carbon sequestration. Most traditional machine learning algorithms are not appropriate for prediction classification due to the lack of historical ground samples and poor model transfer capabilities. Therefore, a transferable learning model including spatiotemporal capability was developed based on the UNet++ model by integrating feature fusion and upsampling of small samples for Sentinel-2A imagery. Classification experiments were conducted for 10 categories from 2019 to 2021 in Xinxiang City, Henan Province. The feature fusion and upsampling methods improved the performance of the UNet++ model, showing lower joint loss and higher mean intersection over union (mIoU) values. Compared with the UNet, DeepLab V3+, and the pyramid scene parsing network (PSPNet), the improved UNet++ model exhibits the best performance, with a joint loss of 0.432 and a mIoU of 0.871. Moreover, the overall accuracy and macro F₁ values of prediction classification results based on the UNet++ model are higher than 83% and 58%, respectively. Based on the reclassification rules, about 3.48% of the farmland was damaged in 2021 due to continuous precipitation. The carbon sequestration of five crops (including corn, peanuts, soybean, rice, and other crops) is estimated, with a total carbon sequestration of 2460.56, 2549.16, and 1814.07 thousand tons in 2019, 2020, and 2021, respectively. The classification accuracy indicates that the improved model exhibits a better feature extraction and transferable learning capability in complex agricultural areas. This study provides a strategy for RS semantic segmentation and carbon sequestration estimation of crops based on a deep learning network.