High-resolution deep-learning-based remote-sensing imagery analysis has been widely used in land-use and crop-classification mapping. However, the influence of composite feature bands, including complex indices arising from different sensors on backbone, patch size, predictions transferable deep models require further testing. The experiments were conducted six sites Henan province 2019 to 2021. This study sought enable transfer classification across regions years for Sentinel-2A (10-m...

Climate change and human activities significantly affect vegetation growth in terrestrial ecosystems. Here, data reconstruction was performed to obtain a time series of the normalized difference index (NDVI) for China (1982–2018) based on Savitzky–Golay filtered GIMMS NDVI3g MOD13A2 datasets. Combining surface temperature precipitation observations from more than 2000 meteorological stations China, Theil–Sen trend analysis, Mann–Kendall significance tests, Pearson correlation residual...

The accurate extraction of cultivated land information is crucial for optimizing regional farmland layouts and enhancing food supply. To address the problem low accuracy in existing products poor applicability methods fragmented, small parcel agricultural landscapes complex terrain mapping, this study develops an advanced model western part Henan Province, China, utilizing Gaofen-2 (GF-2) imagery improved U-Net architecture to achieve a 1 m resolution mapping terrain. We obtained optimal...

Accurate acquisition of cultivated land area and location information is great significance to agricultural management, agro-ecological environment monitoring, national food security. The rapid development deep learning technology provides a new way extract information. However, there are many parameters involved in learning, so it time-consuming find the optimal parameters. In order simplify complex parameter tuning process explore main that affect classification accuracy this study uses...

Propelled by emerging technologies such as artificial intelligence and deep learning, the essence scope of cartography have significantly expanded. The rapid progress in neuroscience has raised high expectations for related disciplines, furnishing theoretical support revealing deepening maps. In this study, CiteSpace was used to examine confluence neural networks over past decade (2013–2023), thus prevailing research trends cutting-edge investigations field machine learning its application...

Abstract The crab-eating frog, Fejervarya cancrivora , is the only frog that lives near seas. It tolerates increased environmental concentrations of sodium, chloride and potassium partly by raising ion urea levels in its blood plasma. molecular mechanism adaptation remains rarely documented. Herein, we analyze transcriptomes closely related saline-intolerant species, F. limnocharis to explore basis adaptations such extreme conditions. Analyses reveal potential genetic underlying salinity for...

Accurate and near-real-time crop mapping from satellite imagery is crucial for agricultural monitoring. However, the seasonal nature of crops makes it challenging to rely on traditional machine learning methods previous samples generated within specific domains. In this study, we improved histogram matching method color correction multi-temporal images tested performance prediction classification accuracy three semantic segmentation models based weak samples. Classification experiments were...

Accurate temporal land use mapping provides important and timely information for decision making large-scale management of crop production. At present, cover classifications within a study area have neglected the differences between subregions. In this paper, we propose classification rule by integrating terrain, time series characteristics, priority, seasonality (TTPSR) with Sentinel-2 satellite imagery. Based on Normalized Difference Water Index (NDWI) Vegetation (NDVI), dynamic tree...

Understanding the regional variations and mechanisms of rural hollowing-out in Yellow River Basin (YRB) is crucial to guiding revitalization. However, further quantitative evaluation analysis are essential address issue caused by decrease population expansion residential land YRB at different spatio-temporal scales. Based on China’s census data areas extracted from remote sensing images, classified into five types: smart development type (SDT), human–-land recession (HRT), loss (PLT), (LET),...

A number of applications have been proposed by using the technique global navigation satellite system reflectometry (GNSS-R) such as ice monitoring and sea surface wind retrieval. Using GNSS-R for target detection has also raised its advantages all-weather capabilities, worldwide coverage ability counter attack anti-radiation missiles (ARM). In this work, ocean positioning is discussed. The analysis mainly focuses on two aspects: 1) detecting principle approach, 2) method based corresponding...

Frequent agricultural activities in farmland ecosystems bring challenges to crop information extraction from remote sensing (RS) imagery. The accurate spatiotemporal of crops serves for regional decision support and ecological assessment, such as disaster monitoring carbon sequestration. Most traditional machine learning algorithms are not appropriate prediction classification due the lack historical ground samples poor model transfer capabilities. Therefore, a transferable including...

After committing an error, humans often adopt strategies to change error behaviors, this phenomenon is termed post-error adjustment. The adjustment ability fundamental flexible behavior and survival in a complex, varying environment. Resting-state neural activity has been associated with variety of cognitive abilities, yet its relevance unclear. fMRI powerful tool for investigating spontaneous brain activity, since it can reveal the correlation intrinsic functional architecture extrinsic...